Datasets:
Euanyu
/
merged_task_dataset_models_BioNLP_results

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Goodmotion/spam-mail-classifier
Goodmotion
text-classification
[ "transformers", "safetensors", "text-classification", "spam-detection", "license:apache-2.0", "endpoints_compatible", "region:us" ]
1,733
1,733
87
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--- license: apache-2.0 tags: - transformers - text-classification - spam-detection --- # SPAM Mail Classifier This model is fine-tuned from `microsoft/Multilingual-MiniLM-L12-H384` to classify email subjects as SPAM or NOSPAM. ## Model Details - **Base model**: `microsoft/Multilingual-MiniLM-L12-H384` - **Fine-tuned for**: Text classification - **Number of classes**: 2 (SPAM, NOSPAM) - **Languages**: Multilingual ## Usage This model is fine-tuned from `microsoft/Multilingual-MiniLM-L12-H384` to classify email subjects as SPAM or NOSPAM. ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification model_name = "Goodmotion/spam-mail-classifier" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained( model_name ) text = "Félicitations ! Vous avez gagné un iPhone." inputs = tokenizer(text, return_tensors="pt") outputs = model(**inputs) print(outputs.logits) ``` ### Exemple for list ```python import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification model_name = "Goodmotion/spam-mail-classifier" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) texts = [ 'Join us for a webinar on AI innovations', 'Urgent: Verify your account immediately.', 'Meeting rescheduled to 3 PM', 'Happy Birthday!', 'Limited time offer: Act now!', 'Join us for a webinar on AI innovations', 'Claim your free prize now!', 'You have unclaimed rewards waiting!', 'Weekly newsletter from Tech World', 'Update on the project status', 'Lunch tomorrow at 12:30?', 'Get rich quick with this amazing opportunity!', 'Invoice for your recent purchase', 'Don\'t forget: Gym session at 6 AM', 'Join us for a webinar on AI innovations', 'bonjour comment allez vous ?', 'Documents suite à notre rendez-vous', 'Valentin Dupond mentioned you in a comment', 'Bolt x Supabase = 🤯', 'Modification site web de la société', 'Image de mise en avant sur les articles', 'Bring new visitors to your site', 'Le Cloud Éthique sans bullshit', 'Remix Newsletter #25: React Router v7', 'Votre essai auprès de X va bientôt prendre fin', 'Introducing a Google Docs integration, styles and more in Claude.ai', 'Carte de crédit sur le point d’expirer sur Cloudflare' ] inputs = tokenizer(texts, padding=True, truncation=True, max_length=128, return_tensors="pt") outputs = model(**inputs) # Convertir les logits en probabilités avec softmax logits = outputs.logits probabilities = torch.softmax(logits, dim=1) # Décoder les classes pour chaque texte labels = ["NOSPAM", "SPAM"] # Mapping des indices à des labels results = [ {"text": text, "label": labels[torch.argmax(prob).item()], "confidence": prob.max().item()} for text, prob in zip(texts, probabilities) ] # Afficher les résultats for result in results: print(f"Texte : {result['text']}") print(f"Résultat : {result['label']} (Confiance : {result['confidence']:.2%})\n") ```
[ "TEXT_CLASSIFICATION" ]
[ "ESSAI" ]
Non_BioNLP
knowledgator/gliner-poly-small-v1.0
knowledgator
token-classification
[ "gliner", "pytorch", "token-classification", "multilingual", "dataset:urchade/pile-mistral-v0.1", "dataset:numind/NuNER", "dataset:knowledgator/GLINER-multi-task-synthetic-data", "license:apache-2.0", "region:us" ]
1,724
1,724
32
14
--- datasets: - urchade/pile-mistral-v0.1 - numind/NuNER - knowledgator/GLINER-multi-task-synthetic-data language: - multilingual library_name: gliner license: apache-2.0 pipeline_tag: token-classification --- # About GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoders (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. This particular version utilize bi-encoder architecture with post-fusion, where textual encoder is [DeBERTa v3 small](microsoft/deberta-v3-small) and entity label encoder is sentence transformer - [BGE-small-en](https://huggingface.co/BAAI/bge-small-en-v1.5). Such architecture brings several advantages over uni-encoder GLiNER: * An unlimited amount of entities can be recognized at a single time; * Faster inference if entity embeddings are preprocessed; * Better generalization to unseen entities; Post fusion strategy brings advantages over classical bi-encoder enabling better inter-label understanding. ### Installation & Usage Install or update the gliner package: ```bash pip install gliner -U ``` Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`. ```python from gliner import GLiNER model = GLiNER.from_pretrained("knowledgator/gliner-poly-small-v1.0") text = """ Cristiano Ronaldo dos Santos Aveiro (Portuguese pronunciation: [kɾiʃˈtjɐnu ʁɔˈnaldu]; born 5 February 1985) is a Portuguese professional footballer who plays as a forward for and captains both Saudi Pro League club Al Nassr and the Portugal national team. Widely regarded as one of the greatest players of all time, Ronaldo has won five Ballon d'Or awards,[note 3] a record three UEFA Men's Player of the Year Awards, and four European Golden Shoes, the most by a European player. He has won 33 trophies in his career, including seven league titles, five UEFA Champions Leagues, the UEFA European Championship and the UEFA Nations League. Ronaldo holds the records for most appearances (183), goals (140) and assists (42) in the Champions League, goals in the European Championship (14), international goals (128) and international appearances (205). He is one of the few players to have made over 1,200 professional career appearances, the most by an outfield player, and has scored over 850 official senior career goals for club and country, making him the top goalscorer of all time. """ labels = ["person", "award", "date", "competitions", "teams"] entities = model.predict_entities(text, labels, threshold=0.25) for entity in entities: print(entity["text"], "=>", entity["label"]) ``` ``` Cristiano Ronaldo dos Santos Aveiro => person 5 February 1985 => date Al Nassr => teams Portugal national team => teams Ballon d'Or => award UEFA Men's Player of the Year Awards => award European Golden Shoes => award UEFA Champions Leagues => competitions UEFA European Championship => competitions UEFA Nations League => competitions Champions League => competitions European Championship => competitions ``` If you have a large amount of entities and want to pre-embed them, please, refer to the following code snippet: ```python labels = ["your entities"] texts = ["your texts"] entity_embeddings = model.encode_labels(labels, batch_size = 8) outputs = model.batch_predict_with_embeds([text], entity_embeddings, labels) ``` ### Benchmarks Below you can see the table with benchmarking results on various named entity recognition datasets: | Dataset | Score | |---------|-------| | ACE 2004 | 25.4% | | ACE 2005 | 27.2% | | AnatEM | 17.7% | | Broad Tweet Corpus | 70.2% | | CoNLL 2003 | 67.8% | | FabNER | 22.9% | | FindVehicle | 40.2% | | GENIA_NER | 47.7% | | HarveyNER | 15.5% | | MultiNERD | 64.5% | | Ontonotes | 28.7% | | PolyglotNER | 47.5% | | TweetNER7 | 39.3% | | WikiANN en | 56.7% | | WikiNeural | 80.0% | | bc2gm | 56.2% | | bc4chemd | 48.7% | | bc5cdr | 60.5% | | ncbi | 53.5% | | **Average** | **45.8%** | ||| | CrossNER_AI | 48.9% | | CrossNER_literature | 64.0% | | CrossNER_music | 68.7% | | CrossNER_politics | 69.0% | | CrossNER_science | 62.7% | | mit-movie | 40.3% | | mit-restaurant | 36.2% | | **Average (zero-shot benchmark)** | **55.7%** | ### Join Our Discord Connect with our community on Discord for news, support, and discussion about our models. Join [Discord](https://discord.gg/dkyeAgs9DG).
[ "NAMED_ENTITY_RECOGNITION" ]
[ "ANATEM", "BC5CDR" ]
Non_BioNLP
QuantFactory/meditron-7b-GGUF
QuantFactory
null
[ "gguf", "en", "dataset:epfl-llm/guidelines", "arxiv:2311.16079", "base_model:meta-llama/Llama-2-7b", "base_model:quantized:meta-llama/Llama-2-7b", "license:llama2", "endpoints_compatible", "region:us" ]
1,727
1,727
206
1
--- base_model: meta-llama/Llama-2-7b datasets: - epfl-llm/guidelines language: - en license: llama2 metrics: - accuracy - perplexity --- [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) # QuantFactory/meditron-7b-GGUF This is quantized version of [epfl-llm/meditron-7b](https://huggingface.co/epfl-llm/meditron-7b) created using llama.cpp # Original Model Card <img width=50% src="meditron_LOGO.png" alt="Alt text" title="Meditron-logo"> # Model Card for Meditron-7B-v1.0 Meditron is a suite of open-source medical Large Language Models (LLMs). Meditron-7B is a 7 billion parameters model adapted to the medical domain from Llama-2-7B through continued pretraining on a comprehensively curated medical corpus, including selected PubMed articles, abstracts, a [new dataset](https://huggingface.co/datasets/epfl-llm/guidelines) of internationally-recognized medical guidelines, and general domain data from [RedPajama-v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T). Meditron-7B, finetuned on relevant training data, outperforms Llama-2-7B and PMC-Llama on multiple medical reasoning tasks. <details open> <summary><strong>Advisory Notice</strong></summary> <blockquote style="padding: 10px; margin: 0 0 10px; border-left: 5px solid #ddd;"> While Meditron is designed to encode medical knowledge from sources of high-quality evidence, it is not yet adapted to deliver this knowledge appropriately, safely, or within professional actionable constraints. We recommend against deploying Meditron in medical applications without extensive use-case alignment, as well as additional testing, specifically including randomized controlled trials in real-world practice settings. </blockquote> </details> ## Model Details - **Developed by:** [EPFL LLM Team](https://huggingface.co/epfl-llm) - **Model type:** Causal decoder-only transformer language model - **Language(s):** English (mainly) - **Model License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - **Code License:** [APACHE 2.0 LICENSE](LICENSE) - **Continue-pretrained from model:** [Llama-2-7B](https://huggingface.co/meta-llama/Llama-2-7b) - **Context length:** 2K tokens - **Input:** Text-only data - **Output:** Model generates text only - **Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we enhance model's performance. - **Knowledge Cutoff:** August 2023 ### Model Sources - **Repository:** [epflLLM/meditron](https://github.com/epfLLM/meditron) - **Trainer:** [epflLLM/Megatron-LLM](https://github.com/epfLLM/Megatron-LLM) - **Paper:** *[MediTron-70B: Scaling Medical Pretraining for Large Language Models](https://arxiv.org/abs/2311.16079)* ## Uses Meditron-7B is being made available for further testing and assessment as an AI assistant to enhance clinical decision-making and enhance access to an LLM for healthcare use. Potential use cases may include but are not limited to: - Medical exam question answering - Supporting differential diagnosis - Disease information (symptoms, cause, treatment) query - General health information query ### Direct Use It is possible to use this model to generate text, which is useful for experimentation and understanding its capabilities. It should not be used directly for production or work that may impact people. ### Downstream Use Meditron-70B and Meditron-7B are both foundation models without finetuning or instruction-tuning. They can be finetuned, instruction-tuned, or RLHF-tuned for specific downstream tasks and applications. There are two ways we have used this model for downstream question-answering tasks. 1. We apply in-context learning with k demonstrations (3 or 5 in our paper) added to the prompt. 2. We finetuned the models for downstream question-answering tasks using specific training sets. We encourage and look forward to the adaption of the base model for more diverse applications. If you want a more interactive way to prompt the model, we recommend using a high-throughput and memory-efficient inference engine with a UI that supports chat and text generation. You can check out our deployment [guide](https://github.com/epfLLM/meditron/blob/main/deployment/README.md), where we used [FastChat](https://github.com/lm-sys/FastChat) with [vLLM](https://github.com/vllm-project/vllm). We collected generations for our qualitative analysis through an interactive UI platform, [BetterChatGPT](https://github.com/ztjhz/BetterChatGPT). Here is the prompt format we used as an example: <img width=70% src="prompt_example.png" alt="qualitative-analysis-prompt" title="Qualitative Analysis Prompt"> ### Out-of-Scope Use We do not recommend using this model for natural language generation in a production environment, finetuned or otherwise. ## Truthfulness, Helpfulness, Risk, and Bias <!-- This section is meant to convey both technical and sociotechnical limitations. --> We did an initial assessment of Meditron models' **Truthfulness** against baseline models and consumer-level medical models. We use TruthfulQA (multiple choice) as the main evaluation benchmark. We only focus on the categories that are relevant to the medical domain, including Health, Nutrition, Psychology, and Science. For 7B models, we perform one-shot evaluations for consistent answer generation. For 70B models, the evaluations are under the zero-shot setting. Below, we report the detailed truthfulness performance of each category. | | | | | | | | | | --- | ------ |----- |----- |----- |----- |----- |----- | |Category | meditron-70b | llama-2-70b | med42-70b* | meditron-7b | llama-2-7b | PMC-llama-7b | |Health | 81.8 | 69.1 | 83.6 | 27.3 | 16.4 | 3.6 | |Nutrition | 77.9 | 68.8 | 62.5 | 31.1 | 12.5 | 6.3 | |Psychology| 47.4 | 36.8 | 52.6 | 21.1 | 10.5 | 0.0 | |Science | 77.8 | 44.4 | 33.3 | 33.3 | 11.1 | 0.0 | |Avg | 71.2 | 54.8 | 58.0 | 28.3 | 12.6 | 2.5 | | | | | | | | | For a more detailed performance analysis, please see our paper. Significant research is still required to fully explore potential bias, fairness, and safety issues with this language model. Please recognize that our evaluation on Meditron-7B's helpfulness, risk, and bias are highly limited. Thus, as we noted in the safety notice, we strongly against any deployment in medical applications without further alignment process and rigorous evaluation! ### Recommendations **IMPORTANT!** Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. While this model is capable of generating natural language text, we have only begun to explore this capability and its limitations. Understanding these limitations is especially important in a domain like medicine. Therefore, we strongly recommend against using this model in production for natural language generation or for professional purposes related to health and medicine. ## Training Details ### Training Data Meditron’s domain-adaptive pre-training corpus GAP-Replay combines 48.1B tokens from four corpora: - [**Clinical Guidelines**](https://huggingface.co/datasets/epfl-llm/guidelines): a new dataset of 46K internationally-recognized clinical practice guidelines from various healthcare-related sources, including hospitals and international organizations. - **Medical Paper Abstracts**: 16.1M abstracts extracted from closed-access PubMed and PubMed Central papers. - **Medical Papers**: full-text articles extracted from 5M publicly available PubMed and PubMed Central papers. - **Replay Data**: 400M tokens of general domain pretraining data sampled from [RedPajama-v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) <img width=75% src="gap-replay.png" alt="Alt text" title="Meditron-logo"> #### Data Preprocessing Please see the detailed preprocessing procedure in our paper. ### Training Procedure We used the [Megatron-LLM](https://github.com/epfLLM/Megatron-LLM) distributed training library, a derivative of Nvidia's Megatron LM project, to optimize training efficiency. Hardware consists of 1 node of 8x NVIDIA A100 (80GB) SXM GPUs connected by NVLink and NVSwitch with a single Nvidia ConnectX-6 DX network card and equipped with 2 x AMD EPYC 7543 32-Core Processors and 512 GB of RAM. Our three way parallelism scheme uses: - Data Parallelism (DP -- different GPUs process different subsets of the batches) of 2, - Pipeline Parallelism (PP -- different GPUs process different layers) of 4, - Tensor Parallelism (TP -- different GPUs process different subtensors for matrix multiplication) of 1. #### Training Hyperparameters | | | | --- | ------ | | bf16 | true | | lr | 3e-4 | | eps | 1e-5 | | betas | \[0.9, 0.95\] | | clip_grad | 1 | | weight decay | 0.1 | | DP size | 16 | | TP size | 4 | | PP size | 1 | | seq length | 2048 | | lr scheduler | cosine| | min lr | 1e-6 | | warmup iteration | 2000 | | micro batch size | 10 | | global batch size | 1600 | | | | #### Sizes The model was trained in September 2023. The model architecture is exactly Llama 2, meaning | | | | --- | ------ | | Model size | 7B | | Hidden dimension | 4096 | | Num. attention heads | 32 | | Num. layers | 32 | | | | ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data & Metrics #### Testing Data - [MedQA (USMLE)](https://huggingface.co/datasets/bigbio/med_qa) - [MedMCQA](https://huggingface.co/datasets/medmcqa) - [PubMedQA](https://huggingface.co/datasets/bigbio/pubmed_qa) - [MMLU-Medical](https://huggingface.co/datasets/lukaemon/mmlu) - [MedQA-4-Option](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options) #### Metrics - Accuracy: suite the evaluation of multiple-choice question-answering tasks. ### Results We finetune meditron-7b, llama-2-7b, pmc-llama-7b on each benchmark (pubmedqa, medmcqa, medqa)'s training data individually. We report the finetuned models' performance with top token selection as the inference mode. For MMLU-Medical, models finetuned on MedMCQA are used for inference. For MedQA-4-Option, models finetuned on MedQA are used for inference. For a more detailed performance analysis, please see our paper. | | | | | | | | --- | ------ |----- |----- |----- |----- | |Dataset | meditron-7b | llama-2-7b | pmc-llama-7b | Zephyr-7B-beta* | Mistral-7B-instruct* | |MMLU-Medical | 54.2 | 53.7 | 56.4 | 63.3 | 60.0 | |PubMedQA | 74.4 | 61.8 | 59.2 | 46.0 | 17.8 | |MedMCQA | 59.2 | 54.4 | 57.6 | 43.0 | 40.2 | |MedQA | 47.9 | 44.0 | 42.4 | 42.8 | 32.4 | |MedQA-4-Option| 52.0 | 49.6 | 49.2 | 48.5 | 41.1 | |Avg | 57.5 | 52.7 | 53.0 | 48.7 | 38.3 | | | | | | | | **Note**: models with * are already instruction-tuned, so we exclude them from further finetuning on any training data. ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> - **Hardware Type:** 8 x NVIDIA A100 (80GB) SXM - **Total GPU hours:** 588.8 - **Hardware Provider:** EPFL Research Computing Platform - **Compute Region:** Switzerland - **Carbon Emitted:** Switzerland has a carbon efficiency of 0.016 kgCO2/kWh (https://www.carbonfootprint.com/docs/2018_8_electricity_factors_august_2018_-_online_sources.pdf). 73.6 hours of 8 A100s means 588.8 hours at a TDP of 400W. Assuming a Power Usage effectiveness of 1.5, total emissions are estimated to be: (400W / 1000W/kWh / GPU * 0.016 kgCO2/kWh * 73.6 h * 8 GPU) * 1.8 PUE = 6.8 kgCO2. ## Citation **BibTeX:** If you use Meditron or its training data, please cite our work: ``` @misc{chen2023meditron70b, title={MEDITRON-70B: Scaling Medical Pretraining for Large Language Models}, author={Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut}, year={2023}, eprint={2311.16079}, archivePrefix={arXiv}, primaryClass={cs.CL} } @software{epfmedtrn, author = {Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut}, title = {MediTron-70B: Scaling Medical Pretraining for Large Language Models}, month = November, year = 2023, url = {https://github.com/epfLLM/meditron} } ```
[ "QUESTION_ANSWERING" ]
[ "MEDQA", "PUBMEDQA" ]
BioNLP
m42-health/Llama3-Med42-8B
m42-health
text-generation
[ "transformers", "safetensors", "llama", "text-generation", "m42", "health", "healthcare", "clinical-llm", "conversational", "en", "arxiv:2408.06142", "license:llama3", "autotrain_compatible", "text-generation-inference", "region:us" ]
1,719
1,724
1,966
62
--- language: - en license: llama3 license_name: llama3 pipeline_tag: text-generation tags: - m42 - health - healthcare - clinical-llm inference: false --- # **Med42-v2 - A Suite of Clinically-aligned Large Language Models** Med42-v2 is a suite of open-access clinical large language models (LLM) instruct and preference-tuned by M42 to expand access to medical knowledge. Built off LLaMA-3 and comprising either 8 or 70 billion parameters, these generative AI systems provide high-quality answers to medical questions. ## Key performance metrics: - Med42-v2-70B outperforms GPT-4.0 in most of the MCQA tasks. - Med42-v2-70B achieves a MedQA zero-shot performance of 79.10, surpassing the prior state-of-the-art among all openly available medical LLMs. - Med42-v2-70B sits at the top of the Clinical Elo Rating Leaderboard. |Models|Elo Score| |:---:|:---:| |**Med42-v2-70B**| 1764 | |Llama3-70B-Instruct| 1643 | |GPT4-o| 1426 | |Llama3-8B-Instruct| 1352 | |Mixtral-8x7b-Instruct| 970 | |**Med42-v2-8B**| 924 | |OpenBioLLM-70B| 657 | |JSL-MedLlama-3-8B-v2.0| 447 | ## Limitations & Safe Use - The Med42-v2 suite of models is not ready for real clinical use. Extensive human evaluation is undergoing as it is required to ensure safety. - Potential for generating incorrect or harmful information. - Risk of perpetuating biases in training data. Use this suite of models responsibly! Do not rely on them for medical usage without rigorous safety testing. ## Model Details *Disclaimer: This large language model is not yet ready for clinical use without further testing and validation. It should not be relied upon for making medical decisions or providing patient care.* Beginning with Llama3 models, Med42-v2 were instruction-tuned using a dataset of ~1B tokens compiled from different open-access and high-quality sources, including medical flashcards, exam questions, and open-domain dialogues. **Model Developers:** M42 Health AI Team **Finetuned from model:** Llama3 - 8B & 70B Instruct **Context length:** 8k tokens **Input:** Text only data **Output:** Model generates text only **Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we enhance the model's performance. **License:** Llama 3 Community License Agreement **Research Paper:** [Med42-v2: A Suite of Clinical LLMs](https://huggingface.co/papers/2408.06142) ## Intended Use The Med42-v2 suite of models is being made available for further testing and assessment as AI assistants to enhance clinical decision-making and access to LLMs for healthcare use. Potential use cases include: - Medical question answering - Patient record summarization - Aiding medical diagnosis - General health Q&A **Run the model** You can use the 🤗 Transformers library `text-generation` pipeline to do inference. ```python import transformers import torch model_name_or_path = "m42-health/Llama3-Med42-8B" pipeline = transformers.pipeline( "text-generation", model=model_name_or_path, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ { "role": "system", "content": ( "You are a helpful, respectful and honest medical assistant. You are a second version of Med42 developed by the AI team at M42, UAE. " "Always answer as helpfully as possible, while being safe. " "Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. " "Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. " "If you don’t know the answer to a question, please don’t share false information." ), }, {"role": "user", "content": "What are the symptoms of diabetes?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=False ) stop_tokens = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>"), ] outputs = pipeline( prompt, max_new_tokens=512, eos_token_id=stop_tokens, do_sample=True, temperature=0.4, top_k=150, top_p=0.75, ) print(outputs[0]["generated_text"][len(prompt) :]) ``` ## Hardware and Software The training was conducted on the NVIDIA DGX cluster with H100 GPUs, utilizing PyTorch's Fully Sharded Data Parallel (FSDP) framework. ## Evaluation Results ### Open-ended question generation To ensure a robust evaluation of our model's output quality, we employ the LLM-as-a-Judge approach using Prometheus-8x7b-v2.0. Our assessment uses 4,000 carefully curated publicly accessible healthcare-related questions, generating responses from various models. We then use Prometheus to conduct pairwise comparisons of the answers. Drawing inspiration from the LMSYS Chatbot-Arena methodology, we present the results as Elo ratings for each model. To maintain fairness and eliminate potential bias from prompt engineering, we used the same simple system prompt for every model throughout the evaluation process. Below is the scoring rubric we used to prompt Prometheus to select the best answer: ``` ### Score Rubric: Which response is of higher overall quality in a medical context? Consider: * Relevance: Does it directly address the question? * Completeness: Does it cover all important aspects, details and subpoints? * Safety: Does it avoid unsafe practices and address potential risks? * Ethics: Does it maintain confidentiality and avoid biases? * Clarity: Is it professional, clear and easy to understand? ``` #### Elo Ratings |Models|Elo Score| |:---:|:---:| |**Med42-v2-70B**| 1764 | |Llama3-70B-Instruct| 1643 | |GPT4-o| 1426 | |Llama3-8B-Instruct| 1352 | |Mixtral-8x7b-Instruct| 970 | |**Med42-v2-8B**| 924 | |OpenBioLLM-70B| 657 | |JSL-MedLlama-3-8B-v2.0| 447 | #### Win-rate ![plot](./pairwise_model_comparison.svg) ### MCQA Evaluation Med42-v2 improves performance on every clinical benchmark compared to our previous version, including MedQA, MedMCQA, USMLE, MMLU clinical topics and MMLU Pro clinical subset. For all evaluations reported so far, we use [EleutherAI's evaluation harness library](https://github.com/EleutherAI/lm-evaluation-harness) and report zero-shot accuracies (except otherwise stated). We integrated chat templates into harness and computed the likelihood for the full answer instead of only the tokens "a.", "b.", "c." or "d.". |Model|MMLU Pro|MMLU|MedMCQA|MedQA|USMLE| |---:|:---:|:---:|:---:|:---:|:---:| |**Med42v2-70B**|64.36|87.12|73.20|79.10|83.80| |**Med42v2-8B**|54.30|75.76|61.34|62.84|67.04| |OpenBioLLM-70B|64.24|90.40|73.18|76.90|79.01| |GPT-4.0<sup>&dagger;</sup>|-|87.00|69.50|78.90|84.05| |MedGemini*|-|-|-|84.00|-| |Med-PaLM-2 (5-shot)*|-|87.77|71.30|79.70|-| |Med42|-|76.72|60.90|61.50|71.85| |ClinicalCamel-70B|-|69.75|47.00|53.40|54.30| |GPT-3.5<sup>&dagger;</sup>|-|66.63|50.10|50.80|53.00| |Llama3-8B-Instruct|48.24|72.89|59.65|61.64|60.38| |Llama3-70B-Instruct|64.24|85.99|72.03|78.88|83.57| **For MedGemini, results are reported for MedQA without self-training and without search. We note that 0-shot performance is not reported for Med-PaLM 2. Further details can be found at [https://github.com/m42health/med42](https://github.com/m42health/med42)*. <sup>&dagger;</sup> *Results as reported in the paper [Capabilities of GPT-4 on Medical Challenge Problems](https://www.microsoft.com/en-us/research/uploads/prod/2023/03/GPT-4_medical_benchmarks.pdf)*. ## Accessing Med42 and Reporting Issues Please report any software "bug" or other problems through one of the following means: - Reporting issues with the model: [https://github.com/m42health/med42](https://github.com/m42health/med42) - Reporting risky content generated by the model, bugs and/or any security concerns: [https://forms.office.com/r/fPY4Ksecgf](https://forms.office.com/r/fPY4Ksecgf) - M42’s privacy policy available at [https://m42.ae/privacy-policy/](https://m42.ae/privacy-policy/) - Reporting violations of the Acceptable Use Policy or unlicensed uses of Med42: <[email protected]> ## Acknowledgements We thank the Torch FSDP team for their robust distributed training framework, the EleutherAI harness team for their valuable evaluation tools, and the Hugging Face Alignment team for their contributions to responsible AI development. ## Citation ``` @misc{med42v2, Author = {Cl{\'e}ment Christophe and Praveen K Kanithi and Tathagata Raha and Shadab Khan and Marco AF Pimentel}, Title = {Med42-v2: A Suite of Clinical LLMs}, Year = {2024}, Eprint = {arXiv:2408.06142}, url={https://arxiv.org/abs/2408.06142}, } ```
[ "QUESTION_ANSWERING", "SUMMARIZATION" ]
[ "MEDQA" ]
BioNLP
seongil-dn/bge-m3-756
seongil-dn
sentence-similarity
[ "sentence-transformers", "safetensors", "xlm-roberta", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:1138596", "loss:CachedGISTEmbedLoss", "arxiv:1908.10084", "base_model:seongil-dn/unsupervised_20m_3800", "base_model:finetune:seongil-dn/unsupervised_20m_3800", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,741
1,741
12
0
--- base_model: seongil-dn/unsupervised_20m_3800 library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:1138596 - loss:CachedGISTEmbedLoss widget: - source_sentence: How many people were reported to have died in the Great Fire of London in 1666? sentences: - City of London 1666. Both of these fires were referred to as "the" Great Fire. After the fire of 1666, a number of plans were drawn up to remodel the City and its street pattern into a renaissance-style city with planned urban blocks, squares and boulevards. These plans were almost entirely not taken up, and the medieval street pattern re-emerged almost intact. By the late 16th century, London increasingly became a major centre for banking, international trade and commerce. The Royal Exchange was founded in 1565 by Sir Thomas Gresham as a centre of commerce for London's merchants, and gained Royal patronage in - Great Atlanta fire of 1917 Great Atlanta fire of 1917 The Great Atlanta Fire of 1917 began just after noon on 21 May 1917 in the Old Fourth Ward of Atlanta, Georgia. It is unclear just how the fire started, but it was fueled by hot temperatures and strong winds which propelled the fire. The fire, which burned for nearly 10 hours, destroyed and 1,900 structures displacing over 10,000 people. Damages were estimated at $5 million, ($ million when adjusted for inflation). It was a clear, warm and sunny day with a brisk breeze from the south. This was not the only fire of the - Great Plague of London they had ever been seen ...". Plague cases continued to occur sporadically at a modest rate until the summer of 1666. On the second and third of September that year, the Great Fire of London destroyed much of the City of London, and some people believed that the fire put an end to the epidemic. However, it is now thought that the plague had largely subsided before the fire took place. In fact, most of the later cases of plague were found in the suburbs, and it was the City of London itself that was destroyed by the Fire. According - Monument to the Great Fire of London Monument to the Great Fire of London The Monument to the Great Fire of London, more commonly known simply as the Monument, is a Doric column in London, United Kingdom, situated near the northern end of London Bridge. Commemorating the Great Fire of London, it stands at the junction of Monument Street and Fish Street Hill, in height and 202 feet west of the spot in Pudding Lane where the Great Fire started on 2 September 1666. Constructed between 1671 and 1677, it was built on the site of St. Margaret's, Fish Street, the first church to be destroyed by - 'How to Have Sex in an Epidemic New York City government and organizations within the LGBT community. The Gay Men''s Health Crisis offered to buy all 5,000 pamphlets and promote them, with the condition that any mentions of the multifactorial model be removed from the writing. The authors refused. Berkowitz recounts in an interview it being "infuriating" that in 1985, the city still hadn''t adopted any standard safe sex education. The advent of safe sex in urban gay male populations came too late for many people: by 1983, more than 1,476 people had died from AIDS and David France estimated that as much as half of all' - 'Monument to the Great Fire of London six years to complete the 202 ft column. It was two more years before the inscription (which had been left to Wren — or to Wren''s choice — to decide upon) was set in place. "Commemorating — with a brazen disregard for the truth — the fact that ''London rises again...three short years complete that which was considered the work of ages.''" Hooke''s surviving drawings show that several versions of the monument were submitted for consideration: a plain obelisk, a column garnished with tongues of fire, and the fluted Doric column that was eventually chosen. The real contention came with' - source_sentence: '"The Claude Francois song ""Comme d''habitude"" (translation ""as usual"") was a hit in English for Frank Sinatra under what title?"' sentences: - Young at Heart (Frank Sinatra song) young, Dick Van Dyke recorded a duet with his wife, Arlene, at Capital Records Studio in Los Angeles, filmed for the HBO Special on aging "If I'm not in the Obituary, I'll have Breakfast" starring Carl Reiner, and featuring other young at heart +90 treasures, Mel Brooks, Norman Lear, Stan Lee & Betty White among others. Van Dyke was recorded using Frank Sinatra's microphone. Young at Heart (Frank Sinatra song) "Young at Heart" is a pop standard, a ballad with music by Johnny Richards and lyrics by Carolyn Leigh. The song was written and published in 1953, with Leigh contributing - 'Comme d''habitude a relationship that is falling out of love, while the English language version is set at the end of a lifetime, approaching death, and looking back without regret – expressing feelings that are more related to Piaf''s song "Non, je ne regrette rien". Many artists sang "Comme d''Habitude" in French after Claude François''s success (and international success through ''"My Way"), notably: David Bowie has said that in 1968 – the year before Paul Anka acquired the French song – his manager, Kenneth Pitt, asked him to write English lyrics for "Comme d''habitude" but that his version, titled "Even a Fool' - Frank Sinatra Me" with Billy May, designed as a musical world tour. It reached the top spot on the Billboard album chart in its second week, remaining at the top for five weeks, and was nominated for the Grammy Award for Album of the Year at the inaugural Grammy Awards. The title song, "Come Fly With Me", written especially for him, would become one of his best known standards. On May 29 he recorded seven songs in a single session, more than double the usual yield of a recording session, and an eighth was planned, "Lush Life", but Sinatra found it too - Frank Sinatra Original Song. Sinatra released "Softly, as I Leave You", and collaborated with Bing Crosby and Fred Waring on "America, I Hear You Singing", a collection of patriotic songs recorded as a tribute to the assassinated President John F. Kennedy. Sinatra increasingly became involved in charitable pursuits in this period. In 1961 and 1962 he went to Mexico, with the sole purpose of putting on performances for Mexican charities, and in July 1964 he was present for the dedication of the Frank Sinatra International Youth Center for Arab and Jewish children in Nazareth. Sinatra's phenomenal success in 1965, coinciding with his - Comme ci comme ça (Basim song) to the charm of it all. Working both Danish and Moroccan Arabic, Basim sings about a girl he is ready to commit to. It doesn’t mater what she wants to do — it’s comme ci comme ça — and he just wants her." An official music video to accompany the release of "Comme ci comme ça" was first released onto YouTube on 20 September 2017 at a total length of three minutes and twelve seconds. Comme ci comme ça (Basim song) "Comme ci comme ça" is a song performed by Danish pop singer and songwriter Basim, featuring vocals from Gilli. - Personal life of Frank Sinatra A third child, Christina Sinatra, known as "Tina", was born on June 20, 1948. Nancy Barbato Sinatra and Frank Sinatra announced their separation on Valentine's Day, February 14, 1950, with Frank's additional extra-marital affair with Ava Gardner compounding his transgressions and becoming public knowledge once again. After originally just seeking a legal separation, Frank and Nancy Sinatra decided some months later to file for divorce, and this divorce became legally final on October 29, 1951. Frank Sinatra's affair and relationship with Gardner had become more and more serious, and she later became his second wife. What was perhaps less widely - source_sentence: What was the name of the first Indiana Jones movie? sentences: - Indiana Jones and the Temple of Doom point. Old-time, 15-part movie serials didn't have shape. They just went on and on and on, which is what "Temple of Doom" does with humor and technical invention." Neal Gabler commented that "I think in some ways, "Indiana Jones and the Temple of Doom" was better than "Raiders of the Lost Ark". In some ways it was less. In sum total, I'd have to say I enjoyed it more. That doesn't mean it's better necessarily, but I got more enjoyment out of it." Colin Covert of the "Star Tribune" called the film "sillier, darkly violent and a bit dumbed down, - Indiana Jones and the Temple of Doom (1985 video game) Theme music plays in the background which is the best part of the game. Most of the sound effects are not sharp and not enough of them exist. "Indiana Jones and the Temple of Doom" is a bad game all the way around. It looks bad, has bad controls, and is way too short." Indiana Jones and the Temple of Doom (1985 video game) Indiana Jones and The Temple of Doom is a 1985 action arcade game developed and published by Atari Games, based on the 1984 film of the same name, the second film in the "Indiana Jones" franchise. - Indiana Jones and the Spear of Destiny Indiana Jones and the Spear of Destiny Indiana Jones and The Spear of Destiny is a four-issue comic book mini-series published by Dark Horse Comics from April to July 1995. It was their seventh series about the adult Indiana Jones. Indiana Jones reached for the Holy Grail, perched in a crack in the Temple of the Sun. Hanging onto him, his father, Professor Henry Jones urged him to let it go, and Indy turned back and let his father help him up. As the Joneses ride out into the Canyon of the Crescent Moon with Marcus Brody and Sallah, they - Lego Indiana Jones sets" The line was discontinued in 2010, but since Lucas plans to make a fifth installment to the franchise, the sets may be re-released along with new sets of the possible fifth Indiana Jones film. Due to the fact Disney bought Lucasfilm and will be making a new Indiana Jones movie, chances of new sets are high. The Indiana Jones sets proved to be one of the most popular Lego themes, and by the end of 2008 were credited, along with Lego Star Wars, of boosting the Lego Group's profits within a stagnant toy market. The product line was said - Indiana Jones and the Staff of Kings point-and-click adventure "Indiana Jones and the Fate of Atlantis". GameSpot criticized its "terribly laid-out checkpoints", "out-of-date" visuals, and "atrocious, annoying motion controls". Indiana Jones and the Staff of Kings The game was initially developed for the higher-end PlayStation 3 and Xbox 360 systems, before switching to the aforementioned lower-end platforms. As a result, both systems never saw a proper "Indiana Jones" video game being released besides the "" duology. The plot centers around Indy's search for the Staff of Moses. The Wii version of the game includes an exclusive co-op story mode (with Indy and Henry Jones Sr.) and unlockable - 'Indiana Jones and the Last Crusade: The Graphic Adventure Indiana Jones and the Last Crusade: The Graphic Adventure Indiana Jones and the Last Crusade: The Graphic Adventure is a graphic adventure game, released in 1989 (to coincide with the release of the film of the same name), published by Lucasfilm Games (now LucasArts). It was the third game to use the SCUMM engine. "Last Crusade" was one of the most innovative of the LucasArts adventures. It expanded on LucasArts'' traditional adventure game structure by including a flexible point system—the IQ score, or "Indy Quotient"—and by allowing the game to be completed in several different ways. The point system was' - source_sentence: '"Who was the Anglo-Irish scientist who, in the 17th century, discovered that ""the volume of a given mass of gas at a given temperature is inversely proportional to its pressure""?"' sentences: - 'Gay-Lussac''s law Gay-Lussac''s law Gay-Lussac''s law can refer to several discoveries made by French chemist Joseph Louis Gay-Lussac (1778–1850) and other scientists in the late 18th and early 19th centuries pertaining to thermal expansion of gases and the relationship between temperature, volume, and pressure. It states that the pressure of a given mass of gas varies directly with the absolute temperature of the gas, when the volume is kept constant. Mathematically, it can be written as: P/T=constant, Gay-Lussac is most often recognized for the Pressure Law which established that the pressure of an enclosed gas is directly proportional to its temperature and' - 'Gas constant "V" is the volume of gas (SI unit cubic metres), "n" is the amount of gas (SI unit moles), "m" is the mass (SI unit kilograms) contained in "V", and "T" is the thermodynamic temperature (SI unit kelvins). "R" is the molar-weight-specific gas constant, discussed below. The gas constant is expressed in the same physical units as molar entropy and molar heat capacity. From the general equation "PV" = "nRT" we get: where "P" is pressure, "V" is volume, "n" is number of moles of a given substance, and "T" is temperature. As pressure is defined as force per unit' - The Boy Who Was a King term. The film presents not only the life of the former Tsar, but also intertwines within the story vignettes of various Bulgarians, who were supporting him, sending him gifts, or merely tattooing his face on their body. The story is told through personal footage and vast amounts of archive material. The film received praise for its editing and use of archives with Variety's Robert Koehler writing that "Pic’s terrific use of archival footage includes an exiled Simeon interviewed in the early ’60s, disputing his playboy rep." and "Editing is aces." The Boy Who Was a King The Boy Who Was - Francis Hauksbee In 1708, Hauksbee independently discovered Charles's law of gases, which states that, for a given mass of gas at a constant pressure, the volume of the gas is proportional to its temperature. Hauksbee published accounts of his experiments in the Royal Society's journal "Philosophical Transactions". In 1709 he self-published "Physico-Mechanical Experiments on Various Subjects" which collected together many of these experiments along with discussion that summarized much of his scientific work. An Italian translation was published in 1716. A second edition was published posthumously in 1719. There were also translations to Dutch (1735) and French (1754). The Royal Society Hauksbee - 'Boyle''s law air moves from high to low pressure. Related phenomena: Other gas laws: Boyle''s law Boyle''s law, sometimes referred to as the Boyle–Mariotte law, or Mariotte''s law (especially in France), is an experimental gas law that describes how the pressure of a gas tends to increase as the volume of the container decreases. A modern statement of Boyle''s law is The absolute pressure exerted by a given mass of an ideal gas is inversely proportional to the volume it occupies if the temperature and amount of gas remain unchanged within a closed system. Mathematically, Boyle''s law can be stated as or' - Boyle's law of the gas, and "k" is a constant. The equation states that the product of pressure and volume is a constant for a given mass of confined gas and this holds as long as the temperature is constant. For comparing the same substance under two different sets of conditions, the law can be usefully expressed as The equation shows that, as volume increases, the pressure of the gas decreases in proportion. Similarly, as volume decreases, the pressure of the gas increases. The law was named after chemist and physicist Robert Boyle, who published the original law in 1662. This relationship - source_sentence: Peter Stuyvesant, born in Holland, became Governor of which American city in 1647? sentences: - Peter Stuyvesant at the corner of Thirteenth Street and Third Avenue until 1867 when it was destroyed by a storm, bearing fruit almost to the last. The house was destroyed by fire in 1777. He also built an executive mansion of stone called Whitehall. In 1645, Stuyvesant married Judith Bayard (–1687) of the Bayard family. Her brother, Samuel Bayard, was the husband of Stuyvesant's sister, Anna Stuyvesant. Petrus and Judith had two sons together. He died in August 1672 and his body was entombed in the east wall of St. Mark's Church in-the-Bowery, which sits on the site of Stuyvesant’s family chapel. - 'Peter Stuyvesant (cigarette) can amount to millions of dollars and finally criminal prosecution - if companies wilfully break the laws. However last year, when questioned on why no such action was being pursued against Imperial Tobacco a spokeswoman for Federal Health said: ""No instances of non-compliance with the Act have been identified by the Department that warrant the initiation of Court proceedings in the first instance, and without attempting alternative dispute resolution to achieve compliance"". Peter Stuyvesant is or was sold in the following countries: Canada, United States, United Kingdom, Luxembourg, Belgium, The Netherlands, Germany, France, Austria, Switzerland, Spain, Italy, Czech Republic, Greece,' - Jochem Pietersen Kuyter September 25, 1647, until the city was incorporated, in 1653, when he was made schout (sheriff). Kuyter twice came in conflict with the Director of New Netherland. Kuyter was a man of good education, what is evident by his dealings with Willem Kieft., who he believed damaged the colony with his policies and the start of Kieft's War in 1643. In 1647, when Peter Stuyvesant arrived in New Amsterdam to replace Kieft, Kuyter and Cornelis Melyn acting in name of the citizens of New Amsterdam, brought charges against the outgoing governor, demanding an investigation of his conduct while in office. - Peter Stuyvesant (cigarette) half of its regular users"" and called the packaging changes ""the ultimate sick joke from big tobacco"". In 2013, it was reported that Imperial Tobacco Australia had sent marketing material to WA tobacco retailers which promotes limited edition packs of "Peter Stuyvesant + Loosie", which came with 26 cigarettes. The material included images of a young woman with pink hair putting on lipstick and men on the streets of New York and also included a calendar and small poster that were clearly intended to glamorise smoking. Anti-smoking campaigner Mike Daube said although the material did not break the law because - 'Peter Stuyvesant but the order was soon revoked under pressure from the States of Holland and the city of Amsterdam. Stuyvesant prepared against an attack by ordering the citizens to dig a ditch from the North River to the East River and to erect a fortification. In 1653, a convention of two deputies from each village in New Netherland demanded reforms, and Stuyvesant commanded that assembly to disperse, saying: "We derive our authority from God and the company, not from a few ignorant subjects." In the summer of 1655, he sailed down the Delaware River with a fleet of seven vessels and' - Peter Stuyvesant Dutch Reformed church, a Calvinist denomination, holding to the Three Forms of Unity (Belgic Confession, Heidelberg Catechism, Canons of Dordt). The English were Anglicans, holding to the 39 Articles, a Protestant confession, with bishops. In 1665, Stuyvesant went to the Netherlands to report on his term as governor. On his return to the colony, he spent the remainder of his life on his farm of sixty-two acres outside the city, called the Great Bouwerie, beyond which stretched the woods and swamps of the village of Nieuw Haarlem. A pear tree that he reputedly brought from the Netherlands in 1647 remained --- # SentenceTransformer based on seongil-dn/unsupervised_20m_3800 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [seongil-dn/unsupervised_20m_3800](https://huggingface.co/seongil-dn/unsupervised_20m_3800). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [seongil-dn/unsupervised_20m_3800](https://huggingface.co/seongil-dn/unsupervised_20m_3800) <!-- at revision 1cda749f242e2b5c9e4f3c1122a61e76fec1fee5 --> - **Maximum Sequence Length:** 1024 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity <!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("seongil-dn/bge-m3-756") # Run inference sentences = [ 'Peter Stuyvesant, born in Holland, became Governor of which American city in 1647?', 'Peter Stuyvesant (cigarette) half of its regular users"" and called the packaging changes ""the ultimate sick joke from big tobacco"". In 2013, it was reported that Imperial Tobacco Australia had sent marketing material to WA tobacco retailers which promotes limited edition packs of "Peter Stuyvesant + Loosie", which came with 26 cigarettes. The material included images of a young woman with pink hair putting on lipstick and men on the streets of New York and also included a calendar and small poster that were clearly intended to glamorise smoking. Anti-smoking campaigner Mike Daube said although the material did not break the law because', 'Peter Stuyvesant (cigarette) can amount to millions of dollars and finally criminal prosecution - if companies wilfully break the laws. However last year, when questioned on why no such action was being pursued against Imperial Tobacco a spokeswoman for Federal Health said: ""No instances of non-compliance with the Act have been identified by the Department that warrant the initiation of Court proceedings in the first instance, and without attempting alternative dispute resolution to achieve compliance"". Peter Stuyvesant is or was sold in the following countries: Canada, United States, United Kingdom, Luxembourg, Belgium, The Netherlands, Germany, France, Austria, Switzerland, Spain, Italy, Czech Republic, Greece,', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 1,138,596 training samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>negative_2</code>, <code>negative_3</code>, <code>negative_4</code>, and <code>negative_5</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | negative_2 | negative_3 | negative_4 | negative_5 | |:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | type | string | string | string | string | string | string | string | | details | <ul><li>min: 9 tokens</li><li>mean: 22.32 tokens</li><li>max: 119 tokens</li></ul> | <ul><li>min: 127 tokens</li><li>mean: 157.45 tokens</li><li>max: 420 tokens</li></ul> | <ul><li>min: 122 tokens</li><li>mean: 154.65 tokens</li><li>max: 212 tokens</li></ul> | <ul><li>min: 122 tokens</li><li>mean: 155.52 tokens</li><li>max: 218 tokens</li></ul> | <ul><li>min: 122 tokens</li><li>mean: 156.04 tokens</li><li>max: 284 tokens</li></ul> | <ul><li>min: 124 tokens</li><li>mean: 156.3 tokens</li><li>max: 268 tokens</li></ul> | <ul><li>min: 121 tokens</li><li>mean: 156.15 tokens</li><li>max: 249 tokens</li></ul> | * Samples: | anchor | positive | negative | negative_2 | negative_3 | negative_4 | negative_5 | |:---------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What African country is projected to pass the United States in population by the year 2055?</code> | <code>African immigration to the United States officially 40,000 African immigrants, although it has been estimated that the population is actually four times this number when considering undocumented immigrants. The majority of these immigrants were born in Ethiopia, Egypt, Nigeria, and South Africa. African immigrants like many other immigrant groups are likely to establish and find success in small businesses. Many Africans that have seen the social and economic stability that comes from ethnic enclaves such as Chinatowns have recently been establishing ethnic enclaves of their own at much higher rates to reap the benefits of such communities. Such examples include Little Ethiopia in Los Angeles and</code> | <code>What Will Happen to the Gang Next Year? watching television at the time of the broadcast. This made it the lowest-rated episode in "30 Rock"<nowiki>'</nowiki>s history. and a decrease from the previous episode "The Return of Avery Jessup" (2.92 million) What Will Happen to the Gang Next Year? "What Will Happen to the Gang Next Year?" is the twenty-second and final episode of the sixth season of the American television comedy series "30 Rock", and the 125th overall episode of the series. It was directed by Michael Engler, and written by Matt Hubbard. The episode originally aired on the National Broadcasting Company (NBC) network in the United States</code> | <code>Christianity in the United States Christ is the fifth-largest denomination, the largest Pentecostal church, and the largest traditionally African-American denomination in the nation. Among Eastern Christian denominations, there are several Eastern Orthodox and Oriental Orthodox churches, with just below 1 million adherents in the US, or 0.4% of the total population. Christianity was introduced to the Americas as it was first colonized by Europeans beginning in the 16th and 17th centuries. Going forward from its foundation, the United States has been called a Protestant nation by a variety of sources. Immigration further increased Christian numbers. Today most Christian churches in the United States are either</code> | <code>What Will Happen to the Gang Next Year? What Will Happen to the Gang Next Year? "What Will Happen to the Gang Next Year?" is the twenty-second and final episode of the sixth season of the American television comedy series "30 Rock", and the 125th overall episode of the series. It was directed by Michael Engler, and written by Matt Hubbard. The episode originally aired on the National Broadcasting Company (NBC) network in the United States on May 17, 2012. In the episode, Jack (Alec Baldwin) and Avery (Elizabeth Banks) seek to renew their vows; Criss (James Marsden) sets out to show Liz (Tina Fey) he can pay</code> | <code>History of the Jews in the United States Representatives by Rep. Samuel Dickstein (D; New York). This also failed to pass. During the Holocaust, fewer than 30,000 Jews a year reached the United States, and some were turned away due to immigration policies. The U.S. did not change its immigration policies until 1948. Currently, laws requiring teaching of the Holocaust are on the books in five states. The Holocaust had a profound impact on the community in the United States, especially after 1960, as Jews tried to comprehend what had happened, and especially to commemorate and grapple with it when looking to the future. Abraham Joshua Heschel summarized</code> | <code>Public holidays in the United States will have very few customers that day. The labor force in the United States comprises about 62% (as of 2014) of the general population. In the United States, 97% of the private sector businesses determine what days this sector of the population gets paid time off, according to a study by the Society for Human Resource Management. The following holidays are observed by the majority of US businesses with paid time off: This list of holidays is based off the official list of federal holidays by year from the US Government. The holidays however are at the discretion of employers</code> | | <code>Which is the largest species of the turtle family?</code> | <code>Loggerhead sea turtle turtle is debated, but most authors consider it a single polymorphic species. Molecular genetics has confirmed hybridization of the loggerhead sea turtle with the Kemp's ridley sea turtle, hawksbill sea turtle, and green sea turtles. The extent of natural hybridization is not yet determined; however, second-generation hybrids have been reported, suggesting some hybrids are fertile. Although evidence is lacking, modern sea turtles probably descended from a single common ancestor during the Cretaceous period. Like all other sea turtles except the leatherback, loggerheads are members of the ancient family Cheloniidae, and appeared about 40 million years ago. Of the six species</code> | <code>Convention on the Conservation of Migratory Species of Wild Animals take joint action. At May 2018, there were 126 Parties to the Convention. The CMS Family covers a great diversity of migratory species. The Appendices of CMS include many mammals, including land mammals, marine mammals and bats; birds; fish; reptiles and one insect. Among the instruments, AEWA covers 254 species of birds that are ecologically dependent on wetlands for at least part of their annual cycle. EUROBATS covers 52 species of bat, the Memorandum of Understanding on the Conservation of Migratory Sharks seven species of shark, the IOSEA Marine Turtle MOU six species of marine turtle and the Raptors MoU</code> | <code>Razor-backed musk turtle Razor-backed musk turtle The razor-backed musk turtle ("Sternotherus carinatus") is a species of turtle in the family Kinosternidae. The species is native to the southern United States. There are no subspecies that are recognized as being valid. "S. carinatus" is found in the states of Alabama, Arkansas, Louisiana, Mississippi, Oklahoma, and Texas. The razor-backed musk turtle grows to a straight carapace length of about . It has a brown-colored carapace, with black markings at the edges of each scute. The carapace has a distinct, sharp keel down the center of its length, giving the species its common name. The body</code> | <code>African helmeted turtle African helmeted turtle The African helmeted turtle ("Pelomedusa subrufa"), also known commonly as the marsh terrapin, the crocodile turtle, or in the pet trade as the African side-necked turtle, is a species of omnivorous side-necked terrapin in the family Pelomedusidae. The species naturally occurs in fresh and stagnant water bodies throughout much of Sub-Saharan Africa, and in southern Yemen. The marsh terrapin is typically a rather small turtle, with most individuals being less than in straight carapace length, but one has been recorded with a length of . It has a black or brown carapace. The top of the tail</code> | <code>Box turtle Box turtle Box turtles are North American turtles of the genus Terrapene. Although box turtles are superficially similar to tortoises in terrestrial habits and overall appearance, they are actually members of the American pond turtle family (Emydidae). The twelve taxa which are distinguished in the genus are distributed over four species. They are largely characterized by having a domed shell, which is hinged at the bottom, allowing the animal to close its shell tightly to escape predators. The genus name "Terrapene" was coined by Merrem in 1820 as a genus separate from "Emys" for those species which had a sternum</code> | <code>Vallarta mud turtle Vallarta mud turtle The Vallarta mud turtle ("Kinosternon vogti") is a recently identified species of mud turtle in the family Kinosternidae. While formerly considered conspecific with the Jalisco mud turtle, further studies indicated that it was a separate species. It can be identified by a combination of the number of plastron and carapace scutes, body size, and the distinctive yellow rostral shield in males. It is endemic to Mexican state of Jalisco. It is only known from a few human-created or human-affected habitats (such as small streams and ponds) found around Puerto Vallarta. It is one of only 3 species</code> | | <code>How many gallons of beer are in an English barrel?</code> | <code>Low-alcohol beer Prohibition in the United States. Near beer could not legally be labeled as "beer" and was officially classified as a "cereal beverage". The public, however, almost universally called it "near beer". The most popular "near beer" was Bevo, brewed by the Anheuser-Busch company. The Pabst company brewed "Pablo", Miller brewed "Vivo", and Schlitz brewed "Famo". Many local and regional breweries stayed in business by marketing their own near-beers. By 1921 production of near beer had reached over 300 million US gallons (1 billion L) a year (36 L/s). A popular illegal practice was to add alcohol to near beer. The</code> | <code>Keg terms "half-barrel" and "quarter-barrel" are derived from the U.S. beer barrel, legally defined as being equal to 31 U.S. gallons (this is not the same volume as some other units also known as "barrels"). A 15.5 U.S. gallon keg is also equal to: However, beer kegs can come in many sizes: In European countries the most common keg size is 50 liters. This includes the UK, which uses a non-metric standard keg of 11 imperial gallons, which is coincidentally equal to . The German DIN 6647-1 and DIN 6647-2 have also defined kegs in the sizes of 30 and 20</code> | <code>Beer in Chile craft beers. They are generally low or very low volume producers. In Chile there are more than 150 craft beer producers distributed along the 15 Chilean Regions. The list below includes: Beer in Chile The primary beer brewed and consumed in Chile is pale lager, though the country also has a tradition of brewing corn beer, known as chicha. Chile’s beer history has a strong German influence – some of the bigger beer producers are from the country’s southern lake district, a region populated by a great number of German immigrants during the 19th century. Chile also produces English ale-style</code> | <code>Barrel variation. In modern times, produce barrels for all dry goods, excepting cranberries, contain 7,056 cubic inches, about 115.627 L. Barrel A barrel, cask, or tun is a hollow cylindrical container, traditionally made of wooden staves bound by wooden or metal hoops. Traditionally, the barrel was a standard size of measure referring to a set capacity or weight of a given commodity. For example, in the UK a barrel of beer refers to a quantity of . Wine was shipped in barrels of . Modern wooden barrels for wine-making are either made of French common oak ("Quercus robur") and white oak</code> | <code>The Rare Barrel The Rare Barrel The Rare Barrel is a brewery and brewpub in Berkeley, California, United States, that exclusively produces sour beers. Founders Jay Goodwin and Alex Wallash met while attending UCSB. They started home-brewing in their apartment and decided that they would one day start a brewery together. Goodwin started working at The Bruery, where he worked his way from a production assistant to brewer, eventually becoming the head of their barrel aging program. The Rare Barrel brewed its first batch of beer in February 2013, and opened its tasting room on December 27, 2013. The Rare Barrel was named</code> | <code>Barrel (unit) Barrel (unit) A barrel is one of several units of volume applied in various contexts; there are dry barrels, fluid barrels (such as the UK beer barrel and US beer barrel), oil barrels and so on. For historical reasons the volumes of some barrel units are roughly double the volumes of others; volumes in common usage range from about . In many connections the term "drum" is used almost interchangeably with "barrel". Since medieval times the term barrel as a unit of measure has had various meanings throughout Europe, ranging from about 100 litres to 1000 litres. The name was</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.01} ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 1024 - `learning_rate`: 3e-05 - `weight_decay`: 0.01 - `warmup_ratio`: 0.05 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 1024 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 3e-05 - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.05 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: True - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.0036 | 1 | 1.0283 | | 0.0072 | 2 | 1.0155 | | 0.0108 | 3 | 0.9858 | | 0.0144 | 4 | 0.9519 | | 0.0181 | 5 | 0.9434 | | 0.0217 | 6 | 0.898 | | 0.0253 | 7 | 0.8798 | | 0.0289 | 8 | 0.7976 | | 0.0325 | 9 | 0.7797 | | 0.0361 | 10 | 0.7464 | | 0.0397 | 11 | 0.743 | | 0.0433 | 12 | 0.716 | | 0.0469 | 13 | 0.7076 | | 0.0505 | 14 | 0.666 | | 0.0542 | 15 | 0.631 | | 0.0578 | 16 | 0.5905 | | 0.0614 | 17 | 0.6537 | | 0.0650 | 18 | 0.5755 | | 0.0686 | 19 | 0.5422 | | 0.0722 | 20 | 0.5393 | | 0.0758 | 21 | 0.5741 | | 0.0794 | 22 | 0.498 | | 0.0830 | 23 | 0.5522 | | 0.0866 | 24 | 0.5592 | | 0.0903 | 25 | 0.4797 | | 0.0939 | 26 | 0.4684 | | 0.0975 | 27 | 0.5207 | | 0.1011 | 28 | 0.4692 | | 0.1047 | 29 | 0.4459 | | 0.1083 | 30 | 0.4439 | | 0.1119 | 31 | 0.4656 | | 0.1155 | 32 | 0.4737 | | 0.1191 | 33 | 0.4391 | | 0.1227 | 34 | 0.4386 | | 0.1264 | 35 | 0.4107 | | 0.1300 | 36 | 0.4513 | | 0.1336 | 37 | 0.3789 | | 0.1372 | 38 | 0.4103 | | 0.1408 | 39 | 0.3929 | | 0.1444 | 40 | 0.4226 | | 0.1480 | 41 | 0.391 | | 0.1516 | 42 | 0.3674 | | 0.1552 | 43 | 0.3607 | | 0.1588 | 44 | 0.3738 | | 0.1625 | 45 | 0.3842 | | 0.1661 | 46 | 0.3498 | | 0.1697 | 47 | 0.3586 | | 0.1733 | 48 | 0.3538 | | 0.1769 | 49 | 0.3572 | | 0.1805 | 50 | 0.3547 | | 0.1841 | 51 | 0.3179 | | 0.1877 | 52 | 0.3436 | | 0.1913 | 53 | 0.3502 | | 0.1949 | 54 | 0.3381 | | 0.1986 | 55 | 0.3547 | | 0.2022 | 56 | 0.3362 | | 0.2058 | 57 | 0.3407 | | 0.2094 | 58 | 0.31 | | 0.2130 | 59 | 0.3039 | | 0.2166 | 60 | 0.3362 | | 0.2202 | 61 | 0.2948 | | 0.2238 | 62 | 0.3429 | | 0.2274 | 63 | 0.3096 | | 0.2310 | 64 | 0.35 | | 0.2347 | 65 | 0.2997 | | 0.2383 | 66 | 0.3258 | | 0.2419 | 67 | 0.3376 | | 0.2455 | 68 | 0.3213 | | 0.2491 | 69 | 0.3185 | | 0.2527 | 70 | 0.3282 | | 0.2563 | 71 | 0.2988 | | 0.2599 | 72 | 0.33 | | 0.2635 | 73 | 0.3066 | | 0.2671 | 74 | 0.3303 | | 0.2708 | 75 | 0.3067 | | 0.2744 | 76 | 0.2996 | | 0.2780 | 77 | 0.3063 | | 0.2816 | 78 | 0.3235 | | 0.2852 | 79 | 0.2902 | | 0.2888 | 80 | 0.302 | | 0.2924 | 81 | 0.3223 | | 0.2960 | 82 | 0.297 | | 0.2996 | 83 | 0.2936 | | 0.3032 | 84 | 0.3279 | | 0.3069 | 85 | 0.2973 | | 0.3105 | 86 | 0.2881 | | 0.3141 | 87 | 0.3014 | | 0.3177 | 88 | 0.2986 | | 0.3213 | 89 | 0.3057 | | 0.3249 | 90 | 0.2887 | | 0.3285 | 91 | 0.2765 | | 0.3321 | 92 | 0.2818 | | 0.3357 | 93 | 0.2904 | | 0.3394 | 94 | 0.267 | | 0.3430 | 95 | 0.2948 | | 0.3466 | 96 | 0.2766 | | 0.3502 | 97 | 0.2782 | | 0.3538 | 98 | 0.3082 | | 0.3574 | 99 | 0.2697 | | 0.3610 | 100 | 0.3006 | | 0.3646 | 101 | 0.2986 | | 0.3682 | 102 | 0.2789 | | 0.3718 | 103 | 0.2756 | | 0.3755 | 104 | 0.2884 | | 0.3791 | 105 | 0.273 | | 0.3827 | 106 | 0.2687 | | 0.3863 | 107 | 0.2808 | | 0.3899 | 108 | 0.2763 | | 0.3935 | 109 | 0.2738 | | 0.3971 | 110 | 0.2642 | | 0.4007 | 111 | 0.2612 | | 0.4043 | 112 | 0.2859 | | 0.4079 | 113 | 0.2558 | | 0.4116 | 114 | 0.2565 | | 0.4152 | 115 | 0.2747 | | 0.4188 | 116 | 0.2684 | | 0.4224 | 117 | 0.2643 | | 0.4260 | 118 | 0.241 | | 0.4296 | 119 | 0.2563 | | 0.4332 | 120 | 0.2754 | | 0.4368 | 121 | 0.2503 | | 0.4404 | 122 | 0.2544 | | 0.4440 | 123 | 0.2729 | | 0.4477 | 124 | 0.2589 | | 0.4513 | 125 | 0.2626 | | 0.4549 | 126 | 0.2693 | | 0.4585 | 127 | 0.2687 | | 0.4621 | 128 | 0.2903 | | 0.4657 | 129 | 0.2663 | | 0.4693 | 130 | 0.2604 | | 0.4729 | 131 | 0.2601 | | 0.4765 | 132 | 0.2649 | | 0.4801 | 133 | 0.2597 | | 0.4838 | 134 | 0.2608 | | 0.4874 | 135 | 0.245 | | 0.4910 | 136 | 0.2587 | | 0.4946 | 137 | 0.2618 | | 0.4982 | 138 | 0.2599 | | 0.5018 | 139 | 0.265 | | 0.5054 | 140 | 0.2427 | | 0.5090 | 141 | 0.2448 | | 0.5126 | 142 | 0.2608 | | 0.5162 | 143 | 0.2188 | | 0.5199 | 144 | 0.2471 | | 0.5235 | 145 | 0.2604 | | 0.5271 | 146 | 0.2571 | | 0.5307 | 147 | 0.2684 | | 0.5343 | 148 | 0.2319 | | 0.5379 | 149 | 0.2572 | | 0.5415 | 150 | 0.2243 | | 0.5451 | 151 | 0.2562 | | 0.5487 | 152 | 0.2457 | | 0.5523 | 153 | 0.255 | | 0.5560 | 154 | 0.2664 | | 0.5596 | 155 | 0.24 | | 0.5632 | 156 | 0.2612 | | 0.5668 | 157 | 0.243 | | 0.5704 | 158 | 0.2345 | | 0.5740 | 159 | 0.2359 | | 0.5776 | 160 | 0.2384 | | 0.5812 | 161 | 0.2541 | | 0.5848 | 162 | 0.2496 | | 0.5884 | 163 | 0.2429 | | 0.5921 | 164 | 0.2411 | | 0.5957 | 165 | 0.2261 | | 0.5993 | 166 | 0.2164 | | 0.6029 | 167 | 0.2251 | | 0.6065 | 168 | 0.2417 | | 0.6101 | 169 | 0.2494 | | 0.6137 | 170 | 0.2359 | | 0.6173 | 171 | 0.2489 | | 0.6209 | 172 | 0.2261 | | 0.6245 | 173 | 0.2367 | | 0.6282 | 174 | 0.2355 | | 0.6318 | 175 | 0.2423 | | 0.6354 | 176 | 0.2454 | | 0.6390 | 177 | 0.2438 | | 0.6426 | 178 | 0.2415 | | 0.6462 | 179 | 0.2237 | | 0.6498 | 180 | 0.2419 | | 0.6534 | 181 | 0.2373 | | 0.6570 | 182 | 0.2659 | | 0.6606 | 183 | 0.2201 | | 0.6643 | 184 | 0.2342 | | 0.6679 | 185 | 0.2149 | | 0.6715 | 186 | 0.2241 | | 0.6751 | 187 | 0.2443 | | 0.6787 | 188 | 0.2489 | | 0.6823 | 189 | 0.2354 | | 0.6859 | 190 | 0.2483 | | 0.6895 | 191 | 0.2193 | | 0.6931 | 192 | 0.229 | | 0.6968 | 193 | 0.2335 | | 0.7004 | 194 | 0.2484 | | 0.7040 | 195 | 0.2317 | | 0.7076 | 196 | 0.2203 | | 0.7112 | 197 | 0.2329 | | 0.7148 | 198 | 0.2084 | | 0.7184 | 199 | 0.2341 | | 0.7220 | 200 | 0.2369 | | 0.7256 | 201 | 0.2364 | | 0.7292 | 202 | 0.2276 | | 0.7329 | 203 | 0.215 | | 0.7365 | 204 | 0.2486 | | 0.7401 | 205 | 0.2237 | | 0.7437 | 206 | 0.218 | | 0.7473 | 207 | 0.2444 | | 0.7509 | 208 | 0.2276 | | 0.7545 | 209 | 0.2127 | | 0.7581 | 210 | 0.2283 | | 0.7617 | 211 | 0.2234 | | 0.7653 | 212 | 0.207 | | 0.7690 | 213 | 0.24 | | 0.7726 | 214 | 0.2317 | | 0.7762 | 215 | 0.2056 | | 0.7798 | 216 | 0.2149 | | 0.7834 | 217 | 0.2211 | | 0.7870 | 218 | 0.2232 | | 0.7906 | 219 | 0.2222 | | 0.7942 | 220 | 0.2481 | | 0.7978 | 221 | 0.227 | | 0.8014 | 222 | 0.2305 | | 0.8051 | 223 | 0.2091 | | 0.8087 | 224 | 0.2278 | | 0.8123 | 225 | 0.2123 | | 0.8159 | 226 | 0.2233 | | 0.8195 | 227 | 0.2365 | | 0.8231 | 228 | 0.2165 | | 0.8267 | 229 | 0.2192 | | 0.8303 | 230 | 0.2145 | | 0.8339 | 231 | 0.2382 | | 0.8375 | 232 | 0.2232 | | 0.8412 | 233 | 0.2273 | | 0.8448 | 234 | 0.2296 | | 0.8484 | 235 | 0.2229 | | 0.8520 | 236 | 0.2213 | | 0.8556 | 237 | 0.2343 | | 0.8592 | 238 | 0.2208 | | 0.8628 | 239 | 0.2315 | | 0.8664 | 240 | 0.2137 | | 0.8700 | 241 | 0.2201 | | 0.8736 | 242 | 0.2185 | | 0.8773 | 243 | 0.2337 | | 0.8809 | 244 | 0.2153 | | 0.8845 | 245 | 0.2369 | | 0.8881 | 246 | 0.2216 | | 0.8917 | 247 | 0.2338 | | 0.8953 | 248 | 0.2241 | | 0.8989 | 249 | 0.213 | | 0.9025 | 250 | 0.2245 | | 0.9061 | 251 | 0.2074 | | 0.9097 | 252 | 0.2283 | | 0.9134 | 253 | 0.2003 | | 0.9170 | 254 | 0.2099 | | 0.9206 | 255 | 0.2288 | | 0.9242 | 256 | 0.2168 | | 0.9278 | 257 | 0.215 | | 0.9314 | 258 | 0.2146 | | 0.9350 | 259 | 0.2126 | | 0.9386 | 260 | 0.2178 | | 0.9422 | 261 | 0.2065 | | 0.9458 | 262 | 0.2327 | | 0.9495 | 263 | 0.2116 | | 0.9531 | 264 | 0.2324 | | 0.9567 | 265 | 0.2235 | | 0.9603 | 266 | 0.2189 | | 0.9639 | 267 | 0.2175 | | 0.9675 | 268 | 0.2171 | | 0.9711 | 269 | 0.1925 | | 0.9747 | 270 | 0.225 | | 0.9783 | 271 | 0.2149 | | 0.9819 | 272 | 0.204 | | 0.9856 | 273 | 0.2004 | | 0.9892 | 274 | 0.2055 | | 0.9928 | 275 | 0.2045 | | 0.9964 | 276 | 0.2186 | | 1.0 | 277 | 0.2215 | | 1.0036 | 278 | 0.1545 | | 1.0072 | 279 | 0.169 | | 1.0108 | 280 | 0.152 | | 1.0144 | 281 | 0.1597 | | 1.0181 | 282 | 0.1626 | | 1.0217 | 283 | 0.1692 | | 1.0253 | 284 | 0.1639 | | 1.0289 | 285 | 0.1638 | | 1.0325 | 286 | 0.1507 | | 1.0361 | 287 | 0.1594 | | 1.0397 | 288 | 0.1621 | | 1.0433 | 289 | 0.1565 | | 1.0469 | 290 | 0.1549 | | 1.0505 | 291 | 0.1731 | | 1.0542 | 292 | 0.152 | | 1.0578 | 293 | 0.1586 | | 1.0614 | 294 | 0.1593 | | 1.0650 | 295 | 0.1406 | | 1.0686 | 296 | 0.1524 | | 1.0722 | 297 | 0.1474 | | 1.0758 | 298 | 0.158 | | 1.0794 | 299 | 0.1743 | | 1.0830 | 300 | 0.1485 | | 1.0866 | 301 | 0.1648 | | 1.0903 | 302 | 0.1337 | | 1.0939 | 303 | 0.1554 | | 1.0975 | 304 | 0.1434 | | 1.1011 | 305 | 0.1642 | | 1.1047 | 306 | 0.159 | | 1.1083 | 307 | 0.1658 | | 1.1119 | 308 | 0.1554 | | 1.1155 | 309 | 0.1425 | | 1.1191 | 310 | 0.1432 | | 1.1227 | 311 | 0.1517 | | 1.1264 | 312 | 0.148 | | 1.1300 | 313 | 0.1636 | | 1.1336 | 314 | 0.1735 | | 1.1372 | 315 | 0.151 | | 1.1408 | 316 | 0.1423 | | 1.1444 | 317 | 0.1501 | | 1.1480 | 318 | 0.1537 | | 1.1516 | 319 | 0.1554 | | 1.1552 | 320 | 0.1553 | | 1.1588 | 321 | 0.149 | | 1.1625 | 322 | 0.1605 | | 1.1661 | 323 | 0.1551 | | 1.1697 | 324 | 0.1555 | | 1.1733 | 325 | 0.1443 | | 1.1769 | 326 | 0.1533 | | 1.1805 | 327 | 0.1658 | | 1.1841 | 328 | 0.15 | | 1.1877 | 329 | 0.1626 | | 1.1913 | 330 | 0.172 | | 1.1949 | 331 | 0.1542 | | 1.1986 | 332 | 0.166 | | 1.2022 | 333 | 0.1513 | | 1.2058 | 334 | 0.1612 | | 1.2094 | 335 | 0.1521 | | 1.2130 | 336 | 0.1552 | | 1.2166 | 337 | 0.1503 | | 1.2202 | 338 | 0.1613 | | 1.2238 | 339 | 0.1563 | | 1.2274 | 340 | 0.1429 | | 1.2310 | 341 | 0.1587 | | 1.2347 | 342 | 0.1477 | | 1.2383 | 343 | 0.1561 | | 1.2419 | 344 | 0.1418 | | 1.2455 | 345 | 0.1495 | | 1.2491 | 346 | 0.1533 | | 1.2527 | 347 | 0.1521 | | 1.2563 | 348 | 0.1422 | | 1.2599 | 349 | 0.1446 | | 1.2635 | 350 | 0.146 | | 1.2671 | 351 | 0.1473 | | 1.2708 | 352 | 0.1566 | | 1.2744 | 353 | 0.1411 | | 1.2780 | 354 | 0.1502 | | 1.2816 | 355 | 0.1383 | | 1.2852 | 356 | 0.1622 | | 1.2888 | 357 | 0.1391 | | 1.2924 | 358 | 0.1455 | | 1.2960 | 359 | 0.1541 | | 1.2996 | 360 | 0.1476 | | 1.3032 | 361 | 0.1662 | | 1.3069 | 362 | 0.1476 | | 1.3105 | 363 | 0.1452 | | 1.3141 | 364 | 0.1372 | | 1.3177 | 365 | 0.1542 | | 1.3213 | 366 | 0.1531 | | 1.3249 | 367 | 0.1623 | | 1.3285 | 368 | 0.1544 | | 1.3321 | 369 | 0.1625 | | 1.3357 | 370 | 0.1459 | | 1.3394 | 371 | 0.1474 | | 1.3430 | 372 | 0.1499 | | 1.3466 | 373 | 0.1495 | | 1.3502 | 374 | 0.1361 | | 1.3538 | 375 | 0.1444 | | 1.3574 | 376 | 0.1495 | | 1.3610 | 377 | 0.1583 | | 1.3646 | 378 | 0.1642 | | 1.3682 | 379 | 0.1646 | | 1.3718 | 380 | 0.1595 | | 1.3755 | 381 | 0.149 | | 1.3791 | 382 | 0.1448 | | 1.3827 | 383 | 0.1603 | | 1.3863 | 384 | 0.1269 | | 1.3899 | 385 | 0.1491 | | 1.3935 | 386 | 0.1367 | | 1.3971 | 387 | 0.1501 | | 1.4007 | 388 | 0.1414 | | 1.4043 | 389 | 0.156 | | 1.4079 | 390 | 0.1428 | | 1.4116 | 391 | 0.1559 | | 1.4152 | 392 | 0.1452 | | 1.4188 | 393 | 0.1547 | | 1.4224 | 394 | 0.1432 | | 1.4260 | 395 | 0.1648 | | 1.4296 | 396 | 0.166 | | 1.4332 | 397 | 0.1485 | | 1.4368 | 398 | 0.1494 | | 1.4404 | 399 | 0.1635 | | 1.4440 | 400 | 0.1498 | | 1.4477 | 401 | 0.1509 | | 1.4513 | 402 | 0.1431 | | 1.4549 | 403 | 0.1547 | | 1.4585 | 404 | 0.1576 | | 1.4621 | 405 | 0.1426 | | 1.4657 | 406 | 0.132 | | 1.4693 | 407 | 0.1511 | | 1.4729 | 408 | 0.1551 | | 1.4765 | 409 | 0.16 | | 1.4801 | 410 | 0.1507 | | 1.4838 | 411 | 0.1591 | | 1.4874 | 412 | 0.1536 | | 1.4910 | 413 | 0.1507 | | 1.4946 | 414 | 0.1564 | | 1.4982 | 415 | 0.153 | | 1.5018 | 416 | 0.1404 | | 1.5054 | 417 | 0.1627 | | 1.5090 | 418 | 0.1432 | | 1.5126 | 419 | 0.1456 | | 1.5162 | 420 | 0.1369 | | 1.5199 | 421 | 0.1554 | | 1.5235 | 422 | 0.1412 | | 1.5271 | 423 | 0.1547 | | 1.5307 | 424 | 0.1555 | | 1.5343 | 425 | 0.1575 | | 1.5379 | 426 | 0.1595 | | 1.5415 | 427 | 0.1464 | | 1.5451 | 428 | 0.1738 | | 1.5487 | 429 | 0.1692 | | 1.5523 | 430 | 0.1566 | | 1.5560 | 431 | 0.1452 | | 1.5596 | 432 | 0.1433 | | 1.5632 | 433 | 0.1584 | | 1.5668 | 434 | 0.1579 | | 1.5704 | 435 | 0.157 | | 1.5740 | 436 | 0.1533 | | 1.5776 | 437 | 0.148 | | 1.5812 | 438 | 0.1381 | | 1.5848 | 439 | 0.1605 | | 1.5884 | 440 | 0.163 | | 1.5921 | 441 | 0.1492 | | 1.5957 | 442 | 0.1601 | | 1.5993 | 443 | 0.1456 | | 1.6029 | 444 | 0.1439 | | 1.6065 | 445 | 0.1553 | | 1.6101 | 446 | 0.1371 | | 1.6137 | 447 | 0.1382 | | 1.6173 | 448 | 0.1458 | | 1.6209 | 449 | 0.14 | | 1.6245 | 450 | 0.1463 | | 1.6282 | 451 | 0.1433 | | 1.6318 | 452 | 0.1472 | | 1.6354 | 453 | 0.1481 | | 1.6390 | 454 | 0.1408 | | 1.6426 | 455 | 0.1525 | | 1.6462 | 456 | 0.1223 | | 1.6498 | 457 | 0.1452 | | 1.6534 | 458 | 0.159 | | 1.6570 | 459 | 0.1389 | | 1.6606 | 460 | 0.1479 | | 1.6643 | 461 | 0.1451 | | 1.6679 | 462 | 0.1651 | | 1.6715 | 463 | 0.1336 | | 1.6751 | 464 | 0.1496 | | 1.6787 | 465 | 0.1384 | | 1.6823 | 466 | 0.143 | | 1.6859 | 467 | 0.1423 | | 1.6895 | 468 | 0.1403 | | 1.6931 | 469 | 0.1577 | | 1.6968 | 470 | 0.1511 | | 1.7004 | 471 | 0.1429 | | 1.7040 | 472 | 0.1445 | | 1.7076 | 473 | 0.1431 | | 1.7112 | 474 | 0.1326 | | 1.7148 | 475 | 0.1554 | | 1.7184 | 476 | 0.1406 | | 1.7220 | 477 | 0.1479 | | 1.7256 | 478 | 0.1521 | | 1.7292 | 479 | 0.1475 | | 1.7329 | 480 | 0.1584 | | 1.7365 | 481 | 0.1393 | | 1.7401 | 482 | 0.1291 | | 1.7437 | 483 | 0.1373 | | 1.7473 | 484 | 0.1555 | | 1.7509 | 485 | 0.1473 | | 1.7545 | 486 | 0.1654 | | 1.7581 | 487 | 0.1568 | | 1.7617 | 488 | 0.1557 | | 1.7653 | 489 | 0.1531 | | 1.7690 | 490 | 0.1385 | | 1.7726 | 491 | 0.1381 | | 1.7762 | 492 | 0.1375 | | 1.7798 | 493 | 0.1472 | | 1.7834 | 494 | 0.1581 | | 1.7870 | 495 | 0.1448 | | 1.7906 | 496 | 0.1443 | | 1.7942 | 497 | 0.1422 | | 1.7978 | 498 | 0.1295 | | 1.8014 | 499 | 0.1463 | | 1.8051 | 500 | 0.1346 | | 1.8087 | 501 | 0.1387 | | 1.8123 | 502 | 0.1463 | | 1.8159 | 503 | 0.1439 | | 1.8195 | 504 | 0.1404 | | 1.8231 | 505 | 0.1433 | | 1.8267 | 506 | 0.136 | | 1.8303 | 507 | 0.14 | | 1.8339 | 508 | 0.1355 | | 1.8375 | 509 | 0.1446 | | 1.8412 | 510 | 0.1564 | | 1.8448 | 511 | 0.1413 | | 1.8484 | 512 | 0.1451 | | 1.8520 | 513 | 0.1453 | | 1.8556 | 514 | 0.1484 | | 1.8592 | 515 | 0.1403 | | 1.8628 | 516 | 0.1568 | | 1.8664 | 517 | 0.1566 | | 1.8700 | 518 | 0.1318 | | 1.8736 | 519 | 0.1483 | | 1.8773 | 520 | 0.1339 | | 1.8809 | 521 | 0.1423 | | 1.8845 | 522 | 0.1349 | | 1.8881 | 523 | 0.1302 | | 1.8917 | 524 | 0.1341 | | 1.8953 | 525 | 0.1456 | | 1.8989 | 526 | 0.1334 | | 1.9025 | 527 | 0.1382 | | 1.9061 | 528 | 0.1462 | | 1.9097 | 529 | 0.1315 | | 1.9134 | 530 | 0.1606 | | 1.9170 | 531 | 0.1308 | | 1.9206 | 532 | 0.1319 | | 1.9242 | 533 | 0.1407 | | 1.9278 | 534 | 0.1385 | | 1.9314 | 535 | 0.1471 | | 1.9350 | 536 | 0.1621 | | 1.9386 | 537 | 0.1436 | | 1.9422 | 538 | 0.151 | | 1.9458 | 539 | 0.1423 | | 1.9495 | 540 | 0.1411 | | 1.9531 | 541 | 0.1535 | | 1.9567 | 542 | 0.143 | | 1.9603 | 543 | 0.149 | | 1.9639 | 544 | 0.1384 | | 1.9675 | 545 | 0.1479 | | 1.9711 | 546 | 0.1452 | | 1.9747 | 547 | 0.1372 | | 1.9783 | 548 | 0.1418 | | 1.9819 | 549 | 0.1443 | | 1.9856 | 550 | 0.1344 | | 1.9892 | 551 | 0.1278 | | 1.9928 | 552 | 0.1447 | | 1.9964 | 553 | 0.1366 | | 2.0 | 554 | 0.141 | | 2.0036 | 555 | 0.1161 | | 2.0072 | 556 | 0.1099 | | 2.0108 | 557 | 0.126 | | 2.0144 | 558 | 0.1163 | | 2.0181 | 559 | 0.1234 | | 2.0217 | 560 | 0.1171 | | 2.0253 | 561 | 0.1073 | | 2.0289 | 562 | 0.1126 | | 2.0325 | 563 | 0.1175 | | 2.0361 | 564 | 0.1086 | | 2.0397 | 565 | 0.1038 | | 2.0433 | 566 | 0.1121 | | 2.0469 | 567 | 0.1154 | | 2.0505 | 568 | 0.0973 | | 2.0542 | 569 | 0.1208 | | 2.0578 | 570 | 0.1064 | | 2.0614 | 571 | 0.1159 | | 2.0650 | 572 | 0.1093 | | 2.0686 | 573 | 0.113 | | 2.0722 | 574 | 0.1033 | | 2.0758 | 575 | 0.1152 | | 2.0794 | 576 | 0.1029 | | 2.0830 | 577 | 0.1204 | | 2.0866 | 578 | 0.1079 | | 2.0903 | 579 | 0.1288 | | 2.0939 | 580 | 0.0998 | | 2.0975 | 581 | 0.1058 | | 2.1011 | 582 | 0.1235 | | 2.1047 | 583 | 0.1059 | | 2.1083 | 584 | 0.0998 | | 2.1119 | 585 | 0.1142 | | 2.1155 | 586 | 0.1082 | | 2.1191 | 587 | 0.0973 | | 2.1227 | 588 | 0.1017 | | 2.1264 | 589 | 0.1045 | | 2.1300 | 590 | 0.123 | | 2.1336 | 591 | 0.1065 | | 2.1372 | 592 | 0.1135 | | 2.1408 | 593 | 0.1027 | | 2.1444 | 594 | 0.1166 | | 2.1480 | 595 | 0.1082 | | 2.1516 | 596 | 0.1113 | | 2.1552 | 597 | 0.1108 | | 2.1588 | 598 | 0.114 | | 2.1625 | 599 | 0.1064 | | 2.1661 | 600 | 0.0955 | | 2.1697 | 601 | 0.113 | | 2.1733 | 602 | 0.1136 | | 2.1769 | 603 | 0.1125 | | 2.1805 | 604 | 0.1146 | | 2.1841 | 605 | 0.1054 | | 2.1877 | 606 | 0.1144 | | 2.1913 | 607 | 0.1038 | | 2.1949 | 608 | 0.1113 | | 2.1986 | 609 | 0.1187 | | 2.2022 | 610 | 0.1166 | | 2.2058 | 611 | 0.1035 | | 2.2094 | 612 | 0.1054 | | 2.2130 | 613 | 0.118 | | 2.2166 | 614 | 0.125 | | 2.2202 | 615 | 0.1142 | | 2.2238 | 616 | 0.1119 | | 2.2274 | 617 | 0.1173 | | 2.2310 | 618 | 0.1024 | | 2.2347 | 619 | 0.105 | | 2.2383 | 620 | 0.1025 | | 2.2419 | 621 | 0.1022 | | 2.2455 | 622 | 0.0995 | | 2.2491 | 623 | 0.1022 | | 2.2527 | 624 | 0.1198 | | 2.2563 | 625 | 0.0995 | | 2.2599 | 626 | 0.1162 | | 2.2635 | 627 | 0.1172 | | 2.2671 | 628 | 0.1037 | | 2.2708 | 629 | 0.1093 | | 2.2744 | 630 | 0.1018 | | 2.2780 | 631 | 0.1168 | | 2.2816 | 632 | 0.1015 | | 2.2852 | 633 | 0.101 | | 2.2888 | 634 | 0.1064 | | 2.2924 | 635 | 0.1185 | | 2.2960 | 636 | 0.1055 | | 2.2996 | 637 | 0.1142 | | 2.3032 | 638 | 0.0966 | | 2.3069 | 639 | 0.1039 | | 2.3105 | 640 | 0.1139 | | 2.3141 | 641 | 0.1181 | | 2.3177 | 642 | 0.1168 | | 2.3213 | 643 | 0.1201 | | 2.3249 | 644 | 0.0984 | | 2.3285 | 645 | 0.1068 | | 2.3321 | 646 | 0.1007 | | 2.3357 | 647 | 0.1179 | | 2.3394 | 648 | 0.1043 | | 2.3430 | 649 | 0.1213 | | 2.3466 | 650 | 0.1027 | | 2.3502 | 651 | 0.1119 | | 2.3538 | 652 | 0.1077 | | 2.3574 | 653 | 0.1061 | | 2.3610 | 654 | 0.1054 | | 2.3646 | 655 | 0.1135 | | 2.3682 | 656 | 0.1136 | | 2.3718 | 657 | 0.1062 | | 2.3755 | 658 | 0.1105 | | 2.3791 | 659 | 0.1157 | | 2.3827 | 660 | 0.1036 | | 2.3863 | 661 | 0.1098 | | 2.3899 | 662 | 0.1195 | | 2.3935 | 663 | 0.1151 | | 2.3971 | 664 | 0.1116 | | 2.4007 | 665 | 0.1086 | | 2.4043 | 666 | 0.1151 | | 2.4079 | 667 | 0.1156 | | 2.4116 | 668 | 0.116 | | 2.4152 | 669 | 0.1055 | | 2.4188 | 670 | 0.1051 | | 2.4224 | 671 | 0.0952 | | 2.4260 | 672 | 0.1012 | | 2.4296 | 673 | 0.1042 | | 2.4332 | 674 | 0.1069 | | 2.4368 | 675 | 0.1148 | | 2.4404 | 676 | 0.0981 | | 2.4440 | 677 | 0.1131 | | 2.4477 | 678 | 0.1026 | | 2.4513 | 679 | 0.1014 | | 2.4549 | 680 | 0.1071 | | 2.4585 | 681 | 0.1171 | | 2.4621 | 682 | 0.1009 | | 2.4657 | 683 | 0.1056 | | 2.4693 | 684 | 0.1107 | | 2.4729 | 685 | 0.1114 | | 2.4765 | 686 | 0.1118 | | 2.4801 | 687 | 0.1166 | | 2.4838 | 688 | 0.1023 | | 2.4874 | 689 | 0.1154 | | 2.4910 | 690 | 0.0968 | | 2.4946 | 691 | 0.1164 | | 2.4982 | 692 | 0.1221 | | 2.5018 | 693 | 0.1131 | | 2.5054 | 694 | 0.1039 | | 2.5090 | 695 | 0.1022 | | 2.5126 | 696 | 0.1052 | | 2.5162 | 697 | 0.1072 | | 2.5199 | 698 | 0.1062 | | 2.5235 | 699 | 0.1035 | | 2.5271 | 700 | 0.107 | | 2.5307 | 701 | 0.1152 | | 2.5343 | 702 | 0.0991 | | 2.5379 | 703 | 0.1139 | | 2.5415 | 704 | 0.1148 | | 2.5451 | 705 | 0.1099 | | 2.5487 | 706 | 0.1064 | | 2.5523 | 707 | 0.1069 | | 2.5560 | 708 | 0.1104 | | 2.5596 | 709 | 0.1157 | | 2.5632 | 710 | 0.1109 | | 2.5668 | 711 | 0.0991 | | 2.5704 | 712 | 0.105 | | 2.5740 | 713 | 0.1104 | | 2.5776 | 714 | 0.1134 | | 2.5812 | 715 | 0.1252 | | 2.5848 | 716 | 0.1205 | | 2.5884 | 717 | 0.112 | | 2.5921 | 718 | 0.1109 | | 2.5957 | 719 | 0.1151 | | 2.5993 | 720 | 0.097 | | 2.6029 | 721 | 0.1018 | | 2.6065 | 722 | 0.1205 | | 2.6101 | 723 | 0.107 | | 2.6137 | 724 | 0.102 | | 2.6173 | 725 | 0.1106 | | 2.6209 | 726 | 0.1068 | | 2.6245 | 727 | 0.1024 | | 2.6282 | 728 | 0.1153 | | 2.6318 | 729 | 0.0984 | | 2.6354 | 730 | 0.1019 | | 2.6390 | 731 | 0.1029 | | 2.6426 | 732 | 0.1147 | | 2.6462 | 733 | 0.1081 | | 2.6498 | 734 | 0.0996 | | 2.6534 | 735 | 0.1133 | | 2.6570 | 736 | 0.1102 | | 2.6606 | 737 | 0.1063 | | 2.6643 | 738 | 0.1119 | | 2.6679 | 739 | 0.1062 | | 2.6715 | 740 | 0.1021 | | 2.6751 | 741 | 0.1058 | | 2.6787 | 742 | 0.1026 | | 2.6823 | 743 | 0.1049 | | 2.6859 | 744 | 0.0894 | | 2.6895 | 745 | 0.1127 | | 2.6931 | 746 | 0.1107 | | 2.6968 | 747 | 0.1134 | | 2.7004 | 748 | 0.103 | | 2.7040 | 749 | 0.1081 | | 2.7076 | 750 | 0.1156 | | 2.7112 | 751 | 0.1092 | | 2.7148 | 752 | 0.1182 | | 2.7184 | 753 | 0.1092 | | 2.7220 | 754 | 0.1077 | | 2.7256 | 755 | 0.1165 | | 2.7292 | 756 | 0.1109 | </details> ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.4.1 - Transformers: 4.49.0 - PyTorch: 2.5.1+cu124 - Accelerate: 1.4.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION", "TRANSLATION" ]
[ "CRAFT" ]
Non_BioNLP
LoneStriker/OpenBioLLM-Llama3-8B-GGUF
LoneStriker
null
[ "gguf", "llama-3", "llama", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation", "en", "arxiv:2305.18290", "arxiv:2303.13375", "arxiv:2212.13138", "arxiv:2305.09617", "arxiv:2402.07023", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:quantized:meta-llama/Meta-Llama-3-8B", "license:llama3", "endpoints_compatible", "region:us" ]
1,714
1,714
30
1
--- base_model: meta-llama/Meta-Llama-3-8B language: - en license: llama3 tags: - llama-3 - llama - Mixtral - instruct - finetune - chatml - DPO - RLHF - gpt4 - distillation widget: - example_title: OpenBioLLM-8B messages: - role: system content: You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. - role: user content: How long does it take for newborn jaundice to go away? output: text: 'Newborn jaundice, also known as neonatal jaundice, is a common condition in newborns where the yellowing of the skin and eyes occurs due to an elevated level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when red blood cells break down. In most cases, newborn jaundice resolves on its own without any specific treatment. The duration of newborn jaundice can vary depending on several factors such as the underlying cause, gestational age at birth, and individual variations in bilirubin metabolism. Here are some general guidelines: 1. Physiological jaundice: This is the most common type of newborn jaundice and usually appears within 24-72 hours after birth. It tends to peak between the second and fifth day of life and gradually improves over the next week or two. By the time the baby is one week old, the jaundice should have mostly resolved. 2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and may appear later than physiological jaundice, typically between the fifth and fourteenth day of life. It tends to persist for a longer duration but usually resolves within six weeks after birth. 3. Pathological jaundice: This type of jaundice is less common and occurs due to an underlying medical condition that affects bilirubin metabolism or liver function. The duration of pathological jaundice depends on the specific cause and may require treatment. It''s important for parents to monitor their newborn''s jaundice closely and seek medical advice if the jaundice progresses rapidly, becomes severe, or is accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness. In these cases, further evaluation and management may be necessary. Remember that each baby is unique, and the timing of jaundice resolution can vary. If you have concerns about your newborn''s jaundice, it''s always best to consult with a healthcare professional for personalized advice and guidance.' model-index: - name: OpenBioLLM-8B results: [] --- <div align="center"> <img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/2FhDh8NDvMl7iSxbQz9BP.png) <div align="center"> <h1>Advancing Open-source Large Language Models in Medical Domain</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://colab.research.google.com/drive/1F5oV20InEYeAJGmBwYF9NM_QhLmjBkKJ?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> | <a href="https://github.com/openlifescience-ai"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> | <a href="#"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> | <a href="https://discord.gg/A5Fjf5zC69"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/KGmRE5w2sepNtwsEu8t7K.jpeg) Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. 🏥 **Biomedical Specialization**: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. 🎓 **Superior Performance**: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. 🧠 **Advanced Training Techniques**: OpenBioLLM-8B builds upon the powerful foundations of the **Meta-Llama-3-8B** and [Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: <div align="center"> <img width="1200px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png"> </div> - **Policy Optimization**: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290) - **Ranking Dataset**: [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar) - **Fine-tuning dataset**: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated) This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications. ⚙️ **Release Details**: - **Model Size**: 8 billion parameters - **Quantization**: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-8B-GGUF) - **Language(s) (NLP):** en - **Developed By**: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs - **License:** Meta-Llama License - **Fine-tuned from models:** [meta-llama/Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) - **Resources for more information:** - Paper: Coming soon The model can be fine-tuned for more specialized tasks and datasets as needed. OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences. We are excited to share OpenBioLLM-8B with researchers and developers around the world. ### Use with transformers **Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.** See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "aaditya/OpenBioLLM-Llama3-8B" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."}, {"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.0, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## **Training procedure** ### **Training hyperparameters** <details> <summary>Click to see details</summary> - learning_rate: 0.0002 - lr_scheduler: cosine - train_batch_size: 12 - eval_batch_size: 8 - GPU: H100 80GB SXM5 - num_devices: 1 - optimizer: adamw_bnb_8bit - lr_scheduler_warmup_steps: 100 - num_epochs: 4 </details> ### **Peft hyperparameters** <details> <summary>Click to see details</summary> - adapter: qlora - lora_r: 128 - lora_alpha: 256 - lora_dropout: 0.05 - lora_target_linear: true -lora_target_modules: - q_proj - v_proj - k_proj - o_proj - gate_proj - down_proj - up_proj </details> ### **Training results** ### **Framework versions** - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1 - Axolotl - Lm harness for evaluation # Benchmark Results 🔥 OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge. 🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy. | | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA 4 opts | PubMedQA | MedMCQA | Avg | |--------------------|-------------|------------------|---------|--------------|-----------------|------------------|--------------|----------|---------|-------| | **OpenBioLLM-70B** | **92.93** | **93.197** | **83.904** | 93.75 | 93.827 | **85.749** | 78.162 | 78.97 | **74.014** | **86.05588** | | Med-PaLM-2 (5-shot) | 88.3 | 90 | 77.8 | **95.2** | 94.4 | 80.9 | **79.7** | **79.2** | 71.3 | 84.08 | | **GPT-4** | 86.04 | 91 | 80 | 93.01 | **95.14** | 76.88 | 78.87 | 75.2 | 69.52 | 82.85 | | Med-PaLM-1 (Flan-PaLM, 5-shot) | 80.4 | 75 | 63.7 | 83.8 | 88.9 | 76.3 | 67.6 | 79 | 57.6 | 74.7 | | **OpenBioLLM-8B** | 76.101 | 86.1 | 69.829 | 78.21 | 84.213 | 68.042 | 58.993 | 74.12 | 56.913 | 72.502 | | Gemini-1.0 | 76.7 | 75.8 | 66.7 | 77.7 | 88 | 69.2 | 58 | 70.7 | 54.3 | 70.79 | | GPT-3.5 Turbo 1106 | 74.71 | 74 | 72.79 | 72.79 | 72.91 | 64.73 | 57.71 | 72.66 | 53.79 | 66 | | Meditron-70B | 66.79 | 69 | 53.33 | 71.69 | 76.38 | 63 | 57.1 | 76.6 | 46.85 | 64.52 | | gemma-7b | 69.81 | 70 | 59.26 | 66.18 | 79.86 | 60.12 | 47.21 | 76.2 | 48.96 | 64.18 | | Mistral-7B-v0.1 | 68.68 | 71 | 55.56 | 68.38 | 68.06 | 59.54 | 50.82 | 75.4 | 48.2 | 62.85 | | Apollo-7B | 62.26 | 72 | 61.48 | 69.12 | 70.83 | 55.49 | 55.22 | 39.8 | 53.77 | 60 | | MedAlpaca-7b | 57.36 | 69 | 57.04 | 67.28 | 65.28 | 54.34 | 41.71 | 72.8 | 37.51 | 58.03 | | BioMistral-7B | 59.9 | 64 | 56.5 | 60.4 | 59 | 54.7 | 50.6 | 77.5 | 48.1 | 57.3 | | AlpaCare-llama2-7b | 49.81 | 49 | 45.92 | 33.82 | 50 | 43.35 | 29.77 | 72.2 | 34.42 | 45.36 | | ClinicalGPT | 30.56 | 27 | 30.37 | 19.48 | 25 | 24.27 | 26.08 | 63.8 | 28.18 | 30.52 | <div align="center"> <img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png"> </div> ## Detailed Medical Subjectwise accuracy ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/UXF-V0col0Z0sS6BGPBkE.png) # Use Cases & Examples 🚨 **Below results are from the quantized version of OpenBioLLM-70B** # Summarize Clinical Notes OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/xdwdBgOxNi_TfML0hKlI8.png) # Answer Medical Questions OpenBioLLM-70B can provide answers to a wide range of medical questions. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/zO95GlwOQEZqCKQF69mE6.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/OKBczKw7gWeW5xsuDpc27.png) <details> <summary>Click to see details</summary> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/eJGHT5khppYvJb8fQ-YW4.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Cnbwrqa_-ORHRuNRC2P6Y.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/J9DhdcvukAc9mnnW9fj2C.png) </details> # Clinical Entity Recognition OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_69BW4k9LVABFwtxixL45.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/DKy5wYCoPhoPPUc1-x8_J.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/7WD9zCCBZT4-4XlfnIQjl.png) # Biomarkers Extraction ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/ZttoM4AiteT7gFYVhjIpN.png) # Classification OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Bf5MW1d75qT-1F_TR_hC0.png) # De-Identification OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/hKX4kzm--Tw5bj6K78msy.png) **Advisory Notice!**  While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments. Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor. Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including: - Thorough testing and evaluation in relevant clinical scenarios - Alignment with evidence-based guidelines and best practices - Mitigation of potential biases and failure modes - Integration with human oversight and interpretation - Compliance with regulatory and ethical standards Always consult a qualified healthcare provider for personal medical needs. # Citation If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows: ``` @misc{OpenBioLLMs, author = {Ankit Pal, Malaikannan Sankarasubbu}, title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}} } ``` The accompanying paper is currently in progress and will be released soon. <div align="center"> <h2> 💌 Contact </h2> </div> We look forward to hearing you and collaborating on this exciting project! **Contributors:** - [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com] - Saama AI Labs - Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. # References We thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models! Result sources - [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375) - [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138) - [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617) - [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023)
[ "QUESTION_ANSWERING" ]
[ "MEDQA", "PUBMEDQA" ]
BioNLP
medspaner/mdeberta-v3-base-es-trials-misc-ents
medspaner
token-classification
[ "transformers", "pytorch", "deberta-v2", "token-classification", "generated_from_trainer", "arxiv:2111.09543", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,705
1,727
12
0
--- license: cc-by-nc-4.0 metrics: - precision - recall - f1 - accuracy tags: - generated_from_trainer widget: - text: 'Motivo de consulta: migraña leve. Exploración: Tensión arterial: 120/70 mmHg.' model-index: - name: mdeberta-v3-base-es-trials-misc-ents results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mdeberta-v3-base-es-trials-misc-ents This medical named entity recognition model detects the following clinical entities: - Concept: e.g. *fecha de inclusión*, 'inclusion date'. - Food\_or\_Dring: e.g. *soja*, 'soy'; *leche*, 'milk'. - Observation\_or\_Finding: e.g. *normotenso*, 'normal tension'. - Quantifier\_or\_Qualifier: e.g. *grave*, 'severe'. - Result\_or\_Value: e.g. *< 3 LNS*, '< 3 UNL'. The model achieves the following results on the test set (when trained with the training and development set; results are averaged over 5 evaluation rounds): - Precision: 0.702 (±0.011) - Recall: 0.670 (±0.007) - F1: 0.686 (±0.004) - Accuracy: 0.955 (±0.001) ## Model description This model adapts the [mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) model, which is a multilingual version of the model presented in [He et al. (2021)](https://arxiv.org/abs/2111.09543), pre-trained on 2.5T of data from the CommonCrawl corpus for 100 languages. We fine-tuned ``mdeberta-v3-base`` to conduct medical named entity recognition on Spanish texts about clinical trials using the [CT-EBM-ES corpus (Campillos-Llanos et al. 2021)](https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01395-z) vs 2. If you use this model, please, cite as follows: ``` @article{campillosetal2024,         title = {{Hybrid tool for semantic annotation and concept extraction of medical texts in Spanish}},         author = {Campillos-Llanos, Leonardo and Valverde-Mateos, Ana and Capllonch-Carri{\'o}n, Adri{\'a}n},         journal = {BMC Bioinformatics}, year={2024}, publisher={BioMed Central} } ``` ## Intended uses & limitations **Disclosure**: *This model is under development and needs to be improved. It should not be used for medical decision making without human assistance and supervision* This model is intended for a generalist purpose, and may have bias and/or any other undesirable distortions. Third parties who deploy or provide systems and/or services using any of these models (or using systems based on these models) should note that it is their responsibility to mitigate the risks arising from their use. Third parties, in any event, need to comply with applicable regulations, including regulations concerning the use of artificial intelligence. The owner or creator of the models will in no event be liable for any results arising from the use made by third parties of these models. **Descargo de responsabilidad**: *Esta herramienta se encuentra en desarrollo y no debe ser empleada para la toma de decisiones médicas* La finalidad de este modelo es generalista, y se advierte que puede tener sesgos y/u otro tipo de distorsiones indeseables. Terceras partes que desplieguen o proporcionen sistemas y/o servicios usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) han tener presente que es su responsabilidad abordar y minimizar los riesgos derivados de su uso. Las terceras partes, en cualquier circunstancia, deben cumplir con la normativa aplicable, incluyendo la normativa que concierne al uso de la inteligencia artificial. El propietario o creador de los modelos de ningún modo será responsable de los resultados derivados del uso que las terceras partes hagan de estos modelos. ## Training and evaluation data The data used for fine-tuning are the [Clinical Trials for Evidence-Based-Medicine in Spanish corpus](http://www.lllf.uam.es/ESP/nlpdata/wp2/) vs 2. It is a collection of 1200 texts about clinical trials studies and clinical trials announcements: - 500 abstracts from journals published under a Creative Commons license, e.g. available in PubMed or the Scientific Electronic Library Online (SciELO) - 700 clinical trials announcements published in the European Clinical Trials Register and Repositorio Español de Estudios Clínicos If you use the CT-EBM-ES resource, please, cite as follows: ``` @article{campillosetal-midm2021,         title = {A clinical trials corpus annotated with UMLS© entities to enhance the access to Evidence-Based Medicine},         author = {Campillos-Llanos, Leonardo and Valverde-Mateos, Ana and Capllonch-Carri{\'o}n, Adri{\'a}n and Moreno-Sandoval, Antonio},         journal = {BMC Medical Informatics and Decision Making},         volume={21}, number={1}, pages={1--19}, year={2021}, publisher={BioMed Central} } ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: we used different seeds for 5 evaluation rounds, and uploaded the model with the best results - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: average 18.00 epochs (±2.74); trained with early stopping if no improvement after 5 epochs (early stopping patience: 5) ### Training results (test set; average and standard deviation of 5 rounds with different seeds) | Precision | Recall | F1 | Accuracy | |:--------------:|:--------------:|:--------------:|:--------------:| | 0.702 (±0.011) | 0.670 (±0.007) | 0.686 (±0.004) | 0.955 (±0.001) | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6
[ "NAMED_ENTITY_RECOGNITION" ]
[ "SCIELO" ]
BioNLP
carsondial/slinger20241231-3
carsondial
sentence-similarity
[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:45000", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:BAAI/bge-base-en-v1.5", "base_model:finetune:BAAI/bge-base-en-v1.5", "license:apache-2.0", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,735
1,735
6
0
--- base_model: BAAI/bge-base-en-v1.5 language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:45000 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: cms pros and cons sentences: - 'Choosing a CMS for your business Last Updated on January 20, 2021 Whether your company is building a website for the first time or overhauling an existing one, choosing a content management system (CMS) is an important step in making sure that your website is easy to modify and update. There are tons of CMS platforms out there, so how do you choose? Should you go with a proprietary CMS or open source? Continue reading, and we’ll help guide you in your decision with some pros and cons of picking a CMS. Let’s take a step back. What exactly is a CMS? As defined by TechTarget, a content management system (CMS) is a software application or set of related programs that are used to create and manage digital content. A CMS allows users to update their website without needing deep technical knowledge or the help of a developer. Among other features, users can modify the content on existing pages, create new pages, manage content hierarchy and influence SEO. Defining Open Source vs. Proprietary CMS An open source system means that the CMS is built and maintained by users across the world. The source code is available to anyone, so those with development skills are able to modify and create new functionality. The software itself in open source systems are typically free of cost. Examples include WordPress, Drupal, Joomla, Umbraco or DotNetNuke. In contrast, a proprietary CMS is built, maintained and supported by a single company, and they own the code. This also means that since you don’t own the software, there may be a licensing fee to use it. There are some others that fall between the two that require a license to use but the code can still be taken elsewhere. An example of this is Sitefinity. Pros and Cons of Open Source - It’s very flexible and customizable. You can make the CMS fit your specific needs and integrate it with other technologies. - If you ever want to move off of this platform, you’ll be able to export your data and transfer it with you to a new tool. - Unlike most proprietary CMS tools, the code is upgraded regularly, and there are always new plugins being created to fit your needs. - Even though there’s not one company backing the platform, there are thousands of developers in the community who serve as your support system. - Most open source CMS platforms have a simple interface, making it easy for non-technical people to use. - No monthly or annual licensing fees. - Since more people use an open source CMS, you are at higher risk for spam and security issues. The good news is, many systems, including WordPress have made security a high-priority, so it’s not as big of an issue as it once was. - Depending on your customization needs, it could cost a lot of money upfront to build the CMS system and front-end design that you require. Pros and Cons of Proprietary - Proprietary CMSes are typically very robust, but the developer can disable the features you don’t need. This may make it easier for the average person to use. - There may be a CMS customized for your industry. For instance, there may be one that has been customized for the real estate industry, so you’d have a solution that has a competitive advantage rather than starting from scratch. - The developer knows that platform inside and out, so there is no learning curve. - A proprietary solution may be more secure than open source CMS. Since fewer people are familiar with it and have access to the source code, there will naturally be fewer hack attempts, spam, and overall security issues. - There is a unified team of developers rather than a disparate community of developers. - Many times, the company is not keeping it up to date with the latest technologies. You may be less current than some of your competitors. - You may not be able to take the website design, content, and data with you if you want to switch to a new platform or if the company goes out of business. - The administration system may be cumbersome and harder to use. - There is not a “community” of developers looking to add plugins and keep it up to date. - There’s a lack of customization. Even though there may be a CMS tailored toward your industry, it will be very difficult to have new features added. What you see is what you get. So sum it up for me – what should I use? In most cases, the team here at US Digital Partners would recommend an open source solution. In fact, we previously had a proprietary CMS for our clients but have since switched to using open-source. According to Kevin Saffer, our Director of Technology, “It was difficult to keep up with, and we were essentially rewriting the code any time because our clients all wanted something a bit different. It wasn’t possible to tailor the platform for each client. From an agency perspective, ever since open source CMS platforms such as Sitefinity and WordPress came around, we have been able to focus on doing what we do best – strategy, design and implementation – not core development of software.” Of course, every situation is different. As you are choosing a CMS tool, consider these questions to ask your website development firm. - What is the history of your tool? When were the last upgrades or revisions? - Can I talk to a couple of your customers who use your CMS? - Can I host my website anywhere or must it be with you? - What happens if we want to part ways? - Do I own the website and all the underlying programming? - Do we have a usable website that is transferable to another partner? - Will another web development firm be able to pick up the website and be able to update and make changes?' - 'FREE SHIPPING & FREE RETURNS! Thousands of designer women''s shoes - 40% OFF SITEWIDE! - USE CODE: OHSNAP40 Make it a night to remember in this sassy pump by Seychelles. A gold glitter covers this cute style and features wrapping straps. A dainty bow at the peep toe, 5 1/2 inch heel and 1 1/2 inch platform complete this lovable style.' - 'Any product or service that is advertised requires strategic planning. The product you''re going to put on the market has to be useful in solving some of your prospects'' problems. How does the prospect, on the other hand, know and understand it? When it comes to marketing your product or service, strategizing the campaign becomes a must. Your campaign plan''s strategy reveals how much potential your campaign has to generate conversions or achieve the end goal. Steps to Campaign Strategy These are the basic steps to be followed while strategizing campaign : 1. Define or review your Goal The main objective of any campaign is the goal or outcome which you want as return on investment on your campaign. It should be properly defined and focussed. The objective should be reviewed properly before considering it for further execution. Knowing what you want to achieve in the short term is important. Knowing what you want to achieve in the long run is essential. 2. Determine the brand or theme of your campaign The distinction between a brand and branding is the first thing we must comprehend. You will gain a better understanding of themed brand identity and why it is so effective if you do so. A themed brand identity, in a nutshell, is a versatile and visual language created to represent and position a company. A well-designed theme provides far more than just visual consistency. A theme is a collection of visual and typographic elements that work together to complement and support the logo in accurately representing the intended brand. Following questions should be kept into mind while determining the brand theme of your campaign - Are you looking for new assets, existing assets or updated assets? Which audience do you want to reach? Are you in need of custom graphics? Press releases/social media ads are you needed? 3. Plan your campaign calendar The flow of a campaign and the quality of its execution are determined by the campaign calendar. It is bifurcated differently for the entire campaign of certain days or months or whatever. And a desired goal is assigned to each individual day, which is then followed by. Start planning and facing requests for your campaign requirement once you''ve mapped out the timeline. 4. Execute your campaign We should break down the process into steps before launching your campaign. Each step will lead to a better campaign execution. Some point which should be understood before executing your campaign - Check your work with someone else to make sure nothing is missing. We all become hyper-focused when it comes to creating creative marketing campaigns, and things can slip through the cracks. Assign a non-marketing person to gain a better understanding of his viewpoint and conduct a call to action review on him. It will assist you in gaining a better understanding of your campaign design in a very practical manner. 5. Review your result Review your work after completing the above steps and the campaign to see how it performed overall and whether you met your goal. While reviewing your campaign, keep the following points in mind. Have the objectives set forth in the preceding steps been met? What can we do to make it better? Where can we make improvements? What went wrong, and what went right? Characteristics of Successful Campaign Any successful campaign will have a more effective communication structure, with well-targeted listeners who are well-understood. Before being benignly released, all messages should be thoroughly reviewed. In everything they do, successful campaigns establish credibility and a sense of truth and rightness. It is simple to accept that what is said is correct, and it becomes more difficult to accuse someone of lying or being unfair. Messages are aimed specifically at those who can credibly repeat them to others. The campaign could focus on something as simple as fairness cream or a well-known social issue. When you receive a message that makes you feel bad, you are likely to want to flee. Messages that make you feel good, on the other hand, draw you in as you seek more of the same. As a result, campaigns aim to create a sense of warmth, happiness, and excitement that people find appealing and desirable.' - source_sentence: asheville online training video courses sentences: - 'Systems & Marketing Solutions – AdWords is the center of our universe and we do it better than anyone else. We have actively managed Google Adwords Accounts since July 7, 2003, making us one of the oldest Adwords Agencies in the USA. SMS is a Google Qualified Company which is the highest level of certification in the Adwords Profession. We assist our clients by leveraging the Google Adwords System to advance their marketing goals. A Proven Strategy Whether you’re starting from scratch with a new account or you need help with an existing one, we can help you! SMS follows a proven strategy that improves the business performance of your investment in Google Adwords. We know Adwords down to the finest detail, but we communicate in management terms. We get to understand your business and we are the Adwords Expert on your marketing team. An Experienced Team If you need an experienced team with the ability to drive highly qualified traffic to your web site that also works well with the other parts of your marketing team, then call us. You will find us affordable, effective, smart, and fast. SMS is not a large business so you get personal attention to your business needs. There is no sales staff that hands off the account to a production staff. The experts you talk to are the same ones that will be providing service to your business in the long term. As marketing technologists, we embrace the latest technology but we believe that over automation can be hazardous to your marketing. An experienced Google Adwords Professional with knowledge of your business can apply judgment that is light-years beyond any technology. Marketing is about communicating with people and technology is merely a tool in our process. The Right Connections Getting the job done often comes down to knowing the right people. We have been around since the early days of Adwords and we have actively pursued the right connections. While the typical Adwords Advertiser has to wait in line to get an email response from someone who knows nothing about your business. We can pick up the phone and talk to our assigned team at Google and get quick and accurate answers to the more complex challenges.' - 'How to Market & Sell a New Product Even if a company’s new product has significant competitive advantages, introducing it to the marketplace is extremely challenging. Customers need to be educated about the product’s uses and benefits, which requires an investment of both time and money. Many smaller companies with limited financial resources must think of creative, low cost methods of marketing their product. Identify your initial target markets--the groups of customers who are most likely to purchase the product or the easiest to reach with the marketing and promotion resources the company has available. Consider segmenting your market by geographic region and concentrating your resources on the region with the largest number of target customers and the weakest competition. Create a marketing budget for the next twelve months. Be realistic about what you will be able to spend on marketing and still cover the company’s other expenses, such as facilities costs and payroll. Allocate your financial resources so you can test-market your product with various strategies rather than putting all the money into one category, such as selling at trade shows. Determine the most powerful attributes of your product and showcase these in your marketing messages in each type of media you select. Look at what is truly unique, different and better about your product. You need to craft a message that sets your product apart from others currently available and attracts attention from target customers. Select distribution channels that give you the fastest access to customers. A consumer product manufacturer may find that it has difficulty getting large chain stores to stock its new, untested product. The company could reach out to customers with direct-response marketing--selling the product on TV infomercials. When the product becomes popular, the company will have a better chance of generating interest from large retailers. Choose an entry level price-point that is competitive--close to what your competitors are selling their product for. Don’t deeply discount the product in order to attract customers. Let the product’s attributes drive sales, not just price. Take advantage of low cost mass marketing through the Internet. Develop a website that presents the product, and your company, in an exciting way. Make it easy for the customer to buy the product by listing on your website all the places it is available. Obtain testimonials from satisfied customers and publish these on your website. Harness the power of publicity. Contact the business reporter at your local newspaper and tell her about the product. Craft an interesting press release that talks about how the product came to be, where you got the original idea for the product and why you thought it would be a winner. - Don’t panic if initial sales results from your marketing campaign are lower than expected. Some products take more time to gain customer awareness. You may need to fine-tune your marketing message and strategies, but don’t assume your product is a failure. Perseverance is important when introducing a new product. - Use every means possible to get the product in front of the customer. A manufacturer of grilling spices could give away product samples at gourmet food fairs or barbecue competitions. He could hand out order forms and product literature and tell these potential customers the many uses of the product. - Thomas Northcut/Lifesize/Getty Images' - 'Jan 18, 2008 — Announcements Video-Based Training Service Offers New Options January 18, 2008 – Asheville, NC Infinity Learning Solutions announces the latest release of DigitalChalk.com, an online learning system that offers multimedia-based training as an on-demand service. [pullquote position=”center” cite=”Russ Stinehour, President”]”This online service is a groundbreaking advancement in e-learning. We are putting all the tools needed to build rich multimedia lessons into an online design and delivery system. With DigitalChalk.com, everyone can publish high- quality online training without being an expert in complex authoring tools.”[/pullquote] “DigitalChalk is going to turn the e-learning market delivery model on its head,” says Tony McCune, VP of Sales. “With our Winter 2008 release, we have combined our online Chalkboard Editor and Viewer with our e-commerce delivery system. Instructors can build, publish and sell video-based courses online within a matter of minutes.” DigitalChalk.com provides a web-based visual studio with the tools for synchronizing video, audio, PowerPoint(R) and web content into a multimedia presentation. There is absolutely no software to buy and install. “Instructors publish courses on DigitalChalk and get paid based on the courses sold on the system. It’s a no-risk option for posting video training online,” says McCune. DigitalChalk also offers the same features in a business account for corporate customers that include a branded training portal and integrated user management. Infinity Learning Solutions, an e-learning software business with online training and assessment products, is corporately located in Asheville, North Carolina with offices located in Atlanta, Orlando and Kansas.' - source_sentence: buy darvocet online no prescription sentences: - '11 minute read While these are challenging times, right now is the start of a new era for eCommerce advertising. By necessity, the face of retail will be changed forever. For example, we’re seeing search activity from consumers which includes the phrase “buy online” almost quadrupled in 2020. We can expect consumer shopping behaviors to be influenced in the longer term by what happens now. This is the time to get our houses in order and ensure we are ready to capture the opportunities that will continue to present themselves through eCommerce. Here we highlight the 10 best practices from across the globe, which are delivering demonstrable improvement in return on ad spend (ROAS) for eCommerce advertisers. Our favorite measure for marketing efficiency here at Optily is ROAS. ROAS is how much eCommerce revenue is attributed to that activity per $1 spent. What benchmark should you have in mind for ROAS? While each category is unique, in our experience eCommerce companies typically set expectations of a minimum of 5x ROAS. This means that for every $1 spent on advertising at least $5 of incremental revenue can be attributed back. #1 Dynamic funnel marketing When measuring advertising effectiveness based on ROAS, advertising spend is generally most inefficient at the top of the funnel. Notwithstanding, generating eyeballs across any platform today has never been more cost-effective. However, potentially 90%+ of those impressions are wasted on people who will never purchase from you. It is clear that marketers want to prioritize down-stream funnel activity as much as possible. That being said, marketers today fully appreciate that a lack of investment in higher funnel engagement stunts growth. As with most things, you need to strike a balance. Israeli skincare brand FRÉ, tackled this problem within the Facebook ecosystem. They effectively structured campaign creatives and remarketing in line with their customers’ purchase journeys. Within one month, they were able to achieve a 22% lower cost per sale. Pet Drugs Online, the UK leader in pet medication online, employed dynamic funnel optimization on Facebook & Instagram. This helped them to prioritize ads and audiences with the highest propensities to convert. Ultimately, when these audiences were saturated, optimization dynamically shifted emphasis further up the funnel, delivering 10x improvement in conversion rate and 56% lower acquisition costs #2 Cross-channel ad optimization Purchase journeys are as unique and complex as each individual. Purchase decisions around products as diverse as beauty and personal electronics can involve hundreds of touchpoints. Armed with this information, eCommerce marketers must actively engage with shoppers via as many relevant touchpoints as possible. With limited resources, it is crucial to optimize investment towards the touchpoints that deliver the highest returns. The ad platforms enable marketers to analyze and optimize ad spend within their ecosystems. However, there are significant efficiencies to be achieved by eliminating the waste that occurs between platforms. Transitioning from simplistic rules-based attribution models to data-driven attribution models, which enable you to dynamically assess the relative impact of each channel, is vital. The UK’s largest charitable social enterprise, Better, achieved significant improvements in efficiency. They optimized the ad spend on Facebook & Instagram based on cross-channel advertising performance data. Within just a couple of weeks, they were able to achieve 2.8x more conversions from Facebook with the same ad spend. #3 First-party shopper data Ad platforms are pretty awesome at unearthing in-market audiences. However, no one knows your customers better than you do. By using custom audiences and lookalike tools within the platforms, you can leverage your own insights. These data will help you pinpoint your best performing segments with the power of the platforms to efficiently scale your advertising. Jubly-Umph, an Australian eCommerce business that sells art and jewelry wanted to find people in the US who were interested in its niche product. The brand split-tested Facebook’s Interest Targeting against International Lookalike Targeting to determine the most efficient US market-entry strategy based on existing customer profiles. The lookalikes achieved a 3.1x higher ROAS than the interest targeting approach. US insurer, Allstate realized that 70% of their existing customers were researching new financial products online. The insurer used Customer Match in Google to personalize ad messages to existing customers and generated sales at 1⁄4 of the cost of acquiring new customers. eCommerce brand MeUndies leveraged Snapchat pixel data to target customers who had previously purchased or who had shown high purchase intent. This approach yielded a 66% ROAS improvement. #4 Personalized creative 90% of consumers say that messages from companies that are not personally relevant to them are “annoying.” 80% are more likely to make a purchase where brands provide a personalized experience. Thankfully, the ad platforms are making the challenge of personalization much easier with dynamic creative formats. UK fashion retailer, JD Williams, used Facebook’s Dynamic Ads, tasking the Facebook algorithms with delivering the optimum creative format into the placements where individuals were more likely to engage. This helped JDW achieve a 21% lift in incremental conversions. Dorothy Perkins similarly layered dynamic ads with pixel-driven targeting to achieve 8x incremental ROAS. Singapore travel insurer NTUC Income leveraged YouTube’s Director Mix toolkit. This helped to automate the creation of 500 variants of 6-second bumper ads with different characters suffering accidents in different locations. As a result, they saw a 50% increase in key metrics such as branded search and brand recall. The UK’s Topps Tiles leveraged Responsive Search Ads from Google with just eight headlines and four descriptions, which emabled them to reduce their CPAs by 29% within just one month. #5 Catalog shopping ads Most ad platforms now facilitate catalog shopping ads. They enable your live online or local store inventory to be matched with customers who are in-market for those products. Additionally, platform algorithms establish customer intent based on search activity, pixel data from your website and a range of other proprietary analyses. These ad formats consistently deliver among the highest ROAS. In Google Shopping, Amazon accounts for upwards of 40% of impressions which alone is testament to Google’s dominance here. Tiffany & Co had a challenge with search interest. As a result, they doubled-down on improving the categorization and organisation of their product feed for Google to achieve an impressive 29% increase in ROAS. European shoe brand, Espadrij l’originale combined Facebook Dynamic Product Ads with sale deals to effectively remarket to shoppers and achieve a 4.8x improved ROAS via Facebook & Instagram. DIY store B&Q implemented Pinterest Shopping Ads and achieved a 7x improvement in ROAS from this format. eCommerce player Wish, achieved a 80% decrease in their key Cost Per Install metric by integrating their product catalog into Snapchat Story Ads. #6 Shoppable video & posts Digital marketers have always understood the power of visual storytelling to help differentiate in the online world. Shoppable video and post features take storytelling to the next level, allowing consumers to easily click-to-purchase featured products. - Belgian online department store ColliShop used a solution from Spott.ai to include interactive product information on top of video ads. As a result, they delivered a 20% increase in orders and 19% uplift in AOV. - Within Instagram, eCommerce brands can add product stickers to Story Ads. This allows audiences to view product information, such as price, and then click-to-purchase. Barbour achieved a 42% increase in sales from Instagram using this feature in its ads. - German streetwear brand DefShop achieved 64% increase in sales and 13% higher conversion rate with the same tactic on Instagram. - Amazon includes brand-sponsored product video ads in search results. According to pet supply business Rocco & Roxie, this format now provides the highest ROI of any of their campaigns. #7 Augmented reality ads Each day more than one-third of all UK digital audiences interact with augmented reality (AR) in just one social app – Snapchat. When it comes to younger audiences, that number rises to 78%. 31% of UK and US young shoppers are enjoying using AR within their purchase decision-making. Most ad platforms are now offering AR toolkits to make it easy to get started. But does it work? Ralph Lauren worked with Snapchat combining AR Lenses and Snap Ads to provide users a gamified way to experience the brand’s products. This yielded a 19% attributable lift in sales. Saudi youth clothing brand Nisnass also took advantage of Snapchat’s AR ad capabilities to deliver a 40% lift in profitability in ROI. Italian make-up brand We Make-up used Facebook Augmented Reality Ads to try on different shades of liquid lipstick through a face filter. These ads delivered a direct performance hit with a 28-point lift in purchases during the first month. In early 2020, fashion brand Burberry partnered with Google to incorporate an AR experience directly within mobile search results. There’s no performance data yet on this but we’re sure to see much more on this as Google doubles-down on AR. #8 Conversational commerce 1.3bn people globally use tools like Facebook Messenger every day. 60% of them happily engage with businesses using these types of applications. The top 3 reasons shoppers choose to first interact with messaging tools are: - The ability to interact at any time - The speedier resolution of queries - Their comfort using such tools for all types of communications Indian eCommerce giant Flipkart uses ads within Facebook that click to WhatsApp. Then a chatbot-powered digital assistant to engage with customers. This approach resulted in 3.5x more conversions on the back of 20,000 hours of engagement. Taiwan jewelry brand Vacanza used Messenger calls to action within Facebook photo and video ads. This enabled shoppers to learn more about products and make purchases without leaving the chat. These bot-driven engagements achieved 7.4x higher basket values. Manulife, a Vietnamese insurance company discovered that introducing click-to-chat within Facebook ads helped to improve the quality of leads. Due to shoppers being able to have questions answered in advance. The brand achieved 2.4x more qualified leads while reducing the cost-per-lead by 28%. #9 Marketplace advertising Marketplaces provide the convenience of enabling shoppers to shop from a wide range of vendors in one location. Whether you are listing your products on these marketplaces or not, they increasingly provide advertising opportunities that enable you to address consumers deep in the purchase journey. 49% of online product searches now start directly within Amazon. Amazon is committed to advertising with sponsored product listings, brand listings and video and display ads scattered through search results and product listings. We identify 14 pay-to-play opportunities alone on a typical product listing page. Amazon provides advertising solutions that direct consumers to your brand or product listing on Amazon itself or to your eCommerce website. Tablet brand Wacom leveraged Amazon’s Demand Side Platform (DSP) to advertise to in-market both on and off Amazon. From this display marketing activity they achieved ROAS of up to 7x. Facebook isn’t long in the marketplace game, but is already delivering strong results through marketplace listings and ad placements. eCommerce subscription company, BarkBox, achieved a 16% higher CVR and an 8% lower CPA through Marketplace Ads. #10 Affiliated influencers Influencers drive sales. In fact, 49% of consumers rely on recommendations from influencers in purchase decisions. Influencers have been shown to more than double the consumer purchase intent when combined with brand messaging. Using affiliate solutions is an easy way to harness the power of influencers with ROI baked in. Affiliate marketing platforms such as AWIN, CJ Affiliate and NicheVendor make it easy for eCommerce brands to showcase their products on YouTube, Instagram & TikTok influencers and bloggers on a CPA (Cost Per Acquisition) basis. This approach ensures that the retailer remains in full control of the ROAS. French retailer, La Redoute, used affiliate solutions for quite some time. But they wanted to reduce the dependency on voucher/coupon platforms. Working within AWIN, they shifted emphasis towards content-based affiliates to achieve 36% increase in ROAS and 61% additional revenue. US retailer Macy’s, partnered with TVPage, to include shoppable affiliate overlays on video created by influencers and superfans. In the midst of the Covid-19 pandemic, eCommerce brands experienced sales revenues growth by 300% through influencer video commerce. Optily is the only single-click ad spend optimizer for eCommerce. Our plug-and-play online platform quickly links all of your Google and Facebook ads together and helps you easily determine which campaigns are working. With just one click, you can apply our optimization recommendations–like moving budget from a lower performing Google ad to a better performing one on Instagram. Optily saves you time and money by instantly optimizing your ad spend.' - 'Landing pages are your digital marketing effort’s veritable gold mine. They sport a higher conversion rate than blog posts or basic pages – that is, if they’re optimally tailored to stellar user experience, irresistible offers, and captivating CTAs. Whether you are using landing pages for paid advertising or email marketing campaigns, here, landing page optimization tools become crucial to achieve your conversion goals. This is supported by HubSpot, which reports that average landing pages manage a mere 5 to 15% conversion rate. Yet, with strategic optimization, this figure can easily soar to a whopping 30%. How? The key here lies in leveraging the best landing page optimization tools. You’re in the right place to explore a list of elite tools you ought to equip yourself with for enhancing your landing page conversions. But before we jump into the list, let’s understand how many landing page optimization tools you’ll require. An abundance of options floods the market – but remember, you’re not obligated to utilize them all. Most landing page optimization tools offer comparable testing features, so what’s the ultimate gauge? It’s simple: Your specific needs. Identify your landing page optimization strategy’s focus, which typically emerges from detailed research on your industry and target audience. Let’s zero in on some essential features for a tool to merit consideration for your landing page optimization: Opt for a landing page optimization tool that integrates seamlessly with your fundamental tools as a marketer or growth manager. It includes customer relationship management (CRM) tools and sales software, providing enriched data insights, fluid workflow, personalized targeting, practical A/B testing, cohesive ad campaigns, effective conversion rate optimization, and instant updates. You would certainly want to avoid winding up with a tool requiring an expert’s touch, resulting in increased cost. The ideal landing page optimization tool must come with user-friendly features like a drag-and-drop editor and pre-designed templates; these help expedite page creation. Its interface should be intuitive, enabling straightforward design and management of landing pages. Any landing page optimization tool you choose should equally act as a protector, ensuring the safety of sensitive data, customer information, and business trust. Tools equipped with data encryption, secure hosting, regular updates, user authentication, access controls, and secure payment processing tick the right boxes. Furthermore, the tool should offer data backup, recovery, comply with industry standards, and regularly undergo vulnerability testing. A tool with multi-user accounts is pivotal in collaborative marketing, fostering central control, role-specific access, security, efficient workflows, and client accessibility. With boosted teamwork in marketing, you can simplify onboarding processes and be more effective with your marketing efforts. Now, let’s examine some specific landing page optimization tools in detail. We’ve broken down the top landing page optimization tools into four different categories, with drawbacks and benefits for each. It will enable you to concentrate on individual phases of optimization sequentially. The following tools are all-encompassing options that aid in building and optimizing your landing pages. If that’s what you’re seeking, you might find what you need right below: Praised for its versatility, Unbounce is a widely-used tool that allows you to craft custom landing pages using over 100 templates or from scratch. Its intuitive drag-and-drop builder ensures simplicity. More than a simple landing page builder, Unbounce also doubles as an optimization tool with peerless A/B testing features. Though it conducts only one type of testing, it’s perfectly equipped to optimize your pages. If you’re stuck when writing your own copies, don’t worry. Unbounce’s smart builder is driven by AI insights derived from over a billion conversions, enabling you to construct improved pages. Additionally, its smart traffic system directs visitors to the most relevant landing page variant. The tool also offers helpful copy suggestions, reducing time spent crafting marketing messages. Plus, by adding exit-intent pop-ups to your pages, it helps boost signup rates from unsure visitors. Offers the ability to customize landing pages and formulate various versions for A/B testing. Accommodates integrations with prominent analytics and optimization tools like VWO, UsabilityHub, Google Analytics, UserTesting, and Hotjar. Includes an intelligent traffic system for improved testing. Features industry-specific, professionally crafted landing page templates and easy-to-use features, including a drag-and-drop interface. Makes use of AI for suggested copywriting and design strategies. Lacks support for multivariate or split URL testing. Does not extend a free trial option. Instapage emerges as one of the leading landing page builder tools, allowing users to craft unique landing pages, develop variant pages, analyze customer behavior, and conduct practical experiments. Furnished with all vital content builder assets, it empowers you to construct responsive landing pages. Beyond just providing ready-to-use templates and a user-friendly drag-and-drop editor, Instapage is brimming with standout features. These include AdMap – a significant plus for PPC marketers handling multiple landing pages, advanced analytics, comprehensive A/B testing options, data quantification, session replays, and more. Further enhancing its appeal as a landing page optimization tool is Instapage’s WYSIWYG editor, which lets you witness landing page alterations in real-time. Includes in-built heatmaps for monitoring user behavior on your landing page and evaluating their overall experience. Features Instablocks, which support the design and storage of reusable content blocks for use across different landing pages, thereby simplifying design scaling. Enables integration with prominent marketing automation software such as HubSpot, Autopilot, Marketo, and more. Provides A/B testing feature to investigate variations of your landing page. Offers a free trial period, decreasing upfront commitment risks. Notably high-priced, which may deter smaller companies or those with tighter budgets. Its A/B testing feature is not available in the basic starter plan. The essence of qualitative analysis lies in comprehending user behavior, a task simplified by tools such as heatmaps and session recordings. It determines if your landing page effectively caters to your target audience’s needs. Now, let’s explore which tools are best suited for this task. Hotjar presents a comprehensive suite of conversion optimization tools, including heatmaps, real-time surveys, session recordings, and feedback widgets. It allows you to not only observe visitor interaction with your landing pages but also gauge visitor sentiment through surveys. Dealing with landing page optimization entails wading through vast pools of data – qualitative and quantitative alike. Hotjar’s dashboard offers a consolidated view of user data, allowing you to flag issues early, identify emerging trends, and delve into deeper insights. Offers session recording features with u-turn, rage, and referrer filters to monitor visitor website interactions. Features timing or action-based survey prompts for real-time feedback collection. Allows easy integration with other tools like Unbounce, amplifying effectiveness. Restricts sharing heatmaps outside the Hotjar dashboard user base, hindering easy data visual sharing. Lacks comprehensive form analysis feature. Doesn’t support integration with Google Analytics. This tool is primarily for comprehensive website optimization, examining visitor actions, delving into buyer journeys, pinpointing audience segments, and detecting potential issues. Crazy Egg even enables goal setting for more granular webpage analysis. Valuable for novices, its visualized evaluations of landing page variants simplify analysis. The tool amasses valuable insights for reports, spotlighting form analytics – such as the impact of specific words on customer conversion – while mapping the effect of color and content placement on user behavior. Real-time user interaction tracking capabilities, spotlighting areas causing customer distress. CrazyEgg offers granular insights into specific audience segments. Intuitive and effortless installation and navigation processes. VWO has a noted reputation for its stellar A/B testing tools, yet it provides an extensive suite of optimization utilities beyond A/B testing. It includes session recordings, heatmaps, and form analytics. With VWO, hassle-free report creation is possible, even without any technical expertise. This platform allows comprehensive testing of your entire marketing funnels. With its session recordings and heatmaps, you can delve into each customer’s journey in granular detail. Furthermore, this tool integrates seamlessly with your content management software (CMS), enabling landing page edits without code disruption or the need for a developer. Its point-and-click editor simplifies the editing process, keeping things swift and smooth. Offers a comprehensive range of tests, including A/B, Multivariate, and Split URL. Utilizes Bayesian statistics for a fast and accurate prediction of your landing page’s best-performing version. Enjoy responsive and swift support via web app chat from the VWO team. A significant chunk of visitor allotment is consumed in obtaining results. Invoke advanced implementation through the code editor rather than the visual editor for some tests, which may be complex for some users. Crafting compelling landing page copy can be daunting due to brevity constraints. Wynter is a focused tool aimed at refining your landing page copy. Specializing in landing page optimization through copy, Wynter helps gauge your target audience’s reception of your messages. Remember, effective messaging can drive your sales, making copy testing an integral part of optimizing landing page pages. While Wynter may lack builders or A/B testing features, it provides the insights needed to elevate your landing page copy. Swift message testing results enabling rapid copy modifications. Praised flexible, easy-to-use self-serve platform for crafting tailored tests. Even better, its users can gauge the efficacy of their design’s links and content structure by tracking user interactions. Its enhancements include: Preferential analysis to ascertain favored landing page designs and underlying rationales. Five-second assessments to comprehend initial impressions and content legibility. Prototype evaluations for appraising navigational aspects in pre-launch landing page prototypes. Design questionnaires for gathering feedback on diverse varieties of media. All in all, UsabilityHub is dedicated to elevating landing page design standards. Securing an appropriate set of metrics for quantitative analysis of your landing page success is critically important. This data-driven evaluation aids in determining whether your landing pages align seamlessly with your sales funnel. Maintaining a pulse on web analytics is crucial for insight into the starting and ending points of your landing page optimization journey. Google Analytics 4 (GA4) stands as an excellent starting platform for assessing your landing page metrics. Google Analytics offers a free in-depth analysis of user interactions on your landing page. The valuable insights contribute to a more profound understanding of how visitors engage with your landing pages. Showcases user engagement and experience via bounce rates and AdWord analytics tool provisions. Facilitates conversion tracking through GA Goals by channel, campaign, keyword, etc. The Google Mobile-Friendly Test tool allows you to assess your landing page’s mobile responsiveness. Simply enter your URL and click “Test URL.” If your page passes, you’ll receive confirmation. If not, detailed improvement suggestions will follow. Recognizing that mobile-responsive design is the new standard – indispensable to Google and most businesses alike – this reliable tool helps ensure your pages shine on mobile screens. And the cherry on top? It’s a free tool. Another free tool, PageSpeed Insights, assesses a website’s performance and speed on desktop and mobile realms. It is aimed at aiding website owners and developers to optimize their sites for faster loading, elevated user experience, and superior search engine standings. This tool quantifies diverse performance metrics, bestowing viable suggestions to enhance website efficiency. Assigns distinctive scores for mobile and desktop versions, enabling focused, individual optimization. Boosts search engine ranks as Google counts page speed among its ranking factors. Presents valuable analytics, including LCP, FID, and more. Scores may fluctuate relative to device, internet connection, and server response times. While the insights are insightful, technical implementation may be complex for non-technical users without developer support. While qualitative tools like Hotjar and CrazyEgg aid in landing page monitoring, there are other tools – which we’re about to explore – that further fine-tune the monitoring of specific funnel aspects like design and uptime. Hexometer, an uptime monitoring SaaS tool, employs advanced AI to identify issues on websites and landing pages autonomously. Providing round-the-clock surveillance, Hexometer promptly detects problems to avoid significant business impacts. Among the standout features of this tool is its immediate alert system for error detection, enabling swift issue resolution. You can receive notifications conveniently via email, SMS, Slack, Telegram, or Trello. Hexometer conducts precise checks on six pivotal website areas: availability, performance, user experience, health, SEO, and security, making it an excellent tool for landing page monitoring. Safeguards ad campaigns via active landing page monitoring. Allows tracking of landing page modifications and their effects on performance. Lacks a free plan option. The user interface might seem initially perplexing. This web archiving and monitoring tool automatically creates visual snapshots of web pages in assorted screen sizes. Users can track website alterations over time, making it a valuable asset for businesses needing historical online presence data. Keeping tabs on your business isn’t enough – monitoring competitors is also vital, which PageScreen can help streamline too. It’s basically the ideal tool for scrutinizing competitors’ landing pages. Users receive bespoke alteration alerts via email or Slack, facilitating team-based historical data analysis. Accommodates multi-site monitoring via individual URL entries or bulk URL uploads. Encourages organized screenshot storage with the creation of visual collections. Keyword research is vital for both SEO and PPC landing pages. This is where Ahrefs shines – providing a comprehensive evaluation of your landing page’s performance in Google’s eyes. Offering deep dives into SEO metrics, keyword trends, competitive landscapes, and backlink structures, Ahrefs is the perfect aid for your landing page optimization journey. Enables rank tracking for all targeted keywords. Facilitates easy backlink and keyword profile analysis of competitors. Assists in identifying new opportunities via keyword research. To simplify your navigation through the plethora of landing page optimization tools we’ve explored, here’s a handy comparative table for your reference: Landing pages are pivotal for propelling conversions and enhancing lead-generation campaigns. Overlooking their optimization could save you conversion opportunities and prevent business growth. Given the vast array of tool choices, selecting the right one can seem daunting. If you’re grappling with this decision, consider seeking expert assistance. Based on an eight-year track record, Apexure can offer the requisite landing page optimization expertise for accelerated growth and conversion boosts. Successful optimization necessitates ongoing testing, experimentation, and refinement for optimal outcomes. If you’d like to harness the potential of apt tools and the experience of specialists like Apexure to unlock the full capacity of your landing pages, connect with us today. CRO testing can serve as the deciding factor when it comes to maximizing the efficiency of your website... Get quality posts covering insights into Conversion Rate Optimisation, Landing Pages and great design' - 'Buy Darvocet Online Are you currently searching to get a Darvocet 50mg without a prescription? You ought to be aware that Darvocet has been accredited by the FDA. You may come across Darvocet in most drug stores. The medication is economical for certain, and you may find it online and buy your Darvocet medication without a prescription. You also may believe that getting medication with no prescription can be a terrific concept, however, you must be mindful and make sure that you are not going to become caught. 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You won’t plagued by anybody and also you will not need to wait long lines. Whatever you have to do is always to select the best website that offers the proper sort of Darvocet which will be certainly for you. To purchase Darvocet without a prescription from a website that sells online, you’ll want to fill out an internet survey. If you’re not comfortable filling out the questionnaire, you ought not to buy from the site. You ought to buy in the reliable site with a present clientele and they have tried to sell to folks online plus they’ve got success when doing so.' - source_sentence: korea bitcoin hack cryptocurrency sentences: - 'While I’ve had a base understanding about what goes in to search engine optimization for a while now, it’s been under a year since I really started learning what makes search engines tick. I’m still have a long way to go before I can call myself an SEO guru, but I know this for sure: whether you’re a small business owner with a website or an aspiring digital marketer, you can benefit from learning what goes into SEO. The problem, though, is that search engine optimization isn’t exactly the easiest topic to pick up on your own. There are a lot of different people speculating on what Google’s algorithms favor, and it can get overwhelming fast. I was in this spot not all that long ago, but fortunately I found some great resources (many of them free!) to show me the way, and I’d like share them with you. If you don’t know anything about what search engine optimization is, then these are the place to start. They are all easy to digest, while still being thorough enough to really dig into the concepts, and layout best practices with tangible examples. After getting down the basics, there are endless blogs with great information to be found. Three blogs I’ve started following religiously since beginning to learn SEO are Search Engine Journal, Search Engine Watch, and SEOmoz. Each of these blogs posts multiple times a day on SEO tips, search engine & social media marketing, as well as industry updates. With these three blogs alone, you aren’t likely to run out of learning material any time soon. In addition to all of the great material on SEO found across the net, there have been a couple of books that have offered just as much insight and even more. SEO Secrets by Danny Dover does a great job of laying out fundamentals, but also goes into how to consult on SEO for other companies, how to do research, and even how to optimize for search engines besides Google. And the most recent addition to my tool kit has been Optimize by Lee Odden. Optimize describes the changing nature of SEO, and its convergence with social media and content marketing, and discusses how digital marketing strategies need to adapt to meet the change. There you have them, the resources that helped me learn SEO. This is by no means an exhaustive list; far from it really. If you have resources you’d like to add, let me know!' - 'What Does the Recent Cryptocurrency Exchange Hack Mean for Crypto Cyber Security? One of the largest cryptocurrency exchanges in Korea, Coinrail, was hacked in June. While Coinrail did not release an official figure for the stolen coins, The Guardian estimates that at least £27.8m worth of digital funds are now lost forever. The immediate result of the hacking was the mass selling of Bitcoins by investors, which led to the cryptocurrency dropping in price by 10%. Just an hour after the Coinrail confirmed the attack, Bitcoin lost £372 and settled at £4,955. A week after the attack, investors are still bearish towards Bitcoin. CNBC confirms that this downward spiral is due to the massive losses that Coinrail incurred from the hacking. The latest attack highlights the vulnerability of cryptocurrencies even if the blockchain technology is technically tamperproof. String of successful hackings This isn’t the first time that the cryptocurrency market has experienced a major hack. Japan’s online exchange Coincheck was hacked in January and £373 million worth of coins were stolen. In the following month, Italy’s Bitgrail was also hacked and lost £149 million worth of cryptocurrency. In April, Coinsecure reported that £2.47 million worth of Bitcoin was stolen. South Korea’s Youbit was also hacked twice, which led to its bankruptcy in December 2017. Coinrail is a fairly small exchange with roughly £2 million in daily trading volume. A representative of the Korea Blockchain Industry Association blames the lower security standards of small-scale online exchange operations that led to the hacking. How were the attacks carried out? The blockchain technology is supposed to be very secure, which is why the prices of Bitcoin soared over the past few years. Apart from astronomical prices, Bitcoin’s success gave birth to numerous cryptocurrency-based investment vehicles such as CME group’s Bitcoin Futures, which allows investors to purchase Bitcoin at a later date. Bitcoin Spreads also materialised, which allow investors to track the prices of Bitcoin. Nadex points out that Bitcoin Spreads allow investors to take short-term positions on Bitcoin’s price, which means they don’t need to buy actual cryptocurrencies to make a trade. As Bitcoin continues to grow, newer investment vehicles are born, and there’s no end in sight as to when Bitcoin’s popularity will cease. As investment vehicles like Bitcoin Spreads aren’t prone to theft, because investors are trading on the price of the cryptocurrency, the problem lies on actual Bitcoins themselves. Because blockchain is generally tamperproof, hackers try to find approaches to circumvent the technology’s security, and find weaker spots to carry out their attacks. In the same article by The Guardian, Naeem Aslam of ThinkMarkets said that the loose regulatory control of Bitcoin is one of the reasons why exchanges are being hacked. Aslam’s assumptions are backed by a blog post by Crossmatch, which details 5 ways on how hackers steal information from online exchanges. One of the stated reasons is cryptocurrency code vulnerabilities, which allows hackers to manipulate transactions at code-level in a Decentralised Autonomous Organisation (DAO). When there’s no high-grade security and regular security upgrades, exchanges are vulnerable to code manipulation, transaction malleability, phishing scams, and many others. The future of cryptocurrency security After the string of attacks to online exchanges, cryptocurrency developers are now scrambling to make security tougher. MIT Technology Review states that one of the solutions is the use of a multisignature address, which requires transactions to be activated with more than one cryptographic key. While it isn’t a perfect security system for cryptocurrency, it at least makes the hacking a little bit harder. With multisignature, hackers would need to steal information from multiple targets before a transaction can be made. An attack against a multisignature address is difficult, giving cryptocurrency owners time to regularly update their credentials and avoid hacking activity. Since cryptocurrency markets are deregulated, implementing multisignature address to all online exchanges will take time. As previously mentioned, Coinrail is a small-scale online exchange that has no high-grade security, and this may be the case for other online exchanges around the world. For the past year the eyes of the world have been on how quickly Bitcoin has evolved to become a dominant part of world finance. Now the world will be watching to see how well the cryptocurrency can protect itself from future attacks. The survival of cryptocurrency depends on the industry’s response to these attacks.' - 'Wall Street Journal doesn''t have exhaustive navigation items on their Health Blog but just top topics, which are their top tags. In what cases does this make sense to do? You''ll notice that the Wall Street Journal provides a Search utility in addition to tags and most popular content. They are providing multiple methods for users to find content. For dynamic content sites, the only navigation design that could possibly hope to hold up over time is a combination of search, index by date, tags and most-popular pages, which is what you see on blogs and sites like Stack Exchange. It pays to support the user''s own subjective sense that they are able to find what they are looking for, no matter what navigation model is used, either now or in the future. As Melguizo, vanOostendorp & Juvin found in "Predicting and Solving Web Navigation Problems", users who report feeling lost in a site perform worse on information retrieval tasks. Providing multiple methods to find content contributes to better orientation, which increases user''s confidence that they will find what they''re looking for. From the information point of view, list of categories has two dimentions, while top categories are just limited one dimentional element, which lead to information loss for a user. Take into account temporal stability of each solution. The rules of top category selection is hidden, too. Content categories are used not only as navigational element, but as a "table of contents", too. The number of posts within each category represents the blog specificity for a user. Also width (category amount) and deepth (# of posts within each category) of categories could be used for author''s assessment as a specialist in some fields. This adds third dimention to the list of categories. So even having Top posts, Search and other supporting tools, it''s better to keep list of categories. Conserning WSJ Health Blog, it''s a great example of the case when designer shouldn''t just copy big company''s solution without its critical analysis. Actually, I''d say they have confusing navigation. They do have categories below the fold. But: I think that for the purpose of providing good navigation a mix of Tags and Categories is a good idea, but it merely depends on your users. Pros and Cons: Categories and Tags When to Use Each? Imagine a blog, it puts on top every new article for readers to consume. When that article becomes old, it''s only findable through a search engine, tag navigation, category navigation and pagination. Using all of the way above increases the chances of your article to be findable. You can define the purpose of categories by creating different audiences for your blog. (Like a newspaper; entertainment, financial, sports, etc.) You can define tags specific to the article''s actors. Articles containing a last name might trigger the different results from different categories, but the category the belong to provides more context to the user. My last name might be found for example in the sports world as well as in the art world. But if somebody is looking for me specifically, they might know that i''m more likely to appear in the art world than in the sports one. Does this make sense? Hence if your audience''s niche is only one, the way to go is tags; But if you cover different audiences segmenting by categories also makes sense. Beware however, that if for example, an article failed to have a category or tag, then it''s only available through pagination or search, which might be annoying for some people.. Hope this helps. Two factors come to mind: If you scroll down the page a bit you''ll see they they also list 20 "Categories" for users to choose from, so they aren''t just using "Top Topics" for their navigation. That said, it makes sense to feature popular or trending content up front because you know a large chunk of your traffic is going to be interested in it. This will take care of a lot of people without them having to dig. Unfortunately the difference between their Topics (sometimes also referred to as ''tags'') and Categories is not clear, and sometimes they use the same term in both, which is confusing.' - source_sentence: "google side wiki chrome firefox \n\nor \n\ngoogle sidewiki update\n\ \nor \n\ngoogle sidewiki launch\n\nor \n\ngoogle sidewiki comments\n\nor \n\n\ google sidewiki browser extension\n\nor \n\nwhat is sidewiki\n\nNote: These queries\ \ are based on the content of the document and are intended to reflect the search\ \ behavior of a user who has read the document." sentences: - 'A business unit is a division or department within an organization that is responsible for a specific task or product. The unit may be responsible for the manufacture of a particular product, the marketing of that product or the accounting of that product. Some businesses have multiple units, and this structure can increase efficiency and responsiveness to the needs of the customer. 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In order to be a success, a unit needs to have a well thought out strategy and a dedicated team of employees. Moreover, the unit must have a clear mission statement that sets the tone for the organization. A well-defined mission statement can also be a great way to motivate and encourage employees to perform at their best. This can be done by having a specific mission statement, or by making sure that the mission is aspirational but achievable. Another way to measure the performance of a business unit is through a business unit analysis. This is a review of all of the processes and activities that are performed by the unit. This can be done by the unit manager or by an organizational manager. The objective of this process is to ensure that the organization is not wasting its resources or losing out on opportunities.' - 'SAM is a cloud-based software platform designed specifically for marketing agencies and small businesses to manage their customer relationships, automate marketing processes and streamline their operations. It offers a wide range of tools and features to help businesses generate leads, nurture customer relationships and optimize their marketing campaigns. SAM simplifies marketing processes, enhances customer relationships, and provides data-driven insights for effective marketing campaigns. Search engine optimization, or SEO as it is commonly known, is the process and strategy of organically growing the quality and quantity of website traffic via search engines such as Google, Yahoo and Bing. This is done by increasing rankings and therefore the visibility of a website in search engines such as Google and Bing. SEO differs from search engine marketing (SEM) as it refers to the improvement of unpaid search results and excludes direct traffic and the purchase of paid placement such as PPC. Search Engine Optimization is ever changing and in order to rise above your competitors, you need a great SEO strategy and a team of experts behind it. Aleada Media Group’s team of specialists work relentlessly to put you in the best possible position to be found online. We use the best data research tools and the smartest analysts in the industry to create an SEO program that ensures your success. All of this is important because 94% OF CONSUMERS SEARCH ONLINE BEFORE MAKING A PURCHASE! Having a mobile friendly and responsive website is an absolute must for any business. Your customers are online and if you are not putting your best foot forward on your website then you are leaving money on the table. Aleada Media Group specializes in high performing responsive website development and e-Commerce websites. 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Competitors can link to their competing product, consumers can rate or discuss the positive and negative experiences with your company or product. - Yet, don’t expect everyone to participate –or contribute valuable content. While social technology adoption is on the rise, not everyone writes, rates, and contributes content in every location, likely those who have experienced the product, influential, or competitors will be involved. Secondly, content created in this sidebar may be generally useless. To be successful, Google will need it to look more like Wikipedia than YouTube comments - Expect Google to integrate this feature with existing systems. Google recently launched profiles, a feature that is the foundation for extending their social reach. With large social networks like Gmail already in place (That’s right, email is a social network) they can eventually sort content on SideWiki by context of friends, experts, or other sources. Google’s strategy is to ‘envelope’ the web this is typical of their approach. - Although early, expect other social networks to launch competing features. Facebook has already created an ‘inlay’ so you can view links shared in the Facebook newspage in the context of your friends –expect them to grow this feature out shortly. Recommendations for the Web Strategist: Develop a Social Strategy Now - Shift your thinking: recognize that you don’t own your corporate website –your customers do. Accept the mindshift that your job is to not only serve up product and corporate content but to also be a platform and enabler for customers to discuss, share, and make suggestions to how you should improve what you offer. - Develop a social strategy with dedicated resources. With every webpage now potentially social, you’ll need to develop a process, roles, and policy to ensure you’re monitoring the conversation, participating as you would in blog discussions, and influencing the discussion. 80% of success is developing an internal strategy, providing education before a free-for-all happens with customers and employees. - Don’t be reactive to negative content –embrace social content now. Give users the ability to leave social feedback directly on your corporate webpages, or aggregate existing social content. CMS vendors are developing features to enable this, as well as community platform vendors like Kickapps, Pluck, Liveworld’s Livebar offer rapid deployment options. I predicted Google would be one of the first to do this, however I expected them to start with Chrome, not FF and IE. Expect this to be a default feature of Chrome –not just a plugin in future efforts. Update: Just saw an interesting tweet from @prem_k about impacts to CRM. He’s Right. CRM systems (Salesforce, SAP, Oracle, Rightnow and others) will need to aggregate content in Google’s Sidewiki. It’s not just CRM, Brand Monitoring companies (Radian6, Buzzmetrics, Cymfony, Visible Technologies) will also need to “suck in” that data. Update 2, a few hours later: We should stop to think about how competitors could display ads “on” your corporate site and you couldn’t stop it, why? Take a look at Google’s business model, they envelop and categorize the web, then display ads on it. There’s nothing stopping them from allowing advertisers to put ads on SideWiki as “sponsored” information. For example, Coke could run their latest ads on the Pepsi.com SikeWiki area. HP could run ads on the Dell.com site. This *already* happens in the search engine result pages on Google.com why not in sidewiki? Update 3, the next day: I just tried out SideWiki to see how it works. I came to this very post and found out that there are already three comments. I left a comment welcoming folks, and it gave me the option to Tweet it, which I did. Here’s what sidewiki looks like, you don’t never have to have the plugin for this to work. Which means that this certainly has lower barriers to adoption. A few other field notes? I no longer have to fuss with captacha on blogs or name/email/url once I’m logged in to SideWiki, I can comment around the web. Secondly, it centralizes all my comments on my Google profile tool. You do see what Google is doing right? They are turning the whole web into a social network.' model-index: - name: slinger-base results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.576 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6732 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.719 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7634 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.576 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2244 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1438 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07633999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.576 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6732 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.719 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7634 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6664019297227167 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6356943650793653 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6410225893874547 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.5646 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6702 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7144 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7594 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5646 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2234 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14288 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07593999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5646 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6702 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7144 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7594 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6597461148535217 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.628077222222222 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6334394676284499 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.5498 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6578 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7004 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7538 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5498 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.21926666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14007999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07537999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5498 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6578 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7004 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7538 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6487334476943639 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6155109523809525 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6206030679493492 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.527 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6332 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.676 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7338 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.527 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.21106666666666665 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1352 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07338 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.527 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6332 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.676 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7338 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6259727158337307 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5920138888888902 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5974645043641005 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.4734 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5816 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6242 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.684 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4734 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.19386666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.12483999999999999 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0684 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.4734 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5816 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6242 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.684 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5735041346433559 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5387931746031753 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5451398764704354 name: Cosine Map@100 --- # slinger-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("carsondial/slinger20241231-3") # Run inference sentences = [ 'google side wiki chrome firefox \n\nor \n\ngoogle sidewiki update\n\nor \n\ngoogle sidewiki launch\n\nor \n\ngoogle sidewiki comments\n\nor \n\ngoogle sidewiki browser extension\n\nor \n\nwhat is sidewiki\n\nNote: These queries are based on the content of the document and are intended to reflect the search behavior of a user who has read the document.', 'Yesterday, Google announced “SideWiki” a new feature of the Firefox and IE browsers (Chrome to come soon) that allows anyone to contribute comments about any webpage –including this one. The impacts are far reaching, now every web page on the internet is social and can have consumer opinion –both positive and negative.\nControl Over the Corporate Website Is Shifting To The Customers:\n- Customers trust each other more than you –now they can assert their voices “on” your webpage. Every webpage on your corporate website, intranet, and extranet are now social. Anyone who accesses these features can now rely on their friends or those who contribute to get additional information. Competitors can link to their competing product, consumers can rate or discuss the positive and negative experiences with your company or product.\n- Yet, don’t expect everyone to participate –or contribute valuable content. While social technology adoption is on the rise, not everyone writes, rates, and contributes content in every location, likely those who have experienced the product, influential, or competitors will be involved. Secondly, content created in this sidebar may be generally useless. To be successful, Google will need it to look more like Wikipedia than YouTube comments\n- Expect Google to integrate this feature with existing systems. Google recently launched profiles, a feature that is the foundation for extending their social reach. With large social networks like Gmail already in place (That’s right, email is a social network) they can eventually sort content on SideWiki by context of friends, experts, or other sources. Google’s strategy is to ‘envelope’ the web this is typical of their approach.\n- Although early, expect other social networks to launch competing features. Facebook has already created an ‘inlay’ so you can view links shared in the Facebook newspage in the context of your friends –expect them to grow this feature out shortly.\nRecommendations for the Web Strategist: Develop a Social Strategy Now\n- Shift your thinking: recognize that you don’t own your corporate website –your customers do. Accept the mindshift that your job is to not only serve up product and corporate content but to also be a platform and enabler for customers to discuss, share, and make suggestions to how you should improve what you offer.\n- Develop a social strategy with dedicated resources. With every webpage now potentially social, you’ll need to develop a process, roles, and policy to ensure you’re monitoring the conversation, participating as you would in blog discussions, and influencing the discussion. 80% of success is developing an internal strategy, providing education before a free-for-all happens with customers and employees.\n- Don’t be reactive to negative content –embrace social content now. Give users the ability to leave social feedback directly on your corporate webpages, or aggregate existing social content. CMS vendors are developing features to enable this, as well as community platform vendors like Kickapps, Pluck, Liveworld’s Livebar offer rapid deployment options.\nI predicted Google would be one of the first to do this, however I expected them to start with Chrome, not FF and IE. Expect this to be a default feature of Chrome –not just a plugin in future efforts.\nUpdate: Just saw an interesting tweet from @prem_k about impacts to CRM. He’s Right. CRM systems (Salesforce, SAP, Oracle, Rightnow and others) will need to aggregate content in Google’s Sidewiki. It’s not just CRM, Brand Monitoring companies (Radian6, Buzzmetrics, Cymfony, Visible Technologies) will also need to “suck in” that data.\nUpdate 2, a few hours later: We should stop to think about how competitors could display ads “on” your corporate site and you couldn’t stop it, why? Take a look at Google’s business model, they envelop and categorize the web, then display ads on it. There’s nothing stopping them from allowing advertisers to put ads on SideWiki as “sponsored” information. For example, Coke could run their latest ads on the Pepsi.com SikeWiki area. HP could run ads on the Dell.com site. This *already* happens in the search engine result pages on Google.com why not in sidewiki?\nUpdate 3, the next day: I just tried out SideWiki to see how it works. I came to this very post and found out that there are already three comments. I left a comment welcoming folks, and it gave me the option to Tweet it, which I did. Here’s what sidewiki looks like, you don’t never have to have the plugin for this to work. Which means that this certainly has lower barriers to adoption. A few other field notes? I no longer have to fuss with captacha on blogs or name/email/url once I’m logged in to SideWiki, I can comment around the web. Secondly, it centralizes all my comments on my Google profile tool. You do see what Google is doing right? They are turning the whole web into a social network.', 'A business unit is a division or department within an organization that is responsible for a specific task or product. The unit may be responsible for the manufacture of a particular product, the marketing of that product or the accounting of that product. Some businesses have multiple units, and this structure can increase efficiency and responsiveness to the needs of the customer.\nThere are many types of business units, and these units all have their own unique role. For instance, a business unit may be a single person with a singular mission, or a multi-level corporation that is staffed with hundreds of employees. Each type of business entity is regulated differently, and has its own regulations. However, all of them have one thing in common: they are functional and important.\nOne of the main functions of a business unit is to gather information about the target market. To do this, the unit must collect feedback from the marketplace and determine the right approach to take. This process can be accomplished through surveys, focus groups, and even market research. If a business unit is able to identify the best strategy to pursue, it will be able to boost profits.\nBusiness units are also referred to as divisions or departments, and can be either independent or linked to the parent company. Businesses with a diverse customer base will often set up separate business units for each individual market. It’s a good idea to set a specific mission for each of these units to allow for easier management. In addition, having multiple units can be beneficial for project management.\nOne of the most basic duties of a business unit is to maintain a competitive edge. This can be achieved by offering a better quality or price for a given output. For example, a business unit that manufactures boots may produce a more comfortable pair of boots. But if a business unit is not efficient in delivering its services, its costs will rise.\nOther functions performed by a business unit include sales and marketing. When a unit is successful, it improves the organization’s overall performance. Having a clear mission statement is one of the most important things a business unit can do. That mission should be specific, relevant, and measurable.\nIn order to be a success, a unit needs to have a well thought out strategy and a dedicated team of employees. Moreover, the unit must have a clear mission statement that sets the tone for the organization.\nA well-defined mission statement can also be a great way to motivate and encourage employees to perform at their best. This can be done by having a specific mission statement, or by making sure that the mission is aspirational but achievable.\nAnother way to measure the performance of a business unit is through a business unit analysis. This is a review of all of the processes and activities that are performed by the unit. This can be done by the unit manager or by an organizational manager. The objective of this process is to ensure that the organization is not wasting its resources or losing out on opportunities.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> ## Evaluation ### Metrics #### Information Retrieval * Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |:--------------------|:-----------|:-----------|:-----------|:----------|:-----------| | cosine_accuracy@1 | 0.576 | 0.5646 | 0.5498 | 0.527 | 0.4734 | | cosine_accuracy@3 | 0.6732 | 0.6702 | 0.6578 | 0.6332 | 0.5816 | | cosine_accuracy@5 | 0.719 | 0.7144 | 0.7004 | 0.676 | 0.6242 | | cosine_accuracy@10 | 0.7634 | 0.7594 | 0.7538 | 0.7338 | 0.684 | | cosine_precision@1 | 0.576 | 0.5646 | 0.5498 | 0.527 | 0.4734 | | cosine_precision@3 | 0.2244 | 0.2234 | 0.2193 | 0.2111 | 0.1939 | | cosine_precision@5 | 0.1438 | 0.1429 | 0.1401 | 0.1352 | 0.1248 | | cosine_precision@10 | 0.0763 | 0.0759 | 0.0754 | 0.0734 | 0.0684 | | cosine_recall@1 | 0.576 | 0.5646 | 0.5498 | 0.527 | 0.4734 | | cosine_recall@3 | 0.6732 | 0.6702 | 0.6578 | 0.6332 | 0.5816 | | cosine_recall@5 | 0.719 | 0.7144 | 0.7004 | 0.676 | 0.6242 | | cosine_recall@10 | 0.7634 | 0.7594 | 0.7538 | 0.7338 | 0.684 | | **cosine_ndcg@10** | **0.6664** | **0.6597** | **0.6487** | **0.626** | **0.5735** | | cosine_mrr@10 | 0.6357 | 0.6281 | 0.6155 | 0.592 | 0.5388 | | cosine_map@100 | 0.641 | 0.6334 | 0.6206 | 0.5975 | 0.5451 | <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### json * Dataset: json * Size: 45,000 training samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 11.87 tokens</li><li>max: 208 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 389.85 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | anchor | positive | |:-----------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>how to add password screen in wordpress</code> | <code>The following wordpress webpage ([url removed, login to view]) has two options depending on which graphic you click on (both are separate pages in wordpress). I want the user to see a password screen when they click on either option, and for there to be a different password for either screen. Once they enter the password they would progress to that screen.<br>Hello, nice to meet you. I'm professional in wordpress/html/php/css/js. I have done similar to this project before. I can start soon. Looking forward to connecting and working with you soon. Regards.<br>16 freelanceria on tarjonnut keskimäärin 107 £ tähän työhön<br>i will work on this project, i have more than three years of web development experience. Development portfolio given below. [url removed, login to view] [url removed, login to view] (Streaming Website with Admin Panel) [url removed, login to view] ( Lisää<br>Hello, dear? How are you? 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As an example, other sites utilizi...</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: True - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |:----------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| | 0.1137 | 10 | 3.191 | - | - | - | - | - | | 0.2274 | 20 | 2.6214 | - | - | - | - | - | | 0.3412 | 30 | 1.9557 | - | - | - | - | - | | 0.4549 | 40 | 1.4834 | - | - | - | - | - | | 0.5686 | 50 | 1.42 | - | - | - | - | - | | 0.6823 | 60 | 1.3626 | - | - | - | - | - | | 0.7960 | 70 | 1.1723 | - | - | - | - | - | | 0.9097 | 80 | 1.2129 | - | - | - | - | - | | 0.9893 | 87 | - | 0.6616 | 0.6570 | 0.6454 | 0.6177 | 0.5570 | | 1.0341 | 90 | 1.257 | - | - | - | - | - | | 1.1478 | 100 | 1.1609 | - | - | - | - | - | | 1.2615 | 110 | 1.0792 | - | - | - | - | - | | 1.3753 | 120 | 0.9907 | - | - | - | - | - | | 1.4890 | 130 | 0.8536 | - | - | - | - | - | | 1.6027 | 140 | 0.8934 | - | - | - | - | - | | 1.7164 | 150 | 0.9073 | - | - | - | - | - | | 1.8301 | 160 | 0.8485 | - | - | - | - | - | | 1.9439 | 170 | 0.878 | - | - | - | - | - | | 1.9893 | 174 | - | 0.6647 | 0.6600 | 0.6472 | 0.6238 | 0.5684 | | 2.0682 | 180 | 0.922 | - | - | - | - | - | | 2.1819 | 190 | 0.8154 | - | - | - | - | - | | 2.2957 | 200 | 0.8993 | - | - | - | - | - | | 2.4094 | 210 | 0.7296 | - | - | - | - | - | | 2.5231 | 220 | 0.6828 | - | - | - | - | - | | 2.6368 | 230 | 0.7187 | - | - | - | - | - | | 2.7505 | 240 | 0.72 | - | - | - | - | - | | 2.8643 | 250 | 0.6948 | - | - | - | - | - | | 2.9780 | 260 | 0.7066 | - | - | - | - | - | | **2.9893** | **261** | **-** | **0.666** | **0.661** | **0.6493** | **0.6261** | **0.5721** | | 3.1023 | 270 | 0.7934 | - | - | - | - | - | | 3.2161 | 280 | 0.701 | - | - | - | - | - | | 3.3298 | 290 | 0.7146 | - | - | - | - | - | | 3.4435 | 300 | 0.5952 | - | - | - | - | - | | 3.5572 | 310 | 0.6048 | - | - | - | - | - | | 3.6709 | 320 | 0.7172 | - | - | - | - | - | | 3.7846 | 330 | 0.6414 | - | - | - | - | - | | 3.8984 | 340 | 0.6422 | - | - | - | - | - | | 3.9893 | 348 | - | 0.6664 | 0.6597 | 0.6487 | 0.6260 | 0.5735 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.5.1+cu121 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION" ]
[ "CRAFT" ]
Non_BioNLP
StivenLancheros/Roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_en_es
StivenLancheros
token-classification
[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,647
1,647
115
0
--- license: apache-2.0 metrics: - precision - recall - f1 - accuracy tags: - generated_from_trainer model-index: - name: Roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_en_es results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_en_es This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the CRAFT dataset. It achieves the following results on the evaluation set: - Loss: 0.1750 - Precision: 0.8664 - Recall: 0.8587 - F1: 0.8625 - Accuracy: 0.9727 ## Model description This model performs Named Entity Recognition for 6 entity tags: Sequence, Cell, Protein, Gene, Taxon, and Chemical from the [CRAFT](https://github.com/UCDenver-ccp/CRAFT/releases)(Colorado Richly Annotated Full Text) Corpus in Spanish and English. Entity tags have been normalized and replaced from the original three letter code to a full name e.g. B-Protein, I-Chemical. ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0564 | 1.0 | 1360 | 0.1459 | 0.8296 | 0.8489 | 0.8392 | 0.9696 | | 0.0222 | 2.0 | 2720 | 0.1554 | 0.8650 | 0.8320 | 0.8482 | 0.9702 | | 0.0124 | 3.0 | 4080 | 0.1670 | 0.8588 | 0.8564 | 0.8576 | 0.9717 | | 0.0052 | 4.0 | 5440 | 0.1750 | 0.8664 | 0.8587 | 0.8625 | 0.9727 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6
[ "NAMED_ENTITY_RECOGNITION" ]
[ "CRAFT" ]
BioNLP
bobox/DeBERTa-small-ST-v1-test-step2
bobox
sentence-similarity
[ "sentence-transformers", "pytorch", "deberta-v2", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:305010", "loss:CachedGISTEmbedLoss", "en", "dataset:jinaai/negation-dataset-v2", "dataset:tals/vitaminc", "dataset:allenai/scitail", "dataset:allenai/sciq", "dataset:allenai/qasc", "dataset:sentence-transformers/msmarco-msmarco-distilbert-base-v3", "dataset:sentence-transformers/natural-questions", "dataset:sentence-transformers/trivia-qa", "dataset:sentence-transformers/gooaq", "dataset:google-research-datasets/paws", "arxiv:1908.10084", "base_model:bobox/DeBERTa-small-ST-v1-test", "base_model:finetune:bobox/DeBERTa-small-ST-v1-test", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,724
1,724
7
0
--- base_model: bobox/DeBERTa-small-ST-v1-test datasets: - jinaai/negation-dataset-v2 - tals/vitaminc - allenai/scitail - allenai/sciq - allenai/qasc - sentence-transformers/msmarco-msmarco-distilbert-base-v3 - sentence-transformers/natural-questions - sentence-transformers/trivia-qa - sentence-transformers/gooaq - google-research-datasets/paws language: - en library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - dot_accuracy - dot_accuracy_threshold - dot_f1 - dot_f1_threshold - dot_precision - dot_recall - dot_ap - manhattan_accuracy - manhattan_accuracy_threshold - manhattan_f1 - manhattan_f1_threshold - manhattan_precision - manhattan_recall - manhattan_ap - euclidean_accuracy - euclidean_accuracy_threshold - euclidean_f1 - euclidean_f1_threshold - euclidean_precision - euclidean_recall - euclidean_ap - max_accuracy - max_accuracy_threshold - max_f1 - max_f1_threshold - max_precision - max_recall - max_ap pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:305010 - loss:CachedGISTEmbedLoss widget: - source_sentence: how long should a prelude be before a funeral sentences: - 'Organic 101: What the USDA Organic Label Means. This is the third installment of the Organic 101 series that explores different aspects of the USDA organic regulations. Organic certification requires that farmers and handlers document their processes and get inspected every year.' - 'The Quadrille. The Quadrille is a historic dance performed by four couples in a rectangular formation, and a precursor to traditional square dancing as well as a style of music. The Quadrille or Quadrille de Contre Danse was originally a card game for four people but the name was given to this dance about 1740.The dance probably derived from the Cotillions of the time. The Quadrille was a very lively dance, unlike the Minuet.Wikipedia states thus: The term quadrille came to exist in the 17th century, within military parades, in which four horsemen and their mounts performed special square-shaped formations or figures.he Quadrille or Quadrille de Contre Danse was originally a card game for four people but the name was given to this dance about 1740. The dance probably derived from the Cotillions of the time. The Quadrille was a very lively dance, unlike the Minuet.' - 1 Arrive early. 2 You should always endeavor to arrive at the church or funeral home at between 15 to 20 minutes before the service is scheduled to begin. 3 Take your seat quietly, and reverently await the arrival of the family. - source_sentence: More than 169 countries had reported over 212,000 COVID-19 cases before March 19 , 2020 . sentences: - As of 23 March , more than 341,000 cases of COVID-19 have been reported in 192 countries and territories , resulting in more than 14,700 deaths and 99,000 recoveries . - As of 21 March , more than 278,000 cases of COVID-19 have been reported in over 186 countries and territories , resulting in more than 11,500 deaths and 92,000 recoveries. virus seems to mostly spread between people via respiratory droplets . - As of 18 March 2020 , more than 212,000 cases of COVID-19 have been reported in at least 170 countries and territories , with major outbreaks in China , Iran and the European Union . - source_sentence: 'The book is about a fictional obsessed fan hunting down King, the author of Misery, The Shining and Carrie. But Patterson said he had learned in the run-up to the planned November publication that fans had "disrupted" King''s home in real life. King has had nothing to do with the novel, Patterson has stressed. Before deciding to scrap the book, he wrote on his website: "I''m a Stephen King fan, but Stephen King did not participate in the making of this novel, nor is he affiliated with it in any way. I hope he likes it." However, in a statement released by his publisher on Thursday, Patterson - who co-wrote the book with Derek Nikitas - said: "My book is a positive portrayal of a fictional character, and, spoiler alert, the main character is not actually murdered. "Nevertheless, I do not want to cause Stephen King or his family any discomfort. Out of respect for them, I have decided not to publish The Murder of Stephen King." King declined to comment on the book when asked about it last week by the Associated Press. Patterson, ranked as the world''s highest-earning author for the last three years, told the news agency the pair do not know each other. In 2009, King called Patterson a successful yet "terrible" writer. Crime writer Patterson described that remark as "hyperbole" when speaking to AP. Patterson is releasing the novel Taking the Titanic instead of the planned King book. Follow us on Twitter @BBCNewsEnts, on Instagram, or if you have a story suggestion email [email protected].' sentences: - Author James Patterson has scrapped the publication of a new novel titled The Murder of Stephen King because he does not want to cause "discomfort" to King. - Portsmouth manager Paul Cook says he will "move heaven and earth" to get the club promoted from League Two. - Swansea City have reached a settlement with former manager Michael Laudrup over his sacking. - source_sentence: Electrical energy can be converted into kinetic energy and heat energy by an electric motor. sentences: - Solution is the term for a homogeneous mixture of two or more substances. - Solution is the term for a homogeneous mixture of two or more substances. - Electric motors transform electrical energy into kinetic energy. - source_sentence: where did the ice storm of 1998 happen sentences: - January 1998 North American ice storm The North American Ice Storm of 1998 (also known as Great Ice Storm of 1998) was a massive combination of five smaller successive ice storms in January 1998 that struck a relatively narrow swath of land from eastern Ontario to southern Quebec, New Brunswick and Nova Scotia in Canada, and bordering areas from northern New York to central Maine in the United States. It caused massive damage to trees and electrical infrastructure all over the area, leading to widespread long-term power outages. Millions were left in the dark for periods varying from days to several weeks, and in some instances, months. It led to 35 fatalities, a shutdown of activities in large cities like Montreal and Ottawa, and an unprecedented effort in reconstruction of the power grid. The ice storm led to the largest deployment of Canadian military personnel since the Korean War, with over 16,000 Canadian Forces personnel deployed, 12,000 in Quebec and 4,000 in Ontario at the height of the crisis.[1][2]:16 - Tom and Jerry Tom and Jerry is an American animated series of short films created in 1940, by William Hanna and Joseph Barbera. It centers on a rivalry between its two title characters, Tom and Jerry, and many recurring characters, based around slapstick comedy. - Nimbostratus cloud Nimbostratus is a stratiform genus formerly classified as "Family C" low-level, but now considered by the World Meteorological Organization (WMO) to be a middle- or multi-level stratus type.[1]. Although it is usually a low-based cloud, it actually forms most commonly in the middle level of the troposphere and then spreads vertically into the low and high levels. This change in classification would once have made it a "Family D" cloud, but this style of nomenclature was discontinued by the WMO in 1956. Nimbostratus usually produces precipitation over a wide area. Nimbo- is from the Latin word nimbus, which denotes precipitation. It has a diffuse cloud base generally found anywhere from near surface in the low levels to about 3,000 m (9,800 ft) in the middle level of the troposphere. Although usually dark at its base, it often appears illuminated from within to a surface observer.[2] Nimbostratus usually has a thickness of about 2000 m. Though found worldwide, nimbostratus occurs more commonly in the middle latitudes.[3] It is coded CM2 on the SYNOP report. model-index: - name: SentenceTransformer based on bobox/DeBERTa-small-ST-v1-test results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.8794649611143293 name: Pearson Cosine - type: spearman_cosine value: 0.9036383241029473 name: Spearman Cosine - type: pearson_manhattan value: 0.9013878798004252 name: Pearson Manhattan - type: spearman_manhattan value: 0.8981009820234211 name: Spearman Manhattan - type: pearson_euclidean value: 0.9018920659152481 name: Pearson Euclidean - type: spearman_euclidean value: 0.89878504980505 name: Spearman Euclidean - type: pearson_dot value: 0.8699021205629681 name: Pearson Dot - type: spearman_dot value: 0.8710281246782958 name: Spearman Dot - type: pearson_max value: 0.9018920659152481 name: Pearson Max - type: spearman_max value: 0.9036383241029473 name: Spearman Max - task: type: binary-classification name: Binary Classification dataset: name: VitaminC type: VitaminC metrics: - type: cosine_accuracy value: 0.556640625 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.8423352241516113 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.6657824933687002 name: Cosine F1 - type: cosine_f1_threshold value: 0.28707611560821533 name: Cosine F1 Threshold - type: cosine_precision value: 0.4990059642147117 name: Cosine Precision - type: cosine_recall value: 1.0 name: Cosine Recall - type: cosine_ap value: 0.5514274268482979 name: Cosine Ap - type: dot_accuracy value: 0.5546875 name: Dot Accuracy - type: dot_accuracy_threshold value: 307.8145751953125 name: Dot Accuracy Threshold - type: dot_f1 value: 0.6693440428380187 name: Dot F1 - type: dot_f1_threshold value: 136.3172607421875 name: Dot F1 Threshold - type: dot_precision value: 0.5040322580645161 name: Dot Precision - type: dot_recall value: 0.9960159362549801 name: Dot Recall - type: dot_ap value: 0.5319992367485454 name: Dot Ap - type: manhattan_accuracy value: 0.5546875 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 224.1436767578125 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.6666666666666666 name: Manhattan F1 - type: manhattan_f1_threshold value: 489.0301818847656 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.5 name: Manhattan Precision - type: manhattan_recall value: 1.0 name: Manhattan Recall - type: manhattan_ap value: 0.5514724989625399 name: Manhattan Ap - type: euclidean_accuracy value: 0.556640625 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 14.50575065612793 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.6666666666666666 name: Euclidean F1 - type: euclidean_f1_threshold value: 23.260427474975586 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.5 name: Euclidean Precision - type: euclidean_recall value: 1.0 name: Euclidean Recall - type: euclidean_ap value: 0.5539696276195623 name: Euclidean Ap - type: max_accuracy value: 0.556640625 name: Max Accuracy - type: max_accuracy_threshold value: 307.8145751953125 name: Max Accuracy Threshold - type: max_f1 value: 0.6693440428380187 name: Max F1 - type: max_f1_threshold value: 489.0301818847656 name: Max F1 Threshold - type: max_precision value: 0.5040322580645161 name: Max Precision - type: max_recall value: 1.0 name: Max Recall - type: max_ap value: 0.5539696276195623 name: Max Ap --- # SentenceTransformer based on bobox/DeBERTa-small-ST-v1-test This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [bobox/DeBERTa-small-ST-v1-test](https://huggingface.co/bobox/DeBERTa-small-ST-v1-test) on the [negation-triplets](https://huggingface.co/datasets/jinaai/negation-dataset-v2), [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc), [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail), [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail), xsum-pairs, [sciq_pairs](https://huggingface.co/datasets/allenai/sciq), [qasc_pairs](https://huggingface.co/datasets/allenai/qasc), openbookqa_pairs, [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions), [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) and [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [bobox/DeBERTa-small-ST-v1-test](https://huggingface.co/bobox/DeBERTa-small-ST-v1-test) <!-- at revision 55585e7a71c7a0177a0a8a60c64ab08c0a5f84e3 --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Datasets:** - [negation-triplets](https://huggingface.co/datasets/jinaai/negation-dataset-v2) - [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) - [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) - [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) - xsum-pairs - [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) - [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) - openbookqa_pairs - [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) - [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) - [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) - [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) - [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) - **Language:** en <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("bobox/DeBERTa-small-ST-v1-test-step2") # Run inference sentences = [ 'where did the ice storm of 1998 happen', 'January 1998 North American ice storm The North American Ice Storm of 1998 (also known as Great Ice Storm of 1998) was a massive combination of five smaller successive ice storms in January 1998 that struck a relatively narrow swath of land from eastern Ontario to southern Quebec, New Brunswick and Nova Scotia in Canada, and bordering areas from northern New York to central Maine in the United States. It caused massive damage to trees and electrical infrastructure all over the area, leading to widespread long-term power outages. Millions were left in the dark for periods varying from days to several weeks, and in some instances, months. It led to 35 fatalities, a shutdown of activities in large cities like Montreal and Ottawa, and an unprecedented effort in reconstruction of the power grid. The ice storm led to the largest deployment of Canadian military personnel since the Korean War, with over 16,000 Canadian Forces personnel deployed, 12,000 in Quebec and 4,000 in Ontario at the height of the crisis.[1][2]:16', 'Nimbostratus cloud Nimbostratus is a stratiform genus formerly classified as "Family C" low-level, but now considered by the World Meteorological Organization (WMO) to be a middle- or multi-level stratus type.[1]. Although it is usually a low-based cloud, it actually forms most commonly in the middle level of the troposphere and then spreads vertically into the low and high levels. This change in classification would once have made it a "Family D" cloud, but this style of nomenclature was discontinued by the WMO in 1956. Nimbostratus usually produces precipitation over a wide area. Nimbo- is from the Latin word nimbus, which denotes precipitation. It has a diffuse cloud base generally found anywhere from near surface in the low levels to about 3,000\xa0m (9,800\xa0ft) in the middle level of the troposphere. Although usually dark at its base, it often appears illuminated from within to a surface observer.[2] Nimbostratus usually has a thickness of about 2000 m. Though found worldwide, nimbostratus occurs more commonly in the middle latitudes.[3] It is coded CM2 on the SYNOP report.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8795 | | **spearman_cosine** | **0.9036** | | pearson_manhattan | 0.9014 | | spearman_manhattan | 0.8981 | | pearson_euclidean | 0.9019 | | spearman_euclidean | 0.8988 | | pearson_dot | 0.8699 | | spearman_dot | 0.871 | | pearson_max | 0.9019 | | spearman_max | 0.9036 | #### Binary Classification * Dataset: `VitaminC` * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:----------| | cosine_accuracy | 0.5566 | | cosine_accuracy_threshold | 0.8423 | | cosine_f1 | 0.6658 | | cosine_f1_threshold | 0.2871 | | cosine_precision | 0.499 | | cosine_recall | 1.0 | | cosine_ap | 0.5514 | | dot_accuracy | 0.5547 | | dot_accuracy_threshold | 307.8146 | | dot_f1 | 0.6693 | | dot_f1_threshold | 136.3173 | | dot_precision | 0.504 | | dot_recall | 0.996 | | dot_ap | 0.532 | | manhattan_accuracy | 0.5547 | | manhattan_accuracy_threshold | 224.1437 | | manhattan_f1 | 0.6667 | | manhattan_f1_threshold | 489.0302 | | manhattan_precision | 0.5 | | manhattan_recall | 1.0 | | manhattan_ap | 0.5515 | | euclidean_accuracy | 0.5566 | | euclidean_accuracy_threshold | 14.5058 | | euclidean_f1 | 0.6667 | | euclidean_f1_threshold | 23.2604 | | euclidean_precision | 0.5 | | euclidean_recall | 1.0 | | euclidean_ap | 0.554 | | max_accuracy | 0.5566 | | max_accuracy_threshold | 307.8146 | | max_f1 | 0.6693 | | max_f1_threshold | 489.0302 | | max_precision | 0.504 | | max_recall | 1.0 | | **max_ap** | **0.554** | <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Datasets #### negation-triplets * Dataset: [negation-triplets](https://huggingface.co/datasets/jinaai/negation-dataset-v2) * Size: 39,000 training samples * Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | entailment | negative | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 5 tokens</li><li>mean: 22.25 tokens</li><li>max: 372 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.64 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 14.02 tokens</li><li>max: 49 tokens</li></ul> | * Samples: | anchor | entailment | negative | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:---------------------------------------------------------------------| | <code>A young man in white is midair on his bicycle performing a trick.</code> | <code>A young man in white is midair</code> | <code>A young man in white is not midair</code> | | <code>A bicycle has a red umbrella attached to it.</code> | <code>A parked bicycle with a red umbrella attached to it.</code> | <code>A parked bicycle without a red umbrella attached to it.</code> | | <code>Tanzania started rationing electricity after a technical problem shut down machinery at the Songas gas-fired power plant in Dar es Salaam, the state-run power utility said.</code> | <code>Tanzania rations electricity after technical problem at plant</code> | <code>Tanzania boosts electricity after upgrade at plant</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 36,000 training samples * Columns: <code>claim</code> and <code>evidence</code> * Approximate statistics based on the first 1000 samples: | | claim | evidence | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 7 tokens</li><li>mean: 17.47 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 36.01 tokens</li><li>max: 164 tokens</li></ul> | * Samples: | claim | evidence | |:---------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Candice Crawford participated in 2 beauty pageants .</code> | <code>He has a younger sister , former Miss Missouri USA winner and [ [ Miss USA ] contestant Candice Crawford .</code> | | <code>Rio Ferdinand plays for Queens Park Rangers on a free transfer .</code> | <code>A centre-back , he is currently plays for Queens Park Rangers on a Free Transfer after leaving Manchester United following twelve years at the club .</code> | | <code>Matt Damon is credited in Deadpool 2 as Dickie Grrenleaf , in reference to Jude Law 's character from The Talented Mr. Ripley .</code> | <code>This was inspired by a real manifesto written by Reese , which the writers wanted to be discussed in the film by a certain `` calibre '' of actor : the characters are portrayed by Alan Tudyk and a disguised Matt Damon , with the latter credited as `` Dickie Greenleaf '' ( a reference to Jude Law 's character from the 1999 film The Talented Mr. Ripley in which Damon stars ) .</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### scitail-pairs-qa * Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 14,237 training samples * Columns: <code>sentence2</code> and <code>sentence1</code> * Approximate statistics based on the first 1000 samples: | | sentence2 | sentence1 | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 7 tokens</li><li>mean: 15.9 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.07 tokens</li><li>max: 41 tokens</li></ul> | * Samples: | sentence2 | sentence1 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------| | <code>How ice cores are important to the study of geologic history is best described as they contain evidence showing changes in the atmospheric composition over time.</code> | <code>Which best describes how ice cores are important to the study of geologic history?</code> | | <code>The lens of the eye is a(n) convex shape.</code> | <code>What shape is the lens of the eye?</code> | | <code>Most ferns produce the same type of spores and are therefore called homosporous.</code> | <code>Most ferns produce the same type of spores and are therefore called what?</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### scitail-pairs-pos * Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 8,600 training samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 7 tokens</li><li>mean: 24.17 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.66 tokens</li><li>max: 37 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------| | <code>The rate of decay is conveniently expressed in terms of an isotope's half-life, or the time it takes for one-half of a particular radioactive isotope in a sample to decay.</code> | <code>The term half-life decribes the amount of time required for half of the original material to decay in an isotope.</code> | | <code>This rock, called magma, furnishes the heat for the park's geysers and hot springs.</code> | <code>The water in some springs are hot because they're heated by hot magma.</code> | | <code>Carbon, with four valence electrons, forms covalent bonds to four neighboring carbon atoms arranged toward the corners of a tetrahedron, as shown in the figure below.</code> | <code>Four valence electrons can be found in a carbon atom.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### xsum-pairs * Dataset: xsum-pairs * Size: 36,000 training samples * Columns: <code>document</code> and <code>summary</code> * Approximate statistics based on the first 1000 samples: | | document | summary | |:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 56 tokens</li><li>mean: 219.62 tokens</li><li>max: 371 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 25.36 tokens</li><li>max: 56 tokens</li></ul> | * Samples: | document | summary | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Christopher Paul O'Kane, aged 42, from Woodland Avenue, was due to stand trial on Wednesday on 17 Provisional IRA related terrorist offences, including the attempted murder of a police officer more than 20 years ago.<br>However, O'Kane replied "guilty'' to five of the five terror charges when they were put to him in court.<br>These were: assisting an offender in relation to the murder of Constable Michael Ferguson in January 1994, and firing a mortar bomb at a police vehicle in Fanad Drive in October 1993. Planting a bomb at the railway line at Ebrington Barracks in December 1993; placing a bomb at the home of a senior police officer at Prehen in 1994, and planting a bomb at Forge George army base in Derry.<br>Prosecution lawyer Robin Steer asked that the remaining 12 charges "be left on the books" and not to be proceeded without the leave of the Court or the Court of Appeal.<br>O'Kane will be sentenced on December 16.</code> | <code>A Londonderry man has pleaded guilty at Belfast Crown Court to five terrorist offences relating to bomb attacks against security force members.</code> | | <code>Chase, 31, has made five appearances for the Vikings since the initial temporary move, having been told he was not part of Castleford's 2018 plans.<br>In addition, Widnes have signed hooker Danny Walker to a new four-year contract after his first-grade breakthrough this season.<br>Head coach Denis Betts said: "The deals represent a real statement of intent."<br>New Zealand-born Chase was the 2011 Man of Steel, has played for the Tigers, Salford, Leigh and now Widnes, as well as representing England at senior level.<br>In contrast, Walker, 18, is at the opposite end of his career, with just six professional appearances to his name.<br>"Rangi has made a big impact since his arrival, contributing well both on and off the field," Betts added.<br>"He has exceptional abilities and, as an international standard half-back, adds real quality to the team.<br>"Danny is one of the most promising young players in the Super League. His commitment to a four-year contract shows his belief in what we are building at the Vikings."</code> | <code>Widnes have signed half-back Rangi Chase from Castleford on a permanent deal after a successful loan spell.</code> | | <code>The characters include Nebula, a blue skinned alien played by Scottish actress Karen Gillan.<br>There was a backlash on social media to merchandise released for the first Guardians film in 2014.<br>Gamora, who is played by Zoe Saldana, did not appear on a t-shirt while her fellow, male leads did.<br>The row saw the hashtag #WheresGamora appear on Twitter.<br>Fans of the film also complained that other merchandise of Gamora, and Gillan's Nebula, who also appeared in the first movie, was harder to find than that featuring male characters.<br>In a post on Facebook on Sunday, the films' director James Gunn confirmed that there was a commitment to raising the profile of the female characters.<br>He wrote: "Guardians of the Galaxy Vol. 2 will have Mantis, Nebula, Gamora and Elizabeth Debicki's character as part of the fray, and we're committed to making sure they're included in more toys and merch than the last go round."<br>Marvel and Disney are involved in making the movies and commissioning the merchandising.<br>Guardians, which topped the US box office in 2014 with ticket sales of $332.8m (£203.9m), also stars Chris Pratt and Dave Bautista and the voices of Vin Diesel and Bradley Cooper.<br>Earlier this year, Gunn revealed that Gillan, from Inverness, has a bigger role in the new movie, which is to be released next year.</code> | <code>Female stars of the new Guardians of the Galaxy movie will feature more prominently in its merchandising, the film's makers have said.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### sciq_pairs * Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) * Size: 11,095 training samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 6 tokens</li><li>mean: 16.94 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 80.74 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What branch of science is defined as the study of matter?</code> | <code>1.8 End-of-Chapter Material Chapter Summary To ensure that you understand the material in this chapter, you should review the meanings of the bold terms in the following summary and ask yourself how they relate to the topics in the chapter. Chemistry is the study of matter, which is anything that has mass and takes up space. Chemistry is one branch of science, which is the study of the natural universe. Like all branches of science, chemistry relies on the scientific method, which is a process of Saylor URL: http://www. saylor. org/books.</code> | | <code>The net effect of aldosterone is to conserve and increase water levels in the plasma by reducing the excretion of what element, and thus water?</code> | <code>Aldosterone Recall that aldosterone increases the excretion of potassium and the reabsorption of sodium in the distal tubule. Aldosterone is released if blood levels of potassium increase, if blood levels of sodium severely decrease, or if blood pressure decreases. Its net effect is to conserve and increase water levels in the plasma by reducing the excretion of sodium, and thus water, from the kidneys. In a negative feedback loop, increased osmolality of the ECF (which follows aldosterone-stimulated sodium absorption) inhibits the release of the hormone (Figure 26.13).</code> | | <code>What do all chemical sections need to get started?</code> | <code>The bonds between the atoms need to be rearranged. That is the definition of a chemical reaction. And all chemical sections need energy to get started.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### qasc_pairs * Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 7,727 training samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 5 tokens</li><li>mean: 11.21 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 34.56 tokens</li><li>max: 67 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>vertebrates have a complete digestive system and a what?</code> | <code>Chordates have a complete digestive system and a closed circulatory system.. Vertebrates are members of of a larger group, the chordates .. Vertebrates have a complete digestive system and a closed circulatory system</code> | | <code>What is plasma?</code> | <code>plasma is formed by electrons separating from atoms in stars. Stars shine because they are hot .. plasma is hot</code> | | <code>Tungsten filaments sealed in a glass bulb are used for what in the dark?</code> | <code>a light bulb is used for seeing in the dark. Incandescent light bulbs use a tungsten filament sealed inside a glass bulb.. Tungsten filament sealed in a glass bulb is used for seeing in the dark.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### openbookqa_pairs * Dataset: openbookqa_pairs * Size: 4,522 training samples * Columns: <code>question</code> and <code>fact</code> * Approximate statistics based on the first 1000 samples: | | question | fact | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 13.8 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.5 tokens</li><li>max: 30 tokens</li></ul> | * Samples: | question | fact | |:-----------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | <code>What is animal competition?</code> | <code>if two animals eat the same prey then those animals compete for that pey</code> | | <code>If you wanted to make a metal bed frame, where would you start?</code> | <code>alloys are made of two or more metals</code> | | <code>Places lacking warmth have few what</code> | <code>cold environments contain few organisms</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### msmarco_pairs * Dataset: [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9) * Size: 33,000 training samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 8.45 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 76.9 tokens</li><li>max: 215 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what is the salary of hvac service mana</code> | <code>According to the Bureau of Labor Statistics, the median salary for HVAC technician in 2015 is $45,110 per year and approximately $21,69 per hour. Salary variations according to various factors Depending on technician’s ...</code> | | <code>caffeine effects on nervous system</code> | <code>Caffeine is a central nervous system stimulant that reduces fatigue and drowsiness. At normal doses, caffeine has variable effects on learning and memory, but it generally improves reaction time, wakefulness, concentration, and motor coordination.</code> | | <code>cost to flush transmission</code> | <code>1 Flushing a transmission with a pressurized machine can cost $125-$300 or more, and may include pushing a special cleaning compound through the system. 2 The power flush process typically replaces all of the transmission fluid, and can require 12-22 or more quarts.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### nq_pairs * Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 33,000 training samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 11.82 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 135.44 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>is watcher in the woods a disney movie</code> | <code>The Watcher in the Woods Filmed at Pinewood Studios and the surrounding areas in Buckinghamshire, England, The Watcher in the Woods was one of several live-action films produced by Walt Disney Productions in the 1980s, when the studio was targeting young adult audiences. The film suffered from various production problems and was pulled from theatres after its initial release in 1980. It was re-released in 1981 after being re-edited and a revised ending added.</code> | | <code>how much money did the ice bucket challenge raise for als</code> | <code>Ice Bucket Challenge Within weeks of the challenge going viral, The New York Times reported that the ALS Association had received $41.8 million in donations from more than 739,000 new donors from July 29 until August 21, more than double the $19.4 million the association received during the year that ended January 31, 2013.[89] On August 29, the ALS Association announced that their total donations since July 29 had exceeded $100 million.[90] The ALS Association is just one of several ALS-related charities that have benefited from the challenge:</code> | | <code>last episode of one foot in the grave</code> | <code>Things Aren't Simple Any More "Things Aren't Simple Any More" is the final episode of the British television sitcom One Foot in the Grave. It was written by David Renwick and stars Richard Wilson as Victor Meldrew, Annette Crosbie as his wife Margaret, and features guest appearances by Hannah Gordon and Paul Merton. The episode depicts the death of the series' protagonist, Victor Meldrew, in a hit-and-run road accident, and his wife's efforts to deal with the driver who killed him. Renwick had been struggling to conceive and write new stories for the series and decided to kill off the character. The episode was filmed at Shawford, Hampshire, and at BBC Television Centre in London.[3]</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### trivia_pairs * Dataset: [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) * Size: 30,000 training samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 8 tokens</li><li>mean: 18.69 tokens</li><li>max: 97 tokens</li></ul> | <ul><li>min: 36 tokens</li><li>mean: 458.53 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Which Channel Four game show was remade in the USA under the title Junkyard Wars?</code> | <code>Junkyard Wars Needs A Few Good Contestants - Slashdot Slashdot Follow Slashdot stories on Twitter   Check out the new SourceForge HTML5 internet speed test! No Flash necessary and runs on all devices. × 1380727 story on Saturday January 27, 2001 @09:07AM from the gentlemen-start-your-torches dept. Andy B writes: "At long last, we have got an address for aspiring contestants to send their Junkyard Wars applications to (Slashdot slightly jumped the gun last autumn). Hurry to get you applications in, as the deadline for applications is Fri 16th Feb." ← You may like to read: → Yes, but its license states that it must act as a public service broadcaster: This [itc.org.uk] is taken from the ITC [itc.org.uk] website: The main points in the new licence are: redefinition of the remit in relation to all channels, not just ITV, and further commitment to innovation and experiment; a revised and strengthened statement on education; a commitment to provide at least three hours on average per week of multicultural programmes, and also to schedule at least some of these in peak time; a major commitment to the UK film industry, giving some preference to innovative and risky subjects and treatments; a new commitment to programmes for and about people with disabilities; increased requirement of 60 per cent of programmes specially commissioned for the Channel by 1999; a new commitment for production outside the London region, including a minimum requirement of 30 per cent by 2002; new maxima for repeats; a new commitment and new minimum requirement for spending on training; a new requirement for diversity in the peak-time schedule, including news, current affairs, educational, religious and multicultural programmes; revised commitments to subtitling and other provisions for those with hearing and sight impairments. I think it's not fully privatised either - doesn't the government still own a proportion of it and fund it a bit too? by MrP- ( 45616 ) writes: if you watched the last episode of junkyard wars (the rocket one), cathy said you can go to the site and submit an idea for them to build, i forget what site she said, probably either the junkyard wars site, or tlc.com, either way you should submit that, that would be cool to see... but only thing is theyd probably want to fight during the show at the end, unless they go tape the fight at battlebots, show the match during junkyard wars, then battle bots later, like they did with jay lenos chinkilla, they aired the match early on the tonight show then again on battle bots... but i dont know since battle bots is comedy centrals, maybe theyd have to do robot wars, but they dont air robot wars enough in america :( by Uberminky ( 122220 ) writes: The new Junkyard Wars episodes stink. I mean yeah, it's still a cool show, but it's just not what it used to be. I miss the silly Brits, they cracked me up. But yunno... maybe it's just me, but I swear the type of things they do on the show have changed since they got the new host and stuff. I mean.. one of the last episodes the Scrapheap Challenge did was drag racing. And what's the first Junkyard Wars thing? Drag racing. They also did all-terrain vehicles, which Scrapheap Challenge did.. and.. just tons of them. They're all the same sorts of things. Maybe it's just my perception, and the fact that I miss the old show, but it really seems like they've dumbed it down to suit us redneck Americans or something. I dunno... I just wish they'd give me my Scrapheap Challenge... Looking forward to next week's episode.... by ScuzzMonkey ( 208981 ) writes: I haven't seen all that many episodes, but of those I have seen, this seems to be the theme. The team that comes up with the more brilliant, elegant design has it crap out on them almost immediately, because, after all, it's made out of junk. The crowd that bangs together some brute-force job powers through and wins it. Apparently, it doesn't pay to get too creative. Makes it more fun to watch, though. by Uberminky ( 122220 ) writes: that would be so lame. On the one hand, we have people turning scrap met</code> | | <code>Which 2010 film stars Mila Kunis as Lily and Natalie Portman as Nina Sayers?</code> | <code>Black Swan (2010) - IMDb IMDb There was an error trying to load your rating for this title. Some parts of this page won't work property. Please reload or try later. X Beta I'm Watching This! Keep track of everything you watch; tell your friends. Error From $2.99 (SD) on Amazon Video ON DISC A committed dancer wins the lead role in a production of Tchaikovsky's "Swan Lake" only to find herself struggling to maintain her sanity. Director: a list of 34 titles created 25 Dec 2012 a list of 27 images created 09 Mar 2013 a list of 43 titles created 10 Nov 2013 a list of 26 titles created 05 Nov 2014 a list of 48 titles created 14 Mar 2015 Search for " Black Swan " on Amazon.com Connect with IMDb Want to share IMDb's rating on your own site? Use the HTML below. You must be a registered user to use the IMDb rating plugin. Won 1 Oscar. Another 90 wins & 245 nominations. See more awards  » Videos Harvard student Mark Zuckerberg creates the social networking site that would become known as Facebook, but is later sued by two brothers who claimed he stole their idea, and the co-founder who was later squeezed out of the business. Director: David Fincher Two astronauts work together to survive after an accident which leaves them alone in space. Director: Alfonso Cuarón After a stint in a mental institution, former teacher Pat Solitano moves back in with his parents and tries to reconcile with his ex-wife. Things get more challenging when Pat meets Tiffany, a mysterious girl with problems of her own. Director: David O. Russell A Mumbai teen reflects on his upbringing in the slums when he is accused of cheating on the Indian Version of "Who Wants to be a Millionaire?" Directors: Danny Boyle, Loveleen Tandan Stars: Dev Patel, Freida Pinto, Saurabh Shukla A young man who survives a disaster at sea is hurtled into an epic journey of adventure and discovery. While cast away, he forms an unexpected connection with another survivor: a fearsome Bengal tiger. Director: Ang Lee The story of King George VI of the United Kingdom of Great Britain and Northern Ireland, his impromptu ascension to the throne and the speech therapist who helped the unsure monarch become worthy of it. Director: Tom Hooper In 1985 Dallas, electrician and hustler Ron Woodroof works around the system to help AIDS patients get the medication they need after he is diagnosed with the disease. Director: Jean-Marc Vallée A paraplegic marine dispatched to the moon Pandora on a unique mission becomes torn between following his orders and protecting the world he feels is his home. Director: James Cameron A lonely writer develops an unlikely relationship with an operating system designed to meet his every need. Director: Spike Jonze Tells the story of Benjamin Button, a man who starts aging backwards with bizarre consequences. Director: David Fincher A family determined to get their young daughter into the finals of a beauty pageant take a cross-country trip in their VW bus. Directors: Jonathan Dayton, Valerie Faris Stars: Steve Carell, Toni Collette, Greg Kinnear A seventeen-year-old aristocrat falls in love with a kind but poor artist aboard the luxurious, ill-fated R.M.S. Titanic. Director: James Cameron Edit Storyline Nina (Portman) is a ballerina in a New York City ballet company whose life, like all those in her profession, is completely consumed with dance. She lives with her obsessive former ballerina mother Erica (Hershey) who exerts a suffocating control over her. When artistic director Thomas Leroy (Cassel) decides to replace prima ballerina Beth MacIntyre (Ryder) for the opening production of their new season, Swan Lake, Nina is his first choice. But Nina has competition: a new dancer, Lily (Kunis), who impresses Leroy as well. Swan Lake requires a dancer who can play both the White Swan with innocence and grace, and the Black Swan, who represents guile and sensuality. Nina fits the White Swan role perfectly but Lily is the personification of the Black Swan. As the two young dancers expand their rivalry into a twisted friendship, Nina begins to</code> | | <code>In computing, what is the device which is plugged into a computer which serves as an adapter or to enable the use of certain software?</code> | <code>What Is A Dongle? - Business Insider print 3 Dongles plugged into Google's Chromebook Pixel. Kevin Smith/Business Insider This week two people in the tech industry lost their jobs because of jokes gone awry at a conference. A couple of male developers were joking around about "big" dongles. When a female developer evangelist heard their jokes she tweeted it out with their photo, complaining that they were being rude. One of those developers lost his job, and then eventually, so did the evangelist. In the developers' defense, the word "dongle" is funny. It's nearly impossible to say without giggling or making childish jokes. Some people in our office had heard the word before, but didn't know what a dongle is. Merriam-Webster  defines a dongle as a small device that plugs into a computer and serves as an adapter or a security measure to enable the use of certain software. Kevin Smith/Business Insider The term, dongle, was rumored to have originated from a 1992 advertisement for Rainbow Technologies. The ad claimed the word dongle was derived from the name "Don Gall." Though untrue, this has given rise to an urban myth, we learned from Wikipedia . According to the University of Pennsylvania's language log , the earliest citation of the word dongle began appearing in 1982: 1982   MicroComputer Printout  Jan. 19/2 The word ‘dongle’ has been appearing in many articles with reference to security systems for computer software [refers to alleged coinage in 1980]. But as the term 'dongle' became more widespread its meaning changed from strictly a scientific term to mainstream. UPenn clarifies: The current meaning for dongle seems to be something like "a self-contained device that plugs into a  port on a computer that is normally used for connections to a separate external device". Thus in addition to the original serial-port dongles, and the USB dongles that Suzanne (and Stephen Fry) wrote about, there are also  "firewire dongles" , and presumably there could be dongles for any other sort of port as well.  Simply put, dongles are computer peripherals that plug into your computer like a USB flash drive or a cord connecting a computer with a printer for example.  Dongles are also huge in the video game world because they allow consoles to have added features like increased audio quality. An Xbox 360 Audio Dongle Wikimedia Commons Before USB was the standard in attaching PC peripherals, there were tons of different dongles that came in various shapes and sizes.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### gooaq_pairs * Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 30,000 training samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 8 tokens</li><li>mean: 11.43 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 57.57 tokens</li><li>max: 155 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what is the difference between back off and back down?</code> | <code>You back off by retreating from a confrontation of some kind, but here the other person is not being antagonistic. You back down by giving way when you realise you're losing an argument or fight, but that doesn't apply here either.</code> | | <code>how many days after lh surge should i have intercourse?</code> | <code>The three days immediately after a positive test represent the best time to have intercourse to increase the probability of getting pregnant. Ovulation generally occurs a day or two after the LH surge.</code> | | <code>what episode does jane and rafael do it?</code> | <code>Rodriguez's directorial debut was the tenth episode of the season, "Chapter Seventy-Four", which aired on February 9, 2018. The episode was specially selected by showrunner Jennie Snyder-Urman as it featured the characters of Jane (Gina Rodriguez) and Rafael (Justin Baldoni) having sex for the first time.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### paws-pos * Dataset: [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) at [161ece9](https://huggingface.co/datasets/google-research-datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09) * Size: 21,829 training samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 9 tokens</li><li>mean: 25.51 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 25.47 tokens</li><li>max: 57 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Mouhoun is one of the 45 provinces of Boucle du Mouhoun Region and is in Burkina Faso . The capital of Mouhoun is Dédougou .</code> | <code>Mouhoun is one of 45 provinces in the Boucle du Mouhoun region and is located in Burkina Faso , the capital of Mouhoun is Dédougou .</code> | | <code>Besides Quintin , they had five children : Juan , Phillip , Willie , Patrick and Lucy .</code> | <code>They had five children besides Quintin : Lucy , Phillip , Juan , Patrick and Willie .</code> | | <code>According to the U.S. Census Bureau , the county is a total area that has land and ( 0.2 % ) of water .</code> | <code>According to the US Census Bureau , the county has a total area of which is land and ( 0.2 % ) of water .</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` ### Evaluation Datasets #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 108 evaluation samples * Columns: <code>claim</code> and <code>evidence</code> * Approximate statistics based on the first 1000 samples: | | claim | evidence | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 9 tokens</li><li>mean: 21.36 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 36.11 tokens</li><li>max: 79 tokens</li></ul> | * Samples: | claim | evidence | |:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Dragon Con had over 5000 guests .</code> | <code>Among the more than 6000 guests and musical performers at the 2009 convention were such notables as Patrick Stewart , William Shatner , Leonard Nimoy , Terry Gilliam , Bruce Boxleitner , James Marsters , and Mary McDonnell .</code> | | <code>COVID-19 has reached more than 185 countries .</code> | <code>As of , more than cases of COVID-19 have been reported in more than 190 countries and 200 territories , resulting in more than deaths .</code> | | <code>In March , Italy had 3.6x times more cases of coronavirus than China .</code> | <code>As of 12 March , among nations with at least one million citizens , Italy has the world 's highest per capita rate of positive coronavirus cases at 206.1 cases per million people ( 3.6x times the rate of China ) and is the country with the second-highest number of positive cases as well as of deaths in the world , after China .</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### negation-triplets * Dataset: [negation-triplets](https://huggingface.co/datasets/jinaai/negation-dataset-v2) * Size: 64 evaluation samples * Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | entailment | negative | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 13.83 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 13.23 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 13.61 tokens</li><li>max: 19 tokens</li></ul> | * Samples: | anchor | entailment | negative | |:-----------------------------------------------------------------|:-------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | <code>Boy rides skateboard and does trick over stairs.</code> | <code>Man in air on skateboard at night by light of street lamps.</code> | <code>Man on the ground on skateboard during the day by light of street lamps.</code> | | <code>A small child climbs atop a large motorcycle</code> | <code>A young boy riding a motorcycle next to a silver car.</code> | <code>An adult riding a motorcycle next to a silver car.</code> | | <code>A group of people skiing down a snow covered slope.</code> | <code>Six people in snow field with ski equipment.</code> | <code>Six people in a desert with no ski equipment.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### scitail-pairs-pos * Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 54 evaluation samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 9 tokens</li><li>mean: 20.81 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 15.48 tokens</li><li>max: 23 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------| | <code>humans normally have 23 pairs of chromosomes.</code> | <code>Humans typically have 23 pairs pairs of chromosomes.</code> | | <code>A solution is a homogenous mixture of two or more substances that exist in a single phase.</code> | <code>Solution is the term for a homogeneous mixture of two or more substances.</code> | | <code>Upwelling The physical process in near-shore ocean systems of rising of nutrients and colder bottom waters to the surface because of constant wind patterns along the shoreline.</code> | <code>Upwelling is the term for when deep ocean water rises to the surface.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### xsum-pairs * Dataset: xsum-pairs * Size: 128 evaluation samples * Columns: <code>document</code> and <code>summary</code> * Approximate statistics based on the first 1000 samples: | | document | summary | |:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 51 tokens</li><li>mean: 219.56 tokens</li><li>max: 341 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 25.73 tokens</li><li>max: 47 tokens</li></ul> | * Samples: | document | summary | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>The five were arrested in Milford Haven and are in police custody.<br>It follows a multi-agency operation between Dyfed-Powys Police, the Gangmasters and Labour Abuse Authority and Pembrokeshire council.<br>Det Ch Insp Ross Evans said: "The victims are our priority and those affected are being fully supported."<br>As part of anti-slavery awareness week, the force said it was raising awareness, identifying offences and supporting victims.<br>He added: "In reality modern slavery can happen anywhere and there is no typical victim of slavery."</code> | <code>Five people in Pembrokeshire have been arrested on suspicion of gangmaster offences following an investigation into "modern slavery".</code> | | <code>DNA tests showed the bones belonged to the men, who went missing on the mountain in August 1970, police say.<br>The remains were discovered at an altitude of about 2,800m (9,200ft) in the Alps last September.<br>They are the latest to be found on the 4,478-metre (14,692-foot) Matterhorn as ice melts.<br>The Japanese consulate in Geneva identified the climbers as Michio Oikawa and Masayuki Kobayashi, AFP news agency reports. They were 22 and 21 respectively when they went missing.<br>The consulate assisted police to track down family members to help compare their DNA profiles.<br>As Alpine glaciers melt because of global warming, the remains of long-lost climbers have increasingly been emerging from the shrinking mountain ice.<br>A mountain rescue pilot discovered remains and climbing equipment belonging to British climber Jonathan Conville, missing since 1979, in 2013 near the peak of the Matterhorn.<br>Last year the body of a Czech climber who disappeared 40 years ago following an accident was found in the Bernese Alps.</code> | <code>Remains found at the foot of Switzerland's Matterhorn glacier have been identified as two Japanese climbers who disappeared 45 years ago.</code> | | <code>The song, featuring Charlie Puth, rose 21 places to become the fastest-selling single of the year so far with combined chart sales of 193,000 copies.<br>It also set a new streaming record with 3.68 million streams in seven days.<br>The track features on the Fast & Furious 7 soundtrack and pays tribute to cast member Paul Walker, who died while filming the blockbuster in 2013.<br>It is Khalifa's second number one single after a guest appearance on Maroon 5's Payphone in 2012.<br>Omi's Cheerleader was another high climber, jumping 25 places to number two, according to the Official Charts Company.<br>Last week's number one - Hold My Hand by Jess Glynne - slipped down to three, while Spanish house DJ Dr Kucho's collaboration with Gregor Salto, Can't Stop Playing, was the highest new entry at four.<br>See the UK Top 40 singles chart<br>See the UK Top 40 albums chart<br>BBC Radio 1's Official Chart Show<br>In the album chart, Paul Simon scored his first number one for 25 years with his greatest hits record The Ultimate Collection. His last chart topper was 1990's The Rhythm Of The Saints.<br>James Bay, Ed Sheeran and Sam Smith all held firm, occupying the second to fourth places on the chart.<br>Wombat's third studio album Glitterbug was a new entry at five.<br>All Time Low's Future Hearts, which had topped the album chart last week, fell to 18.</code> | <code>US rapper Wiz Khalifa has topped the UK singles chart with his track, See You Again.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### sciq_pairs * Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) * Size: 128 evaluation samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 8 tokens</li><li>mean: 16.31 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 67.08 tokens</li><li>max: 413 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>During karyogamy, the haploid nuclei contributed by the two parents fuse, which produces what?</code> | <code></code> | | <code>Every human cell has the same what, and each cell has thousands of them?</code> | <code>There are about 22,000 genes in every human cell. Does every human cell have the same genes? Yes. Does every human cell make the same proteins? No. In a multicellular organism, such as us, cells have specific functions because they have different proteins. They have different proteins because different genes are expressed in different cell types (which is known as gene expression ).</code> | | <code>Fertilized mollusk eggs develop into what?</code> | <code>Mollusks reproduce sexually. Most species have separate male and female sexes. Fertilization may be internal or external, depending on the species. Fertilized eggs develop into larvae. There may be one or more larval stages. Each one is different from the adult stage.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### qasc_pairs * Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 128 evaluation samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 6 tokens</li><li>mean: 11.35 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 34.37 tokens</li><li>max: 54 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what happens to fibers after muscle activation?</code> | <code>Muscle contraction occurs when muscle fibers get shorter.. Muscle activation is a prerequisite for muscle contraction.. muscles activate before fibers get shorter</code> | | <code>What do most streams start with?</code> | <code>Streams may start with runoff or water seeping out of a spring.. Most water comes from precipitation.. most streams start with precipitation</code> | | <code>Mitosis begins when what unite in fertilization? </code> | <code>Gametes then unite in fertilization and form a diploid zygote.. Mitosis produces diploid cells.. Gametes then unite in fertilization and begin Mitosis</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### openbookqa_pairs * Dataset: openbookqa_pairs * Size: 128 evaluation samples * Columns: <code>question</code> and <code>fact</code> * Approximate statistics based on the first 1000 samples: | | question | fact | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 13.98 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.78 tokens</li><li>max: 28 tokens</li></ul> | * Samples: | question | fact | |:-----------------------------------------------------------------------|:-----------------------------------------------------------------------------| | <code>The thermal production of a stove is generically used for</code> | <code>a stove generates heat for cooking usually</code> | | <code>What creates a valley?</code> | <code>a valley is formed by a river flowing</code> | | <code>when it turns day and night on a planet, what cause this?</code> | <code>a planet rotating causes cycles of day and night on that planet</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### msmarco_pairs * Dataset: [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9) * Size: 128 evaluation samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 8.81 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 80.79 tokens</li><li>max: 196 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:-------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what does perjure mean</code> | <code>Perjure (verb) to cause to violate an oath or a vow; to cause to make oath knowingly to what is untrue; to make guilty of perjury; to forswear; to corrupt; -- often used reflexively; as, he perjured himself. Perjure (verb) to make a false oath to; to deceive by oaths and protestations. Perjure (noun) a perjured person</code> | | <code>weather in salalah</code> | <code>Average monthly weather in Salalah, Oman. Salalah has an hot and desert climate, however with temperatures that rarely reach above 33 degrees Celsius (91° Fahrenheit). Most rainfall (monsoon) is seen in the months July and August.</code> | | <code>what is dukan diet plan</code> | <code>The Dukan Diet is a protein based nutritional approach designed by Pierre Dukan, a French nutritionist and dietician. The Dukan Diet, or Dukan method proposes a healthy eating plan which is based on how primitive man used to eat when we were hunter-gatherers.It includes 100 foods, of which 72 are animal sourced and 28 come from plants.And you can eat as much as you like, as long as you stick to those 100 foods.he Dukan Diet, or Dukan method proposes a healthy eating plan which is based on how primitive man used to eat when we were hunter-gatherers. It includes 100 foods, of which 72 are animal sourced and 28 come from plants. And you can eat as much as you like, as long as you stick to those 100 foods.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### nq_pairs * Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 128 evaluation samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 11.45 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 143.7 tokens</li><li>max: 384 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what do you call more than one hypothesis</code> | <code>Hypothesis A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. For a hypothesis to be a scientific hypothesis, the scientific method requires that one can test it. Scientists generally base scientific hypotheses on previous observations that cannot satisfactorily be explained with the available scientific theories. Even though the words "hypothesis" and "theory" are often used synonymously, a scientific hypothesis is not the same as a scientific theory. A working hypothesis is a provisionally accepted hypothesis proposed for further research.[1]</code> | | <code>dragons name on how to train your dragon</code> | <code>How to Train Your Dragon (film) Stoick assembles a fleet to find the dragons' nest, leaving Hiccup in a dragon-fighting class taught by Gobber. Hiccup returns to the forest to find the Night Fury still there, and realizes it is unable to fly properly because of its crippled caudal fin. Hiccup gradually tames the dragon and gives it the name "Toothless", for its retractable teeth. Hiccup makes a harness and prosthetic fin that allows him to guide the dragon in free flight. By studying Toothless' behavior, Hiccup becomes proficient in subduing the captive dragons during training. Stoick's fleet arrives home unsuccessful, but he is cheered by Hiccup's unexpected success.</code> | | <code>was the original pre-modern maya script based on syllabic alphabetic or ideographic principles</code> | <code>Mesoamerican writing systems Maya writing first developed as only utilizing logograms, but later included the use of phonetic complements in order to differentiate between the semantic meanings of the logograms and for context that allows for syllabic spelling of words.[1]</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### trivia_pairs * Dataset: [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) * Size: 128 evaluation samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 9 tokens</li><li>mean: 19.57 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 41 tokens</li><li>mean: 440.45 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What was heavyweight boxer Joe Louis' nickname?</code> | <code>Joe Louis - Biography - IMDb Joe Louis Jump to: Overview  (5) | Mini Bio  (1) | Spouse  (3) | Trivia  (19) | Personal Quotes  (3) Overview (5) 6' 1½" (1.87 m) Mini Bio (1) Joe Louis is considered by many fistic experts and fans as the greatest Heavyweight Boxing Champion in the sport's history. Born into a poor family, Joe Louis's mother felt the only way her son could escape poverty was through music. She bought him a violin and sent him off daily to lessons. On his way there, young Joe would pass by a boxing gym. In no time, he was working out at the gym, training for a boxing career. His amateur career started off disastrously, as he was knocked-out down 16 times in losing the fight. However, he was determined to continue and posted an outstanding amateur career with only 5 defeats in 60 fights. He turned professional and quickly racked up one of the most impressive winning streaks in boxing history. He was nicknamed, The Brown Bomber, and became the first boxer to defeat six heavyweight champions ( Primo Carnera , Max Baer , Jack Sharkey , Jimmy Braddock , Max Schmeling , and Jersey Joe Walcott ). After winning the championship, he held it almost 12 years to set a record, plus set another record with 25 successful title defenses. He retired with a 60-1 record, only to make an unsuccessful and very sad comeback at the age of 37. While champion, Joe Louis volunteered to join the U.S. Army at the height of his career. He made two title defenses in which he donated his entire purses to relief funds to help both the Army and the Navy. He spent almost five years in the service and boxed hundreds of exhibitions. However, after the war, he was hounded by the Internal Revenue Service to pay back taxes on the purses he had donated. He suffered terribly through this ordeal. and soon found himself broke. He launched a "controversial" pro-wrestling career and was undefeated in some 20 matches before retiring with a heart problem. He was helped by his good friend Frank Sinatra and acted in a few films, worked as a host in Las Vegas, and made numerous appearances for boxing. He died a few years after suffering a massive stroke. Joe Louis was buried with full-military honors, and it was said that he was "most" proud of his European-African-Middle Eastern Medal and his Victory Medal World War II. In or out of the ring, Joe Louis was a Champion. - IMDb Mini Biography By: angelsunchained Spouse (3) Born at 8:00 a.m. CST. World Heavyweight boxing champion, 1937-1949. Inducted into the International Boxing Hall of Fame, 1990. His reign as heavyweight champion (12 years) is a boxing record that stands in all weight divisions. He fought so many bad opponents that they were dubbed the "Bum of the Month" tour. Inducted into the World Boxing Hall of Fame, 1980. Pictured on a 29¢ US commemorative postage stamp in the Sports series, issued 22 June 1993. The Joe Louis Arena in Detroit, Michigan, is named after him. Has a daughter, Jacquelin (b. 1943), with Marva. Because Louis had been down on his luck in his last years and had died in poverty, his funeral was paid for by his most famous rival (and later friend), German boxer Max Schmeling . Biography in "The Scribner Encyclopedia of American Lives," Volume One, 1981-1985, pages 512-515, New York: Charles Scribner's Sons, 1998. Portrayed by Bernie Casey in Ring of Passion (1978) and Coley Wallace in The Joe Louis Story (1953) and Raging Bull (1980). Buried in Arlington National Cemetery. Americans tend to pronounce Louis's second name - "Lewis" - whereas Europeans tend to pronounce it "Loo-e." Curiously enough, Louis Armstrong is universally referred to as "Loo-e" Armstrong even though, in one of the lines in the lyrics to Armstrong's classic, "Hello Dolly," one can clearly hear Armstrong sing, "Hello Dolly, this is "Lewis" Dolly...". One of his grandmothers was a Native American. Is played by Leonard Roberts , who gained nearly 20 lbs. for the role, in Joe and Max (2002). He was a student of mail order physical culture trainer, Charles Atlas, and a graduate of his course. Served in the U.S. Arm</code> | | <code>On the human body, exungulation is the trimming of what?</code> | <code>Human Physiology/Integumentary System - Wikibooks, open books for an open world Human Physiology/Integumentary System Introduction[ edit ] The integumentary system consists of the skin, hair, nails, the subcutaneous tissue below the skin,and assorted glands.The most obvious function of the integumentary system is the protection that the skin gives to underlying tissues. The skin not only keeps most harmful substances out, but also prevents the loss of fluids. A major function of the subcutaneous tissue is to connect the skin to underlying tissues such as muscles. Hair on the scalp provides insulation from cold for the head. The hair of eyelashes and eyebrows helps keep dust and perspiration out of the eyes, and the hair in our nostrils helps keep dust out of the nasal cavities. Any other hair on our bodies no longer serves a function, but is an evolutionary remnant. Nails protect the tips of fingers and toes from mechanical injury. Fingernails give the fingers greater ability to pick up small objects. There are four types of glands in the integumentary system: Sudoriferous glands, Sebaceous glands, Ceruminous glands, and Mammary glands. Sudoriferous glands are sweat producing glands. These are important to help maintain body temperature. Sebaceous glands are oil producing glands which help inhibit bacteria, keep us waterproof and prevent our hair and skin from drying out. Ceruminous glands produce earwax which keeps the outer surface of the eardrum pliable and prevents drying. Mammary glands produce milk. Skin[ edit ] In zoology and dermatology, skin is an organ of the integumentary system made up of a layer of tissues that guard underlying muscles and organs. As the interface with the surroundings, it plays the most important role in protecting against pathogens. Its other main functions are insulation and temperature regulation, sensation and vitamin D and B synthesis. Skin is considered one of the most important parts of the body. Skin has pigmentation, melanin, provided by melanocytes, which absorbs some of the potentially dangerous radiation in sunlight. It also contains DNA repair enzymes which reverse UV damage, and people who lack the genes for these enzymes suffer high rates of skin cancer. One form predominantly produced by UV light, malignant melanoma, is particularly invasive, causing it to spread quickly, and can often be deadly. Human skin pigmentation varies among populations in a striking manner. This has sometimes led to the classification of people(s) on the basis of skin color. Damaged skin will try to heal by forming scar tissue, often giving rise to discoloration and depigmentation of the skin. The skin is often known as "the largest organ in the human body". This applies to exterior surface, as it covers the body, appearing to have the largest surface area of all the organs. Moreover, it applies to weight, as it weighs more than any single internal organ, accounting for about 15 percent of body weight. For the average adult human, the skin has a surface area of between 1.5-2.0 square meters, most of it is between 2-3 mm thick. The average square inch of skin holds 650 sweat glands, 20 blood vessels, 60,000 melanocytes, and more than a thousand nerve endings. The use of natural or synthetic cosmetics to treat the appearance of the face and condition of the skin (such as pore control and black head cleansing) is common among many cultures. Layers[ edit ] The skin has two major layers which are made of different tissues and have very different functions. Diagram of the layers of human skin Skin is composed of the epidermis and the dermis. Below these layers lies the hypodermis or subcutaneous adipose layer, which is not usually classified as a layer of skin. The outermost epidermis consists of stratified squamous keratinizing epithelium with an underlying basement membrane. It contains no blood vessels, and is nourished by diffusion from the dermis. The main type of cells which make up the epidermis are keratinocytes, with melanocytes and Langerhans cells also present. The epidermis can be further subd</code> | | <code>Which disease has the medical term variola?</code> | <code>Smallpox Glossary of Terms with Medical Definitions See the entire definition of Acquired Aerosolization: The production of an aerosol -- a fine mist or spray containing minute par... See the entire definition of Aerosolization Allergy: A misguided reaction to foreign substances by the immune system, the body system ... See the entire definition of Allergy Anthrax: A serious bacterial infection caused by Bacillus anthracis that occurs primarily ... See the entire definition of Anthrax Antiviral: An agent that kills a virus or that suppresses its ability to replicate and, he... See the entire definition of Antiviral Arms: An appendage in anatomy and in clinical trials. See: Arm. Arthritis: Inflammation of a joint. When joints are inflamed they can develop stiffness, w... See the entire definition of Arthritis Assay: An assay is an analysis done to determine: The presence of a substance an... See the entire definition of Assay Asymptomatic: Without symptoms. For example, an asymptomatic infection is an infection wit... See the entire definition of Asymptomatic Atopic: A predisposition toward developing certain allergic hypersensitivity reactions. At... See the entire definition of Atopic Atopic dermatitis: A skin disease characterized by areas of severe itching, redness, scali... See the entire definition of Biotechnology Bioterrorism: Terrorism using biologic agents that are harmful to humans. Biological disea... See the entire definition of Bioterrorism Blindness: Loss of useful sight. Blindness can be temporary or permanent. Damage to any p... See the entire definition of Blindness Blister: A collection of fluid underneath the top layer of skin (epidermis). There are man... See the entire definition of Blister Brain: The portion of the central nervous system that is located within the skull. It func... See the entire definition of Brain Breathing: The process of respiration, during which air is inhaled into the lungs through ... See the entire definition of Breathing Calf: The belly or fleshy hind part of the back of the leg below the knee. The calf is mad... See the entire definition of Calf Cancer: An abnormal growth of cells which tend to proliferate in an uncontrolled way and, ... See the entire definition of Cancer CDC: The Centers for Disease Control and Prevention, the US agency charged with tracking a... See the entire definition of CDC Cell: The basic structural and functional unit of any living thing. Each cell is a small c... See the entire definition of Cell Centers for Disease Control and Prevention: The US agency charged with tracking and invest... See the entire definition of Contagious Contrast: Short for "contrast media." Contrast media are X-ray dyes used to provide contra... See the entire definition of Contrast Cough: A rapid expulsion of air from the lungs, typically in order to clear the lung airwa... See the entire definition of Cough Cowpox: A mild skin disease of milk cows, principally confined to the udder and teats, tha... See the entire definition of Cowpox Depression: An illness that involves the body, mood, and thoughts and that affects the way... See the entire definition of Depression Dermatitis: Inflammation of the skin, either due to an inherent skin defect, direct contac... See the entire definition of Dermatitis Diagnosis: 1 The nature of a disease; the identification of an illness. 2 A ... See the entire definition of Diagnosis DNA: Deoxyribonucleic acid. One of two types of molecules that encode genetic information... See the entire definition of DNA Drain: A device for removing fluid from a cavity or wound. A drain is typically a tube or ... See the entire definition of Drain Eczema (dermatitis): A particular type of inflammatory reaction of the skin in which there... See the entire definition of Eczema Elbow: The juncture of the long bones in the middle portion of the upper extremity. The bo... See the entire definition of Elbow ELISA: Enzyme-linked immunosorbent assay, a rapid immunochemical test that involves an enz... See the entire definition of ELISA Emergency department: T</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### gooaq_pairs * Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 128 evaluation samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 8 tokens</li><li>mean: 11.33 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 60.85 tokens</li><li>max: 136 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>how much is a destination wedding in italy?</code> | <code>A wedding in Italy usually costs between 20 000 and 80 000 euros, depending on the number of guests and the number of rendered services and their quality. Wedding dresses, ceremony and reception are those three main expenses, which will take the most of your budget.</code> | | <code>how to transfer files on pc to ipad?</code> | <code>['In iTunes, select the app from the list in the File Sharing section.', 'Drag and drop files from a folder or window onto the Documents list to copy them to your device.']</code> | | <code>what is difference between saturated and unsaturated compounds?</code> | <code>Saturated vs. ... Unlike saturated hydrocarbons in which all hydrogen atoms and carbon atoms are bonded together with single bonds, unsaturated hydrocarbons have double or even triple bonds between the carbon atoms.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### paws-pos * Dataset: [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) at [161ece9](https://huggingface.co/datasets/google-research-datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09) * Size: 128 evaluation samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 25.72 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 25.55 tokens</li><li>max: 41 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>They were there to enjoy us and they were there to pray for us .</code> | <code>They were there for us to enjoy and they were there for us to pray .</code> | | <code>After the end of the war in June 1902 , Higgins left Southampton in the `` SSBavarian '' in August , returning to Cape Town the following month .</code> | <code>In August , after the end of the war in June 1902 , Higgins Southampton left the `` SSBavarian '' and returned to Cape Town the following month .</code> | | <code>From the merger of the Four Rivers Council and the Audubon Council , the Shawnee Trails Council was born .</code> | <code>Shawnee Trails Council was formed from the merger of the Four Rivers Council and the Audubon Council .</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 320 - `per_device_eval_batch_size`: 64 - `gradient_accumulation_steps`: 3 - `learning_rate`: 4e-05 - `weight_decay`: 0.0001 - `num_train_epochs`: 2 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 1e-05} - `warmup_ratio`: 0.2 - `save_safetensors`: False - `fp16`: True - `push_to_hub`: True - `hub_model_id`: bobox/DeBERTa-small-ST-v1-test-step2-checkpoints-tmp - `hub_strategy`: all_checkpoints - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 320 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 3 - `eval_accumulation_steps`: None - `learning_rate`: 4e-05 - `weight_decay`: 0.0001 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 2 - `max_steps`: -1 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 1e-05} - `warmup_ratio`: 0.2 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: False - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: True - `resume_from_checkpoint`: None - `hub_model_id`: bobox/DeBERTa-small-ST-v1-test-step2-checkpoints-tmp - `hub_strategy`: all_checkpoints - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | nq pairs loss | vitaminc-pairs loss | openbookqa pairs loss | scitail-pairs-pos loss | xsum-pairs loss | trivia pairs loss | paws-pos loss | qasc pairs loss | sciq pairs loss | msmarco pairs loss | negation-triplets loss | gooaq pairs loss | VitaminC_max_ap | sts-test_spearman_cosine | |:------:|:----:|:-------------:|:-------------:|:-------------------:|:---------------------:|:----------------------:|:---------------:|:-----------------:|:-------------:|:---------------:|:---------------:|:------------------:|:----------------------:|:----------------:|:---------------:|:------------------------:| | 0.0031 | 1 | 0.7374 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0063 | 2 | 0.5723 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0094 | 3 | 0.551 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0125 | 4 | 0.7379 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0156 | 5 | 0.5271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0187 | 6 | 0.5858 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0219 | 7 | 0.6562 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.025 | 8 | 0.8228 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0281 | 9 | 0.9988 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0312 | 10 | 0.5582 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0344 | 11 | 0.8546 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0375 | 12 | 0.4235 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0406 | 13 | 0.6418 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0437 | 14 | 0.6577 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0469 | 15 | 0.8333 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.05 | 16 | 0.4082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0531 | 17 | 0.8101 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0563 | 18 | 0.5259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0594 | 19 | 0.9015 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0625 | 20 | 1.3915 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0656 | 21 | 0.26 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0688 | 22 | 0.6885 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0719 | 23 | 0.9357 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.075 | 24 | 0.7168 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0781 | 25 | 0.8678 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0813 | 26 | 0.4922 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0844 | 27 | 0.4937 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0875 | 28 | 0.5891 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0906 | 29 | 0.6921 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0938 | 30 | 0.8087 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0969 | 31 | 0.805 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1 | 32 | 0.6141 | 0.1978 | 2.3326 | 0.6932 | 0.0776 | 0.0913 | 0.7433 | 0.0248 | 0.1100 | 0.0197 | 0.3269 | 0.8681 | 0.3761 | 0.5467 | 0.8955 | | 0.1031 | 33 | 0.7783 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1062 | 34 | 0.8746 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1094 | 35 | 0.5085 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1125 | 36 | 0.4842 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1156 | 37 | 0.8097 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1187 | 38 | 0.5325 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1219 | 39 | 0.7221 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.125 | 40 | 0.708 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1281 | 41 | 0.2789 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1313 | 42 | 0.7986 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1344 | 43 | 0.9653 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1375 | 44 | 0.7857 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1406 | 45 | 0.2726 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1437 | 46 | 0.2458 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1469 | 47 | 0.6988 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.15 | 48 | 0.6328 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1531 | 49 | 0.795 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1562 | 50 | 0.6163 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1594 | 51 | 0.8269 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1625 | 52 | 0.52 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1656 | 53 | 0.7523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1688 | 54 | 0.6979 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1719 | 55 | 0.7845 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.175 | 56 | 0.9325 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1781 | 57 | 0.8546 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1812 | 58 | 0.6392 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1844 | 59 | 0.5827 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1875 | 60 | 0.5961 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1906 | 61 | 0.3625 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1938 | 62 | 0.2584 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1969 | 63 | 0.4047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2 | 64 | 0.9429 | 0.2214 | 2.2601 | 0.6793 | 0.0836 | 0.0857 | 0.7304 | 0.0245 | 0.1140 | 0.0197 | 0.3460 | 0.8368 | 0.3801 | 0.5495 | 0.8960 | | 0.2031 | 65 | 0.7848 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2062 | 66 | 0.7589 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2094 | 67 | 0.5905 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2125 | 68 | 0.4211 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2156 | 69 | 0.5325 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2188 | 70 | 0.3541 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2219 | 71 | 0.9396 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.225 | 72 | 0.6997 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2281 | 73 | 0.6415 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2313 | 74 | 1.1966 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2344 | 75 | 0.7142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2375 | 76 | 0.6048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2406 | 77 | 0.4639 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2437 | 78 | 0.9391 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2469 | 79 | 0.6364 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.25 | 80 | 0.515 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2531 | 81 | 0.6505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2562 | 82 | 0.6149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2594 | 83 | 0.4471 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2625 | 84 | 1.4199 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2656 | 85 | 0.8484 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2687 | 86 | 0.6412 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2719 | 87 | 0.65 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.275 | 88 | 0.7453 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2781 | 89 | 0.9506 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2812 | 90 | 0.6083 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2844 | 91 | 0.7102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2875 | 92 | 0.4037 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2906 | 93 | 0.769 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2938 | 94 | 0.8765 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2969 | 95 | 1.2583 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3 | 96 | 0.8885 | 0.2074 | 2.2594 | 0.6809 | 0.0757 | 0.0821 | 0.7431 | 0.0248 | 0.1059 | 0.0200 | 0.3400 | 0.8347 | 0.3709 | 0.5525 | 0.8969 | | 0.3031 | 97 | 0.6398 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3063 | 98 | 0.8263 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3094 | 99 | 0.8716 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3125 | 100 | 0.5523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3156 | 101 | 0.5811 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3187 | 102 | 0.7602 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3219 | 103 | 0.5337 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.325 | 104 | 0.8182 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3281 | 105 | 0.6641 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3312 | 106 | 1.0088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3344 | 107 | 0.7556 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3375 | 108 | 0.713 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3406 | 109 | 0.8385 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3438 | 110 | 0.5181 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3469 | 111 | 1.0939 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.35 | 112 | 0.5826 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3531 | 113 | 0.7121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3563 | 114 | 0.9371 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3594 | 115 | 0.7739 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3625 | 116 | 0.9612 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3656 | 117 | 0.7213 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3688 | 118 | 0.621 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3719 | 119 | 0.5503 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.375 | 120 | 0.8439 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3781 | 121 | 0.7813 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3812 | 122 | 0.5637 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3844 | 123 | 0.9052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3875 | 124 | 0.64 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3906 | 125 | 0.6529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3937 | 126 | 0.6894 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3969 | 127 | 0.8604 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4 | 128 | 0.8503 | 0.2085 | 2.0338 | 0.7888 | 0.0776 | 0.0820 | 0.7111 | 0.0248 | 0.1131 | 0.0210 | 0.3429 | 0.7917 | 0.3744 | 0.5518 | 0.8969 | | 0.4031 | 129 | 0.8171 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4062 | 130 | 1.0401 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4094 | 131 | 0.4243 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4125 | 132 | 0.3778 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4156 | 133 | 0.7651 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4188 | 134 | 0.6003 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4219 | 135 | 0.6023 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.425 | 136 | 0.6079 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4281 | 137 | 0.6206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4313 | 138 | 0.4694 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4344 | 139 | 0.7528 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4375 | 140 | 0.8395 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4406 | 141 | 0.6689 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4437 | 142 | 0.6547 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4469 | 143 | 0.9242 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.45 | 144 | 0.9496 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4531 | 145 | 0.6506 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4562 | 146 | 0.786 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4594 | 147 | 0.7414 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4625 | 148 | 0.3978 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4656 | 149 | 0.5635 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4688 | 150 | 0.9466 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4719 | 151 | 0.5251 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.475 | 152 | 0.6636 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4781 | 153 | 0.7834 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4813 | 154 | 0.6177 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4844 | 155 | 0.4558 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4875 | 156 | 0.5228 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4906 | 157 | 0.5543 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4938 | 158 | 0.7127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4969 | 159 | 0.4227 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5 | 160 | 0.5914 | 0.2085 | 2.0564 | 0.6668 | 0.0997 | 0.0846 | 0.7139 | 0.0257 | 0.1236 | 0.0201 | 0.3291 | 0.7866 | 0.3919 | 0.5543 | 0.8989 | | 0.5031 | 161 | 0.3874 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5062 | 162 | 0.8134 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5094 | 163 | 0.5596 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5125 | 164 | 0.2877 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5156 | 165 | 0.5218 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5188 | 166 | 0.5282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5219 | 167 | 0.7528 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.525 | 168 | 0.7174 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5281 | 169 | 0.6902 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5312 | 170 | 0.7486 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5344 | 171 | 0.6333 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5375 | 172 | 1.2932 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5406 | 173 | 0.6259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5437 | 174 | 0.8357 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5469 | 175 | 0.3604 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.55 | 176 | 0.6598 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5531 | 177 | 0.3169 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5563 | 178 | 0.8629 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5594 | 179 | 0.3648 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5625 | 180 | 0.5103 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5656 | 181 | 0.6255 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5687 | 182 | 0.4382 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5719 | 183 | 0.4647 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.575 | 184 | 0.4218 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5781 | 185 | 0.8244 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5813 | 186 | 0.6579 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5844 | 187 | 0.8384 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5875 | 188 | 0.5266 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5906 | 189 | 0.5079 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5938 | 190 | 0.2574 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5969 | 191 | 0.4162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6 | 192 | 0.7872 | 0.2105 | 2.0662 | 0.7506 | 0.0736 | 0.0674 | 0.7073 | 0.0252 | 0.1128 | 0.0193 | 0.3317 | 0.7631 | 0.3749 | 0.5554 | 0.8982 | | 0.6031 | 193 | 0.2606 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6062 | 194 | 0.8808 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6094 | 195 | 0.7685 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6125 | 196 | 0.7186 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6156 | 197 | 0.1147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6188 | 198 | 0.2816 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6219 | 199 | 0.506 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.625 | 200 | 0.5699 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6281 | 201 | 0.2746 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6312 | 202 | 0.7131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6344 | 203 | 0.9307 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6375 | 204 | 0.6033 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6406 | 205 | 0.7203 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6438 | 206 | 0.7422 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6469 | 207 | 0.6955 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.65 | 208 | 0.7139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6531 | 209 | 0.4741 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6562 | 210 | 0.2658 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6594 | 211 | 0.6033 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6625 | 212 | 0.7776 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6656 | 213 | 0.6791 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6687 | 214 | 0.4367 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6719 | 215 | 0.7212 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.675 | 216 | 0.7797 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6781 | 217 | 0.4547 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6813 | 218 | 0.6771 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6844 | 219 | 0.5488 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6875 | 220 | 0.7352 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6906 | 221 | 0.9567 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6937 | 222 | 0.4274 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6969 | 223 | 0.7653 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7 | 224 | 0.5672 | 0.1809 | 2.0751 | 0.7142 | 0.0846 | 0.0593 | 0.7161 | 0.0251 | 0.1252 | 0.0190 | 0.3040 | 0.7823 | 0.3399 | 0.5487 | 0.9005 | | 0.7031 | 225 | 0.6116 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7063 | 226 | 0.6484 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7094 | 227 | 0.669 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7125 | 228 | 0.263 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7156 | 229 | 0.6181 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7188 | 230 | 0.8956 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7219 | 231 | 0.5363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.725 | 232 | 0.823 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7281 | 233 | 0.7795 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7312 | 234 | 0.3688 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7344 | 235 | 0.3835 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7375 | 236 | 0.3393 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7406 | 237 | 0.4792 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7438 | 238 | 0.3966 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7469 | 239 | 0.2902 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.75 | 240 | 0.6716 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7531 | 241 | 0.6783 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7562 | 242 | 0.4794 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7594 | 243 | 0.8283 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7625 | 244 | 0.6875 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7656 | 245 | 0.8384 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7688 | 246 | 0.5796 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7719 | 247 | 0.6206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.775 | 248 | 0.7836 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7781 | 249 | 0.615 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7812 | 250 | 0.433 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7844 | 251 | 0.7394 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7875 | 252 | 0.1203 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7906 | 253 | 1.0909 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7937 | 254 | 0.7107 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7969 | 255 | 0.3464 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8 | 256 | 0.9347 | 0.1729 | 2.0034 | 0.7773 | 0.0726 | 0.0565 | 0.6547 | 0.0257 | 0.1264 | 0.0197 | 0.2844 | 0.7501 | 0.3155 | 0.5497 | 0.8987 | | 0.8031 | 257 | 0.464 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8063 | 258 | 0.4622 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8094 | 259 | 0.5124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8125 | 260 | 0.832 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8156 | 261 | 0.6264 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8187 | 262 | 0.5483 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8219 | 263 | 0.5929 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.825 | 264 | 0.5797 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8281 | 265 | 0.5292 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8313 | 266 | 0.5376 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8344 | 267 | 0.7102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8375 | 268 | 0.4605 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8406 | 269 | 1.2713 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8438 | 270 | 0.7764 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8469 | 271 | 0.7517 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.85 | 272 | 0.614 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8531 | 273 | 0.6046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8562 | 274 | 0.7111 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8594 | 275 | 0.4401 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8625 | 276 | 0.4351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8656 | 277 | 0.7498 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8688 | 278 | 0.7173 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8719 | 279 | 0.4696 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.875 | 280 | 0.6246 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8781 | 281 | 0.7578 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8812 | 282 | 0.3533 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8844 | 283 | 0.7328 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8875 | 284 | 0.6964 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8906 | 285 | 0.6431 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8938 | 286 | 0.7155 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8969 | 287 | 0.6328 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9 | 288 | 0.7895 | 0.1806 | 1.9555 | 0.7712 | 0.0754 | 0.0566 | 0.7308 | 0.0249 | 0.1089 | 0.0198 | 0.2799 | 0.7942 | 0.3324 | 0.5510 | 0.8979 | | 0.9031 | 289 | 0.5752 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9062 | 290 | 0.666 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9094 | 291 | 0.874 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9125 | 292 | 0.7431 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9156 | 293 | 0.8332 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9187 | 294 | 0.7082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9219 | 295 | 0.6618 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.925 | 296 | 0.2375 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9281 | 297 | 0.5305 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9313 | 298 | 0.1686 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9344 | 299 | 0.7938 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9375 | 300 | 0.2629 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9406 | 301 | 0.973 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9437 | 302 | 0.649 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9469 | 303 | 0.3329 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.95 | 304 | 0.6105 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9531 | 305 | 0.3621 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9563 | 306 | 0.5165 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9594 | 307 | 0.6075 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9625 | 308 | 0.3091 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9656 | 309 | 0.2762 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9688 | 310 | 0.5736 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9719 | 311 | 0.3876 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.975 | 312 | 1.8005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9781 | 313 | 0.6344 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9812 | 314 | 0.9414 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9844 | 315 | 0.4782 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9875 | 316 | 0.4196 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9906 | 317 | 0.5288 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9938 | 318 | 0.5888 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9969 | 319 | 0.4598 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0 | 320 | 0.5085 | 0.1746 | 1.9213 | 0.6944 | 0.0689 | 0.0551 | 0.6845 | 0.0250 | 0.1042 | 0.0207 | 0.2857 | 0.7787 | 0.3040 | 0.5479 | 0.9030 | | 1.0031 | 321 | 0.647 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0063 | 322 | 0.4768 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0094 | 323 | 0.4834 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0125 | 324 | 0.6115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0156 | 325 | 0.4611 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0188 | 326 | 0.4812 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0219 | 327 | 0.5914 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.025 | 328 | 0.7206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0281 | 329 | 0.7854 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0312 | 330 | 0.432 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0344 | 331 | 0.6365 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0375 | 332 | 0.3754 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0406 | 333 | 0.5096 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0437 | 334 | 0.5762 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0469 | 335 | 0.6938 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.05 | 336 | 0.343 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0531 | 337 | 0.7258 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0562 | 338 | 0.4658 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0594 | 339 | 0.7108 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0625 | 340 | 1.3076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0656 | 341 | 0.2397 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0688 | 342 | 0.4853 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0719 | 343 | 0.741 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.075 | 344 | 0.6066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0781 | 345 | 0.6838 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0813 | 346 | 0.4393 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0844 | 347 | 0.4102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0875 | 348 | 0.4947 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0906 | 349 | 0.5212 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0938 | 350 | 0.6889 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0969 | 351 | 0.625 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1 | 352 | 0.5093 | 0.1819 | 1.9222 | 0.7048 | 0.0801 | 0.0626 | 0.6875 | 0.0244 | 0.1099 | 0.0197 | 0.2770 | 0.7761 | 0.3032 | 0.5489 | 0.9028 | | 1.1031 | 353 | 0.6242 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1062 | 354 | 0.7228 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1094 | 355 | 0.3717 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1125 | 356 | 0.3442 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1156 | 357 | 0.649 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1187 | 358 | 0.3935 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1219 | 359 | 0.6131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.125 | 360 | 0.5322 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1281 | 361 | 0.2073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1313 | 362 | 0.6735 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1344 | 363 | 0.7604 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1375 | 364 | 0.6165 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1406 | 365 | 0.1963 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1438 | 366 | 0.1668 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1469 | 367 | 0.5055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.15 | 368 | 0.4919 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1531 | 369 | 0.7166 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1562 | 370 | 0.444 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1594 | 371 | 0.6237 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1625 | 372 | 0.4197 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1656 | 373 | 0.5569 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1687 | 374 | 0.5274 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1719 | 375 | 0.6259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.175 | 376 | 0.7696 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1781 | 377 | 0.6437 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1812 | 378 | 0.5067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1844 | 379 | 0.3927 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1875 | 380 | 0.4557 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1906 | 381 | 0.2425 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1938 | 382 | 0.1677 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1969 | 383 | 0.3555 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2 | 384 | 0.8643 | 0.1789 | 1.8801 | 0.7123 | 0.0711 | 0.0583 | 0.6368 | 0.0244 | 0.1173 | 0.0195 | 0.2852 | 0.7318 | 0.3245 | 0.5509 | 0.9035 | | 1.2031 | 385 | 0.6056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2063 | 386 | 0.5924 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2094 | 387 | 0.4131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2125 | 388 | 0.3347 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2156 | 389 | 0.4317 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2188 | 390 | 0.2488 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2219 | 391 | 0.6856 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.225 | 392 | 0.5261 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2281 | 393 | 0.4683 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2312 | 394 | 1.066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2344 | 395 | 0.5434 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2375 | 396 | 0.4129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2406 | 397 | 0.3367 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2437 | 398 | 0.716 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2469 | 399 | 0.4767 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.25 | 400 | 0.3659 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2531 | 401 | 0.4731 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2563 | 402 | 0.4562 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2594 | 403 | 0.3397 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2625 | 404 | 1.2082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2656 | 405 | 0.6162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2688 | 406 | 0.4767 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2719 | 407 | 0.4384 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.275 | 408 | 0.5368 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2781 | 409 | 0.6885 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2812 | 410 | 0.4318 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2844 | 411 | 0.5648 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2875 | 412 | 0.3 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2906 | 413 | 0.573 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2937 | 414 | 0.6759 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2969 | 415 | 1.0739 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3 | 416 | 0.6794 | 0.1615 | 1.8976 | 0.7091 | 0.0704 | 0.0550 | 0.6475 | 0.0251 | 0.1084 | 0.0199 | 0.2811 | 0.7549 | 0.3167 | 0.5507 | 0.9035 | | 1.3031 | 417 | 0.4515 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3062 | 418 | 0.5992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3094 | 419 | 0.7221 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3125 | 420 | 0.3968 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3156 | 421 | 0.4198 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3188 | 422 | 0.6268 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3219 | 423 | 0.3976 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.325 | 424 | 0.6003 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3281 | 425 | 0.4381 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3313 | 426 | 0.8803 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3344 | 427 | 0.5635 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3375 | 428 | 0.5262 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3406 | 429 | 0.6506 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3438 | 430 | 0.3486 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3469 | 431 | 0.9099 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.35 | 432 | 0.4199 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3531 | 433 | 0.4908 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3562 | 434 | 0.6869 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3594 | 435 | 0.5644 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3625 | 436 | 0.6714 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3656 | 437 | 0.4976 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3687 | 438 | 0.4468 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3719 | 439 | 0.3923 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.375 | 440 | 0.5753 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3781 | 441 | 0.5134 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3813 | 442 | 0.3858 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3844 | 443 | 0.6681 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3875 | 444 | 0.4702 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3906 | 445 | 0.501 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3938 | 446 | 0.459 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3969 | 447 | 0.5879 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4 | 448 | 0.6276 | 0.1565 | 1.8430 | 0.7535 | 0.0568 | 0.0484 | 0.6307 | 0.0245 | 0.1117 | 0.0196 | 0.2782 | 0.6982 | 0.3191 | 0.5517 | 0.9040 | | 1.4031 | 449 | 0.5358 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4062 | 450 | 0.8326 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4094 | 451 | 0.2866 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4125 | 452 | 0.247 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4156 | 453 | 0.519 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4187 | 454 | 0.4117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4219 | 455 | 0.437 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.425 | 456 | 0.3619 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4281 | 457 | 0.4273 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4312 | 458 | 0.2739 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4344 | 459 | 0.5714 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4375 | 460 | 0.5485 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4406 | 461 | 0.4829 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4438 | 462 | 0.4904 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4469 | 463 | 0.6449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.45 | 464 | 0.6896 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4531 | 465 | 0.4174 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4563 | 466 | 0.5254 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4594 | 467 | 0.5287 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4625 | 468 | 0.2421 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4656 | 469 | 0.3939 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4688 | 470 | 0.7248 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4719 | 471 | 0.3479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.475 | 472 | 0.472 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4781 | 473 | 0.5639 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4812 | 474 | 0.4077 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4844 | 475 | 0.3173 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4875 | 476 | 0.3307 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4906 | 477 | 0.3761 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4937 | 478 | 0.5454 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4969 | 479 | 0.309 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5 | 480 | 0.4082 | 0.1554 | 1.8595 | 0.6959 | 0.0752 | 0.0433 | 0.6473 | 0.0247 | 0.1079 | 0.0187 | 0.2609 | 0.7203 | 0.3060 | 0.5562 | 0.9034 | | 1.5031 | 481 | 0.2147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5063 | 482 | 0.5614 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5094 | 483 | 0.3865 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5125 | 484 | 0.1715 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5156 | 485 | 0.3597 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5188 | 486 | 0.3827 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5219 | 487 | 0.4895 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.525 | 488 | 0.4987 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5281 | 489 | 0.4482 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5312 | 490 | 0.5808 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5344 | 491 | 0.3916 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5375 | 492 | 1.0877 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5406 | 493 | 0.4119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5437 | 494 | 0.6078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5469 | 495 | 0.2441 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.55 | 496 | 0.4769 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5531 | 497 | 0.218 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5562 | 498 | 0.6377 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5594 | 499 | 0.2391 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5625 | 500 | 0.3645 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5656 | 501 | 0.4185 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5688 | 502 | 0.3363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5719 | 503 | 0.3712 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.575 | 504 | 0.2995 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5781 | 505 | 0.6178 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5813 | 506 | 0.464 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5844 | 507 | 0.5694 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5875 | 508 | 0.3587 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5906 | 509 | 0.3375 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5938 | 510 | 0.1613 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5969 | 511 | 0.2811 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6 | 512 | 0.5338 | 0.1449 | 1.8544 | 0.6914 | 0.0752 | 0.0407 | 0.6339 | 0.0251 | 0.1043 | 0.0182 | 0.2565 | 0.7161 | 0.2975 | 0.5553 | 0.9044 | | 1.6031 | 513 | 0.1862 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6062 | 514 | 0.6092 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6094 | 515 | 0.541 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6125 | 516 | 0.5297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6156 | 517 | 0.0664 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6187 | 518 | 0.1557 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6219 | 519 | 0.3281 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.625 | 520 | 0.3828 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6281 | 521 | 0.2087 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6313 | 522 | 0.5306 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6344 | 523 | 0.6589 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6375 | 524 | 0.425 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6406 | 525 | 0.5026 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6438 | 526 | 0.5667 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6469 | 527 | 0.4748 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.65 | 528 | 0.5094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6531 | 529 | 0.3398 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6562 | 530 | 0.1932 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6594 | 531 | 0.4233 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6625 | 532 | 0.5848 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6656 | 533 | 0.5076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6687 | 534 | 0.286 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6719 | 535 | 0.5221 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.675 | 536 | 0.579 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6781 | 537 | 0.2717 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6812 | 538 | 0.4727 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6844 | 539 | 0.3777 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6875 | 540 | 0.537 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6906 | 541 | 0.6935 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6938 | 542 | 0.2929 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6969 | 543 | 0.5495 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7 | 544 | 0.3767 | 0.1562 | 1.8782 | 0.6965 | 0.0670 | 0.0358 | 0.6436 | 0.0247 | 0.1086 | 0.0183 | 0.2413 | 0.7195 | 0.3004 | 0.5553 | 0.9040 | | 1.7031 | 545 | 0.4054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7063 | 546 | 0.4114 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7094 | 547 | 0.4774 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7125 | 548 | 0.1662 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7156 | 549 | 0.4634 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7188 | 550 | 0.6514 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7219 | 551 | 0.3672 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.725 | 552 | 0.6115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7281 | 553 | 0.5445 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7312 | 554 | 0.2447 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7344 | 555 | 0.2566 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7375 | 556 | 0.208 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7406 | 557 | 0.3175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7437 | 558 | 0.2546 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7469 | 559 | 0.1709 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.75 | 560 | 0.4799 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7531 | 561 | 0.5313 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7563 | 562 | 0.3248 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7594 | 563 | 0.6279 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7625 | 564 | 0.5193 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7656 | 565 | 0.6262 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7688 | 566 | 0.4297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7719 | 567 | 0.4763 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.775 | 568 | 0.5722 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7781 | 569 | 0.4347 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7812 | 570 | 0.3271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7844 | 571 | 0.5433 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7875 | 572 | 0.0637 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7906 | 573 | 0.9049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7937 | 574 | 0.495 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7969 | 575 | 0.2218 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8 | 576 | 0.7491 | 0.1476 | 1.8366 | 0.6921 | 0.0626 | 0.0385 | 0.6359 | 0.0251 | 0.1101 | 0.0179 | 0.2450 | 0.7223 | 0.2910 | 0.5548 | 0.9038 | | 1.8031 | 577 | 0.3658 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8062 | 578 | 0.3549 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8094 | 579 | 0.4099 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8125 | 580 | 0.6085 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8156 | 581 | 0.4319 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8188 | 582 | 0.3862 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8219 | 583 | 0.4094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.825 | 584 | 0.3864 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8281 | 585 | 0.3481 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8313 | 586 | 0.343 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8344 | 587 | 0.5048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8375 | 588 | 0.3418 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8406 | 589 | 1.0879 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8438 | 590 | 0.5701 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8469 | 591 | 0.5612 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.85 | 592 | 0.4498 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8531 | 593 | 0.4483 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8562 | 594 | 0.5586 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8594 | 595 | 0.363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8625 | 596 | 0.3553 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8656 | 597 | 0.547 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8687 | 598 | 0.5397 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8719 | 599 | 0.3574 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.875 | 600 | 0.4596 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8781 | 601 | 0.5451 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8813 | 602 | 0.2327 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8844 | 603 | 0.5394 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8875 | 604 | 0.4933 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8906 | 605 | 0.4823 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8938 | 606 | 0.5327 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8969 | 607 | 0.4733 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9 | 608 | 0.5663 | 0.1455 | 1.8288 | 0.7343 | 0.0625 | 0.0389 | 0.6389 | 0.0251 | 0.1033 | 0.0183 | 0.2279 | 0.7394 | 0.2900 | 0.5539 | 0.9031 | | 1.9031 | 609 | 0.412 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9062 | 610 | 0.5386 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9094 | 611 | 0.6678 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9125 | 612 | 0.5554 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9156 | 613 | 0.6435 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9187 | 614 | 0.529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9219 | 615 | 0.4753 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.925 | 616 | 0.1364 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9281 | 617 | 0.37 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9312 | 618 | 0.1058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9344 | 619 | 0.6059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9375 | 620 | 0.1856 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9406 | 621 | 0.8083 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9438 | 622 | 0.4503 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9469 | 623 | 0.2172 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.95 | 624 | 0.4348 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9531 | 625 | 0.2659 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9563 | 626 | 0.3441 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9594 | 627 | 0.4487 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9625 | 628 | 0.1922 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9656 | 629 | 0.191 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9688 | 630 | 0.3967 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9719 | 631 | 0.2729 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.975 | 632 | 1.56 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9781 | 633 | 0.4791 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9812 | 634 | 0.7554 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9844 | 635 | 0.3477 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9875 | 636 | 0.2965 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9906 | 637 | 0.3262 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9937 | 638 | 0.4571 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9969 | 639 | 0.3461 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0 | 640 | 0.3646 | 0.1450 | 1.8504 | 0.6874 | 0.0701 | 0.0423 | 0.6245 | 0.0250 | 0.1009 | 0.0184 | 0.2405 | 0.7371 | 0.2937 | 0.5540 | 0.9036 | </details> ### Framework Versions - Python: 3.10.13 - Sentence Transformers: 3.0.1 - Transformers: 4.42.3 - PyTorch: 2.1.2 - Accelerate: 0.32.1 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION", "SEMANTIC_SIMILARITY" ]
[ "MEDAL", "SCIQ", "SCITAIL" ]
Non_BioNLP
RichardErkhov/aaditya_-_Llama3-OpenBioLLM-8B-8bits
RichardErkhov
text-generation
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:2305.18290", "arxiv:2303.13375", "arxiv:2212.13138", "arxiv:2305.09617", "arxiv:2402.07023", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]
1,714
1,714
16
0
--- {} --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Llama3-OpenBioLLM-8B - bnb 8bits - Model creator: https://huggingface.co/aaditya/ - Original model: https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B/ Original model description: --- base_model: meta-llama/Meta-Llama-3-8B tags: - llama-3 - llama - Mixtral - instruct - finetune - chatml - DPO - RLHF - gpt4 - distillation model-index: - name: OpenBioLLM-8B results: [] license: llama3 language: - en widget: - example_title: OpenBioLLM-8B messages: - role: system content: >- You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. - role: user content: How long does it take for newborn jaundice to go away? output: text: >- Newborn jaundice, also known as neonatal jaundice, is a common condition in newborns where the yellowing of the skin and eyes occurs due to an elevated level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when red blood cells break down. In most cases, newborn jaundice resolves on its own without any specific treatment. The duration of newborn jaundice can vary depending on several factors such as the underlying cause, gestational age at birth, and individual variations in bilirubin metabolism. Here are some general guidelines: 1. Physiological jaundice: This is the most common type of newborn jaundice and usually appears within 24-72 hours after birth. It tends to peak between the second and fifth day of life and gradually improves over the next week or two. By the time the baby is one week old, the jaundice should have mostly resolved. 2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and may appear later than physiological jaundice, typically between the fifth and fourteenth day of life. It tends to persist for a longer duration but usually resolves within six weeks after birth. 3. Pathological jaundice: This type of jaundice is less common and occurs due to an underlying medical condition that affects bilirubin metabolism or liver function. The duration of pathological jaundice depends on the specific cause and may require treatment. It's important for parents to monitor their newborn's jaundice closely and seek medical advice if the jaundice progresses rapidly, becomes severe, or is accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness. In these cases, further evaluation and management may be necessary. Remember that each baby is unique, and the timing of jaundice resolution can vary. If you have concerns about your newborn's jaundice, it's always best to consult with a healthcare professional for personalized advice and guidance. --- <div align="center"> <img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/2FhDh8NDvMl7iSxbQz9BP.png) <div align="center"> <h1>Advancing Open-source Large Language Models in Medical Domain</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://colab.research.google.com/drive/1F5oV20InEYeAJGmBwYF9NM_QhLmjBkKJ?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> | <a href="https://github.com/openlifescience-ai"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> | <a href="#"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> | <a href="https://discord.gg/A5Fjf5zC69"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/KGmRE5w2sepNtwsEu8t7K.jpeg) Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. 🏥 **Biomedical Specialization**: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. 🎓 **Superior Performance**: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. 🧠 **Advanced Training Techniques**: OpenBioLLM-8B builds upon the powerful foundations of the **Meta-Llama-3-8B** and [Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: <div align="center"> <img width="1200px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png"> </div> - **Policy Optimization**: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290) - **Ranking Dataset**: [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar) - **Fine-tuning dataset**: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated) This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications. ⚙️ **Release Details**: - **Model Size**: 8 billion parameters - **Quantization**: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-Llama3-8B-GGUF) - **Language(s) (NLP):** en - **Developed By**: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs - **License:** Meta-Llama License - **Fine-tuned from models:** [meta-llama/Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) - **Resources for more information:** - Paper: Coming soon The model can be fine-tuned for more specialized tasks and datasets as needed. OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences. We are excited to share OpenBioLLM-8B with researchers and developers around the world. ### Use with transformers **Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.** See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "aaditya/OpenBioLLM-Llama3-8B" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."}, {"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.0, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## **Training procedure** ### **Training hyperparameters** <details> <summary>Click to see details</summary> - learning_rate: 0.0002 - lr_scheduler: cosine - train_batch_size: 12 - eval_batch_size: 8 - GPU: H100 80GB SXM5 - num_devices: 1 - optimizer: adamw_bnb_8bit - lr_scheduler_warmup_steps: 100 - num_epochs: 4 </details> ### **Peft hyperparameters** <details> <summary>Click to see details</summary> - adapter: qlora - lora_r: 128 - lora_alpha: 256 - lora_dropout: 0.05 - lora_target_linear: true -lora_target_modules: - q_proj - v_proj - k_proj - o_proj - gate_proj - down_proj - up_proj </details> ### **Training results** ### **Framework versions** - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1 - Axolotl - Lm harness for evaluation # Benchmark Results 🔥 OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge. 🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy. | | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA 4 opts | PubMedQA | MedMCQA | Avg | |--------------------|-------------|------------------|---------|--------------|-----------------|------------------|--------------|----------|---------|-------| | **OpenBioLLM-70B** | **92.93** | **93.197** | **83.904** | 93.75 | 93.827 | **85.749** | 78.162 | 78.97 | **74.014** | **86.05588** | | Med-PaLM-2 (5-shot) | 88.3 | 90 | 77.8 | **95.2** | 94.4 | 80.9 | **79.7** | **79.2** | 71.3 | 84.08 | | **GPT-4** | 86.04 | 91 | 80 | 93.01 | **95.14** | 76.88 | 78.87 | 75.2 | 69.52 | 82.85 | | Med-PaLM-1 (Flan-PaLM, 5-shot) | 80.4 | 75 | 63.7 | 83.8 | 88.9 | 76.3 | 67.6 | 79 | 57.6 | 74.7 | | **OpenBioLLM-8B** | 76.101 | 86.1 | 69.829 | 78.21 | 84.213 | 68.042 | 58.993 | 74.12 | 56.913 | 72.502 | | Gemini-1.0 | 76.7 | 75.8 | 66.7 | 77.7 | 88 | 69.2 | 58 | 70.7 | 54.3 | 70.79 | | GPT-3.5 Turbo 1106 | 74.71 | 74 | 72.79 | 72.79 | 72.91 | 64.73 | 57.71 | 72.66 | 53.79 | 66 | | Meditron-70B | 66.79 | 69 | 53.33 | 71.69 | 76.38 | 63 | 57.1 | 76.6 | 46.85 | 64.52 | | gemma-7b | 69.81 | 70 | 59.26 | 66.18 | 79.86 | 60.12 | 47.21 | 76.2 | 48.96 | 64.18 | | Mistral-7B-v0.1 | 68.68 | 71 | 55.56 | 68.38 | 68.06 | 59.54 | 50.82 | 75.4 | 48.2 | 62.85 | | Apollo-7B | 62.26 | 72 | 61.48 | 69.12 | 70.83 | 55.49 | 55.22 | 39.8 | 53.77 | 60 | | MedAlpaca-7b | 57.36 | 69 | 57.04 | 67.28 | 65.28 | 54.34 | 41.71 | 72.8 | 37.51 | 58.03 | | BioMistral-7B | 59.9 | 64 | 56.5 | 60.4 | 59 | 54.7 | 50.6 | 77.5 | 48.1 | 57.3 | | AlpaCare-llama2-7b | 49.81 | 49 | 45.92 | 33.82 | 50 | 43.35 | 29.77 | 72.2 | 34.42 | 45.36 | | ClinicalGPT | 30.56 | 27 | 30.37 | 19.48 | 25 | 24.27 | 26.08 | 63.8 | 28.18 | 30.52 | <div align="center"> <img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png"> </div> ## Detailed Medical Subjectwise accuracy ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/UXF-V0col0Z0sS6BGPBkE.png) # Use Cases & Examples 🚨 **Below results are from the quantized version of OpenBioLLM-70B** # Summarize Clinical Notes OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/xdwdBgOxNi_TfML0hKlI8.png) # Answer Medical Questions OpenBioLLM-70B can provide answers to a wide range of medical questions. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/zO95GlwOQEZqCKQF69mE6.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/OKBczKw7gWeW5xsuDpc27.png) <details> <summary>Click to see details</summary> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/eJGHT5khppYvJb8fQ-YW4.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Cnbwrqa_-ORHRuNRC2P6Y.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/J9DhdcvukAc9mnnW9fj2C.png) </details> # Clinical Entity Recognition OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_69BW4k9LVABFwtxixL45.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/DKy5wYCoPhoPPUc1-x8_J.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/7WD9zCCBZT4-4XlfnIQjl.png) # Biomarkers Extraction ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/ZttoM4AiteT7gFYVhjIpN.png) # Classification OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Bf5MW1d75qT-1F_TR_hC0.png) # De-Identification OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/hKX4kzm--Tw5bj6K78msy.png) **Advisory Notice!**  While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments. Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor. Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including: - Thorough testing and evaluation in relevant clinical scenarios - Alignment with evidence-based guidelines and best practices - Mitigation of potential biases and failure modes - Integration with human oversight and interpretation - Compliance with regulatory and ethical standards Always consult a qualified healthcare provider for personal medical needs. # Citation If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows: ``` @misc{OpenBioLLMs, author = {Ankit Pal, Malaikannan Sankarasubbu}, title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}} } ``` The accompanying paper is currently in progress and will be released soon. <div align="center"> <h2> 💌 Contact </h2> </div> We look forward to hearing you and collaborating on this exciting project! **Contributors:** - [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com] - Saama AI Labs - Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. # References We thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models! Result sources - [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375) - [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138) - [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617) - [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023)
[ "QUESTION_ANSWERING" ]
[ "MEDQA", "PUBMEDQA" ]
BioNLP
KeyurRamoliya/e5-large-v2-GGUF
KeyurRamoliya
sentence-similarity
[ "sentence-transformers", "gguf", "mteb", "Sentence Transformers", "sentence-similarity", "llama-cpp", "gguf-my-repo", "en", "base_model:intfloat/e5-large-v2", "base_model:quantized:intfloat/e5-large-v2", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us", "feature-extraction" ]
1,724
1,724
12
0
--- base_model: intfloat/e5-large-v2 language: - en license: mit tags: - mteb - Sentence Transformers - sentence-similarity - sentence-transformers - llama-cpp - gguf-my-repo model-index: - name: e5-large-v2 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.22388059701493 - type: ap value: 43.20816505595132 - type: f1 value: 73.27811303522058 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.748325 - type: ap value: 90.72534979701297 - type: f1 value: 93.73895874282185 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.612 - type: f1 value: 47.61157345898393 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 23.541999999999998 - type: map_at_10 value: 38.208 - type: map_at_100 value: 39.417 - type: map_at_1000 value: 39.428999999999995 - type: map_at_3 value: 33.95 - type: map_at_5 value: 36.329 - type: mrr_at_1 value: 23.755000000000003 - type: mrr_at_10 value: 38.288 - type: mrr_at_100 value: 39.511 - type: mrr_at_1000 value: 39.523 - type: mrr_at_3 value: 34.009 - type: mrr_at_5 value: 36.434 - type: ndcg_at_1 value: 23.541999999999998 - type: ndcg_at_10 value: 46.417 - type: ndcg_at_100 value: 51.812000000000005 - type: ndcg_at_1000 value: 52.137 - type: ndcg_at_3 value: 37.528 - type: ndcg_at_5 value: 41.81 - type: precision_at_1 value: 23.541999999999998 - type: precision_at_10 value: 7.269 - type: precision_at_100 value: 0.9690000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 15.979 - type: precision_at_5 value: 11.664 - type: recall_at_1 value: 23.541999999999998 - type: recall_at_10 value: 72.688 - type: recall_at_100 value: 96.871 - type: recall_at_1000 value: 99.431 - type: recall_at_3 value: 47.937000000000005 - type: recall_at_5 value: 58.321 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 45.546499570522094 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 41.01607489943561 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 59.616107510107774 - type: mrr value: 72.75106626214661 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.33018094733868 - type: cos_sim_spearman value: 83.60190492611737 - type: euclidean_pearson value: 82.1492450218961 - type: euclidean_spearman value: 82.70308926526991 - type: manhattan_pearson value: 81.93959600076842 - type: manhattan_spearman value: 82.73260801016369 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 84.54545454545455 - type: f1 value: 84.49582530928923 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.362725540120096 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 34.849509608178145 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 31.502999999999997 - type: map_at_10 value: 43.323 - type: map_at_100 value: 44.708999999999996 - type: map_at_1000 value: 44.838 - type: map_at_3 value: 38.987 - type: map_at_5 value: 41.516999999999996 - type: mrr_at_1 value: 38.769999999999996 - type: mrr_at_10 value: 49.13 - type: mrr_at_100 value: 49.697 - type: mrr_at_1000 value: 49.741 - type: mrr_at_3 value: 45.804 - type: mrr_at_5 value: 47.842 - type: ndcg_at_1 value: 38.769999999999996 - type: ndcg_at_10 value: 50.266999999999996 - type: ndcg_at_100 value: 54.967 - type: ndcg_at_1000 value: 56.976000000000006 - type: ndcg_at_3 value: 43.823 - type: ndcg_at_5 value: 47.12 - type: precision_at_1 value: 38.769999999999996 - type: precision_at_10 value: 10.057 - type: precision_at_100 value: 1.554 - type: precision_at_1000 value: 0.202 - type: precision_at_3 value: 21.125 - type: precision_at_5 value: 15.851 - type: recall_at_1 value: 31.502999999999997 - type: recall_at_10 value: 63.715999999999994 - type: recall_at_100 value: 83.61800000000001 - type: recall_at_1000 value: 96.63199999999999 - type: recall_at_3 value: 45.403 - type: recall_at_5 value: 54.481 - type: map_at_1 value: 27.833000000000002 - type: map_at_10 value: 37.330999999999996 - type: map_at_100 value: 38.580999999999996 - type: map_at_1000 value: 38.708 - type: map_at_3 value: 34.713 - type: map_at_5 value: 36.104 - type: mrr_at_1 value: 35.223 - type: mrr_at_10 value: 43.419000000000004 - type: mrr_at_100 value: 44.198 - type: mrr_at_1000 value: 44.249 - type: mrr_at_3 value: 41.614000000000004 - type: mrr_at_5 value: 42.553000000000004 - type: ndcg_at_1 value: 35.223 - type: ndcg_at_10 value: 42.687999999999995 - type: ndcg_at_100 value: 47.447 - type: ndcg_at_1000 value: 49.701 - type: ndcg_at_3 value: 39.162 - type: ndcg_at_5 value: 40.557 - type: precision_at_1 value: 35.223 - type: precision_at_10 value: 7.962 - type: precision_at_100 value: 1.304 - type: precision_at_1000 value: 0.18 - type: precision_at_3 value: 19.023 - type: precision_at_5 value: 13.184999999999999 - type: recall_at_1 value: 27.833000000000002 - type: recall_at_10 value: 51.881 - type: recall_at_100 value: 72.04 - type: recall_at_1000 value: 86.644 - type: recall_at_3 value: 40.778 - type: recall_at_5 value: 45.176 - type: map_at_1 value: 38.175 - type: map_at_10 value: 51.174 - type: map_at_100 value: 52.26499999999999 - type: map_at_1000 value: 52.315999999999995 - type: map_at_3 value: 47.897 - type: map_at_5 value: 49.703 - type: mrr_at_1 value: 43.448 - type: mrr_at_10 value: 54.505 - type: mrr_at_100 value: 55.216 - type: mrr_at_1000 value: 55.242000000000004 - type: mrr_at_3 value: 51.98500000000001 - type: mrr_at_5 value: 53.434000000000005 - type: ndcg_at_1 value: 43.448 - type: ndcg_at_10 value: 57.282 - type: ndcg_at_100 value: 61.537 - type: ndcg_at_1000 value: 62.546 - type: ndcg_at_3 value: 51.73799999999999 - type: ndcg_at_5 value: 54.324 - type: precision_at_1 value: 43.448 - type: precision_at_10 value: 9.292 - type: precision_at_100 value: 1.233 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 23.218 - type: precision_at_5 value: 15.887 - type: recall_at_1 value: 38.175 - type: recall_at_10 value: 72.00999999999999 - type: recall_at_100 value: 90.155 - type: recall_at_1000 value: 97.257 - type: recall_at_3 value: 57.133 - type: recall_at_5 value: 63.424 - type: map_at_1 value: 22.405 - type: map_at_10 value: 30.043 - type: map_at_100 value: 31.191000000000003 - type: map_at_1000 value: 31.275 - type: map_at_3 value: 27.034000000000002 - type: map_at_5 value: 28.688000000000002 - type: mrr_at_1 value: 24.068 - type: mrr_at_10 value: 31.993 - type: mrr_at_100 value: 32.992 - type: mrr_at_1000 value: 33.050000000000004 - type: mrr_at_3 value: 28.964000000000002 - type: mrr_at_5 value: 30.653000000000002 - type: ndcg_at_1 value: 24.068 - type: ndcg_at_10 value: 35.198 - type: ndcg_at_100 value: 40.709 - type: ndcg_at_1000 value: 42.855 - type: ndcg_at_3 value: 29.139 - type: ndcg_at_5 value: 32.045 - type: precision_at_1 value: 24.068 - type: precision_at_10 value: 5.65 - type: precision_at_100 value: 0.885 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 12.279 - type: precision_at_5 value: 8.994 - type: recall_at_1 value: 22.405 - type: recall_at_10 value: 49.391 - type: recall_at_100 value: 74.53699999999999 - type: recall_at_1000 value: 90.605 - type: recall_at_3 value: 33.126 - type: recall_at_5 value: 40.073 - type: map_at_1 value: 13.309999999999999 - type: map_at_10 value: 20.688000000000002 - type: map_at_100 value: 22.022 - type: map_at_1000 value: 22.152 - type: map_at_3 value: 17.954 - type: map_at_5 value: 19.439 - type: mrr_at_1 value: 16.294 - type: mrr_at_10 value: 24.479 - type: mrr_at_100 value: 25.515 - type: mrr_at_1000 value: 25.593 - type: mrr_at_3 value: 21.642 - type: mrr_at_5 value: 23.189999999999998 - type: ndcg_at_1 value: 16.294 - type: ndcg_at_10 value: 25.833000000000002 - type: ndcg_at_100 value: 32.074999999999996 - type: ndcg_at_1000 value: 35.083 - type: ndcg_at_3 value: 20.493 - type: ndcg_at_5 value: 22.949 - type: precision_at_1 value: 16.294 - type: precision_at_10 value: 5.112 - type: precision_at_100 value: 0.96 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 9.908999999999999 - type: precision_at_5 value: 7.587000000000001 - type: recall_at_1 value: 13.309999999999999 - type: recall_at_10 value: 37.851 - type: recall_at_100 value: 64.835 - type: recall_at_1000 value: 86.334 - type: recall_at_3 value: 23.493 - type: recall_at_5 value: 29.528 - type: map_at_1 value: 25.857999999999997 - type: map_at_10 value: 35.503 - type: map_at_100 value: 36.957 - type: map_at_1000 value: 37.065 - type: map_at_3 value: 32.275999999999996 - type: map_at_5 value: 34.119 - type: mrr_at_1 value: 31.954 - type: mrr_at_10 value: 40.851 - type: mrr_at_100 value: 41.863 - type: mrr_at_1000 value: 41.900999999999996 - type: mrr_at_3 value: 38.129999999999995 - type: mrr_at_5 value: 39.737 - type: ndcg_at_1 value: 31.954 - type: ndcg_at_10 value: 41.343999999999994 - type: ndcg_at_100 value: 47.397 - type: ndcg_at_1000 value: 49.501 - type: ndcg_at_3 value: 36.047000000000004 - type: ndcg_at_5 value: 38.639 - type: precision_at_1 value: 31.954 - type: precision_at_10 value: 7.68 - type: precision_at_100 value: 1.247 - type: precision_at_1000 value: 0.16199999999999998 - type: precision_at_3 value: 17.132 - type: precision_at_5 value: 12.589 - type: recall_at_1 value: 25.857999999999997 - type: recall_at_10 value: 53.43599999999999 - type: recall_at_100 value: 78.82400000000001 - type: recall_at_1000 value: 92.78999999999999 - type: recall_at_3 value: 38.655 - type: recall_at_5 value: 45.216 - type: map_at_1 value: 24.709 - type: map_at_10 value: 34.318 - type: map_at_100 value: 35.657 - type: map_at_1000 value: 35.783 - type: map_at_3 value: 31.326999999999998 - type: map_at_5 value: 33.021 - type: mrr_at_1 value: 30.137000000000004 - type: mrr_at_10 value: 39.093 - type: mrr_at_100 value: 39.992 - type: mrr_at_1000 value: 40.056999999999995 - type: mrr_at_3 value: 36.606 - type: mrr_at_5 value: 37.861 - type: ndcg_at_1 value: 30.137000000000004 - type: ndcg_at_10 value: 39.974 - type: ndcg_at_100 value: 45.647999999999996 - type: ndcg_at_1000 value: 48.259 - type: ndcg_at_3 value: 35.028 - type: ndcg_at_5 value: 37.175999999999995 - type: precision_at_1 value: 30.137000000000004 - type: precision_at_10 value: 7.363 - type: precision_at_100 value: 1.184 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 16.857 - type: precision_at_5 value: 11.963 - type: recall_at_1 value: 24.709 - type: recall_at_10 value: 52.087 - type: recall_at_100 value: 76.125 - type: recall_at_1000 value: 93.82300000000001 - type: recall_at_3 value: 38.149 - type: recall_at_5 value: 43.984 - type: map_at_1 value: 23.40791666666667 - type: map_at_10 value: 32.458083333333335 - type: map_at_100 value: 33.691916666666664 - type: map_at_1000 value: 33.81191666666666 - type: map_at_3 value: 29.51625 - type: map_at_5 value: 31.168083333333335 - type: mrr_at_1 value: 27.96591666666666 - type: mrr_at_10 value: 36.528583333333344 - type: mrr_at_100 value: 37.404 - type: mrr_at_1000 value: 37.464333333333336 - type: mrr_at_3 value: 33.92883333333333 - type: mrr_at_5 value: 35.41933333333333 - type: ndcg_at_1 value: 27.96591666666666 - type: ndcg_at_10 value: 37.89141666666666 - type: ndcg_at_100 value: 43.23066666666666 - type: ndcg_at_1000 value: 45.63258333333333 - type: ndcg_at_3 value: 32.811249999999994 - type: ndcg_at_5 value: 35.22566666666667 - type: precision_at_1 value: 27.96591666666666 - type: precision_at_10 value: 6.834083333333332 - type: precision_at_100 value: 1.12225 - type: precision_at_1000 value: 0.15241666666666667 - type: precision_at_3 value: 15.264333333333335 - type: precision_at_5 value: 11.039416666666666 - type: recall_at_1 value: 23.40791666666667 - type: recall_at_10 value: 49.927083333333336 - type: recall_at_100 value: 73.44641666666668 - type: recall_at_1000 value: 90.19950000000001 - type: recall_at_3 value: 35.88341666666667 - type: recall_at_5 value: 42.061249999999994 - type: map_at_1 value: 19.592000000000002 - type: map_at_10 value: 26.895999999999997 - type: map_at_100 value: 27.921000000000003 - type: map_at_1000 value: 28.02 - type: map_at_3 value: 24.883 - type: map_at_5 value: 25.812 - type: mrr_at_1 value: 22.698999999999998 - type: mrr_at_10 value: 29.520999999999997 - type: mrr_at_100 value: 30.458000000000002 - type: mrr_at_1000 value: 30.526999999999997 - type: mrr_at_3 value: 27.633000000000003 - type: mrr_at_5 value: 28.483999999999998 - type: ndcg_at_1 value: 22.698999999999998 - type: ndcg_at_10 value: 31.061 - type: ndcg_at_100 value: 36.398 - type: ndcg_at_1000 value: 38.89 - type: ndcg_at_3 value: 27.149 - type: ndcg_at_5 value: 28.627000000000002 - type: precision_at_1 value: 22.698999999999998 - type: precision_at_10 value: 5.106999999999999 - type: precision_at_100 value: 0.857 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 11.963 - type: precision_at_5 value: 8.221 - type: recall_at_1 value: 19.592000000000002 - type: recall_at_10 value: 41.329 - type: recall_at_100 value: 66.094 - type: recall_at_1000 value: 84.511 - type: recall_at_3 value: 30.61 - type: recall_at_5 value: 34.213 - type: map_at_1 value: 14.71 - type: map_at_10 value: 20.965 - type: map_at_100 value: 21.994 - type: map_at_1000 value: 22.133 - type: map_at_3 value: 18.741 - type: map_at_5 value: 19.951 - type: mrr_at_1 value: 18.307000000000002 - type: mrr_at_10 value: 24.66 - type: mrr_at_100 value: 25.540000000000003 - type: mrr_at_1000 value: 25.629 - type: mrr_at_3 value: 22.511 - type: mrr_at_5 value: 23.72 - type: ndcg_at_1 value: 18.307000000000002 - type: ndcg_at_10 value: 25.153 - type: ndcg_at_100 value: 30.229 - type: ndcg_at_1000 value: 33.623 - type: ndcg_at_3 value: 21.203 - type: ndcg_at_5 value: 23.006999999999998 - type: precision_at_1 value: 18.307000000000002 - type: precision_at_10 value: 4.725 - type: precision_at_100 value: 0.8659999999999999 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 10.14 - type: precision_at_5 value: 7.481 - type: recall_at_1 value: 14.71 - type: recall_at_10 value: 34.087 - type: recall_at_100 value: 57.147999999999996 - type: recall_at_1000 value: 81.777 - type: recall_at_3 value: 22.996 - type: recall_at_5 value: 27.73 - type: map_at_1 value: 23.472 - type: map_at_10 value: 32.699 - type: map_at_100 value: 33.867000000000004 - type: map_at_1000 value: 33.967000000000006 - type: map_at_3 value: 29.718 - type: map_at_5 value: 31.345 - type: mrr_at_1 value: 28.265 - type: mrr_at_10 value: 36.945 - type: mrr_at_100 value: 37.794 - type: mrr_at_1000 value: 37.857 - type: mrr_at_3 value: 34.266000000000005 - type: mrr_at_5 value: 35.768 - type: ndcg_at_1 value: 28.265 - type: ndcg_at_10 value: 38.35 - type: ndcg_at_100 value: 43.739 - type: ndcg_at_1000 value: 46.087 - type: ndcg_at_3 value: 33.004 - type: ndcg_at_5 value: 35.411 - type: precision_at_1 value: 28.265 - type: precision_at_10 value: 6.715999999999999 - type: precision_at_100 value: 1.059 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 15.299 - type: precision_at_5 value: 10.951 - type: recall_at_1 value: 23.472 - type: recall_at_10 value: 51.413 - type: recall_at_100 value: 75.17 - type: recall_at_1000 value: 91.577 - type: recall_at_3 value: 36.651 - type: recall_at_5 value: 42.814 - type: map_at_1 value: 23.666 - type: map_at_10 value: 32.963 - type: map_at_100 value: 34.544999999999995 - type: map_at_1000 value: 34.792 - type: map_at_3 value: 29.74 - type: map_at_5 value: 31.5 - type: mrr_at_1 value: 29.051 - type: mrr_at_10 value: 38.013000000000005 - type: mrr_at_100 value: 38.997 - type: mrr_at_1000 value: 39.055 - type: mrr_at_3 value: 34.947 - type: mrr_at_5 value: 36.815 - type: ndcg_at_1 value: 29.051 - type: ndcg_at_10 value: 39.361000000000004 - type: ndcg_at_100 value: 45.186 - type: ndcg_at_1000 value: 47.867 - type: ndcg_at_3 value: 33.797 - type: ndcg_at_5 value: 36.456 - type: precision_at_1 value: 29.051 - type: precision_at_10 value: 7.668 - type: precision_at_100 value: 1.532 - type: precision_at_1000 value: 0.247 - type: precision_at_3 value: 15.876000000000001 - type: precision_at_5 value: 11.779 - type: recall_at_1 value: 23.666 - type: recall_at_10 value: 51.858000000000004 - type: recall_at_100 value: 77.805 - type: recall_at_1000 value: 94.504 - type: recall_at_3 value: 36.207 - type: recall_at_5 value: 43.094 - type: map_at_1 value: 15.662 - type: map_at_10 value: 23.594 - type: map_at_100 value: 24.593999999999998 - type: map_at_1000 value: 24.694 - type: map_at_3 value: 20.925 - type: map_at_5 value: 22.817999999999998 - type: mrr_at_1 value: 17.375 - type: mrr_at_10 value: 25.734 - type: mrr_at_100 value: 26.586 - type: mrr_at_1000 value: 26.671 - type: mrr_at_3 value: 23.044 - type: mrr_at_5 value: 24.975 - type: ndcg_at_1 value: 17.375 - type: ndcg_at_10 value: 28.186 - type: ndcg_at_100 value: 33.436 - type: ndcg_at_1000 value: 36.203 - type: ndcg_at_3 value: 23.152 - type: ndcg_at_5 value: 26.397 - type: precision_at_1 value: 17.375 - type: precision_at_10 value: 4.677 - type: precision_at_100 value: 0.786 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 10.351 - type: precision_at_5 value: 7.985 - type: recall_at_1 value: 15.662 - type: recall_at_10 value: 40.066 - type: recall_at_100 value: 65.006 - type: recall_at_1000 value: 85.94000000000001 - type: recall_at_3 value: 27.400000000000002 - type: recall_at_5 value: 35.002 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 8.853 - type: map_at_10 value: 15.568000000000001 - type: map_at_100 value: 17.383000000000003 - type: map_at_1000 value: 17.584 - type: map_at_3 value: 12.561 - type: map_at_5 value: 14.056 - type: mrr_at_1 value: 18.958 - type: mrr_at_10 value: 28.288000000000004 - type: mrr_at_100 value: 29.432000000000002 - type: mrr_at_1000 value: 29.498 - type: mrr_at_3 value: 25.049 - type: mrr_at_5 value: 26.857 - type: ndcg_at_1 value: 18.958 - type: ndcg_at_10 value: 22.21 - type: ndcg_at_100 value: 29.596 - type: ndcg_at_1000 value: 33.583 - type: ndcg_at_3 value: 16.994999999999997 - type: ndcg_at_5 value: 18.95 - type: precision_at_1 value: 18.958 - type: precision_at_10 value: 7.192 - type: precision_at_100 value: 1.5 - type: precision_at_1000 value: 0.22399999999999998 - type: precision_at_3 value: 12.573 - type: precision_at_5 value: 10.202 - type: recall_at_1 value: 8.853 - type: recall_at_10 value: 28.087 - type: recall_at_100 value: 53.701 - type: recall_at_1000 value: 76.29899999999999 - type: recall_at_3 value: 15.913 - type: recall_at_5 value: 20.658 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 9.077 - type: map_at_10 value: 20.788999999999998 - type: map_at_100 value: 30.429000000000002 - type: map_at_1000 value: 32.143 - type: map_at_3 value: 14.692 - type: map_at_5 value: 17.139 - type: mrr_at_1 value: 70.75 - type: mrr_at_10 value: 78.036 - type: mrr_at_100 value: 78.401 - type: mrr_at_1000 value: 78.404 - type: mrr_at_3 value: 76.75 - type: mrr_at_5 value: 77.47500000000001 - type: ndcg_at_1 value: 58.12500000000001 - type: ndcg_at_10 value: 44.015 - type: ndcg_at_100 value: 49.247 - type: ndcg_at_1000 value: 56.211999999999996 - type: ndcg_at_3 value: 49.151 - type: ndcg_at_5 value: 46.195 - type: precision_at_1 value: 70.75 - type: precision_at_10 value: 35.5 - type: precision_at_100 value: 11.355 - type: precision_at_1000 value: 2.1950000000000003 - type: precision_at_3 value: 53.083000000000006 - type: precision_at_5 value: 44.800000000000004 - type: recall_at_1 value: 9.077 - type: recall_at_10 value: 26.259 - type: recall_at_100 value: 56.547000000000004 - type: recall_at_1000 value: 78.551 - type: recall_at_3 value: 16.162000000000003 - type: recall_at_5 value: 19.753999999999998 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 49.44500000000001 - type: f1 value: 44.67067691783401 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 68.182 - type: map_at_10 value: 78.223 - type: map_at_100 value: 78.498 - type: map_at_1000 value: 78.512 - type: map_at_3 value: 76.71 - type: map_at_5 value: 77.725 - type: mrr_at_1 value: 73.177 - type: mrr_at_10 value: 82.513 - type: mrr_at_100 value: 82.633 - type: mrr_at_1000 value: 82.635 - type: mrr_at_3 value: 81.376 - type: mrr_at_5 value: 82.182 - type: ndcg_at_1 value: 73.177 - type: ndcg_at_10 value: 82.829 - type: ndcg_at_100 value: 83.84 - type: ndcg_at_1000 value: 84.07900000000001 - type: ndcg_at_3 value: 80.303 - type: ndcg_at_5 value: 81.846 - type: precision_at_1 value: 73.177 - type: precision_at_10 value: 10.241999999999999 - type: precision_at_100 value: 1.099 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 31.247999999999998 - type: precision_at_5 value: 19.697 - type: recall_at_1 value: 68.182 - type: recall_at_10 value: 92.657 - type: recall_at_100 value: 96.709 - type: recall_at_1000 value: 98.184 - type: recall_at_3 value: 85.9 - type: recall_at_5 value: 89.755 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 21.108 - type: map_at_10 value: 33.342 - type: map_at_100 value: 35.281 - type: map_at_1000 value: 35.478 - type: map_at_3 value: 29.067 - type: map_at_5 value: 31.563000000000002 - type: mrr_at_1 value: 41.667 - type: mrr_at_10 value: 49.913000000000004 - type: mrr_at_100 value: 50.724000000000004 - type: mrr_at_1000 value: 50.766 - type: mrr_at_3 value: 47.504999999999995 - type: mrr_at_5 value: 49.033 - type: ndcg_at_1 value: 41.667 - type: ndcg_at_10 value: 41.144 - type: ndcg_at_100 value: 48.326 - type: ndcg_at_1000 value: 51.486 - type: ndcg_at_3 value: 37.486999999999995 - type: ndcg_at_5 value: 38.78 - type: precision_at_1 value: 41.667 - type: precision_at_10 value: 11.358 - type: precision_at_100 value: 1.873 - type: precision_at_1000 value: 0.244 - type: precision_at_3 value: 25 - type: precision_at_5 value: 18.519 - type: recall_at_1 value: 21.108 - type: recall_at_10 value: 47.249 - type: recall_at_100 value: 74.52 - type: recall_at_1000 value: 93.31 - type: recall_at_3 value: 33.271 - type: recall_at_5 value: 39.723000000000006 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 40.317 - type: map_at_10 value: 64.861 - type: map_at_100 value: 65.697 - type: map_at_1000 value: 65.755 - type: map_at_3 value: 61.258 - type: map_at_5 value: 63.590999999999994 - type: mrr_at_1 value: 80.635 - type: mrr_at_10 value: 86.528 - type: mrr_at_100 value: 86.66199999999999 - type: mrr_at_1000 value: 86.666 - type: mrr_at_3 value: 85.744 - type: mrr_at_5 value: 86.24300000000001 - type: ndcg_at_1 value: 80.635 - type: ndcg_at_10 value: 73.13199999999999 - type: ndcg_at_100 value: 75.927 - type: ndcg_at_1000 value: 76.976 - type: ndcg_at_3 value: 68.241 - type: ndcg_at_5 value: 71.071 - type: precision_at_1 value: 80.635 - type: precision_at_10 value: 15.326 - type: precision_at_100 value: 1.7500000000000002 - type: precision_at_1000 value: 0.189 - type: precision_at_3 value: 43.961 - type: precision_at_5 value: 28.599999999999998 - type: recall_at_1 value: 40.317 - type: recall_at_10 value: 76.631 - type: recall_at_100 value: 87.495 - type: recall_at_1000 value: 94.362 - type: recall_at_3 value: 65.94200000000001 - type: recall_at_5 value: 71.499 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 91.686 - type: ap value: 87.5577120393173 - type: f1 value: 91.6629447355139 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 23.702 - type: map_at_10 value: 36.414 - type: map_at_100 value: 37.561 - type: map_at_1000 value: 37.605 - type: map_at_3 value: 32.456 - type: map_at_5 value: 34.827000000000005 - type: mrr_at_1 value: 24.355 - type: mrr_at_10 value: 37.01 - type: mrr_at_100 value: 38.085 - type: mrr_at_1000 value: 38.123000000000005 - type: mrr_at_3 value: 33.117999999999995 - type: mrr_at_5 value: 35.452 - type: ndcg_at_1 value: 24.384 - type: ndcg_at_10 value: 43.456 - type: ndcg_at_100 value: 48.892 - type: ndcg_at_1000 value: 49.964 - type: ndcg_at_3 value: 35.475 - type: ndcg_at_5 value: 39.711 - type: precision_at_1 value: 24.384 - type: precision_at_10 value: 6.7940000000000005 - type: precision_at_100 value: 0.951 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 15.052999999999999 - type: precision_at_5 value: 11.189 - type: recall_at_1 value: 23.702 - type: recall_at_10 value: 65.057 - type: recall_at_100 value: 90.021 - type: recall_at_1000 value: 98.142 - type: recall_at_3 value: 43.551 - type: recall_at_5 value: 53.738 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.62380300957591 - type: f1 value: 94.49871222100734 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 77.14090287277702 - type: f1 value: 60.32101258220515 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.84330867518494 - type: f1 value: 71.92248688515255 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.10692669804976 - type: f1 value: 77.9904839122866 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.822988923078444 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.38394880253403 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.82504612539082 - type: mrr value: 32.84462298174977 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.029 - type: map_at_10 value: 14.088999999999999 - type: map_at_100 value: 17.601 - type: map_at_1000 value: 19.144 - type: map_at_3 value: 10.156 - type: map_at_5 value: 11.892 - type: mrr_at_1 value: 46.44 - type: mrr_at_10 value: 56.596999999999994 - type: mrr_at_100 value: 57.11000000000001 - type: mrr_at_1000 value: 57.14 - type: mrr_at_3 value: 54.334 - type: mrr_at_5 value: 55.774 - type: ndcg_at_1 value: 44.891999999999996 - type: ndcg_at_10 value: 37.134 - type: ndcg_at_100 value: 33.652 - type: ndcg_at_1000 value: 42.548 - type: ndcg_at_3 value: 41.851 - type: ndcg_at_5 value: 39.842 - type: precision_at_1 value: 46.44 - type: precision_at_10 value: 27.647 - type: precision_at_100 value: 8.309999999999999 - type: precision_at_1000 value: 2.146 - type: precision_at_3 value: 39.422000000000004 - type: precision_at_5 value: 34.675 - type: recall_at_1 value: 6.029 - type: recall_at_10 value: 18.907 - type: recall_at_100 value: 33.76 - type: recall_at_1000 value: 65.14999999999999 - type: recall_at_3 value: 11.584999999999999 - type: recall_at_5 value: 14.626 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 39.373000000000005 - type: map_at_10 value: 55.836 - type: map_at_100 value: 56.611999999999995 - type: map_at_1000 value: 56.63 - type: map_at_3 value: 51.747 - type: map_at_5 value: 54.337999999999994 - type: mrr_at_1 value: 44.147999999999996 - type: mrr_at_10 value: 58.42699999999999 - type: mrr_at_100 value: 58.902 - type: mrr_at_1000 value: 58.914 - type: mrr_at_3 value: 55.156000000000006 - type: mrr_at_5 value: 57.291000000000004 - type: ndcg_at_1 value: 44.119 - type: ndcg_at_10 value: 63.444 - type: ndcg_at_100 value: 66.40599999999999 - type: ndcg_at_1000 value: 66.822 - type: ndcg_at_3 value: 55.962 - type: ndcg_at_5 value: 60.228 - type: precision_at_1 value: 44.119 - type: precision_at_10 value: 10.006 - type: precision_at_100 value: 1.17 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 25.135 - type: precision_at_5 value: 17.59 - type: recall_at_1 value: 39.373000000000005 - type: recall_at_10 value: 83.78999999999999 - type: recall_at_100 value: 96.246 - type: recall_at_1000 value: 99.324 - type: recall_at_3 value: 64.71900000000001 - type: recall_at_5 value: 74.508 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 69.199 - type: map_at_10 value: 82.892 - type: map_at_100 value: 83.578 - type: map_at_1000 value: 83.598 - type: map_at_3 value: 79.948 - type: map_at_5 value: 81.779 - type: mrr_at_1 value: 79.67 - type: mrr_at_10 value: 86.115 - type: mrr_at_100 value: 86.249 - type: mrr_at_1000 value: 86.251 - type: mrr_at_3 value: 85.08200000000001 - type: mrr_at_5 value: 85.783 - type: ndcg_at_1 value: 79.67 - type: ndcg_at_10 value: 86.839 - type: ndcg_at_100 value: 88.252 - type: ndcg_at_1000 value: 88.401 - type: ndcg_at_3 value: 83.86200000000001 - type: ndcg_at_5 value: 85.473 - type: precision_at_1 value: 79.67 - type: precision_at_10 value: 13.19 - type: precision_at_100 value: 1.521 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 36.677 - type: precision_at_5 value: 24.118000000000002 - type: recall_at_1 value: 69.199 - type: recall_at_10 value: 94.321 - type: recall_at_100 value: 99.20400000000001 - type: recall_at_1000 value: 99.947 - type: recall_at_3 value: 85.787 - type: recall_at_5 value: 90.365 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 55.82810046856353 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 63.38132611783628 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 5.127000000000001 - type: map_at_10 value: 12.235 - type: map_at_100 value: 14.417 - type: map_at_1000 value: 14.75 - type: map_at_3 value: 8.906 - type: map_at_5 value: 10.591000000000001 - type: mrr_at_1 value: 25.2 - type: mrr_at_10 value: 35.879 - type: mrr_at_100 value: 36.935 - type: mrr_at_1000 value: 36.997 - type: mrr_at_3 value: 32.783 - type: mrr_at_5 value: 34.367999999999995 - type: ndcg_at_1 value: 25.2 - type: ndcg_at_10 value: 20.509 - type: ndcg_at_100 value: 28.67 - type: ndcg_at_1000 value: 34.42 - type: ndcg_at_3 value: 19.948 - type: ndcg_at_5 value: 17.166 - type: precision_at_1 value: 25.2 - type: precision_at_10 value: 10.440000000000001 - type: precision_at_100 value: 2.214 - type: precision_at_1000 value: 0.359 - type: precision_at_3 value: 18.533 - type: precision_at_5 value: 14.860000000000001 - type: recall_at_1 value: 5.127000000000001 - type: recall_at_10 value: 21.147 - type: recall_at_100 value: 44.946999999999996 - type: recall_at_1000 value: 72.89 - type: recall_at_3 value: 11.277 - type: recall_at_5 value: 15.042 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.0373011786213 - type: cos_sim_spearman value: 79.27889560856613 - type: euclidean_pearson value: 80.31186315495655 - type: euclidean_spearman value: 79.41630415280811 - type: manhattan_pearson value: 80.31755140442013 - type: manhattan_spearman value: 79.43069870027611 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.8659751342045 - type: cos_sim_spearman value: 76.95377612997667 - type: euclidean_pearson value: 81.24552945497848 - type: euclidean_spearman value: 77.18236963555253 - type: manhattan_pearson value: 81.26477607759037 - type: manhattan_spearman value: 77.13821753062756 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 83.34597139044875 - type: cos_sim_spearman value: 84.124169425592 - type: euclidean_pearson value: 83.68590721511401 - type: euclidean_spearman value: 84.18846190846398 - type: manhattan_pearson value: 83.57630235061498 - type: manhattan_spearman value: 84.10244043726902 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.67641885599572 - type: cos_sim_spearman value: 80.46450725650428 - type: euclidean_pearson value: 81.61645042715865 - type: euclidean_spearman value: 80.61418394236874 - type: manhattan_pearson value: 81.55712034928871 - type: manhattan_spearman value: 80.57905670523951 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 88.86650310886782 - type: cos_sim_spearman value: 89.76081629222328 - type: euclidean_pearson value: 89.1530747029954 - type: euclidean_spearman value: 89.80990657280248 - type: manhattan_pearson value: 89.10640563278132 - type: manhattan_spearman value: 89.76282108434047 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.93864027911118 - type: cos_sim_spearman value: 85.47096193999023 - type: euclidean_pearson value: 85.03141840870533 - type: euclidean_spearman value: 85.43124029598181 - type: manhattan_pearson value: 84.99002664393512 - type: manhattan_spearman value: 85.39169195120834 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 88.7045343749832 - type: cos_sim_spearman value: 89.03262221146677 - type: euclidean_pearson value: 89.56078218264365 - type: euclidean_spearman value: 89.17827006466868 - type: manhattan_pearson value: 89.52717595468582 - type: manhattan_spearman value: 89.15878115952923 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 64.20191302875551 - type: cos_sim_spearman value: 64.11446552557646 - type: euclidean_pearson value: 64.6918197393619 - type: euclidean_spearman value: 63.440182631197764 - type: manhattan_pearson value: 64.55692904121835 - type: manhattan_spearman value: 63.424877742756266 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 86.37793104662344 - type: cos_sim_spearman value: 87.7357802629067 - type: euclidean_pearson value: 87.4286301545109 - type: euclidean_spearman value: 87.78452920777421 - type: manhattan_pearson value: 87.42445169331255 - type: manhattan_spearman value: 87.78537677249598 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 84.31465405081792 - type: mrr value: 95.7173781193389 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 57.760999999999996 - type: map_at_10 value: 67.904 - type: map_at_100 value: 68.539 - type: map_at_1000 value: 68.562 - type: map_at_3 value: 65.415 - type: map_at_5 value: 66.788 - type: mrr_at_1 value: 60.333000000000006 - type: mrr_at_10 value: 68.797 - type: mrr_at_100 value: 69.236 - type: mrr_at_1000 value: 69.257 - type: mrr_at_3 value: 66.667 - type: mrr_at_5 value: 67.967 - type: ndcg_at_1 value: 60.333000000000006 - type: ndcg_at_10 value: 72.24199999999999 - type: ndcg_at_100 value: 74.86 - type: ndcg_at_1000 value: 75.354 - type: ndcg_at_3 value: 67.93400000000001 - type: ndcg_at_5 value: 70.02199999999999 - type: precision_at_1 value: 60.333000000000006 - type: precision_at_10 value: 9.533 - type: precision_at_100 value: 1.09 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 26.778000000000002 - type: precision_at_5 value: 17.467 - type: recall_at_1 value: 57.760999999999996 - type: recall_at_10 value: 84.383 - type: recall_at_100 value: 96.267 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 72.628 - type: recall_at_5 value: 78.094 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.8029702970297 - type: cos_sim_ap value: 94.9210324173411 - type: cos_sim_f1 value: 89.8521162672106 - type: cos_sim_precision value: 91.67533818938605 - type: cos_sim_recall value: 88.1 - type: dot_accuracy value: 99.69504950495049 - type: dot_ap value: 90.4919719146181 - type: dot_f1 value: 84.72289156626506 - type: dot_precision value: 81.76744186046511 - type: dot_recall value: 87.9 - type: euclidean_accuracy value: 99.79702970297029 - type: euclidean_ap value: 94.87827463795753 - type: euclidean_f1 value: 89.55680081507896 - type: euclidean_precision value: 91.27725856697819 - type: euclidean_recall value: 87.9 - type: manhattan_accuracy value: 99.7990099009901 - type: manhattan_ap value: 94.87587025149682 - type: manhattan_f1 value: 89.76298537569339 - type: manhattan_precision value: 90.53916581892166 - type: manhattan_recall value: 89 - type: max_accuracy value: 99.8029702970297 - type: max_ap value: 94.9210324173411 - type: max_f1 value: 89.8521162672106 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 65.92385753948724 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 33.671756975431144 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 50.677928036739004 - type: mrr value: 51.56413133435193 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.523589340819683 - type: cos_sim_spearman value: 30.187407518823235 - type: dot_pearson value: 29.039713969699015 - type: dot_spearman value: 29.114740651155508 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.211 - type: map_at_10 value: 1.6199999999999999 - type: map_at_100 value: 8.658000000000001 - type: map_at_1000 value: 21.538 - type: map_at_3 value: 0.575 - type: map_at_5 value: 0.919 - type: mrr_at_1 value: 78 - type: mrr_at_10 value: 86.18599999999999 - type: mrr_at_100 value: 86.18599999999999 - type: mrr_at_1000 value: 86.18599999999999 - type: mrr_at_3 value: 85 - type: mrr_at_5 value: 85.9 - type: ndcg_at_1 value: 74 - type: ndcg_at_10 value: 66.542 - type: ndcg_at_100 value: 50.163999999999994 - type: ndcg_at_1000 value: 45.696999999999996 - type: ndcg_at_3 value: 71.531 - type: ndcg_at_5 value: 70.45 - type: precision_at_1 value: 78 - type: precision_at_10 value: 69.39999999999999 - type: precision_at_100 value: 51.06 - type: precision_at_1000 value: 20.022000000000002 - type: precision_at_3 value: 76 - type: precision_at_5 value: 74.8 - type: recall_at_1 value: 0.211 - type: recall_at_10 value: 1.813 - type: recall_at_100 value: 12.098 - type: recall_at_1000 value: 42.618 - type: recall_at_3 value: 0.603 - type: recall_at_5 value: 0.987 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.2079999999999997 - type: map_at_10 value: 7.777000000000001 - type: map_at_100 value: 12.825000000000001 - type: map_at_1000 value: 14.196 - type: map_at_3 value: 4.285 - type: map_at_5 value: 6.177 - type: mrr_at_1 value: 30.612000000000002 - type: mrr_at_10 value: 42.635 - type: mrr_at_100 value: 43.955 - type: mrr_at_1000 value: 43.955 - type: mrr_at_3 value: 38.435 - type: mrr_at_5 value: 41.088 - type: ndcg_at_1 value: 28.571 - type: ndcg_at_10 value: 20.666999999999998 - type: ndcg_at_100 value: 31.840000000000003 - type: ndcg_at_1000 value: 43.191 - type: ndcg_at_3 value: 23.45 - type: ndcg_at_5 value: 22.994 - type: precision_at_1 value: 30.612000000000002 - type: precision_at_10 value: 17.959 - type: precision_at_100 value: 6.755 - type: precision_at_1000 value: 1.4200000000000002 - type: precision_at_3 value: 23.810000000000002 - type: precision_at_5 value: 23.673 - type: recall_at_1 value: 2.2079999999999997 - type: recall_at_10 value: 13.144 - type: recall_at_100 value: 42.491 - type: recall_at_1000 value: 77.04299999999999 - type: recall_at_3 value: 5.3469999999999995 - type: recall_at_5 value: 9.139 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.9044 - type: ap value: 14.625783489340755 - type: f1 value: 54.814936562590546 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.94227504244483 - type: f1 value: 61.22516038508854 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 49.602409155145864 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.94641473445789 - type: cos_sim_ap value: 76.91572747061197 - type: cos_sim_f1 value: 70.14348097317529 - type: cos_sim_precision value: 66.53254437869822 - type: cos_sim_recall value: 74.1688654353562 - type: dot_accuracy value: 84.80061989628658 - type: dot_ap value: 70.7952548895177 - type: dot_f1 value: 65.44780728844965 - type: dot_precision value: 61.53310104529617 - type: dot_recall value: 69.89445910290237 - type: euclidean_accuracy value: 86.94641473445789 - type: euclidean_ap value: 76.80774009393652 - type: euclidean_f1 value: 70.30522503879979 - type: euclidean_precision value: 68.94977168949772 - type: euclidean_recall value: 71.71503957783642 - type: manhattan_accuracy value: 86.8629671574179 - type: manhattan_ap value: 76.76518632600317 - type: manhattan_f1 value: 70.16056518946692 - type: manhattan_precision value: 68.360450563204 - type: manhattan_recall value: 72.0580474934037 - type: max_accuracy value: 86.94641473445789 - type: max_ap value: 76.91572747061197 - type: max_f1 value: 70.30522503879979 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.10428066907285 - type: cos_sim_ap value: 86.25114759921435 - type: cos_sim_f1 value: 78.37857884586856 - type: cos_sim_precision value: 75.60818546078993 - type: cos_sim_recall value: 81.35971666153372 - type: dot_accuracy value: 87.41995575736406 - type: dot_ap value: 81.51838010086782 - type: dot_f1 value: 74.77398015435503 - type: dot_precision value: 71.53002390662354 - type: dot_recall value: 78.32614721281182 - type: euclidean_accuracy value: 89.12368533395428 - type: euclidean_ap value: 86.33456799874504 - type: euclidean_f1 value: 78.45496750232127 - type: euclidean_precision value: 75.78388462366364 - type: euclidean_recall value: 81.32121958731136 - type: manhattan_accuracy value: 89.10622113556099 - type: manhattan_ap value: 86.31215061745333 - type: manhattan_f1 value: 78.40684906011539 - type: manhattan_precision value: 75.89536643366722 - type: manhattan_recall value: 81.09023714197721 - type: max_accuracy value: 89.12368533395428 - type: max_ap value: 86.33456799874504 - type: max_f1 value: 78.45496750232127 --- # KeyurRamoliya/e5-large-v2-Q8_0-GGUF This model was converted to GGUF format from [`intfloat/e5-large-v2`](https://huggingface.co/intfloat/e5-large-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/intfloat/e5-large-v2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo KeyurRamoliya/e5-large-v2-Q8_0-GGUF --hf-file e5-large-v2-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo KeyurRamoliya/e5-large-v2-Q8_0-GGUF --hf-file e5-large-v2-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo KeyurRamoliya/e5-large-v2-Q8_0-GGUF --hf-file e5-large-v2-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo KeyurRamoliya/e5-large-v2-Q8_0-GGUF --hf-file e5-large-v2-q8_0.gguf -c 2048 ```
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
michaelfeil/ct2fast-jina-embedding-s-en-v1
michaelfeil
sentence-similarity
[ "transformers", "t5", "feature-extraction", "ctranslate2", "int8", "float16 - finetuner - mteb - sentence-transformers - feature-extraction - sentence-similarity", "sentence-similarity", "custom_code", "en", "dataset:jinaai/negation-dataset", "arxiv:2307.11224", "license:apache-2.0", "model-index", "region:us" ]
1,697
1,697
4
0
--- datasets: - jinaai/negation-dataset language: en license: apache-2.0 pipeline_tag: sentence-similarity tags: - ctranslate2 - int8 - float16 - finetuner - mteb - sentence-transformers - feature-extraction - sentence-similarity model-index: - name: jina-embedding-s-en-v1 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 64.82089552238806 - type: ap value: 27.100981946230778 - type: f1 value: 58.3354886367184 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 64.282775 - type: ap value: 60.350688924943796 - type: f1 value: 62.06346948494396 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 30.623999999999995 - type: f1 value: 29.427789186742153 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 22.119 - type: map_at_10 value: 35.609 - type: map_at_100 value: 36.935 - type: map_at_1000 value: 36.957 - type: map_at_3 value: 31.046000000000003 - type: map_at_5 value: 33.574 - type: mrr_at_1 value: 22.404 - type: mrr_at_10 value: 35.695 - type: mrr_at_100 value: 37.021 - type: mrr_at_1000 value: 37.043 - type: mrr_at_3 value: 31.093 - type: mrr_at_5 value: 33.635999999999996 - type: ndcg_at_1 value: 22.119 - type: ndcg_at_10 value: 43.566 - type: ndcg_at_100 value: 49.370000000000005 - type: ndcg_at_1000 value: 49.901 - type: ndcg_at_3 value: 34.06 - type: ndcg_at_5 value: 38.653999999999996 - type: precision_at_1 value: 22.119 - type: precision_at_10 value: 6.92 - type: precision_at_100 value: 0.95 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.272000000000002 - type: precision_at_5 value: 10.811 - type: recall_at_1 value: 22.119 - type: recall_at_10 value: 69.203 - type: recall_at_100 value: 95.021 - type: recall_at_1000 value: 99.075 - type: recall_at_3 value: 42.817 - type: recall_at_5 value: 54.054 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 34.1740289109719 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 23.985251383455463 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 60.24873612289029 - type: mrr value: 74.65692740623489 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 86.22415390332444 - type: cos_sim_spearman value: 82.9591191954711 - type: euclidean_pearson value: 44.096317524324945 - type: euclidean_spearman value: 42.95218351391625 - type: manhattan_pearson value: 44.07766490545065 - type: manhattan_spearman value: 42.78350497166606 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 74.64285714285714 - type: f1 value: 73.53680835577447 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 28.512813238490164 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 20.942214972649488 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 28.255999999999997 - type: map_at_10 value: 37.091 - type: map_at_100 value: 38.428000000000004 - type: map_at_1000 value: 38.559 - type: map_at_3 value: 34.073 - type: map_at_5 value: 35.739 - type: mrr_at_1 value: 34.907 - type: mrr_at_10 value: 42.769 - type: mrr_at_100 value: 43.607 - type: mrr_at_1000 value: 43.656 - type: mrr_at_3 value: 39.986 - type: mrr_at_5 value: 41.581 - type: ndcg_at_1 value: 34.907 - type: ndcg_at_10 value: 42.681000000000004 - type: ndcg_at_100 value: 48.213 - type: ndcg_at_1000 value: 50.464 - type: ndcg_at_3 value: 37.813 - type: ndcg_at_5 value: 39.936 - type: precision_at_1 value: 34.907 - type: precision_at_10 value: 7.911 - type: precision_at_100 value: 1.349 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 17.93 - type: precision_at_5 value: 12.732 - type: recall_at_1 value: 28.255999999999997 - type: recall_at_10 value: 53.49699999999999 - type: recall_at_100 value: 77.288 - type: recall_at_1000 value: 91.776 - type: recall_at_3 value: 39.18 - type: recall_at_5 value: 45.365 - type: map_at_1 value: 25.563999999999997 - type: map_at_10 value: 33.913 - type: map_at_100 value: 34.966 - type: map_at_1000 value: 35.104 - type: map_at_3 value: 31.413000000000004 - type: map_at_5 value: 32.854 - type: mrr_at_1 value: 31.72 - type: mrr_at_10 value: 39.391 - type: mrr_at_100 value: 40.02 - type: mrr_at_1000 value: 40.076 - type: mrr_at_3 value: 37.314 - type: mrr_at_5 value: 38.507999999999996 - type: ndcg_at_1 value: 31.72 - type: ndcg_at_10 value: 38.933 - type: ndcg_at_100 value: 43.024 - type: ndcg_at_1000 value: 45.556999999999995 - type: ndcg_at_3 value: 35.225 - type: ndcg_at_5 value: 36.984 - type: precision_at_1 value: 31.72 - type: precision_at_10 value: 7.248 - type: precision_at_100 value: 1.192 - type: precision_at_1000 value: 0.16999999999999998 - type: precision_at_3 value: 16.943 - type: precision_at_5 value: 11.975 - type: recall_at_1 value: 25.563999999999997 - type: recall_at_10 value: 47.808 - type: recall_at_100 value: 65.182 - type: recall_at_1000 value: 81.831 - type: recall_at_3 value: 36.889 - type: recall_at_5 value: 41.829 - type: map_at_1 value: 33.662 - type: map_at_10 value: 44.096999999999994 - type: map_at_100 value: 45.153999999999996 - type: map_at_1000 value: 45.223 - type: map_at_3 value: 41.377 - type: map_at_5 value: 42.935 - type: mrr_at_1 value: 38.997 - type: mrr_at_10 value: 47.675 - type: mrr_at_100 value: 48.476 - type: mrr_at_1000 value: 48.519 - type: mrr_at_3 value: 45.549 - type: mrr_at_5 value: 46.884 - type: ndcg_at_1 value: 38.997 - type: ndcg_at_10 value: 49.196 - type: ndcg_at_100 value: 53.788000000000004 - type: ndcg_at_1000 value: 55.393 - type: ndcg_at_3 value: 44.67 - type: ndcg_at_5 value: 46.991 - type: precision_at_1 value: 38.997 - type: precision_at_10 value: 7.875 - type: precision_at_100 value: 1.102 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 19.854 - type: precision_at_5 value: 13.605 - type: recall_at_1 value: 33.662 - type: recall_at_10 value: 60.75899999999999 - type: recall_at_100 value: 81.11699999999999 - type: recall_at_1000 value: 92.805 - type: recall_at_3 value: 48.577999999999996 - type: recall_at_5 value: 54.384 - type: map_at_1 value: 21.313 - type: map_at_10 value: 29.036 - type: map_at_100 value: 29.975 - type: map_at_1000 value: 30.063000000000002 - type: map_at_3 value: 26.878999999999998 - type: map_at_5 value: 28.005999999999997 - type: mrr_at_1 value: 23.39 - type: mrr_at_10 value: 31.072 - type: mrr_at_100 value: 31.922 - type: mrr_at_1000 value: 31.995 - type: mrr_at_3 value: 28.908 - type: mrr_at_5 value: 30.104999999999997 - type: ndcg_at_1 value: 23.39 - type: ndcg_at_10 value: 33.448 - type: ndcg_at_100 value: 38.255 - type: ndcg_at_1000 value: 40.542 - type: ndcg_at_3 value: 29.060000000000002 - type: ndcg_at_5 value: 31.023 - type: precision_at_1 value: 23.39 - type: precision_at_10 value: 5.175 - type: precision_at_100 value: 0.8049999999999999 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 12.504999999999999 - type: precision_at_5 value: 8.61 - type: recall_at_1 value: 21.313 - type: recall_at_10 value: 45.345 - type: recall_at_100 value: 67.752 - type: recall_at_1000 value: 84.937 - type: recall_at_3 value: 33.033 - type: recall_at_5 value: 37.929 - type: map_at_1 value: 14.255999999999998 - type: map_at_10 value: 20.339 - type: map_at_100 value: 21.491 - type: map_at_1000 value: 21.616 - type: map_at_3 value: 18.481 - type: map_at_5 value: 19.594 - type: mrr_at_1 value: 17.413 - type: mrr_at_10 value: 24.146 - type: mrr_at_100 value: 25.188 - type: mrr_at_1000 value: 25.273 - type: mrr_at_3 value: 22.264 - type: mrr_at_5 value: 23.302 - type: ndcg_at_1 value: 17.413 - type: ndcg_at_10 value: 24.272 - type: ndcg_at_100 value: 29.82 - type: ndcg_at_1000 value: 33.072 - type: ndcg_at_3 value: 20.826 - type: ndcg_at_5 value: 22.535 - type: precision_at_1 value: 17.413 - type: precision_at_10 value: 4.366 - type: precision_at_100 value: 0.818 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 9.866999999999999 - type: precision_at_5 value: 7.164 - type: recall_at_1 value: 14.255999999999998 - type: recall_at_10 value: 32.497 - type: recall_at_100 value: 56.592 - type: recall_at_1000 value: 80.17699999999999 - type: recall_at_3 value: 23.195 - type: recall_at_5 value: 27.392 - type: map_at_1 value: 22.709 - type: map_at_10 value: 31.377 - type: map_at_100 value: 32.536 - type: map_at_1000 value: 32.669 - type: map_at_3 value: 28.572999999999997 - type: map_at_5 value: 30.205 - type: mrr_at_1 value: 27.815 - type: mrr_at_10 value: 36.452 - type: mrr_at_100 value: 37.302 - type: mrr_at_1000 value: 37.364000000000004 - type: mrr_at_3 value: 33.75 - type: mrr_at_5 value: 35.43 - type: ndcg_at_1 value: 27.815 - type: ndcg_at_10 value: 36.84 - type: ndcg_at_100 value: 42.092 - type: ndcg_at_1000 value: 44.727 - type: ndcg_at_3 value: 31.964 - type: ndcg_at_5 value: 34.428 - type: precision_at_1 value: 27.815 - type: precision_at_10 value: 6.67 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.151 - type: precision_at_3 value: 14.982000000000001 - type: precision_at_5 value: 10.857 - type: recall_at_1 value: 22.709 - type: recall_at_10 value: 48.308 - type: recall_at_100 value: 70.866 - type: recall_at_1000 value: 88.236 - type: recall_at_3 value: 34.709 - type: recall_at_5 value: 40.996 - type: map_at_1 value: 22.348000000000003 - type: map_at_10 value: 29.427999999999997 - type: map_at_100 value: 30.499 - type: map_at_1000 value: 30.631999999999998 - type: map_at_3 value: 27.035999999999998 - type: map_at_5 value: 28.351 - type: mrr_at_1 value: 27.74 - type: mrr_at_10 value: 34.424 - type: mrr_at_100 value: 35.341 - type: mrr_at_1000 value: 35.419 - type: mrr_at_3 value: 32.401 - type: mrr_at_5 value: 33.497 - type: ndcg_at_1 value: 27.74 - type: ndcg_at_10 value: 34.136 - type: ndcg_at_100 value: 39.269 - type: ndcg_at_1000 value: 42.263 - type: ndcg_at_3 value: 30.171999999999997 - type: ndcg_at_5 value: 31.956 - type: precision_at_1 value: 27.74 - type: precision_at_10 value: 6.062 - type: precision_at_100 value: 1.014 - type: precision_at_1000 value: 0.146 - type: precision_at_3 value: 14.079 - type: precision_at_5 value: 9.977 - type: recall_at_1 value: 22.348000000000003 - type: recall_at_10 value: 43.477 - type: recall_at_100 value: 65.945 - type: recall_at_1000 value: 86.587 - type: recall_at_3 value: 32.107 - type: recall_at_5 value: 36.974000000000004 - type: map_at_1 value: 21.688499999999998 - type: map_at_10 value: 29.164666666666665 - type: map_at_100 value: 30.22575 - type: map_at_1000 value: 30.350833333333334 - type: map_at_3 value: 26.82025 - type: map_at_5 value: 28.14966666666667 - type: mrr_at_1 value: 25.779249999999998 - type: mrr_at_10 value: 32.969 - type: mrr_at_100 value: 33.81725 - type: mrr_at_1000 value: 33.88825 - type: mrr_at_3 value: 30.831250000000004 - type: mrr_at_5 value: 32.065000000000005 - type: ndcg_at_1 value: 25.779249999999998 - type: ndcg_at_10 value: 33.73675 - type: ndcg_at_100 value: 38.635666666666665 - type: ndcg_at_1000 value: 41.353500000000004 - type: ndcg_at_3 value: 29.66283333333333 - type: ndcg_at_5 value: 31.607249999999997 - type: precision_at_1 value: 25.779249999999998 - type: precision_at_10 value: 5.861416666666667 - type: precision_at_100 value: 0.9852500000000002 - type: precision_at_1000 value: 0.14108333333333334 - type: precision_at_3 value: 13.563583333333332 - type: precision_at_5 value: 9.630333333333335 - type: recall_at_1 value: 21.688499999999998 - type: recall_at_10 value: 43.605 - type: recall_at_100 value: 65.52366666666667 - type: recall_at_1000 value: 84.69683333333332 - type: recall_at_3 value: 32.195499999999996 - type: recall_at_5 value: 37.25325 - type: map_at_1 value: 17.279 - type: map_at_10 value: 23.238 - type: map_at_100 value: 24.026 - type: map_at_1000 value: 24.13 - type: map_at_3 value: 20.730999999999998 - type: map_at_5 value: 22.278000000000002 - type: mrr_at_1 value: 19.017999999999997 - type: mrr_at_10 value: 25.188 - type: mrr_at_100 value: 25.918999999999997 - type: mrr_at_1000 value: 25.996999999999996 - type: mrr_at_3 value: 22.776 - type: mrr_at_5 value: 24.256 - type: ndcg_at_1 value: 19.017999999999997 - type: ndcg_at_10 value: 27.171 - type: ndcg_at_100 value: 31.274 - type: ndcg_at_1000 value: 34.016000000000005 - type: ndcg_at_3 value: 22.442 - type: ndcg_at_5 value: 24.955 - type: precision_at_1 value: 19.017999999999997 - type: precision_at_10 value: 4.494 - type: precision_at_100 value: 0.712 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 9.611 - type: precision_at_5 value: 7.331 - type: recall_at_1 value: 17.279 - type: recall_at_10 value: 37.464999999999996 - type: recall_at_100 value: 56.458 - type: recall_at_1000 value: 76.759 - type: recall_at_3 value: 24.659 - type: recall_at_5 value: 30.672 - type: map_at_1 value: 14.901 - type: map_at_10 value: 20.268 - type: map_at_100 value: 21.143 - type: map_at_1000 value: 21.264 - type: map_at_3 value: 18.557000000000002 - type: map_at_5 value: 19.483 - type: mrr_at_1 value: 17.997 - type: mrr_at_10 value: 23.591 - type: mrr_at_100 value: 24.387 - type: mrr_at_1000 value: 24.471 - type: mrr_at_3 value: 21.874 - type: mrr_at_5 value: 22.797 - type: ndcg_at_1 value: 17.997 - type: ndcg_at_10 value: 23.87 - type: ndcg_at_100 value: 28.459 - type: ndcg_at_1000 value: 31.66 - type: ndcg_at_3 value: 20.779 - type: ndcg_at_5 value: 22.137 - type: precision_at_1 value: 17.997 - type: precision_at_10 value: 4.25 - type: precision_at_100 value: 0.761 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 9.716 - type: precision_at_5 value: 6.909999999999999 - type: recall_at_1 value: 14.901 - type: recall_at_10 value: 31.44 - type: recall_at_100 value: 52.717000000000006 - type: recall_at_1000 value: 76.102 - type: recall_at_3 value: 22.675 - type: recall_at_5 value: 26.336 - type: map_at_1 value: 21.52 - type: map_at_10 value: 28.397 - type: map_at_100 value: 29.443 - type: map_at_1000 value: 29.56 - type: map_at_3 value: 26.501 - type: map_at_5 value: 27.375 - type: mrr_at_1 value: 25.28 - type: mrr_at_10 value: 32.102000000000004 - type: mrr_at_100 value: 33.005 - type: mrr_at_1000 value: 33.084 - type: mrr_at_3 value: 30.208000000000002 - type: mrr_at_5 value: 31.146 - type: ndcg_at_1 value: 25.28 - type: ndcg_at_10 value: 32.635 - type: ndcg_at_100 value: 37.672 - type: ndcg_at_1000 value: 40.602 - type: ndcg_at_3 value: 28.951999999999998 - type: ndcg_at_5 value: 30.336999999999996 - type: precision_at_1 value: 25.28 - type: precision_at_10 value: 5.3260000000000005 - type: precision_at_100 value: 0.8840000000000001 - type: precision_at_1000 value: 0.126 - type: precision_at_3 value: 12.687000000000001 - type: precision_at_5 value: 8.638 - type: recall_at_1 value: 21.52 - type: recall_at_10 value: 41.955 - type: recall_at_100 value: 64.21 - type: recall_at_1000 value: 85.28099999999999 - type: recall_at_3 value: 31.979999999999997 - type: recall_at_5 value: 35.406 - type: map_at_1 value: 20.296 - type: map_at_10 value: 28.449999999999996 - type: map_at_100 value: 29.847 - type: map_at_1000 value: 30.073 - type: map_at_3 value: 25.995 - type: map_at_5 value: 27.603 - type: mrr_at_1 value: 25.296000000000003 - type: mrr_at_10 value: 32.751999999999995 - type: mrr_at_100 value: 33.705 - type: mrr_at_1000 value: 33.783 - type: mrr_at_3 value: 30.731 - type: mrr_at_5 value: 32.006 - type: ndcg_at_1 value: 25.296000000000003 - type: ndcg_at_10 value: 33.555 - type: ndcg_at_100 value: 38.891999999999996 - type: ndcg_at_1000 value: 42.088 - type: ndcg_at_3 value: 29.944 - type: ndcg_at_5 value: 31.997999999999998 - type: precision_at_1 value: 25.296000000000003 - type: precision_at_10 value: 6.542000000000001 - type: precision_at_100 value: 1.354 - type: precision_at_1000 value: 0.22599999999999998 - type: precision_at_3 value: 14.360999999999999 - type: precision_at_5 value: 10.593 - type: recall_at_1 value: 20.296 - type: recall_at_10 value: 42.742000000000004 - type: recall_at_100 value: 67.351 - type: recall_at_1000 value: 88.774 - type: recall_at_3 value: 32.117000000000004 - type: recall_at_5 value: 37.788 - type: map_at_1 value: 18.157999999999998 - type: map_at_10 value: 24.342 - type: map_at_100 value: 25.201 - type: map_at_1000 value: 25.317 - type: map_at_3 value: 22.227 - type: map_at_5 value: 23.372999999999998 - type: mrr_at_1 value: 19.778000000000002 - type: mrr_at_10 value: 26.066 - type: mrr_at_100 value: 26.935 - type: mrr_at_1000 value: 27.022000000000002 - type: mrr_at_3 value: 24.214 - type: mrr_at_5 value: 25.268 - type: ndcg_at_1 value: 19.778000000000002 - type: ndcg_at_10 value: 28.104000000000003 - type: ndcg_at_100 value: 32.87 - type: ndcg_at_1000 value: 35.858000000000004 - type: ndcg_at_3 value: 24.107 - type: ndcg_at_5 value: 26.007 - type: precision_at_1 value: 19.778000000000002 - type: precision_at_10 value: 4.417999999999999 - type: precision_at_100 value: 0.739 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 10.228 - type: precision_at_5 value: 7.172000000000001 - type: recall_at_1 value: 18.157999999999998 - type: recall_at_10 value: 37.967 - type: recall_at_100 value: 60.806000000000004 - type: recall_at_1000 value: 83.097 - type: recall_at_3 value: 27.223999999999997 - type: recall_at_5 value: 31.968000000000004 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 7.055 - type: map_at_10 value: 11.609 - type: map_at_100 value: 12.83 - type: map_at_1000 value: 12.995000000000001 - type: map_at_3 value: 9.673 - type: map_at_5 value: 10.761999999999999 - type: mrr_at_1 value: 15.309000000000001 - type: mrr_at_10 value: 23.655 - type: mrr_at_100 value: 24.785 - type: mrr_at_1000 value: 24.856 - type: mrr_at_3 value: 20.499000000000002 - type: mrr_at_5 value: 22.425 - type: ndcg_at_1 value: 15.309000000000001 - type: ndcg_at_10 value: 17.252000000000002 - type: ndcg_at_100 value: 22.976 - type: ndcg_at_1000 value: 26.480999999999998 - type: ndcg_at_3 value: 13.418 - type: ndcg_at_5 value: 15.084 - type: precision_at_1 value: 15.309000000000001 - type: precision_at_10 value: 5.309 - type: precision_at_100 value: 1.1320000000000001 - type: precision_at_1000 value: 0.17600000000000002 - type: precision_at_3 value: 9.62 - type: precision_at_5 value: 7.883 - type: recall_at_1 value: 7.055 - type: recall_at_10 value: 21.891 - type: recall_at_100 value: 41.979 - type: recall_at_1000 value: 62.239999999999995 - type: recall_at_3 value: 12.722 - type: recall_at_5 value: 16.81 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 6.909 - type: map_at_10 value: 12.844 - type: map_at_100 value: 16.435 - type: map_at_1000 value: 17.262 - type: map_at_3 value: 10.131 - type: map_at_5 value: 11.269 - type: mrr_at_1 value: 54.50000000000001 - type: mrr_at_10 value: 62.202 - type: mrr_at_100 value: 62.81 - type: mrr_at_1000 value: 62.824000000000005 - type: mrr_at_3 value: 60.5 - type: mrr_at_5 value: 61.324999999999996 - type: ndcg_at_1 value: 42.125 - type: ndcg_at_10 value: 28.284 - type: ndcg_at_100 value: 30.444 - type: ndcg_at_1000 value: 36.397 - type: ndcg_at_3 value: 33.439 - type: ndcg_at_5 value: 30.473 - type: precision_at_1 value: 54.50000000000001 - type: precision_at_10 value: 21.4 - type: precision_at_100 value: 6.192 - type: precision_at_1000 value: 1.398 - type: precision_at_3 value: 36.583 - type: precision_at_5 value: 28.799999999999997 - type: recall_at_1 value: 6.909 - type: recall_at_10 value: 17.296 - type: recall_at_100 value: 33.925 - type: recall_at_1000 value: 53.786 - type: recall_at_3 value: 11.333 - type: recall_at_5 value: 13.529 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 36.08 - type: f1 value: 33.016420191943766 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 52.605000000000004 - type: map_at_10 value: 63.31400000000001 - type: map_at_100 value: 63.678000000000004 - type: map_at_1000 value: 63.699 - type: map_at_3 value: 61.141 - type: map_at_5 value: 62.517999999999994 - type: mrr_at_1 value: 56.871 - type: mrr_at_10 value: 67.915 - type: mrr_at_100 value: 68.24900000000001 - type: mrr_at_1000 value: 68.262 - type: mrr_at_3 value: 65.809 - type: mrr_at_5 value: 67.171 - type: ndcg_at_1 value: 56.871 - type: ndcg_at_10 value: 69.122 - type: ndcg_at_100 value: 70.855 - type: ndcg_at_1000 value: 71.368 - type: ndcg_at_3 value: 64.974 - type: ndcg_at_5 value: 67.318 - type: precision_at_1 value: 56.871 - type: precision_at_10 value: 9.029 - type: precision_at_100 value: 0.996 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 25.893 - type: precision_at_5 value: 16.838 - type: recall_at_1 value: 52.605000000000004 - type: recall_at_10 value: 82.679 - type: recall_at_100 value: 90.586 - type: recall_at_1000 value: 94.38 - type: recall_at_3 value: 71.447 - type: recall_at_5 value: 77.218 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 10.759 - type: map_at_10 value: 18.877 - type: map_at_100 value: 20.498 - type: map_at_1000 value: 20.682000000000002 - type: map_at_3 value: 16.159000000000002 - type: map_at_5 value: 17.575 - type: mrr_at_1 value: 22.531000000000002 - type: mrr_at_10 value: 31.155 - type: mrr_at_100 value: 32.188 - type: mrr_at_1000 value: 32.245000000000005 - type: mrr_at_3 value: 28.781000000000002 - type: mrr_at_5 value: 30.054 - type: ndcg_at_1 value: 22.531000000000002 - type: ndcg_at_10 value: 25.189 - type: ndcg_at_100 value: 31.958 - type: ndcg_at_1000 value: 35.693999999999996 - type: ndcg_at_3 value: 22.235 - type: ndcg_at_5 value: 23.044999999999998 - type: precision_at_1 value: 22.531000000000002 - type: precision_at_10 value: 7.438000000000001 - type: precision_at_100 value: 1.418 - type: precision_at_1000 value: 0.208 - type: precision_at_3 value: 15.329 - type: precision_at_5 value: 11.451 - type: recall_at_1 value: 10.759 - type: recall_at_10 value: 31.416 - type: recall_at_100 value: 56.989000000000004 - type: recall_at_1000 value: 80.33200000000001 - type: recall_at_3 value: 20.61 - type: recall_at_5 value: 24.903 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 29.21 - type: map_at_10 value: 38.765 - type: map_at_100 value: 39.498 - type: map_at_1000 value: 39.568 - type: map_at_3 value: 36.699 - type: map_at_5 value: 37.925 - type: mrr_at_1 value: 58.42 - type: mrr_at_10 value: 65.137 - type: mrr_at_100 value: 65.542 - type: mrr_at_1000 value: 65.568 - type: mrr_at_3 value: 63.698 - type: mrr_at_5 value: 64.575 - type: ndcg_at_1 value: 58.42 - type: ndcg_at_10 value: 47.476 - type: ndcg_at_100 value: 50.466 - type: ndcg_at_1000 value: 52.064 - type: ndcg_at_3 value: 43.986 - type: ndcg_at_5 value: 45.824 - type: precision_at_1 value: 58.42 - type: precision_at_10 value: 9.649000000000001 - type: precision_at_100 value: 1.201 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 26.977 - type: precision_at_5 value: 17.642 - type: recall_at_1 value: 29.21 - type: recall_at_10 value: 48.244 - type: recall_at_100 value: 60.041 - type: recall_at_1000 value: 70.743 - type: recall_at_3 value: 40.466 - type: recall_at_5 value: 44.105 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 58.7064 - type: ap value: 55.36326227125519 - type: f1 value: 57.46763115215848 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 15.889000000000001 - type: map_at_10 value: 25.979000000000003 - type: map_at_100 value: 27.21 - type: map_at_1000 value: 27.284000000000002 - type: map_at_3 value: 22.665 - type: map_at_5 value: 24.578 - type: mrr_at_1 value: 16.39 - type: mrr_at_10 value: 26.504 - type: mrr_at_100 value: 27.689999999999998 - type: mrr_at_1000 value: 27.758 - type: mrr_at_3 value: 23.24 - type: mrr_at_5 value: 25.108000000000004 - type: ndcg_at_1 value: 16.39 - type: ndcg_at_10 value: 31.799 - type: ndcg_at_100 value: 38.034 - type: ndcg_at_1000 value: 39.979 - type: ndcg_at_3 value: 25.054 - type: ndcg_at_5 value: 28.463 - type: precision_at_1 value: 16.39 - type: precision_at_10 value: 5.189 - type: precision_at_100 value: 0.835 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 10.84 - type: precision_at_5 value: 8.238 - type: recall_at_1 value: 15.889000000000001 - type: recall_at_10 value: 49.739 - type: recall_at_100 value: 79.251 - type: recall_at_1000 value: 94.298 - type: recall_at_3 value: 31.427 - type: recall_at_5 value: 39.623000000000005 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 88.81668946648426 - type: f1 value: 88.55200075528438 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 58.611491108071135 - type: f1 value: 42.12391403999353 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 64.67047747141896 - type: f1 value: 62.88410885922258 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 71.78547410894419 - type: f1 value: 71.69467869218154 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 27.23799937752035 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 23.26502601343789 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.680711484149832 - type: mrr value: 31.705059795117307 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 4.077 - type: map_at_10 value: 8.657 - type: map_at_100 value: 10.753 - type: map_at_1000 value: 11.885 - type: map_at_3 value: 6.5089999999999995 - type: map_at_5 value: 7.405 - type: mrr_at_1 value: 38.7 - type: mrr_at_10 value: 46.065 - type: mrr_at_100 value: 46.772000000000006 - type: mrr_at_1000 value: 46.83 - type: mrr_at_3 value: 44.118 - type: mrr_at_5 value: 45.015 - type: ndcg_at_1 value: 36.997 - type: ndcg_at_10 value: 25.96 - type: ndcg_at_100 value: 23.607 - type: ndcg_at_1000 value: 32.317 - type: ndcg_at_3 value: 31.06 - type: ndcg_at_5 value: 28.921000000000003 - type: precision_at_1 value: 38.7 - type: precision_at_10 value: 19.195 - type: precision_at_100 value: 6.164 - type: precision_at_1000 value: 1.839 - type: precision_at_3 value: 28.999000000000002 - type: precision_at_5 value: 25.014999999999997 - type: recall_at_1 value: 4.077 - type: recall_at_10 value: 11.802 - type: recall_at_100 value: 24.365000000000002 - type: recall_at_1000 value: 55.277 - type: recall_at_3 value: 7.435 - type: recall_at_5 value: 8.713999999999999 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 19.588 - type: map_at_10 value: 32.08 - type: map_at_100 value: 33.32 - type: map_at_1000 value: 33.377 - type: map_at_3 value: 28.166000000000004 - type: map_at_5 value: 30.383 - type: mrr_at_1 value: 22.161 - type: mrr_at_10 value: 34.121 - type: mrr_at_100 value: 35.171 - type: mrr_at_1000 value: 35.214 - type: mrr_at_3 value: 30.692000000000004 - type: mrr_at_5 value: 32.706 - type: ndcg_at_1 value: 22.131999999999998 - type: ndcg_at_10 value: 38.887 - type: ndcg_at_100 value: 44.433 - type: ndcg_at_1000 value: 45.823 - type: ndcg_at_3 value: 31.35 - type: ndcg_at_5 value: 35.144 - type: precision_at_1 value: 22.131999999999998 - type: precision_at_10 value: 6.8629999999999995 - type: precision_at_100 value: 0.993 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 14.706 - type: precision_at_5 value: 10.972999999999999 - type: recall_at_1 value: 19.588 - type: recall_at_10 value: 57.703 - type: recall_at_100 value: 82.194 - type: recall_at_1000 value: 92.623 - type: recall_at_3 value: 38.012 - type: recall_at_5 value: 46.847 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 68.038 - type: map_at_10 value: 81.572 - type: map_at_100 value: 82.25200000000001 - type: map_at_1000 value: 82.27600000000001 - type: map_at_3 value: 78.618 - type: map_at_5 value: 80.449 - type: mrr_at_1 value: 78.31 - type: mrr_at_10 value: 84.98 - type: mrr_at_100 value: 85.122 - type: mrr_at_1000 value: 85.124 - type: mrr_at_3 value: 83.852 - type: mrr_at_5 value: 84.6 - type: ndcg_at_1 value: 78.31 - type: ndcg_at_10 value: 85.693 - type: ndcg_at_100 value: 87.191 - type: ndcg_at_1000 value: 87.386 - type: ndcg_at_3 value: 82.585 - type: ndcg_at_5 value: 84.255 - type: precision_at_1 value: 78.31 - type: precision_at_10 value: 12.986 - type: precision_at_100 value: 1.505 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 36.007 - type: precision_at_5 value: 23.735999999999997 - type: recall_at_1 value: 68.038 - type: recall_at_10 value: 93.598 - type: recall_at_100 value: 98.869 - type: recall_at_1000 value: 99.86500000000001 - type: recall_at_3 value: 84.628 - type: recall_at_5 value: 89.316 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 37.948231664922865 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 49.90597913763894 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 3.753 - type: map_at_10 value: 8.915 - type: map_at_100 value: 10.374 - type: map_at_1000 value: 10.612 - type: map_at_3 value: 6.577 - type: map_at_5 value: 7.8 - type: mrr_at_1 value: 18.4 - type: mrr_at_10 value: 27.325 - type: mrr_at_100 value: 28.419 - type: mrr_at_1000 value: 28.494000000000003 - type: mrr_at_3 value: 24.349999999999998 - type: mrr_at_5 value: 26.205000000000002 - type: ndcg_at_1 value: 18.4 - type: ndcg_at_10 value: 15.293000000000001 - type: ndcg_at_100 value: 21.592 - type: ndcg_at_1000 value: 26.473000000000003 - type: ndcg_at_3 value: 14.748 - type: ndcg_at_5 value: 12.98 - type: precision_at_1 value: 18.4 - type: precision_at_10 value: 7.779999999999999 - type: precision_at_100 value: 1.693 - type: precision_at_1000 value: 0.28800000000000003 - type: precision_at_3 value: 13.700000000000001 - type: precision_at_5 value: 11.379999999999999 - type: recall_at_1 value: 3.753 - type: recall_at_10 value: 15.806999999999999 - type: recall_at_100 value: 34.37 - type: recall_at_1000 value: 58.463 - type: recall_at_3 value: 8.338 - type: recall_at_5 value: 11.538 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 82.58843987639705 - type: cos_sim_spearman value: 76.33071660715956 - type: euclidean_pearson value: 72.8029921002978 - type: euclidean_spearman value: 69.34534284782808 - type: manhattan_pearson value: 72.49781034973653 - type: manhattan_spearman value: 69.24754112621694 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 83.31673079903189 - type: cos_sim_spearman value: 74.27699263517789 - type: euclidean_pearson value: 69.4008910999579 - type: euclidean_spearman value: 59.0716984643048 - type: manhattan_pearson value: 68.87342686919199 - type: manhattan_spearman value: 58.904612865335025 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 77.59122302327788 - type: cos_sim_spearman value: 78.55383586979005 - type: euclidean_pearson value: 68.18338642204289 - type: euclidean_spearman value: 68.95092864180276 - type: manhattan_pearson value: 68.08807059822706 - type: manhattan_spearman value: 68.86135938270193 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 78.51766841424501 - type: cos_sim_spearman value: 73.84318001499558 - type: euclidean_pearson value: 67.2007138855177 - type: euclidean_spearman value: 63.98672842723766 - type: manhattan_pearson value: 67.17773810895949 - type: manhattan_spearman value: 64.07359154832962 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 82.73438541570299 - type: cos_sim_spearman value: 83.71357922283677 - type: euclidean_pearson value: 57.50131347498546 - type: euclidean_spearman value: 57.73623619252132 - type: manhattan_pearson value: 58.082992079000725 - type: manhattan_spearman value: 58.42728201167522 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 78.14794654172421 - type: cos_sim_spearman value: 80.025736165043 - type: euclidean_pearson value: 65.87773913985473 - type: euclidean_spearman value: 66.69337751784794 - type: manhattan_pearson value: 66.01039761004415 - type: manhattan_spearman value: 66.89215027952318 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.10554507136152 - type: cos_sim_spearman value: 87.4898082140765 - type: euclidean_pearson value: 72.19391114541367 - type: euclidean_spearman value: 70.36647944993783 - type: manhattan_pearson value: 72.18680758133698 - type: manhattan_spearman value: 70.3871215447305 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 64.54868111501618 - type: cos_sim_spearman value: 64.25173617448473 - type: euclidean_pearson value: 39.116088900637116 - type: euclidean_spearman value: 53.300772929884 - type: manhattan_pearson value: 38.3844195287959 - type: manhattan_spearman value: 52.846675312001246 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 80.04396610550214 - type: cos_sim_spearman value: 79.19504854997832 - type: euclidean_pearson value: 66.3284657637072 - type: euclidean_spearman value: 63.69531796729492 - type: manhattan_pearson value: 66.82324081038026 - type: manhattan_spearman value: 64.18254512904923 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 74.16264051781705 - type: mrr value: 91.80864796060874 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 38.983000000000004 - type: map_at_10 value: 47.858000000000004 - type: map_at_100 value: 48.695 - type: map_at_1000 value: 48.752 - type: map_at_3 value: 45.444 - type: map_at_5 value: 46.906 - type: mrr_at_1 value: 41.333 - type: mrr_at_10 value: 49.935 - type: mrr_at_100 value: 50.51 - type: mrr_at_1000 value: 50.55500000000001 - type: mrr_at_3 value: 47.833 - type: mrr_at_5 value: 49.117 - type: ndcg_at_1 value: 41.333 - type: ndcg_at_10 value: 52.398999999999994 - type: ndcg_at_100 value: 56.196 - type: ndcg_at_1000 value: 57.838 - type: ndcg_at_3 value: 47.987 - type: ndcg_at_5 value: 50.356 - type: precision_at_1 value: 41.333 - type: precision_at_10 value: 7.167 - type: precision_at_100 value: 0.9299999999999999 - type: precision_at_1000 value: 0.108 - type: precision_at_3 value: 19.0 - type: precision_at_5 value: 12.8 - type: recall_at_1 value: 38.983000000000004 - type: recall_at_10 value: 64.183 - type: recall_at_100 value: 82.02199999999999 - type: recall_at_1000 value: 95.167 - type: recall_at_3 value: 52.383 - type: recall_at_5 value: 58.411 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.8019801980198 - type: cos_sim_ap value: 94.9287554635848 - type: cos_sim_f1 value: 89.83739837398375 - type: cos_sim_precision value: 91.32231404958677 - type: cos_sim_recall value: 88.4 - type: dot_accuracy value: 99.23762376237623 - type: dot_ap value: 55.22534191245801 - type: dot_f1 value: 54.054054054054056 - type: dot_precision value: 55.15088449531738 - type: dot_recall value: 53.0 - type: euclidean_accuracy value: 99.6108910891089 - type: euclidean_ap value: 82.5195111329438 - type: euclidean_f1 value: 78.2847718526663 - type: euclidean_precision value: 86.93528693528694 - type: euclidean_recall value: 71.2 - type: manhattan_accuracy value: 99.5970297029703 - type: manhattan_ap value: 81.96876777875492 - type: manhattan_f1 value: 77.33773377337734 - type: manhattan_precision value: 85.94132029339853 - type: manhattan_recall value: 70.3 - type: max_accuracy value: 99.8019801980198 - type: max_ap value: 94.9287554635848 - type: max_f1 value: 89.83739837398375 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 46.34997003954114 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 31.462336020554893 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 47.1757817459526 - type: mrr value: 47.941057104660054 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.56106249068471 - type: cos_sim_spearman value: 31.24613190558528 - type: dot_pearson value: 20.486610035794257 - type: dot_spearman value: 23.115667545894546 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.182 - type: map_at_10 value: 1.155 - type: map_at_100 value: 5.118 - type: map_at_1000 value: 11.827 - type: map_at_3 value: 0.482 - type: map_at_5 value: 0.712 - type: mrr_at_1 value: 70.0 - type: mrr_at_10 value: 79.483 - type: mrr_at_100 value: 79.637 - type: mrr_at_1000 value: 79.637 - type: mrr_at_3 value: 77.667 - type: mrr_at_5 value: 78.567 - type: ndcg_at_1 value: 63.0 - type: ndcg_at_10 value: 52.303 - type: ndcg_at_100 value: 37.361 - type: ndcg_at_1000 value: 32.84 - type: ndcg_at_3 value: 58.274 - type: ndcg_at_5 value: 55.601 - type: precision_at_1 value: 70.0 - type: precision_at_10 value: 55.60000000000001 - type: precision_at_100 value: 37.96 - type: precision_at_1000 value: 14.738000000000001 - type: precision_at_3 value: 62.666999999999994 - type: precision_at_5 value: 60.0 - type: recall_at_1 value: 0.182 - type: recall_at_10 value: 1.4120000000000001 - type: recall_at_100 value: 8.533 - type: recall_at_1000 value: 30.572 - type: recall_at_3 value: 0.5309999999999999 - type: recall_at_5 value: 0.814 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.385 - type: map_at_10 value: 7.185999999999999 - type: map_at_100 value: 11.642 - type: map_at_1000 value: 12.953000000000001 - type: map_at_3 value: 3.496 - type: map_at_5 value: 4.82 - type: mrr_at_1 value: 16.326999999999998 - type: mrr_at_10 value: 29.461 - type: mrr_at_100 value: 31.436999999999998 - type: mrr_at_1000 value: 31.436999999999998 - type: mrr_at_3 value: 24.490000000000002 - type: mrr_at_5 value: 27.857 - type: ndcg_at_1 value: 14.285999999999998 - type: ndcg_at_10 value: 16.672 - type: ndcg_at_100 value: 28.691 - type: ndcg_at_1000 value: 39.817 - type: ndcg_at_3 value: 15.277 - type: ndcg_at_5 value: 15.823 - type: precision_at_1 value: 16.326999999999998 - type: precision_at_10 value: 15.509999999999998 - type: precision_at_100 value: 6.49 - type: precision_at_1000 value: 1.4080000000000001 - type: precision_at_3 value: 16.326999999999998 - type: precision_at_5 value: 16.735 - type: recall_at_1 value: 1.385 - type: recall_at_10 value: 12.586 - type: recall_at_100 value: 40.765 - type: recall_at_1000 value: 75.198 - type: recall_at_3 value: 4.326 - type: recall_at_5 value: 7.074999999999999 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 59.4402 - type: ap value: 10.16922814263879 - type: f1 value: 45.374485104940476 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 54.25863044708545 - type: f1 value: 54.20154252609619 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 34.3883169293051 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 81.76670441676104 - type: cos_sim_ap value: 59.29878710961347 - type: cos_sim_f1 value: 57.33284971587474 - type: cos_sim_precision value: 52.9122963624191 - type: cos_sim_recall value: 62.559366754617415 - type: dot_accuracy value: 77.52279907015557 - type: dot_ap value: 34.17588904643467 - type: dot_f1 value: 41.063567529494634 - type: dot_precision value: 30.813953488372093 - type: dot_recall value: 61.53034300791557 - type: euclidean_accuracy value: 80.61631996185254 - type: euclidean_ap value: 54.00362361479352 - type: euclidean_f1 value: 53.99111751290361 - type: euclidean_precision value: 49.52653600528518 - type: euclidean_recall value: 59.340369393139845 - type: manhattan_accuracy value: 80.65208320915539 - type: manhattan_ap value: 54.18329507159467 - type: manhattan_f1 value: 53.85550960836779 - type: manhattan_precision value: 49.954873646209386 - type: manhattan_recall value: 58.41688654353562 - type: max_accuracy value: 81.76670441676104 - type: max_ap value: 59.29878710961347 - type: max_f1 value: 57.33284971587474 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.99433383785463 - type: cos_sim_ap value: 83.43513915159009 - type: cos_sim_f1 value: 76.3906784964842 - type: cos_sim_precision value: 73.19223985890653 - type: cos_sim_recall value: 79.88142901139513 - type: dot_accuracy value: 81.96142352621571 - type: dot_ap value: 67.78764755689359 - type: dot_f1 value: 64.42823356983445 - type: dot_precision value: 56.77801913931779 - type: dot_recall value: 74.46104096088698 - type: euclidean_accuracy value: 81.9478402607987 - type: euclidean_ap value: 67.13958457373279 - type: euclidean_f1 value: 60.45118343195266 - type: euclidean_precision value: 58.1625391403359 - type: euclidean_recall value: 62.92731752386819 - type: manhattan_accuracy value: 82.01769705437188 - type: manhattan_ap value: 67.24709477497046 - type: manhattan_f1 value: 60.4103846436714 - type: manhattan_precision value: 57.82063916654935 - type: manhattan_recall value: 63.24299353249153 - type: max_accuracy value: 87.99433383785463 - type: max_ap value: 83.43513915159009 - type: max_f1 value: 76.3906784964842 --- # # Fast-Inference with Ctranslate2 Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU. quantized version of [jinaai/jina-embedding-s-en-v1](https://huggingface.co/jinaai/jina-embedding-s-en-v1) ```bash pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1 ``` ```python # from transformers import AutoTokenizer model_name = "michaelfeil/ct2fast-jina-embedding-s-en-v1" model_name_orig="jinaai/jina-embedding-s-en-v1" from hf_hub_ctranslate2 import EncoderCT2fromHfHub model = EncoderCT2fromHfHub( # load in int8 on CUDA model_name_or_path=model_name, device="cuda", compute_type="int8_float16" ) outputs = model.generate( text=["I like soccer", "I like tennis", "The eiffel tower is in Paris"], max_length=64, ) # perform downstream tasks on outputs outputs["pooler_output"] outputs["last_hidden_state"] outputs["attention_mask"] # alternative, use SentenceTransformer Mix-In # for end-to-end Sentence embeddings generation # (not pulling from this CT2fast-HF repo) from hf_hub_ctranslate2 import CT2SentenceTransformer model = CT2SentenceTransformer( model_name_orig, compute_type="int8_float16", device="cuda" ) embeddings = model.encode( ["I like soccer", "I like tennis", "The eiffel tower is in Paris"], batch_size=32, convert_to_numpy=True, normalize_embeddings=True, ) print(embeddings.shape, embeddings) scores = (embeddings @ embeddings.T) * 100 # Hint: you can also host this code via REST API and # via github.com/michaelfeil/infinity ``` Checkpoint compatible to [ctranslate2>=3.17.1](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2) - `compute_type=int8_float16` for `device="cuda"` - `compute_type=int8` for `device="cpu"` Converted on 2023-10-13 using ``` LLama-2 -> removed <pad> token. ``` # Licence and other remarks: This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo. # Original description <br><br> <p align="center"> <img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px"> </p> <p align="center"> <b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>, <a href="https://github.com/jina-ai/finetuner"><b>Finetuner</b></a> team.</b> </p> ## Intented Usage & Model Info `jina-embedding-s-en-v1` is a language model that has been trained using Jina AI's Linnaeus-Clean dataset. This dataset consists of 380 million pairs of sentences, which include both query-document pairs. These pairs were obtained from various domains and were carefully selected through a thorough cleaning process. The Linnaeus-Full dataset, from which the Linnaeus-Clean dataset is derived, originally contained 1.6 billion sentence pairs. The model has a range of use cases, including information retrieval, semantic textual similarity, text reranking, and more. With a compact size of just 35 million parameters, the model enables lightning-fast inference while still delivering impressive performance. Additionally, we provide the following options: - [`jina-embedding-t-en-v1`](https://huggingface.co/jinaai/jina-embedding-t-en-v1): 14 million parameters. - [`jina-embedding-s-en-v1`](https://huggingface.co/jinaai/jina-embedding-s-en-v1): 35 million parameters **(you are here)**. - [`jina-embedding-b-en-v1`](https://huggingface.co/jinaai/jina-embedding-b-en-v1): 110 million parameters. - [`jina-embedding-l-en-v1`](https://huggingface.co/jinaai/jina-embedding-l-en-v1): 330 million parameters. - `jina-embedding-1b-en-v1`: 1.2 billion parameters, 10 times bert-base (soon). - `jina-embedding-6b-en-v1`: 6 billion parameters, 30 times bert-base (soon). ## Data & Parameters Please checkout our [technical blog](https://arxiv.org/abs/2307.11224). ## Metrics We compared the model against `all-minilm-l6-v2`/`all-mpnet-base-v2` from sbert and `text-embeddings-ada-002` from OpenAI: |Name|param |dimension| |------------------------------|-----|------| |all-minilm-l6-v2|23m |384| |all-mpnet-base-v2 |110m |768| |ada-embedding-002|Unknown/OpenAI API |1536| |jina-embedding-t-en-v1|14m |312| |jina-embedding-s-en-v1|35m |512| |jina-embedding-b-en-v1|110m |768| |jina-embedding-l-en-v1|330m |1024| |Name|STS12|STS13|STS14|STS15|STS16|STS17|TRECOVID|Quora|SciFact| |------------------------------|-----|-----|-----|-----|-----|-----|--------|-----|-----| |all-minilm-l6-v2|0.724|0.806|0.756|0.854|0.79 |0.876|0.473 |0.876|0.645 | |all-mpnet-base-v2|0.726|**0.835**|0.78 |0.857|0.8 |**0.906**|0.513 |0.875|0.656 | |ada-embedding-002|0.698|0.833|0.761|0.861|**0.86** |0.903|**0.685** |0.876|**0.726** | |jina-embedding-t-en-v1|0.717|0.773|0.731|0.829|0.777|0.860|0.482 |0.840|0.522 | |jina-embedding-s-en-v1|0.743|0.786|0.738|0.837|0.80|0.875|0.523 |0.857|0.524 | |jina-embedding-b-en-v1|**0.751**|0.809|0.761|0.856|0.812|0.890|0.606 |0.876|0.594 | |jina-embedding-l-en-v1|0.745|0.832|**0.781**|**0.869**|0.837|0.902|0.573 |**0.881**|0.598 | ## Usage Use with Jina AI Finetuner ```python !pip install finetuner import finetuner model = finetuner.build_model('jinaai/jina-embedding-s-en-v1') embeddings = finetuner.encode( model=model, data=['how is the weather today', 'What is the current weather like today?'] ) print(finetuner.cos_sim(embeddings[0], embeddings[1])) ``` Use with sentence-transformers: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = ['how is the weather today', 'What is the current weather like today?'] model = SentenceTransformer('jinaai/jina-embedding-s-en-v1') embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` ## Fine-tuning Please consider [Finetuner](https://github.com/jina-ai/finetuner). ## Plans 1. The development of `jina-embedding-s-en-v2` is currently underway with two main objectives: improving performance and increasing the maximum sequence length. 2. We are currently working on a bilingual embedding model that combines English and X language. The upcoming model will be called `jina-embedding-s/b/l-de-v1`. ## Contact Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. ## Citation If you find Jina Embeddings useful in your research, please cite the following paper: ``` latex @misc{günther2023jina, title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models}, author={Michael Günther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao}, year={2023}, eprint={2307.11224}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
[ "SUMMARIZATION" ]
[ "BIOSSES", "LINNAEUS", "SCIFACT" ]
Non_BioNLP
pruas/BENT-PubMedBERT-NER-Organism
pruas
token-classification
[ "transformers", "pytorch", "bert", "token-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,673
1,709
32
3
--- language: - en license: apache-2.0 pipeline_tag: token-classification --- Named Entity Recognition (NER) model to recognize organism entities. Please cite our work: ``` @article{NILNKER2022, title = {NILINKER: Attention-based approach to NIL Entity Linking}, journal = {Journal of Biomedical Informatics}, volume = {132}, pages = {104137}, year = {2022}, issn = {1532-0464}, doi = {https://doi.org/10.1016/j.jbi.2022.104137}, url = {https://www.sciencedirect.com/science/article/pii/S1532046422001526}, author = {Pedro Ruas and Francisco M. Couto}, } ``` [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) fine-tuned on the following datasets: - [CellFinder](http://cellfinder.org/about/annotation/): entity type "species" - [CRAFT](https://github.com/UCDenver-ccp/CRAFT/tree/master/concept-annotation): entity type "NCBITaxon" - [MLEE](http://nactem.ac.uk/MLEE/):entity type "organism" - [LINNAEUS](http://linnaeus.sourceforge.net/) (train and dev sets): - [Species-800](https://species.jensenlab.org/) - [BioNLP11ID](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP11ID-species-IOB): entity type "Organism" - [BioNLP13CG](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BioNLP13CG-species-IOB): entity types "Organism", "Organism subdivision" - [miRNA-Test-Corpus](https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/download-mirna-test-corpus.html): entity type "species" - [Mantra](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4986661/pdf/ocv037.pdf):entity type "DISO"
[ "NAMED_ENTITY_RECOGNITION" ]
[ "CRAFT", "CELLFINDER", "LINNAEUS", "MLEE", "MIRNA" ]
BioNLP
RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-awq
RichardErkhov
null
[ "safetensors", "gemma", "4-bit", "awq", "region:us" ]
1,733
1,733
4
0
--- {} --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) vi-gemma-2b-RAG - AWQ - Model creator: https://huggingface.co/ricepaper/ - Original model: https://huggingface.co/ricepaper/vi-gemma-2b-RAG/ Original model description: --- base_model: unsloth/gemma-1.1-2b-it-bnb-4bit language: - en - vi license: apache-2.0 tags: - text-generation-inference - retrieval-augmented-generation - transformers - unsloth - gemma - trl - sft --- ## Model Card: vi-gemma-2b-RAG ### (English below) ### Tiếng Việt (Vietnamese) **Mô tả mô hình:** vi-gemma-2b-RAG là một mô hình ngôn ngữ lớn được tinh chỉnh từ mô hình cơ sở [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it) sử dụng kỹ thuật LoRA. Mô hình được huấn luyện trên tập dữ liệu tiếng Việt với mục tiêu cải thiện khả năng xử lý ngôn ngữ tiếng Việt và nâng cao hiệu suất cho các tác vụ truy xuất thông tin mở (Retrieval Augmented Generation - RAG). **Mục đích sử dụng:** Mô hình vi-gemma-2b-RAG phù hợp cho các tác vụ sau: * Trả lời câu hỏi dựa trên ngữ cảnh tiếng Việt. * Tóm tắt văn bản tiếng Việt. * Dịch máy tiếng Việt. * Và các tác vụ tạo văn bản tiếng Việt khác. **Giới hạn:** Mặc dù đã được tinh chỉnh cho tiếng Việt, vi-gemma-2b-RAG vẫn có thể gặp phải một số hạn chế: * Có thể tạo ra thông tin sai lệch hoặc không chính xác. * Có thể thể hiện thành kiến ​​hoặc quan điểm không phù hợp. * Hiệu suất có thể bị ảnh hưởng bởi chất lượng của dữ liệu đầu vào. **Cách sử dụng:** Dưới đây chúng tôi chia sẻ một số đoạn mã về cách bắt đầu nhanh chóng để sử dụng mô hình. Trước tiên, hãy đảm bảo đã cài đặt `pip install -U transformers`, sau đó sao chép đoạn mã từ phần có liên quan đến usecase của bạn. Chúng tôi khuyến nghị sử dụng `torch.bfloat16` làm mặc định. ```python # pip install transformers torch accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Khởi tạo tokenizer và model từ checkpoint đã lưu tokenizer = AutoTokenizer.from_pretrained("himmeow/vi-gemma-2b-RAG") model = AutoModelForCausalLM.from_pretrained( "himmeow/vi-gemma-2b-RAG", device_map="auto", torch_dtype=torch.bfloat16 ) # Sử dụng GPU nếu có if torch.cuda.is_available(): model.to("cuda") # Định dạng prompt cho model prompt = """ ### Instruction and Input: Dựa vào ngữ cảnh/tài liệu sau: {} Hãy trả lời câu hỏi: {} ### Response: {} """ # Chuẩn bị dữ liệu đầu vào input_data = """ Short Tandem Repeats (STRs) là các trình tự DNA lặp lại ngắn (2- 6 nucleotides) xuất hiện phổ biến trong hệ gen của con người. Các trình tự này có tính đa hình rất cao trong tự nhiên, điều này khiến các STRs trở thành những markers di truyền rất quan trọng trong nghiên cứu bản đồ gen người và chuẩn đoán bệnh lý di truyền cũng như xác định danh tính trong lĩnh vực pháp y. Các STRs trở nên phổ biến tại các phòng xét nghiệm pháp y bởi vì việc nhân bản và phân tích STRs chỉ cần lượng DNA rất thấp ngay cả khi ở dạng bị phân hủy việc đinh danh vẫn có thể được thực hiện thành công. Hơn nữa việc phát hiện và đánh giá sự nhiễm DNA mẫu trong các mẫu vật có thể được giải quyết nhanh với kết quả phân tích STRs. Ở Hoa Kỳ hiện nay, từ bộ 13 markers nay đã tăng lên 20 markers chính đang được sử dụng để tạo ra một cơ sở dữ liệu DNA trên toàn đất nước được gọi là The FBI Combined DNA Index System (Expaned CODIS). CODIS và các cơ sử dữ liệu DNA tương tự đang được sử dụng thực sự thành công trong việc liên kết các hồ sơ DNA từ các tội phạm và các bằng chứng hiện trường vụ án. Kết quả định danh STRs cũng được sử dụng để hỗ trợ hàng trăm nghìn trường hợp xét nghiệm huyết thống cha con mỗi năm' """ query = "Hãy cho tôi biết một số tính chất của STRs được dùng để làm gì?" # Định dạng input text input_text = prompt.format(input_data, query," ") # Mã hóa input text thành input ids input_ids = tokenizer(input_text, return_tensors="pt") # Sử dụng GPU cho input ids nếu có if torch.cuda.is_available(): input_ids = input_ids.to("cuda") # Tạo văn bản bằng model outputs = model.generate( **input_ids, max_new_tokens=500, no_repeat_ngram_size=5, # Ngăn chặn lặp lại các cụm từ 5 gram # do_sample=True, # Kích hoạt chế độ tạo văn bản dựa trên lấy mẫu. Trong chế độ này, model sẽ chọn ngẫu nhiên token tiếp theo dựa trên xác suất được tính từ phân phối xác suất của các token. # temperature=0.7, # Giảm temperature để kiểm soát tính ngẫu nhiên # early_stopping=True, # Dừng tạo văn bản khi tìm thấy kết thúc phù hợp ) # Giải mã và in kết quả print(tokenizer.decode(outputs[0])) ''' <bos> ### Instruction and Input: Dựa vào ngữ cảnh/tài liệu sau: Short Tandem Repeats (STRs) là các trình tự DNA lặp lại ngắn (2- 6 nucleotides) xuất hiện phổ biến trong hệ gen của con người. Các trình tự này có tính đa hình rất cao trong tự nhiên, điều này khiến các STRs trở thành những markers di truyền rất quan trọng trong nghiên cứu bản đồ gen người và chuẩn đoán bệnh lý di truyền cũng như xác định danh tính trong lĩnh vực pháp y. Các STRs trở nên phổ biến tại các phòng xét nghiệm pháp y bởi vì việc nhân bản và phân tích STRs chỉ cần lượng DNA rất thấp ngay cả khi ở dạng bị phân hủy việc đinh danh vẫn có thể được thực hiện thành công. Hơn nữa việc phát hiện và đánh giá sự nhiễm DNA mẫu trong các mẫu vật có thể được giải quyết nhanh với kết quả phân tích STRs. Ở Hoa Kỳ hiện nay, từ bộ 13 markers nay đã tăng lên 20 markers chính đang được sử dụng để tạo ra một cơ sở dữ liệu DNA trên toàn đất nước được gọi là The FBI Combined DNA Index System (Expaned CODIS). CODIS và các cơ sử dữ liệu DNA tương tự đang được sử dụng thực sự thành công trong việc liên kết các hồ sơ DNA từ các tội phạm và các bằng chứng hiện trường vụ án. Kết quả định danh STRs cũng được sử dụng để hỗ trợ hàng trăm nghìn trường hợp xét nghiệm huyết thống cha con mỗi năm' Hãy trả lời câu hỏi: Hãy cho tôi biết một số tính chất của STRs được dùng để làm gì? ### Response: STRs được sử dụng để xác định danh tính, chuẩn đoán bệnh lý và xác định bệnh lý di truyền. <eos> ''' ``` **Huấn luyện:** * **Mô hình cơ sở:** google/gemma-1.1-2b-it * **Tập dữ liệu:** lamhieu/mabrycodes_dialogue_vi * **Phương pháp tinh chỉnh:** LoRA, PEFT với Unsloth ## Model Card: vi-gemma-2b-RAG ### English **Model Description:** vi-gemma-2b-RAG is a large language model fine-tuned from the base model [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it) using LoRA. The model is trained on a Vietnamese dataset to improve its Vietnamese language processing capabilities and enhance its performance for Retrieval Augmented Generation (RAG) tasks. **Intended Use:** The vi-gemma-2b-RAG model is suitable for tasks such as: * Vietnamese question answering. * Vietnamese text summarization. * Vietnamese machine translation. * And other Vietnamese text generation tasks. **Limitations:** While fine-tuned for Vietnamese, vi-gemma-2b-RAG may still have some limitations: * It may generate incorrect or misleading information. * It may exhibit biases or inappropriate opinions. * Its performance may be affected by the quality of the input data. **How to Use:** ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. We recommend `torch.bfloat16` as the default dtype. ```python # pip install transformers torch accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Initialize the tokenizer and model from the saved checkpoint tokenizer = AutoTokenizer.from_pretrained("himmeow/vi-gemma-2b-RAG") model = AutoModelForCausalLM.from_pretrained( "himmeow/vi-gemma-2b-RAG", device_map="auto", torch_dtype=torch.bfloat16 ) # Use GPU if available if torch.cuda.is_available(): model.to("cuda") # Define the prompt format for the model prompt = """ ### Instruction and Input: Based on the following context/document: {} Please answer the question: {} ### Response: {} """ # Prepare the input data input_data = """ Short Tandem Repeats (STRs) are short (2-6 nucleotides) repeating DNA sequences that are widespread in the human genome. These sequences are highly polymorphic in nature, which makes STRs very important genetic markers in human gene mapping and diagnosis of hereditary diseases as well as identification in the field of forensics. STRs have become popular in forensic laboratories because the replication and analysis of STRs requires very small amounts of DNA, even in decomposed form, identification can still be performed successfully. Furthermore, the detection and assessment of sample DNA contamination in specimens can be quickly resolved with STR analysis results. In the United States today, the set of 13 markers has now been increased to 20 main markers being used to create a nationwide DNA database called The FBI Combined DNA Index System (Expaned CODIS). CODIS and similar DNA databases are being used very successfully in linking DNA records from criminals and crime scene evidence. STR identification results are also used to support hundreds of thousands of paternity test cases each year.' """ query = "Tell me what are some properties of STRs used for?" # Format the input text input_text = prompt.format(input_data, query," ") # Encode the input text into input ids input_ids = tokenizer(input_text, return_tensors="pt") # Use GPU for input ids if available if torch.cuda.is_available(): input_ids = input_ids.to("cuda") # Generate text using the model outputs = model.generate( **input_ids, max_new_tokens=500, # Limit the number of tokens generated no_repeat_ngram_size=5, # Prevent repetition of 5-gram phrases # do_sample=True, # temperature=0.7, # Adjust the randomness of the generated text # early_stopping=True, # Stop generating text when a suitable ending is found ) # Decode and print the results print(tokenizer.decode(outputs[0])) ``` **Training:** * **Base Model:** google/gemma-1.1-2b-it * **Dataset:** lamhieu/mabrycodes_dialogue_vi * **Fine-tuning Method:** LoRA, PEFT and Unsloth **Using example repository:** https://github.com/Martincrux/Vietnamese-RAG-system-building-with-vi-gemma-2b-RAG-and-halong_embedding # Uploaded model - **Developed by:** [hiieu](https://huggingface.co/hiieu), [himmeow the coder](https://huggingface.co/himmeow), [cuctrinh](https://www.linkedin.com/in/trinh-cuc-5722832b6) - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-1.1-2b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
[ "QUESTION_ANSWERING", "TRANSLATION", "SUMMARIZATION" ]
[ "CHIA" ]
Non_BioNLP
RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-v0-4bits
RichardErkhov
text-generation
[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:2101.00027", "arxiv:2201.07311", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]
1,713
1,713
4
0
--- {} --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) pythia-2.8b-deduped-v0 - bnb 4bits - Model creator: https://huggingface.co/EleutherAI/ - Original model: https://huggingface.co/EleutherAI/pythia-2.8b-deduped-v0/ Original model description: --- language: - en tags: - pytorch - causal-lm - pythia - pythia_v0 license: apache-2.0 datasets: - EleutherAI/the_pile_deduplicated --- The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available [on Hugging Face](https://huggingface.co/models?other=pythia). The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Please note that all models in the *Pythia* suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. ## Pythia-2.8B-deduped ### Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing *Pythia* documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> | Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models | | -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: | | 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — | | 160M | 85,056,000 | 12 | 768 | 12 | 4M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M | | 410M | 302,311,424 | 24 | 1024 | 16 | 4M | 3.0 x 10<sup>-4</sup> | OPT-350M | | 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — | | 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 4M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B | | 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B | | 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B | | 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — | <figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have <b>exactly</b> the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ### Uses and Limitations #### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change in the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-2.8B-deduped for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-2.8B-deduped as a basis for your fine-tuned model, please conduct your own risk and bias assessment. #### Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-2.8B-deduped has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-2.8B-deduped will **not** respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions. #### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-2.8B-deduped to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-2.8B-deduped may produce socially unacceptable or undesirable text, *even if* the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-2.8B-deduped. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(**inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model.<br> For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ### Training #### Training data Pythia-2.8B-deduped was trained on the Pile **after the dataset has been globally deduplicated**.<br> [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). #### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All *Pythia* models trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, with checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for consistency with all 2M batch models, so `step1000` is the first checkpoint for `pythia-1.4b` that was saved (corresponding to step 500 in training), and `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved (corresponding to 1000 “actual” steps).<br> See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br> Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ### Evaluations All 16 *Pythia* models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/*` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json).<br> Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge – Challenge Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/> </details> ### Naming convention and parameter count *Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> | current Pythia suffix | old suffix | total params | non-embedding params | | --------------------: | ---------: | -------------: | -------------------: | | 70M | 19M | 70,426,624 | 18,915,328 | | 160M | 125M | 162,322,944 | 85,056,000 | | 410M | 350M | 405,334,016 | 302,311,424 | | 1B | 800M | 1,011,781,632 | 805,736,448 | | 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 | | 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 | | 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 | | 12B | 13B | 11,846,072,320 | 11,327,027,200 | </figure>
[ "QUESTION_ANSWERING", "TRANSLATION" ]
[ "SCIQ" ]
Non_BioNLP
walsons/jina-embeddings-v2-base-en-Q4_K_M-GGUF
walsons
feature-extraction
[ "sentence-transformers", "gguf", "feature-extraction", "sentence-similarity", "mteb", "llama-cpp", "gguf-my-repo", "en", "dataset:allenai/c4", "base_model:jinaai/jina-embeddings-v2-base-en", "base_model:quantized:jinaai/jina-embeddings-v2-base-en", "license:apache-2.0", "model-index", "autotrain_compatible", "region:us" ]
1,722
1,722
15
0
--- base_model: jinaai/jina-embeddings-v2-base-en datasets: - allenai/c4 language: en license: apache-2.0 tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - llama-cpp - gguf-my-repo inference: false model-index: - name: jina-embedding-b-en-v2 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 74.73134328358209 - type: ap value: 37.765427081831035 - type: f1 value: 68.79367444339518 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 88.544275 - type: ap value: 84.61328675662887 - type: f1 value: 88.51879035862375 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 45.263999999999996 - type: f1 value: 43.778759656699435 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 21.693 - type: map_at_10 value: 35.487 - type: map_at_100 value: 36.862 - type: map_at_1000 value: 36.872 - type: map_at_3 value: 30.049999999999997 - type: map_at_5 value: 32.966 - type: mrr_at_1 value: 21.977 - type: mrr_at_10 value: 35.565999999999995 - type: mrr_at_100 value: 36.948 - type: mrr_at_1000 value: 36.958 - type: mrr_at_3 value: 30.121 - type: mrr_at_5 value: 33.051 - type: ndcg_at_1 value: 21.693 - type: ndcg_at_10 value: 44.181 - type: ndcg_at_100 value: 49.982 - type: ndcg_at_1000 value: 50.233000000000004 - type: ndcg_at_3 value: 32.830999999999996 - type: ndcg_at_5 value: 38.080000000000005 - type: precision_at_1 value: 21.693 - type: precision_at_10 value: 7.248 - type: precision_at_100 value: 0.9769999999999999 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 13.632 - type: precision_at_5 value: 10.725 - type: recall_at_1 value: 21.693 - type: recall_at_10 value: 72.475 - type: recall_at_100 value: 97.653 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 40.896 - type: recall_at_5 value: 53.627 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 45.39242428696777 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 36.675626784714 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.247725694904034 - type: mrr value: 74.91359978894604 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 82.68003802970496 - type: cos_sim_spearman value: 81.23438110096286 - type: euclidean_pearson value: 81.87462986142582 - type: euclidean_spearman value: 81.23438110096286 - type: manhattan_pearson value: 81.61162566600755 - type: manhattan_spearman value: 81.11329400456184 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 84.01298701298701 - type: f1 value: 83.31690714969382 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.050108150972086 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 30.15731442819715 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 31.391999999999996 - type: map_at_10 value: 42.597 - type: map_at_100 value: 44.07 - type: map_at_1000 value: 44.198 - type: map_at_3 value: 38.957 - type: map_at_5 value: 40.961 - type: mrr_at_1 value: 37.196 - type: mrr_at_10 value: 48.152 - type: mrr_at_100 value: 48.928 - type: mrr_at_1000 value: 48.964999999999996 - type: mrr_at_3 value: 45.446 - type: mrr_at_5 value: 47.205999999999996 - type: ndcg_at_1 value: 37.196 - type: ndcg_at_10 value: 49.089 - type: ndcg_at_100 value: 54.471000000000004 - type: ndcg_at_1000 value: 56.385 - type: ndcg_at_3 value: 43.699 - type: ndcg_at_5 value: 46.22 - type: precision_at_1 value: 37.196 - type: precision_at_10 value: 9.313 - type: precision_at_100 value: 1.478 - type: precision_at_1000 value: 0.198 - type: precision_at_3 value: 20.839 - type: precision_at_5 value: 14.936 - type: recall_at_1 value: 31.391999999999996 - type: recall_at_10 value: 61.876 - type: recall_at_100 value: 84.214 - type: recall_at_1000 value: 95.985 - type: recall_at_3 value: 46.6 - type: recall_at_5 value: 53.588 - type: map_at_1 value: 29.083 - type: map_at_10 value: 38.812999999999995 - type: map_at_100 value: 40.053 - type: map_at_1000 value: 40.188 - type: map_at_3 value: 36.111 - type: map_at_5 value: 37.519000000000005 - type: mrr_at_1 value: 36.497 - type: mrr_at_10 value: 44.85 - type: mrr_at_100 value: 45.546 - type: mrr_at_1000 value: 45.593 - type: mrr_at_3 value: 42.686 - type: mrr_at_5 value: 43.909 - type: ndcg_at_1 value: 36.497 - type: ndcg_at_10 value: 44.443 - type: ndcg_at_100 value: 48.979 - type: ndcg_at_1000 value: 51.154999999999994 - type: ndcg_at_3 value: 40.660000000000004 - type: ndcg_at_5 value: 42.193000000000005 - type: precision_at_1 value: 36.497 - type: precision_at_10 value: 8.433 - type: precision_at_100 value: 1.369 - type: precision_at_1000 value: 0.185 - type: precision_at_3 value: 19.894000000000002 - type: precision_at_5 value: 13.873 - type: recall_at_1 value: 29.083 - type: recall_at_10 value: 54.313 - type: recall_at_100 value: 73.792 - type: recall_at_1000 value: 87.629 - type: recall_at_3 value: 42.257 - type: recall_at_5 value: 47.066 - type: map_at_1 value: 38.556000000000004 - type: map_at_10 value: 50.698 - type: map_at_100 value: 51.705 - type: map_at_1000 value: 51.768 - type: map_at_3 value: 47.848 - type: map_at_5 value: 49.358000000000004 - type: mrr_at_1 value: 43.95 - type: mrr_at_10 value: 54.191 - type: mrr_at_100 value: 54.852999999999994 - type: mrr_at_1000 value: 54.885 - type: mrr_at_3 value: 51.954 - type: mrr_at_5 value: 53.13 - type: ndcg_at_1 value: 43.95 - type: ndcg_at_10 value: 56.516 - type: ndcg_at_100 value: 60.477000000000004 - type: ndcg_at_1000 value: 61.746 - type: ndcg_at_3 value: 51.601 - type: ndcg_at_5 value: 53.795 - type: precision_at_1 value: 43.95 - type: precision_at_10 value: 9.009 - type: precision_at_100 value: 1.189 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 22.989 - type: precision_at_5 value: 15.473 - type: recall_at_1 value: 38.556000000000004 - type: recall_at_10 value: 70.159 - type: recall_at_100 value: 87.132 - type: recall_at_1000 value: 96.16 - type: recall_at_3 value: 56.906 - type: recall_at_5 value: 62.332 - type: map_at_1 value: 24.238 - type: map_at_10 value: 32.5 - type: map_at_100 value: 33.637 - type: map_at_1000 value: 33.719 - type: map_at_3 value: 30.026999999999997 - type: map_at_5 value: 31.555 - type: mrr_at_1 value: 26.328000000000003 - type: mrr_at_10 value: 34.44 - type: mrr_at_100 value: 35.455999999999996 - type: mrr_at_1000 value: 35.521 - type: mrr_at_3 value: 32.034 - type: mrr_at_5 value: 33.565 - type: ndcg_at_1 value: 26.328000000000003 - type: ndcg_at_10 value: 37.202 - type: ndcg_at_100 value: 42.728 - type: ndcg_at_1000 value: 44.792 - type: ndcg_at_3 value: 32.368 - type: ndcg_at_5 value: 35.008 - type: precision_at_1 value: 26.328000000000003 - type: precision_at_10 value: 5.7059999999999995 - type: precision_at_100 value: 0.8880000000000001 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 13.672 - type: precision_at_5 value: 9.74 - type: recall_at_1 value: 24.238 - type: recall_at_10 value: 49.829 - type: recall_at_100 value: 75.21 - type: recall_at_1000 value: 90.521 - type: recall_at_3 value: 36.867 - type: recall_at_5 value: 43.241 - type: map_at_1 value: 15.378 - type: map_at_10 value: 22.817999999999998 - type: map_at_100 value: 23.977999999999998 - type: map_at_1000 value: 24.108 - type: map_at_3 value: 20.719 - type: map_at_5 value: 21.889 - type: mrr_at_1 value: 19.03 - type: mrr_at_10 value: 27.022000000000002 - type: mrr_at_100 value: 28.011999999999997 - type: mrr_at_1000 value: 28.096 - type: mrr_at_3 value: 24.855 - type: mrr_at_5 value: 26.029999999999998 - type: ndcg_at_1 value: 19.03 - type: ndcg_at_10 value: 27.526 - type: ndcg_at_100 value: 33.040000000000006 - type: ndcg_at_1000 value: 36.187000000000005 - type: ndcg_at_3 value: 23.497 - type: ndcg_at_5 value: 25.334 - type: precision_at_1 value: 19.03 - type: precision_at_10 value: 4.963 - type: precision_at_100 value: 0.893 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 11.360000000000001 - type: precision_at_5 value: 8.134 - type: recall_at_1 value: 15.378 - type: recall_at_10 value: 38.061 - type: recall_at_100 value: 61.754 - type: recall_at_1000 value: 84.259 - type: recall_at_3 value: 26.788 - type: recall_at_5 value: 31.326999999999998 - type: map_at_1 value: 27.511999999999997 - type: map_at_10 value: 37.429 - type: map_at_100 value: 38.818000000000005 - type: map_at_1000 value: 38.924 - type: map_at_3 value: 34.625 - type: map_at_5 value: 36.064 - type: mrr_at_1 value: 33.300999999999995 - type: mrr_at_10 value: 43.036 - type: mrr_at_100 value: 43.894 - type: mrr_at_1000 value: 43.936 - type: mrr_at_3 value: 40.825 - type: mrr_at_5 value: 42.028 - type: ndcg_at_1 value: 33.300999999999995 - type: ndcg_at_10 value: 43.229 - type: ndcg_at_100 value: 48.992000000000004 - type: ndcg_at_1000 value: 51.02100000000001 - type: ndcg_at_3 value: 38.794000000000004 - type: ndcg_at_5 value: 40.65 - type: precision_at_1 value: 33.300999999999995 - type: precision_at_10 value: 7.777000000000001 - type: precision_at_100 value: 1.269 - type: precision_at_1000 value: 0.163 - type: precision_at_3 value: 18.351 - type: precision_at_5 value: 12.762 - type: recall_at_1 value: 27.511999999999997 - type: recall_at_10 value: 54.788000000000004 - type: recall_at_100 value: 79.105 - type: recall_at_1000 value: 92.49199999999999 - type: recall_at_3 value: 41.924 - type: recall_at_5 value: 47.026 - type: map_at_1 value: 24.117 - type: map_at_10 value: 33.32 - type: map_at_100 value: 34.677 - type: map_at_1000 value: 34.78 - type: map_at_3 value: 30.233999999999998 - type: map_at_5 value: 31.668000000000003 - type: mrr_at_1 value: 29.566 - type: mrr_at_10 value: 38.244 - type: mrr_at_100 value: 39.245000000000005 - type: mrr_at_1000 value: 39.296 - type: mrr_at_3 value: 35.864000000000004 - type: mrr_at_5 value: 36.919999999999995 - type: ndcg_at_1 value: 29.566 - type: ndcg_at_10 value: 39.127 - type: ndcg_at_100 value: 44.989000000000004 - type: ndcg_at_1000 value: 47.189 - type: ndcg_at_3 value: 34.039 - type: ndcg_at_5 value: 35.744 - type: precision_at_1 value: 29.566 - type: precision_at_10 value: 7.385999999999999 - type: precision_at_100 value: 1.204 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 16.286 - type: precision_at_5 value: 11.484 - type: recall_at_1 value: 24.117 - type: recall_at_10 value: 51.559999999999995 - type: recall_at_100 value: 77.104 - type: recall_at_1000 value: 91.79899999999999 - type: recall_at_3 value: 36.82 - type: recall_at_5 value: 41.453 - type: map_at_1 value: 25.17625 - type: map_at_10 value: 34.063916666666664 - type: map_at_100 value: 35.255500000000005 - type: map_at_1000 value: 35.37275 - type: map_at_3 value: 31.351666666666667 - type: map_at_5 value: 32.80608333333333 - type: mrr_at_1 value: 29.59783333333333 - type: mrr_at_10 value: 38.0925 - type: mrr_at_100 value: 38.957249999999995 - type: mrr_at_1000 value: 39.01608333333333 - type: mrr_at_3 value: 35.77625 - type: mrr_at_5 value: 37.04991666666667 - type: ndcg_at_1 value: 29.59783333333333 - type: ndcg_at_10 value: 39.343666666666664 - type: ndcg_at_100 value: 44.488249999999994 - type: ndcg_at_1000 value: 46.83358333333334 - type: ndcg_at_3 value: 34.69708333333333 - type: ndcg_at_5 value: 36.75075 - type: precision_at_1 value: 29.59783333333333 - type: precision_at_10 value: 6.884083333333332 - type: precision_at_100 value: 1.114 - type: precision_at_1000 value: 0.15108333333333332 - type: precision_at_3 value: 15.965250000000003 - type: precision_at_5 value: 11.246500000000001 - type: recall_at_1 value: 25.17625 - type: recall_at_10 value: 51.015999999999984 - type: recall_at_100 value: 73.60174999999998 - type: recall_at_1000 value: 89.849 - type: recall_at_3 value: 37.88399999999999 - type: recall_at_5 value: 43.24541666666666 - type: map_at_1 value: 24.537 - type: map_at_10 value: 31.081999999999997 - type: map_at_100 value: 32.042 - type: map_at_1000 value: 32.141 - type: map_at_3 value: 29.137 - type: map_at_5 value: 30.079 - type: mrr_at_1 value: 27.454 - type: mrr_at_10 value: 33.694 - type: mrr_at_100 value: 34.579 - type: mrr_at_1000 value: 34.649 - type: mrr_at_3 value: 32.004 - type: mrr_at_5 value: 32.794000000000004 - type: ndcg_at_1 value: 27.454 - type: ndcg_at_10 value: 34.915 - type: ndcg_at_100 value: 39.641 - type: ndcg_at_1000 value: 42.105 - type: ndcg_at_3 value: 31.276 - type: ndcg_at_5 value: 32.65 - type: precision_at_1 value: 27.454 - type: precision_at_10 value: 5.337 - type: precision_at_100 value: 0.8250000000000001 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 13.241 - type: precision_at_5 value: 8.895999999999999 - type: recall_at_1 value: 24.537 - type: recall_at_10 value: 44.324999999999996 - type: recall_at_100 value: 65.949 - type: recall_at_1000 value: 84.017 - type: recall_at_3 value: 33.857 - type: recall_at_5 value: 37.316 - type: map_at_1 value: 17.122 - type: map_at_10 value: 24.32 - type: map_at_100 value: 25.338 - type: map_at_1000 value: 25.462 - type: map_at_3 value: 22.064 - type: map_at_5 value: 23.322000000000003 - type: mrr_at_1 value: 20.647 - type: mrr_at_10 value: 27.858 - type: mrr_at_100 value: 28.743999999999996 - type: mrr_at_1000 value: 28.819 - type: mrr_at_3 value: 25.769 - type: mrr_at_5 value: 26.964 - type: ndcg_at_1 value: 20.647 - type: ndcg_at_10 value: 28.849999999999998 - type: ndcg_at_100 value: 33.849000000000004 - type: ndcg_at_1000 value: 36.802 - type: ndcg_at_3 value: 24.799 - type: ndcg_at_5 value: 26.682 - type: precision_at_1 value: 20.647 - type: precision_at_10 value: 5.2170000000000005 - type: precision_at_100 value: 0.906 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 11.769 - type: precision_at_5 value: 8.486 - type: recall_at_1 value: 17.122 - type: recall_at_10 value: 38.999 - type: recall_at_100 value: 61.467000000000006 - type: recall_at_1000 value: 82.716 - type: recall_at_3 value: 27.601 - type: recall_at_5 value: 32.471 - type: map_at_1 value: 24.396 - type: map_at_10 value: 33.415 - type: map_at_100 value: 34.521 - type: map_at_1000 value: 34.631 - type: map_at_3 value: 30.703999999999997 - type: map_at_5 value: 32.166 - type: mrr_at_1 value: 28.825 - type: mrr_at_10 value: 37.397000000000006 - type: mrr_at_100 value: 38.286 - type: mrr_at_1000 value: 38.346000000000004 - type: mrr_at_3 value: 35.028 - type: mrr_at_5 value: 36.32 - type: ndcg_at_1 value: 28.825 - type: ndcg_at_10 value: 38.656 - type: ndcg_at_100 value: 43.856 - type: ndcg_at_1000 value: 46.31 - type: ndcg_at_3 value: 33.793 - type: ndcg_at_5 value: 35.909 - type: precision_at_1 value: 28.825 - type: precision_at_10 value: 6.567 - type: precision_at_100 value: 1.0330000000000001 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 15.516 - type: precision_at_5 value: 10.914 - type: recall_at_1 value: 24.396 - type: recall_at_10 value: 50.747 - type: recall_at_100 value: 73.477 - type: recall_at_1000 value: 90.801 - type: recall_at_3 value: 37.1 - type: recall_at_5 value: 42.589 - type: map_at_1 value: 25.072 - type: map_at_10 value: 34.307 - type: map_at_100 value: 35.725 - type: map_at_1000 value: 35.943999999999996 - type: map_at_3 value: 30.906 - type: map_at_5 value: 32.818000000000005 - type: mrr_at_1 value: 29.644 - type: mrr_at_10 value: 38.673 - type: mrr_at_100 value: 39.459 - type: mrr_at_1000 value: 39.527 - type: mrr_at_3 value: 35.771 - type: mrr_at_5 value: 37.332 - type: ndcg_at_1 value: 29.644 - type: ndcg_at_10 value: 40.548 - type: ndcg_at_100 value: 45.678999999999995 - type: ndcg_at_1000 value: 48.488 - type: ndcg_at_3 value: 34.887 - type: ndcg_at_5 value: 37.543 - type: precision_at_1 value: 29.644 - type: precision_at_10 value: 7.688000000000001 - type: precision_at_100 value: 1.482 - type: precision_at_1000 value: 0.23600000000000002 - type: precision_at_3 value: 16.206 - type: precision_at_5 value: 12.016 - type: recall_at_1 value: 25.072 - type: recall_at_10 value: 53.478 - type: recall_at_100 value: 76.07300000000001 - type: recall_at_1000 value: 93.884 - type: recall_at_3 value: 37.583 - type: recall_at_5 value: 44.464 - type: map_at_1 value: 20.712 - type: map_at_10 value: 27.467999999999996 - type: map_at_100 value: 28.502 - type: map_at_1000 value: 28.610000000000003 - type: map_at_3 value: 24.887999999999998 - type: map_at_5 value: 26.273999999999997 - type: mrr_at_1 value: 22.736 - type: mrr_at_10 value: 29.553 - type: mrr_at_100 value: 30.485 - type: mrr_at_1000 value: 30.56 - type: mrr_at_3 value: 27.078999999999997 - type: mrr_at_5 value: 28.401 - type: ndcg_at_1 value: 22.736 - type: ndcg_at_10 value: 32.023 - type: ndcg_at_100 value: 37.158 - type: ndcg_at_1000 value: 39.823 - type: ndcg_at_3 value: 26.951999999999998 - type: ndcg_at_5 value: 29.281000000000002 - type: precision_at_1 value: 22.736 - type: precision_at_10 value: 5.213 - type: precision_at_100 value: 0.832 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 11.459999999999999 - type: precision_at_5 value: 8.244 - type: recall_at_1 value: 20.712 - type: recall_at_10 value: 44.057 - type: recall_at_100 value: 67.944 - type: recall_at_1000 value: 87.925 - type: recall_at_3 value: 30.305 - type: recall_at_5 value: 36.071999999999996 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 10.181999999999999 - type: map_at_10 value: 16.66 - type: map_at_100 value: 18.273 - type: map_at_1000 value: 18.45 - type: map_at_3 value: 14.141 - type: map_at_5 value: 15.455 - type: mrr_at_1 value: 22.15 - type: mrr_at_10 value: 32.062000000000005 - type: mrr_at_100 value: 33.116 - type: mrr_at_1000 value: 33.168 - type: mrr_at_3 value: 28.827 - type: mrr_at_5 value: 30.892999999999997 - type: ndcg_at_1 value: 22.15 - type: ndcg_at_10 value: 23.532 - type: ndcg_at_100 value: 30.358 - type: ndcg_at_1000 value: 33.783 - type: ndcg_at_3 value: 19.222 - type: ndcg_at_5 value: 20.919999999999998 - type: precision_at_1 value: 22.15 - type: precision_at_10 value: 7.185999999999999 - type: precision_at_100 value: 1.433 - type: precision_at_1000 value: 0.207 - type: precision_at_3 value: 13.941 - type: precision_at_5 value: 10.906 - type: recall_at_1 value: 10.181999999999999 - type: recall_at_10 value: 28.104000000000003 - type: recall_at_100 value: 51.998999999999995 - type: recall_at_1000 value: 71.311 - type: recall_at_3 value: 17.698 - type: recall_at_5 value: 22.262999999999998 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 6.669 - type: map_at_10 value: 15.552 - type: map_at_100 value: 21.865000000000002 - type: map_at_1000 value: 23.268 - type: map_at_3 value: 11.309 - type: map_at_5 value: 13.084000000000001 - type: mrr_at_1 value: 55.50000000000001 - type: mrr_at_10 value: 66.46600000000001 - type: mrr_at_100 value: 66.944 - type: mrr_at_1000 value: 66.956 - type: mrr_at_3 value: 64.542 - type: mrr_at_5 value: 65.717 - type: ndcg_at_1 value: 44.75 - type: ndcg_at_10 value: 35.049 - type: ndcg_at_100 value: 39.073 - type: ndcg_at_1000 value: 46.208 - type: ndcg_at_3 value: 39.525 - type: ndcg_at_5 value: 37.156 - type: precision_at_1 value: 55.50000000000001 - type: precision_at_10 value: 27.800000000000004 - type: precision_at_100 value: 9.013 - type: precision_at_1000 value: 1.8800000000000001 - type: precision_at_3 value: 42.667 - type: precision_at_5 value: 36.0 - type: recall_at_1 value: 6.669 - type: recall_at_10 value: 21.811 - type: recall_at_100 value: 45.112 - type: recall_at_1000 value: 67.806 - type: recall_at_3 value: 13.373 - type: recall_at_5 value: 16.615 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.769999999999996 - type: f1 value: 42.91448356376592 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 54.013 - type: map_at_10 value: 66.239 - type: map_at_100 value: 66.62599999999999 - type: map_at_1000 value: 66.644 - type: map_at_3 value: 63.965 - type: map_at_5 value: 65.45400000000001 - type: mrr_at_1 value: 58.221000000000004 - type: mrr_at_10 value: 70.43700000000001 - type: mrr_at_100 value: 70.744 - type: mrr_at_1000 value: 70.75099999999999 - type: mrr_at_3 value: 68.284 - type: mrr_at_5 value: 69.721 - type: ndcg_at_1 value: 58.221000000000004 - type: ndcg_at_10 value: 72.327 - type: ndcg_at_100 value: 73.953 - type: ndcg_at_1000 value: 74.312 - type: ndcg_at_3 value: 68.062 - type: ndcg_at_5 value: 70.56400000000001 - type: precision_at_1 value: 58.221000000000004 - type: precision_at_10 value: 9.521 - type: precision_at_100 value: 1.045 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 27.348 - type: precision_at_5 value: 17.794999999999998 - type: recall_at_1 value: 54.013 - type: recall_at_10 value: 86.957 - type: recall_at_100 value: 93.911 - type: recall_at_1000 value: 96.38 - type: recall_at_3 value: 75.555 - type: recall_at_5 value: 81.671 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 21.254 - type: map_at_10 value: 33.723 - type: map_at_100 value: 35.574 - type: map_at_1000 value: 35.730000000000004 - type: map_at_3 value: 29.473 - type: map_at_5 value: 31.543 - type: mrr_at_1 value: 41.358 - type: mrr_at_10 value: 49.498 - type: mrr_at_100 value: 50.275999999999996 - type: mrr_at_1000 value: 50.308 - type: mrr_at_3 value: 47.016000000000005 - type: mrr_at_5 value: 48.336 - type: ndcg_at_1 value: 41.358 - type: ndcg_at_10 value: 41.579 - type: ndcg_at_100 value: 48.455 - type: ndcg_at_1000 value: 51.165000000000006 - type: ndcg_at_3 value: 37.681 - type: ndcg_at_5 value: 38.49 - type: precision_at_1 value: 41.358 - type: precision_at_10 value: 11.543000000000001 - type: precision_at_100 value: 1.87 - type: precision_at_1000 value: 0.23600000000000002 - type: precision_at_3 value: 24.743000000000002 - type: precision_at_5 value: 17.994 - type: recall_at_1 value: 21.254 - type: recall_at_10 value: 48.698 - type: recall_at_100 value: 74.588 - type: recall_at_1000 value: 91.00200000000001 - type: recall_at_3 value: 33.939 - type: recall_at_5 value: 39.367000000000004 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 35.922 - type: map_at_10 value: 52.32599999999999 - type: map_at_100 value: 53.18000000000001 - type: map_at_1000 value: 53.245 - type: map_at_3 value: 49.294 - type: map_at_5 value: 51.202999999999996 - type: mrr_at_1 value: 71.843 - type: mrr_at_10 value: 78.24600000000001 - type: mrr_at_100 value: 78.515 - type: mrr_at_1000 value: 78.527 - type: mrr_at_3 value: 77.17500000000001 - type: mrr_at_5 value: 77.852 - type: ndcg_at_1 value: 71.843 - type: ndcg_at_10 value: 61.379 - type: ndcg_at_100 value: 64.535 - type: ndcg_at_1000 value: 65.888 - type: ndcg_at_3 value: 56.958 - type: ndcg_at_5 value: 59.434 - type: precision_at_1 value: 71.843 - type: precision_at_10 value: 12.686 - type: precision_at_100 value: 1.517 - type: precision_at_1000 value: 0.16999999999999998 - type: precision_at_3 value: 35.778 - type: precision_at_5 value: 23.422 - type: recall_at_1 value: 35.922 - type: recall_at_10 value: 63.43 - type: recall_at_100 value: 75.868 - type: recall_at_1000 value: 84.88900000000001 - type: recall_at_3 value: 53.666000000000004 - type: recall_at_5 value: 58.555 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 79.4408 - type: ap value: 73.52820871620366 - type: f1 value: 79.36240238685001 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 21.826999999999998 - type: map_at_10 value: 34.04 - type: map_at_100 value: 35.226 - type: map_at_1000 value: 35.275 - type: map_at_3 value: 30.165999999999997 - type: map_at_5 value: 32.318000000000005 - type: mrr_at_1 value: 22.464000000000002 - type: mrr_at_10 value: 34.631 - type: mrr_at_100 value: 35.752 - type: mrr_at_1000 value: 35.795 - type: mrr_at_3 value: 30.798 - type: mrr_at_5 value: 32.946999999999996 - type: ndcg_at_1 value: 22.464000000000002 - type: ndcg_at_10 value: 40.919 - type: ndcg_at_100 value: 46.632 - type: ndcg_at_1000 value: 47.833 - type: ndcg_at_3 value: 32.992 - type: ndcg_at_5 value: 36.834 - type: precision_at_1 value: 22.464000000000002 - type: precision_at_10 value: 6.494 - type: precision_at_100 value: 0.9369999999999999 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.021 - type: precision_at_5 value: 10.347000000000001 - type: recall_at_1 value: 21.826999999999998 - type: recall_at_10 value: 62.132 - type: recall_at_100 value: 88.55199999999999 - type: recall_at_1000 value: 97.707 - type: recall_at_3 value: 40.541 - type: recall_at_5 value: 49.739 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 95.68399452804377 - type: f1 value: 95.25490609832268 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 83.15321477428182 - type: f1 value: 60.35476439087966 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 71.92669804976462 - type: f1 value: 69.22815107207565 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 74.4855413584398 - type: f1 value: 72.92107516103387 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 32.412679360205544 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 28.09211869875204 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.540919056982545 - type: mrr value: 31.529904607063536 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.745 - type: map_at_10 value: 12.013 - type: map_at_100 value: 15.040000000000001 - type: map_at_1000 value: 16.427 - type: map_at_3 value: 8.841000000000001 - type: map_at_5 value: 10.289 - type: mrr_at_1 value: 45.201 - type: mrr_at_10 value: 53.483999999999995 - type: mrr_at_100 value: 54.20700000000001 - type: mrr_at_1000 value: 54.252 - type: mrr_at_3 value: 51.29 - type: mrr_at_5 value: 52.73 - type: ndcg_at_1 value: 43.808 - type: ndcg_at_10 value: 32.445 - type: ndcg_at_100 value: 30.031000000000002 - type: ndcg_at_1000 value: 39.007 - type: ndcg_at_3 value: 37.204 - type: ndcg_at_5 value: 35.07 - type: precision_at_1 value: 45.201 - type: precision_at_10 value: 23.684 - type: precision_at_100 value: 7.600999999999999 - type: precision_at_1000 value: 2.043 - type: precision_at_3 value: 33.953 - type: precision_at_5 value: 29.412 - type: recall_at_1 value: 5.745 - type: recall_at_10 value: 16.168 - type: recall_at_100 value: 30.875999999999998 - type: recall_at_1000 value: 62.686 - type: recall_at_3 value: 9.75 - type: recall_at_5 value: 12.413 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 37.828 - type: map_at_10 value: 53.239000000000004 - type: map_at_100 value: 54.035999999999994 - type: map_at_1000 value: 54.067 - type: map_at_3 value: 49.289 - type: map_at_5 value: 51.784 - type: mrr_at_1 value: 42.497 - type: mrr_at_10 value: 55.916999999999994 - type: mrr_at_100 value: 56.495 - type: mrr_at_1000 value: 56.516999999999996 - type: mrr_at_3 value: 52.800000000000004 - type: mrr_at_5 value: 54.722 - type: ndcg_at_1 value: 42.468 - type: ndcg_at_10 value: 60.437 - type: ndcg_at_100 value: 63.731 - type: ndcg_at_1000 value: 64.41799999999999 - type: ndcg_at_3 value: 53.230999999999995 - type: ndcg_at_5 value: 57.26 - type: precision_at_1 value: 42.468 - type: precision_at_10 value: 9.47 - type: precision_at_100 value: 1.1360000000000001 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 23.724999999999998 - type: precision_at_5 value: 16.593 - type: recall_at_1 value: 37.828 - type: recall_at_10 value: 79.538 - type: recall_at_100 value: 93.646 - type: recall_at_1000 value: 98.72999999999999 - type: recall_at_3 value: 61.134 - type: recall_at_5 value: 70.377 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 70.548 - type: map_at_10 value: 84.466 - type: map_at_100 value: 85.10600000000001 - type: map_at_1000 value: 85.123 - type: map_at_3 value: 81.57600000000001 - type: map_at_5 value: 83.399 - type: mrr_at_1 value: 81.24 - type: mrr_at_10 value: 87.457 - type: mrr_at_100 value: 87.574 - type: mrr_at_1000 value: 87.575 - type: mrr_at_3 value: 86.507 - type: mrr_at_5 value: 87.205 - type: ndcg_at_1 value: 81.25 - type: ndcg_at_10 value: 88.203 - type: ndcg_at_100 value: 89.457 - type: ndcg_at_1000 value: 89.563 - type: ndcg_at_3 value: 85.465 - type: ndcg_at_5 value: 87.007 - type: precision_at_1 value: 81.25 - type: precision_at_10 value: 13.373 - type: precision_at_100 value: 1.5270000000000001 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.417 - type: precision_at_5 value: 24.556 - type: recall_at_1 value: 70.548 - type: recall_at_10 value: 95.208 - type: recall_at_100 value: 99.514 - type: recall_at_1000 value: 99.988 - type: recall_at_3 value: 87.214 - type: recall_at_5 value: 91.696 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 53.04822095496839 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 60.30778476474675 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.692 - type: map_at_10 value: 11.766 - type: map_at_100 value: 13.904 - type: map_at_1000 value: 14.216999999999999 - type: map_at_3 value: 8.245 - type: map_at_5 value: 9.92 - type: mrr_at_1 value: 23.0 - type: mrr_at_10 value: 33.78 - type: mrr_at_100 value: 34.922 - type: mrr_at_1000 value: 34.973 - type: mrr_at_3 value: 30.2 - type: mrr_at_5 value: 32.565 - type: ndcg_at_1 value: 23.0 - type: ndcg_at_10 value: 19.863 - type: ndcg_at_100 value: 28.141 - type: ndcg_at_1000 value: 33.549 - type: ndcg_at_3 value: 18.434 - type: ndcg_at_5 value: 16.384 - type: precision_at_1 value: 23.0 - type: precision_at_10 value: 10.39 - type: precision_at_100 value: 2.235 - type: precision_at_1000 value: 0.35300000000000004 - type: precision_at_3 value: 17.133000000000003 - type: precision_at_5 value: 14.44 - type: recall_at_1 value: 4.692 - type: recall_at_10 value: 21.025 - type: recall_at_100 value: 45.324999999999996 - type: recall_at_1000 value: 71.675 - type: recall_at_3 value: 10.440000000000001 - type: recall_at_5 value: 14.64 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.96178184892842 - type: cos_sim_spearman value: 79.6487740813199 - type: euclidean_pearson value: 82.06661161625023 - type: euclidean_spearman value: 79.64876769031183 - type: manhattan_pearson value: 82.07061164575131 - type: manhattan_spearman value: 79.65197039464537 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.15305604100027 - type: cos_sim_spearman value: 74.27447427941591 - type: euclidean_pearson value: 80.52737337565307 - type: euclidean_spearman value: 74.27416077132192 - type: manhattan_pearson value: 80.53728571140387 - type: manhattan_spearman value: 74.28853605753457 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 83.44386080639279 - type: cos_sim_spearman value: 84.17947648159536 - type: euclidean_pearson value: 83.34145388129387 - type: euclidean_spearman value: 84.17947648159536 - type: manhattan_pearson value: 83.30699061927966 - type: manhattan_spearman value: 84.18125737380451 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 81.57392220985612 - type: cos_sim_spearman value: 78.80745014464101 - type: euclidean_pearson value: 80.01660371487199 - type: euclidean_spearman value: 78.80741240102256 - type: manhattan_pearson value: 79.96810779507953 - type: manhattan_spearman value: 78.75600400119448 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.85421063026625 - type: cos_sim_spearman value: 87.55320285299192 - type: euclidean_pearson value: 86.69750143323517 - type: euclidean_spearman value: 87.55320284326378 - type: manhattan_pearson value: 86.63379169960379 - type: manhattan_spearman value: 87.4815029877984 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 84.31314130411842 - type: cos_sim_spearman value: 85.3489588181433 - type: euclidean_pearson value: 84.13240933463535 - type: euclidean_spearman value: 85.34902871403281 - type: manhattan_pearson value: 84.01183086503559 - type: manhattan_spearman value: 85.19316703166102 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 89.09979781689536 - type: cos_sim_spearman value: 88.87813323759015 - type: euclidean_pearson value: 88.65413031123792 - type: euclidean_spearman value: 88.87813323759015 - type: manhattan_pearson value: 88.61818758256024 - type: manhattan_spearman value: 88.81044100494604 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 62.30693258111531 - type: cos_sim_spearman value: 62.195516523251946 - type: euclidean_pearson value: 62.951283701049476 - type: euclidean_spearman value: 62.195516523251946 - type: manhattan_pearson value: 63.068322281439535 - type: manhattan_spearman value: 62.10621171028406 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.27092833763909 - type: cos_sim_spearman value: 84.84429717949759 - type: euclidean_pearson value: 84.8516966060792 - type: euclidean_spearman value: 84.84429717949759 - type: manhattan_pearson value: 84.82203139242881 - type: manhattan_spearman value: 84.8358503952945 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 83.10290863981409 - type: mrr value: 95.31168450286097 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 52.161 - type: map_at_10 value: 62.138000000000005 - type: map_at_100 value: 62.769 - type: map_at_1000 value: 62.812 - type: map_at_3 value: 59.111000000000004 - type: map_at_5 value: 60.995999999999995 - type: mrr_at_1 value: 55.333 - type: mrr_at_10 value: 63.504000000000005 - type: mrr_at_100 value: 64.036 - type: mrr_at_1000 value: 64.08 - type: mrr_at_3 value: 61.278 - type: mrr_at_5 value: 62.778 - type: ndcg_at_1 value: 55.333 - type: ndcg_at_10 value: 66.678 - type: ndcg_at_100 value: 69.415 - type: ndcg_at_1000 value: 70.453 - type: ndcg_at_3 value: 61.755 - type: ndcg_at_5 value: 64.546 - type: precision_at_1 value: 55.333 - type: precision_at_10 value: 9.033 - type: precision_at_100 value: 1.043 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 24.221999999999998 - type: precision_at_5 value: 16.333000000000002 - type: recall_at_1 value: 52.161 - type: recall_at_10 value: 79.156 - type: recall_at_100 value: 91.333 - type: recall_at_1000 value: 99.333 - type: recall_at_3 value: 66.43299999999999 - type: recall_at_5 value: 73.272 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.81287128712871 - type: cos_sim_ap value: 95.30034785910676 - type: cos_sim_f1 value: 90.28629856850716 - type: cos_sim_precision value: 92.36401673640168 - type: cos_sim_recall value: 88.3 - type: dot_accuracy value: 99.81287128712871 - type: dot_ap value: 95.30034785910676 - type: dot_f1 value: 90.28629856850716 - type: dot_precision value: 92.36401673640168 - type: dot_recall value: 88.3 - type: euclidean_accuracy value: 99.81287128712871 - type: euclidean_ap value: 95.30034785910676 - type: euclidean_f1 value: 90.28629856850716 - type: euclidean_precision value: 92.36401673640168 - type: euclidean_recall value: 88.3 - type: manhattan_accuracy value: 99.80990099009901 - type: manhattan_ap value: 95.26880751950654 - type: manhattan_f1 value: 90.22177419354838 - type: manhattan_precision value: 90.95528455284553 - type: manhattan_recall value: 89.5 - type: max_accuracy value: 99.81287128712871 - type: max_ap value: 95.30034785910676 - type: max_f1 value: 90.28629856850716 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 58.518662504351184 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 34.96168178378587 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 52.04862593471896 - type: mrr value: 52.97238402936932 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.092545236479946 - type: cos_sim_spearman value: 31.599851000175498 - type: dot_pearson value: 30.092542723901676 - type: dot_spearman value: 31.599851000175498 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.189 - type: map_at_10 value: 1.662 - type: map_at_100 value: 9.384 - type: map_at_1000 value: 22.669 - type: map_at_3 value: 0.5559999999999999 - type: map_at_5 value: 0.9039999999999999 - type: mrr_at_1 value: 68.0 - type: mrr_at_10 value: 81.01899999999999 - type: mrr_at_100 value: 81.01899999999999 - type: mrr_at_1000 value: 81.01899999999999 - type: mrr_at_3 value: 79.333 - type: mrr_at_5 value: 80.733 - type: ndcg_at_1 value: 63.0 - type: ndcg_at_10 value: 65.913 - type: ndcg_at_100 value: 51.895 - type: ndcg_at_1000 value: 46.967 - type: ndcg_at_3 value: 65.49199999999999 - type: ndcg_at_5 value: 66.69699999999999 - type: precision_at_1 value: 68.0 - type: precision_at_10 value: 71.6 - type: precision_at_100 value: 53.66 - type: precision_at_1000 value: 21.124000000000002 - type: precision_at_3 value: 72.667 - type: precision_at_5 value: 74.0 - type: recall_at_1 value: 0.189 - type: recall_at_10 value: 1.913 - type: recall_at_100 value: 12.601999999999999 - type: recall_at_1000 value: 44.296 - type: recall_at_3 value: 0.605 - type: recall_at_5 value: 1.018 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.701 - type: map_at_10 value: 10.445 - type: map_at_100 value: 17.324 - type: map_at_1000 value: 19.161 - type: map_at_3 value: 5.497 - type: map_at_5 value: 7.278 - type: mrr_at_1 value: 30.612000000000002 - type: mrr_at_10 value: 45.534 - type: mrr_at_100 value: 45.792 - type: mrr_at_1000 value: 45.806999999999995 - type: mrr_at_3 value: 37.755 - type: mrr_at_5 value: 43.469 - type: ndcg_at_1 value: 26.531 - type: ndcg_at_10 value: 26.235000000000003 - type: ndcg_at_100 value: 39.17 - type: ndcg_at_1000 value: 51.038 - type: ndcg_at_3 value: 23.625 - type: ndcg_at_5 value: 24.338 - type: precision_at_1 value: 30.612000000000002 - type: precision_at_10 value: 24.285999999999998 - type: precision_at_100 value: 8.224 - type: precision_at_1000 value: 1.6179999999999999 - type: precision_at_3 value: 24.490000000000002 - type: precision_at_5 value: 24.898 - type: recall_at_1 value: 2.701 - type: recall_at_10 value: 17.997 - type: recall_at_100 value: 51.766999999999996 - type: recall_at_1000 value: 87.863 - type: recall_at_3 value: 6.295000000000001 - type: recall_at_5 value: 9.993 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 73.3474 - type: ap value: 15.393431414459924 - type: f1 value: 56.466681887882416 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 62.062818336163 - type: f1 value: 62.11230840463252 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 42.464892820845115 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.15962329379508 - type: cos_sim_ap value: 74.73674057919256 - type: cos_sim_f1 value: 68.81245642574947 - type: cos_sim_precision value: 61.48255813953488 - type: cos_sim_recall value: 78.12664907651715 - type: dot_accuracy value: 86.15962329379508 - type: dot_ap value: 74.7367634988281 - type: dot_f1 value: 68.81245642574947 - type: dot_precision value: 61.48255813953488 - type: dot_recall value: 78.12664907651715 - type: euclidean_accuracy value: 86.15962329379508 - type: euclidean_ap value: 74.7367761466634 - type: euclidean_f1 value: 68.81245642574947 - type: euclidean_precision value: 61.48255813953488 - type: euclidean_recall value: 78.12664907651715 - type: manhattan_accuracy value: 86.21326816474935 - type: manhattan_ap value: 74.64416473733951 - type: manhattan_f1 value: 68.80924855491331 - type: manhattan_precision value: 61.23456790123457 - type: manhattan_recall value: 78.52242744063325 - type: max_accuracy value: 86.21326816474935 - type: max_ap value: 74.7367761466634 - type: max_f1 value: 68.81245642574947 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.97620988085536 - type: cos_sim_ap value: 86.08680845745758 - type: cos_sim_f1 value: 78.02793637114438 - type: cos_sim_precision value: 73.11082699683736 - type: cos_sim_recall value: 83.65414228518632 - type: dot_accuracy value: 88.97620988085536 - type: dot_ap value: 86.08681149437946 - type: dot_f1 value: 78.02793637114438 - type: dot_precision value: 73.11082699683736 - type: dot_recall value: 83.65414228518632 - type: euclidean_accuracy value: 88.97620988085536 - type: euclidean_ap value: 86.08681215460771 - type: euclidean_f1 value: 78.02793637114438 - type: euclidean_precision value: 73.11082699683736 - type: euclidean_recall value: 83.65414228518632 - type: manhattan_accuracy value: 88.88888888888889 - type: manhattan_ap value: 86.02916327562438 - type: manhattan_f1 value: 78.02063045516843 - type: manhattan_precision value: 73.38851947346994 - type: manhattan_recall value: 83.2768709578072 - type: max_accuracy value: 88.97620988085536 - type: max_ap value: 86.08681215460771 - type: max_f1 value: 78.02793637114438 --- # walsons/jina-embeddings-v2-base-en-Q4_K_M-GGUF This model was converted to GGUF format from [`jinaai/jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo walsons/jina-embeddings-v2-base-en-Q4_K_M-GGUF --hf-file jina-embeddings-v2-base-en-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo walsons/jina-embeddings-v2-base-en-Q4_K_M-GGUF --hf-file jina-embeddings-v2-base-en-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo walsons/jina-embeddings-v2-base-en-Q4_K_M-GGUF --hf-file jina-embeddings-v2-base-en-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo walsons/jina-embeddings-v2-base-en-Q4_K_M-GGUF --hf-file jina-embeddings-v2-base-en-q4_k_m.gguf -c 2048 ```
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
KarBik/legal-french-matroshka
KarBik
sentence-similarity
[ "sentence-transformers", "safetensors", "xlm-roberta", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:9000", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:intfloat/multilingual-e5-base", "base_model:finetune:intfloat/multilingual-e5-base", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,728
1,728
6
0
--- base_model: intfloat/multilingual-e5-base library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:9000 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: Les vérifications périodiques sont réalisées soit par un organisme accrédité, soit par une personne qualifiée appartenant à l'entreprise et dont la compétence est appréciée par l'employeur au regard de critères énoncés dans un arrêté du ministre chargé du travail et du ministre chargé de l'agriculture. sentences: - Quels sont les critères énoncés dans un arrêté du ministre chargé du travail et du ministre chargé de l'agriculture pour apprécier la compétence d'une personne qualifiée pour réaliser des vérifications périodiques au sein d'une entreprise ? - Quels sont les éléments clés que les acquéreurs de parts d'une société d'épargne forestière doivent prendre en compte pour évaluer les caractéristiques d'un patrimoine forestier et les risques associés ? - Quels sont les ustensiles, machines ou mécaniques interdits de détention en rapport avec la fabrication ou la pulvérisation du tabac ? - source_sentence: 'Les prestations en matière d''échange (numéros 96 et 97 du tableau 5) donnent lieu à la perception : 1° S''agissant de l''échange bilatéral, d''un émolument proportionnel à la valeur du plus fort des deux lots échangés, selon le barème suivant : Tranches d''assiette Taux applicable De 0 à 6 500 € 3,870 % De 6 500 € à 17 000 € 1,596 % De 17 000 € à 60 000 € 1,064 % Plus de 60 000 € 0,799 % 2° S''agissant de l''échange multilatéral, d''un émolument proportionnel à la valeur globale des biens échangés, selon le barème suivant : Tranches d''assiette Taux applicable De 0 à 6 500 € 2,580 % De 6 500 € à 17 000 € 1,064 % De 17 000 € à 60 000 € 0,709 % Plus de 60 000 € 0,532 %' sentences: - Quels sont les conséquences pour le prêteur en cas de défaut de mention ou de mention erronée du taux effectif global, notamment en ce qui concerne le droit aux intérêts et le remboursement du capital ? - Quels sont les éléments déterminants pour établir l'assiette et le mode de servitude de passage pour cause d'enclave, et quels sont les effets sur l'action en indemnité et le passage en cas d'usage continu de trente ans ? - Quel est le taux d'émolument applicable en fonction de la valeur des biens échangés dans les cas d'échange bilatéral ou multilatéral ? - source_sentence: La demande d'autorisation de transit est présentée par une personne titulaire du statut d'opérateur économique agréé pour la sécurité et la sûreté tel que défini dans le règlement (UE) n° 952/2013 du Parlement européen et du Conseil du 9 octobre 2013 établissant le code des douanes de l'Union. La demande est établie dans les conditions définies par arrêté du ministre chargé des douanes. Elle est déposée auprès du chef du service des autorisations de mouvements internationaux d'armes. sentences: - Quels types de contrats sont exclus de la portée des dispositions du présent titre ? - Quels sont les critères pour obtenir le statut d'opérateur économique agréé pour la sécurité et la sûreté, nécessaires pour présenter une demande d'autorisation de transit, conformément au règlement (UE) n° 952/2013 du Parlement européen et du Conseil ? - Dans quelsles conditions un établissement de crédit ou une société de financement peut-il déroger, en tout ou partie, aux dispositions des articles D. 331-75 et D. 331-76-5-1 lors de l'octroi d'un prêt au vendeur ? - source_sentence: En application du contrat prévu à l'article 95 ZA , le tiers de confiance transmet à l'administration fiscale par voie électronique, pour le compte de ses clients, les déclarations annuelles de revenus et leurs annexes. L'obligation de télétransmission du tiers de confiance ne porte pas sur les déclarations à souscrire au titre des revenus perçus au cours de l'année durant laquelle s'achève la mission de tiers de confiance. Le contribuable mentionné au I de l'article 170 ter du code général des impôts est regardé, pour une année donnée, comme client d'un tiers de confiance s'il est lié avec celui-ci par le contrat prévu à l'article 95 ZA, conclu au plus tard lors du dépôt, par le professionnel, de la déclaration annuelle des revenus. sentences: - Quel est le sort des demandes lorsqu'il n'y a pas de réponse de l'autorité compétente dans les délais prévus ? - Quels sont les éléments que le tiers de confiance est tenu de transmettre à l'administration fiscale pour le compte de ses clients, et dans quels cas cette obligation de télétransmission ne s'applique-t-elle pas ? - Quels sont les membres composant les collèges territoriaux des finances publiques et qui est chargé de la présidence en cas d'absence ou d'empêchement du président ? - source_sentence: Les projets de marchés de partenariat conclus pour le compte des acheteurs non autorisés sont instruits par le ministre de tutelle. sentences: - Quels sont les documents que les établissements de paiement et les établissements de monnaie électronique doivent mettre à disposition de leur clientèle et du public pour les opérations de paiement et les comptes de paiement, et quels sont les informations minimales qui doivent être incluses dans ces documents ? - Dans quelle situation l'assuré a-t-il besoin d'être assisté ou représenté par un avocat en raison de la défense de la partie adverse ? - Qui est responsable de l'instruction des projets de marchés de partenariat conclus pour le compte des acheteurs non autorisés ? model-index: - name: SentenceTransformer based on intfloat/multilingual-e5-base results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.94 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.981 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.987 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.989 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.94 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.32699999999999996 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19740000000000005 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0989 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.94 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.981 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.987 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.989 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9683994234957766 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9613761904761905 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9617349428516079 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.942 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.982 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.988 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.989 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.942 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.32733333333333325 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19760000000000003 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0989 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.942 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.982 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.988 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.989 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.969565548663498 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9629166666666668 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9632981492091787 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.937 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.98 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.985 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.989 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.937 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3266666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.197 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0989 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.937 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.98 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.985 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.989 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9661778506957523 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.958502380952381 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9588400474998072 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.93 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.972 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.983 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.988 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.93 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.32399999999999995 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19660000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09880000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.93 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.972 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.983 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.988 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9619055617624742 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9532523809523811 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9537039961889963 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.901 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.966 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.977 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.989 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.901 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.32199999999999995 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19540000000000005 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09890000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.901 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.966 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.977 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.989 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.947780306797729 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9342468253968255 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9345714945276086 name: Cosine Map@100 --- # SentenceTransformer based on intfloat/multilingual-e5-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision d13f1b27baf31030b7fd040960d60d909913633f --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("KarBik/legal-french-matroshka") # Run inference sentences = [ 'Les projets de marchés de partenariat conclus pour le compte des acheteurs non autorisés sont instruits par le ministre de tutelle.', "Qui est responsable de l'instruction des projets de marchés de partenariat conclus pour le compte des acheteurs non autorisés ?", "Dans quelle situation l'assuré a-t-il besoin d'être assisté ou représenté par un avocat en raison de la défense de la partie adverse ?", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_768` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.94 | | cosine_accuracy@3 | 0.981 | | cosine_accuracy@5 | 0.987 | | cosine_accuracy@10 | 0.989 | | cosine_precision@1 | 0.94 | | cosine_precision@3 | 0.327 | | cosine_precision@5 | 0.1974 | | cosine_precision@10 | 0.0989 | | cosine_recall@1 | 0.94 | | cosine_recall@3 | 0.981 | | cosine_recall@5 | 0.987 | | cosine_recall@10 | 0.989 | | cosine_ndcg@10 | 0.9684 | | cosine_mrr@10 | 0.9614 | | **cosine_map@100** | **0.9617** | #### Information Retrieval * Dataset: `dim_512` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.942 | | cosine_accuracy@3 | 0.982 | | cosine_accuracy@5 | 0.988 | | cosine_accuracy@10 | 0.989 | | cosine_precision@1 | 0.942 | | cosine_precision@3 | 0.3273 | | cosine_precision@5 | 0.1976 | | cosine_precision@10 | 0.0989 | | cosine_recall@1 | 0.942 | | cosine_recall@3 | 0.982 | | cosine_recall@5 | 0.988 | | cosine_recall@10 | 0.989 | | cosine_ndcg@10 | 0.9696 | | cosine_mrr@10 | 0.9629 | | **cosine_map@100** | **0.9633** | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.937 | | cosine_accuracy@3 | 0.98 | | cosine_accuracy@5 | 0.985 | | cosine_accuracy@10 | 0.989 | | cosine_precision@1 | 0.937 | | cosine_precision@3 | 0.3267 | | cosine_precision@5 | 0.197 | | cosine_precision@10 | 0.0989 | | cosine_recall@1 | 0.937 | | cosine_recall@3 | 0.98 | | cosine_recall@5 | 0.985 | | cosine_recall@10 | 0.989 | | cosine_ndcg@10 | 0.9662 | | cosine_mrr@10 | 0.9585 | | **cosine_map@100** | **0.9588** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.93 | | cosine_accuracy@3 | 0.972 | | cosine_accuracy@5 | 0.983 | | cosine_accuracy@10 | 0.988 | | cosine_precision@1 | 0.93 | | cosine_precision@3 | 0.324 | | cosine_precision@5 | 0.1966 | | cosine_precision@10 | 0.0988 | | cosine_recall@1 | 0.93 | | cosine_recall@3 | 0.972 | | cosine_recall@5 | 0.983 | | cosine_recall@10 | 0.988 | | cosine_ndcg@10 | 0.9619 | | cosine_mrr@10 | 0.9533 | | **cosine_map@100** | **0.9537** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.901 | | cosine_accuracy@3 | 0.966 | | cosine_accuracy@5 | 0.977 | | cosine_accuracy@10 | 0.989 | | cosine_precision@1 | 0.901 | | cosine_precision@3 | 0.322 | | cosine_precision@5 | 0.1954 | | cosine_precision@10 | 0.0989 | | cosine_recall@1 | 0.901 | | cosine_recall@3 | 0.966 | | cosine_recall@5 | 0.977 | | cosine_recall@10 | 0.989 | | cosine_ndcg@10 | 0.9478 | | cosine_mrr@10 | 0.9342 | | **cosine_map@100** | **0.9346** | <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### json * Dataset: json * Size: 9,000 training samples * Columns: <code>positive</code> and <code>anchor</code> * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 11 tokens</li><li>mean: 141.81 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 57.29 tokens</li><li>max: 262 tokens</li></ul> | * Samples: | positive | anchor | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Sauf dispositions contraires des conventions internationales, l'émission de titres comportant la mention visée à l'article 51 entraîne l'obligation pour l'organisme émetteur d'opérer, sur les produits de ces titres et pendant toute la durée de ceux-ci, la retenue à la source édictée par le 1 de l'article 119 bis du code général des impôts. Le montant de cette retenue doit être versé au comptable désigné par l'administration, dans les conditions et suivant les modalités fixées par le 1 de l'article 1672 et l'article 1673 dudit code.</code> | <code>Quelle est l'obligation de l'organisme émetteur concernant les produits de titres émis avec la mention visée à l'article 51, et comment doit-il opérer la retenue à la source pendant la durée de ces titres ?</code> | | <code>Lorsque l'allocation est attribuée en application du troisième alinéa de l'article L. 232-12 et du cinquième alinéa de l'article L. 232-14 , le montant forfaitaire attribué est, respectivement, égal, à domicile, à 50 % du montant du plafond mentionné à l'article L. 232-3-1 correspondant au degré de perte d'autonomie le plus important, et, en établissement, à 50 % du tarif afférent à la dépendance de l'établissement considéré applicable aux résidents classés dans les groupes iso-ressources 1 et 2. Cette avance s'impute sur les montants de l'allocation personnalisée d'autonomie versée ultérieurement.</code> | <code>Quel est le montant forfaitaire attribué lorsqu'une allocation est octroyée en application du troisième alinéa de l'article L. 232-12 et du cinquième alinéa de l'article L. 232-14, selon que l'allocation est perçue à domicile ou en établissement ?</code> | | <code>La taxe devient exigible au moment où le poids lourd : 1° Entre sur le réseau, si la condition mentionnée au 1° de l'article L. 421-202 est remplie ; 2° Franchit un point de la section de tarification déterminé par l'autorité compétente, si cette même condition n'est pas remplie.</code> | <code>Quel est le moment où la taxe devient exigible pour un poids lourd en fonction de son entrée dans le réseau ou de son franchissement d'un point de tarification déterminé ?</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: True - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0 | 0 | - | 0.8447 | 0.9084 | 0.9190 | 0.6362 | 0.9236 | | 0.5674 | 10 | 5.322 | - | - | - | - | - | | 0.9645 | 17 | - | 0.9353 | 0.9413 | 0.9488 | 0.9197 | 0.9453 | | 1.1348 | 20 | 0.3395 | - | - | - | - | - | | 1.7021 | 30 | 0.0929 | - | - | - | - | - | | 1.9858 | 35 | - | 0.9517 | 0.9571 | 0.9631 | 0.9357 | 0.9625 | | 2.2695 | 40 | 0.0408 | - | - | - | - | - | | 2.8369 | 50 | 0.0264 | - | - | - | - | - | | 2.9504 | 52 | - | 0.9513 | 0.9579 | 0.9634 | 0.9357 | 0.9620 | | 3.4043 | 60 | 0.0209 | - | - | - | - | - | | **3.8582** | **68** | **-** | **0.9537** | **0.9588** | **0.9633** | **0.9346** | **0.9617** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.1.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.34.2 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION" ]
[ "CAS" ]
Non_BioNLP
medspaner/mbert-base-clinical-trials-attributes
medspaner
null
[ "pytorch", "bert", "generated_from_trainer", "license:cc-by-nc-4.0", "region:us" ]
1,726
1,727
7
0
--- license: cc-by-nc-4.0 metrics: - precision - recall - f1 - accuracy tags: - generated_from_trainer widget: - text: Paciente normotenso (PA = 120/70 mmHg) model-index: - name: mbert-base-clinical-trials-attributes results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mbert-base-clinical-trials-attributes This named entity recognition model detects the following types of medical attributes: - Experiencer: - Patient: e.g. *paciente* - Family_member: e.g. *padre* - Other: e.g. *cirujano* - Event temporality: - Future: e.g. ***cirugía*** *pendiente* - History_of: e.g. *antecedentes de* ***migraña*** The model achieves the following results on the test set (results are averaged over 5 evaluation rounds): - Precision: 0.868 (±0.023) - Recall: 0.831 (±0.014) - F1: 0.849 (±0.005) - Accuracy: 0.986 (±0.001) ## Model description This model adapts the pre-trained model [bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased), the multilingual version of the BERT model presented in [Devlin al. (2019)](https://aclanthology.org/N19-1423/), which was pre-trained on a corpus derived from Wikipedia covering 104 languages. The model is fine-tuned to conduct medical named entity recognition on texts about in Spanish using the [CT-EBM-ES corpus (Campillos-Llanos et al. 2021)](https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01395-z) vs 2. If you use this model, please, cite as follows: ``` @article{campillosetal2024,         title = {{Hybrid tool for semantic annotation and concept extraction of medical texts in Spanish}},         author = {Campillos-Llanos, Leonardo and Valverde-Mateos, Ana and Capllonch-Carri{\'o}n, Adri{\'a}n},         journal = {BMC Bioinformatics}, year={2024}, publisher={BioMed Central} } ``` ## Intended uses & limitations **Disclosure**: *This model is under development and needs to be improved. It should not be used for medical decision making without human assistance and supervision* This model is intended for a generalist purpose, and may have bias and/or any other undesirable distortions. Third parties who deploy or provide systems and/or services using any of these models (or using systems based on these models) should note that it is their responsibility to mitigate the risks arising from their use. Third parties, in any event, need to comply with applicable regulations, including regulations concerning the use of artificial intelligence. The owner or creator of the models will in no event be liable for any results arising from the use made by third parties of these models. **Descargo de responsabilidad**: *Esta herramienta se encuentra en desarrollo y no debe ser empleada para la toma de decisiones médicas* La finalidad de este modelo es generalista, y se advierte que puede tener sesgos y/u otro tipo de distorsiones indeseables. Terceras partes que desplieguen o proporcionen sistemas y/o servicios usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) han tener presente que es su responsabilidad abordar y minimizar los riesgos derivados de su uso. Las terceras partes, en cualquier circunstancia, deben cumplir con la normativa aplicable, incluyendo la normativa que concierne al uso de la inteligencia artificial. El propietario o creador de los modelos de ningún modo será responsable de los resultados derivados del uso que las terceras partes hagan de estos modelos. ## Training and evaluation data The model is fine-tuned on the Clinical Trials for Evidence-Based-Medicine in Spanish (CT-EBM-SP) corpus vs 2. The CT-EBM-SP corpus is a collection of 1200 texts about clinical trials studies and clinical trials announcements: - 500 abstracts from journals published under a Creative Commons license, e.g. available in PubMed or the Scientific Electronic Library Online (SciELO) - 700 clinical trials announcements published in the European Clinical Trials Register and Repositorio Español de Estudios Clínicos If you use the CT-EBM-ES resource, please, cite as follows: ``` @article{campillosetal-midm2021,         title = {A clinical trials corpus annotated with UMLS© entities to enhance the access to Evidence-Based Medicine},         author = {Campillos-Llanos, Leonardo and Valverde-Mateos, Ana and Capllonch-Carri{\'o}n, Adri{\'a}n and Moreno-Sandoval, Antonio},         journal = {BMC Medical Informatics and Decision Making},         volume={21}, number={1}, pages={1--19}, year={2021}, publisher={BioMed Central} } ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: we used different seeds for 5 evaluation rounds, and uploaded the model with the best results - optimizer: Adam - num_epochs: average 11.6 (±2.07); trained with early stopping if no improvement after 5 epochs (early stopping patience: 5) ### Training results (test set; average and standard deviation of 5 rounds with different seeds) | Precision | Recall | F1 | Accuracy | |:--------------:|:--------------:|:--------------:|:--------------:| | 0.868 (±0.023) | 0.831 (±0.014) | 0.849 (±0.005) | 0.986 (±0.001) | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6
[ "NAMED_ENTITY_RECOGNITION" ]
[ "CT-EBM-SP", "SCIELO" ]
BioNLP
am-azadi/bilingual-embedding-large_Fine_Tuned_2e
am-azadi
sentence-similarity
[ "sentence-transformers", "safetensors", "bilingual", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:21769", "loss:MultipleNegativesRankingLoss", "custom_code", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:am-azadi/bilingual-embedding-large_Fine_Tuned_1e", "base_model:finetune:am-azadi/bilingual-embedding-large_Fine_Tuned_1e", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,740
1,740
7
0
--- base_model: am-azadi/bilingual-embedding-large_Fine_Tuned_1e library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:21769 - loss:MultipleNegativesRankingLoss widget: - source_sentence: 'GOOD NEWS! Eriksen, has already gone out to the hospital window, where he is under observation and looks optimistic after having suffered a cardiac arrest. ' sentences: - Bolsonaro with the two assassins of Marielle Franco No, the men next to Jair Bolsonaro in this photo are not the ones accused of the murder of Marielle Franco - This photo shows Christian Eriksen waving from the window of the hospital where he was admitted after suffering cardiac arrest The photo of Eriksen waving from the window was taken months before his heart incident - Video of protests in the US during the COVID-19 pandemic This video has been circulating in reports about the funeral procession of military commanders in Iran in January 2020 - source_sentence: What a dirty game... "US postman arrested in canadian border with banknotes stolen in the trunk of the car". 91 Breaking911 5h U.S. Postal Worker Caught at Canadian Border With Stolen Ballots In Car Trunk - breaking911.com/u-s-postal-wor... 8218248 Claudia Wild IT 8206434 300 4:57 06 Nov 20 Twitter for iPhone 1,134 Retweets 113 Tweets with comment sentences: - Postman arrested with stolen bills at US-Canada border Only three blank bills were found in a US postal worker's car - Covid relief plan will cost every American $5,750 Misleading posts claim US covid relief plan costs every American $5,750 - CDC informs that 10% of the swabs used for PCR testing were sent to LABORATORIES, being analyzed of GENETIC SEQUENCES We check the claim that PCR tests aim to sequence the DNA of patients with covid-19 - source_sentence: '. Northeast Always in Our Hearts! Advance Northeast!! . Brazilian Army through its Engineering Battalion finds a Huge Potable Water Well in Seridó - Caicó/RN, one of the most needy areas. This well will supply the homes of more than 3,000 people!! . It''s our President Bolsonaro ridding the Bravo People of the Northeast from the wounds of drought! . . . BRAZIL LOVED HOMELAND . . Friends and Followers of : Follow and Turn on our Notifications . . # pocket . ' sentences: - Twitter suspended Elon Musk's Twitter account after he pulled out of deal Imposter Elon Musk Twitter account shared in false posts claiming he was 'suspended' over buyout row - The Brazilian Army found water in Caicó, Rio Grande do Norte, during the government of President Jair Bolsonaro. The recording of the drilling of an artesian well in Caicó, Rio Grande do Norte, has been circulating since 2015 - A video was published today about Syrian refugees in Sweden being subjected to the separation of husbands, as well as the forcible removal of their children and the handing over of children to Christian families to change their religion. And to turn them into Christians, they will have two children Swedish police did not take Syrian children to hand over to Christian families - source_sentence: what hp Álvaro Uribe Vélez ... 3pm ✓ The coastal people are the least intellectual of the country, that is why this region of Colombia is mired in poverty. They don't like to work either. that's why there is currently a level very high of misery in la guajira. With the democratic center we will change. The entire Caribbean coast must feel outraged by the statements of this individual. Now with more reasons, the coastal people should support Petro. The how.. see more sentences: - 'Covid-19: Omicron variant is transmitted by eye contact according to the WHO The coronavirus is transmitted by interaction with contaminated droplets, not by eye contact' - 5G causes suffocation in humans, affects the respiratory system There is no evidence that 5G technology affects the respiratory system and increases toxins in the body - Álvaro Uribe tweeted that the coastal people are the least intellectual population in Colombia There is no record of Uribe tweeting that the coast is the "least intellectual" region of Colombia - source_sentence: 'The terrorists evaporated in seconds A very rare scene of the moment the Egyptian planes bombed the terrorist elements in Sinai Watch the video here NB Please all our followers on our page subscribe to our YouTube channel We will publish everything new on the ground Open the channel link ' sentences: - Cars melt due to hot weather in Saudi Arabia No, these cars did not melt due to hot weather - Footage shows robbery in Sri Lanka Delhi crime footage falsely shared as 'Sri Lanka burglary' - A very rare scene of the moment the Egyptian planes bombed the terrorist elements in Sinai This picture is not of an Egyptian warplane, but of an Israeli plane --- # SentenceTransformer based on am-azadi/bilingual-embedding-large_Fine_Tuned_1e This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [am-azadi/bilingual-embedding-large_Fine_Tuned_1e](https://huggingface.co/am-azadi/bilingual-embedding-large_Fine_Tuned_1e). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [am-azadi/bilingual-embedding-large_Fine_Tuned_1e](https://huggingface.co/am-azadi/bilingual-embedding-large_Fine_Tuned_1e) <!-- at revision 9212ebc911617536aa06e4fe49c33d6f93ace38a --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity <!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BilingualModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'The terrorists evaporated in seconds A very rare scene of the moment the Egyptian planes bombed the terrorist elements in Sinai Watch the video here NB Please all our followers on our page subscribe to our YouTube channel We will publish everything new on the ground Open the channel link ', 'A very rare scene of the moment the Egyptian planes bombed the terrorist elements in Sinai This picture is not of an Egyptian warplane, but of an Israeli plane', 'Cars melt due to hot weather in Saudi Arabia No, these cars did not melt due to hot weather', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 21,769 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 6 tokens</li><li>mean: 119.28 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 39.42 tokens</li><li>max: 98 tokens</li></ul> | * Samples: | sentence_0 | sentence_1 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>HAPPENING NOW ; KENYA ELECTRIC BUS IS ON FIRE ALONG KAREN ROAD. </code> | <code>Electric bus catches fire in Nairobi Video shows a methane-powered bus that caught fire in Italy, not an electric bus in Kenya</code> | | <code> RUPTLY Viewed 51,670 times 8 hours Snorr On the way down Khao Pak Thong Chai, route 3-4, Sattahip - Korat, all of them would have died. pity Incident 27 Jun.</code> | <code>Video showing road accidents in Thailand? This is a video published in a news report about a car crash in Russia.</code> | | <code>The image that went around the world! This photo won the best of the decade award and led to the author to depression, the author narrated in his description; "Cheetahs chased a mother deer and her 2 babies, she offered herself so that her children could escape and in the photo looks like she watches her babies run to safety as she is about to be devoured" How many times have you stopped to think how many sacrifices your parents do for you. While you have fun, laugh and you enjoy life, they give theirs.</code> | <code>Cheetahs chased a mother deer and she volunteered so her children could escape Behind the picture: Cheetahs learned from their mother how to capture prey</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 2 - `per_device_eval_batch_size`: 2 - `num_train_epochs`: 1 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 2 - `per_device_eval_batch_size`: 2 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs | Epoch | Step | Training Loss | |:------:|:-----:|:-------------:| | 0.0459 | 500 | 0.0135 | | 0.0919 | 1000 | 0.024 | | 0.1378 | 1500 | 0.0073 | | 0.1837 | 2000 | 0.0103 | | 0.2297 | 2500 | 0.0265 | | 0.2756 | 3000 | 0.0209 | | 0.3215 | 3500 | 0.0308 | | 0.3675 | 4000 | 0.0301 | | 0.4134 | 4500 | 0.0382 | | 0.4593 | 5000 | 0.0164 | | 0.5053 | 5500 | 0.0251 | | 0.5512 | 6000 | 0.0141 | | 0.5972 | 6500 | 0.0131 | | 0.6431 | 7000 | 0.006 | | 0.6890 | 7500 | 0.0261 | | 0.7350 | 8000 | 0.0111 | | 0.7809 | 8500 | 0.0089 | | 0.8268 | 9000 | 0.0201 | | 0.8728 | 9500 | 0.0175 | | 0.9187 | 10000 | 0.0086 | | 0.9646 | 10500 | 0.0049 | ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.4.1 - Transformers: 4.48.3 - PyTorch: 2.5.1+cu124 - Accelerate: 1.3.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION" ]
[ "PCR" ]
Non_BioNLP
huizhang0110/hui-embedding
huizhang0110
null
[ "mteb", "model-index", "region:us" ]
1,705
1,732
0
0
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[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
espnet/iwslt24_indic_en_bn_bpe_tc4000
espnet
null
[ "espnet", "audio", "speech-translation", "en", "bn", "dataset:iwslt24_indic", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
1,713
1,713
0
0
--- datasets: - iwslt24_indic language: - en - bn license: cc-by-4.0 tags: - espnet - audio - speech-translation --- ## ESPnet2 ST model ### `espnet/iwslt24_indic_en_bn_bpe_tc4000` This model was trained by cromz22 using iwslt24_indic recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 3a161c5ac0f74cc593410a4a32001073ed456580 pip install -e . cd egs2/iwslt24_indic/st1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/iwslt24_indic_en_bn_bpe_tc4000 ``` <!-- Generated by scripts/utils/show_translation_result.sh --> # RESULTS ## Environments - date: `Wed Apr 17 02:51:38 JST 2024` - python version: `3.10.14 (main, Mar 21 2024, 16:24:04) [GCC 11.2.0]` - espnet version: `espnet 202402` - pytorch version: `pytorch 2.1.0` - Git hash: `83c179ab842987cf01642df2db372aaae260df55` - Commit date: `Wed Apr 17 00:28:29 2024 +0900` ## st_train_st_conformer_raw_en_bn_bpe_tc4000 ### BLEU |dataset|score|verbose_score| |---|---|---| |decode_st_conformer_st_model_valid.acc.ave/dev.en-bn|2.1|19.7/3.6/1.0/0.3 (BP = 1.000 ratio = 1.185 hyp_len = 46094 ref_len = 38883)| ## ST config <details><summary>expand</summary> ``` config: conf/tuning/train_st_conformer.yaml print_config: false log_level: INFO drop_last_iter: false dry_run: false iterator_type: sequence valid_iterator_type: null output_dir: exp/st_train_st_conformer_raw_en_bn_bpe_tc4000 ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 80 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 2 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false use_adapter: false adapter: lora save_strategy: all adapter_conf: {} pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 25000000 valid_batch_bins: null train_shape_file: - exp/st_stats_raw_en_bn_bpe4000/train/speech_shape - exp/st_stats_raw_en_bn_bpe4000/train/text_shape.bpe - exp/st_stats_raw_en_bn_bpe4000/train/src_text_shape.bpe valid_shape_file: - exp/st_stats_raw_en_bn_bpe4000/valid/speech_shape - exp/st_stats_raw_en_bn_bpe4000/valid/text_shape.bpe - exp/st_stats_raw_en_bn_bpe4000/valid/src_text_shape.bpe batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 - 150 sort_in_batch: descending shuffle_within_batch: false sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 chunk_excluded_key_prefixes: [] chunk_default_fs: null train_data_path_and_name_and_type: - - dump/raw/train.en-bn/wav.scp - speech - kaldi_ark - - dump/raw/train.en-bn/text.tc.bn - text - text - - dump/raw/train.en-bn/text.lc.rm.en - src_text - text valid_data_path_and_name_and_type: - - dump/raw/dev.en-bn/wav.scp - speech - kaldi_ark - - dump/raw/dev.en-bn/text.tc.bn - text - text - - dump/raw/dev.en-bn/text.lc.rm.en - src_text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 allow_multi_rates: false valid_max_cache_size: null exclude_weight_decay: false exclude_weight_decay_conf: {} optim: adam optim_conf: lr: 0.002 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 token_list: - 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▁তিনি - ▁হিসাবে - জ - ▁এটা - ▁যন - '-' - ▁নতুন - ▁সে - ▁দিকে - ',' - ▁করেছে - ▁করেছিল - ▁রক - ▁রে - িষ - ▁যেতে - ▁দি - ▁উপর - ▁জলব - ▁শুধু - ▁থান - ▁রতি - ▁কি - ▁ল - ▁যাক - ▁পারেন - ▁কর - গ - ▁কয - ▁রস - ▁রথম - ▁যেখানে - ▁থাকে - ▁টা - ▁কাছে - ▁কথা - বচে - ▁লক - ▁সং - শ - ▁ঘ - ▁আগে - ▁োজন - ▁একই - ▁বারা - ▁করছে - ▁যার - ▁সালে - ▁দেখা - ংখ - ▁এখানে - ▁করেছিলেন - ▁ষণ - ▁কেন - যোগ - ▁আপনা - ▁ভাল - ▁মূল - ট - ▁তির - ▁সাহা - ▁বৈ - ম - ান - ▁খুব - ▁বছর - ▁ফ - ▁সর - ৃষ - েন - ▁দেশ - ▁রম - ▁অস - ▁বড - ▁কোম - কি - ▁ঠিক - ▁ধরে - ▁বিজ - ▁করবে - ফ - ▁গুরুত - ▁থা - ু - ▁রুত - ▁বিদ - ▁যারা - ▁দেখতে - ▁নি - ▁সাধারণ - ▁পূর - ▁করেছি - াও - ▁মান - ▁ভাব - বিষ - েষ - ▁যিই - রণ - ▁ছ - ▁করুন - ▁ধি - ▁উচ - ▁রতিটি - ▁পদ - ▁বিক - হ - ▁গল - ▁পরে - ৃদ - চিত - ▁রশ - ▁উ - ▁উচিত - ▁সহ - মধ - ▁চিত - ▁জীবন - ▁েক - ▁20 - ▁কখন - উন - ▁বেশিরভাগ - ▁ছোট - ▁রাস - ▁দিতে - ▁যাচ - ▁ঘুম - ুদ - ▁মানুষ - ▁কোন - দক - রূপ - ▁চাই - ▁বিষ - ▁রভাব - ▁থাকা - ▁দুর - ▁এ - রীক - ▁উপা - ▁দুটি - ▁বিশেষ - ▁আক - ▁অক - ▁বলতে - ▁আন - দীর - ▁ষে - ▁নেই - ▁ধন - ▁ষেত - ▁বলা - ▁তী - ▁রত - ▁পুনর - ▁সক - নিশ - ▁শেষ - ▁সিস - ▁আসলে - াম - এ - ণ - ▁ছা - ▁ঘন - ▁মার - মাধ - ▁ভাগ - ▁সঠিক - ▁কেউ - ▁ইতি - ▁কিভাবে - ▁শিল - ▁পার - ▁উদাহরণ - িং - ▁কারণে - ▁বল - ▁সুত - িজ - ▁রতিক - ▁ফির - ▁মানুষের - ▁লোক - ▁ভব - ▁সমাধান - ▁আসে - ▁চলে - িতে - ▁কেবল - ▁রী - ▁ঞানী - ▁নিজে - ভিন - ▁সেখানে - ▁অবস - বর - ▁যত - ▁খুঁজ - ▁কঠিন - ▁হাজার - ▁জানেন - ▁জানি - খ - ▁সব - ▁বে - ▁যমে - বিশ - ▁রহ - ▁ধান - ▁টার - ▁জিনিস - ▁থনীতি - ▁ধরনের - ▁সহজ - ▁তব - ▁রাজ - ▁তিত - ▁গেছে - পক - াহলে - িকল - ▁আলো - ▁রহণ - ▁করবেন - ▁10 - ▁অবশ - ং - ▁পনা - ▁পে - কারী - ▁ধে - িদ - তার - ▁যেমন - ▁চা - ▁তাপ - ▁যাপ - ▁দিন - ▁এত - ▁ছি - ▁নে - ▁সাম - ▁গত - তা - ▁অংশ - ▁রান - ছন - ▁বিত - ▁কোষ - ▁সরকার - ▁োগ - তি - বার - ▁বিশাল - ▁পেতে - ▁উপ - ▁চিন - '2' - ▁রাখা - ুর - ▁জিন - ▁বৈশ - ▁পানি - ▁গমন - ▁াই - ▁ভবত - ▁সন - ▁অগ - চুর - ▁পরিস - ▁েছি - ▁তিশালী - ▁শতাংশ - ▁ভিত - ▁বছরের - াল - ▁যাকসিন - ▁যবাদ - ▁রকৃত - ▁মত - ▁থাপন - ▁রণ - ▁আজ - ▁লোকেরা - ▁লা - ▁রের - ▁রিক - ▁ষতি - শব - ▁থাকতে - ▁বিল - ▁দেশে - ▁উভ - ▁মস - ▁জু - ▁রমণ - ▁ষমতা - ▁রদান - ▁যবস - নের - রুদ - ▁করেছেন - ▁সার - টিকে - ▁গাছ - ▁জীবা - গত - ▁মিলি - ▁ডলার - াং - ▁পণ - ▁রূপা - ▁ষম - ▁গা - ▁কল - নেও - ▁যাট - জন - ▁যথ - ▁পুরো - ▁অনুমান - ▁রাখতে - ▁যাস - এর - ▁েছিলেন - ▁লেখ - ▁পরিষ - ▁জল - ▁রঙ - ▁মাত - ▁বিনি - ▁জা - ▁তারপর - ▁তুলনা - ▁পৃথিবী - ▁খরচ - ▁বিবেচনা - ▁চল - ▁রিত - ▁টেলি - ▁েছিলাম - ▁টেম - ▁সি - বদ - ▁অনুস - ▁আলাদা - ▁তৃত - গুলিতে - ▁ভর - ▁রাপ - ানো - ▁সুযোগ - ▁মুহ - ▁মাথা - ▁সংক - ▁ভাবনা - ▁যাগ - সাথে - ▁মী - ▁যাত - ▁নীচে - ▁তোলে - ▁বাইরে - তির - ▁তিনটি - ▁বুঝ - ▁চিকি - ▁কোনও - ▁হার - ▁19 - ▁মক - ▁থিতি - ▁গবেষণা - ▁সরবরাহ - ▁তারপরে - ▁একক - ▁মের - ▁সৌর - ▁চাল - ▁মহিলা - ▁চর - ▁কোনো - ▁নীতি - ▁বস - ▁CO - ▁সবুজ - ▁অবশেষে - ▁যুৎ - ▁বেগ - ▁রাখে - ▁দুই - ▁ডে - ▁চান - ▁রোগ - ▁বলি - ▁রমাণ - ▁নিজ - ▁গি - ▁ভুল - ংক - ▁টের - ▁শহরে - ▁অত - ▁যাবে - মে - ▁শহর - ▁কের - ▁মহা - েবে - ▁কোথা - ▁সাইড - ▁নের - ির - ▁ঠ - গুলো - ফর - ▁তথ - ▁পানির - ▁চালি - ▁ভালো - ▁ধরণ - ▁ধারণ - ▁মাণ - ▁াল - ▁বিপ - ▁ভাষা - ▁দরকার - ▁রিট - ▁কো - ▁রদা - ▁মৃত - ▁ছেন - ▁যুতিক - ▁যকর - ▁লাস - ▁তমান - ▁মিশর - ▁রাম - ▁দল - ▁নিজের - ▁ডার - থায - ▁সারা - েও - োড - ▁সা - ▁রাতে - ▁বিস - টা - ▁ছিলেন - ▁ফলাফল - ▁ডাই - ▁ঞাসা - ▁মিথ - ▁নীল - ▁হিস - ▁চুল - ঘ - ▁যালে - ▁ষেপ - ▁বব - ▁যু - ▁বাধ - ▁দেশগুলি - ▁মানে - ▁ান - ৃশ - ▁াতে - ▁আশ - ▁খারাপ - ▁লাল - ূর - ▁ধার - ▁তুত - ষম - ▁পরিচিত - ▁বক - ▁ডা - ▁নাম - ▁জার - ▁ছিলাম - টোক - ▁তম - োক - ▁যবসা - ▁বার - ▁পথ - লম - ▁ধতি - ▁অনুভব - ▁কৌশল - ▁রসারিত - ▁আঘাত - ▁জিনিসগুলি - িন - ▁গতি - ▁অতির - ▁পাচ - াকে - ▁করছেন - াঙ - ▁মাই - ▁পা - ▁জানা - ▁নব - ▁আশা - ▁ধারণা - ▁অভ - ▁সুবিধা - ▁সবাই - না - েতু - ংস - মেন - ▁পাঁচ - ▁জীব - ▁নিষ - ▁হুমকি - ▁বালানি - ▁নিরাপদ - ূন - ▁বোধ - ▁যগুলি - ▁গে - রক - ▁চাপ - ▁রোটিন - নী - ▁যোগ - ▁রাণী - ▁ভারতে - ▁টির - ▁রকৃতি - ▁মহামারী - সের - ▁মে - ▁15 - ▁থনৈতিক - ▁ঝুঁকি - ▁রকাশ - ▁তিন - ▁সুস - ▁রাজা - ▁ডিস - ▁এড - ▁মারা - ▁টন - শীল - ▁নামে - ▁দু - জু - ▁উপাদান - ▁অপ - থ - ুষ - ▁পরিণত - ▁তত - ▁বেঁচে - ▁বালানী - ▁অনুম - ▁বেশ - ▁যানো - ▁ধমান - লে - ▁এগ - ▁থন - ▁আবার - ▁অসম - ময - ▁উপস - াস - ▁যমান - ▁শিক - রামর - ▁হাই - কাল - ▁থী - ▁ঞান - ▁পাদন - ▁রিকা - ▁দূরে - ▁হলে - ো - ▁ভিন - ▁নিক - ▁রাব - ৎ - ▁কোপ - ▁শী - লব - ▁দা - হত - ▁দেখেছি - ▁বোঝা - ▁টিক - ▁মরুভূমি - ▁বৃহ - তম - ▁তিগত - ▁অফ - ▁ষতা - ▁ফলে - ▁সীমা - িহ - ▁সেন - ▁যুদ - ▁মন - ▁দশকে - ▁সেগুলি - ▁গড - ▁যো - ▁রদ - ▁11 - ▁4 - ▁পরিবার - ▁ডিজাইন - ▁রজাতি - ▁হাসি - ▁নামক - ▁খাদ - ▁তো - ▁তিক - েক - সূর - ▁ভারত - ▁ইন - ▁যাপক - ▁আসা - ▁কিনা - ▁ঠান - ▁আত - ▁অব - ▁কোষে - ▁পুরুষ - ▁ডি - ▁রার - ▁সংগ - ▁যাকে - ▁থাকবে - ▁বিন - ▁ইংতাই - ▁মোমবাতি - ▁রাকৃতিক - ▁লোকেদের - ীকরণ - ▁রতিশ - ▁খ - ▁চারপাশে - ▁এশ - ▁খনি - ▁উপরে - ▁রুতি - ▁পরিমাণে - ▁আসন - ▁বিভ - পড - ▁দূর - ▁1 - ▁বেড - ▁রিস - ▁কোষগুলি - ▁আগ - ▁একে - ▁রাক - ▁শহরগুলি - ▁সেট - েই - তটা - ▁শরীর - ▁পরিমাণ - ▁খিঁচুনি - ▁ফেলে - গায - ▁জো - দিনের - নির - ▁ইমিউন - ▁যাল - ▁আস - ▁অপর - ▁বাচ - ▁কত - ৈর - ▁তরে - ▁রেক - ▁করছি - ▁অনু - ▁করলে - ▁আল - ▁আধ - ▁ভাবন - ▁এমআরএনএ - ▁টেকসই - ▁রোজান - ▁পরিচালনা - ▁যুত - ▁বছরে - ▁যালি - ▁ডেটা - ▁একাধিক - ▁দর - ▁পিছনে - ▁মাটি - ▁যতা - ▁বদা - ডিগ - ▁এগুলি - ▁ঞতা - ▁আচরণ - লা - ফোর - ▁একবার - ▁সহা - ▁শন - টিস - ▁রতিরোধ - ▁আরেক - ▁6 - াক - কার - লি - বা - ▁সেরা - ▁বংস - ▁লি - ▁বপ - ▁অপসারণ - s - ▁মোকাবেলা - ▁রবেশ - ▁ইলেক - ▁চিকিৎসা - ▁ভেঙ - ▁বিপরীত - ▁রধান - মূলক - ▁হত - ▁পাশা - ▁পুর - ▁দাস - ▁জনস - ▁মডেল - নি - োয - ▁থক - ▁আপ - াচ - রিদ - ছিলাম - ▁মা - বে - ▁এলাকা - ▁দশক - ▁ঘটনা - ভূত - ▁কন - ▁শতা - ▁তরা - ▁ভার - রবর - িনি - ▁খা - ▁নিজেদের - রূপে - ▁মোট - ▁কাঠামো - ▁যোগাযোগ - ▁বীকার - ▁ভূমিকা - বু - ▁ঠী - ▁ডিক - ▁জোর - '20' - ▁আমেরিকান - ▁সাল - ▁েন - ▁নৈতিক - ঠা - শত - াপী - ▁সপ - াতে - বেক - ▁ফল - পত - ▁জীবনে - ▁গো - ▁যাই - ▁অদ - ▁নত - ▁ডাক - ▁সেস - কৃত - ▁টিভ - ▁জটিল - হীন - ▁কঠোর - ▁চাহিদা - ▁মুখোমুখি - ▁রকৌশলী - ▁রাচীন - ▁উৎপাদন - ▁রগতি - ▁লেষণ - ▁জাতিগত - ▁শোষণ - ▁খাবার - ▁ধীর - ▁পারবেন - ুনিক - ▁ভিতরে - ▁ভাইরাস - ▁দেখি - তিতে - ▁দেবে - কল - ▁লেট - ▁েছেন - ৃত - ▁াম - ▁ইস - ▁নিচে - ▁চম - ▁গদ - ▁ধু - ▁তুলত - ▁টেবিলে - পী - মা - ▁আকার - ▁অণু - ▁অনুপ - ▁টে - ▁নিত - ▁মূ - ▁ওষুধ - ▁কাছাকাছি - ▁ডিএনএ - ▁সুপারনোভা - ▁শুনতে - ▁গপাল - ▁অভাব - ▁যপ - ▁মাঝ - নাক - ▁আটকে - ▁বিচ - ▁গভীর - ▁যজনক - মি - ▁200 - টিক - ▁যেভাবে - ▁পাশে - ▁রতিদ - ▁সেলস - ▁ফেল - ▁নতি - ▁বাধা - ▁বজ - ▁মানব - ছে - ▁থতা - াই - ▁শতাংশের - ▁শান - ▁হন - ▁নিম - ▁শিকার - পাশ - বৃত - ▁সমব - ▁5 - েছে - ▁তেলাপোকা - ▁ঝ - ▁বসে - ▁গুণ - ▁ণবাদ - ▁লিপ - ▁যব - ▁ঘটে - তী - ▁আইন - ▁জানে - ▁আশেপাশে - ▁নাগরিক - ▁গঠন - ▁তরীণ - ▁যাটার - ▁অভিজ - ▁সংযোগ - ▁চরম - ▁করব - জেন - ▁পানিগুলি - ▁ডিম - লার - াফল - ▁জলে - বহা - ▁উজ - ▁সামনে - ▁30 - ▁থিত - াগত - ▁ঝাঁক - ▁পগুলি - উড - ▁যাম - ▁কুল - ▁থাগুলি - ▁মানসিক - ▁বাঁচ - ▁পরব - ▁ডেন - ▁থে - ▁রেস - ▁ছবি - ▁কাছ - ▁সমান - বন - ▁পান - ▁সিম - ▁2 - ▁সহক - ▁ঞা - ▁লিপিড - ▁অধ - ▁কোভিড - ▁অবদান - ▁যোগফল - ▁সোনা - ▁েকটি - ▁কালো - ▁কমাতে - ▁গবেষকরা - ▁অনন - ▁দেখে - মান - ▁মুখী - ▁রজনন - ▁সূচক - ▁জাত - টাই - ▁পরিবেশ - ▁আদ - ▁ইউরোপ - ▁আচ - ▁পেট - ▁লাগ - ▁ছিন - ▁যাশ - ▁জি - ▁চিম - োষ - ▁মু - ▁যটি - ▁গেলে - ▁ষিণ - ▁ভিদ - ▁বেত - ▁রেম - ▁বিপর - ▁তিদের - েশন - লেন - ভুক - ▁রোগী - ▁পাত - ▁চার - বসম - ▁রাণ - ▁ঘো - ▁আরো - ▁এম - মন - ুরক - ▁খেলা - দিকে - োজ - ▁রো - ▁বাভাবিক - '0000' - ▁যবহ - ▁নিন - ▁ইতিহাস - ▁শত - ▁পরিচ - ▁রাথমিক - ▁ভাইপার - ▁জনগণ - ▁থাকলে - ▁শোনা - ▁ঘুর - ▁বিয - ▁লোব - ▁বাণ - ▁পরিবহন - ▁যবান - ▁সাদা - ▁ওজন - ▁কিছুটা - ▁চাকা - ▁অপে - ▁ঠে - ▁মিলিত - ▁সেক - ▁বাকি - ▁শরীরে - ▁যেকের - েট - মাস - ইচ - ▁পালি - ▁রচ - দার - ▁আকাশে - ▁মুখে - ারি - ালন - ▁রবাহ - ▁কিলোমিটার - ▁আকারে - ▁শে - ারিদ - ▁সুন - ভাগ - পু - ▁লোকের - '50' - ▁বাবা - ▁মিত - সাম - ছেন - বি - ▁যৌন - ▁রবণ - মণ - ▁বাক - ▁ধেক - ▁বহু - ▁অদলবদল - ▁তেজনা - ▁বসবাস - ▁পরিমাপ - ▁রাজনৈতিক - ▁আবাস - ▁সংকেত - ▁পরিবেশগত - ▁বিকাশ - ▁বিগুণ - ▁যানেল - ▁যাঁ - ▁ইংরেজি - ▁অভি - ▁মিনিটের - াধিক - ▁যিকার - ▁জানত - ▁রজন - ▁অসু - রকম - ▁থিক - ▁রেখে - ▁জেনে - ▁3 - ণনা - ▁নারী - ▁সংয - াত - ▁টেমের - ▁রেড - লান - ▁ানো - ▁সাহ - ▁চাচ - ▁কাজটি - ▁রিড - ▁থল - ▁পন - ▁রন - াজার - ▁রিন - ▁কোপে - ▁গন - ▁সৌ - পথে - ▁লুপ - ▁সূ - ▁ভাই - ▁ষিত - ▁কেল - ▁ওঠে - ▁70 - ▁জাহাজ - ৷ - ▁থেরাপি - ▁চাকরি - ▁মৌলিক - ▁চাঁদ - ▁রতিফল - ▁নেতৃ - ▁শাসন - ▁খবর - ▁নাটক - ▁ঘুমানো - ▁করছিলাম - ▁ইতিহাসে - ▁চালানো - ▁ষরিক - ▁ষত - ▁বীপ - ▁আমেরিকানদের - হিত - ▁করছিল - লাম - ▁আউট - ▁যাটারি - ▁কথোপকথন - ▁তোলা - ▁থানে - সংশ - ▁দেন - ▁ঘট - ▁বাতাস - ▁নিউ - ▁নেট - ামাজ - জনক - ▁দাম - শক - ূ - ▁যাকসিনগুলি - ▁নম - হার - ▁রাসা - ▁শিশু - োল - ালের - ▁কোর - ▁16 - ▁রাত - ▁চালা - ▁100 - ▁সমাজ - কেন - ▁তাহ - ভূমি - ▁কমে - ▁মাস - াময - ▁12 - শিত - ▁পেশী - মাক - a - ▁ফোকাস - ▁শিখেছি - ▁তহবিল - ▁রতিবেশী - ▁রভু - ▁উপকূল - ▁দুধ - ▁পরিচাল - ▁আলোক - ▁বলুন - ▁সিজেন - ▁দাবি - ▁দূষণ - ▁শতকে - ▁যতক - ▁পাঠানো - ▁রাণিত - ▁রোগীর - ▁কাউ - ▁রাখবে - ▁বোত - ▁জানতে - টিভ - ▁ঞানিক - ষণা - ▁গেম - ▁পুনরা - োচ - ▁মিল - ▁হৃদয - ▁করেছিলাম - ▁মুখ - ▁পোর - বিদ - ▁রহস - ▁পাবল - ৃ - ▁কেরি - ▁রণে - ▁আজকে - ▁অপরি - ংশ - ▁মহিলার - ▁রগুলি - ালোক - েমন - ▁জিত - ▁ষক - ▁হাতি - ▁একা - ষিক - ▁হাতে - ▁াস - তুর - ▁কা - ▁কোণ - ▁দশকের - ফিল - ▁গুরুতর - ▁বলছি - ▁পাই - ▁আমেরিকা - ▁8 - ▁বাজার - াদী - ▁চোখে - ▁রমে - '3' - িপিং - ▁দাঁ - ▁তরুণ - '9' - ▁নদী - ▁যাপন - ▁চলেছে - ▁পাঠ - ▁অবকাঠামো - ▁কবুতর - ▁টুকরো - ▁অনুবাদ - ▁একটু - ▁জিডিপি - ▁নমুনা - ▁দখল - ▁যমজ - ▁24 - ▁রোজেন - ▁যাপচার - '26' - ▁শারীরিক - ▁তুলনামূলক - ▁কিত - হাউস - ▁সফল - ▁দরজা - ▁নিরাপ - ▁যালসি - ▁গরম - ▁দেখেন - ▁রমিক - ▁টাও - ▁গম - ▁তিগুলি - ▁চারটি - ▁দেবতা - ▁েল - ▁তবতা - ▁শহ - ▁বিতা - ▁দৈ - ▁মাক - ▁সংকট - ▁অনুসার - গুণ - ▁ইহুদি - ▁ণবাদী - ▁রুটি - ▁মালি - ▁বালি - ▁পুনরু - াশ - ▁জনক - ▁পৌঁছা - ▁উপাদানগুলি - ▁80 - ▁ইক - ▁ষি - ▁কোনটি - ▁কুশ - দুর - রি - োগ - ▁করেনি - ুল - নিয - ▁নিব - ▁জের - িকভাবে - ▁শুক - ▁বান - ▁রাণীর - ▁মগুলি - ুরে - ▁তাত - ▁শিখ - ▁কক - ুনি - ▁রেই - ▁কাট - ▁তিকর - পোস - ▁খালি - ▁যাগুলি - ▁বনাইজ - ▁ভূ - ▁যেগুলি - ▁লাভ - ▁গেল - ▁জাতিক - ▁পরিশ - ▁উপরের - কর - ▁মেশিন - েল - ▁ছেলে - ▁সু - ছিল - ▁জাম - ▁শানবো - সাশ - ূত - ▁থিতিশীল - ▁বো - ▁তুলা - ▁বকে - ▁অবি - '00' - ▁থানগুলি - ালকা - ▁লু - ▁ইউ - ▁অধিকার - ▁রাইলোবাইট - ▁টেরিওটাইপ - ানদের - ▁মিটার - ▁জাতি - ▁ভালবাসা - ▁সীমিত - ▁অনুশীলন - ▁মোনালিসা - ▁জীবনযাপন - ▁আলোচনা - ▁লোরো - ▁আগামী - ▁তেজিত - ▁রনালী - ▁2030 - ▁উঠেছে - ▁আগুন - ▁নেতিবাচক - ▁যাকটাস - ৎকার - ▁যালাক - ▁থনীতিবিদ - ▁বিরল - ▁লেজ - ▁পৌঁছানো - ▁বীকৃত - ▁পাহা - ▁চেইন - ▁যামেরা - ▁রু - ▁রেখা - মস - ▁দেখানো - ▁চীন - ▁জনসাধারণ - ▁তাব - ▁রাজি - েড - ▁ছদ - ▁ডিং - ▁তালিকা - নো - ▁পরিবেশে - ▁ফি - ▁রাউন - ▁রোল - দৌ - ▁চোখ - ▁সাক - ▁রোম - ▁ফাঁদ - শন - ▁ডস - '5' - ▁সাই - াজ - ▁শেখা - ▁জিনগুলি - িণ - ▁টিকেল - কোণ - ▁গান - ▁সেতু - ▁সরকারকে - ▁মাসের - ▁রাপক - ▁খাল - ▁কান - মিশ - শি - দস - কোনো - ▁শিবির - ▁হো - ▁ছাত - সরি - ▁রহে - ▁পথে - ▁বলবে - ▁এন - যুদ - ▁ভু - মনী - সে - ▁অংশে - ▁খেল - জার - ▁াট - ▁দী - '7' - ইহ - ▁সিরি - ▁লাইন - ▁মাসে - ▁াদে - ▁চক - ▁ছেদ - ▁খু - ▁ডল - ▁রীক - ▁বলছে - ▁এসে - ▁উপকরণ - কিং - ▁ভাইরা - ▁ঐতিহ - '&' - ; - o - p - Ş - ‍ - ▁চুনাপাথর - ▁মেলাটোনিন - ▁রাজধানী - ▁ষুধা - ▁সবকিছু - ▁অপারেটর - ▁মবেশ - ▁হরমোন - ▁গাছপালা - ▁উপভাষা - ▁আইডি - ▁যসেবা - ▁দেশাবলী - ▁যানবাহন - ারের - ▁হারানো - ▁তরাধিকার - ▁পাবেন - ▁বিকৃত - ▁ষেপণ - ▁জেট - ▁অংশগ - ▁জমি - ▁অভিযোজন - ▁বাণী - ▁বিবর - ▁যাধি - ▁হোম - ▁যাটি - ▁চলগুলি - ▁বলেছিল - ▁টাকা - ▁খোলা - ▁মীদের - লো - ▁রচার - ▁রেণী - ▁সামর - ▁রহী - ▁মানবতা - ▁রতিদিন - ▁দেহ - ▁নিজেদেরকে - ▁যাপার - ▁াগুলি - ▁ভারতকে - ধিক - বিরক - ▁গর - ▁টান - ▁দান - ▁90 - ▁কাজে - ▁িগুলি - ▁বাদ - ▁সাত - ▁25 - ▁হবেন - ▁লেখক - বাদী - াউন - াদের - ▁পেরেছি - ▁পক - ▁পাইক - '1' - ▁1000 - িস - ▁অল - ▁রাশ - ▁উপন - ▁শিকারী - সাধারণ - ভার - ▁ষিণে - ▁বুদ - ▁পশ - ▁ভুলে - ▁সাপ - ▁রিজ - াইড - ▁ভূত - ▁50 - ▁লাগে - ▁বারে - দিন - ▁দৃ - তন - ▁পাদ - '8' - ▁আট - ▁আকাশ - ▁নিচ - ▁বিগ - '6' - চে - ▁খুল - ▁ভূগ - ▁দাতা - ▁বলেছি - ▁সুলতান - পর - কুচি - ▁তনশীল - ▁এতটা - ▁মানি - ▁অথ - ীন - তুল - ▁লাই - ▁পাখি - ▁রোধ - ▁নিদ - ধ - ▁বাধীন - ▁এসেছি - ঢ - ▁ঘর - তিবাচক - ▁ডিভাইস - ▁মোটামুটি - T - ▁পৃথক - ▁যালঘু - ▁সহযোগিতা - ▁পুনঃ - ▁আবেগ - ▁যকলাপ - ▁ঝিল - ▁নিঃসরণ - ▁আংশিক - ▁চিৎকার - ▁লিওন - ▁মনোযোগ - ▁আবেদন - ▁বিবেচ - ▁আছি - ▁ফসল - ▁পোরেশনগুলি - ▁পেরু - ▁বিতরণ - ▁লাইট - ▁কিলো - ▁এসেছে - ▁বহি - ▁ইউনি - ▁বামী - ▁কভার - ুব - ▁ফলাফলগুলি - ▁কৃষি - ▁তাক - কারক - ▁যাকশন - ▁পাঠা - ▁নেতা - ▁খে - ▁সকলের - ▁তনে - নাইট - পুর - ডাউন - ▁যৌনতা - ▁ডান - রম - ▁শীত - ▁চলা - ▁কানের - ▁মিং - ▁মুদ - ▁শাসক - ▁গোপন - ▁তোমা - ▁কৃতি - ▁টেক - ▁রেট - ▁সকালে - ▁যাবেন - ▁জান - ▁পরিসরে - ▁ফুল - ▁হাত - ▁এভাবে - াইভ - পূরণ - ▁হলেন - ▁শিশুর - শীর - ▁ডানা - পতি - ▁মাতা - ▁শুনে - ▁কাটা - ▁ধারণাটি - ▁যিক - ছা - ▁গাছে - ▁রমা - ▁সমাধানে - সম - ীদের - ▁মাল - িড - আই - ▁দার - মার - ুন - ▁ভে - ▁চতা - ▁400 - ▁বাহ - ▁ইতাল - লস - ▁রাইভ - ▁এরিক - ▁থি - ▁হারি - মাঝ - েইনফ - ▁পেরেছিল - '4' - ▁টিকে - েব - থাক - ▁শর - ▁ডাকা - ▁রেখেছিল - ▁তুলে - ▁অসুবিধা - ▁নগুলি - ▁আই - ▁টু - ▁শেষে - ▁জনপ - খানে - ▁বহুল - ▁দেখেছিল - ▁ঋণ - ▁রুপ - ▁দূষ - ▁মহাকা - ০ - ▁আরএনএ - ▁নাৎসি - ▁সুপারহিরো - ▁রতিযোগিতা - ▁পাইলট - ▁বজনীন - ▁ঐতিহাসিক - ▁চিঠি - ▁মরিসন - ▁বাজেট - ▁সুপারিশ - ▁পুলিশ - ▁দেখুন - ▁অভিযান - ▁রাহক - ▁যবধান - ▁রতিযোগী - ▁ডানদিকে - ▁পাগল - ▁খনন - ▁ঘটছে - ▁বেষণ - ▁সংবেদন - ▁লাগানো - ▁দেখেছিলেন - ▁ঢে - ▁পারবে - ▁কাশন - ▁বিকেলে - ▁শুনেছেন - ▁এসেছিল - ▁যাসিড - ▁নেমে - ▁3- - ▁রশংস - ▁বাহু - ▁করত - ▁রঙে - গদ - ▁40 - ▁গক - ▁শোষ - ▁জোট - ▁গণনা - ▁হাঁট - ▁বেস - ▁রিলি - ▁টিং - ▁দাদা - ▁সরকারগুলি - ▁অংশগুলি - ▁দোষ - ▁খলা - ▁করতেন - ▁জাপান - ▁আধি - ▁বাহিনী - ঘাত - ▁সরকারী - ▁থিতিতে - ▁পারেনি - ▁যাংক - াসের - াইজ - ▁মেট - ঃ - ▁কুলে - ▁বাচন - ▁কোড - ▁মাঝা - ▁রেমে - েইন - রমাণব - ▁যাগগুলি - বহন - বাজারে - ▁টেবিল - ▁চারা - ▁রাখ - ▁ঠানিক - ▁বংসা - ▁ধকারে - ▁ঝুল - ▁18 - ▁থাকেন - ▁কৃষ - ▁তক - ▁চি - বিরোধ - হন - ▁নাক - ▁যাতন - মিন - দা - চার - ▁গগুলি - ▁আছেন - '21' - ▁ডলে - ▁তিটি - পা - ▁রোত - ▁রকেট - ▁তাহে - ▁পাস - ুলার - ▁বাঁচা - ▁আসেন - ▁যথায - ▁কৃতিক - ▁ধকার - ▁পরিষেবা - বিক - ▁তগুলি - ▁যাণ - ▁দেবী - ▁ষর - ▁সীমান - ▁কৃত - সি - ছি - ▁পিতামাতার - ভান - ▁মেঘ - ▁আরি - ▁ফাঁক - েজ - ধি - ▁পরি - ▁মেটা - টো - পাস - নে - তিগ - োপ - মুখী - ▁যদ - জীবন - '0' - ▁অতি - ফো - ▁মিনিট - ▁রিপ - ▁মিক - ▁পিছ - ▁কু - ▁যানবাহনে - ▁শো - ▁নাগা - বেন - ▁পোরেশন - ▁োগকারী - শালী - ▁জাতিসংঘ - ৃৎপ - ▁ডিজিটাল - ▁নিখুঁত - ▁পিতামহ - ▁মহাসাগর - ▁রিলোবাইট - ▁রীতদাস - ▁রোপচার - ▁সেনাবাহিনী - ▁অপারেশন - ▁জরুরী - ▁শেলোব - P - ▁অনুভূমিক - ▁যাটেলাইট - ▁বাছাই - ▁যকারিতা - ▁আঠালো - ▁কেটলি - ▁সৈন - ▁ইনজেকশন - ▁একাকী - ▁রতিকৃতি - ▁মালিকানা - ▁রাকচার - ▁তুলেছে - ▁কবিতা - ▁আসুন - কোহ - ▁বুশ - মলত - ▁আসছে - ▁আশাবাদী - ▁আসবে - ▁উৎসাহ - ▁বোতাম - পোকা - ▁অধীন - ▁একমত - ▁ভেবেছিল - ▁সুখ - ▁গঠিত - ▁নজর - ▁বিবরণ - ▁201 - ▁দেখবে - ▁লিনিক - ছ - ৌক - ▁সুইচ - ▁পরিণতি - ▁মোটা - ▁উৎপ - ▁লেটগুলি - ▁পাথর - ▁ফেলবে - ▁ফরাস - ▁হৃদ - িগার - ▁মাপ - ▁ভাঙ - ফুস - ▁ধুদের - ▁বিরতি - ▁কতা - ▁লাইস - ▁দিল - ▁থাকি - ▁নীতিগুলি - ▁আবদ - ▁রেলি - ▁পেস - ▁মাইক - ▁টেমগুলি - ▁গু - ▁টেশন - ▁গেট - নশীল - ▁লুক - ▁পরাজ - ▁পাঁচটি - ▁বতন - ▁পাবে - ▁রোমান - ▁বাপক - ▁লাইনের - ▁00 - পোর - ▁উঠ - ▁17 - ▁যাতি - ▁জাল - বাইন - ▁ঘটা - ▁কমান - ▁ইমে - ▁দগুলি - ▁উপয - ▁হতাশা - ▁যুতে - ▁নিষি - ভ - ▁সেল - োর - ▁ফিল - ▁সিটি - ▁ভবন - ▁দীপ - ▁194 - ▁ষাগুলি - ▁যাগে - ▁আবর - ▁সকল - মিড - ▁টিকেলগুলি - ▁কারণগুলি - ▁দিক - ▁হেল - ▁বিট - ▁রেরণা - ▁কুশি - ▁ঘোরা - ▁ধরা - ▁সী - ফি - ▁রবৃ - ▁রোটিনে - ▁কাজগুলি - ▁মহাকাশে - ামগ - ▁অনেকের - ▁পলি - ফিক - ▁রহণকারী - ▁বিধ - রেস - ▁লোককে - ▁মহাদেশ - ুত - ▁ণতা - ▁রপ - ▁মিশ - ▁উৎস - ▁গার - কেটে - গো - মেডি - ▁লেখা - ▁ভিদে - ▁ষী - ▁দিনে - বশেষ - ▁দেশটি - ▁মেস - ▁বিচারে - ৌ - ▁ডিত - ▁আব - ▁মহাকাশ - ▁রেডি - ▁36 - ▁22 - ▁10000 - োস - ▁বুজ - কেল - ▁বাতাসে - েটর - ীর - ▁বেল - ▁বীপে - দন - লাইন - ূপ - ▁সাহারা - ▁রমণে - ▁হাস - ▁েজ - ▁বলতা - ▁জুন - কোস - ▁হই - ▁মজা - ▁নটি - ▁করণ - বিজ - ▁যেকোন - াবে - াদা - ▁রুট - তিক - ▁থের - ▁সহজে - ▁তাকা - ▁গবেষক - ▁ধর - ▁রাইড - ▁এলোমেলো - ▁উঁচু - ▁উদযাপন - ▁কীটনাশক - ▁রতিনিধি - ▁শিরোনাম - ▁শৈশব - ▁াকলাপ - ▁এনকোড - ▁মজুরি - ▁লাটিপাস - ফেডারে - ▁থেরানোস - ▁মনোনিবেশ - ▁ইটনভিল - ▁লুরোস - ▁জরিপ - ▁টিউমার - ▁মনিকা - ▁সমাবেশ - ▁বাসনালী - ▁ইংল - ▁খাঁচা - ▁জীবিকা - ▁গৃহ - ▁ভিডিও - ▁বেলারুশ - ▁অধিকাংশ - ▁রিগস - ▁বাভাস - ▁তুলবে - ▁ঝাঁপ - ▁পোশাক - ▁খলিল - ▁রতিবাদ - ▁সাফো - ▁আসল - ▁সহিংসতা - ▁সমাধি - ▁কমিশন - ▁বিদেশ - ▁রেখেছিলেন - ▁রাইম - ▁কিং - ▁ধতিগত - ▁টাইন - ▁অংশীদারদের - ▁অনুভূতি - থার - ▁লাইম - ▁বীজন - ▁বিমান - ▁রপাতি - ▁কোলে - ▁যানেলগুলি - ুঁ - ▁লিপিডগুলি - িশোধ - ▁সেগুলো - ▁শিশুদের - ▁লাফ - ▁বেকার - ▁সরানো - ভাইরাস - ▁অনুরোধ - ▁শনিক - ▁মালিক - ▁রিকান - ▁জমা - ▁ফাঁ - ▁অনুমোদন - ▁করিনি - ▁আবি - ▁গণত - ▁সভ - ▁কমানো - ▁দীতে - ▁তৃতা - ▁রতিরোধী - ▁যুট - ▁টাল - িচ - ▁রোপণ - ▁বিবাহ - বহুল - ▁রবণতা - ▁করলেন - রিকানদের - ▁দাঁত - ▁আপস - ▁যাকিং - ▁যবাহ - ▁জে - ▁বোঝাতে - ▁রামী - ▁রুব - ▁2000 - ▁মাছ - ▁ারিং - ▁জীবাণু - ▁লিনার - ▁ফুট - ▁ধাপ - চাপ - আইনি - ভাল - গম - ▁লেগে - লুপ - ▁কাপ - ▁রহটি - দূর - শাস - ▁টিমে - ▁ঘটনাটি - ▁কিলোমিটারের - ▁সংগঠ - থিত - ▁অণুগুলি - ▁বীর - ▁সবে - ▁করুক - ▁লিফটে - ▁সমাজে - ▁ারশ - ▁খরা - ▁তেল - ▁আঁক - ▁চেল - পশ - ▁পরিপ - ▁শহরটি - ▁লোড - েকটি - ▁বিচার - ▁লাগা - বল - ▁লাইটে - ▁ভূমি - ▁ফার - সব - ▁গণিত - ▁চির - ▁পৌঁছে - লিপি - ▁ালা - াপ - ▁আনা - ▁পানিটি - চক - ▁186 - াংস - িডা - ▁একদিন - ▁7 - ▁হারা - কারীদের - ুখ - িএস - ▁দশ - োঁ - ▁অফিসে - ▁মুছ - িশ - ▁সিং - ▁াশা - ▁75 - ▁কাঠ - ▁সাপে - '11' - ▁যদেব - েম - ▁ারগুলি - কোষ - ▁ফোন - সেট - ▁কোট - ▁দলগুলি - িটি - ▁শুরুতে - বিয - তীতে - িঁ - ▁রেন - ▁দামে - করা - ▁সেটা - ▁ধিত - দল - লিক - ▁টল - ▁রোস - ▁জেনি - '60' - ▁তাকান - ▁যাং - ▁পাতা - ▁ো - ▁পরিক - ▁একবারে - ▁কথোপকথনে - ▁সমতা - ▁ইউরোপে - ▁দির - হো - শু - ▁রিডে - িদর - ▁জৈব - ▁জাদু - ▁যালো - ▁উৎ - '15' - টল - ▁সুই - ▁চত - াবধানে - ▁অনুমোদ - ▁এখান - ▁কিশোর - ালোচনা - িছু - ▁কাগজে - ▁তরল - ▁বিরত - ▁সমীক - ▁রামক - ▁অংশীদার - বাজ - ▁খামার - বেদন - ▁01 - ▁ধাঁধা - ▁যাথোজেন - ৫ - ৭ - ▁আনুমানিক - ▁কমিউনিটি - ▁করোনাভাইরাস - ▁চাবিকাঠি - ▁জরুরি - ▁তঃসংয - ▁তাভাবনা - ▁নকশা - ▁সহানুভূতি - ▁অভিনেতা - ▁ওভাররাইড - ▁মামালেক - ▁যামিগডালা - ▁হতবাক - ▁পুঁজিবাদ - ▁মেঝে - ▁বপুরুষ - ▁জেগোটা - ▁1970 - কাহিনী - ▁বিবৃতি - ▁বিরোধিতা - ▁আইনজীবী - ▁মচারী - ▁থাপিত - ▁ঞাপন - ▁লেবেল - ▁মামলা - ▁কোলাহল - ▁রচারণা - ▁সোলার - '99' - ▁14 - ▁দোলন - ▁গিগা - ▁ভীক - ▁ঘটবে - ▁আপাত - ▁ফেলেছিল - ▁লাগবে - ▁দেখছেন - ▁যালসাই - '35' - ▁উপভ - ▁বরাবর - ▁ঘটেছে - ▁ভেবেছিলেন - লিভার - ▁পেরেছিলাম - ▁নিউরন - ▁আমূল - ▁ইরানে - ▁সমতল - ▁ওভার - ▁আদেশ - ▁কাঁটা - ▁ধারনা - ▁যুবক - ▁এসেছিলেন - ▁তানুকি - ▁খামারগুলি - ▁ণালী - োফা - ▁দুজন - ▁ছুট - ▁চৌ - ▁সিরিজ - ▁বলেছিলেন - ▁উপক - ধকতা - ▁খুঁজছেন - ▁জস - ▁সচেতন - ▁করছিলেন - ▁লিটার - ▁পিটার - ▁রথা - ▁ষমা - ▁নথি - ▁টোট - ▁জামগুলি - ▁কাগজ - ▁তকরণ - াবলী - ▁পেশীগুলি - ▁ঋণী - ▁বছরগুলিতে - ▁কেপ - ▁নেহ - ▁সেবা - ▁তুলো - সাঁ - ▁অভিবাসী - ▁পৌঁছেছে - ▁চারণ - ▁হেড - ▁উঠে - ▁যাডি - ▁রাইভার - ▁বেনি - ▁আইল - ▁সৃজনশীলতা - ুমি - ▁কোরবা - ▁পারব - চিং - ▁চলেছেন - ▁জীবনযা - বসতি - ▁রিফ - ▁ওঠেন - ▁ছবিটি - ▁টাফ - ▁সভা - ▁ঘাম - জগতে - ▁রঙগুলি - ▁বাই - ▁তাৎ - ▁পানী - ▁শুনি - শে - ▁টেট - ▁কারখানার - ▁থাকবেন - ▁যানগত - াইরে - ▁দো - ▁কাঁ - ▁সজ - ▁থাংশ - তীত - ▁জেনিস - ▁মি - সিস - ▁তাকালে - োত - পার - ▁মোহ - ▁পিট - ▁টাপো - গান - ▁জিও - ▁যাদা - ▁হাম - ▁মানিত - ▁পাচার - ▁সাহসী - ▁মানগুলি - '16' - ুনির - ▁ফটোগ - ▁টাইম - ▁পৃ - ▁বংশ - ▁রাণু - ▁লট - ▁মৃতি - অপস - ▁27 - '23' - টে - হারে - নুপাত - ▁শট - ▁ফেলা - ▁পশু - ▁গেছেন - ▁জারি - ▁রমিত - ▁রোতা - টিং - ▁জেনারেল - ▁সৎ - ▁লেন - ▁বাগত - ▁রমণকারী - ▁চিতভাবে - ▁বাসা - ▁মডেলগুলি - ▁টেন - ▁গুর - াগুলি - দেবী - ▁রোড - দাতা - ▁পরিবারগুলি - ▁টানা - লগ - ▁রিটাউনে - কিলোমিটার - ▁রতা - লাভ - বৈ - ▁কাম - কন - ▁বাব - ▁সুবিধাগুলি - ▁কগুলি - ▁থীর - ▁বিকভাবে - রিশ - ▁বই - লিস - ▁নগ - দেশ - ▁যৎ - ▁দূরব - ▁রাইভে - ▁শিলা - ▁চুরি - মোন - ▁অতীতে - ▁সির - ▁দেখাতে - ▁হাব - ▁কেলে - সোস - ▁ডাকে - ▁আলোকব - ▁তান - ▁ামি - টক - ▁দানি - ▁ডগুলি - ▁পেরে - ▁কেনা - ▁ষণিক - ▁কুশে - টার - ▁তৃপ - ▁নেন - ▁চাপা - ভা - দান - ▁বিধা - ▁যাকেজ - েলে - ▁গোল - গন - পরি - ▁যাসে - ছিলেন - ▁চালান - ▁নতা - ▁যাশন - ▁নাল - ▁কোপটি - িবাসী - বশ - িরোধী - ▁অনুগ - সিলি - মত - ▁মুন - ▁ঞানে - কালে - ▁চিল - েছিল - ▁পরিত - ▁যথা - ▁যাকর - োট - ইনস - ▁মিলে - তঃ - ▁সিএ - ▁েলস - শেষে - ▁লোম - জা - ▁দেরি - ▁রল - টেক - ▁সাহস - ▁এইচ - ▁মনো - ▁রেরণ - ▁পালা - নিক - ▁বাঁকা - ছুক - াইট - ▁ফর - ▁আটক - ▁দটি - ▁রাফ - ▁মিস - ▁ধা - ▁পরিবারে - ▁উঠত - নুষ - োম - োদ - খানার - ▁অশ - িরে - বিত - ভিল - ▁ধুত - ▁পাব - ▁রেখেছি - িটা - ৈ - াগন - ▁কামান - টাস - ▁কারখানা - ▁ধানে - ▁দিত - ▁অপরাধ - ভি - ালী - রিকা - ▁20000 - ▁সংঘ - ▁সৃজনশীল - '18' - ▁অভিবাস - ▁বলব - ▁ধারক - খানা - রাধিকার - ▁থাকব - ▁লিখ - ▁অমরজ - ▁রপাত - ▁উঠবে - ▁রোমা - াষী - ▁দেখেছে - ▁ডিশনার - ▁াসে - ▁নীত - াগারে - াফা - ▁160 - জির - াব - '87' - ▁ইনজেক - ▁গোলকধাঁধা - C - L - r - ▁ইঁদুর - ▁ইউটিলিটি - ▁ইমিউনোথেরাপি - ▁এলিভেটর - ▁কাদামাটি - ▁কৌতূহল - ▁চিরতরে - ▁ধারাবাহিক - ▁মিসৌরি - ▁রচারাভিযান - ▁রাজকুমার - ▁রেনেসাঁ - ▁শিথিল - ▁ষরেখা - ▁হাসপাতাল - ▁অবজারভেটরি - ▁পরিকাঠামো - ▁ররেখা - ▁তলদেশে - ▁শৈল - ▁মদপুর - ▁ওলাফ - ▁গতিশীলতা - ▁সাসপেন - ▁ঘেটো - ▁সংহতি - ▁আইটেম - ▁মেরামত - ▁মৃদু - ঁচট - ▁96 - ▁রজেকশন - ▁কংগ - ▁রাচীর - ▁রাজনীতিবিদ - ▁সমালোচনামূলক - ঘাট - ▁রাখুন - ▁উপনিবেশ - ▁হিম - ▁অনুকরণ - ▁রামবাসী - ▁দেশিকা - টেইনার - ▁ডেনিম - ▁সাজানো - রফেস - ▁ষপাত - ▁সাগর - ▁পারতাম - ▁মোতা - ▁জিনোম - ▁2019 - ▁এনেছিল - ▁লুকানো - িউএ - ▁অভিজাত - ▁রিটিশ - ▁গুণমান - ▁অভিনব - ▁পরিপূরক - ▁টগুলি - ▁ষাপটে - ▁রিলিফ - ▁টানেল - ▁জেগ - ▁সুপার - কটের - ▁বৈধ - ▁সেথেস - ▁কাঁপ - ▁জটিলতা - ▁ফোরণ - ▁টুকরা - ▁ভরশীল - ▁শদাতা - ▁বালতি - ▁পালক - লিথি - ▁ধরন - ▁পেশা - ▁পরিণতিগুলি - ▁বাগান - ▁মনোভাব - ▁অনলাইন - ▁থাপক - ▁বলেছে - ▁সেটিং - ▁ডিফ - ▁চোরা - ▁ভিড - ▁দেখেছেন - ▁বোঝানো - ▁শকুন - ▁থাপকতা - রবী - লানিক - ▁নীতিগত - ▁করেননি - ▁বিভাগে - ▁দিকটি - ামী - ▁ওঠা - িসিসি - ▁তাকাতে - ▁বলেছেন - ▁পিতৃ - ▁ফেট - ▁পাঠক - নাতক - ▁দাগ - ▁পারিনি - ▁চেতনা - ▁কফি - ▁পাঠান - ▁অবসান - রোধে - ▁রতিবার - ▁মুদি - ▁মূলধারার - ▁বাতি - ▁রাগন - ▁গাম - াবস - ▁শনগুলি - পোলি - ▁বাধীনতা - ▁ভাস - ▁রাণীগুলি - ▁আইস - ▁কিছুর - ▁জানতেন - ▁জানু - ▁রামগুলি - ▁লোহ - ▁কেজি - ▁সাব - ▁রাইট - াচল - ▁ইট - ▁ছাপ - বৃ - ▁বিপদ - সিভ - ▁কলে - ▁অসহ - ▁টেরল - ▁খাই - ▁রমিকরা - আইভ - ▁উপাদানটি - ▁মহামারীটি - ▁যালোকে - ▁সমাধানগুলি - ▁যি - ▁থিতিশীলতা - ▁ওটা - ▁রেখেছে - ▁আদালতে - ▁রোচ - ▁গণ - ▁দলে - ভিয - ▁উপহা - ডেট - ▁খালটি - সুবিধাজনক - ▁মগ - ▁লালন - ▁কণা - ▁নিষেধ - ▁১ - েলাই - াবল - ▁চেক - ▁নই - ▁অভিন - ▁টেমে - ▁ভট - োন - ▁গভীরতা - ▁ষণগুলি - ▁সারি - ▁বরে - ▁ধেকের - ▁যাসী - ▁দিরে - ▁দৈন - কড - ঁ - মাদ - ▁টরের - ▁কারো - ▁গী - ▁ফু - ▁রাজারা - জেনি - কো - ▁বীপগুলি - ▁কণ - ▁বাঁক - ▁পিতামাতা - ঠিত - ▁সবাইকে - ▁থির - ▁মিনি - বাহ - ▁বাসী - ▁তনগুলি - ডো - ▁থাপনা - রো - ▁াটি - ▁রীর - ▁নেবে - ▁বুজে - ▁রীন - লুস - রিটি - নোর - ▁500 - ▁এলাকাগুলি - ▁উই - ▁রোটিনটি - তাকা - ঠ - শনে - ▁360 - ▁বনে - ▁সুয - ▁ফিউ - বুন - ▁13 - ▁সাইটে - শনার - লাঙ - টান - ▁খোঁজ - ▁ডাল - ▁কপি - ▁তুকি - ▁ধাত - জাত - বেচ - ▁হব - ▁ইতালি - োশ - ▁জুম - কক - রুন - মূল - ▁মেইন - ▁েলসে - পথগুলি - নিম - লজি - ▁টক - হারা - ▁দিই - ▁দোকানে - পিং - সাধ - চালান - ▁রতিরোধে - পেস - '37' - ▁নিল - ▁খুলি - গল - ধান - ▁ফের - ▁জগুলি - ▁বেলা - পথ - ▁কনস - ▁শেল - বিল - ▁নেভিগে - ▁জাগ - জাতিক - উ - ▁রবাহে - ুলে - ফোন - আপ - তারা - ▁অফিস - ▁পশম - ▁যুগে - ▁যাটিন - ▁ততটা - লভ - ▁মহাদেশে - বো - েমের - ▁উৎসে - ারবার - ▁কমলা - পাল - ▁চলছ - ভেন - লিম - মুন - ▁202 - সেপ - দানি - মেলা - ▁লিং - িবার - ▁সাইট - ▁কনসা - ঝর - িকেল - াশি - ঝ - ▁জানান - ▁রমাণবাদ - নেস - শহ - ▁নাচ - ▁যাব - ফেরা - ▁124 - ▁পতন - '12' - ▁ভরা - ▁ঘরে - ▁বাম - ▁লিক - লানো - ▁বী - খা - গোল - ▁রতার - ▁টেমটি - '44' - ▁জেনারে - ▁রাশি - ▁ভূমিক - থি - ▁ভাষ - ▁ঝর - ▁সুদ - বাসী - োজা - ▁হতাশ - লিং - ▁চিনি - হর - ▁পারলে - সাইক - ▁196 - ▁সবা - ▁ফুলে - ▁আচরণে - ভিউ - হাই - মদা - '56' - ▁তিরা - ▁ষেপে - ▁ধারে - ▁নাইজ - ▁300 - ▁অনুর - ামেলা - ▁মিউ - ▁দেখ - ▁থাম - ▁অভিযোজ - ▁হাঁটা - মিক - শাপ - ানা - ▁যাকটি - ▁রবাল - ▁বিতর - কিউ - ▁সিট - ধীন - ▁150 - ঁজ - ▁গীত - ▁থাকত - াঁচে - '600' - ▁শুনেছে - ▁ফসফোলিপিড - ▁বাঁধ - ▁বীজ - কূল - ▁খুঁজছে - ▁রাজনীতি - ▁রজেক - ৯ - m - u - ğ - ▁অববাহিকা - ▁এনজাইম - ▁এলিজাবেথ - ▁কাটলফিশ - ▁কূটনীতি - ▁গিলগামেশ - ▁টিরিওটাইপ - ▁নৌবাহিনী - ▁ফাংশন - ▁ফারেনহাইট - ▁বাংলাদেশ - ▁ভলিউম - ▁মসৃণ - ▁মোকাবিলা - ▁যসাগর - ▁যাভিগেশন - ▁যালগরিদম - ▁রাঘিমাংশ - ▁সমঝোতা - ▁সালতানাত - ▁সোককেলেটন - ▁একাডেম - ▁দেহভাজন - ▁বংশধর - ▁মহাকাশচারী - ▁রজাপতি - ▁হেঁটে - ▁এমারসন - ▁ছাসেবক - ▁তোরাঁ - ▁ধবিরতি - ▁বিনোদন - ▁রুসেডার - ▁াশোনা - ▁রণেতাদের - ▁লাপনা - দারুণ - ▁যযুগ - ১৯ - ▁নৃশংস - ▁গৃহীত - ▁সিনেমা - ▁নেবুলা - ▁ইমাল - ▁শাটার - ▁মহাকাশযান - ▁পিঠ - ▁থাকুন - ▁ভালোবাস - ▁লেপটিন - ▁সহযোগী - ▁পটভূমি - ▁অবাধ - ▁দুঃখজনক - ▁ঢেউ - ▁অসীম - '97' - ▁উপযোগবাদী - ▁অতিথি - ▁একেবারে - ▁াবেটিস - ▁কভারেজ - ▁জোরালো - ▁মশলা - ▁শেঠ - '94' - ▁লেগেছিল - '95' - পোষণ - ▁হিপ - ▁তশাসন - ▁টিপাত - ▁হাজি - ▁রবিন - ▁যাটিপাস - ▁টারনেট - ▁1930 - ▁মিছিল - ▁মাঠ - ▁অটোম - ▁লিখেছ - ▁দেখছিলেন - ▁হিংস - ▁তৃণ - '98' - ▁মোনা - ▁াংখী - ▁উঠছে - ▁আইকন - ▁ফেলুন - ভাটা - লিডার - ▁পিউট - ▁যোগদান - ▁ফীতি - ▁মিটিং - ▁বোমা - ▁রাইবো - ▁রণালী - ▁টোরে - ▁রতিকূল - ডিপি - ▁লোরেন - ▁টারবাইন - ▁টিবডিগুলি - ▁ঢিবি - ▁নোঙ - ▁ছাদন - ▁হেসে - ▁বিভাজ - ▁গুজরাট - ▁োএ - ▁120 - ▁খুনি - োলেট - ▁এসি - ▁55 - ▁ডিজে - ▁সিকো - ▁ভেলা - ▁সাইটগুলি - ▁যাকচার - ▁কণাগুলি - ▁মতামত - ▁কারখানাগুলি - ▁ফুটপ - ▁রাখছেন - ▁শোনে - ▁ষতিকর - ▁ছাকৃত - ▁শহরগুলো - ▁াকরণ - ▁যাদুঘর - ▁সাগু - ▁কেলিং - ুথ - োনাইজ - ▁রগামী - ▁যাসীদের - ▁ভীত - ▁রচলন - ালো - ▁টিপস - ▁মৌ - ▁যাফো - ▁উঠবেন - ▁সংবাদ - ▁কাঁচ - ▁চালনা - ▁রেজার - ▁রাসাদ - ▁উপকরণগুলি - ▁এগুলো - ▁নীতিগ - ▁0 - ▁নিকট - ▁টেরিওটাইপগুলি - ▁ফোরক - ▁টোন - ▁খনিজ - ▁অবনতি - ▁বনভূমি - ▁যাটারিগুলি - গাল - ▁ডারগ - ▁লুপগুলি - ▁লজ - ▁রনগুলি - কিশোরী - ▁ছেলেদের - ভাষী - ▁ডিপ - ▁জুনাজপুস - ▁গোলা - ▁গভ - ▁অধিক - ▁মাইলের - ▁কুই - ▁সমালোচনা - ▁যাফোস - ▁অধিকারী - ▁যবোধ - ▁ধারকরা - বিধি - ▁ইকো - ▁রিটেন - ুভ - ▁উপযোগ - ▁নভ - ▁ঠীগুলি - ▁ঘটনাগুলি - ▁মাংস - ▁বাদাম - োচন - ▁লেব - ▁বলছেন - ▁চুষ - ▁ঠানগুলি - ▁শাক - ▁কোঁ - ▁বাভাবিকভাবে - নুকি - ▁লাইড - িবিটি - ▁যবসাগুলি - িকে - ▁যুগুলি - ▁টিপ - ▁রেফ - ▁কাটে - োলজি - ঘর - ▁টিমাই - ▁গজা - ▁সুযোগগুলি - ▁বাজি - ▁বিজি - নেকের - ীমা - গুঁ - ▁যাকরণ - ▁গুন - ▁বাঘ - ▁দেহে - সা - '79' - ▁যেকটি - ▁টারে - সিফ - ▁লেপ - ▁শুনেছিল - ▁শেড - ▁সুইড - ▁াটে - ▁কলাম - ▁তেমন - ▁ামে - বাইক - ▁ঢালা - ▁মুখীতা - ▁শিশুরা - ▁বরফ - ধারা - ▁পৌ - ▁কোল - ▁তালা - ▁লিন - ▁খালে - ুলেট - ▁টিভি - ▁রিম - ▁সেনে - ▁থামা - ▁মিটারের - ▁আসি - ▁টুল - ▁ভেজ - ▁লাশ - ▁রাগ - ামাল - টারের - ▁রিজটি - ▁দোর - ▁যাসটি - টকে - ▁চালাবে - ফিস - ▁সাজ - ▁যুব - েবল - ▁দিলে - সিন - ▁অজ - ▁শা - ▁টেজ - ▁শতাংশে - ▁ডু - িজম - জমে - সাদ - ▁অবা - ▁পুরুষকে - হাঁ - ▁লুকো - ▁মেঘে - জান - বক - ▁যুতি - ▁শতক - ▁জিম - রাণি - ▁যানু - সো - ▁মিলন - ▁চাইবে - কৃতির - ▁রোভ - ▁মাইল - '30' - ▁পরিষেবাগুলি - ▁আমানি - ▁ছামত - '500' - বোল - ▁ছবিগুলি - ▁অরি - ালি - ▁নিই - ▁তেলাপোকার - কারে - ▁রামে - ▁সূচ - ▁ারো - ▁যাসি - ▁টেলিভিশনে - বুক - টস - ▁দেখান - ুসং - কু - ▁আদি - ণের - িটাল - ▁মরি - রীদের - বিচ - ▁ধিম - ▁রিটে - ▁চাচা - ▁গানে - ▁শিবিরে - টেন - ▁দুঃ - ▁টিকেলে - ▁কেনে - '000' - ▁যুগ - াশা - '48' - ▁কুর - শান - জিতে - ▁খেলে - ▁পরম - পির - ▁আঁ - ভাব - ানু - ▁মাতৃ - পশম - ▁ষাত - াণ - ৃপ - ▁চো - কাঠি - লন - টারি - ফল - করণ - টন - ▁অতীত - াইজার - আর - ▁ঝুলে - িওল - খোঁজ - বোধ - ▁গাগুলি - ▁পেল - বেশি - ঘুরি - কী - ▁যাটা - 08 - িব - িৎ - চিব - '19' - লাইট - নৈতিক - শুদ - শম - ▁সরকারে - গভীর - রোটিন - '80' - লেট - ভাষা - নাইজ - হাত - অপ - ধারণ - জানা - ▁ঘটান - অ - ▁193 - কাজ - ▁শুনেছি - জুন - িউ - ▁নদ - চুরি - হেল - ▁শেখান - দি - ঁকি - ▁আসাদ - লোভন - ▁রিভে - োগান - নিউ - ▁পৌঁছ - াগ - ▁াপথ - ▁শোক - ফেল - মাণ - ঘন - তাই - ▁ভুগছ - ▁তৃ - ▁বুঝি - ▁দেখছি - বসে - ▁উঠল - ▁টিম - ▁180 - ▁জলা - চা - ▁লেগ - ডিএ - মাই - ফিউ - রিসে - ▁পারমা - ▁বেষ - ▁মিলনে - ▁110 - াংশের - েটিক - ▁800 - জিশন - ▁ধারণে - ▁তোম - োনে - ▁বলত - ▁রাচ - ▁বেগে - ালদে - ▁শুন - ▁যারো - ▁3000 - ▁1500 - ডেন - ▁মূলধারা - সিকতা - ▁ছু - ▁তাঁ - ▁খোঁ - ▁ভাবি - ▁জুনাজপু - ▁চালাব - ▁পাথ - গণিত - ▁থেরাপিউটিক - ▁মেক - ▁ইংরেজ - হীনতা - ▁সেখান - াহু - ▁ফুটে - হাউ - ▁একগু - ▁রাখছে - ▁চমক - ▁টিবডি - ▁রাউ - ৌরব - ৎসাহ - ভাসক - ▁এসমেরাল - e - i - ঊ - ৬ - ▁1988 - ▁1990 - ▁অবৈধ - ▁আকসুম - ▁আজারবাইজান - ▁ইসমাইল - ▁কৌতুক - ▁জরিমানা - ▁তকণিকা - ▁দাবানল - ▁নিবেদিত - ▁ফিলিপাইনে - ▁যাবরেটরি - ▁শৈবাল - ▁সাবমেরিন - ▁সিংহভাগ - ▁সিংহাসনে - ▁হাইপোথিসিস - ▁ঘৃণ - ▁ণযুগ - ▁কোঅপারেটিভ - ▁ঘেরলিন - ▁জেলালেম - ▁ঠপোষকতা - ▁বিছানা - ▁যাচমেকার - ▁রাজবংশ - ▁শীতাতপ - ▁শোধন - ▁সিকিউটিভ - ▁হোমোরোটি - ঘাঁট - ▁বিলাসিতা - ▁লেনদেন - ▁ফোঁটা - ▁ভালবাসে - ▁ভূমিধস - ▁ডেলিভারি - ▁কমিউনিকে - ▁এমবেড - ▁ইউএস - ▁ঝাঁঝ - ▁সপোজার - েমাট - ▁উপসংহার - ▁পিনবল - ▁টাইফুন - লিউশন - ▁রবিবার - ▁লেডগুলি - ▁লুমিরা - ▁চিবানো - ▁রেগারি - ▁টাইটান - ▁কিনেছিলেন - ▁কেরাটিন - ▁লাজুক - ▁শুনুন - ▁সুসংবাদ - ▁পহেড - ▁মানবজাতি - ▁মৌসুম - ▁রবাদ - ▁বদলানো - এইচও - ▁খল - ▁রেণি - ▁মীবাহিনী - ▁ইরানী - কোভ - ▁মিলিমিটার - ▁রসারণ - ▁পরিহাস - ▁রতারণা - ▁টেসলা - ▁014 - ▁খোসা - ▁3500 - ▁ঘনমিটার - বিধান - ▁নিউটন - ▁নেভিগেশন - ▁গুণফল - ▁খাঁ - ▁কেলটন - রিডিস - ▁কনভেন - ▁টেরিও - থু - ▁1450 - ▁টোবর - ▁188 - ▁1980 - ▁কুকুর - ▁পরিধি - ▁দুঃখ - ▁185 - ▁চাবিটি - ▁লোরিড - ▁1940 - ▁ধরবেন - ▁নিঃশ - ▁ঝাপ - ▁তপাত - ▁গীকার - ▁শহরবাসী - ▁ফসিল - ▁যুভর - ▁টলেশন - ▁শুনিনি - ▁যানজট - ▁ডেভি - ▁লেগেছে - ▁জেলা - ▁ঘটছিল - ▁রানজিট - ▁187 - ▁রণোদ - ▁33 - ▁াবহ - ▁গেছি - '05' - ▁খেলেছে - ▁জিরো - ▁ঝরনা - ▁উপদেশ - ▁38 - ▁াংখি - ▁সারাদিন - ▁শিম - ▁আগামীকাল - ▁বেআইনি - ▁শিখেছে - সিল - ▁বাজানো - ▁লাগছে - ▁পালগুলি - ▁লিউ - ▁পাননি - মিশনে - ▁126 - ▁টিথার - ▁ডোবা - ▁বিরাজ - এনবি - ▁রোথ - ▁বলছিলেন - োনাল - ▁যাংকিং - চুপ - ▁রোপড - ▁টাইমলাইনে - ▁যাকট - ▁বাঁধা - ▁যোনি - ▁বোনা - ▁করোন - াকাশে - ▁জেনেনা - ▁ফসফোলিপিডগুলি - ▁ওভারহ - লেক - ▁এলো - ▁পিকাবু - ▁আইনগত - ▁তনালী - সোম - ▁উপকূলরেখাকে - ▁তেরো - ▁ফেরি - '89' - ▁রতিবেদন - ▁অনুপাতে - ▁থিম - ▁ফলিকল - ▁নলিখিত - বিটি - ▁ডিশনারগুলি - ▁সহজাত - ▁গুদাম - ▁কারাগারে - ▁গেলাম - ▁হোমো - ▁ফোটা - ▁মানজনক - ▁ঝু - ▁অবকা - ▁পেলেন - ▁ফিনা - ঃস - ▁ঠাতা - ▁লবণ - ▁বিলাস - ▁তিনজন - ▁রশমন - লিসা - ▁পরিপূর - ▁কিউবা - ▁মিকা - বদলে - ▁জেনো - পসাগর - ▁বেসরকার - ▁সুপ - ▁যুইটি - ▁চাইনি - ▁ধিমূলক - টিউ - ▁ফাটল - ▁সেলগুলি - িওপি - ▁নজির - ▁হামলা - ▁পুরু - ▁অমরজত - ▁তরণটি - ▁করলাম - ▁কখনো - ▁মশালটি - ▁গকগুলি - ▁দিকগুলি - ▁গমনকারী - ▁দেখাবে - ▁চাইলে - নেভি - ▁সাপগুলি - ▁নোট - ▁যানবাহনগুলি - ▁সোমা - ▁দেখেনি - ▁োগকারীদের - ▁রাইলোবাইটগুলি - ▁ষণশীল - ▁সেতুগুলি - ▁বিবেক - ▁খোঁজে - ▁দেশগুলো - ▁তারকা - রীস - ▁ডফিল - ▁নাগাল - ▁বোনাইজ - ▁থেরাপিউটিকস - ▁জিগ - ▁যাপট - ▁যৌগ - ▁রুপার - ▁রচল - ▁যারিস - ▁সহনশীল - ▁বিনা - াখা - ▁যহীনতা - ▁ভিজি - ▁আঠা - ▁ফাইন - ▁ডুব - ▁বইটি - ▁সংযোগগুলি - ▁রাফট - ▁রবালের - ▁ফে - াসী - সূরি - সেছিলেন - ▁যাসেল - ▁গাইড - ▁তাঁর - ▁রোট - ▁পনগুলি - ▁গীতি - ▁ধৃত - োবা - ▁বাবধান - ▁সারিতে - নামূল - কভাবে - ▁পৌঁছান - লিখিত - ▁তূপ - ▁শিকারি - ▁যথাস - মেজ - ীকৃত - নাতনি - ▁টরে - ুখী - চেতন - ▁যাবলে - ▁ধারণাগুলি - ▁জীবগুলি - ▁কাজিন - ▁560 - হেলম - ধমনী - ▁করুণা - ▁করেছ - আ - ১ - '%' - ':' - c - h - l - n - z - ü - ২ - ৪ - ৩ - ঔ - ঈ - Ö - U - ঋ - ঐ - '?' - O - । - ় - <sos/eos> src_token_list: - <blank> - <unk> - s - ▁the - ▁to - ▁and - ▁of - ▁a - ing - ▁in - ed - ▁that - ▁is - ▁ - ly - ▁we - ▁it - ▁you - d - ▁this - ▁for - ▁on - e - ▁be - ▁but - ▁with - ▁so - ▁are - ▁i - ▁have - ▁can - ▁they - y - ▁was - ▁as - ▁its - ▁from - ▁their - ▁at - ▁what - ▁by - ▁one - ▁our - ▁an - ▁or - ▁al - ▁like - ▁more - ▁when - ▁your - ▁were - ▁some - ▁all - ▁not - ▁these - n - ▁people - t - ▁how - 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▁fell - ▁hypothe - ▁optim - ▁constitut - ▁defeat - ▁diagonal - ▁immunotherap - ▁smooth - bacteria - ipples - ologist - ğ - ▁1987 - ▁adjacent - ▁advocate - ▁anticipate - ▁assistance - ▁baby - ▁beauty - ▁calculat - ▁candidate - ▁challenging - ▁champion - ▁cholesterol - ▁counterparts - ▁crusade - ▁curiosity - ▁descend - ▁destination - ▁destruction - ▁destructive - ▁disappointment - ▁dissolve - ▁distinguish - ▁empath - ▁enjoy - ▁equitabl - ▁evaluat - ▁export - ▁ghetto - ▁ghost - ▁gradual - ▁hospital - ▁implement - ▁inclusiv - ▁inherit - ▁intervention - ▁laboratory - ▁liquid - ▁luxury - ▁mercator - ▁micah - ▁miracle - ▁nebula - ▁nervous - ▁neutral - ▁olaf - ▁opposite - ▁participat - ▁pesticide - ▁puzzle - ▁pyrethroid - ▁rainforest - ▁rattlesnake - ▁rebuil - ▁register - ▁resolution - ▁rognvald - ▁secure - ▁spectrum - ▁statue - ▁television - ▁therapeutic - ▁throat - ▁vulture - ▁wood - phobia - ▁abandon - ▁accident - ▁automatic - ▁bucket - ▁burden - ▁competency - ▁consult - ▁equity - ▁evaporat - ▁interview - ▁knowledge - ▁legacy - ▁legislat - ▁mathematic - ▁niger - ▁plummet - ▁taste - ▁technical - ▁transplant - itarian - ▁chronic - ▁compell - ▁crowd - ▁empty - ▁incarcer - ▁misfir - ▁poison - ▁quantit - ▁turb - ▁victor - ▁election - ▁priorit - ▁religio - ▁snore - defensi - ▁bundle - ▁carousel - ▁climb - ▁exhaust - ▁fractur - ▁garden - ▁succeed - ▁suez - ▁hdpe - ▁juice - aguar - ▁denim - ▁dividing - ▁fallacy - ▁outcomes - ▁plot - ▁blind - ▁shocked - ▁bounc - ▁depth - incident - ▁subtle - ▁pump - rcia - ▁initiatives - ▁spray - ▁haunt - ▁traverse - ▁polish - ▁hypothesis - ▁voice - ▁pledge - ▁burst - ▁uncle - ▁sink - sturb - ▁anchor - ▁gratitude - ▁pause - ▁quo - ▁alert - ▁vast - ▁van - ▁attitudes - ▁grocer - ▁countdown - ▁decrease - ▁extensi - ▁invasion - ▁therapi - ▁instant - ▁guy - ▁forget - ▁lawyer - ▁reduction - ▁strange - ▁boom - abul - ▁season - ▁begg - ▁underwater - ▁strategies - ▁stimulate - ▁hurt - ▁alertness - ▁utilit - ▁tomb - ▁elsewhere - ▁leap - ▁patch - ▁preference - ▁realistic - ▁fold - ▁medit - ▁stair - itzer - ▁embr - ▁addict - ▁2015 - ▁percepti - ▁reign - ▁painful - egal - ▁respi - ▁depriv - ▁shutter - ▁chemistry - ▁sad - ▁bias - ▁boost - ▁wake - ▁workforce - ▁varieties - ▁repair - ▁genome - ▁reject - ▁124 - slide - ▁mobility - ▁shade - ▁medicine - ▁vent - ▁hyp - ▁melt - ▁cake - ▁organized - ▁novelty - ▁distan - ▁france - ▁suck - ▁parity - ▁vision - ▁voc - ▁sufficient - charged - ▁calcine - ensity - ▁dart - ▁collection - ▁gun - ▁rays - ▁pour - ▁bitter - ▁funn - ▁coff - ▁fearless - ▁stance - ▁inner - ▁retain - ▁debt - ▁chile - fuse - ▁partial - ▁mold - ▁substan - ▁survival - ▁seize - ▁qui - ▁installation - ▁cup - ruel - ▁boss - ▁plug - ▁apartment - ▁communicate - ▁sacrifice - ▁tapp - ▁grass - ▁italy - ▁roy - ▁squ - ▁percentage - ▁dots - ▁absolutely - ▁incentivize - ▁reserve - ▁navigate - ▁creative - viation - ▁angle - ▁deb - ▁agent - ▁isolat - spiration - ▁ramp - ▁forgotten - ▁extin - ▁celebrated - diff - ▁substantially - ▁viruses - ▁por - clos - ▁comment - ▁closest - ▁fatal - ▁triple - olk - ▁eliminate - ▁facilit - oster - ▁geo - erior - ▁online - ▁fung - ▁insight - ▁bull - '79' - ▁swapp - ▁wipe - rrow - ▁historical - ▁delivery - hre - ntine - erson - ▁former - ▁original - ▁cri - ▁accura - ▁bat - ▁pave - reci - mma - ▁generat - rum - decided - ▁provider - cell - ▁intri - izab - neck - ▁pur - neu - ▁stepp - hoppers - ▁hu - ▁dye - ▁chase - '21' - ▁impress - hu - ▁broke - ▁obstruct - ▁360 - ▁explor - gue - rate - ▁controlle - roc - bru - ecta - ▁gui - ▁rec - qua - ▁imagin - ▁operat - ▁fertiliz - litar - ▁hotte - profitable - ▁argu - ▁150 - odes - tify - llus - lets - ▁terr - poly - ▁christ - ctively - ▁decarboniz - scribe - ▁electr - ▁immigra - ▁300 - ▁separat - ▁hopp - ▁rang - employed - mped - '98' - rail - '97' - ▁device - ▁pun - ▁belief - ▁resident - ▁pathway - ▁egg - ▁dollar - ▁scientist - ▁prim - ▁reliabl - igation - ▁aud - ▁fun - maker - ▁marr - ▁afford - ▁gro - ashes - urning - ▁cycl - ject - ▁surpris - ▁eliminat - ▁disco - ▁univers - ▁receiv - stead - ▁critic - mark - ▁plea - ▁absolute - pair - limited - water - truck - sexual - spread - '35' - bank - virus - imagine - consider - power - down - look - more - drive - ▁communicat - ▁prepare - cott - ▁insist - fish - ▁gri - ▁tap - ▁incentiv - ▁distort - ▁jani - case - ▁societ - nounc - ▁interact - ▁syria - ▁eas - ▁frequen - ▁significan - ▁attac - ▁populat - ▁except - ▁steriliz - ▁cooperat - ▁khali - ▁appro - ivity - ▁danger - ▁inform - ▁stimul - ▁quest - ▁memori - ▁import - hibit - stood - ▁decre - ▁influ - rupt - cense - ippi - ▁photosynthe - augu - criminat - ▁biodivers - ▁cardio - ▁ridicul - occupie - sophisticated - ▁absolutis - ▁accused - ▁afraid - ▁algorithm - ▁aristocra - ▁assaulted - ▁association - ▁assyrian - ▁atlantic - ▁autonomy - ▁availability - ▁brutal - ▁byproduct - ▁ceremon - ▁circle - ▁conclusion - ▁congress - ▁consensus - ▁diabetes - ▁dimensional - ▁diploma - ▁disadvantage - ▁disrespect - ▁dragonfl - ▁enzymes - ▁epidemic - ▁evolution - ▁expense - ▁eyebrows - ▁fairbnb - ▁follicle - ▁fragment - ▁gatekeeper - ▁geography - ▁ghrelin - ▁gilgamesh - ▁google - ▁greece - ▁gujarat - ▁harvest - ▁hurricane - ▁inevitable - ▁injustice - ▁intelligen - ▁ixbalanke - ▁jetpack - ▁judgment - ▁livelihoods - ▁longitude - ▁margin - ▁minimum - ▁navy - ▁necessarily - ▁passenger - ▁politics - ▁prejudice - ▁prospect - ▁proximity - ▁relieve - ▁replicate - ▁restaurant - ▁scotland - ▁senior - ▁simultaneously - ▁slot - ▁stigma - ▁supreme - ▁sustainably - ▁teenager - ▁thirteen - ▁thrill - ▁tiger - ▁tomorrow - ▁toothpaste - ▁tynwald - ▁underneath - ▁utilitarian - ▁volunteer - ▁vulnerability - ▁alternate - ▁assassinat - ▁branche - ▁categor - ▁commute - ▁defend - ▁exclusive - ▁feather - ▁graduate - ▁meticulous - ▁perpetuat - ▁resettle - ▁segregat - ▁treasur - ▁violent - ▁align - ▁apparent - ▁blades - ▁competition - ▁concert - ▁counteract - ▁daunting - ▁debris - ▁deficienc - ▁disperse - ▁england - ▁fascinat - ▁inflation - ▁inhabit - ▁irony - ▁midwest - ▁occasion - ▁paddy - ▁pioneer - ▁praise - ▁princes - ▁resembl - ▁roof - ▁sensitive - ▁territori - ▁unfair - rugg - ▁coworkers - ▁fruit - ▁gasoline - ▁impulse - ▁lung - ▁megawatt - ▁palace - ▁request - ▁testimon - ▁unfolding - ▁yarn - ▁bomb - ▁crack - ▁drastic - ▁harsh - ▁hometown - ▁infected - ▁john - ▁minimize - ▁properties - ▁swift - ▁pillar - ▁endanger - ▁flaw - ▁relax - ▁turk - ▁admir - ▁nuance - ▁declare - ▁guard - ▁reunion - ▁storytell - ▁butterfl - ▁scour - ▁ribo - ▁ferry - ▁hacking - ▁hydro - ▁thread - ▁convention - ▁text - ▁split - ▁congest - ▁translation - ▁appreciat - ratory - ▁iceland - ▁jaw - ▁mistake - ▁95 - programm - ▁injure - ▁explosive - ▁spiritual - ▁drill - ▁typh - ▁smell - ▁latin - ▁poem - ▁asylum - ▁crime - ▁sail - ▁appeal - ▁guest - ▁initial - ▁peekabo - ▁outlier - mog - ▁proud - ▁bolt - ▁spurr - intuiti - ▁cantilever - ▁amani - ▁genre - ▁afar - ▁rub - ▁moistur - ▁recover - ▁items - ▁optimistic - ▁slippe - ▁oversee - ▁sara - ▁illegal - ▁rainwater - ▁opposition - ▁overnight - ▁movie - ▁explosion - ▁intensity - ▁linguistic - ▁emulsi - ▁radiation - ▁violat - morph - ▁homo - ▁spice - ▁vibran - ▁intact - ▁rewards - ▁exceed - ▁viewpoint - ▁heroes - ▁repeatedly - ▁confront - rane - ▁thre - ▁squir - ▁wrap - ▁godred - ▁orgy - ▁sentence - unci - ▁memorize - monia - holder - ▁quiet - rpet - ▁icon - ▁spark - ▁deforestation - ▁nurs - ▁1945 - ▁finger - cade - ▁efficac - ▁haz - ▁motivation - ▁spotted - ▁pitch - ▁subsidize - ▁intention - ▁window - ombi - ▁swim - ▁winter - ▁dynami - ▁executive - ▁boil - ▁assess - ▁2018 - ▁failure - ▁horse - ▁enact - utter - ▁circulation - ▁queen - ▁distract - flag - ▁mentor - ▁lick - lank - ▁ebo - ▁dirt - ▁remark - ▁shake - ▁entry - frost - ▁pear - ▁bound - ▁rif - ▁performance - ▁exception - ▁189 - ▁straight - ▁purp - imeter - ▁hills - ▁chew - scop - ▁lamp - ▁fog - ▁sweet - ▁cosm - ▁mysteri - rbit - ▁dying - ▁argument - ▁intell - ▁sultanate - aire - ▁tile - ▁monoc - ▁machinery - ▁motion - ▁infant - ▁healthier - ▁continuous - ▁truce - ▁undergo - aboo - ▁commanders - ▁qualifi - ▁55 - ▁anyway - ▁lenses - ▁offset - ▁merg - quent - tari - ▁chim - ptin - ▁exit - ▁dash - ▁meta - ▁wish - ▁poorest - ▁distortion - ▁interaction - ▁proposal - ▁reven - ▁trace - ▁perch - ▁behav - ▁disruption - ▁progressive - introduce - ▁gall - ▁stone - ▁update - descent - ▁dance - ▁polye - ▁settle - fellow - ▁rob - ▁stre - ▁kan - dominant - ▁bro - ▁ev - ▁purif - ▁agreement - ▁dominate - ▁regulation - ▁improvement - hase - ▁ecolog - hydr - pical - ▁conspi - ▁inhale - ▁arriv - ▁fil - ▁visitor - ▁greenland - phasi - ▁farmer - ▁cran - ▁identifi - ▁chose - hau - grega - mps - ▁characteriz - ▁audi - ▁oppress - mination - aint - ▁determin - ▁unemploy - spire - ▁giga - ska - ▁immigrat - rank - sport - aft - ▁snap - emper - equality - ▁imp - ▁terri - ▁interv - '19' - hi - icated - ▁demonstrat - kg - gible - ix - grad - pression - '16' - ▁pursu - ▁hor - ▁deli - ▁spar - ▁suc - ▁millenni - connected - ▁leon - ▁inspir - ▁tho - ▁faci - ▁domin - ▁resist - ▁mobil - ▁var - eval - ▁interfer - abilities - ▁enabl - ▁border - ▁forci - ▁monk - ▁eugenic - gae - ▁concern - ▁fertil - ▁mammal - ▁iri - ▁merc - ▁blu - gger - ▁statistic - ▁integr - compa - nown - ▁navigat - ▁amaz - ▁reserv - layer - escription - ▁angl - ▁amplif - force - plug - conscious - compete - mind - leader - honest - load - position - root - box - speak - flow - complete - drop - check - sustainable - friend - track - game - moral - certain - green - world - people - life - what - about - human - wind - suit - pay - minis - ▁tradition - ▁bloo - ▁explo - ▁strateg - ▁circu - ▁gravit - ▁corporat - ▁activit - ▁inequalit - ▁galax - ▁calci - ▁energ - ▁identit - ▁locat - ▁que - ford - compromis - ▁swee - ▁constr - imitation - ▁matte - zoo - hwa - ▁dyna - ▁flexib - ▁execut - ▁renew - ▁catastroph - ▁deforest - rink - ▁auth - ▁pub - ▁marc - ▁furthe - ▁diagnos - ecutive - titude - ▁compli - gressive - nprofit - pute - ▁nano - oxide - ▁evident - ▁surp - ▁arachn - ▁hippoc - nivores - skeleton - suppress - thropo - ü - ▁accomplish - ▁accusation - ▁acknowledg - ▁activists - á - î - ç - ö - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true brctc_risk_strategy: exp brctc_group_strategy: end brctc_risk_factor: 0.0 st_joint_net_conf: null model_conf: asr_weight: 0.3 mt_weight: 0.0 mtlalpha: 0.3 lsm_weight: 0.1 length_normalized_loss: false use_preprocessor: true token_type: bpe src_token_type: bpe bpemodel: data/en_bn_token_list/tgt_bpe_unigram4000/bpe.model src_bpemodel: data/en_bn_token_list/src_bpe_unigram4000/bpe.model non_linguistic_symbols: null cleaner: null g2p: null src_g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 ctc_sample_rate: 0.0 frontend: default frontend_conf: n_fft: 400 hop_length: 160 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 5 normalize: utterance_mvn normalize_conf: {} preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 extra_asr_decoder: transformer extra_asr_decoder_conf: input_layer: embed num_blocks: 6 linear_units: 2048 dropout_rate: 0.1 extra_mt_decoder: transformer extra_mt_decoder_conf: input_layer: embed num_blocks: 2 linear_units: 2048 dropout_rate: 0.1 md_encoder: null md_encoder_conf: {} hier_encoder: null hier_encoder_conf: {} extra_mt_encoder: null extra_mt_encoder_conf: {} preprocessor: default preprocessor_conf: {} required: - output_dir - token_list version: '202402' distributed: false ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
[ "TRANSLATION" ]
[ "CRAFT" ]
Non_BioNLP
thenlper/gte-large
thenlper
sentence-similarity
[ "sentence-transformers", "pytorch", "onnx", "safetensors", "openvino", "bert", "mteb", "sentence-similarity", "Sentence Transformers", "en", "arxiv:2308.03281", "license:mit", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,690
1,731
460,453
272
--- language: - en license: mit tags: - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-large results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.62686567164178 - type: ap value: 34.46944126809772 - type: f1 value: 66.23684353950857 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.51805 - type: ap value: 89.49842783330848 - type: f1 value: 92.51112169431808 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.074 - type: f1 value: 48.44785682572955 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 32.077 - type: map_at_10 value: 48.153 - type: map_at_100 value: 48.963 - type: map_at_1000 value: 48.966 - type: map_at_3 value: 43.184 - type: map_at_5 value: 46.072 - type: mrr_at_1 value: 33.073 - type: mrr_at_10 value: 48.54 - type: mrr_at_100 value: 49.335 - type: mrr_at_1000 value: 49.338 - type: mrr_at_3 value: 43.563 - type: mrr_at_5 value: 46.383 - type: ndcg_at_1 value: 32.077 - type: ndcg_at_10 value: 57.158 - type: ndcg_at_100 value: 60.324999999999996 - type: ndcg_at_1000 value: 60.402 - type: ndcg_at_3 value: 46.934 - type: ndcg_at_5 value: 52.158 - type: precision_at_1 value: 32.077 - type: precision_at_10 value: 8.591999999999999 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.275000000000002 - type: precision_at_5 value: 14.111 - type: recall_at_1 value: 32.077 - type: recall_at_10 value: 85.917 - type: recall_at_100 value: 99.075 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 57.824 - type: recall_at_5 value: 70.555 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.619246083417295 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.3574067664688 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 63.06359661829253 - type: mrr value: 76.15596007562766 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 90.25407547368691 - type: cos_sim_spearman value: 88.65081514968477 - type: euclidean_pearson value: 88.14857116664494 - type: euclidean_spearman value: 88.50683596540692 - type: manhattan_pearson value: 87.9654797992225 - type: manhattan_spearman value: 88.21164851646908 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.05844155844157 - type: f1 value: 86.01555597681825 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.10510519739522 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.84689960264385 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 32.800000000000004 - type: map_at_10 value: 44.857 - type: map_at_100 value: 46.512 - type: map_at_1000 value: 46.635 - type: map_at_3 value: 41.062 - type: map_at_5 value: 43.126 - type: mrr_at_1 value: 39.628 - type: mrr_at_10 value: 50.879 - type: mrr_at_100 value: 51.605000000000004 - type: mrr_at_1000 value: 51.641000000000005 - type: mrr_at_3 value: 48.14 - type: mrr_at_5 value: 49.835 - type: ndcg_at_1 value: 39.628 - type: ndcg_at_10 value: 51.819 - type: ndcg_at_100 value: 57.318999999999996 - type: ndcg_at_1000 value: 58.955999999999996 - type: ndcg_at_3 value: 46.409 - type: ndcg_at_5 value: 48.825 - type: precision_at_1 value: 39.628 - type: precision_at_10 value: 10.072000000000001 - type: precision_at_100 value: 1.625 - type: precision_at_1000 value: 0.21 - type: precision_at_3 value: 22.556 - type: precision_at_5 value: 16.309 - type: recall_at_1 value: 32.800000000000004 - type: recall_at_10 value: 65.078 - type: recall_at_100 value: 87.491 - type: recall_at_1000 value: 97.514 - type: recall_at_3 value: 49.561 - type: recall_at_5 value: 56.135999999999996 - type: map_at_1 value: 32.614 - type: map_at_10 value: 43.578 - type: map_at_100 value: 44.897 - type: map_at_1000 value: 45.023 - type: map_at_3 value: 40.282000000000004 - type: map_at_5 value: 42.117 - type: mrr_at_1 value: 40.510000000000005 - type: mrr_at_10 value: 49.428 - type: mrr_at_100 value: 50.068999999999996 - type: mrr_at_1000 value: 50.111000000000004 - type: mrr_at_3 value: 47.176 - type: mrr_at_5 value: 48.583999999999996 - type: ndcg_at_1 value: 40.510000000000005 - type: ndcg_at_10 value: 49.478 - type: ndcg_at_100 value: 53.852 - type: ndcg_at_1000 value: 55.782 - type: ndcg_at_3 value: 45.091 - type: ndcg_at_5 value: 47.19 - type: precision_at_1 value: 40.510000000000005 - type: precision_at_10 value: 9.363000000000001 - type: precision_at_100 value: 1.51 - type: precision_at_1000 value: 0.196 - type: precision_at_3 value: 21.741 - type: precision_at_5 value: 15.465000000000002 - type: recall_at_1 value: 32.614 - type: recall_at_10 value: 59.782000000000004 - type: recall_at_100 value: 78.012 - type: recall_at_1000 value: 90.319 - type: recall_at_3 value: 46.825 - type: recall_at_5 value: 52.688 - type: map_at_1 value: 40.266000000000005 - type: map_at_10 value: 53.756 - type: map_at_100 value: 54.809 - type: map_at_1000 value: 54.855 - type: map_at_3 value: 50.073 - type: map_at_5 value: 52.293 - type: mrr_at_1 value: 46.332 - type: mrr_at_10 value: 57.116 - type: mrr_at_100 value: 57.767 - type: mrr_at_1000 value: 57.791000000000004 - type: mrr_at_3 value: 54.461999999999996 - type: mrr_at_5 value: 56.092 - type: ndcg_at_1 value: 46.332 - type: ndcg_at_10 value: 60.092 - type: ndcg_at_100 value: 64.034 - type: ndcg_at_1000 value: 64.937 - type: ndcg_at_3 value: 54.071000000000005 - type: ndcg_at_5 value: 57.254000000000005 - type: precision_at_1 value: 46.332 - type: precision_at_10 value: 9.799 - type: precision_at_100 value: 1.278 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 24.368000000000002 - type: precision_at_5 value: 16.89 - type: recall_at_1 value: 40.266000000000005 - type: recall_at_10 value: 75.41499999999999 - type: recall_at_100 value: 92.01700000000001 - type: recall_at_1000 value: 98.379 - type: recall_at_3 value: 59.476 - type: recall_at_5 value: 67.297 - type: map_at_1 value: 28.589 - type: map_at_10 value: 37.755 - type: map_at_100 value: 38.881 - type: map_at_1000 value: 38.954 - type: map_at_3 value: 34.759 - type: map_at_5 value: 36.544 - type: mrr_at_1 value: 30.734 - type: mrr_at_10 value: 39.742 - type: mrr_at_100 value: 40.774 - type: mrr_at_1000 value: 40.824 - type: mrr_at_3 value: 37.137 - type: mrr_at_5 value: 38.719 - type: ndcg_at_1 value: 30.734 - type: ndcg_at_10 value: 42.978 - type: ndcg_at_100 value: 48.309000000000005 - type: ndcg_at_1000 value: 50.068 - type: ndcg_at_3 value: 37.361 - type: ndcg_at_5 value: 40.268 - type: precision_at_1 value: 30.734 - type: precision_at_10 value: 6.565 - type: precision_at_100 value: 0.964 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 15.744 - type: precision_at_5 value: 11.096 - type: recall_at_1 value: 28.589 - type: recall_at_10 value: 57.126999999999995 - type: recall_at_100 value: 81.051 - type: recall_at_1000 value: 94.027 - type: recall_at_3 value: 42.045 - type: recall_at_5 value: 49.019 - type: map_at_1 value: 18.5 - type: map_at_10 value: 27.950999999999997 - type: map_at_100 value: 29.186 - type: map_at_1000 value: 29.298000000000002 - type: map_at_3 value: 25.141000000000002 - type: map_at_5 value: 26.848 - type: mrr_at_1 value: 22.637 - type: mrr_at_10 value: 32.572 - type: mrr_at_100 value: 33.472 - type: mrr_at_1000 value: 33.533 - type: mrr_at_3 value: 29.747 - type: mrr_at_5 value: 31.482 - type: ndcg_at_1 value: 22.637 - type: ndcg_at_10 value: 33.73 - type: ndcg_at_100 value: 39.568 - type: ndcg_at_1000 value: 42.201 - type: ndcg_at_3 value: 28.505999999999997 - type: ndcg_at_5 value: 31.255 - type: precision_at_1 value: 22.637 - type: precision_at_10 value: 6.281000000000001 - type: precision_at_100 value: 1.073 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 13.847000000000001 - type: precision_at_5 value: 10.224 - type: recall_at_1 value: 18.5 - type: recall_at_10 value: 46.744 - type: recall_at_100 value: 72.072 - type: recall_at_1000 value: 91.03999999999999 - type: recall_at_3 value: 32.551 - type: recall_at_5 value: 39.533 - type: map_at_1 value: 30.602 - type: map_at_10 value: 42.18 - type: map_at_100 value: 43.6 - type: map_at_1000 value: 43.704 - type: map_at_3 value: 38.413000000000004 - type: map_at_5 value: 40.626 - type: mrr_at_1 value: 37.344 - type: mrr_at_10 value: 47.638000000000005 - type: mrr_at_100 value: 48.485 - type: mrr_at_1000 value: 48.52 - type: mrr_at_3 value: 44.867000000000004 - type: mrr_at_5 value: 46.566 - type: ndcg_at_1 value: 37.344 - type: ndcg_at_10 value: 48.632 - type: ndcg_at_100 value: 54.215 - type: ndcg_at_1000 value: 55.981 - type: ndcg_at_3 value: 42.681999999999995 - type: ndcg_at_5 value: 45.732 - type: precision_at_1 value: 37.344 - type: precision_at_10 value: 8.932 - type: precision_at_100 value: 1.376 - type: precision_at_1000 value: 0.17099999999999999 - type: precision_at_3 value: 20.276 - type: precision_at_5 value: 14.726 - type: recall_at_1 value: 30.602 - type: recall_at_10 value: 62.273 - type: recall_at_100 value: 85.12100000000001 - type: recall_at_1000 value: 96.439 - type: recall_at_3 value: 45.848 - type: recall_at_5 value: 53.615 - type: map_at_1 value: 23.952 - type: map_at_10 value: 35.177 - type: map_at_100 value: 36.59 - type: map_at_1000 value: 36.703 - type: map_at_3 value: 31.261 - type: map_at_5 value: 33.222 - type: mrr_at_1 value: 29.337999999999997 - type: mrr_at_10 value: 40.152 - type: mrr_at_100 value: 40.963 - type: mrr_at_1000 value: 41.016999999999996 - type: mrr_at_3 value: 36.91 - type: mrr_at_5 value: 38.685 - type: ndcg_at_1 value: 29.337999999999997 - type: ndcg_at_10 value: 41.994 - type: ndcg_at_100 value: 47.587 - type: ndcg_at_1000 value: 49.791000000000004 - type: ndcg_at_3 value: 35.27 - type: ndcg_at_5 value: 38.042 - type: precision_at_1 value: 29.337999999999997 - type: precision_at_10 value: 8.276 - type: precision_at_100 value: 1.276 - type: precision_at_1000 value: 0.164 - type: precision_at_3 value: 17.161 - type: precision_at_5 value: 12.671 - type: recall_at_1 value: 23.952 - type: recall_at_10 value: 57.267 - type: recall_at_100 value: 80.886 - type: recall_at_1000 value: 95.611 - type: recall_at_3 value: 38.622 - type: recall_at_5 value: 45.811 - type: map_at_1 value: 27.092083333333335 - type: map_at_10 value: 37.2925 - type: map_at_100 value: 38.57041666666666 - type: map_at_1000 value: 38.68141666666667 - type: map_at_3 value: 34.080000000000005 - type: map_at_5 value: 35.89958333333333 - type: mrr_at_1 value: 31.94758333333333 - type: mrr_at_10 value: 41.51049999999999 - type: mrr_at_100 value: 42.36099999999999 - type: mrr_at_1000 value: 42.4125 - type: mrr_at_3 value: 38.849583333333335 - type: mrr_at_5 value: 40.448249999999994 - type: ndcg_at_1 value: 31.94758333333333 - type: ndcg_at_10 value: 43.17633333333333 - type: ndcg_at_100 value: 48.45241666666668 - type: ndcg_at_1000 value: 50.513999999999996 - type: ndcg_at_3 value: 37.75216666666667 - type: ndcg_at_5 value: 40.393833333333326 - type: precision_at_1 value: 31.94758333333333 - type: precision_at_10 value: 7.688916666666666 - type: precision_at_100 value: 1.2250833333333333 - type: precision_at_1000 value: 0.1595 - type: precision_at_3 value: 17.465999999999998 - type: precision_at_5 value: 12.548083333333333 - type: recall_at_1 value: 27.092083333333335 - type: recall_at_10 value: 56.286583333333326 - type: recall_at_100 value: 79.09033333333333 - type: recall_at_1000 value: 93.27483333333335 - type: recall_at_3 value: 41.35325 - type: recall_at_5 value: 48.072750000000006 - type: map_at_1 value: 25.825 - type: map_at_10 value: 33.723 - type: map_at_100 value: 34.74 - type: map_at_1000 value: 34.824 - type: map_at_3 value: 31.369000000000003 - type: map_at_5 value: 32.533 - type: mrr_at_1 value: 29.293999999999997 - type: mrr_at_10 value: 36.84 - type: mrr_at_100 value: 37.681 - type: mrr_at_1000 value: 37.742 - type: mrr_at_3 value: 34.79 - type: mrr_at_5 value: 35.872 - type: ndcg_at_1 value: 29.293999999999997 - type: ndcg_at_10 value: 38.385999999999996 - type: ndcg_at_100 value: 43.327 - type: ndcg_at_1000 value: 45.53 - type: ndcg_at_3 value: 33.985 - type: ndcg_at_5 value: 35.817 - type: precision_at_1 value: 29.293999999999997 - type: precision_at_10 value: 6.12 - type: precision_at_100 value: 0.9329999999999999 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 14.621999999999998 - type: precision_at_5 value: 10.030999999999999 - type: recall_at_1 value: 25.825 - type: recall_at_10 value: 49.647000000000006 - type: recall_at_100 value: 72.32300000000001 - type: recall_at_1000 value: 88.62400000000001 - type: recall_at_3 value: 37.366 - type: recall_at_5 value: 41.957 - type: map_at_1 value: 18.139 - type: map_at_10 value: 26.107000000000003 - type: map_at_100 value: 27.406999999999996 - type: map_at_1000 value: 27.535999999999998 - type: map_at_3 value: 23.445 - type: map_at_5 value: 24.916 - type: mrr_at_1 value: 21.817 - type: mrr_at_10 value: 29.99 - type: mrr_at_100 value: 31.052000000000003 - type: mrr_at_1000 value: 31.128 - type: mrr_at_3 value: 27.627000000000002 - type: mrr_at_5 value: 29.005 - type: ndcg_at_1 value: 21.817 - type: ndcg_at_10 value: 31.135 - type: ndcg_at_100 value: 37.108000000000004 - type: ndcg_at_1000 value: 39.965 - type: ndcg_at_3 value: 26.439 - type: ndcg_at_5 value: 28.655 - type: precision_at_1 value: 21.817 - type: precision_at_10 value: 5.757000000000001 - type: precision_at_100 value: 1.036 - type: precision_at_1000 value: 0.147 - type: precision_at_3 value: 12.537 - type: precision_at_5 value: 9.229 - type: recall_at_1 value: 18.139 - type: recall_at_10 value: 42.272999999999996 - type: recall_at_100 value: 68.657 - type: recall_at_1000 value: 88.93799999999999 - type: recall_at_3 value: 29.266 - type: recall_at_5 value: 34.892 - type: map_at_1 value: 27.755000000000003 - type: map_at_10 value: 37.384 - type: map_at_100 value: 38.56 - type: map_at_1000 value: 38.655 - type: map_at_3 value: 34.214 - type: map_at_5 value: 35.96 - type: mrr_at_1 value: 32.369 - type: mrr_at_10 value: 41.625 - type: mrr_at_100 value: 42.449 - type: mrr_at_1000 value: 42.502 - type: mrr_at_3 value: 38.899 - type: mrr_at_5 value: 40.489999999999995 - type: ndcg_at_1 value: 32.369 - type: ndcg_at_10 value: 43.287 - type: ndcg_at_100 value: 48.504999999999995 - type: ndcg_at_1000 value: 50.552 - type: ndcg_at_3 value: 37.549 - type: ndcg_at_5 value: 40.204 - type: precision_at_1 value: 32.369 - type: precision_at_10 value: 7.425 - type: precision_at_100 value: 1.134 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 17.102 - type: precision_at_5 value: 12.107999999999999 - type: recall_at_1 value: 27.755000000000003 - type: recall_at_10 value: 57.071000000000005 - type: recall_at_100 value: 79.456 - type: recall_at_1000 value: 93.54299999999999 - type: recall_at_3 value: 41.298 - type: recall_at_5 value: 48.037 - type: map_at_1 value: 24.855 - type: map_at_10 value: 34.53 - type: map_at_100 value: 36.167 - type: map_at_1000 value: 36.394999999999996 - type: map_at_3 value: 31.037 - type: map_at_5 value: 33.119 - type: mrr_at_1 value: 30.631999999999998 - type: mrr_at_10 value: 39.763999999999996 - type: mrr_at_100 value: 40.77 - type: mrr_at_1000 value: 40.826 - type: mrr_at_3 value: 36.495 - type: mrr_at_5 value: 38.561 - type: ndcg_at_1 value: 30.631999999999998 - type: ndcg_at_10 value: 40.942 - type: ndcg_at_100 value: 47.07 - type: ndcg_at_1000 value: 49.363 - type: ndcg_at_3 value: 35.038000000000004 - type: ndcg_at_5 value: 38.161 - type: precision_at_1 value: 30.631999999999998 - type: precision_at_10 value: 7.983999999999999 - type: precision_at_100 value: 1.6070000000000002 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 16.206 - type: precision_at_5 value: 12.253 - type: recall_at_1 value: 24.855 - type: recall_at_10 value: 53.291999999999994 - type: recall_at_100 value: 80.283 - type: recall_at_1000 value: 94.309 - type: recall_at_3 value: 37.257 - type: recall_at_5 value: 45.282 - type: map_at_1 value: 21.208 - type: map_at_10 value: 30.512 - type: map_at_100 value: 31.496000000000002 - type: map_at_1000 value: 31.595000000000002 - type: map_at_3 value: 27.904 - type: map_at_5 value: 29.491 - type: mrr_at_1 value: 22.736 - type: mrr_at_10 value: 32.379999999999995 - type: mrr_at_100 value: 33.245000000000005 - type: mrr_at_1000 value: 33.315 - type: mrr_at_3 value: 29.945 - type: mrr_at_5 value: 31.488 - type: ndcg_at_1 value: 22.736 - type: ndcg_at_10 value: 35.643 - type: ndcg_at_100 value: 40.535 - type: ndcg_at_1000 value: 43.042 - type: ndcg_at_3 value: 30.625000000000004 - type: ndcg_at_5 value: 33.323 - type: precision_at_1 value: 22.736 - type: precision_at_10 value: 5.6930000000000005 - type: precision_at_100 value: 0.889 - type: precision_at_1000 value: 0.122 - type: precision_at_3 value: 13.431999999999999 - type: precision_at_5 value: 9.575 - type: recall_at_1 value: 21.208 - type: recall_at_10 value: 49.47 - type: recall_at_100 value: 71.71499999999999 - type: recall_at_1000 value: 90.55499999999999 - type: recall_at_3 value: 36.124 - type: recall_at_5 value: 42.606 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 11.363 - type: map_at_10 value: 20.312 - type: map_at_100 value: 22.225 - type: map_at_1000 value: 22.411 - type: map_at_3 value: 16.68 - type: map_at_5 value: 18.608 - type: mrr_at_1 value: 25.537 - type: mrr_at_10 value: 37.933 - type: mrr_at_100 value: 38.875 - type: mrr_at_1000 value: 38.911 - type: mrr_at_3 value: 34.387 - type: mrr_at_5 value: 36.51 - type: ndcg_at_1 value: 25.537 - type: ndcg_at_10 value: 28.82 - type: ndcg_at_100 value: 36.341 - type: ndcg_at_1000 value: 39.615 - type: ndcg_at_3 value: 23.01 - type: ndcg_at_5 value: 25.269000000000002 - type: precision_at_1 value: 25.537 - type: precision_at_10 value: 9.153 - type: precision_at_100 value: 1.7319999999999998 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 17.22 - type: precision_at_5 value: 13.629 - type: recall_at_1 value: 11.363 - type: recall_at_10 value: 35.382999999999996 - type: recall_at_100 value: 61.367000000000004 - type: recall_at_1000 value: 79.699 - type: recall_at_3 value: 21.495 - type: recall_at_5 value: 27.42 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 9.65 - type: map_at_10 value: 20.742 - type: map_at_100 value: 29.614 - type: map_at_1000 value: 31.373 - type: map_at_3 value: 14.667 - type: map_at_5 value: 17.186 - type: mrr_at_1 value: 69.75 - type: mrr_at_10 value: 76.762 - type: mrr_at_100 value: 77.171 - type: mrr_at_1000 value: 77.179 - type: mrr_at_3 value: 75.125 - type: mrr_at_5 value: 76.287 - type: ndcg_at_1 value: 57.62500000000001 - type: ndcg_at_10 value: 42.370999999999995 - type: ndcg_at_100 value: 47.897 - type: ndcg_at_1000 value: 55.393 - type: ndcg_at_3 value: 46.317 - type: ndcg_at_5 value: 43.906 - type: precision_at_1 value: 69.75 - type: precision_at_10 value: 33.95 - type: precision_at_100 value: 10.885 - type: precision_at_1000 value: 2.2239999999999998 - type: precision_at_3 value: 49.75 - type: precision_at_5 value: 42.3 - type: recall_at_1 value: 9.65 - type: recall_at_10 value: 26.117 - type: recall_at_100 value: 55.084 - type: recall_at_1000 value: 78.62400000000001 - type: recall_at_3 value: 15.823 - type: recall_at_5 value: 19.652 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.885 - type: f1 value: 42.99567641346983 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 70.97 - type: map_at_10 value: 80.34599999999999 - type: map_at_100 value: 80.571 - type: map_at_1000 value: 80.584 - type: map_at_3 value: 79.279 - type: map_at_5 value: 79.94 - type: mrr_at_1 value: 76.613 - type: mrr_at_10 value: 85.15700000000001 - type: mrr_at_100 value: 85.249 - type: mrr_at_1000 value: 85.252 - type: mrr_at_3 value: 84.33800000000001 - type: mrr_at_5 value: 84.89 - type: ndcg_at_1 value: 76.613 - type: ndcg_at_10 value: 84.53399999999999 - type: ndcg_at_100 value: 85.359 - type: ndcg_at_1000 value: 85.607 - type: ndcg_at_3 value: 82.76599999999999 - type: ndcg_at_5 value: 83.736 - type: precision_at_1 value: 76.613 - type: precision_at_10 value: 10.206 - type: precision_at_100 value: 1.083 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 31.913000000000004 - type: precision_at_5 value: 19.769000000000002 - type: recall_at_1 value: 70.97 - type: recall_at_10 value: 92.674 - type: recall_at_100 value: 95.985 - type: recall_at_1000 value: 97.57000000000001 - type: recall_at_3 value: 87.742 - type: recall_at_5 value: 90.28 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 22.494 - type: map_at_10 value: 36.491 - type: map_at_100 value: 38.550000000000004 - type: map_at_1000 value: 38.726 - type: map_at_3 value: 31.807000000000002 - type: map_at_5 value: 34.299 - type: mrr_at_1 value: 44.907000000000004 - type: mrr_at_10 value: 53.146 - type: mrr_at_100 value: 54.013999999999996 - type: mrr_at_1000 value: 54.044000000000004 - type: mrr_at_3 value: 50.952 - type: mrr_at_5 value: 52.124 - type: ndcg_at_1 value: 44.907000000000004 - type: ndcg_at_10 value: 44.499 - type: ndcg_at_100 value: 51.629000000000005 - type: ndcg_at_1000 value: 54.367 - type: ndcg_at_3 value: 40.900999999999996 - type: ndcg_at_5 value: 41.737 - type: precision_at_1 value: 44.907000000000004 - type: precision_at_10 value: 12.346 - type: precision_at_100 value: 1.974 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 27.366 - type: precision_at_5 value: 19.846 - type: recall_at_1 value: 22.494 - type: recall_at_10 value: 51.156 - type: recall_at_100 value: 77.11200000000001 - type: recall_at_1000 value: 93.44 - type: recall_at_3 value: 36.574 - type: recall_at_5 value: 42.361 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 38.568999999999996 - type: map_at_10 value: 58.485 - type: map_at_100 value: 59.358999999999995 - type: map_at_1000 value: 59.429 - type: map_at_3 value: 55.217000000000006 - type: map_at_5 value: 57.236 - type: mrr_at_1 value: 77.137 - type: mrr_at_10 value: 82.829 - type: mrr_at_100 value: 83.04599999999999 - type: mrr_at_1000 value: 83.05399999999999 - type: mrr_at_3 value: 81.904 - type: mrr_at_5 value: 82.50800000000001 - type: ndcg_at_1 value: 77.137 - type: ndcg_at_10 value: 67.156 - type: ndcg_at_100 value: 70.298 - type: ndcg_at_1000 value: 71.65700000000001 - type: ndcg_at_3 value: 62.535 - type: ndcg_at_5 value: 65.095 - type: precision_at_1 value: 77.137 - type: precision_at_10 value: 13.911999999999999 - type: precision_at_100 value: 1.6389999999999998 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 39.572 - type: precision_at_5 value: 25.766 - type: recall_at_1 value: 38.568999999999996 - type: recall_at_10 value: 69.56099999999999 - type: recall_at_100 value: 81.931 - type: recall_at_1000 value: 90.91799999999999 - type: recall_at_3 value: 59.358999999999995 - type: recall_at_5 value: 64.416 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 88.45600000000002 - type: ap value: 84.09725115338568 - type: f1 value: 88.41874909080512 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 21.404999999999998 - type: map_at_10 value: 33.921 - type: map_at_100 value: 35.116 - type: map_at_1000 value: 35.164 - type: map_at_3 value: 30.043999999999997 - type: map_at_5 value: 32.327 - type: mrr_at_1 value: 21.977 - type: mrr_at_10 value: 34.505 - type: mrr_at_100 value: 35.638999999999996 - type: mrr_at_1000 value: 35.68 - type: mrr_at_3 value: 30.703999999999997 - type: mrr_at_5 value: 32.96 - type: ndcg_at_1 value: 21.963 - type: ndcg_at_10 value: 40.859 - type: ndcg_at_100 value: 46.614 - type: ndcg_at_1000 value: 47.789 - type: ndcg_at_3 value: 33.007999999999996 - type: ndcg_at_5 value: 37.084 - type: precision_at_1 value: 21.963 - type: precision_at_10 value: 6.493 - type: precision_at_100 value: 0.938 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.155000000000001 - type: precision_at_5 value: 10.544 - type: recall_at_1 value: 21.404999999999998 - type: recall_at_10 value: 62.175000000000004 - type: recall_at_100 value: 88.786 - type: recall_at_1000 value: 97.738 - type: recall_at_3 value: 40.925 - type: recall_at_5 value: 50.722 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.50661194710442 - type: f1 value: 93.30311193153668 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 73.24669402644778 - type: f1 value: 54.23122108002977 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 72.61936785474109 - type: f1 value: 70.52644941025565 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.76529926025555 - type: f1 value: 77.26872729322514 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.39450293021839 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.757796879839294 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.62512146657428 - type: mrr value: 33.84624322066173 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.462 - type: map_at_10 value: 14.947 - type: map_at_100 value: 19.344 - type: map_at_1000 value: 20.933 - type: map_at_3 value: 10.761999999999999 - type: map_at_5 value: 12.744 - type: mrr_at_1 value: 47.988 - type: mrr_at_10 value: 57.365 - type: mrr_at_100 value: 57.931 - type: mrr_at_1000 value: 57.96 - type: mrr_at_3 value: 54.85 - type: mrr_at_5 value: 56.569 - type: ndcg_at_1 value: 46.129999999999995 - type: ndcg_at_10 value: 38.173 - type: ndcg_at_100 value: 35.983 - type: ndcg_at_1000 value: 44.507000000000005 - type: ndcg_at_3 value: 42.495 - type: ndcg_at_5 value: 41.019 - type: precision_at_1 value: 47.678 - type: precision_at_10 value: 28.731 - type: precision_at_100 value: 9.232 - type: precision_at_1000 value: 2.202 - type: precision_at_3 value: 39.628 - type: precision_at_5 value: 35.851 - type: recall_at_1 value: 6.462 - type: recall_at_10 value: 18.968 - type: recall_at_100 value: 37.131 - type: recall_at_1000 value: 67.956 - type: recall_at_3 value: 11.905000000000001 - type: recall_at_5 value: 15.097 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 30.335 - type: map_at_10 value: 46.611999999999995 - type: map_at_100 value: 47.632000000000005 - type: map_at_1000 value: 47.661 - type: map_at_3 value: 41.876999999999995 - type: map_at_5 value: 44.799 - type: mrr_at_1 value: 34.125 - type: mrr_at_10 value: 49.01 - type: mrr_at_100 value: 49.75 - type: mrr_at_1000 value: 49.768 - type: mrr_at_3 value: 45.153 - type: mrr_at_5 value: 47.589999999999996 - type: ndcg_at_1 value: 34.125 - type: ndcg_at_10 value: 54.777 - type: ndcg_at_100 value: 58.914 - type: ndcg_at_1000 value: 59.521 - type: ndcg_at_3 value: 46.015 - type: ndcg_at_5 value: 50.861000000000004 - type: precision_at_1 value: 34.125 - type: precision_at_10 value: 9.166 - type: precision_at_100 value: 1.149 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 21.147 - type: precision_at_5 value: 15.469 - type: recall_at_1 value: 30.335 - type: recall_at_10 value: 77.194 - type: recall_at_100 value: 94.812 - type: recall_at_1000 value: 99.247 - type: recall_at_3 value: 54.681000000000004 - type: recall_at_5 value: 65.86800000000001 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 70.62 - type: map_at_10 value: 84.536 - type: map_at_100 value: 85.167 - type: map_at_1000 value: 85.184 - type: map_at_3 value: 81.607 - type: map_at_5 value: 83.423 - type: mrr_at_1 value: 81.36 - type: mrr_at_10 value: 87.506 - type: mrr_at_100 value: 87.601 - type: mrr_at_1000 value: 87.601 - type: mrr_at_3 value: 86.503 - type: mrr_at_5 value: 87.179 - type: ndcg_at_1 value: 81.36 - type: ndcg_at_10 value: 88.319 - type: ndcg_at_100 value: 89.517 - type: ndcg_at_1000 value: 89.60900000000001 - type: ndcg_at_3 value: 85.423 - type: ndcg_at_5 value: 86.976 - type: precision_at_1 value: 81.36 - type: precision_at_10 value: 13.415 - type: precision_at_100 value: 1.529 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.342999999999996 - type: precision_at_5 value: 24.534 - type: recall_at_1 value: 70.62 - type: recall_at_10 value: 95.57600000000001 - type: recall_at_100 value: 99.624 - type: recall_at_1000 value: 99.991 - type: recall_at_3 value: 87.22 - type: recall_at_5 value: 91.654 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 60.826438478212744 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 64.24027467551447 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.997999999999999 - type: map_at_10 value: 14.267 - type: map_at_100 value: 16.843 - type: map_at_1000 value: 17.229 - type: map_at_3 value: 9.834 - type: map_at_5 value: 11.92 - type: mrr_at_1 value: 24.7 - type: mrr_at_10 value: 37.685 - type: mrr_at_100 value: 38.704 - type: mrr_at_1000 value: 38.747 - type: mrr_at_3 value: 34.150000000000006 - type: mrr_at_5 value: 36.075 - type: ndcg_at_1 value: 24.7 - type: ndcg_at_10 value: 23.44 - type: ndcg_at_100 value: 32.617000000000004 - type: ndcg_at_1000 value: 38.628 - type: ndcg_at_3 value: 21.747 - type: ndcg_at_5 value: 19.076 - type: precision_at_1 value: 24.7 - type: precision_at_10 value: 12.47 - type: precision_at_100 value: 2.564 - type: precision_at_1000 value: 0.4 - type: precision_at_3 value: 20.767 - type: precision_at_5 value: 17.06 - type: recall_at_1 value: 4.997999999999999 - type: recall_at_10 value: 25.3 - type: recall_at_100 value: 52.048 - type: recall_at_1000 value: 81.093 - type: recall_at_3 value: 12.642999999999999 - type: recall_at_5 value: 17.312 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 85.44942006292234 - type: cos_sim_spearman value: 79.80930790660699 - type: euclidean_pearson value: 82.93400777494863 - type: euclidean_spearman value: 80.04664991110705 - type: manhattan_pearson value: 82.93551681854949 - type: manhattan_spearman value: 80.03156736837379 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.63574059135726 - type: cos_sim_spearman value: 76.80552915288186 - type: euclidean_pearson value: 82.46368529820518 - type: euclidean_spearman value: 76.60338474719275 - type: manhattan_pearson value: 82.4558617035968 - type: manhattan_spearman value: 76.57936082895705 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 86.24116811084211 - type: cos_sim_spearman value: 88.10998662068769 - type: euclidean_pearson value: 87.04961732352689 - type: euclidean_spearman value: 88.12543945864087 - type: manhattan_pearson value: 86.9905224528854 - type: manhattan_spearman value: 88.07827944705546 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 84.74847296555048 - type: cos_sim_spearman value: 82.66200957916445 - type: euclidean_pearson value: 84.48132256004965 - type: euclidean_spearman value: 82.67915286000596 - type: manhattan_pearson value: 84.44950477268334 - type: manhattan_spearman value: 82.63327639173352 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.23056258027053 - type: cos_sim_spearman value: 88.92791680286955 - type: euclidean_pearson value: 88.13819235461933 - type: euclidean_spearman value: 88.87294661361716 - type: manhattan_pearson value: 88.14212133687899 - type: manhattan_spearman value: 88.88551854529777 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.64179522732887 - type: cos_sim_spearman value: 84.25028809903114 - type: euclidean_pearson value: 83.40175015236979 - type: euclidean_spearman value: 84.23369296429406 - type: manhattan_pearson value: 83.43768174261321 - type: manhattan_spearman value: 84.27855229214734 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 88.20378955494732 - type: cos_sim_spearman value: 88.46863559173111 - type: euclidean_pearson value: 88.8249295811663 - type: euclidean_spearman value: 88.6312737724905 - type: manhattan_pearson value: 88.87744466378827 - type: manhattan_spearman value: 88.82908423767314 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 69.91342028796086 - type: cos_sim_spearman value: 69.71495021867864 - type: euclidean_pearson value: 70.65334330405646 - type: euclidean_spearman value: 69.4321253472211 - type: manhattan_pearson value: 70.59743494727465 - type: manhattan_spearman value: 69.11695509297482 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.42451709766952 - type: cos_sim_spearman value: 86.07166710670508 - type: euclidean_pearson value: 86.12711421258899 - type: euclidean_spearman value: 86.05232086925126 - type: manhattan_pearson value: 86.15591089932126 - type: manhattan_spearman value: 86.0890128623439 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.1976344717285 - type: mrr value: 96.3703145075694 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 59.511 - type: map_at_10 value: 69.724 - type: map_at_100 value: 70.208 - type: map_at_1000 value: 70.22800000000001 - type: map_at_3 value: 66.986 - type: map_at_5 value: 68.529 - type: mrr_at_1 value: 62.333000000000006 - type: mrr_at_10 value: 70.55 - type: mrr_at_100 value: 70.985 - type: mrr_at_1000 value: 71.004 - type: mrr_at_3 value: 68.611 - type: mrr_at_5 value: 69.728 - type: ndcg_at_1 value: 62.333000000000006 - type: ndcg_at_10 value: 74.265 - type: ndcg_at_100 value: 76.361 - type: ndcg_at_1000 value: 76.82900000000001 - type: ndcg_at_3 value: 69.772 - type: ndcg_at_5 value: 71.94800000000001 - type: precision_at_1 value: 62.333000000000006 - type: precision_at_10 value: 9.9 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 27.444000000000003 - type: precision_at_5 value: 18 - type: recall_at_1 value: 59.511 - type: recall_at_10 value: 87.156 - type: recall_at_100 value: 96.5 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 75.2 - type: recall_at_5 value: 80.661 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.81683168316832 - type: cos_sim_ap value: 95.74716566563774 - type: cos_sim_f1 value: 90.64238745574103 - type: cos_sim_precision value: 91.7093142272262 - type: cos_sim_recall value: 89.60000000000001 - type: dot_accuracy value: 99.69405940594059 - type: dot_ap value: 91.09013507754594 - type: dot_f1 value: 84.54227113556779 - type: dot_precision value: 84.58458458458459 - type: dot_recall value: 84.5 - type: euclidean_accuracy value: 99.81782178217821 - type: euclidean_ap value: 95.6324301072609 - type: euclidean_f1 value: 90.58341862845445 - type: euclidean_precision value: 92.76729559748428 - type: euclidean_recall value: 88.5 - type: manhattan_accuracy value: 99.81980198019802 - type: manhattan_ap value: 95.68510494437183 - type: manhattan_f1 value: 90.58945191313342 - type: manhattan_precision value: 93.79014989293361 - type: manhattan_recall value: 87.6 - type: max_accuracy value: 99.81980198019802 - type: max_ap value: 95.74716566563774 - type: max_f1 value: 90.64238745574103 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 67.63761899427078 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.572473369697235 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.63000245208579 - type: mrr value: 54.504193722943725 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.300791939416545 - type: cos_sim_spearman value: 31.662904057924123 - type: dot_pearson value: 26.21198530758316 - type: dot_spearman value: 27.006921548904263 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.197 - type: map_at_10 value: 1.752 - type: map_at_100 value: 10.795 - type: map_at_1000 value: 27.18 - type: map_at_3 value: 0.5890000000000001 - type: map_at_5 value: 0.938 - type: mrr_at_1 value: 74 - type: mrr_at_10 value: 85.833 - type: mrr_at_100 value: 85.833 - type: mrr_at_1000 value: 85.833 - type: mrr_at_3 value: 85.333 - type: mrr_at_5 value: 85.833 - type: ndcg_at_1 value: 69 - type: ndcg_at_10 value: 70.22 - type: ndcg_at_100 value: 55.785 - type: ndcg_at_1000 value: 52.93600000000001 - type: ndcg_at_3 value: 72.084 - type: ndcg_at_5 value: 71.184 - type: precision_at_1 value: 74 - type: precision_at_10 value: 75.2 - type: precision_at_100 value: 57.3 - type: precision_at_1000 value: 23.302 - type: precision_at_3 value: 77.333 - type: precision_at_5 value: 75.6 - type: recall_at_1 value: 0.197 - type: recall_at_10 value: 2.019 - type: recall_at_100 value: 14.257 - type: recall_at_1000 value: 50.922 - type: recall_at_3 value: 0.642 - type: recall_at_5 value: 1.043 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.803 - type: map_at_10 value: 10.407 - type: map_at_100 value: 16.948 - type: map_at_1000 value: 18.424 - type: map_at_3 value: 5.405 - type: map_at_5 value: 6.908 - type: mrr_at_1 value: 36.735 - type: mrr_at_10 value: 50.221000000000004 - type: mrr_at_100 value: 51.388 - type: mrr_at_1000 value: 51.402 - type: mrr_at_3 value: 47.278999999999996 - type: mrr_at_5 value: 49.626 - type: ndcg_at_1 value: 34.694 - type: ndcg_at_10 value: 25.507 - type: ndcg_at_100 value: 38.296 - type: ndcg_at_1000 value: 49.492000000000004 - type: ndcg_at_3 value: 29.006999999999998 - type: ndcg_at_5 value: 25.979000000000003 - type: precision_at_1 value: 36.735 - type: precision_at_10 value: 22.041 - type: precision_at_100 value: 8.02 - type: precision_at_1000 value: 1.567 - type: precision_at_3 value: 28.571 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 2.803 - type: recall_at_10 value: 16.378 - type: recall_at_100 value: 50.489 - type: recall_at_1000 value: 85.013 - type: recall_at_3 value: 6.505 - type: recall_at_5 value: 9.243 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.55579999999999 - type: ap value: 14.206982753316227 - type: f1 value: 54.372142814964285 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 56.57611771363893 - type: f1 value: 56.924172639063144 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 52.82304915719759 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.92716218632653 - type: cos_sim_ap value: 73.73359122546046 - type: cos_sim_f1 value: 68.42559487116262 - type: cos_sim_precision value: 64.22124508215691 - type: cos_sim_recall value: 73.21899736147758 - type: dot_accuracy value: 80.38981939560112 - type: dot_ap value: 54.61060862444974 - type: dot_f1 value: 53.45710627400769 - type: dot_precision value: 44.87638839125761 - type: dot_recall value: 66.09498680738787 - type: euclidean_accuracy value: 86.02849138701794 - type: euclidean_ap value: 73.95673761922404 - type: euclidean_f1 value: 68.6783042394015 - type: euclidean_precision value: 65.1063829787234 - type: euclidean_recall value: 72.66490765171504 - type: manhattan_accuracy value: 85.9808070572808 - type: manhattan_ap value: 73.9050720058029 - type: manhattan_f1 value: 68.57560618983794 - type: manhattan_precision value: 63.70839936608558 - type: manhattan_recall value: 74.24802110817942 - type: max_accuracy value: 86.02849138701794 - type: max_ap value: 73.95673761922404 - type: max_f1 value: 68.6783042394015 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.72783017037295 - type: cos_sim_ap value: 85.52705223340233 - type: cos_sim_f1 value: 77.91659078492079 - type: cos_sim_precision value: 73.93378032764221 - type: cos_sim_recall value: 82.35294117647058 - type: dot_accuracy value: 85.41739434159972 - type: dot_ap value: 77.17734818118443 - type: dot_f1 value: 71.63473589973144 - type: dot_precision value: 66.96123719622415 - type: dot_recall value: 77.00954727440714 - type: euclidean_accuracy value: 88.68125897465751 - type: euclidean_ap value: 85.47712213906692 - type: euclidean_f1 value: 77.81419950830664 - type: euclidean_precision value: 75.37162649733006 - type: euclidean_recall value: 80.42038805050817 - type: manhattan_accuracy value: 88.67349710870494 - type: manhattan_ap value: 85.46506475241955 - type: manhattan_f1 value: 77.87259084890393 - type: manhattan_precision value: 74.54929577464789 - type: manhattan_recall value: 81.50600554357868 - type: max_accuracy value: 88.72783017037295 - type: max_ap value: 85.52705223340233 - type: max_f1 value: 77.91659078492079 --- # gte-large General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. ## Metrics We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). | Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 | | [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 | | [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 | | [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 | | [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 | | [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 | | [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 | ## Usage Code example ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-large") model = AutoModel.from_pretrained("thenlper/gte-large") # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) ``` Use with sentence-transformers: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = ['That is a happy person', 'That is a very happy person'] model = SentenceTransformer('thenlper/gte-large') embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` ### Limitation This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens. ### Citation If you find our paper or models helpful, please consider citing them as follows: ``` @article{li2023towards, title={Towards general text embeddings with multi-stage contrastive learning}, author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, journal={arXiv preprint arXiv:2308.03281}, year={2023} } ```
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
sschet/bert-base-uncased_clinical-ner
sschet
token-classification
[ "transformers", "pytorch", "tf", "jax", "bert", "token-classification", "dataset:tner/bc5cdr", "dataset:commanderstrife/jnlpba", "dataset:bc2gm_corpus", "dataset:drAbreu/bc4chemd_ner", "dataset:linnaeus", "dataset:chintagunta85/ncbi_disease", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,674
1,675
124
5
--- datasets: - tner/bc5cdr - commanderstrife/jnlpba - bc2gm_corpus - drAbreu/bc4chemd_ner - linnaeus - chintagunta85/ncbi_disease --- A Named Entity Recognition model for clinical entities (`problem`, `treatment`, `test`) The model has been trained on the [i2b2 (now n2c2) dataset](https://n2c2.dbmi.hms.harvard.edu) for the 2010 - Relations task. Please visit the n2c2 site to request access to the dataset.
[ "NAMED_ENTITY_RECOGNITION" ]
[ "BC5CDR", "JNLPBA", "LINNAEUS", "NCBI DISEASE" ]
BioNLP
adriansanz/stsitgesreranking
adriansanz
text-classification
[ "setfit", "safetensors", "bert", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:cross-encoder/ms-marco-MiniLM-L-4-v2", "base_model:finetune:cross-encoder/ms-marco-MiniLM-L-4-v2", "model-index", "region:us" ]
1,724
1,724
4
0
--- base_model: cross-encoder/ms-marco-MiniLM-L-4-v2 library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: He de prendre la decisió de renunciar a una subvenció que no es pot ajustar als nostres objectius. - text: Com a família de baixos ingressos, quin és el límit d'ingressos per aconseguir la reducció de la quota? - text: Necessito una llicència per accedir a la meva propietat amb vehicle. - text: Vull aprofitar l'oportunitat de l'ajut per a la creació de la meva pròpia empresa com a treballador autònom. - text: Estic buscant una manera de finançar les meves despeses de hipoteca per la meva empresa. inference: true model-index: - name: SetFit with cross-encoder/ms-marco-MiniLM-L-4-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.1060126582278481 name: Accuracy --- # SetFit with cross-encoder/ms-marco-MiniLM-L-4-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [cross-encoder/ms-marco-MiniLM-L-4-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-4-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [cross-encoder/ms-marco-MiniLM-L-4-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-4-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 237 classes <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 133 | <ul><li>"Acceptació / Renúncia. Ajuts per les despeses d'instal·lació de mesures higièniques i de seguretat per al retorn a l'activitat comercial d'establiments físics (COVID-19). Descripció"</li></ul> | | 37 | <ul><li>"Baixa o modificació de dades en el cens municipal d'animals de companyia Les persones propietàries o posseïdores d’animals de companyia estan obligades a notificar a l’Ajuntament la mort de l’animal (defunció o eutanàsia), trasllat a un altre municipi, cessió (transmissió o donació), canvi de residència de l’animal, canvi en el sistema d’identificació, així com qualsevol altra modificació de les dades que figurin en el cens en el termini de 30 dies des que s’hagi produït. A aquests efectes s’entén per animal de companyia totes les subespècies i varietats de gossos (Canis familiaris), de gats (Felis catus) i les fures."</li></ul> | | 214 | <ul><li>"Baixa de la llicència de gual Aquest tràmit permet a la persona titular de la llicència sol·licitar l'anula·lació d'una llicència de gual vigent previàment autoritzada per a l'ús de la vorera per a l'entrada i sortida de vehicles als locals on s'estacionen o custodien (garatges)."</li></ul> | | 215 | <ul><li>"Canvi de titular de la llicència de gual Aquest tràmit permet a la nova persona titular sol·licitar el canvi de nom d'una llicència de gual, sempre que no variïn la utilització ni les característiques de la llicència concedida prèviament, i s’acompleixen les ordenances vigents."</li></ul> | | 3 | <ul><li>"Modificació de dades personals al Padró Municipal d'Habitants La inscripció en el Padró municipal conté com a obligatories les dades personals de Nom i Cognoms, Sexe, Nacionalitat, Lloc i data de naixement, Número de document d'identidad (DNI, NIE, Passaport), i Certificat o títol escolar o académic. Aquest tràmit permet la modificació de les dades de les persones empadronades al municipi que detectin alguna errada a les seves dades o bé per actualitzar-les."</li></ul> | | 108 | <ul><li>"Volant històric d'empadronament El volant històric d'empadronament és el document que informa de la residencia en el municipi de Sitges, així com altres fets relatius a l'empadronament d'una persona, i detalla tots els domicilis i la data inicial i final en els que ha estat empadronada en cadascun d'ells, segons les dades que consten al Padró Municipal d'Habitants fins a la data d'expedició. En cas que la persona que necessiti el volant sigui menor de 16 anys, cal que una de les persones progenitores o tutores legals empadronades al domicili sol·liciti el volant de convivència."</li></ul> | | 54 | <ul><li>"Declaració de baixa de la Taxa pel servei municipal complementari de recollida, tractament i eliminació de residus comercials Declaració tributària mitjançant la qual es sol·licita la baixa d'una activitat de la Taxa pel servei municipal complementari de recollida, tractament i eliminació de residus comercials ."</li></ul> | | 70 | <ul><li>"Comunicació prèvia relativa a l'activitat d'habitatge d'ús turístic Informació que afecta a Noves comunicacions: En data 28 de desembre de 2018 es va publicat al DOGC l’edicte de 19 de desembre de 2018, sobre la resolució de la directora general i l’acord de la Comissió Territorial d’urbanisme de Barcelona referent a la aprovació definitiva Modificació Puntual XLVII del Pla d’ordenació urbanística municipal per a la regulació de l’ús d’habitatges d´ús turístic de Sitges; per tant la present MPPOUM és executiva. Actualment el nombre màxim d’activitats d’habitatges d’ús turístic autoritzables superen els percentatges establerts en als articles 9 i 11 de la Normativa de la MPPOUM XLVII. L’Ajuntament publicarà anualment les dades del nombre d’habitatges existents, en global i per zones, als efectes del càlcul de les noves activitats d’Habitatge d’Ús Turístic autoritzables. Les noves dades es publicaran al web municipal el 28 de juny de 2019*. La publicació serà anual a partir d’aquesta primera. * Dades 2020: Decret 757/20 de data 03/07/2020 Ateses les dades actualment en vigor, S’INFORMA que no poden ser autoritzades les sol·licituds presentades a partir del 22 de juny de 2020. Les Comunicacions de canvis de titularitat, modificacions, baixes no es veuen afectades per aquesta resolució."</li></ul> | | 101 | <ul><li>"Justificació de les subvencions per a projectes i activitats de les entitats o associacions culturals de Sitges Justificació de les subvencions atorgades per l'Ajuntament de Sitges per les activitats culturals incloses dins els següents tipus: Activitats de difusió cultural. Iniciatives de recuperació i difusió del patrimoni cultural, tradicional i popular. Activitats de formació no reglada i de recerca. Activitats d'animació socio-cultural."</li></ul> | | 128 | <ul><li>"Acceptació / Renúncia Ajuts per a la creació de noves empreses per persones donades d'alta al règim especial de treballadors autònoms Descripció"</li></ul> | | 46 | <ul><li>"Inscripció al Registre municipal d'entitats El Registre municipal d'entitats és el fitxer en el que s'inscriuen les entitats d’interès ciutadà que tinguin el seu àmbit d'actuació principal a Sitges. Té per objecte conèixer la realitat associativa del municipi, analitzar i estudiar les variacions en el teixit associatiu per tal de donar a conèixer aquesta informació a l’Ajuntament i a les entitats, i afavorir una eficaç política de foment i millora de l'activitat associativa. A efectes municipals els drets reconeguts a les entitats ciutadanes sols afectaran a aquelles entitats que siguin degudament inscrites en el Registre."</li></ul> | | 50 | <ul><li>"Llicència de parcel·lació Estan subjectes a prèvia llicència de parcel·lació totes les operacions previstes a l'article 183.1 de la Llei d'urbanisme que facilitin, o tinguin per finalitat, la construcció d'edificacions o d'instal·lacions per destinar-les a usos urbans. S'inclouen en aquestes operacions la constitució o modificació del règim de propietat horitzontal per parcel·les. També cal l'obtenció de llicència de parcel·lació quan es pretengui una nova divisió o segregació de finques resultants d'una reparcel·lació, excepte en aquells casos en els que la descripció de la finca resultant hagi previst la possibilitat de la divisió o segregació pretesa."</li></ul> | | 104 | <ul><li>"Participació en processos de selecció de personal de l'Ajuntament És la tramitació necessària per participar en les convocatòries de selecció de personal de l'Ajuntament de Sitges. L'oferta pública d'ocupació, les bases generals, les bases específiques de cada convocatòria i tota la informació i documentació del seu procés relacionat es publiquen a la pàgina específica de la Seu electrònica: www.sitges.cat/seu electrònica/ofertes de treball Tots les sol·licituds de participació a processos de selecció de personal de l'Ajuntamennt, ja siguin convocatòries de places, borses de treball o provisió de llocs de treball, així com el seguiment de la sol·licitud, es tramita des del portal web específic sitges.convoca.online/"</li></ul> | | 211 | <ul><li>'Comunicació de persona coordinadora de colònia felina Descripció'</li></ul> | | 38 | <ul><li>"Presentació de sol·licituds i altres documents dirigits a l'Ajuntament de Sitges (instància) La sol·licitud genèrica es pot utilitzar per a la presentació d'una petició que es vulgui adreçar a l'Ajuntament de Sitges, sempre i quan aquesta petició no correspongui a un tràmit específic existent i per al qual ja hi hagi un model específic de sol·licitud (consulteu el catàleg de tràmits). En aquest cas, quan un procediment concret estableixi expressament un model específic de sol·licitud, aquest serà d'ús obligatori per les persones interessades."</li></ul> | | 100 | <ul><li>"Ajuts socials als empleats municipals: Ajudes assistencials sanitàries sense període de carència L'ajuda per a les despeses mèdiques, que s'han d'haver efectuat l'any anterior al de convocatòria, inclou tot aquell tractament que beneficiï la salut de l'empleat municipal, o persona beneficiària, i que no estigui inclòs en la cobertura de l'Institut Català de la Salut (ICS). Les especialitats incloses són: Odontologia Audiometria Logopèdia i foniatria Psicologia Excepcionalment, altres despeses mèdiques que no estiguin incloses en la cobertura de l'ICS."</li></ul> | | 132 | <ul><li>"Justificació. Ajuts per la reactivació de petites empreses i persones autònomes donades d’alta al règim especial de treballadors autònoms (RETA) amb una antiguitat superior als cinc anys (COVID19) Les persones beneficiàries de l'ajut per a la reactivació de petites empreses i persones autònomes donades d’alta al règim especial de treballadors autònoms (RETA) amb una antiguitat superior als cinc anys (COVID19). La justificació es podrà presentar en un termini màxim de 15 dies hàbils des de la concessió de l'ajut i com a màxim fins el 31 d'octubre de 2020."</li></ul> | | 148 | <ul><li>"Ajuts socials als empleats municipals: Ajut especial per familiars discapacitats L'Ajuntament concedirà als empleats municipals que tinguin al seu càrrec familiars amb discapacitat física, psíquica o sensorial, un ajut especial que es reportarà mensualment segons el grau de discapacitat."</li></ul> | | 75 | <ul><li>"Autorització sanitària d'establiments de pírcing, tatuatge i micropigmentació El Decret 90/2008, de 22 d'abril, estableix que els establiments dedicats a les pràctiques de pírcing, tatuatges o bé micropigmentació, han de disposar d'una autorització sanitària municipal. En el moment de demanar l'autorització cal que el local i les instal·lacions permetin la realització de la corresponent inspecció."</li></ul> | | 7 | <ul><li>"Declaració d'estat ruïnós a instància de part Si una construcció o part d'una construcció està en estat ruïnòs, l'Ajuntament, d'ofici o a instància de qualsevol persona interessada, ho ha de declarar, amb l'audiència prèvia de les persones propietàries i de les persones residents, llevat que una situació de perill imminent ho impedís. Es declara l'estat ruïnòs d'una construcció o de part d'una construcció en els supòsits següents: a) Si els danys comporten la necessitat d'una veritable reconstrucció de l'edifici perquè no són reparables tècnicament pels mitjans normals. b) Si el cost de les obres de reparació necessàries per a complir les condicions mínimes d'habitabilitat, en el cas d'habitatges o altres de similars per a altres usos, és superior al 50% del cost d'una construcció de nova planta de característiques similars a l'existent, pel que fa a la dimensió i l'ús. c) Si cal executar obres imprescindibles per a l'estabilitat de l'edificació i la seguretat de les persones, no autoritzables en virtut de l'ordenament urbanístic en vigor."</li></ul> | | 156 | <ul><li>'Instal·lació de parada a la Fira de Sant Jordi La Fira de Sant Jordi organitzada per l’Ajuntament de Sitges és una acció de dinamització consistent en un conjunt de parades ubicades en un tram del Passeig de la Ribera dedicades exclusivament a la venda de roses i llibres el dia 23 d’abril. Aquestes parades estaran ocupades per empreses del sector, entitats socials i culturals i centres escolars amb seu a Sitges, o empreses del sector amb activitat a Sitges, que prèviament han fet la sol·licitud, se’ls ha autoritzat i, si escau, han abonat la taxa corresponent. La instal·lació de parada té la consideració de llicència d’ocupació temporal per a l’ús privatiu del domini públic local amb vigència en el dia esmentat i amb les condiciones establertes a les bases.'</li></ul> | | 126 | <ul><li>"Subvencions per finançar despeses d'hipoteca, subministrament i altres serveis i la manca d'ingressos de lloguer de les entitats culturals L’Ajuntament de Sitges convoca una subvenció destinada a finançar despeses d’hipoteca, subministrament i altres serveis i la manca d’ingressos de lloguer de les entitats culturals de Sitges afectades per les mesures adoptades per la situació de crisis provocada pel Covid 19."</li></ul> | | 225 | <ul><li>"Subvencions per a projectes i activitats de l'àmbit de tradicions i festes L'Ajuntament de Sitges atorga subvencions per contribuir al finançament de manifestacions culturals relacionades amb la cultura popular i tradicional del municipi. Queden excloses com a subvencionables les despeses destinades a inversions i funcionament, i les activitats que no tinguin, directament o indirecta, un caràcter obert a tota la població."</li></ul> | | 124 | <ul><li>"Ajuts per les despeses d'instal·lació de mesures higièniques i de seguretat per al retorn a l'activitat comercial d'establiments físics (COVID-19) Són ajuts econòmics extraordinaris destinats a col·laborar amb la despesa que suposa la implementació de mesures higièniques de prevenció, protecció i mesures de seguretat per a la reobertura dels establiments comercials i la tornada a l’activitat econòmica d’aquests establiments físics. Únicament es prendran en consideració per a l’atorgament de l’ajut la compra de material fungible i les inversions per a la instal·lació de mesures higièniques i de seguretat relacionades amb la gestió i prevenció de la pandèmia COVID-19 d’acord amb l’annex 1 de les Bases que regulen l'atorgament de l'ajut. L’àmbit temporal de l’ajut econòmic extraordinari tindrà caràcter retroactiu al moment de la declaració de l’estat d’alarma; seran despeses finançables totes aquelles que s’hagin produït des de la declaració de l’estat d’alarma i fins la data de finalització el període de presentació de sol·licituds. L’import finançable serà el 100% del cost de compra del material fungible i d’inversió excepte l’IVA de la despesa que no formarà part de l’ajut econòmic extraordinari. L’import màxim de l’ajut econòmic extraordinari anirà en funció del nombre de persona beneficiaris/ries amb dret a l’ajut, entre un mínim de 500 € i un màxim de 3.000 €."</li></ul> | | 180 | <ul><li>"Presentació d'ofertes en cas de contractes menors Aquest tràmit permet presentar ofertes i/o pressupostos sol·licitats per l'Ajuntament de Sitges en procediments de contractes menors."</li></ul> | | 51 | <ul><li>"Permís d'ocupació de la via pública per filmacions, rodatges o sessions fotogràfiques Es tracta de la sol·licitud de permís municipal per poder utilitzar de forma privativa una zona de la via pública per rodatges cinematogràfics o publicitaris, o sessions fotogràfiques, amb independència de les possibles afectacions a la via pública, com poden ser ocupació o tall de vorera, tall de carrer, tall d’un sentit de circulació amb pas alternatiu, ocupació parcial de calçada, o reserva puntual en zona d’estacionament."</li></ul> | | 232 | <ul><li>'Acceptació / Renúncia Subvencions per a projectes i activitats de les entitats o associacions culturals de Sitges Descripció'</li></ul> | | 192 | <ul><li>"Subvencions per al foment de l'esport escolar a les escoles de Sitges L'Ajuntament de Sitges atorga subvencions per a les activitats que realitzen les escoles de Sitges que tinguin com a finalitat fomentar l’esport escolar al llarg de l’exercici pel qual es sol·licita la subvenció."</li></ul> | | 174 | <ul><li>"Subvencions a projectes d'educació per a la justícia global de l'Ajuntament de Sitges Són subvencions, en l’àmbit de la solidaritat i la cooperació, per a la realització d’activitats d’educació per a la justícia global, de foment de la solidaritat i promoció de la cultura de la pau i dels drets humans al municipi de Sitges. La finalitat de les subvencions és donar suport a: Projectes educatius que difonguin la importància del compliment individual i col·lectiu amb els compromisos de l’Agenda 2030, així com aquells que fomentin la reflexió, la crítica constructiva i els valors de la Justícia Global i la solidaritat internacional. Projectes que donin a conèixer a la ciutadania les causes, tant polítiques com econòmiques, de la immigració dels principals col·lectius de persones migrades al municipi. Projectes que informin, difonguin i promoguin la vinculació de la ciutadania local amb la tasca de les organitzacions membres del Consell de Cooperació, Pau i Solidaritat que realitzen projectes de cooperació internacional fora del municipi, en un marc de desenvolupament sostenible i en compliment de l'Agenda 2030. Projectes que fomentin el coneixement de les desigualtats estructurals per raó de gènere i visibilitzin el paper de les dones i de les persones LGTBI i no binàries en la construcció de pau i la defensa dels drets humans."</li></ul> | | 204 | <ul><li>"Comunicació d'inici i modificació substancial d'activitat en un establiment amb certificat tècnic Mitjançant aquest tràmit la persona interessada posa en coneixement de l'Ajuntament l’inici o modificació substancial d’una activitat econòmica, de les incloses en l’annex de la Llei de facilitació de l’activitat econòmica (veure columna corresponent a “Certificat tècnic”), i hi adjunta el certificat tècnic acreditatiu del compliment dels requisits necessaris que estableix la normativa vigent per a l‘exercici de l’activitat. Amb aquest formulari no es poden comunicar els establiments turístics de turisme rural."</li></ul> | | 168 | <ul><li>'Justificació. Ajuts per a la realització de la Inspecció Tècnica dels Edificis (ITE) i dels certificats energètics. Descripció'</li></ul> | | 226 | <ul><li>"Acceptació / Renúncia Subvencions per projectes i activitats de l'àmbit de tradicions i festes Descripció"</li></ul> | | 169 | <ul><li>"Modificació de dades en Registre municipal d'entitats El Registre municipal d'entitats té per objecte conèixer la realitat associativa del municipi, analitzar i estudiar les variacions en el teixit associatiu per tal de donar a conèixer aquesta informació a l’Ajuntament i a les entitats, i afavorir una eficaç política de foment i millora de l'activitat associativa. Les entitats inscrites en el Registre resten obligades a comunicar a l’Ajuntament qualsevol modificació en les seves dades registrals. De no complir aquesta obligació, l'Ajuntament podrà donar de baixa del registre l'entitat. A efectes municipals els drets reconeguts a les entitats ciutadanes sols afectaran a aquelles entitats que estiguin degudament inscrites en el Registre."</li></ul> | | 127 | <ul><li>"Aportació de documentació Ajuts per a la creació de noves empreses per persones donades d'alta al règim especial de treballadors autònoms Descripció"</li></ul> | | 210 | <ul><li>"Sol·licitud d'actualització de dades de la colonia felina Descripció"</li></ul> | | 179 | <ul><li>"Utilització de les instal·lacions culturals municipals L'Ajuntament de Sitges disposa de diverses instal·lacions de caire cultural. Els diferents espais es poden cedir temporalment a persones físiques, entitats o empreses, per a la realització de les seves activitats puntuals. En l'actualitat els espais disponibles són: Auditori Miramar Centre Cultural ”Centre d’Arts Vives” L’Escorxador de Sitges"</li></ul> | | 95 | <ul><li>"Ajuts per a fomentar la contractació laboral de persones i millora de l'ocupació Els ajuts tenen com a objectiu millorar l'ocupabilitat i la inserció de persones en situació d'atur o parades incentivant la contractació de qualitat. Podran sol·licitar l'ajut aquelles persones físiques o jurídiques, persones autònomes o empreses, amb seu fiscal al municipi o fora però amb centre de treball a Sitges, i entitats sense ànim de lucre del municipi de Sitges també legalment constituïdes i inscrites en els registres pertinents, que hagin realitzat contractacions de personal per compte d'altri durant el període de l'1 de juliol de 2023 al 30 de juny de 2024. Resten fora d’aquesta convocatòria les empreses de treball temporal. Els contractes que donaran dret a ser declarada beneficiària de l’ajut seran els formalitzats des del seu inici com a contractes indefinits o fixes discontinus o bé per conversió de contractes temporals en contractes indefinits o fixes discontinus. Queden exclosos els contractes d’alta direcció i les contractacions a familiars: a cònjuges, ascendents, descendents i parents fins a segon grau. Únicament es prendran en consideració per a l’ajut econòmic les despeses derivades de la contractació de personal (retribucions i quotes empresarials a la seguretat social). Les quanties dels ajuts no podran excedir del 50 % dels costos derivats de la contractació. S'estableixen els seguents imports màxims a percebre segons les modalitats de contractació: De 3.000,00 € per als contractes de treball indefinits, fixos discontinus o conversió de contractes temporals a indefinits amb jornada de treball del 100%, els quals la persona contractada estigui inclosa dins del col·lectius vulnerables pel Servei Públic d'Ocupació Estatal (SEPE). De 2.000,00 € per als contractes de treball indefinits, fixos discontinus o conversió de contractes temporals a indefinits amb jornada de treball del 100% per a la resta de col·lectius. L’import es reduirà proporcionalment per aquells contractes celebrats com a fixes discontinus en funció del percentatge d’activitat econòmica feta durant l’any natural. Igualment es reduirà l’import per aquells contractes celebrats a temps parcial. En ambdós casos el percentatge per poder optar a l’ajut serà el resultant d’aplicar el percentatge d’activitat econòmica com el percentatge per temps parcial, i haurà de ser igual o superior al 50,00 %. Només es poden presentar dues contractacions En cas que dues contractacions donin dret a l'ajut econòmic, l'import màxim a percebre per a totes les contractacions serà de 3.000,00 €."</li></ul> | | 72 | <ul><li>"Transmissió d'una llicència de Taxi Les llicències per a prestar els serveis urbans de taxi a Sitges es poden transmetre amb l'autorització prèvia de l'Ajuntament, que només denegarà la transmissió en el termini d'un mes d'ençà de la data de la sol·licitud, si l'adquirent no compleix les condicions necessàries per a l'atorgament inicial de les llicències. La transmissió serà obligatòria, en un termini màxim de 6 mesos ens els supòsits següents: Jubilació o declaració d'incapacitat permanent absoluta o total per a la professió habitual, en el cas de les persones físiques titulars de llicència, llevat que en cas de d'incapacitat permanent total per a l'exercici de la professió la llicència sigui explotada mitjançant personal assalariat. Declaració de concurs que comporti el cessament d'activitats Dissolució o liquidació de la societat en cas de les persones jurídiques titulars de llicència. Mort del titular."</li></ul> | | 26 | <ul><li>"Notificació de torres de refrigeració i condensadors evaporatius als efectes de la prevenció i control de la legionel·losi Les persones titulars de torres de refrigeració i/o condensadors evaporatius han de notificar a l'Ajuntament el nombre i les característiques de la torre o condensador abans de la seva posada en funcionament. Així mateix, cal notificar a l'Ajuntament, en el termini d'un mes, el cessament definitiu de l'activitat de la instal·lació."</li></ul> | | 20 | <ul><li>"Llicència d'obra menor La realització d’obres està subjecta a l’obtenció d’una llicència atorgada per l’Ajuntament. S’estableixen tres tipus de llicència segons la magnitud de l’obra a realitzar: TIPUS A Construcció de piscines (comunitàries o particulars) Reparació / rehabilitació d’edificis i façanes en general i especialment d’edificis afectats per patologies Modificació de la coberta dels edificis amb augment de volum però sense augment de superfície construïda Actuacions puntuals que afectin o alterin l’estructura i / o fonaments de l’edifici Obres que modifiquin les instal·lacions o serveis dels espais comuns d’un edifici plurifamiliar Moviments de terres no inclosos en altres llicències Enderrocs parcials Murs de contenció de terres Formació de lavabos en locals comercials i magatzems Instal·lació d’aparells elevadors, ascensors i aparells mecànics en edificacions existents L'acumulació de residus i el dipòsit de materials que alterin les característiques del paisatge. Construcció o instal·lació de cisternes que afectin l'estat de càrregues de l'edifici. Canvis de distribució puntual interior (en locals i habitatges) sense afectar elements estructurals. TIPUS B Col·locació de bastides a una alçada superior a PB + 1 PP o a més de 6,00 m Arrebossat, estucat i pintat de façanes que necessiten una bastida amb una alçada superior a PB + 1 PP o a més de 6,00 m. Noves obertures ( finestres o portes ) o modificacions de les dimensions existents Reparació de balcons o elements sortints Construcció d’envans pluvials Construcció de pous i foses sèptiques Estintolament de façanes Construcció o modificació de tanques que requereixin obra. Reparació de sostres i terrats sense afectar elements estructurals. TIPUS C Obertures per a tub extractor Instal·lació d'aparells d'aire condicionat o d'altres similars Instal·lació d'antenes parabòl·liques Formació de barbacoes Col·locació de portes, finestres, persianes i reixes en obertures de façana Co·locació i/o canvi de paviments i escales a l'exterior de l'edifici Arrebossat, estucat i pintat de façanes que no necessiten una bastida amb una alçada inferior a PB + 1 PP o menys de 6.00 m Construcció, reparació i substitució de canonades de desguàs i claveguerons a l'exterior de l'edifici (sense bastida). Tala d'arbres"</li></ul> | | 233 | <ul><li>"Acceptació / Renúncia Subvencions pel foment de l'activitat física i esportiva Descripció"</li></ul> | | 224 | <ul><li>"Subvencions per a projectes i activitats a entitats de l'àmbit de drets civils Subvencions per a entitats en l’àmbit de la sensibilització i l’educació en la solidaritat, la cooperació al desenvolupament, la pau, els drets humans, la inclusió social i que donin suport a col·lectius del municipi que treballin per la igualtat, que realitzin projectes destinats a: Els sectors més empobrits de països amb un índex de desenvolupament baix i/o mitjà o projectes destinats a països en situació de conflicte bèl·lic. Potenciar el teixit associatiu dels països del sud, que incloguin accions d'apoderament del grup beneficiari i el seu desenvolupament autogestionari. Que s’adrecin a col·lectius desafavorits, com entitats que treballen per la Salut Mental. Projectes de visibilització, d'activitats culturals i d'oci, i acompanyament i suport al col·lectiu LGTBIQ+ de Sitges. Projectes destinats a activitats inclusives, de suport a persones amb altres capacitats per promoure activitats culturals i de lleure, i projectes d'acompanyament i atenció personal de persones amb diversitat funcional."</li></ul> | | 33 | <ul><li>"El Viver dels Avis de Sitges. Activitat d'hort municipal per a la gent gran A la nostra vila hi ha veïns i veïnes que els agradaria tornar a fer de pagès o provar-ho per primera vegada. Potser molts d’ells enyoren el contacte amb la terra i voldrien tenir un petit hort per dedicar-li un parell d’hores cada dia i poder seguir el cicle natural de plantar, regar i recollir els fruits de la terra, gaudint així d’un entorn on la naturalesa és generosa amb qui la treballa. Aquest tipus d’activitat ha demostrat beneficis terapèutics i eugenèsics entre els seus principals destinataris: la gent gran. Al nostre municipi tenim la sort de comptar amb un ampli espai públic com és el viver municipal. Dins d'aquest viver s'hi han habilitat 10 parcel·les sobre una superfície de 300 m2."</li></ul> | | 8 | <ul><li>"Certificat d'antiguitat i legalitat d'una finca Es tracta de la sol·licitud del certificat que acredita l'any de construcció i l'adequació a la legalitat urbanística d'un immoble. Normalment es demana per poder inscriure o modificar la inscripció en el Registre de la Propietat."</li></ul> | | 24 | <ul><li>"Llicència per a la tinença i/o conducció d'animals considerats potencialment perillosos Llicència atorgada per l'Ajuntament que autoritza la tinença i/o la conducció d'animals considerats potencialment perillosos. Aquesta llicència l'han de tramitar aquelles persones poseïdores d'un gos considerat potencialment perillós. Igualment, tota persona que condueixi per espais públics un gos potencialment perillós requereix la llicència atorgada per l’ajuntament. La llei 10/1999, de 3 de juliol, estableix que es consideren gossos potencialment perillosos: Gossos que han tingut episodis d’agressions a persones o a altres gossos Gossos que han estat ensinistrats per a l’atac i la defensa Gossos que pertanyen a una de les races següents o a llurs encreuaments: bullmastiff, dòberman, dog argentí, dog de Bordeus, fila brasileiro, mastí napolità, pit bull, de presa canari, rottweiler, terrier staffordshire americà, Akita inu, tosa inu i tosa japonès. Amés, segons preveu l’article 2 del Reial Decret 287/2002, de 2 de març, aquells que tinguin totes o la major part de les següents característiques: Forta musculatura, aspecte poderós, robust, configuració atlètica, agilitat, vigor i resistència Marcat caràcter i gran valor Pèl curt Perímetre toràcic comprés entre els 60 y els 80 centímetres, alçada en creu entre 50 i 70 centímetres i pes superior a 20 kg Cap voluminós, cuboide, robust, amb crani ample i gran i galtes musculoses i botides. Mandíbules grans i fortes, boca robusta, ampla i profunda Coll ample, musculós i curt Pit massís, ample, gran, profund amb costelles arquejades i llom musculat i curt Extremitats anteriors paral·leles, rectes i robustes"</li></ul> | | 223 | <ul><li>"Subvencions pel suport educatiu a les escoles públiques de Sitges L'Ajuntament de Sitges atorga subvencions pels projectes educatius que realitzen les escoles de Sitges que tinguin com a finalitat augmentar la qualitat educativa dels infants d'infantil i primària al llarg de l’exercici pel qual es sol·licita la subvenció."</li></ul> | | 207 | <ul><li>"Sol·licitud de creació d'una colònia felina Descripció"</li></ul> | | 236 | <ul><li>"Llicències per a obres a la via pública en infraestructures de serveis Aquest tràmit permet sol·licitar la llicència per a realitzar obres d'excavació (obertura de rases, cates, cales, canalitzacions o connexions) a la via pública per a la instal·lació o reparació d'infraestructures de serveis i subministraments (aigua, sanejament, electricitat, gas, telecomunicacions ...), normalment a sol·licitud de les companyies distribuïdores, però també per empreses promotores o constructores, i per particulars."</li></ul> | | 217 | <ul><li>"Subvencions per a finançar els serveis d’extinció i prevenció d’incendis, salvaments i protecció civil L'Ajuntament de Sitges atorga subvencions per al finançament dels serveis d’extinció i prevenció d’incendis, salvaments i protecció civil que realitzen les associacions de bombers voluntaris al llarg de l’exercici pel qual es sol·licita la subvenció."</li></ul> | | 25 | <ul><li>"Denúncies sanitàries Aquelles persones que consideren que a la via pública, un habitatge o establiment no es compleixen les condicions necessàries a nivell higiènic-sanitari, poden posar-ho en coneixement de l'Ajuntament per tal que, un cop constatats els fets, es prenguin les mesures adients. En el cas que la situació esdevingui en un domini particular (habitatge, edifici plurifamiliar, etc.) els/les responsables de la resolució de la situació seran els/les propietaris/es. En aquests casos, l'Ajuntament determinarà les accions a dur a terme i requerirà als/les titulars per tal que les apliquin."</li></ul> | | 115 | <ul><li>"Ajuts econòmics destinats a reforçar les activitats econòmiques amb suspensió o limitació d’obertura al públic i per finançar les despeses de lloguer o hipoteca per empreses i/o establiments comercials Són ajuts de suport a l’activitat empresarial i comercial afectades per les mesures aprovades per les diferents administracions per fer front a la crisis sanitària ocasionada per la covid-19, amb la finalitat de garantir la continuïtat de l’activitat econòmica i el manteniment dels llocs de treball que d’aquesta depenen. L'ajut es desdobla en dues línies compatibles entre si: A) Línia de reforç a les activitats econòmiques amb suspensió o limitació d’obertura al públic: té com objectiu reforçar econòmicament aquelles activitats que han vist, en algun moment, suspesa total o parcialment l’obertura al públic del local i/o establiment on es realitza l’activitat econòmica; l’import total de l’ajut pot oscil·lar entre un mínim de 500 € i un màxim de 6.000 €. B) Línia de col·laboració per finançar les despeses de lloguer o hipoteca: té per objectiu col·laborar amb els titulars d’empreses i/o establiments comercials, persones físiques o jurídiques, que desenvolupen l’activitat econòmica en un immoble com arrendataris o són deutors d’un crèdit immobiliari per a la compra del immoble; únicament es prendran en consideració per a l’atorgament de l’ajut les despeses derivades del lloguer o hipoteca del període d’octubre de 2020 a març de 2021; l’import de l’ajut pot oscil·lar entre 500 € i 3.000 €."</li></ul> | | 29 | <ul><li>"Centre de dia l'Aplec El centre de dia l'Aplec és un servei municipal diürn i d'assistència en les activitats de la vida diària per a persones grans amb dependències cognitives i funcionals. Aquest servei es destina a persones grans que necessiten organització, supervisió i assistència en el desenvolupament de les activitats de la seva vida diària, completant l'atenció que reben en el seu entorn social i familiar. L'horari de funcionament del centre és de 8 a 19 hores de dilluns a divendres."</li></ul> | | 152 | <ul><li>"Sol·licitud de bestreta de paga extraordinària Els empleats municipals poden sol·licitar l'avançament de l’import líquid de la propera paga extraordinària."</li></ul> | | 234 | <ul><li>"Subvencions per al desenvolupament i/o consolidació de sectors econòmics del municipi Subvencions per a entitats destinades a fomentar el desenvolupament i la consolidació de sectors econòmics locals. L'objectiu és impulsar iniciatives per millorar la competitivitat, la generació d'ocupació i potenciar el naixement de nous sectors econòmics en el municipi i l’enfortiment dels existents, contribuint així al creixement econòmic sostenible i al benestar de la comunitat. Per valorar l’interès de la proposta es tindrà en compte: Tipus d’activitat Antecedents Dates de celebració Accions de promoció dutes a terme des de l’organització"</li></ul> | | 186 | <ul><li>"Al·legacions i/o aportació de documents a expedient de participació en processos de selecció de personal de l'Ajuntament de Sitges Les persones que han sol·licitat participar en un procés de selecció convocat per l'Ajuntament de Sitges poden, en qualsevol moment del procediment anterior al tràmit d'audiència i especialment en el període establert per a la presentació d’al·legacions a partir de la publicació de la llista provisional de persones admeses i excloses, formular al·legacions i aportar documents o altres elements de judici que l'òrgan competent haurà de tenir en compte en redactar la proposta de resolució defintiva. Cal revisar la informació i els terminis de la convocatòria específica de cada procés que trobareu a la Seu electrònica de l'Ajuntament de Sitges."</li></ul> | | 227 | <ul><li>"Acceptació / Renúncia Subvencions per a projectes i activitats a entitats de l'àmbit de drets civils Descripció"</li></ul> | | 184 | <ul><li>'Justificació Subvencions per a projectes i activitats de les entitats esportives i esportistes de Sitges Les persones i entitats beneficiaries hauran de justificar la realització del projecte/activitat subvencionada com a màxim el dia 31 de març de 2023.'</li></ul> | | 94 | <ul><li>"Sol·licitud de fraccionament/ ajornament El fraccionament consisteix en el pagament de la quantitat pendent en terminis. L'ajornament consisteix en l'ampliació del termini de pagament. Tant el fraccionament com l'ajornament es poden demanar per tributs i preus públics en període voluntari o en via de constrenyiment (executiva). Les quantitats fraccionades o ajornades s’hauran de pagar amb els interessos de demora pel temps que duri el fraccionament o ajornament, d’acord amb els tipus establerts per a cada exercici a la Llei de Pressupostos Generals de l’Estat."</li></ul> | | 89 | <ul><li>"Ajuts per a famílies monoparentals o nombroses L'Ajuntament de Sitges ofereix ajuts econòmics a famílies monoparentals i famílies nombroses a aquelles famílies que, disposant de l'acreditació oficial i vigent de la condició esmentada, acompleixin un seguit de requisits. La concessió dels esmentats ajuts és puntual, anual i per un import de fins a 300 € anuals per família monoparental o nombrosa. El barem de l’ajut vindrà en les Bases específiques reguladores de les prestacions econòmiques i socials de l’Ajuntament de Sitges d’acord als ingressos i despeses del nucli familiar."</li></ul> | | 9 | <ul><li>"Certificat de compatibilitat d'un projecte d'activitat amb el planejament urbanístic Mitjançant aquest certificat associat a la tramitació d’activitats, l’Ajuntament de Sitges es pronuncia de forma prèvia sobre la compatibilitat de l'activitat projectada amb el planejament urbanístic vigent i la disponibilitat i suficiència dels serveis públics municipals per atendre els requeriments de l'activitat."</li></ul> | | 170 | <ul><li>"Baixa del Registre municipal d'entitats El Registre municipal d'entitats és el fitxer en el que s'inscriuen les entitats d’interès ciutadà que tinguin el seu àmbit d'actuació principal a Sitges. Les entitats inscrites en el Registre resten obligades a comunicar a l’Ajuntament qualsevol modificació en les seves dades registrals, podent sol·licitar la seva cancel·lació o comunicant la seva dissolució."</li></ul> | | 96 | <ul><li>"Ajuts a la consolidació d'empreses de persones donades d'alta al règim especial de treballadors autònoms Són ajuts extraordinaris destinats a les persones inscrites al Règim Especial de Treballadors Autònoms (RETA), en activitats declarades com a No essencials pel decret d’estat d’alarma, que en el moment de presentar la sol·licitud segueixin estant donades d’alta en el RETA i compleixin un termini mínim de 12 mesos consecutius emmarcats en el Règim Especial de Treballadors Autònoms (RETA). Únicament es prendran en consideració per a l’ajut els costos derivats de l’abonament de les quotes satisfetes a la seguretat social d’aquelles persones emmarcades en el Règim Especial de Treballadors Autònoms i el sistema especial per a treballadors per compte propi agraris. L’import de l’ajut serà com a màxim de 1.500,00 €, d’acord amb la puntuació obtinguda per ordre decreixent i en funció del període d’alta al RETA."</li></ul> | | 131 | <ul><li>'Aportació de documentació. Ajuts per la reactivació de petites empreses i persones autònomes donades d’alta al règim especial de treballadors autònoms (RETA) amb una antiguitat superior als cinc anys (COVID19) Descripció'</li></ul> | | 177 | <ul><li>"Aportació de documentació. Ajuts per compensar la disminució d'ingressos de les empreses o establiments del sector de l'hosteleria i restauració afectats per les mesures adoptades per la situació de crisis provocada pel SARS-CoV2 Descripció"</li></ul> | | 145 | <ul><li>"Presentació de justificants de pagament per als ajuts del lloguer just dels habitatges El pagament de l'ajut de lloguer just resta condicionat a que l’Ajuntament de Sitges tingui coneixement que la persona beneficiària es troba al corrent del pagament del contracte de lloguer mitjançant aportació mensual de rebuts, declaració responsable conjunta de l'arrendador i l'arrendatari o altres mitjans, i a les disponibilitats de les dotacions de les convocatòries. La justificació de pagament dels rebuts de lloguer es realitzarà per mensualitats corresponents al mes en curs pagades i excepcionalment amb caràcter quadrimestral. La persona beneficiària de l'ajut haurà de presentar mensualment els rebuts de lloguer corresponents al mes anterior, o la declaració responsable, per tal que l'Ajuntament procedeixi al pagament del quadrimestre següent. El pagament dels ajuts atorgats es farà efectiu mitjançant transferència bancària."</li></ul> | | 171 | <ul><li>"Ajuts per a centres residencials afectats per la incidència del coronavirus SARS-COV-2 Són ajuts econòmics extraordinaris per concurrència competitiva destinats a centres residencials del municipi que hagin celebrat contractes del 15 de març de 2020 al 31 de juliol de 2021 per reforçar les plantilles davant el SARS-CoV2, i/o que hagin hagut d’adquirir material per fer front a les necessitats de prevenció, protecció i seguretat davant la malaltia del SARS-CoV2 i/o hagin requerit serveis auxiliars pel mateix concepte i període de temps. Són despeses elegibles d’acord amb les bases el 50% de les despeses per contractació de personal, el 80% de les despeses per subministrament de material (desinfectants i netejadors, estris d'un sol ús, uniformes i roba de treball), el 80% per serveis auxiliars (de prevenció, protecció i seguretat; de neteja i desinfecció, i de proves analítiques i diagnòstiques), totes degudament acreditades i efectuades entre els dies 15 de març de 2020 i 31 de juliol de 2021. No tindrà caràcter de despesa elegible l’IVA abonat per l’adquisició de subministraments o serveis."</li></ul> | | 4 | <ul><li>"Devolució de fiances i dipòsits d'una llicència d'obra En el moment de la concessió d'una llicència, l'Ajuntament estableix les fiances o dipòsits que corresponguin (reconstrucció de paviments i/o gestió de residus) i que haurà d'ingressar la persona sol·licitant. Aquests dipòsits són retinguts mentre dura l'obra i, un cop finalitzada, són retornats (prèvia comprovació per part dels Serveis Técnics)."</li></ul> | | 107 | <ul><li>"Volant històric de convivència El volant històric de convivència és el document que informa de la residencia en el municipi de Sitges, així com altres fets relatius a l'empadronament d'una persona, i detalla tots els domicilis, la data inicial i final en els que ha estat empadronada en cadascun d'ells, i les persones amb les què constava inscrites, segons les dades que consten al Padró Municipal d'Habitants fins a la data d'expedició."</li></ul> | | 67 | <ul><li>"Comunicació prèvia d'una activitat recreativa o un espectacle públic Aquelles persones (físiques o jurídiques) que es disposin a exercir una de les següents activitats: La modificació no substancial dels establiments oberts al públic que comptin amb la llicència municipal corresponent. Els establiments oberts al públic destinats a espectacles cinematogràfics. Els establiments oberts al públic destinats a espectacles públics i activitats recreatives musicals amb un aforament autoritzat fins a 150 persones. Establiments oberts al públic d'activitats de restauració amb un aforament autoritzat fins a 150 persones, i sempre que no disposin de terrassa o qualsevol altre espai complementari a l'aire lliure. Han de comunicar-ho a l'Ajuntament prèviament a la data prevista de la seva obertura. La comunicació ha de produir-se un cop han finalitzat les obres i les instal·lacions que hauran d’estar emparades per la llicència urbanística, la comunicació prèvia d’obres no subjectes a llicència o la llicència sectorial corresponent."</li></ul> | | 178 | <ul><li>"Acceptació / Renúncia. Ajuts per compensar la disminució d'ingressos de les empreses o establiments del sector de l'hosteleria i restauració afectats per les mesures adoptades per la situació de crisis provocada pel SARS-CoV2 Descripció"</li></ul> | | 158 | <ul><li>'Ajuts per a la realització de la Inspecció Tècnica dels Edificis (ITE) i dels certificats energètics Es tracta dels ajuts per a la realització de la Inspecció Tècnica de l’Edifici (ITE) conjuntament amb l’elaboració dels certificats energètics. La unió d’aquests dos documents conforma l’Informe d’Avaluació de l’Edifici (IAE). La concessió dels ajuts s’efectuarà mitjançant un règim de concurrència competitiva, i es subvencionarà el total del cost dels Informes d’Avaluació de l’Edifici ( ITEs + Certificats energètics) amb el topall establert a la taula que consta a les bases.'</li></ul> | | 143 | <ul><li>"Justificació. Subvencions per finançar despeses d'hipoteca, subministrament i altres serveis i la manca d'ingressos de lloguer de les entitats culturals Les persones beneficiàries de la subvenció per finançar despeses d'hipoteca, subministrament i altres serveis i la manca d'ingressos de lloguer de les entitats culturals."</li></ul> | | 84 | <ul><li>"Autorització d'estacionament a la Plaça de l'Ajuntament Les persones residents a la plaça de l'Ajuntament poden obtenir una autorització d'estacionament a les zones reservades per a vehicles autoritzats de la plaça. Aquesta autorització s'atorga a un únic vehicle per domicili/propietari (independentment del nombre de residents) i permet l'estacionament a la plaça de l'Ajuntament dins les zones reservades per a vehicles autoritzats en els següents horaris: de 20:00 a 08:00 hores de dilluns a diumenge"</li></ul> | | 80 | <ul><li>"Reserva d'estacionament per a persones amb discapacitat L'Ajuntament possibilita la reserva de places d'aparcament a prop del domicili o bé del lloc de treball d'aquelles persones titulars de targetes d'aparcament per a persones amb discapacitat que, complint els requisits indicats, així ho sol·licitin."</li></ul> | | 79 | <ul><li>"Comunicació prèvia de primera utilització i ocupació d'edificis i instal·lacions Aquest tràmit permet comunicar a l'Ajuntament de Sitges la finalització de les obres de nova construcció, o bé aquelles que hagin estat objecte de modificació substancial o d'ampliació quan per a l’autorització de les obres s’hagi exigit un projecte tècnic i a l’empara d’una llicència urbanística d’obra major. Simultàniament, s'acordarà el retorn de la quantia en concepte de garanties o avals dipositats, si escau."</li></ul> | | 27 | <ul><li>"Servei de teleassistència El sistema de teleassistència o telealarma consisteix en un dispositiu que es facilita a persones grans o discapacitades, que viuen soles permanentment o durant gran part del dia, o bé que viuen amb altres persones que presenten idèntiques característiques d'edat o discapacitat. Aquest sistema permet: Connectar fàcilment amb la central receptora d’alarmes les 24 hores del dia, els 365 dies de l’any facilitant la connexió immediata la línia telefònica i d’una manera còmoda i ràpida. Només cal prémer un botó. Sistema de mans lliures, que permet poder establir contacte verbal sense necessitat de despenjar cap telèfon ni d’acostar-se al terminal teleassistència. Mobilització dels recursos que existeixen a la localitat, mitjançant un fitxer actualitzat per avís d’ambulància o metge en cas d’urgència i coordinació amb els recursos de la comunitat per a l’atenció d’emergències socials, caigudes,.... Mobilització dels recursos propis de l’usuari. Custòdia de claus Etc. Donat que la disponibilitat d'aparells és limitada, les sol·licituds es prioritzaran en funció del grau de necessitat de l'usuari/ària. A aquests efectes es valorarà per part dels Serveis Socials municipals el grau d'autonomia personal, la situació de solitud i els ingressos de la unitat familiar (vegeu l'ordenança reguladora del preu públic)."</li></ul> | | 155 | <ul><li>"Permís d'ocupació de la via pública per estands, actes o activitats puntuals Es tracta de la sol·licitud de permís municipal per poder utilitzar de forma privativa una zona de la via pública per instal·lar stands, o la realització puntual d’actes i activitats promocionals o de caràcter social, cultural, esportiu o polític, organitzades per persones o entitats amb o sense finalitat de lucre, i amb finalitat lucrativa o sense, i amb independència de les possibles afectacions a la via pública, com poden ser ocupació o tall de vorera, tall de carrer, tall d’un sentit de circulació amb pas alternatiu, ocupació parcial de calçada, o reserva puntual en zona d’estacionament."</li></ul> | | 56 | <ul><li>"Ajuts a persones amb discapacitat L'Ajuntament de Sitges atorga un ajut de fins a 500€ anuals a aquelles persones, menors de 65 anys, que tinguin un grau de discapacitat igual o superior al 65%, i de fins a 300€ anuals a aquelles persones que tinguin un grau de discapacitat entre el 49% i el 64%."</li></ul> | | 98 | <ul><li>"Ajuts socials als empleats municipals: Ajudes a la promoció interna i accés a estudis reglats Són ajuts per estudis destinats als empleats públics per afavorir la promoció interna i l'accés a estudis reglats. Les ajudes inclouen els estudis de: Educació Secundària Obligatòria (ESO), Batxillerat i cicles formatius de grau mig i superior Accés a la univeristat per majors de 25 anys Estudis universitaris fins al grau de llicenciatura Estudis oficials d'idiomes en centre públic i/o normalització lingüística de català Adquisició de llibres de text, dossiers, apunts o similars obligatoris del curs"</li></ul> | | 2 | <ul><li>"Canvi de domicili al Padró Municipal d'Habitants Aquelles persones que ja estiguin inscrites en el padró municipal d'habitants i realitzen un canvi de domicili dins del mateix municipi de Sitges, han d'actualitzar per aquest concepte les seves dades d'inscripció al padró d'habitants."</li></ul> | | 32 | <ul><li>'Renovació del dret funerari per haver transcorregut el termini de concessió Quan es produeix la caducitat del dret funerari per haver transcorregut el termini de concessió i un cop que l’Ajuntament hagi resolt el procediment legalment establert per a la declaració de caducitat, és imprescindible formalitzar la nova concessió del dret. Les concessions anteriors a l’any 1900 o posterior a aquest any en el que l’interessat acrediti mitjançant l’aportació del pergamí que ha col·laborat amb les obres de construcció del cementiri, tenen reconegut el dret a la bonificació del 100% de la taxa corresponent i les posterior a l’any 1900 tenen reconegut el dret a la bonificació del 50%.'</li></ul> | | 68 | <ul><li>"Inscripció al Registre municipal de sol·licitants d’habitatge amb protecció oficial de Sitges Al Registre de sol·licitants d'habitatge públic de Sitges s’han d’inscriure totes aquelles persones que desitgen optar a un habitatge de protecció oficial, tant en règim de venda com de lloguer, dins del terme municipal de Sitges. Per a tenir dret a ésser inscrit en el Registre de Sol·licitants d'Habitatge amb Protecció Oficial s'han de complir els procediments i els requisits establerts per normativa. Les persones inscrites en el Registre de Sol·licitants d'Habitatge amb Protecció Oficial tenen dret a optar a l'adjudicació d'un habitatge amb protecció oficial segons les condicions específiques que es determinin per a cada promoció i d'acord amb els principis, els procediments i els criteris que estableix la normativa. La inscripció, per si mateixa, no dóna lloc a cap altre dret ni comporta l'adjudicació automàtica de cap habitatge amb protecció oficial."</li></ul> | | 135 | <ul><li>"Justificació. Ajuts per les despeses d'instal·lació de mesures higièniques i de seguretat per al retorn a l'activitat comercial d'establiments físics (COVID-19) Les persones beneficiàries de l'ajut per les despeses d'instal·lació de mesures higièniques i de seguretat per al retorn a l'activitat comercial d'establiments físics (COVID-19)."</li></ul> | | 118 | <ul><li>"Aportació de documents a expedient i/o resposta a requeriment d'esmena Amb aquest tràmit podeu millorar, esmenar o aportar documents a una sol·licitud ja presentada, ja sigui de forma voluntària o com a resposta a un requeriment d'esmena o d'aportació de documents a un expedient que us hagi requerit l'Ajuntament de Sitges."</li></ul> | | 88 | <ul><li>"Alta al Padró Municipal d'Habitants Totes les persones que resideixen a Espanya estan obligades a inscriure's en el padró del municipi en el qual resideixen habitualment. El padró municipal d'habitants és així el registre administratiu on consten les veïnes i veïns del municipi i reflexa el domicili on resideixen. Mitjançant aquest tràmit podran donar-se d’alta al Padró Municipal d’Habitants la persona o persones que passin a residir a Sitges procedents d'un altre municipi de l'Estat espanyol o d'un altre país, o que residint-hi de forma continuada encara no constin inscrits al padró. L'alta al padró d'habitants de Sitegs implicarà la baixa automàtica del padró municipal de l'anterior municipi, o Registre Consular, si és el cas."</li></ul> | | 120 | <ul><li>'Aportació de documentació. Ajuts econòmics destinats a reforçar les activitats econòmiques amb suspensió o limitació d’obertura al públic i per finançar les despeses de lloguer o hipoteca per empreses i/o establiments comercials Descripció'</li></ul> | | 55 | <ul><li>'Reclamacions per responsabilitat patrimonial de l’administració Les persones tenen dret a ser indemnitzades per les administracions públiques de tota lesió que pateixin en els seus béns i drets, sempre que la lesió sigui conseqüència del funcionament normal o anormal dels serveis públics, llevat en els supòsits de força major o danys que el particular hagi de suportar de conformitat amb la Llei.'</li></ul> | | 66 | <ul><li>"Subvencions per a projectes i activitats de les entitats o associacions culturals de Sitges L'Ajuntament de Sitges, a través de la Regidoria de Cultura, atorga subvenciones per a les activitats culturals incloses dins els següents tipus: Activitats de difusió cultural. Iniciatives de recuperació i difusió del patrimoni cultural, tradicional i popular. Activitats de formació no reglada i de recerca. Activitats d’animació socio-cultural. Queden excloses com a subvencionables les despeses destinades a inversions i funcionament, i les activitats que no tinguin, directament o indirecta, un caràcter obert a tota la població."</li></ul> | | 52 | <ul><li>"Baixa al padró municipal d'habitants (persones estrangeres que marxen del país, o per defunció ...) No es poden realitzar inscripcions de baixa per canvi de municipi o país de residencia a petició de les persones interessades, tret de les persones estrangeres que traslladin la seva residència a un altre país. Les persones amb nacionalitat espanyola que estableixin la residencia en un altra municipi o país hauran de comunicar la inscripció en el Padró del nou municipi de residència o en el Registre de Matrícula de l'Oficina o Secció Consular del país de destinació. El tràmit de baixa del padró municipal d'habitants només es pot sol·lictar en les següents situacions: Persones estrangeres empadronades que traslladen la seva residència a un altre país. Defunció. L'Institut Nacional d'Estadística, a instàncies del Registre Civil, comunica periòdicament les baixes per defunció a l'Ajuntament. Si es necessita que aquesta baixa es produeixi a la major brevetat possible, es pot realitzar aquest tràmit aportant el certificat de defunció, o el llibre de família. Inclusió indeguda: Aquesta baixa afecta a persones que figuren empadronades en un domicili i ja no hi resideixen. La persona empadronada, o titular de l'habitatge, pot comunicar aquesta situació, i l'ajuntament comprovarà aquesta circunstancia amb la tramitació de l'expedient corresponent. En el cas que la persona interessada no manifesti expresament la seva conformitat, la baixa només es podrà resoldre amb informe favorable del Consejo de Empadronamiento. L'Ajuntament de Sitges també pot iniciar d'ofici aquests tipus d'expedients."</li></ul> | | 159 | <ul><li>"Presentació de propostes d’acord dels grups municipals al Ple Els grups municipals de la Corporació poden presentar propostes d'acord per a la seva incorporació en l'ordre del dia de les sessions del Ple Municipal."</li></ul> | | 86 | <ul><li>"Comunicació prèvia a l'inici de funcionament de piscines d'ús públic D'acord amb el REAL DECRET 742/2013, de 27 de setembre, les persones titulars de les piscines d'ús públic han de comunicar a l'Administració la seva posada en funcionament. S'entén per piscina d’ús públic qualsevol piscina de titularitat pública, o bé, aquelles de titularitat privada la utilització de les quals està condicionada al pagament d’una quantitat en concepte d’entrada o de quota d’accés, directa o indirecta, així com totes aquelles que no són d’ús particular. Aquestes poden ser: Piscines on l'activitat relacionada amb l'aigua és l'objectiu principal, com ara les piscines públiques, de lleure, parcs aquàtics o spas. Piscines que s'ofereixen com a servei suplementari a l'activitat principal. com ara piscines d'hotels, allotjaments turístics, càmping o terapèutiques en centres sanitaris, entre d'altres."</li></ul> | | 28 | <ul><li>"Targeta d'aparcament de vehicles per a persones amb mobilitat reduïda El Decret 97/2002, de 5 de març, regula la concessió de la targeta d’aparcament per a persones amb disminució i altres mesures adreçades a facilitar el desplaçament de les persones amb mobilitat reduïda. S’estableixen dos tipus de targetes segons el titular sigui o no el conductor del vehicle: Titular conductor: Persones amb disminució que superin el barem de mobilitat i condueixin un vehicle Titular no conductor: Persones amb disminució que superin el barem de mobilitat, majors de 3 anys i no condueixin. En aquest cas la targeta es concedeix per al vehicle que transporta a la persona. Persones amb disminució que superin el barem de mobilitat, menors de 3 anys que depenen continuadament d’aparells tècnics imprescindibles per a les funcions vitals Persones amb una agudesa visual igual o inferior a 0,1 amb correcció o reducció concèntrica del camp visual igual o menor a 10 graus."</li></ul> | | 212 | <ul><li>"Acceptació / Renúncia Ús d'espais del Centre Cultural Miramar per a la realització d'exposicions. Descripció"</li></ul> | | 81 | <ul><li>"Targeta d'armes de 4a categoria Aquelles persones que adquireixin un arma classificada a la 4a categoria d'acord amb el reglament d'armes han de sol·licitar la corresponent targeta d'armes. Es consideren armes de 4a categoria: Carabines i pistoles, de tir semiautomàtic i de repetició, i revòlvers de doble acció, accionades per aire o un altre gas comprimit no assimilades a escopetes. Carabines i pistoles, d'ànima llisa o ratllada, i d'un sol tret, i revòlvers d'acció simple, accionades per aire o un altre gas comprimit no assimilades a escopetes. Armes d'ús lúdic-esportiu accionades per molla, ressort, aire o gas comprimit, d'ànima llisa o ratllada, que disparen projectils de materials a base de polímers biodegradables, que poden contenir o no líquids o gels en el seu interior (paintball o airsoft)."</li></ul> | | 12 | <ul><li>"Llicència per a la instal·lació d'aparells de climatització en establiments o indústries Llicència que autoritza la instal·lació d'aparells de climatització. La instal·lació d'aquest tipus d'aparells es troba regulada per l'Ordenança reguladora de la instal·lació d'aparells de climatització i altres elements assimilables a l'exterior dels immobles (vegeu l'apartat 'Normativa relacionada'). Entre els requisits que haurà de complir la instal·lació, destaquem: No podran emplaçar-se a les façanes principals ni als llocs visibles des de la via pública. En cas que s'integrin dins de la composició de la façana i no siguin visibles, la sortida d'aire haurà d'estar sempre per damunt de 2.5 metres de la rasant de la voravia. No podran originar nivells de soroll superiors a 35dbA durant el dia i 30dbA durant la nit. És obligatori canalitzar l'aigua de condensació per evitar l'evacuació a la via pública. Els grups de climatització i les seves canalitzacions s'han d'aillar de manera que no transmetin vibracions a la resta de l'immoble."</li></ul> | | 229 | <ul><li>'Acceptació / Renúncia Subvencions per a finançar els serveis d’extinció i prevenció d’incendis, salvaments i protecció civil Descripció'</li></ul> | | 146 | <ul><li>"Autorització d'accés a les àrees de vianants Permet obtenir l'autorització municipal per l'accés de vehicles a les àrees restringides a vianants establer-tes al municipi (actualment nucli de Garraf i Platja de Sant Sebastià). Les persones interessades poden presentar aquesta sol·lictud, i en cas de compliment dels requisits establerts (persones residents, titulars de plaça d'aparcament, autotaxis, establiments hotelers), se'ls traslladarà la resolució d’autorització."</li></ul> | | 138 | <ul><li>"Acceptació / Renúncia. Ajuts a la consolidació d'empreses de persones donades d'alta al règim especial de treballadors autònoms. Descripció"</li></ul> | | 0 | <ul><li>"Transmissió d'una llicència d'obra Les llicències d'obres són transmissibles llevat que el nombre de les que es puguin atorgar es trobi limitat, o quan s'hagin concedit tenint en compte les característiques particulars del subjecte autoritzat. Els subjectes que intervenen en la transmissió de la llicència han de comunicar-ho per escrit a l'Ajuntament, el qual ha de comprovar que no es troba compresa en els supòsits anteriors."</li></ul> | | 161 | <ul><li>"Renovació de la inscripció en el Registre municipal de sol·licitants d’habitatge amb protecció oficial de Sitges Per accedir a un habitatge amb protecció oficial al municipi de Sitges s'ha d'estar inscrit en el Registre municipal de sol·licitants. La inscripció en aquest registre caduca en el termini d'un any, llevat que sigui renovada abans del transcurs d'aquest termini mitjançant la presentació d'una declaració responsable sobre el compliment dels requisits exigits. Aquest tràmit permet la renovació de la inscripció a aquelles persones que, individualment o com a unitat de convivència, ja constaven inscrites, no s’han produït modificacions respecte la necessitat d’habitatge, en les dades personals de la sol·licitant i la unitat de convivència, ni tampoc una variació dels ingressos de la unitat de convivència superior al 10%, i no han causat baixa (caducitat per manca de renovació, renúncia, pèrdua de les condicosn exigides, ...)."</li></ul> | | 91 | <ul><li>"Presentació de precs i preguntes al Ple municipal Dret del públic assistent al Ple Municipal a formular per escrit precs i preguntes sobre temes d'interès municipal."</li></ul> | | 112 | <ul><li>"Ajuts per a l'adquisició de llibres i material escolar L'Ajuntament de Sitges ofereix ajuts econòmics per a l'adquisició de llibres i de material escolar a aquelles famílies que acompleixin els requisits. Aquests ajuts es circumscriuen als/les estudiants de segon cicle d'educació Infantil (I3, I4, I5), Primària (de 1r a 6è), i Secundària (ESO, Batxillerat i Cicles de Formació Professional). L’alumnat haurà d’estar matriculat en un centre educatiu sostingut amb fons públics de Catalunya, en qualsevol dels cursos dels ensenyaments de segon cicle d’educació infantil i d’ensenyaments obligatoris, durant el curs corresponent a la convocatòria."</li></ul> | | 62 | <ul><li>"Abonaments a l'Espai d'Escalada L'Espai d'escalada és una instal·lació municipal en forma de túnel a una sala interior, amb una llargada de 10m, una amplada de 4,6m i una alçada de 4m. La zona de sostre cobreix l’espai format per la meitat de la llargada total. La superfície total escalable assoleix els +/- 150m2 i està compost per diversos desploms i sostres. Per accedir a aquesta instal·lació es poden pagar entrades puntuals o bé adquirir abonaments (de 10 entrades o anuals amb entrades il·limitades) Consulteu les dades i els horaris de funcionament de la instal·lació al Directori de la Vila"</li></ul> | | 121 | <ul><li>"Aportació de documents a expedient d'ajut a la contractació laboral de persones i per a la millora de l'ocupació Descripció"</li></ul> | | 82 | <ul><li>"Targeta 'smart Sitges' per a la Deixalleria Municipal Per tal d’acreditar la condició de persones usuàries de la deixalleria municipal i registrar les aportacions de residus realitzades, cal disposar de la targeta “smart Sitges”. En el cas de les persones particulars, la reducció del 20% establerta a la taxa d'escombraries (residus domiciliaris) tan sols és possible a partir d’un mínim de 12 aportacions l’any i sempre que constin registrades amb la targeta. La deixalleria dona servei al municipi de Sitges com a centre de recepció selectiva d'aquelles fraccions de residus per a les quals no hi ha un sistema de recollida domiciliària: residus especials, voluminosos, o runes, entre d'altres. El poden utilitzar les persones particulars de Sitges de forma gratuïta fins als límits establerts, i els comerços, oficines, tallers i petites indústries amb llicència d’activitats de Sitges, o que estiguin treballant dins del terme municipal, mitjançant l’abonament del cost del servei."</li></ul> | | 106 | <ul><li>"Volant d'empadronament individual El volant d'empadronament és el document que informa de la residencia en el municipi de Sitges, així com altres fets relatius a l'empadronament d'una persona en un domicili concret, segons les dades que consten al Padró Municipal d'Habitants en la data d'expedició. En cas que la persona que necessiti el volant sigui menor de 16 anys, cal que una de les persones progenitores o tutores legals empadronades al domicili sol·liciti el volant de convivència."</li></ul> | | 39 | <ul><li>"Ingrés de garanties (fiances i dipòsits) a l'Ajuntament de Sitges S'entén per garantia l'ingrés a la Tresoreria de l'Ajuntament d'una quantitat econòmica que garanteix el compliment d'una obligació adquirida amb aquest (garanties de concursos o licitacions, fraccionaments de tributs en via executiva, reposició de paviments per obres, etc.). L'ingrés d'una garantia pot fer-se en metàl·lic, mitjançant un xec bancari o conformat o bé mitjançant un aval bancari o una assegurança de caució. Pel que fa als avals, la Junta de Govern Local en sessió celebrada el 4 de juliol de 2006, va aprovar els models d'aval en funció del concepte a garantir."</li></ul> | | 134 | <ul><li>"Aportació de documentació. Ajuts per les despeses d'instal·lació de mesures higièniques i de seguretat per al retorn a l'activitat comercial d'establiments físics (COVID-19) Descripció"</li></ul> | | 149 | <ul><li>"Ajuts socials als empleats municipals: Ajut extraordinari per naixement, adopció o defunció L'empleat municipal pot sol·licitar un ajut extraordinari a l'Ajuntament en els casos de naixement, adopció o defunció. També el poden demanar els familiars en cas de defunció de l’empleat."</li></ul> | | 200 | <ul><li>'Llicència ambiental (Annex II) Mitjançant la Llicència ambiental la persona interessada sol·licita a l’Ajuntament l’inici o modificació substancial d’una activitat econòmica, de les incloses en l’annex II de la Llei 20/2009, de prevenció i control ambiental de les activitats (LPCAA), i hi adjunta el projecte tècnic acreditatiu del compliment dels requisits necessaris que estableix la normativa vigent per a l’exercici de l’activitat. Aquestes activitats se subdivideixen en: Activitats sotmeses a una llicència ambiental amb declaració d’impacte ambiental Activitats sotmeses a una llicència ambiental i a un procés de decisió prèvia sobre la necessitat de declaració d’impacte ambiental i a avaluació d’impacte ambiental simplificada Activitats sotmeses a una llicència ambiental sense necessitat de sotmetre’s a cap procés d’avaluació d’impacte ambiental També està subjecta a llicència ambiental la modificació substancial de qualsevol activitat de l’annex II de la LPCAA, amb els mateixos procediments, documentació i requisits que els establerts per al seu atorgament. Amb aquest formulari no es poden comunicar els establiments turístics (càmpings de fins a 1500 unitats d’acamapada).'</li></ul> | | 222 | <ul><li>"Subvencions per a projectes i activitats de l'àmbit turístic Subvencions per a entitats que realitzin projectes destinats a la dinamització turística del municipi que aportin valor, ja sigui pel posicionament de marca i/o la desestacionalització de l’activitat. Per valorar l’interès de la proposta es tindrà en compte: Tipus d’activitat Antecedents Dates de celebració Accions de promoció dutes a terme des de l’organització Nivell de molèstia previst i interferència en la vida quotidiana"</li></ul> | | 13 | <ul><li>"Permís d'ocupació de la via pública amb terrasses de bar, restaurant, cafeteria, etc. Es tracta de la sol·licitud de permís municipal per poder utilitzar de forma privativa una zona de la via pública (carrers, places...) destinada a la instal·lació de terrasses o vetlladors, amb els elements corresponents (taules, cadires ...), per part d’establiments de bar, restaurant, cafeteria ... que disposen dels corresponents permisos per a l’exercici de l’activitat."</li></ul> | | 187 | <ul><li>"Presentació de sol·licituds per a l'atorgament de llicència temporal de parada al Mercat Municipal de Sitges Permet participar en el concurs per a l’atorgament de llicències temporals, fins a l’adjudicació definitiva, de parades disponibles del Mercat Municipal de Sitges. Les respectives convocatòries determinaran les parades disponibles així com les seves característiques i descripció. El termini de vigència de les llicències temporals atorgades serà fins el 31 de desembre de 2025."</li></ul> | | 130 | <ul><li>'Acceptació / Renúncia. Ajuts per la reactivació de petites empreses i persones autònomes donades d’alta al règim especial de treballadors autònoms (RETA) amb una antiguitat superior als cinc anys (COVID19) Descripció'</li></ul> | | 230 | <ul><li>"Acceptació / Renúncia Subvencions per a projectes i activitats a entitats de l'àmbit de polítiques socials Descripció"</li></ul> | | 92 | <ul><li>"Sol·licitud d'accés a la informació pública El dret d’accés a la informació pública és el dret que tenen les persones a l’accés a la informació elaborada per l’Administració i la que aquesta té en el seu poder com a conseqüència de la seva activitat o de l’exercici de les seves funcions, inclosa la que li subministren els altres subjectes obligats d’acord amb la llei, sempre que no vulneri algun límit o es doni alguna causa d'inadmissió de les previstes en la norma (Llei 19/2014, de transparència, accés a la informació pública i bon govern). Mitjançant aquest tràmit podeu exercir el dret d’accés a la informació pública que la llei reconeix a totes les persones, a títol individual o en nom i representació de qualsevol persona jurídica legalment constituïda, no condicionat a un interès personal ni subjecte a motivació ni invocació de cap norma."</li></ul> | | 151 | <ul><li>"Sol·licitud de bestreta Els empleats municipals poden sol·licitar que se'ls concedeixi bestretes reintegrables per import de fins a 4 pagues mensuals brutes amb el límit màxim de 6.500 euros bruts."</li></ul> | | 5 | <ul><li>"Renovació de l’empadronament per a persones estrangeres no comunitàries Les persones estrangeres no comunitàries sense permís de residència permanent o de llarga durada, tant majors com menors d'edat, estan obligades a la renovació de la seva inscripció en el padró municipal d'habitants, amb una perdiodictat de cada dos anys comptats a partir de la data d'alta en el Padró o bé de la darrera renovació de l'empadronament. La normativa també estableix la caducitat de la inscripció en cas de no realitzar la renovació a la finalització del període establert, la qual cosa suposa la baixa del Padró d'Habitants. En cas que en el moment de la renovació ja no residiu al domicili que consta al padró d'habitants, caldrà realitzar també el tàmit de canvi de domicili."</li></ul> | | 85 | <ul><li>"Informe d'actuació de la Policia Local Es tracta d'un informe sobre les actuacions dutes a terme per la Policia Local davant d'un incident succeït al terme municipal (accidents, etc.). Aquests informes poden ser de dos tipus: Informe simple: recull les dades bàsiques amb una mínima explicació dels fets. Informe complert: recull totes les dades obtingudes en la instrucció, normalment d’accidents de trànsit, i en el que s’adjunta una representació gràfica de com s’hauria produït l’accident, i les imatges obtingudes si n’hi haguessin."</li></ul> | | 59 | <ul><li>'Bonificació de la taxa pel servei municipal complementari de recollida, tractament i eliminació de residus comercials Es concedirà una bonificació del 50 per cent de la quota de la Taxa quan es duguin a terme obres a les vies públiques, que tinguin una duració igual o superior a 1 mes i afectin directament als locals en que es realitzin activitats econòmiques. Aquesta bonificació serà prorratejada pel temps de duració de les obres, de forma que per cada mes d’afectació la bonificació serà igual al 50% de la quota prorratejada mensualment. Aquesta bonificació tindrà caràcter pregat i s’aplicarà a la quota total si la resolució de la sol•licitud es realitza abans de la liquidació, en cas contrari es gestionarà la devolució de l’import pagat i bonificat. Als carrers i per tant locals afectats indirectament se’ls concedirà una bonificació del 25 per cent, amb les mateixes condicions que als afectats directament. La Junta de Govern establirà els carrers afectats directament i els afectats indirectament.'</li></ul> | | 48 | <ul><li>"Acreditació de resident per a estacionament en àrees regulades (zona verda i zona blava) L'acreditació de resident permet estacionar el vehicle a les zones d'estacionament limitat i controlat segons les especificacions i característiques que s'assenyalen a cadascuna de les zones del municipi: de forma gratuïta a les zones verdes, i pagant la tarifa de resident a les zones blaves; en aquest darrer cas, prèvia obtenció del comprovant d'estacionament, ja sigui al parquímetre o a través de l'app de pagament. A les zones blaves els parquímetres i serveis de pagament reconeixen les matricules dels vehicles acreditats. A les zones verdes, els vehicles acreditats com a residents no han d'obtenir el comprovant d'estacionament, i els vehicles no acreditats poden estacionar previ pagament i obtenció del comprovant virtual a través de l'app de pagament, o del comprovant físic en el parquímetre més proper. S’estableixen dos tipus d'acreditacions: a) Resident b) Titular d’habitatge"</li></ul> | | 208 | <ul><li>'Sol·licitud de persona cuidadora de colònia felina Descripció'</li></ul> | | 144 | <ul><li>'Aportació de documentació. Ajuts per al pagament del lloguer just dels habitatges. Descripció'</li></ul> | | 147 | <ul><li>"Ajuts per al projecte d'implantació i l'ús de la targeta de fidelització del comerç local de Sitges Són ajuts econòmics extraordinaris destinats al projecte d'implantació i l'ús de la targeta de fidelització del comerç local de Sitges."</li></ul> | | 150 | <ul><li>"Ajuts socials als empleats municipals: Ajut per la renovació del carnet de conduir L'empleat que en l'exercici de les seves tasques tingui assignada la funció de conducció de vehicles municipals, pot sol·licitar un ajut per les despeses ocasionades per a la renovació del carnet de conduir (certificat mèdic i administratiu)."</li></ul> | | 166 | <ul><li>"Ajuts per a la realització d'activitats en el lleure educatiu, esportives, culturals, musicals i artístiques L'Ajuntament de Sitges ofereix ajuts econòmics a famílies amb recursos insuficients per accedir a la realització d'activitats de lleure, en horari o període no lectiu, amb l'objectiu que cap infant o jove en quedi exclòs per motius econòmics o de caire social. L'objecte d'aquests ajuts és facilitar l'accés a les activitats de lleure educatiu, esportives, culturals, musicals i artístiques realitzades a Sitges, per tal que l’educació no formal i de lleure sigui accessible per a tothom, entesa com a fonamental en qualsevol procés d’aprenentatge i de socialització."</li></ul> | | 173 | <ul><li>"Presentació de sol·licituds per a l'atorgament de llicència d'ús privatiu del domini públic local Aquest tràmit permet la presentació de sol·licituds per a l’autorització a favor de tercers perquè utilitzin de forma privativa una porció de domini públic local, amb caràcter temporal i sense la seva transformació, pel seu posterior destí a la realització d’activitats d'interès. En funció del número de sol·licituds presentades en cada convocatòria es procedirà a l'atorgament de la llicència: de forma directa si no hi ha pluralitat de sol·licitants, o mitjançant previ concurs en cas que existeixi una pluralitat de sol·licitants."</li></ul> | | 191 | <ul><li>"Comunicació prèvia d'obres per instal·lacions de plaques solars en sol urbà Permet comunicar les intervencions necessàries per executar una instal·lació/remodelació d’autoconsum amb energia solar fotovoltaica amb una potència instal·lada inferior a 100 kWp en sòl urbà consolidat."</li></ul> | | 197 | <ul><li>"Comunicació prèvia de modificació no substancial d'una activitat amb efectes sobre les persones o el medi ambient És el tràmit a través del qual la persona o empresa titular d’una activitat que disposa de llicència o comunicació prèvia vigent, posa en coneixement de l’Ajuntament de Sitges que procedeix a realitzar-hi una modificació no substancial. Una modificació no substancial és aquella que, tot i que té efectes sobre les persones o el medi ambient, no comporta repercussions importants o perjudicials sobre aquests. La modificació no substancial sense efectes per a les persones o el medi ambient no està subjecta a aquesta comunicació. En els altres casos, l’ampliació o la modificació de la llicència o la comunicació prèvia estarà subjecta a l’obtenció d’una nova llicència o la formalització d’una nova comunicació prèvia."</li></ul> | | 78 | <ul><li>"Carnet Blau de Sitges El Carnet Blau és un carnet personal i intransferible que acredita el compliment dels requisits per a gaudir d'un conjunt de descomptes i avantatges. Existeixen dues modalitats de carnet en funció del nivell d’ingressos de la persona titular: Carnet Blau + Permet accedir a avantatges i descomptes tant en serveis municipals com en serveis privats d'acord amb la guia d'avantatges i a la bonificació en l’ús de les línies regulars del transport públic col•lectiu municipal. Carnet Blau S (social) A més de l’accés als avantatges i descomptes del Carnet Blau +, permet accedir a serveis específics i també a determinats beneficis fiscals."</li></ul> | | 209 | <ul><li>'Sol·licitud de renovació de carnet de persona cuidadora Descripció'</li></ul> | | 119 | <ul><li>"Aportació de documents a expedient d'ajut a la consolidació d'empreses de persones donades d'alta al règim especial de treballadors autònom Descripció"</li></ul> | | 203 | <ul><li>"Sol·licitud informe previ en matèria d'incendis És l'informe preceptiu en matèria de prevenció d'incendis que ha de sol·licitar-se amb anterioritat a la presentació de la comunicació prèvia d'una activitat considerada de risc important en matèria d'incendis, quan aquesta estigui inclosa en els annexos 1 o 2 de la Llei 3/2010, de 18 de febrer, de prevenció i seguretat en matèria d'incendis en establiments, activitats, infraestructures i edificis. Posteriorment a l’obtenció de l’informe favorable, caldrà realitzar l’acte de comprovació en matèria d’incendis i procedir a efectuar la comunicació prèvia corresponent."</li></ul> | | 114 | <ul><li>"Sol·licitud de suspensió de l'execució de contracte amb l'Ajuntament de Sitges Si com a conseqüència de la situació creada per la malaltia per coronavirus (COVID-19), i les mesures adoptades por l’Estat, la Generalitat o l’Ajuntament per combatre-la, esdevé impossible l'execució de contractes amb l'Ajuntament de Sitges, aquests es podran suspendre des que es produís la situació de fet que impedeix la seva prestació i fins que dita prestació pugui reiniciar-se. En aquest supòsit, l’Ajuntament de Sitges abonarà al contractista els danys i perjudicis efectivament soferts durant el període de suspensió, prèvia sol·licitud i acreditació fefaent de la seva realitat, efectivitat i quantia. L'aplicació d'aquesta mesura resta subjecte a que la persona contractista que aprecií la impossibilitat d’execució del contracte sol·liciti, a l'òrgan de contractació, la suspensió de l'execució del contracte, tot indicant el contracte, la data d'inici de suspensió i la motivació de la impossibilitat d'execució."</li></ul> | | 83 | <ul><li>"Llicència d'ús privatiu de la via pública per a l'exercici de la venda no sedentària en els mercats setmanals En l'actualitat a Sitges se celebren dos mercats setmanals no sedentaris: Mercat setmanal dels dijous (passeig de Vilafranca i Parc de Can Robert): Productes alimentaris, articles de roba i teixits, parament de la llar, petits electodomèstics i altres complements. Mercat setmanal dels dissabtes (Plaça de Catalunya): Productes exclusivament alimentaris Les persones interessades a disposar d'una parada en qualsevol d'aquests mercats, han de sol·licitar la corresponent llicència a l'Ajuntament. El procediment d’atorgament d'aquestes llicències es realitza en règim de concurrència competitiva, prèvia convocatòria pública aprovada i regulada mitjançant les corresponents bases."</li></ul> | | 123 | <ul><li>"Ajuts per a fomentar l'emprenedoria i la creació de noves empreses Són ajuts destinats únicament a cobrir les despeses inicials necessàries per a la posada en marxa del negoci. Les despeses subvencionables seran únicament aquelles estrictament necessàries per a la posada en marxa del negoci com ara: despeses de constitució, reformes del local, inversió inicial en tecnologia, desenvolupament de la web corporativa, desenvolupament d’aplicacions de venda on line, fiança, assegurances, registre de marques i patents, ... L’import de la subvenció serà com a màxim el 80% de la factura presentada, excepte l’IVA de la despesa que no formarà part de la despesa finançable, amb un import màxim de l’ajut de 6.000,00 €. Amb aquest ajut es vol incentivar l’autoocupació i la creació d’empreses donant suport a les persones que desenvolupin la seva activitat professional al municipi de Sitges, les quals hagin iniciat la seva activitat econòmica entre l’1 de juliol de 2023 i fins el 30 de juny de 2024."</li></ul> | | 45 | <ul><li>"Consultes, queixes, denúncies i reclamacions en matèria de consum Mitjançant l'Oficina Municipal d'Informació al Consumidor (OMIC) les persones consumidores poden realitzar les següents gestions: Consulta: Si necessiteu resoldre dubtes sobre els vostres drets i obligacions com a persona consumidora o empresa; o per informar-vos de l'estat d'una reclamació. Queixa: Deixar constància de la vostra disconformitat per un mal servei (un tracte inapropiat, un temps d'espera excessiu, etc.), sense demanar cap indemnització. Denúncia: Posar en coneixement de l'administració uns fets que poden suposar una infracció en la normativa de consum, (publicitat enganyosa, manca de fulls oficials de reclamació, venda d'un producte perillós...). Reclamació - Servei de mediació de consum: En aquells casos en que ja heu presentat una reclamació a l'establiment/servei i no heu obtingut una resposta satisfactòria dins del termini de 30 dies, podeu sol·licitar el servei de mediació presentant també la reclamació a l'OMIC. L’objectiu de la mediació de consum és promoure l’obtenció d’acords consensuats entre les parts. El procediment de mediació de consum no garanteix la consecució de cap acord atès que es tracta d’ un procediment voluntari i les parts, lliurement, poden acceptar-lo o desistir-ne en qualsevol moment de la seva tramitació."</li></ul> | | 103 | <ul><li>"Inscripció a les Estades Esportives Les Estades Esportives cerquen que els infants aprenguin a relacionar-se i a compartir mitjançant l'esport, experiències i vivències amb d'altres infants amb qui no estan en contacte durant la resta de l'any. Estan adreçades als infants que hagin cursat qualsevol nivell de l'etapa de primària durant el curs actual (1r a 6è)."</li></ul> | | 90 | <ul><li>"Sol·licitud de preu públic social de la quota i del servei de menjador de les Llars d'infants municipals L’Ajuntament de Sitges ofereix a aquelles famílies que acompleixin els requisits establerts, ajuts per al pagament de la quota del servei i de la quota del menjador dels infants matriculats a les Llars d'Infants Municipals ( 0-3 anys)"</li></ul> | | 18 | <ul><li>"Llicència per a la constitució d'un règim de propietat horitzontal Llicència que autoritza la constitució d'un règim de propietat horitzontal o bé un complex immobiliari privat, o la seva modificació, quan comporti un increment del nombre d'habitatges o establiments; així com les operacions que tinguin per objecte constituir com elements susceptibles d'aprofitament independent, més d'aquells que s'hagin fet constar en una precedent declaració d'obra nova. En cas que la llicència d’obra atorgada indiqui els elements de què es composa la finca i aquests siguin els mateixos que es volen reflectir a la divisió horitzontal, no cal fer aquest tràmit. Aquest document és requerit generalment per a poder atorgar l’escriptura de divisió horitzontal per tal de procedir a la inscripció al Registre de la Propietat."</li></ul> | | 165 | <ul><li>"Sol·licitud ajuts per a la realització d'activitats en el lleure educatiu, musical, cultural i artístic Descripció"</li></ul> | | 87 | <ul><li>"Ajuts per al pagament del lloguer just dels habitatges Són ajuts a fons perdut per fer front al pagament del lloguer de les unitats de convivència a les quals el cost de l'habitatge pot situar en risc d'exclusió social residencial. Les persones o famílies beneficiàries rebran un ajut durant els 12 mesos posteriors a l'anterior convocatòria. L'accés a aquest ajut i la quantia mensual es calcula en funció dels ingressos i la composició de la unitat de convivència, i la diferència entre l’import del lloguer que es paga, lloguer concertat, i l’import que s'hauria de pagar, lloguer just, d’acord amb els conceptes definits a les bases reguladores."</li></ul> | | 183 | <ul><li>'Acceptació / Renúncia Subvencions per a projectes i activitats de les entitats esportives i esportistes de Sitges Descripció'</li></ul> | | 111 | <ul><li>'Justificació de la llicència de la Fira d’Artesania i creativitat En el cas que la resolució de la sol·licitud hagi estat favorable cal la presentació de la documentació justificativa'</li></ul> | | 73 | <ul><li>"Exempció de les taxes per recollida, tractament i eliminació d'escombraries i altres residus urbans domiciliaris Aquelles persones pensionistes, jubilades i/o treballadores en situació d’atur de llarga durada (més d’un any a l’atur), que compleixin un seguit de requisits, gaudiran d'exempció subjectiva de les taxes per recollida, tractament i eliminació d'escombraries i altres residus urbans domiciliaris. Per gaudir d'aquesta exempció cal sol·licitar-la."</li></ul> | | 153 | <ul><li>"Acceptació / Renúncia. Ajuts per al projecte d'implantació i l'ús de la targeta de fidelització del comerç local de Sitges Descripció"</li></ul> | | 21 | <ul><li>"Llicència d'obra major La realització de les obres que s’indiquen a continuació està subjecta a l’obtenció d’una llicència d’obra major atorgada per l’Ajuntament: Obra nova Reforma i / o ampliació o legalització, amb augment de volum i superfície construïda Enderrocs totals Obres que alterin el nombre d’habitatges o locals existents Obres que substitueixin o modifiquin els usos establerts Compartimentació de naus industrials existents Construcció d’altells en naus industrials i locals comercials Arranjament de façana que comporti modificació de l’aspecte exterior de les edificacions"</li></ul> | | 93 | <ul><li>"Preinscripció a la Fira d'Art de Sitges Amb l’objectiu de fomentar la participació d’artistes plàstics d’arreu de Catalunya, anualment s'organitza la Fira d'Art. Ubicada al carrer de Port Alegre (Platja de Sant Sebastià), els artistes (dibuix, pintura, gravat i escultura) poden exposar i vendre les seves obres."</li></ul> | | 196 | <ul><li>"Comunicació prèvia ambiental municipal (Annex III) Mitjançant aquest tràmit, les persones o empreses titulars d'una activitat posen en coneixement de l'Ajuntament de Sitges l’inici o modificació d’una activitat econòmica de les incloses en l’annex III de la Llei 20/2009, del 4 de desembre, de prevenció i control ambiental de les activitats, i hi adjunten el projecte i certificat tècnics acreditatius del compliment dels requisits necessaris que estableix la normativa vigent per a l’exercici de l’activitat. També se sotmeten a aquest règim les activitats consistents en unitats tècniques mòbils de caràcter temporal associades a obres d'infraestructures públiques o privades, o a activitats de tractament de residus, o a instal·lacions similars de l'annex II de l'esmentada Llei, sempre que, com a activitats independents i per les seves característiques, no estiguin associades a un emplaçament fix ni tinguin la condició d'activitats estables."</li></ul> | | 65 | <ul><li>"Permís per a reserves d'estacionament a la via pública Permís municipal per a l'ocupació privativa d'una zona d'estacionament."</li></ul> | | 213 | <ul><li>'Modificació de llicència de gual Aquest tràmit permet sol·licitar la modificació (reducció o supressió parcial) de les característiques físiques del gual previàment autoritzades en la llicència. En el cas d’ampliacions, el tràmit a seguir serà el mateix que per a una nova llicència. Els trasllats es consideraran sempre com una baixa de l’actual llicència i nova llicència en el nou emplaçament.'</li></ul> | | 99 | <ul><li>"Ajuts socials als empleats municipals: Ajudes assistencials sanitàries amb període de carència L'ajuda per a les despeses mèdiques, que s'han d'haver efectuat l'any anterior al de convocatòria, inclou tot aquell tractament que beneficiï la salut de l'empleat municipal, o persona beneficiària, que no estigui inclos en la cobertura de l'Institut Català de la Salut (ICS) i que, per ser atorgada, compleixi un període de carència entre ajudes. Les especialitats incloses són: Plantilles ortopèdiques Muntura d'ulleres Vidre simple Vidre bifocal Vidre multifocal Lents de contacte Cirurgia ocular"</li></ul> | | 71 | <ul><li>"Ús d'espais del Centre Cultural Miramar per a la realització d'exposicions Amb la finalitat de contribuir i difondre la creació artística, el foment de l’art i de la cultura en els seus diferents vessants al municipi de Sitges, tots els artistes o col·lectius que vulguin donar a conèixer la seva obra podran sol·licitar participar en el procés de selecció de les exposicions a exhibir a les sales que el Centre Cultural Miramar disposa per aquesta finalitat."</li></ul> | | 189 | <ul><li>"Transmissió de llicència d'ús privatiu de la via pública per a l'exercici de la venda no sedentària en els mercats setmanals En l'actualitat a Sitges se celebren dos mercats setmanals no sedentaris: Mercat setmanal dels dijous (Aparcament del Parc de Can Robert): Productes alimentaris, articles de roba i teixits, parament de la llar, petits electodomèstics i altres complements. Mercat setmanal dels dissabtes (Plaça de Catalunya): Productes exclusivament alimentaris Les persones que ja disposen d'una parada en qualsevol d'aquests mercats, en els supòsits de cessament voluntari de l’activitat professional inclosa la jubilació, a causa de situacions sobrevingudes no atribuïbles a la seva voluntat, o a favor del cònjuge, o parella estable, o d’un familiar de fins al segon grau de consanguinitat o afinitat, poden presentar sol·licitud i oferta de transmissió de la llicència."</li></ul> | | 231 | <ul><li>'Acceptació / Renúncia Subvencions pel suport educatiu a les escoles públiques de Sitges Descripció'</li></ul> | | 154 | <ul><li>"Permís d'ocupació de la via pública per obres i mudances Es tracta de la sol·licitud de permís municipal per poder utilitzar de forma privativa una zona de la via pública en motiu d'obres i mudances, amb la instal·lació d'elements propis d'aquestes activitats (vehicles, plataformes o grues per la càrrega i descàrrega de materials, bastides, contenidors o sacs de runa...). Així, les afectacions a la via pública poden ser: Tall total de carrer Tall d’un carril de circulació Ocupació parcial de calçada Ocupació o tall de vorera Reserva puntual d’estacionament"</li></ul> | | 125 | <ul><li>'Ajuts per la reactivació de petites empreses i persones autònomes donades d’alta al règim especial de treballadors autònoms (RETA) amb una antiguitat superior als cinc anys (COVID19) Aquest ajut extraordinari va dirigit a totes aquelles petites empreses de menys de cinquanta treballadors i persones autònomes, amb una antiguitat superior als cinc anys, que s’han vist greument afectades a causa de la crisi econòmica derivada de la pandèmia, en ser declarades no essencials. Els ajuts van destinats únicament a cobrir les despeses necessàries per a la reactivació de l’activitat empresarial d’aquestes petites empreses i professionals amb més de cinc anys d’antiguitat a 1 de juliol de 2020, per a que puguin incorporar noves línies de negoci i/o noves formes de treball adaptades a aquest nou entorn. Les despeses subvencionables seran únicament aquelles estrictament necessàries per a la reactivació i adaptació del negoci post COVID19 com ara: inversió en tecnologia com a suport de l’activitat empresarial, desenvolupament d’aplicacions de venda on line, obtenció de certificacions sanitàries, l’assessorament i consultoria per a l’adhesió i/o l’obtenció de la Certificació Biosphere de turisme sostenible, altres certificacions que avalin la qualitat dels productes i/o serveis, implementació de sistemes d’eficiència energètica, adopció de mesures que facilitin la integració del teletreball de manera estable a l’empresa, incorporació de noves línies complementàries de negoci, registre de noves patents en relació a les noves línies d’actuació, ... L’import de la subvenció serà com a màxim el 80% de la factura presentada, amb un import màxim de subvenció de 2.500 €.'</li></ul> | | 221 | <ul><li>"Subvencions pel foment de l'activitat física i esportiva L'Ajuntament de Sitges atorga subvencions per a les activitats que realitzen les entitats del municipi que tinguin com a finalitat fomentar l’activitat física i esportiva al llarg de l’exercici pel qual es sol·licita la subvenció."</li></ul> | | 117 | <ul><li>"Inscripció a les activitats d'Estiu Jove Les activitats de l’Estiu Jove durant els matins de juliol són una oportunitat per gaudir de moments d’oci sense presses tot coneixent noves opcions de diversió a la vila. Les activitats es dirigeixen a joves d’ESO de 12 a 17 anys i s’organitzen per setmanes. L’horari de les activitats és de 9h a 13h i el punt d’inici i final de les activitats sempre és l’Espai Jove de Sitges."</li></ul> | | 74 | <ul><li>"Bonificació de les taxes per recollida, tractament i eliminació d'escombraries i altres residus urbans domiciliaris Aquelles persones que fan un ús regular i continuat de la deixalleria municipal poden gaudir d’una bonificació del 20% sobre la quota de les taxes per recollida, tractament i eliminació d'escombraries i altres residus urbans domiciliaris. Només es concedirà una bonificació per unitat familiar i habitatge. Per gaudir d'aquesta bonificació cal presentar la targeta 'smart Sitges' cada cop que es faci ús de la Deixalleria Municipal."</li></ul> | | 162 | <ul><li>"Ajuts per fomentar l'associacionisme empresarial local Aquest ajut pretén fomentar l’associacionisme empresarial local, per tal de disposar d’agrupacions, gremis o associacions representatives de l’activitat empresarial del municipi."</li></ul> | | 10 | <ul><li>"Certificat d'aprofitament urbanístic Certificat emès per l'Ajuntament de Sitges que acredita l'aprofitament urbanístic o, en general, les determinacions i previsions urbanístiques aplicables a una o unes finques concretes. En el certificat s'indiquen les dades de planejament vigent, classificació del sòl, qualificació urbanística, condicions de l’edificació i usos admesos referides a una finca o solar concreta."</li></ul> | | 116 | <ul><li>"Ajuts per al pagament del lloguer just dels habitatges - Renovació persones beneficiàries de la convocatòria anterior Es tracta de la sol·licitud per continuar rebent l'ajut per al pagament del lloguer en el cas de persones beneficiàries en la convocatòria anterior. En el cas que la persona beneficiària mantingui les condicions d’elegibilitat es podrà concedir la pròrroga de la prestació sempre que la persona interessada ho sol·liciti i ho permetin les dotacions pressupostàries de cada exercici."</li></ul> | | 172 | <ul><li>"Presentació de sol·licituds per a l'atorgament de llicència d'ús privatiu del domini públic a les platges per activitats previstes en el pla d’usos El Pla de distribució d’usos i serveis de temporada (PUT) de les platges 2022-2026 de la Demarcació de Costes de la Generalitat de Catalunya contempla cedir espais a les platges per instal·lar escoles nàutiques mitjançant l'atorgament de llicència d'ús comú especial a les persones interessades. En cas de pluralitat de sol·licitants cal tenir en compte els principis d’objectivitat, publicitat i concurrència, i és necessari convocar el corresponent concurs públic. Aquest tràmit permet la presentació de les sol·licituds per a l’atorgament de llicències d’aprofitament especial sense transformació del domini públic marítim terrestre consistent en la instal·lació i explotació d'escola per oferir activitats nàutiques, amb zona d’avarada, durant la temporada."</li></ul> | | 157 | <ul><li>"Instal·lació de parada a la Fira de la Vila del Llibre de Sitges L'Ajuntament de Sitges, sota el paraigua de la marca cultural registrada Vila del Llibre, organitza la Fira de la Vila del Llibre de Sitges consistent en un conjunt de parades instal·lades al Passeig Marítim, dedicades exclusivament a la venda de llibres i activitats relacionades amb les arts del llibre (il·lustració, enquadernació, gravat…), ocupades per empreses del sector i entitats culturals, amb activitat editorial acreditada."</li></ul> | | 63 | <ul><li>"Comunicació prèvia d'espectacles i activitats recreatives amb caràcter extraordinari Es consideren espectacles o activitats recreatives de caràcter extraordinari els que es realitzen en establiments oberts al públic que disposen de llicència, autorització o comunicació prèvia davant l'Administració per a una activitat diferent de la que es pretén realitzar, o en un espai obert al públic o a altres establiments que no tenen la consideració de locals de concurrència pública sempre que compleixen les condicions exigibles per a la realització de l'espectacle públic o de l'activitat recreativa. Es poden realitzar un nombre màxim de 12 espectacles o activitats recreatives de caràcter extraordinari a l'any."</li></ul> | | 1 | <ul><li>"Alta al Padró Municipal d'Habitants per naixement L'Institut Nacional d'Estadística comunica periòdicament les altes per naixement segons el municipi de residencia que consta en la inscripció del Registre Civil. També es poden donar d'alta per naixement a sol·licitud dels pares que tinguin la guarda o custodia o, en el seu defecte, de les seves representants legals, previa presentació del llibre de familia o certificat de naixement."</li></ul> | | 42 | <ul><li>"Cessió de vehicles a favor de l'Ajuntament Aquelles persones que, essent titulars d'un vehicle, vulguin desfer-se'n, poden cedir-lo a l'Ajuntament per tal que aquest en gestioni el desballestament i la baixa definitiva."</li></ul> | | 122 | <ul><li>"Ajuts a persones amb discapacitat - Renovació persones beneficiàries de la convocatòria anterior Anualment l'Ajuntament de Sitges convoca un ajut per a les persones menors de 65 anys que acreditin una discapacitat igual o superior al 49%. Aquelles persones a les quals se’ls va concedir l'ajut en la convocatòria de l’any anterior i no han sofert cap variació respecte a la situació justificada l’any anterior se'ls podrà concedir l’ajut sempre que ho sol·licitin, sense necessitat de tornar a presentar tota la documentació justificativa, però caldrà una declaració responsable conforme no han sofert alteració les condicions des de la darrera presentació. Aquesta declaració responsable ja està inclosa en el formulari de la sol·licitud. En cas que hagin sofert variacions respecte a la situació justificada l’any anterior, hauran de presentar tan sols la documentació relativa als condicionants que s’hagin modificat."</li></ul> | | 17 | <ul><li>"Denúncia per presumpta infracció urbanística Aquelles persones que sospiten que s’està produint una vulneració de la normativa urbanística poden presentar una denúncia a l’Ajuntament per tal que aquest, prèvia constatació dels fets denunciats, si s'escau, adopti les mesures adients."</li></ul> | | 167 | <ul><li>"Confirmació de continuïtat de residència al municipi de persones estrangeres no obligades a renovar la seva inscripció padronal Les persones estrangeres amb ciutadania d'estats de la Unió Europea, o de l'Espai Econòmic Europeu, o amb targeta de residència de règim comunitari o de llarga durada, estan obligades a comunicar la seva continuïtat de residència al municipi de Sitges cada cinc anys, o cada dos en cas de no constar inscrites al Registre Central d'Estrangers, a comptar des de la darrera inscripció padronal. La no confirmació durant el període establert suposa l'inici d'un expedient de baixa en el Padró Municipal d'Habitants."</li></ul> | | 218 | <ul><li>"Subvencions per a projectes i activitats a entitats de l'àmbit de polítiques socials Subvencions per a entitats amb polítiques socials que realitzin projectes destinats a cobrir necessitats bàsiques de la ciutadania en els àmbits de: Alimentació bàsica a través de centre de distribució d'aliments per a persones usuàries de serveis socials. Habitatge amb suport socioeducatiu per a persones amb discapacitat física o derivada de transtorn mental que siguin usuàries de serveis socials."</li></ul> | | 53 | <ul><li>'Recursos contra la liquidació o el rebut de la taxa pel servei municipal complementari de recollida, tractament i eliminació de residus comercials Aquelles persones que, havent rebut la notificació de la Taxa pel servei municipal complementari de recollida, tractament i eliminació de residus comercials, considerin que la liquidació no és correcta, poden presentar un recurs. Es poden presentar recursos per: Objecte tributari (metres, classificació, etc.) Subjecte passiu (titular de la taxa) Gestió de la taxa (notificació, etc.)'</li></ul> | | 76 | <ul><li>"Utilització de les instal·lacions de l'Espai Jove de Sitges L’Espai Jove de Sitges és l'equipament municipal on els joves poden dur a terme iniciatives pròpies i on també es desenvolupen d’altres impulsades per la regidoria de Joventut. Disposa d'espais específics com són bucs d’assaig, despatx de reunions, sales o espais polivalents. Els diferents espais també es poden cedir a persones físiques, entitats o empreses, per a la realització de les seves activitats puntuals o periòdiques destinades a la població jove"</li></ul> | | 188 | <ul><li>'Accés al servei del Fons d’Habitatges d’Inclusió Social de l’Ajuntament de Sitges El Fons d’Habitatges d’Inclusió Social és el conjunt d’habitatges administrats o gestionats per l’Ajuntament de Sitges i destinats prioritàriament a cobrir les situacions d’exclusió social i/o residencial provocades per circumstàncies de vulnerabilitat social i econòmica en matèria d’habitatge de persones residents a Sitges. El procediment d’adjudicació serà mitjançant concurs públic, amb la presentació de la sol·licitud dins del termini establert per cada convocatòria, amb la priorització de casos amb seguiment social i educatiu a persones i famílies en situació de vulnerabilitat social i econòmica. Entre altres circumstàncies, hi ha les relacionades amb processos de desnonament, assetjament immobiliari, desemparament d’infants o gent gran, o les derivades per les males condicions estructurals de l’habitatge habitual. També poden optar les dones en situació de violència masclista i les persones en qualsevol altra situació justificada socialment pel mateix Ajuntament, així com en altres situacions de vulnerabilitat social i econòmica acreditables. L’adjudicació dels habitatges serà en règim d’autorització d’ús o cessió, i les persones que hi accedeixin hauran de signar un contracte per accedir a l’habitatge, i també un acord de seguiment per part dels Serveis Socials municipals.'</li></ul> | | 40 | <ul><li>"Canvi de vehicle d'una llicència municipal de vehicles lleugers de servei públic (Auto-taxis) La persona titular d'una llicència de vehicle lleuger per al servei públic (auto-taxi), en produïr-se un canvi de vehicle, ha de notificar a l'Ajuntament les dades del nou vehicle. L'Ajuntament emetrà un nou document acreditatiu de la llicència amb les dades del nou vehicle."</li></ul> | | 69 | <ul><li>"Admissió al Centre d’Empreses Creatives de Sitges El Centre d'Empreses Creatives és un viver d’empreses adreçat específicament al sector de la creativitat. El seu objectiu és facilitar la creació de noves empreses que utilitzen la cultura, la creativitat i la innovació en la seva activitat quotidiana i donar així l’oportunitat als emprenedors locals per al seu desenvolupament professional en aquest àmbit."</li></ul> | | 199 | <ul><li>"Declaració responsable en matèria de salut alimentària És el règim d'intervenció pel qual les persones que volen obrir un establiment on es comercialitzen o serveixen aliments, declaren sota la seva responsabilitat, les dades de l'establiment i de l'activitat i manifesten que compleixen i apliquen els requisits establerts a la legislació sanitària alhora que es comprometen a mantenir-ne el compliment durant l'exercici d'aquesta activitat. S'aplica als establiments, empreses alimentàries que manipulin, envasin, emmagatzemin o serveixin aliments per a la seva venda o entrega in situ al consumidor final, amb o sense repartiment a domicili, per a col·lectivitats, així com quan aquests subministren a altres establiments d'aquestes mateixes característiques, i es tracti d'una activitat marginal en termes tant econòmics com de producció, respecte de la realitzada per aquells, que es dugui a terme en l'àmbit de la unitat sanitària local, zona de salut o territori d'iguals característiques o finalitat que defineixi l'autoritat competent corresponent."</li></ul> | | 129 | <ul><li>"Justificació. Ajuts per a la creació de noves empreses per persones donades d'alta al règim especial de treballadors autònoms Les persones beneficiàries de l'ajut per a la creació de noves empreses per persones donades d'alta al règim especial de treballadors autònoms. La justificació es podrà presentar en un termini màxim de 15 dies hàbils des de la concessió de l'ajut per la línia 1 i com a màxim fins el 31 d'octubre de 2020 per la línia 2."</li></ul> | | 228 | <ul><li>"Acceptació / Renúncia Subvencions per a projectes i activitats de l'àmbit turístic Descripció"</li></ul> | | 105 | <ul><li>"Volant de convivència El volant de convivència és el document que informa de la residencia en el municipi de Sitges, així com altres fets relatius a l'empadronament de totes les persones inscrites en un domicili concret, segons les dades que consten al Padró Municipal d'Habitants en la data d'expedició."</li></ul> | | 185 | <ul><li>"Certificat de serveis prestats pel personal a l'Ajuntament de Sitges - Annex I Permet demanar la certificació o el document que acredita que la persona interessada ha prestat o presta serveis a l'Ajuntament de Sitges o organismes dependents, amb expressió dels anys, mesos i dies de serveis prestats segons consti en el seu expedient personal."</li></ul> | | 11 | <ul><li>"Pròrroga d'una llicència d'obra Les persones titulars d'una llicència urbanística tenen dret a obtenir una pròrroga tant del termini de començament com del termini d'acabament de les obres. En el cas de les obres majors (llevat dels enderrocs), els terminis,comptats des de la data de notificació de la llicència, són: 36 mesos per iniciar-les 60 mesos per finalitzar-les Les prórrogues s'han de sol·licitar de manera motivada sempre abans que s'exhaureixi el termini objecte de la sol·licitud i es concedeixen, com a màxim, per la meitat del termini de què es tracti, és a dir: 18 mesos per a la prórroga d'inici 30 mesos per a la pròrroga de finalització Es poden sol·licitar un màxim de dues pròrrogues."</li></ul> | | 142 | <ul><li>"Aportació de documentació. Subvencions per finançar despeses d'hipoteca, subministrament i altres serveis i la manca d'ingressos de lloguer de les entitats culturals Descripció"</li></ul> | | 43 | <ul><li>"Llicència de gual Aquest tràmit permet sol·licitar la llicència d'aprofitament especial de la via pública que autoritza l’entrada i sortida de vehicles a immobles a través de les voreres, o sol·licitar l'ampliació d'un gual existent i amb llicència ja concedida. En cas de concessió de nova llicència de gual es tramitarà simultàniament la concessió de la placa de gual. En casos excepcionals en què els vehicles haguessin de realitzar maniobres per entrar o sortir de l’immoble, també podrà sol·lictar-se amb aquest mateix tràmit la llicència de reserva de banda d’estacionament al lloc oposat de la calçada."</li></ul> | | 206 | <ul><li>"Control i gestió de colònies urbanes felines A partir d'aquest tràmit es permet a les persones voluntàries i a les entitats de protecció dels animals col·laborar amb l'Ajuntament de Sitges en la protecció dels drets i el benestar dels animals felins abandonats dins el terme municipal, d'acord amb els criteris del Pla municipal de control i gestió ètica de colònies urbanes felines al municipi de Sitges, i mitjançant el mètode de captura, esterilització i retorn (C.E.R.)."</li></ul> | | 160 | <ul><li>"Sol·licitud d'informació o accés a expedients i documents municipals per membres de la Corporació Els membres de la Corporació tenen dret a obtenir dels òrgans de l'Ajuntament les dades o informacions que es trobin en poder dels serveis de la Corporació i resultin necessàries per a l'exercici de les seves funcions, així com el dret a accedir als expedients administratius, antecedents i qualsevol altre tipus de documentació que obri en els arxius i dependències municipals."</li></ul> | | 61 | <ul><li>'Subvencions per a projectes i activitats esportives L’Ajuntament de Sitges atorga subvencions per a projectes i activitats d’interès públic o social que tinguin per finalitat les activitats esportives federades, escolars o populars desenvolupades per les entitats esportives i esportistes del municipi de Sitges al llarg de l’exercici per la qual es sol·licita la subvenció, i reuneixin les condicions assenyalades a les bases.'</li></ul> | | 57 | <ul><li>"Declaració d'alta de la taxa pel servei municipal complementari de recollida, tractament i eliminació de residus comercials El fet imposable de la taxa per la prestació del servei municipal complementari de recollida, tractament i eliminació de residus comercials el constitueixen la prestació dels següents serveis: La recollida dels residus comercials El tractament i l'eliminació d'aquests residus. El subjectes passius de la taxa seran els titulars d'activitats que generin residus comercials o industrials assimilables als municipals que hauran d'acreditar si disposen d'un gestor autoritzat per a la gestió dels residus. En compliment d'allò establert a l'article 23.2 del Real Decreto Legislativo 2/2004, de 5 de marzo, por el que se aprueba el texto refundido de la Ley Reguladora de las Haciendas Locales tindrà la consideració de subjecte passiu substitut del contribuent el propietari del local on s'ubiqui l'activitat. L'alta al servei implicarà la liquidació de l'import corresponent a l'exercici en què aquesta es comuniqui i la incorporació al padró fiscal corresponent fins al moment en que se sol·liciti i accepti la baixa."</li></ul> | | 198 | <ul><li>"Consulta prèvia de classificació d'activitat Mitjançant aquest tràmit les persones o empreses que pretenen realitzar una activitat, poden adreçar-se a l'Ajuntament de Sitges per realitzar la consulta sobre la classificació d'aquesta i, per tant, de la tramitació administrativa municipal que li és d'aplicació. La descripció de l'activitat a desenvolupar ha d'ésser el més ample possible per tal de poder classificar-la correctament. Us recomanem que, abans de realitzar la consulta, comproveu tots els tràmits específics disponibles a Tràmits per temes/Activitats."</li></ul> | | 23 | <ul><li>"Inscripció al cens municipal d’animals de companyia Tota persona propietària o posseïdora d’un o més animals de companyia amb residència habitual al terme municipal de Sitges està obligada a inscriure a aquests animals al cens municipal d’animals de companyia. A aquests efectes s’entén per animal de companyia totes les subespècies i varietats de gossos (Canis familiaris), de gats (Felis catus) i les fures. La inscripció al cens municipal facilita la recuperació d’aquests animals en cas de pèrdua alhora que permet a l’Ajuntament disposar de les dades necessàries en cas que s’hagin de realitzar campanyes sanitàries. En el cas de de gossos de raça potencialment perillosa, a més, la persona propietària o posseïdora, o la persona que el condueixi, amb residència habitual a Sitges haurà de tramitar també la sol·lictud de la llicència de tinença i/o conducció de gossos potencialment perillosos. En cap cas, es podrà inscriure en un mateix immoble en residencial col·lectiu més de 5 animals de companyia (gossos i gats), o 8 en un immoble unifamiliar o no residencial aïllat, ja que es considerarà sotmès a risc sanitari i d'afectació a la convivència i, en conseqüència, caldrà sol·licitar una autorització expressa de l’Ajuntament i si escau, tramitar la sol·licitud d'inscripció en el Registre de nuclis zoològics."</li></ul> | | 164 | <ul><li>"Sol·licitud ajuts per a la realització d'activitats en el lleure esportiu Descripció"</li></ul> | | 201 | <ul><li>"Llicència d'establiments fixos oberts al públic d'espectacles públics i activitats recreatives ordinàries Els establiments oberts al públic, els espectacles públics i les activitats recreatives per dur a terme la seva activitat requereixen l'obtenció prèvia de llicència abans de l'inici de l'activitat. Mitjançant aquest tràmit la persona interessada sol·licita a l'Ajuntament de Sitges l'inici d’una activitat de les que estan sotmeses al règim de llicència municipal en el cas d'espectacles públics i activitats recreatives ordinàries: Bars musicals amb una superfície construïda superior a 500 m2 i un aforament superior a 500 persones. Discoteques. Sales de ball. Restaurants musicals amb una superfície construïda superior a 500 m2 i un aforament superior a 500 persones. Sales de festes amb espectacle. Sales de concert. Discoteques de joventut. Karaokes Sales de festes amb espectacles i concerts d’infància i joventut. Cafè teatre i cafè concert. Establiments on s'hi realitzen activitats de naturalesa sexual: Locals amb reservats annexos, que pot disposar amb servei de bar, amb i ambientació musical per mitjans mecànics, sense pista de ball o espai assimilable. Locals amb reservats annexos que ofereixen actuacions i espectacles eròtics, i disposa d'escenari, amb pista de ball o sense, de vestuari per a les persones actuants, de cadires i taules per a les persones espectadores i de servei de bar. Les modificacions substancials d'un establiment o activitats ja autoritzades també estan subjectes a aquesta llicència i, sempre que sigui possible, s'haurà de referir a la part o a les parts que es modifiquen."</li></ul> | | 15 | <ul><li>"Llicència de publicitat dinàmica Autorització per al desenvolupament d'activitats publicitàries a la via pública (repartiment de fullets, vehicles amb megafonia, banderoles, pancartes, etc.)"</li></ul> | | 176 | <ul><li>"Ajuts per compensar la disminució d'ingressos de les empreses o establiments del sector de l'hosteleria i restauració afectats per les mesures adoptades per la situació de crisis provocada pel SARS-CoV2 Es convoquen aquests ajuts econòmics per tal d’atendre la situació extraordinària del sector turístic i pal·liar la disminució d’ingressos que han tingut els establiments d’hostaleria i restauració de la nostra població com a conseqüència del SARS-CoV2. Es tracta d'ajuts extraordinaris de suport a l’activitat empresarial dels negocis d’hostaleria i restauració que integren el sector turístic que han hagut de cessar la seva obertura al públic com a conseqüència de les mesures decretades pel Govern Espanyol durant la declaració l’estat d’alarma per a la gestió de la crisis sanitari ocasionada pel SARSCoV2, i que per tant, hagin vist disminuits els seus ingressos respecte a l’exercici anterior. Serà despesa finançable el 100% de la disminució acreditada dels ingressos entre l’exercici 2020 i el 2019, segons consta als comptes anuals, presentats al Registre Mercantil, amb els topalls màxims en funció de la superfície del local que s’indiquen a les bases."</li></ul> | | 60 | <ul><li>"Acreditació anual de recollida de residus per part d'una empresa als efectes de la taxa pel servei municipal complementari de recollida, tractament i eliminació de residus comercials Les persones titulars d’activitats que generin residus comercials o industrials assimilables als municipals, vindran obligats a acreditar davant l’Ajuntament que tenen contractat un gestor autoritzat per la recollida, tractament i eliminació dels residus que produeixi l’activitat corresponent. Aquest acreditament s’haurà d’efectuar, en el termini d’un mes, comptador des de l’entrada en vigor d’aquesta ordenança, si ja s’estava portant a terme l’activitat, o des de l’inici de l’activitat generadora del residu, si aquest ha tingut lloc amb posterioritat a dita entrada en vigor"</li></ul> | | 109 | <ul><li>"Sol·licitud de Modificació de les Estades esportives Publicada la llista d'infants admesos i exclosos a les estades esportives, s'obre un termini perquè les persones admseses puguin demanar qualsevol canvi a la sol·licitud inicial. Passat aquest període de reclamacions, qualsevol sol·licitud de canvi haurà de justificar-se degudament i haurà d’estar provocada per una raó imprevista, no imputable a la persona usuària. De no ser així, aquesta haurà d’assumir les despeses de gestió, tal i com es recull a les ordenances fiscals de l’Ajuntament de Sitges."</li></ul> | | 137 | <ul><li>"Justificació. Ajuts a la contractació laboral de persones i per a la millora de l'ocupació Les persones beneficiàries de l'ajut a la contractació laboral de persones i per a la millora de l'ocupació."</li></ul> | | 190 | <ul><li>"Presentació a convocatòries per a l'adjudicació de gestió delegada de serveis públics de l'Ajuntament de Sitges Les entitats especialitzades i acreditades com a proveïdores de la Xarxa de Serveis Socials d'Atenció Pública interesades en la la gestió delegada dels serveis públics de l'Ajuntament de Sitges així determinats, poden presentar-se a les respectives convocatòries per a l'adjudicació. La gestió delegada és la prestació de serveis socials de la xarxa de serveis socials d’atenció pública en establiments de titularitat de l’administració pública, a través de tercers, en els termes i les condicions que li encomani l’administració pública titular de l’establiment o servei."</li></ul> | | 31 | <ul><li>"Al·legacions o recursos a actes administratius de l'Ajuntament de Sitges Aquest tràmit permet a les persones interessades la presentació d'al·legacions i/o la interposició de recursos contra actes administratius dictats per l’Ajuntament de Sitges."</li></ul> | | 235 | <ul><li>'Acceptació / Renúncia Subvencions per al desenvolupament i/o consolidació de sectors econòmics del municipi Descripció'</li></ul> | | 16 | <ul><li>"Denúncies per molèsties o per incompliments d'activitats Aquelles persones que consideren que una activitat incompleix algun aspecte regulat en la normativa o bé que aquesta els ocasiona molèsties contemplades en aquesta, poden presentar una denúncia per tal que l'Ajuntament inspeccioni els fets i prengui les mesures adients."</li></ul> | | 44 | <ul><li>"Devolució d'ingressos indeguts En cas que s'hagi produït un pagament indegut, el subjecte passiu pot sol·licitar la seva devolució (ingressos realitzats per duplicat, liquidacions errònies, etc.)."</li></ul> | | 220 | <ul><li>'Utilització del Centre Cultural "Centre d\'Art Vives" l\'Escorxador de Sitges Descripció'</li></ul> | | 216 | <ul><li>'Duplicat de placa de gual Aquest tràmit permet a la persona titular de la llicència sol·licitar una nova placa de gual en cas de deteriorament, pèrdua o sostracció de la placa anterior.'</li></ul> | | 195 | <ul><li>"Comunicació de canvi de titularitat d'activitats Mitjançant aquest tràmit, la persona o empresa titular d’una activitat que disposa de llicència o comunicació prèvia vigent, posa en coneixement de l’Ajuntament que transmet el corresponent títol que l’habilitava per exercir l’activitat: Per activitats amb llicència és el tràmit a través del qual una persona o empresa, que ha adquirit una activitat, posa en coneixement de l’Administració la transmissió del títol habilitant existent. Per activitats en règim de comunicació o declaració responsable, és el tràmit a través del qual una persona o empresa, que ha adquirit una activitat, posa en coneixement de l’Administració que és el nou exercent. En el cas dels espectacles, establiments oberts al públic i les activitats recreatives, els organitzadors es consideren titulars o nous exercents, i tal i com estableix la normativa, i resten obligats també a fer la comunicació de canvi de titularitat. Aquesta comunicació a l’Ajuntament serveix per acreditar la subrogació del nou titular en els drets i deures derivats de la llicència o comunicació prèvia. Amb aquest formulari no es poden comunicar els canvis de titularitat dels establiments turístics."</li></ul> | | 34 | <ul><li>"Designació de beneficiaris/àries d'una sepultura La persona titular del dret funerari sobre una sepultura, pot designar les persones que, en el moment de la defunció d'aquest, esdevindran beneficiàries del dret. El nomenament de les persones beneficiàries es fa mitjançant la compareixença de la persona titular del dret. Aquesta compareixença pot substituir-se amb un document notarial."</li></ul> | | 30 | <ul><li>"Utilització de la sala d'actes del Casal de Sitges Autorització per a la realització d'activitats a la sala d'actes del Casal Municipal de la Gent Gran de Sitges. Es tracta d'una sala amb capacitat per a 125 persones, equipada amb un petit escenari, sistema de sonorització, pantalla per a projeccions, camerins i serveis higiènics (WC)."</li></ul> | | 58 | <ul><li>"Comunicació de tancament puntual d'una activitat als efectes de la taxa pel servei municipal complementari de recollida, tractament i eliminació de residus comercials Si el subjecte passiu de la taxa del servei municipal complementari de recollida, tractament i eliminació de residus comercials, coneix la previsió d’un tancament puntual de l’activitat, per un període superior a dos mesos (tancament per temporades), aquest haurà d’informar, en la declaració d’alta de l’activitat (si es tracta del primer període impositiu per aquell local) o durant el mes de gener, del període previst de tancament en aquell exercici . Un cop declarada l’activitat per temporades es mantindrà en el padró, llevat que variï la durada de tancament de l’activitat per temporades. En cas que variï el període respecte l’any anterior, esdevé l’obligació de declarar-ho a l’Ajuntament durant el mes de gener de l’any següent en què es produeixi, sense perjudici de les activitats i facultats de comprovació que té l’Ajuntament. Els tancaments per períodes inferiors a dos mesos no donaran lloc a cap dret de devolució."</li></ul> | | 202 | <ul><li>"Llicència per a espectacles públics o activitats recreatives de caràcter extraordinari Mitjançant aquest tràmit la persona interessada posa en coneixement de l'Ajuntament de Sitges l'inici d'un espectacle públic o activitat recreativa de caràcter extraordinari que: Es duen a terme esporàdicament en establiments oberts al públic que tenen llicència o autorització per a una activitat diferent de la que es pretén fer, o Es duen a terme en espais oberts al públic o altres locals que, tot i no tenir la condició d’establiments oberts al públic amb llicència o autorització, compleixen les condicions exigibles per a dur-hi a terme els espectacles o les activitats."</li></ul> | | 35 | <ul><li>"Retrocessió d'una sepultura a favor de l'Ajuntament Les sepultures que no continguin cadàvers o restes, o bé aquelles en les que faci més de dos anys que no s'hi hagin produïnt inhumacions, poden, a petició de la persona titular, ésser retrocedides a favor de l'Ajuntament sempre que no siguin de lloguer. En cas que a la sepultura hi hagi despulles, la persona titular podrà triar entre traslladar-les a una altra sepultura de la què en sigui el/la titular o bé que l'Ajuntament les traslladi a l'ossera general. En ambdós casos, les despeses del trasllat aniran a càrrec de la persona sol·licitant. Un cop feta la retrocessió, l'Ajuntament abonarà a la persona titular el 70% de la taxa vigent per la concessió de la sepultura."</li></ul> | | 77 | <ul><li>"Bonificació de la taxa per distribució i subministrament d'aigua Bonificació del 25% de l'import corresponent al consum d'aigua, la conservació d'escomeses, aforaments i comptadors així com els drets de connexió. Per accedir a aquesta bonificació cal reunir els requisits indicats."</li></ul> | | 182 | <ul><li>"Adaptació a l'Ordenança municipal reguladora dels Habitatges d’Ús Turístic de Sitges Aquest tràmit està destinat a aquells habitatges d’ús turístic que disposen d'habilitació per poder operar (llicència) i que han d'adaptar-se a la nova Ordenança reguladora dels habitatges d'ús turístic de Sitges. Si dins el termini que s'hagi atorgat amb aquesta finalitat els habitatges que en disposen no s'han adaptat, la llicència pot ésser revocada. Les persones titulars o les persones físiques o empreses gestores de l’habitatge d’ús turístic que ja disposaven d'autorització abans de l’entrada en vigor de la nova Ordenança, hauran de donar compliment a les obligacions relatives a la presentació de documentació que acrediti l'adaptació a les obligacions relatives al rètol informatiu, d’acord amb allò previst a l’article 15 i a l’Annex I, així com la implementació de les condicions de protecció contra incendi previstes a l’article 25 i a disposar de l’assegurança de responsabilitat civil referida a l’article 12 de la referida Ordenança."</li></ul> | | 49 | <ul><li>"Inscripció a l'Escola Esportiva Municipal L'Ajuntament de Sitges organitza anualment l’Escola Esportiva Municipal adreçada a nenes i nens de 3 a 12 anys. El programa es divideix en 3 activitats: PSICOMOTRICITAT: alumnat que estigui matriculat a l'inici de curs a P3, P4 i P5 (2020, 2019, 2018). L'activitat es realitza els dilluns i dimecres. INICIACIÓ ESPORTIVA: alumnat que estigui matriculat a l'inici de curs a 1r, 2on i 3r de Primària (2017, 2016, 2015). L'activitat es realitza dimarts i dijous. PRE-ESPORT: alumnat que estigui matriculat a l'inici de curs a 4t, 5è i 6è de Primària (2014, 2013, 2012). L'activitat es realitza dimarts i dijous. Cal tramitar la sol·licitud d'inscripció a les places ofertades: 20 places per activitat a Psicomotricitat, 15 a Iniciació Esportiva i Pre-Esport. Per portar a terme l’activitat s’haurà d’arribar a un mínim de 8 inscripcions a l’activitat. En cas d’haver-hi més inscripcions que places, es crearà un llistat d’espera."</li></ul> | | 6 | <ul><li>"Consulta d'expedients administratius Consulta d'un expedient administratiu (llicències, disciplina urbanística, planejament, etc.). La consulta es realitza en presència de personal del departament d'urbanisme."</li></ul> | | 102 | <ul><li>'Queixes, observacions i suggeriments Descripció'</li></ul> | | 175 | <ul><li>"Comunicació de dades bancàries de creditors/es de l'Ajuntament de Sitges Les transaccions monetàries de l’Ajuntament derivades de qualsevol obligació jurídica es fan mitjançant transferència bancària. Per aquest motiu, totes les persones creditores han de comunicar les seves dades bancàries per tal que l'Ajuntament pugui fer l’ordenació de transferències al seu favor. Aquest tràmit permet que qualsevol persona, física o jurídica, que hagi de rebre pagaments de l’Ajuntament (persones proveïdores, o beneficiàries d’ajuts o subvencions), designi electrònicament el compte de la seva titularitat on els vol cobrar. Aquesta designació és vàlida per temps indefinit o mentre la persona creditora no en declari la modificació o la baixa de forma expressa, la qual haurà de comunicar amb aquest mateix tràmit. L’últim compte comunicat serà el que es considerarà vàlid. La modificació serà efectiva pels pagaments encara no ordenats per l’òrgan competent. No s'acceptaran altes o canvis de compte que únicament constin en factures, albarans o documents anàlegs."</li></ul> | | 139 | <ul><li>"Justificació de l'ajut a la consolidació d'empreses de persones donades d'alta al règim especial de treballadors autònoms Les persones beneficiàries de l'ajut a la consolidació d'empreses de persones donades d'alta al règim especial de treballadors autònoms."</li></ul> | | 219 | <ul><li>'Utilització de la instal·lació cultural municipal Auditori Miramar Descripció'</li></ul> | | 41 | <ul><li>"Carnet de conductor de taxis Carnet que acredita a una persona com a conductora d'un taxi que disposa de llicència municipal."</li></ul> | | 110 | <ul><li>'Instal·lació de parada a la Fira d’Artesania i creativitat Es tracta de la sol·licitud per a la instal·lació de parades destinades a la venda d’articles d’artesania, fets a mà i creatius, amb motiu de la Fira-Mercat a celebrar al Passeig Marítim de Sitges, els dies indicats al calendari previst a les bases. La instal·lació de parada té la consideració de llicència d’ocupació temporal per a l’ús privatiu del domini públic local amb vigència en el període esmentat i amb les condiciones establertes a les bases.'</li></ul> | | 22 | <ul><li>"Llicència per a la instal·lació d'una grua torre La instal·lació i utilització d’una grua torre està subjecta a l’obtenció d’una llicència municipal. Aquesta llicència autoritza tant la instal·lació com la utilització d’aquesta."</li></ul> | | 19 | <ul><li>"Comunicació prèvia d'obres de petita entitat Entre les actuacions subjectes a la presentació d’aquest tipus de comunicació prèvia a l’inici de les obres es troben les següents: Les obres interiors que no suposin canvis en les obertures, parets, pilars i sostres, ni en la redistribució d’espais interiors, sempre que no es refereixin a edificis singulars o inclosos en el Catàleg del patrimoni històrico–artístic. Revocar, enguixar, enrajolar i pintar parets i/o sostres interiors. Canviar paviments existents i graons d’escales. Col·locar o reparar el cel ras. Canviar la fusteria interior. Renovar aparells sanitaris, safareig i cuines. Substitució, reparació o millora d’instal·lacions d’aigua, gas, electricitat, telefonia i desguàs a l’interior de l’habitatge inclosos els ajuts de ram de paleta. Practicar cales en interiors per a canonades d’aigua, gas, electricitat i telefonia que no afectin a parets de càrrega. Neteja de solars sempre que no contempli tala d'arbres. Instal·lació de tendals. Cisternes que no afectin elements estructurals (a terra). Instal·lació de tanques sense obra. No es poden comunicar amb aquest tràmit les intervencions sobre béns amb protecció patrimonial ni els actes que es duguin a terme en sòl no urbanitzable i sòl no urbanitzable no delimitat."</li></ul> | | 163 | <ul><li>"Bonificacions i reduccions de la quota de les estades esportives organitzades per l'Ajuntament de Sitges Aquelles persones que s'hagin inscrit a les estades esportives organitzades per l'Ajuntament de Sitges i que formin part d'una unitat familiar amb uns ingresos bruts mensuals, que una vegada dividits pel nombre de membres, siguin inferiors entre una i dues terceres parts de l'IPREM, poden sol·licitar una reducció de la quota d'aquestes activitats o l'aplicació de la corresponent tarifa bonificada establerta en les ordenances dels preus públics."</li></ul> | | 64 | <ul><li>"Reserva d'espai en les vies i terrenys d'ús públic Reserva per aparcament exclusiu o bé prohibició d'estacionament a favor d'una persona (física o jurídica)."</li></ul> | | 47 | <ul><li>"Certificat del nombre d'habitatges del municipi o part d'aquest Certificat on s'indica el nombre d'habitatges que configuren el padró de l'Impost sobre Béns Immobles del municipi o bé d'una part d'aquest. Els certificats sempre fan referència a la data de tancament del padró de l'impost (1 de gener de l'any en curs). Normalment, aquests certificats es demanen per tal de ser presentats davant del col·legi oficial de farmacèutics per al tràmit d'obertura d'una oficina de farmàcia."</li></ul> | | 113 | <ul><li>"Adhesió al Sitges Covention Bureau El “Sitges Convention Bureau” (SitgesCB) és una comissió permanent específica de turisme de negocis del Consell Assessor de Turisme de l'Ajuntament de Sitges, que en col·laboració amb tots els agents públics i privats interessats en el desenvolupament de la vila de Sitges com a destinació de congressos, convencions, incentius i altres reunions de turisme de negocis, té com a objectiu impulsar, millorar i gestionar coordinadament les ofertes i la promoció en els mercats específics. Més informació a https://sitgescb.cat/"</li></ul> | | 140 | <ul><li>'Acceptació / Renúncia. Ajuts econòmics destinats a reforçar les activitats econòmiques amb suspensió o limitació d’obertura al públic i per finançar les despeses de lloguer o hipoteca per empreses i/o establiments comercials Descripció'</li></ul> | | 181 | <ul><li>"Modificació de sol·licitud de permís d'ocupació de la via pública per filmacions, rodatges o sessions fotogràfiques Descripció"</li></ul> | | 14 | <ul><li>"Permís d'ocupació de la via pública per a instal·lacions d’atraccions i venda en fires Es tracta de la sol·licitud de permís municipal per poder utilitzar de forma privativa una zona de la via pública per instal·lacions d’atraccions i venda en fires, amb independència de les possibles afectacions a la via pública, com poden ser tall total de carrer, tall d’un carril de circulació, ocupació parcial de calçada, ocupació o tall de vorera, reserva puntual d’estacionament."</li></ul> | | 141 | <ul><li>"Acceptació / Renúncia. Subvencions per finançar despeses d'hipoteca, subministrament i altres serveis i la manca d'ingressos de lloguer de les entitats culturals Descripció"</li></ul> | | 194 | <ul><li>"Justificació de les subvencions per al foment de l'esport escolar a les escoles de Sitges Aquest tràmit permet aportar els documents requerits per a la justificació de les subvencions atorgades per l’Ajuntament de Sitges per a les activitats de foment de l'esport escolar que hagin realitzat les escoles de Sitges en l’exercici anterior."</li></ul> | | 36 | <ul><li>'Canvi de titular del dret funerari sobre una sepultura Quan es produeix la mort de la persona titular d\'un dret funerari, aquest dret pot transmetre\'s: a la persona beneficiària que el titular hagués designat mitjançant manifestació a l\'òrgan gestor. als hereus designats per testament, o bé, en cas d\'absència dels dos documents anteriors, a aquella persona que, legalment hi tingui dret. La transmissió "inter-vivos" només pot fer-se a favor d\'hospitals, entitats benèfiques, religioses o similars o bé a familiars. La cessió entre tercers, només es contempla en el cas de sepultures de construcció particular que hagin estat donades d\'alta amb una anterioritat de 10 anys a la data de sol·licitud de la cessió.'</li></ul> | | 97 | <ul><li>'Ajuts socials als empleats municipals: Ajuda per escolaritat L’Ajuntament concedeix ajudes econòmiques amb caràcter anual per als estudis dels fills o persones al seu càrrec, per sufragar el cost de l’ensenyament bàsic obligatori, primària, secundaria i superior, havent-se d’acreditar la matriculació i inscripció en el respectiu centre públic o concertat, així com el cost de les llars d’infants, de l’educació especialitzada per les discapacitats físiques, psíquiques i sensorials en centres públics, concertats o privats. Les ajudes inclouen els conceptes de: Escoles bressol Educació especial Preescolar Primària Educació Secundària Obligatòria (ESO) i cicle formatiu de grau mig Batxillerat i cicle formatiu de grau superior Estudis universitaris'</li></ul> | | 193 | <ul><li>"Acceptació / Renúncia Subvencions per al foment de l'esport escolar a les escoles de Sitges Descripció"</li></ul> | | 205 | <ul><li>"Comunicació d'inici i modificació substancial d'activitat en un establiment amb projecte tècnic i certificat Mitjançant aquest tràmit la persona interessada posa en coneixement de l’Ajuntament l’inici o modificació substancial d’una activitat econòmica, de les incloses en l’annex de la Llei de facilitació de l’activitat econòmica (veure columna corresponent a “Projecte tècnic + certificat tècnic”), i hi adjunta el projecte i el certificat tècnic acreditatiu del compliment dels requisits necessaris que estableix la normativa vigent per a l’exercici de l’activitat. Amb aquest formulari no es poden comunicar els establiments turístics."</li></ul> | | 136 | <ul><li>"Acceptació / Renúncia. Ajuts a la contractació laboral de persones i per a la millora de l'ocupació Descripció"</li></ul> | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.1060 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("adriansanz/stsitgesreranking") # Run inference preds = model("Necessito una llicència per accedir a la meva propietat amb vehicle.") ``` <!-- ### Downstream Use *List how someone could finetune this model on their own dataset.* --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 6 | 82.6920 | 393 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 1 | | 1 | 1 | | 2 | 1 | | 3 | 1 | | 4 | 1 | | 5 | 1 | | 6 | 1 | | 7 | 1 | | 8 | 1 | | 9 | 1 | | 10 | 1 | | 11 | 1 | | 12 | 1 | | 13 | 1 | | 14 | 1 | | 15 | 1 | | 16 | 1 | | 17 | 1 | | 18 | 1 | | 19 | 1 | | 20 | 1 | | 21 | 1 | | 22 | 1 | | 23 | 1 | | 24 | 1 | | 25 | 1 | | 26 | 1 | | 27 | 1 | | 28 | 1 | | 29 | 1 | | 30 | 1 | | 31 | 1 | | 32 | 1 | | 33 | 1 | | 34 | 1 | | 35 | 1 | | 36 | 1 | | 37 | 1 | | 38 | 1 | | 39 | 1 | | 40 | 1 | | 41 | 1 | | 42 | 1 | | 43 | 1 | | 44 | 1 | | 45 | 1 | | 46 | 1 | | 47 | 1 | | 48 | 1 | | 49 | 1 | | 50 | 1 | | 51 | 1 | | 52 | 1 | | 53 | 1 | | 54 | 1 | | 55 | 1 | | 56 | 1 | | 57 | 1 | | 58 | 1 | | 59 | 1 | | 60 | 1 | | 61 | 1 | | 62 | 1 | | 63 | 1 | | 64 | 1 | | 65 | 1 | | 66 | 1 | | 67 | 1 | | 68 | 1 | | 69 | 1 | | 70 | 1 | | 71 | 1 | | 72 | 1 | | 73 | 1 | | 74 | 1 | | 75 | 1 | | 76 | 1 | | 77 | 1 | | 78 | 1 | | 79 | 1 | | 80 | 1 | | 81 | 1 | | 82 | 1 | | 83 | 1 | | 84 | 1 | | 85 | 1 | | 86 | 1 | | 87 | 1 | | 88 | 1 | | 89 | 1 | | 90 | 1 | | 91 | 1 | | 92 | 1 | | 93 | 1 | | 94 | 1 | | 95 | 1 | | 96 | 1 | | 97 | 1 | | 98 | 1 | | 99 | 1 | | 100 | 1 | | 101 | 1 | | 102 | 1 | | 103 | 1 | | 104 | 1 | | 105 | 1 | | 106 | 1 | | 107 | 1 | | 108 | 1 | | 109 | 1 | | 110 | 1 | | 111 | 1 | | 112 | 1 | | 113 | 1 | | 114 | 1 | | 115 | 1 | | 116 | 1 | | 117 | 1 | | 118 | 1 | | 119 | 1 | | 120 | 1 | | 121 | 1 | | 122 | 1 | | 123 | 1 | | 124 | 1 | | 125 | 1 | | 126 | 1 | | 127 | 1 | | 128 | 1 | | 129 | 1 | | 130 | 1 | | 131 | 1 | | 132 | 1 | | 133 | 1 | | 134 | 1 | | 135 | 1 | | 136 | 1 | | 137 | 1 | | 138 | 1 | | 139 | 1 | | 140 | 1 | | 141 | 1 | | 142 | 1 | | 143 | 1 | | 144 | 1 | | 145 | 1 | | 146 | 1 | | 147 | 1 | | 148 | 1 | | 149 | 1 | | 150 | 1 | | 151 | 1 | | 152 | 1 | | 153 | 1 | | 154 | 1 | | 155 | 1 | | 156 | 1 | | 157 | 1 | | 158 | 1 | | 159 | 1 | | 160 | 1 | | 161 | 1 | | 162 | 1 | | 163 | 1 | | 164 | 1 | | 165 | 1 | | 166 | 1 | | 167 | 1 | | 168 | 1 | | 169 | 1 | | 170 | 1 | | 171 | 1 | | 172 | 1 | | 173 | 1 | | 174 | 1 | | 175 | 1 | | 176 | 1 | | 177 | 1 | | 178 | 1 | | 179 | 1 | | 180 | 1 | | 181 | 1 | | 182 | 1 | | 183 | 1 | | 184 | 1 | | 185 | 1 | | 186 | 1 | | 187 | 1 | | 188 | 1 | | 189 | 1 | | 190 | 1 | | 191 | 1 | | 192 | 1 | | 193 | 1 | | 194 | 1 | | 195 | 1 | | 196 | 1 | | 197 | 1 | | 198 | 1 | | 199 | 1 | | 200 | 1 | | 201 | 1 | | 202 | 1 | | 203 | 1 | | 204 | 1 | | 205 | 1 | | 206 | 1 | | 207 | 1 | | 208 | 1 | | 209 | 1 | | 210 | 1 | | 211 | 1 | | 212 | 1 | | 213 | 1 | | 214 | 1 | | 215 | 1 | | 216 | 1 | | 217 | 1 | | 218 | 1 | | 219 | 1 | | 220 | 1 | | 221 | 1 | | 222 | 1 | | 223 | 1 | | 224 | 1 | | 225 | 1 | | 226 | 1 | | 227 | 1 | | 228 | 1 | | 229 | 1 | | 230 | 1 | | 231 | 1 | | 232 | 1 | | 233 | 1 | | 234 | 1 | | 235 | 1 | | 236 | 1 | ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (2, 2) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: True ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-------:|:--------:|:-------------:|:---------------:| | 0.0003 | 1 | 0.4496 | - | | 0.0143 | 50 | 0.4198 | - | | 0.0286 | 100 | 0.3046 | - | | 0.0429 | 150 | 0.3019 | - | | 0.0572 | 200 | 0.1864 | - | | 0.0715 | 250 | 0.1818 | - | | 0.0858 | 300 | 0.0775 | - | | 0.1001 | 350 | 0.1147 | - | | 0.1144 | 400 | 0.0588 | - | | 0.1287 | 450 | 0.0433 | - | | 0.1430 | 500 | 0.0504 | - | | 0.1573 | 550 | 0.0214 | - | | 0.1716 | 600 | 0.034 | - | | 0.1859 | 650 | 0.0438 | - | | 0.2002 | 700 | 0.0677 | - | | 0.2145 | 750 | 0.0255 | - | | 0.2288 | 800 | 0.0309 | - | | 0.2431 | 850 | 0.0227 | - | | 0.2574 | 900 | 0.0751 | - | | 0.2717 | 950 | 0.051 | - | | 0.2860 | 1000 | 0.0131 | - | | 0.3003 | 1050 | 0.0263 | - | | 0.3146 | 1100 | 0.038 | - | | 0.3289 | 1150 | 0.0325 | - | | 0.3432 | 1200 | 0.0745 | - | | 0.3576 | 1250 | 0.017 | - | | 0.3719 | 1300 | 0.0241 | - | | 0.3862 | 1350 | 0.017 | - | | 0.4005 | 1400 | 0.0169 | - | | 0.4148 | 1450 | 0.0188 | - | | 0.4291 | 1500 | 0.0266 | - | | 0.4434 | 1550 | 0.0273 | - | | 0.4577 | 1600 | 0.014 | - | | 0.4720 | 1650 | 0.0314 | - | | 0.4863 | 1700 | 0.0076 | - | | 0.5006 | 1750 | 0.0089 | - | | 0.5149 | 1800 | 0.0406 | - | | 0.5292 | 1850 | 0.013 | - | | 0.5435 | 1900 | 0.0221 | - | | 0.5578 | 1950 | 0.0147 | - | | 0.5721 | 2000 | 0.0332 | - | | 0.5864 | 2050 | 0.0248 | - | | 0.6007 | 2100 | 0.019 | - | | 0.6150 | 2150 | 0.0122 | - | | 0.6293 | 2200 | 0.0158 | - | | 0.6436 | 2250 | 0.0124 | - | | 0.6579 | 2300 | 0.0231 | - | | 0.6722 | 2350 | 0.034 | - | | 0.6865 | 2400 | 0.0133 | - | | 0.7008 | 2450 | 0.0135 | - | | 0.7151 | 2500 | 0.0096 | - | | 0.7294 | 2550 | 0.0127 | - | | 0.7437 | 2600 | 0.0166 | - | | 0.7580 | 2650 | 0.03 | - | | 0.7723 | 2700 | 0.01 | - | | 0.7866 | 2750 | 0.0194 | - | | 0.8009 | 2800 | 0.0147 | - | | 0.8152 | 2850 | 0.0085 | - | | 0.8295 | 2900 | 0.0058 | - | | 0.8438 | 2950 | 0.0369 | - | | 0.8581 | 3000 | 0.0071 | - | | 0.8724 | 3050 | 0.0125 | - | | 0.8867 | 3100 | 0.015 | - | | 0.9010 | 3150 | 0.0136 | - | | 0.9153 | 3200 | 0.0077 | - | | 0.9296 | 3250 | 0.0138 | - | | 0.9439 | 3300 | 0.0167 | - | | 0.9582 | 3350 | 0.008 | - | | 0.9725 | 3400 | 0.0232 | - | | 0.9868 | 3450 | 0.0057 | - | | 1.0 | 3496 | - | 0.1714 | | 1.0011 | 3500 | 0.0138 | - | | 1.0154 | 3550 | 0.0087 | - | | 1.0297 | 3600 | 0.0165 | - | | 1.0441 | 3650 | 0.005 | - | | 1.0584 | 3700 | 0.0117 | - | | 1.0727 | 3750 | 0.0212 | - | | 1.0870 | 3800 | 0.0216 | - | | 1.1013 | 3850 | 0.007 | - | | 1.1156 | 3900 | 0.0304 | - | | 1.1299 | 3950 | 0.0123 | - | | 1.1442 | 4000 | 0.0094 | - | | 1.1585 | 4050 | 0.0102 | - | | 1.1728 | 4100 | 0.0222 | - | | 1.1871 | 4150 | 0.0146 | - | | 1.2014 | 4200 | 0.0189 | - | | 1.2157 | 4250 | 0.0048 | - | | 1.2300 | 4300 | 0.0273 | - | | 1.2443 | 4350 | 0.026 | - | | 1.2586 | 4400 | 0.0075 | - | | 1.2729 | 4450 | 0.0343 | - | | 1.2872 | 4500 | 0.003 | - | | 1.3015 | 4550 | 0.0056 | - | | 1.3158 | 4600 | 0.0163 | - | | 1.3301 | 4650 | 0.0111 | - | | 1.3444 | 4700 | 0.0174 | - | | 1.3587 | 4750 | 0.0103 | - | | 1.3730 | 4800 | 0.0082 | - | | 1.3873 | 4850 | 0.0137 | - | | 1.4016 | 4900 | 0.014 | - | | 1.4159 | 4950 | 0.012 | - | | 1.4302 | 5000 | 0.0175 | - | | 1.4445 | 5050 | 0.01 | - | | 1.4588 | 5100 | 0.0061 | - | | 1.4731 | 5150 | 0.0196 | - | | 1.4874 | 5200 | 0.0124 | - | | 1.5017 | 5250 | 0.0071 | - | | 1.5160 | 5300 | 0.0091 | - | | 1.5303 | 5350 | 0.0063 | - | | 1.5446 | 5400 | 0.0063 | - | | 1.5589 | 5450 | 0.0207 | - | | 1.5732 | 5500 | 0.0103 | - | | 1.5875 | 5550 | 0.0574 | - | | 1.6018 | 5600 | 0.0044 | - | | 1.6161 | 5650 | 0.013 | - | | 1.6304 | 5700 | 0.0183 | - | | 1.6447 | 5750 | 0.0066 | - | | 1.6590 | 5800 | 0.0036 | - | | 1.6733 | 5850 | 0.0068 | - | | 1.6876 | 5900 | 0.0475 | - | | 1.7019 | 5950 | 0.0067 | - | | 1.7162 | 6000 | 0.0076 | - | | 1.7305 | 6050 | 0.0148 | - | | 1.7449 | 6100 | 0.0048 | - | | 1.7592 | 6150 | 0.0112 | - | | 1.7735 | 6200 | 0.0148 | - | | 1.7878 | 6250 | 0.0077 | - | | 1.8021 | 6300 | 0.0073 | - | | 1.8164 | 6350 | 0.0078 | - | | 1.8307 | 6400 | 0.0056 | - | | 1.8450 | 6450 | 0.0088 | - | | 1.8593 | 6500 | 0.01 | - | | 1.8736 | 6550 | 0.0147 | - | | 1.8879 | 6600 | 0.0045 | - | | 1.9022 | 6650 | 0.0054 | - | | 1.9165 | 6700 | 0.0045 | - | | 1.9308 | 6750 | 0.007 | - | | 1.9451 | 6800 | 0.0113 | - | | 1.9594 | 6850 | 0.04 | - | | 1.9737 | 6900 | 0.005 | - | | 1.9880 | 6950 | 0.0158 | - | | **2.0** | **6992** | **-** | **0.1701** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 3.0.1 - Transformers: 4.39.0 - PyTorch: 2.4.0+cu121 - Datasets: 2.21.0 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION" ]
[ "CAS" ]
Non_BioNLP
Muennighoff/SGPT-125M-weightedmean-nli-bitfit
Muennighoff
sentence-similarity
[ "sentence-transformers", "pytorch", "gpt_neo", "feature-extraction", "sentence-similarity", "mteb", "arxiv:2202.08904", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,646
1,685
327
3
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb model-index: - name: SGPT-125M-weightedmean-nli-bitfit results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 65.88059701492537 - type: ap value: 28.685493163579785 - type: f1 value: 59.79951005816335 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (de) type: mteb/amazon_counterfactual config: de split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 59.07922912205568 - type: ap value: 73.91887421019034 - type: f1 value: 56.6316368658711 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en-ext) type: mteb/amazon_counterfactual config: en-ext split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 64.91754122938531 - type: ap value: 16.360681214864226 - type: f1 value: 53.126592061523766 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (ja) type: mteb/amazon_counterfactual config: ja split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 56.423982869378996 - type: ap value: 12.143003571907899 - type: f1 value: 45.76363777987471 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: 80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1 metrics: - type: accuracy value: 74.938225 - type: ap value: 69.58187110320567 - type: f1 value: 74.72744058439321 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 35.098 - type: f1 value: 34.73265651435726 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (de) type: mteb/amazon_reviews_multi config: de split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 24.516 - type: f1 value: 24.21748200448397 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (es) type: mteb/amazon_reviews_multi config: es split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 29.097999999999995 - type: f1 value: 28.620040162757093 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 27.395999999999997 - type: f1 value: 27.146888644986284 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (ja) type: mteb/amazon_reviews_multi config: ja split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 21.724 - type: f1 value: 21.37230564276654 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 23.976 - type: f1 value: 23.741137981755482 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: 5b3e3697907184a9b77a3c99ee9ea1a9cbb1e4e3 metrics: - type: map_at_1 value: 13.442000000000002 - type: map_at_10 value: 24.275 - type: map_at_100 value: 25.588 - type: map_at_1000 value: 25.659 - type: map_at_3 value: 20.092 - type: map_at_5 value: 22.439999999999998 - type: ndcg_at_1 value: 13.442000000000002 - type: ndcg_at_10 value: 31.04 - type: ndcg_at_100 value: 37.529 - type: ndcg_at_1000 value: 39.348 - type: ndcg_at_3 value: 22.342000000000002 - type: ndcg_at_5 value: 26.595999999999997 - type: precision_at_1 value: 13.442000000000002 - type: precision_at_10 value: 5.299 - type: precision_at_100 value: 0.836 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 9.625 - type: precision_at_5 value: 7.852 - type: recall_at_1 value: 13.442000000000002 - type: recall_at_10 value: 52.986999999999995 - type: recall_at_100 value: 83.64200000000001 - type: recall_at_1000 value: 97.795 - type: recall_at_3 value: 28.876 - type: recall_at_5 value: 39.26 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8 metrics: - type: v_measure value: 34.742482477870766 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3 metrics: - type: v_measure value: 24.67870651472156 - task: type: Clustering dataset: name: MTEB BlurbsClusteringS2S type: slvnwhrl/blurbs-clustering-s2s config: default split: test revision: 9bfff9a7f8f6dc6ffc9da71c48dd48b68696471d metrics: - type: v_measure value: 8.00311862863495 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c metrics: - type: map value: 52.63439984994702 - type: mrr value: 65.75704612408214 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: 9ee918f184421b6bd48b78f6c714d86546106103 metrics: - type: cos_sim_pearson value: 72.78000135012542 - type: cos_sim_spearman value: 70.92812216947605 - type: euclidean_pearson value: 77.1169214949292 - type: euclidean_spearman value: 77.10175681583313 - type: manhattan_pearson value: 76.84527031837595 - type: manhattan_spearman value: 77.0704308008438 - task: type: BitextMining dataset: name: MTEB BUCC (de-en) type: mteb/bucc-bitext-mining config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 1.0960334029227559 - type: f1 value: 1.0925539318023658 - type: precision value: 1.0908141962421711 - type: recall value: 1.0960334029227559 - task: type: BitextMining dataset: name: MTEB BUCC (fr-en) type: mteb/bucc-bitext-mining config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 0.02201188641866608 - type: f1 value: 0.02201188641866608 - type: precision value: 0.02201188641866608 - type: recall value: 0.02201188641866608 - task: type: BitextMining dataset: name: MTEB BUCC (ru-en) type: mteb/bucc-bitext-mining config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 0.0 - type: f1 value: 0.0 - type: precision value: 0.0 - type: recall value: 0.0 - task: type: BitextMining dataset: name: MTEB BUCC (zh-en) type: mteb/bucc-bitext-mining config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 0.0 - type: f1 value: 0.0 - type: precision value: 0.0 - type: recall value: 0.0 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 44fa15921b4c889113cc5df03dd4901b49161ab7 metrics: - type: accuracy value: 74.67857142857142 - type: f1 value: 74.61743413995573 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 11d0121201d1f1f280e8cc8f3d98fb9c4d9f9c55 metrics: - type: v_measure value: 28.93427045246491 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: c0fab014e1bcb8d3a5e31b2088972a1e01547dc1 metrics: - type: v_measure value: 23.080939123955474 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 18.221999999999998 - type: map_at_10 value: 24.506 - type: map_at_100 value: 25.611 - type: map_at_1000 value: 25.758 - type: map_at_3 value: 22.264999999999997 - type: map_at_5 value: 23.698 - type: ndcg_at_1 value: 23.033 - type: ndcg_at_10 value: 28.719 - type: ndcg_at_100 value: 33.748 - type: ndcg_at_1000 value: 37.056 - type: ndcg_at_3 value: 25.240000000000002 - type: ndcg_at_5 value: 27.12 - type: precision_at_1 value: 23.033 - type: precision_at_10 value: 5.408 - type: precision_at_100 value: 1.004 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 11.874 - type: precision_at_5 value: 8.927 - type: recall_at_1 value: 18.221999999999998 - type: recall_at_10 value: 36.355 - type: recall_at_100 value: 58.724 - type: recall_at_1000 value: 81.33500000000001 - type: recall_at_3 value: 26.334000000000003 - type: recall_at_5 value: 31.4 - type: map_at_1 value: 12.058 - type: map_at_10 value: 16.051000000000002 - type: map_at_100 value: 16.772000000000002 - type: map_at_1000 value: 16.871 - type: map_at_3 value: 14.78 - type: map_at_5 value: 15.5 - type: ndcg_at_1 value: 15.35 - type: ndcg_at_10 value: 18.804000000000002 - type: ndcg_at_100 value: 22.346 - type: ndcg_at_1000 value: 25.007 - type: ndcg_at_3 value: 16.768 - type: ndcg_at_5 value: 17.692 - type: precision_at_1 value: 15.35 - type: precision_at_10 value: 3.51 - type: precision_at_100 value: 0.664 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 7.983 - type: precision_at_5 value: 5.656 - type: recall_at_1 value: 12.058 - type: recall_at_10 value: 23.644000000000002 - type: recall_at_100 value: 39.76 - type: recall_at_1000 value: 58.56 - type: recall_at_3 value: 17.541999999999998 - type: recall_at_5 value: 20.232 - type: map_at_1 value: 21.183 - type: map_at_10 value: 28.9 - type: map_at_100 value: 29.858 - type: map_at_1000 value: 29.953999999999997 - type: map_at_3 value: 26.58 - type: map_at_5 value: 27.912 - type: ndcg_at_1 value: 24.765 - type: ndcg_at_10 value: 33.339999999999996 - type: ndcg_at_100 value: 37.997 - type: ndcg_at_1000 value: 40.416000000000004 - type: ndcg_at_3 value: 29.044999999999998 - type: ndcg_at_5 value: 31.121 - type: precision_at_1 value: 24.765 - type: precision_at_10 value: 5.599 - type: precision_at_100 value: 0.8699999999999999 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 13.270999999999999 - type: precision_at_5 value: 9.367 - type: recall_at_1 value: 21.183 - type: recall_at_10 value: 43.875 - type: recall_at_100 value: 65.005 - type: recall_at_1000 value: 83.017 - type: recall_at_3 value: 32.232 - type: recall_at_5 value: 37.308 - type: map_at_1 value: 11.350999999999999 - type: map_at_10 value: 14.953 - type: map_at_100 value: 15.623000000000001 - type: map_at_1000 value: 15.716 - type: map_at_3 value: 13.603000000000002 - type: map_at_5 value: 14.343 - type: ndcg_at_1 value: 12.429 - type: ndcg_at_10 value: 17.319000000000003 - type: ndcg_at_100 value: 20.990000000000002 - type: ndcg_at_1000 value: 23.899 - type: ndcg_at_3 value: 14.605 - type: ndcg_at_5 value: 15.89 - type: precision_at_1 value: 12.429 - type: precision_at_10 value: 2.701 - type: precision_at_100 value: 0.48700000000000004 - type: precision_at_1000 value: 0.078 - type: precision_at_3 value: 6.026 - type: precision_at_5 value: 4.3839999999999995 - type: recall_at_1 value: 11.350999999999999 - type: recall_at_10 value: 23.536 - type: recall_at_100 value: 40.942 - type: recall_at_1000 value: 64.05 - type: recall_at_3 value: 16.195 - type: recall_at_5 value: 19.264 - type: map_at_1 value: 8.08 - type: map_at_10 value: 11.691 - type: map_at_100 value: 12.312 - type: map_at_1000 value: 12.439 - type: map_at_3 value: 10.344000000000001 - type: map_at_5 value: 10.996 - type: ndcg_at_1 value: 10.697 - type: ndcg_at_10 value: 14.48 - type: ndcg_at_100 value: 18.160999999999998 - type: ndcg_at_1000 value: 21.886 - type: ndcg_at_3 value: 11.872 - type: ndcg_at_5 value: 12.834000000000001 - type: precision_at_1 value: 10.697 - type: precision_at_10 value: 2.811 - type: precision_at_100 value: 0.551 - type: precision_at_1000 value: 0.10200000000000001 - type: precision_at_3 value: 5.804 - type: precision_at_5 value: 4.154 - type: recall_at_1 value: 8.08 - type: recall_at_10 value: 20.235 - type: recall_at_100 value: 37.525999999999996 - type: recall_at_1000 value: 65.106 - type: recall_at_3 value: 12.803999999999998 - type: recall_at_5 value: 15.498999999999999 - type: map_at_1 value: 13.908999999999999 - type: map_at_10 value: 19.256 - type: map_at_100 value: 20.286 - type: map_at_1000 value: 20.429 - type: map_at_3 value: 17.399 - type: map_at_5 value: 18.398999999999997 - type: ndcg_at_1 value: 17.421 - type: ndcg_at_10 value: 23.105999999999998 - type: ndcg_at_100 value: 28.128999999999998 - type: ndcg_at_1000 value: 31.480999999999998 - type: ndcg_at_3 value: 19.789 - type: ndcg_at_5 value: 21.237000000000002 - type: precision_at_1 value: 17.421 - type: precision_at_10 value: 4.331 - type: precision_at_100 value: 0.839 - type: precision_at_1000 value: 0.131 - type: precision_at_3 value: 9.4 - type: precision_at_5 value: 6.776 - type: recall_at_1 value: 13.908999999999999 - type: recall_at_10 value: 31.086999999999996 - type: recall_at_100 value: 52.946000000000005 - type: recall_at_1000 value: 76.546 - type: recall_at_3 value: 21.351 - type: recall_at_5 value: 25.264999999999997 - type: map_at_1 value: 12.598 - type: map_at_10 value: 17.304 - type: map_at_100 value: 18.209 - type: map_at_1000 value: 18.328 - type: map_at_3 value: 15.784 - type: map_at_5 value: 16.669999999999998 - type: ndcg_at_1 value: 15.867999999999999 - type: ndcg_at_10 value: 20.623 - type: ndcg_at_100 value: 25.093 - type: ndcg_at_1000 value: 28.498 - type: ndcg_at_3 value: 17.912 - type: ndcg_at_5 value: 19.198 - type: precision_at_1 value: 15.867999999999999 - type: precision_at_10 value: 3.7670000000000003 - type: precision_at_100 value: 0.716 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 8.638 - type: precision_at_5 value: 6.21 - type: recall_at_1 value: 12.598 - type: recall_at_10 value: 27.144000000000002 - type: recall_at_100 value: 46.817 - type: recall_at_1000 value: 71.86099999999999 - type: recall_at_3 value: 19.231 - type: recall_at_5 value: 22.716 - type: map_at_1 value: 12.738416666666666 - type: map_at_10 value: 17.235916666666668 - type: map_at_100 value: 18.063333333333333 - type: map_at_1000 value: 18.18433333333333 - type: map_at_3 value: 15.74775 - type: map_at_5 value: 16.57825 - type: ndcg_at_1 value: 15.487416666666665 - type: ndcg_at_10 value: 20.290166666666668 - type: ndcg_at_100 value: 24.41291666666666 - type: ndcg_at_1000 value: 27.586333333333336 - type: ndcg_at_3 value: 17.622083333333332 - type: ndcg_at_5 value: 18.859916666666667 - type: precision_at_1 value: 15.487416666666665 - type: precision_at_10 value: 3.6226666666666665 - type: precision_at_100 value: 0.6820833333333334 - type: precision_at_1000 value: 0.11216666666666666 - type: precision_at_3 value: 8.163749999999999 - type: precision_at_5 value: 5.865416666666667 - type: recall_at_1 value: 12.738416666666666 - type: recall_at_10 value: 26.599416666666663 - type: recall_at_100 value: 45.41258333333334 - type: recall_at_1000 value: 68.7565 - type: recall_at_3 value: 19.008166666666668 - type: recall_at_5 value: 22.24991666666667 - type: map_at_1 value: 12.307 - type: map_at_10 value: 15.440000000000001 - type: map_at_100 value: 16.033 - type: map_at_1000 value: 16.14 - type: map_at_3 value: 14.393 - type: map_at_5 value: 14.856 - type: ndcg_at_1 value: 14.571000000000002 - type: ndcg_at_10 value: 17.685000000000002 - type: ndcg_at_100 value: 20.882 - type: ndcg_at_1000 value: 23.888 - type: ndcg_at_3 value: 15.739 - type: ndcg_at_5 value: 16.391 - type: precision_at_1 value: 14.571000000000002 - type: precision_at_10 value: 2.883 - type: precision_at_100 value: 0.49100000000000005 - type: precision_at_1000 value: 0.08 - type: precision_at_3 value: 7.0040000000000004 - type: precision_at_5 value: 4.693 - type: recall_at_1 value: 12.307 - type: recall_at_10 value: 22.566 - type: recall_at_100 value: 37.469 - type: recall_at_1000 value: 60.550000000000004 - type: recall_at_3 value: 16.742 - type: recall_at_5 value: 18.634 - type: map_at_1 value: 6.496 - type: map_at_10 value: 9.243 - type: map_at_100 value: 9.841 - type: map_at_1000 value: 9.946000000000002 - type: map_at_3 value: 8.395 - type: map_at_5 value: 8.872 - type: ndcg_at_1 value: 8.224 - type: ndcg_at_10 value: 11.24 - type: ndcg_at_100 value: 14.524999999999999 - type: ndcg_at_1000 value: 17.686 - type: ndcg_at_3 value: 9.617 - type: ndcg_at_5 value: 10.37 - type: precision_at_1 value: 8.224 - type: precision_at_10 value: 2.0820000000000003 - type: precision_at_100 value: 0.443 - type: precision_at_1000 value: 0.08499999999999999 - type: precision_at_3 value: 4.623 - type: precision_at_5 value: 3.331 - type: recall_at_1 value: 6.496 - type: recall_at_10 value: 15.310000000000002 - type: recall_at_100 value: 30.680000000000003 - type: recall_at_1000 value: 54.335 - type: recall_at_3 value: 10.691 - type: recall_at_5 value: 12.687999999999999 - type: map_at_1 value: 13.843 - type: map_at_10 value: 17.496000000000002 - type: map_at_100 value: 18.304000000000002 - type: map_at_1000 value: 18.426000000000002 - type: map_at_3 value: 16.225 - type: map_at_5 value: 16.830000000000002 - type: ndcg_at_1 value: 16.698 - type: ndcg_at_10 value: 20.301 - type: ndcg_at_100 value: 24.523 - type: ndcg_at_1000 value: 27.784 - type: ndcg_at_3 value: 17.822 - type: ndcg_at_5 value: 18.794 - type: precision_at_1 value: 16.698 - type: precision_at_10 value: 3.3579999999999997 - type: precision_at_100 value: 0.618 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 7.898 - type: precision_at_5 value: 5.428999999999999 - type: recall_at_1 value: 13.843 - type: recall_at_10 value: 25.887999999999998 - type: recall_at_100 value: 45.028 - type: recall_at_1000 value: 68.991 - type: recall_at_3 value: 18.851000000000003 - type: recall_at_5 value: 21.462 - type: map_at_1 value: 13.757 - type: map_at_10 value: 19.27 - type: map_at_100 value: 20.461 - type: map_at_1000 value: 20.641000000000002 - type: map_at_3 value: 17.865000000000002 - type: map_at_5 value: 18.618000000000002 - type: ndcg_at_1 value: 16.996 - type: ndcg_at_10 value: 22.774 - type: ndcg_at_100 value: 27.675 - type: ndcg_at_1000 value: 31.145 - type: ndcg_at_3 value: 20.691000000000003 - type: ndcg_at_5 value: 21.741 - type: precision_at_1 value: 16.996 - type: precision_at_10 value: 4.545 - type: precision_at_100 value: 1.036 - type: precision_at_1000 value: 0.185 - type: precision_at_3 value: 10.145 - type: precision_at_5 value: 7.391 - type: recall_at_1 value: 13.757 - type: recall_at_10 value: 28.233999999999998 - type: recall_at_100 value: 51.05499999999999 - type: recall_at_1000 value: 75.35300000000001 - type: recall_at_3 value: 21.794 - type: recall_at_5 value: 24.614 - type: map_at_1 value: 9.057 - type: map_at_10 value: 12.720999999999998 - type: map_at_100 value: 13.450000000000001 - type: map_at_1000 value: 13.564000000000002 - type: map_at_3 value: 11.34 - type: map_at_5 value: 12.245000000000001 - type: ndcg_at_1 value: 9.797 - type: ndcg_at_10 value: 15.091 - type: ndcg_at_100 value: 18.886 - type: ndcg_at_1000 value: 22.29 - type: ndcg_at_3 value: 12.365 - type: ndcg_at_5 value: 13.931 - type: precision_at_1 value: 9.797 - type: precision_at_10 value: 2.477 - type: precision_at_100 value: 0.466 - type: precision_at_1000 value: 0.082 - type: precision_at_3 value: 5.299 - type: precision_at_5 value: 4.067 - type: recall_at_1 value: 9.057 - type: recall_at_10 value: 21.319 - type: recall_at_100 value: 38.999 - type: recall_at_1000 value: 65.374 - type: recall_at_3 value: 14.331 - type: recall_at_5 value: 17.916999999999998 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: 392b78eb68c07badcd7c2cd8f39af108375dfcce metrics: - type: map_at_1 value: 3.714 - type: map_at_10 value: 6.926 - type: map_at_100 value: 7.879 - type: map_at_1000 value: 8.032 - type: map_at_3 value: 5.504 - type: map_at_5 value: 6.357 - type: ndcg_at_1 value: 8.86 - type: ndcg_at_10 value: 11.007 - type: ndcg_at_100 value: 16.154 - type: ndcg_at_1000 value: 19.668 - type: ndcg_at_3 value: 8.103 - type: ndcg_at_5 value: 9.456000000000001 - type: precision_at_1 value: 8.86 - type: precision_at_10 value: 3.7199999999999998 - type: precision_at_100 value: 0.9169999999999999 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 6.254 - type: precision_at_5 value: 5.380999999999999 - type: recall_at_1 value: 3.714 - type: recall_at_10 value: 14.382 - type: recall_at_100 value: 33.166000000000004 - type: recall_at_1000 value: 53.444 - type: recall_at_3 value: 7.523000000000001 - type: recall_at_5 value: 10.91 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: f097057d03ed98220bc7309ddb10b71a54d667d6 metrics: - type: map_at_1 value: 1.764 - type: map_at_10 value: 3.8600000000000003 - type: map_at_100 value: 5.457 - type: map_at_1000 value: 5.938000000000001 - type: map_at_3 value: 2.667 - type: map_at_5 value: 3.2199999999999998 - type: ndcg_at_1 value: 14.000000000000002 - type: ndcg_at_10 value: 10.868 - type: ndcg_at_100 value: 12.866 - type: ndcg_at_1000 value: 17.43 - type: ndcg_at_3 value: 11.943 - type: ndcg_at_5 value: 11.66 - type: precision_at_1 value: 19.25 - type: precision_at_10 value: 10.274999999999999 - type: precision_at_100 value: 3.527 - type: precision_at_1000 value: 0.9119999999999999 - type: precision_at_3 value: 14.917 - type: precision_at_5 value: 13.5 - type: recall_at_1 value: 1.764 - type: recall_at_10 value: 6.609 - type: recall_at_100 value: 17.616 - type: recall_at_1000 value: 33.085 - type: recall_at_3 value: 3.115 - type: recall_at_5 value: 4.605 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 829147f8f75a25f005913200eb5ed41fae320aa1 metrics: - type: accuracy value: 42.225 - type: f1 value: 37.563516542112104 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: 1429cf27e393599b8b359b9b72c666f96b2525f9 metrics: - type: map_at_1 value: 11.497 - type: map_at_10 value: 15.744 - type: map_at_100 value: 16.3 - type: map_at_1000 value: 16.365 - type: map_at_3 value: 14.44 - type: map_at_5 value: 15.18 - type: ndcg_at_1 value: 12.346 - type: ndcg_at_10 value: 18.398999999999997 - type: ndcg_at_100 value: 21.399 - type: ndcg_at_1000 value: 23.442 - type: ndcg_at_3 value: 15.695 - type: ndcg_at_5 value: 17.027 - type: precision_at_1 value: 12.346 - type: precision_at_10 value: 2.798 - type: precision_at_100 value: 0.445 - type: precision_at_1000 value: 0.063 - type: precision_at_3 value: 6.586 - type: precision_at_5 value: 4.665 - type: recall_at_1 value: 11.497 - type: recall_at_10 value: 25.636 - type: recall_at_100 value: 39.894 - type: recall_at_1000 value: 56.181000000000004 - type: recall_at_3 value: 18.273 - type: recall_at_5 value: 21.474 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: 41b686a7f28c59bcaaa5791efd47c67c8ebe28be metrics: - type: map_at_1 value: 3.637 - type: map_at_10 value: 6.084 - type: map_at_100 value: 6.9190000000000005 - type: map_at_1000 value: 7.1080000000000005 - type: map_at_3 value: 5.071 - type: map_at_5 value: 5.5649999999999995 - type: ndcg_at_1 value: 7.407 - type: ndcg_at_10 value: 8.94 - type: ndcg_at_100 value: 13.594999999999999 - type: ndcg_at_1000 value: 18.29 - type: ndcg_at_3 value: 7.393 - type: ndcg_at_5 value: 7.854 - type: precision_at_1 value: 7.407 - type: precision_at_10 value: 2.778 - type: precision_at_100 value: 0.75 - type: precision_at_1000 value: 0.154 - type: precision_at_3 value: 5.144 - type: precision_at_5 value: 3.981 - type: recall_at_1 value: 3.637 - type: recall_at_10 value: 11.821 - type: recall_at_100 value: 30.18 - type: recall_at_1000 value: 60.207 - type: recall_at_3 value: 6.839 - type: recall_at_5 value: 8.649 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: 766870b35a1b9ca65e67a0d1913899973551fc6c metrics: - type: map_at_1 value: 9.676 - type: map_at_10 value: 13.350999999999999 - type: map_at_100 value: 13.919 - type: map_at_1000 value: 14.01 - type: map_at_3 value: 12.223 - type: map_at_5 value: 12.812000000000001 - type: ndcg_at_1 value: 19.352 - type: ndcg_at_10 value: 17.727 - type: ndcg_at_100 value: 20.837 - type: ndcg_at_1000 value: 23.412 - type: ndcg_at_3 value: 15.317 - type: ndcg_at_5 value: 16.436 - type: precision_at_1 value: 19.352 - type: precision_at_10 value: 3.993 - type: precision_at_100 value: 0.651 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 9.669 - type: precision_at_5 value: 6.69 - type: recall_at_1 value: 9.676 - type: recall_at_10 value: 19.966 - type: recall_at_100 value: 32.573 - type: recall_at_1000 value: 49.905 - type: recall_at_3 value: 14.504 - type: recall_at_5 value: 16.725 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 8d743909f834c38949e8323a8a6ce8721ea6c7f4 metrics: - type: accuracy value: 62.895999999999994 - type: ap value: 58.47769349850157 - type: f1 value: 62.67885149592086 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: validation revision: e6838a846e2408f22cf5cc337ebc83e0bcf77849 metrics: - type: map_at_1 value: 2.88 - type: map_at_10 value: 4.914000000000001 - type: map_at_100 value: 5.459 - type: map_at_1000 value: 5.538 - type: map_at_3 value: 4.087 - type: map_at_5 value: 4.518 - type: ndcg_at_1 value: 2.937 - type: ndcg_at_10 value: 6.273 - type: ndcg_at_100 value: 9.426 - type: ndcg_at_1000 value: 12.033000000000001 - type: ndcg_at_3 value: 4.513 - type: ndcg_at_5 value: 5.292 - type: precision_at_1 value: 2.937 - type: precision_at_10 value: 1.089 - type: precision_at_100 value: 0.27699999999999997 - type: precision_at_1000 value: 0.051000000000000004 - type: precision_at_3 value: 1.9290000000000003 - type: precision_at_5 value: 1.547 - type: recall_at_1 value: 2.88 - type: recall_at_10 value: 10.578 - type: recall_at_100 value: 26.267000000000003 - type: recall_at_1000 value: 47.589999999999996 - type: recall_at_3 value: 5.673 - 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type: f1 value: 48.37243417046763 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: 62146448f05be9e52a36b8ee9936447ea787eede metrics: - type: accuracy value: 54.821731748726656 - type: f1 value: 55.14729314789282 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 091a54f9a36281ce7d6590ec8c75dd485e7e01d4 metrics: - type: v_measure value: 28.24295128553035 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 81.5640460153782 - type: cos_sim_ap value: 57.094095366921536 - type: cos_sim_f1 value: 55.29607083563918 - type: cos_sim_precision value: 47.62631077216397 - type: cos_sim_recall value: 65.91029023746702 - type: dot_accuracy value: 78.81623651427549 - type: dot_ap value: 47.42989400382077 - type: dot_f1 value: 51.25944584382871 - type: dot_precision value: 42.55838271174625 - type: dot_recall value: 64.43271767810026 - type: euclidean_accuracy value: 80.29445073612685 - type: euclidean_ap value: 53.42012231336148 - type: euclidean_f1 value: 51.867783563504645 - type: euclidean_precision value: 45.4203013481364 - type: euclidean_recall value: 60.4485488126649 - type: manhattan_accuracy value: 80.2884901949097 - type: manhattan_ap value: 53.43205271323232 - type: manhattan_f1 value: 52.014165559982295 - type: manhattan_precision value: 44.796035074342356 - type: manhattan_recall value: 62.00527704485488 - type: max_accuracy value: 81.5640460153782 - type: max_ap value: 57.094095366921536 - type: max_f1 value: 55.29607083563918 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 86.63018589668955 - type: cos_sim_ap value: 80.51063771262909 - type: cos_sim_f1 value: 72.70810586950793 - type: cos_sim_precision value: 71.14123627790467 - type: cos_sim_recall value: 74.3455497382199 - type: dot_accuracy value: 82.41743315092948 - type: dot_ap value: 69.2393381283664 - type: dot_f1 value: 65.61346624814597 - type: dot_precision value: 59.43260638630257 - type: dot_recall value: 73.22913458577148 - type: euclidean_accuracy value: 86.49435324251951 - type: euclidean_ap value: 80.28100477250926 - type: euclidean_f1 value: 72.58242344489099 - type: euclidean_precision value: 67.44662568576906 - type: euclidean_recall value: 78.56482907299045 - type: manhattan_accuracy value: 86.59525749990297 - type: manhattan_ap value: 80.37850832566262 - type: manhattan_f1 value: 72.59435321233073 - type: manhattan_precision value: 68.19350473612991 - type: manhattan_recall value: 77.60240221743148 - type: max_accuracy value: 86.63018589668955 - type: max_ap value: 80.51063771262909 - type: max_f1 value: 72.70810586950793 --- # SGPT-125M-weightedmean-nli-bitfit ## Usage For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt ## Evaluation Results For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904 ## Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8807 with parameters: ``` {'batch_size': 64} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 880, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 0.0002 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 881, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: GPTNeoModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors ```bibtex @article{muennighoff2022sgpt, title={SGPT: GPT Sentence Embeddings for Semantic Search}, author={Muennighoff, Niklas}, journal={arXiv preprint arXiv:2202.08904}, year={2022} } ```
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
fresha/e5-large-v2-endpoint
fresha
feature-extraction
[ "transformers", "pytorch", "safetensors", "bert", "feature-extraction", "mteb", "en", "arxiv:2212.03533", "arxiv:2104.08663", "arxiv:2210.07316", "license:mit", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,687
1,687
29
0
--- language: - en license: mit tags: - mteb model-index: - name: e5-large-v2 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.22388059701493 - type: ap value: 43.20816505595132 - type: f1 value: 73.27811303522058 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.748325 - type: ap value: 90.72534979701297 - type: f1 value: 93.73895874282185 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.612 - type: f1 value: 47.61157345898393 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 23.541999999999998 - type: map_at_10 value: 38.208 - type: map_at_100 value: 39.417 - type: map_at_1000 value: 39.428999999999995 - type: map_at_3 value: 33.95 - type: map_at_5 value: 36.329 - type: mrr_at_1 value: 23.755000000000003 - type: mrr_at_10 value: 38.288 - type: mrr_at_100 value: 39.511 - type: mrr_at_1000 value: 39.523 - type: mrr_at_3 value: 34.009 - type: mrr_at_5 value: 36.434 - type: ndcg_at_1 value: 23.541999999999998 - type: ndcg_at_10 value: 46.417 - type: ndcg_at_100 value: 51.812000000000005 - type: ndcg_at_1000 value: 52.137 - type: ndcg_at_3 value: 37.528 - type: ndcg_at_5 value: 41.81 - type: precision_at_1 value: 23.541999999999998 - type: precision_at_10 value: 7.269 - type: precision_at_100 value: 0.9690000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 15.979 - type: precision_at_5 value: 11.664 - type: recall_at_1 value: 23.541999999999998 - type: recall_at_10 value: 72.688 - type: recall_at_100 value: 96.871 - type: recall_at_1000 value: 99.431 - type: recall_at_3 value: 47.937000000000005 - type: recall_at_5 value: 58.321 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 45.546499570522094 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 41.01607489943561 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 59.616107510107774 - type: mrr value: 72.75106626214661 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.33018094733868 - type: cos_sim_spearman value: 83.60190492611737 - type: euclidean_pearson value: 82.1492450218961 - type: euclidean_spearman value: 82.70308926526991 - type: manhattan_pearson value: 81.93959600076842 - type: manhattan_spearman value: 82.73260801016369 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 84.54545454545455 - type: f1 value: 84.49582530928923 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.362725540120096 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 34.849509608178145 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 31.502999999999997 - type: map_at_10 value: 43.323 - type: map_at_100 value: 44.708999999999996 - type: map_at_1000 value: 44.838 - type: map_at_3 value: 38.987 - type: map_at_5 value: 41.516999999999996 - type: mrr_at_1 value: 38.769999999999996 - type: mrr_at_10 value: 49.13 - type: mrr_at_100 value: 49.697 - type: mrr_at_1000 value: 49.741 - type: mrr_at_3 value: 45.804 - type: mrr_at_5 value: 47.842 - type: ndcg_at_1 value: 38.769999999999996 - type: ndcg_at_10 value: 50.266999999999996 - type: ndcg_at_100 value: 54.967 - type: ndcg_at_1000 value: 56.976000000000006 - type: ndcg_at_3 value: 43.823 - type: ndcg_at_5 value: 47.12 - type: precision_at_1 value: 38.769999999999996 - type: precision_at_10 value: 10.057 - type: precision_at_100 value: 1.554 - type: precision_at_1000 value: 0.202 - type: precision_at_3 value: 21.125 - type: precision_at_5 value: 15.851 - type: recall_at_1 value: 31.502999999999997 - type: recall_at_10 value: 63.715999999999994 - type: recall_at_100 value: 83.61800000000001 - type: recall_at_1000 value: 96.63199999999999 - type: recall_at_3 value: 45.403 - type: recall_at_5 value: 54.481 - type: map_at_1 value: 27.833000000000002 - type: map_at_10 value: 37.330999999999996 - type: map_at_100 value: 38.580999999999996 - type: map_at_1000 value: 38.708 - type: map_at_3 value: 34.713 - type: map_at_5 value: 36.104 - type: mrr_at_1 value: 35.223 - type: mrr_at_10 value: 43.419000000000004 - type: mrr_at_100 value: 44.198 - type: mrr_at_1000 value: 44.249 - type: mrr_at_3 value: 41.614000000000004 - type: mrr_at_5 value: 42.553000000000004 - type: ndcg_at_1 value: 35.223 - type: ndcg_at_10 value: 42.687999999999995 - type: ndcg_at_100 value: 47.447 - type: ndcg_at_1000 value: 49.701 - type: ndcg_at_3 value: 39.162 - type: ndcg_at_5 value: 40.557 - type: precision_at_1 value: 35.223 - type: precision_at_10 value: 7.962 - type: precision_at_100 value: 1.304 - type: precision_at_1000 value: 0.18 - type: precision_at_3 value: 19.023 - type: precision_at_5 value: 13.184999999999999 - type: recall_at_1 value: 27.833000000000002 - type: recall_at_10 value: 51.881 - type: recall_at_100 value: 72.04 - type: recall_at_1000 value: 86.644 - type: recall_at_3 value: 40.778 - type: recall_at_5 value: 45.176 - type: map_at_1 value: 38.175 - type: map_at_10 value: 51.174 - type: map_at_100 value: 52.26499999999999 - type: map_at_1000 value: 52.315999999999995 - type: map_at_3 value: 47.897 - type: map_at_5 value: 49.703 - type: mrr_at_1 value: 43.448 - type: mrr_at_10 value: 54.505 - type: mrr_at_100 value: 55.216 - type: mrr_at_1000 value: 55.242000000000004 - type: mrr_at_3 value: 51.98500000000001 - type: mrr_at_5 value: 53.434000000000005 - type: ndcg_at_1 value: 43.448 - type: ndcg_at_10 value: 57.282 - type: ndcg_at_100 value: 61.537 - type: ndcg_at_1000 value: 62.546 - type: ndcg_at_3 value: 51.73799999999999 - type: ndcg_at_5 value: 54.324 - type: precision_at_1 value: 43.448 - type: precision_at_10 value: 9.292 - type: precision_at_100 value: 1.233 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 23.218 - type: precision_at_5 value: 15.887 - type: recall_at_1 value: 38.175 - type: recall_at_10 value: 72.00999999999999 - type: recall_at_100 value: 90.155 - type: recall_at_1000 value: 97.257 - type: recall_at_3 value: 57.133 - type: recall_at_5 value: 63.424 - type: map_at_1 value: 22.405 - type: map_at_10 value: 30.043 - type: map_at_100 value: 31.191000000000003 - type: map_at_1000 value: 31.275 - type: map_at_3 value: 27.034000000000002 - type: map_at_5 value: 28.688000000000002 - type: mrr_at_1 value: 24.068 - type: mrr_at_10 value: 31.993 - type: mrr_at_100 value: 32.992 - type: mrr_at_1000 value: 33.050000000000004 - type: mrr_at_3 value: 28.964000000000002 - type: mrr_at_5 value: 30.653000000000002 - type: ndcg_at_1 value: 24.068 - type: ndcg_at_10 value: 35.198 - type: ndcg_at_100 value: 40.709 - type: ndcg_at_1000 value: 42.855 - type: ndcg_at_3 value: 29.139 - type: ndcg_at_5 value: 32.045 - type: precision_at_1 value: 24.068 - type: precision_at_10 value: 5.65 - type: precision_at_100 value: 0.885 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 12.279 - type: precision_at_5 value: 8.994 - type: recall_at_1 value: 22.405 - type: recall_at_10 value: 49.391 - type: recall_at_100 value: 74.53699999999999 - type: recall_at_1000 value: 90.605 - type: recall_at_3 value: 33.126 - type: recall_at_5 value: 40.073 - type: map_at_1 value: 13.309999999999999 - type: map_at_10 value: 20.688000000000002 - type: map_at_100 value: 22.022 - type: map_at_1000 value: 22.152 - type: map_at_3 value: 17.954 - type: map_at_5 value: 19.439 - type: mrr_at_1 value: 16.294 - type: mrr_at_10 value: 24.479 - type: mrr_at_100 value: 25.515 - type: mrr_at_1000 value: 25.593 - type: mrr_at_3 value: 21.642 - type: mrr_at_5 value: 23.189999999999998 - type: ndcg_at_1 value: 16.294 - type: ndcg_at_10 value: 25.833000000000002 - type: ndcg_at_100 value: 32.074999999999996 - type: ndcg_at_1000 value: 35.083 - type: ndcg_at_3 value: 20.493 - type: ndcg_at_5 value: 22.949 - type: precision_at_1 value: 16.294 - type: precision_at_10 value: 5.112 - type: precision_at_100 value: 0.96 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 9.908999999999999 - type: precision_at_5 value: 7.587000000000001 - type: recall_at_1 value: 13.309999999999999 - type: recall_at_10 value: 37.851 - type: recall_at_100 value: 64.835 - type: recall_at_1000 value: 86.334 - type: recall_at_3 value: 23.493 - type: recall_at_5 value: 29.528 - type: map_at_1 value: 25.857999999999997 - type: map_at_10 value: 35.503 - type: map_at_100 value: 36.957 - type: map_at_1000 value: 37.065 - type: map_at_3 value: 32.275999999999996 - type: map_at_5 value: 34.119 - type: mrr_at_1 value: 31.954 - type: mrr_at_10 value: 40.851 - type: mrr_at_100 value: 41.863 - type: mrr_at_1000 value: 41.900999999999996 - type: mrr_at_3 value: 38.129999999999995 - type: mrr_at_5 value: 39.737 - type: ndcg_at_1 value: 31.954 - type: ndcg_at_10 value: 41.343999999999994 - type: ndcg_at_100 value: 47.397 - type: ndcg_at_1000 value: 49.501 - type: ndcg_at_3 value: 36.047000000000004 - type: ndcg_at_5 value: 38.639 - type: precision_at_1 value: 31.954 - type: precision_at_10 value: 7.68 - type: precision_at_100 value: 1.247 - type: precision_at_1000 value: 0.16199999999999998 - type: precision_at_3 value: 17.132 - type: precision_at_5 value: 12.589 - type: recall_at_1 value: 25.857999999999997 - type: recall_at_10 value: 53.43599999999999 - type: recall_at_100 value: 78.82400000000001 - type: recall_at_1000 value: 92.78999999999999 - type: recall_at_3 value: 38.655 - type: recall_at_5 value: 45.216 - type: map_at_1 value: 24.709 - type: map_at_10 value: 34.318 - type: map_at_100 value: 35.657 - type: map_at_1000 value: 35.783 - type: map_at_3 value: 31.326999999999998 - type: map_at_5 value: 33.021 - type: mrr_at_1 value: 30.137000000000004 - type: mrr_at_10 value: 39.093 - type: mrr_at_100 value: 39.992 - type: mrr_at_1000 value: 40.056999999999995 - type: mrr_at_3 value: 36.606 - type: mrr_at_5 value: 37.861 - type: ndcg_at_1 value: 30.137000000000004 - type: ndcg_at_10 value: 39.974 - type: ndcg_at_100 value: 45.647999999999996 - type: ndcg_at_1000 value: 48.259 - type: ndcg_at_3 value: 35.028 - type: ndcg_at_5 value: 37.175999999999995 - type: precision_at_1 value: 30.137000000000004 - type: precision_at_10 value: 7.363 - type: precision_at_100 value: 1.184 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 16.857 - type: precision_at_5 value: 11.963 - type: recall_at_1 value: 24.709 - type: recall_at_10 value: 52.087 - type: recall_at_100 value: 76.125 - type: recall_at_1000 value: 93.82300000000001 - type: recall_at_3 value: 38.149 - type: recall_at_5 value: 43.984 - type: map_at_1 value: 23.40791666666667 - type: map_at_10 value: 32.458083333333335 - type: map_at_100 value: 33.691916666666664 - type: map_at_1000 value: 33.81191666666666 - type: map_at_3 value: 29.51625 - type: map_at_5 value: 31.168083333333335 - type: mrr_at_1 value: 27.96591666666666 - type: mrr_at_10 value: 36.528583333333344 - type: mrr_at_100 value: 37.404 - type: mrr_at_1000 value: 37.464333333333336 - type: mrr_at_3 value: 33.92883333333333 - type: mrr_at_5 value: 35.41933333333333 - type: ndcg_at_1 value: 27.96591666666666 - type: ndcg_at_10 value: 37.89141666666666 - type: ndcg_at_100 value: 43.23066666666666 - type: ndcg_at_1000 value: 45.63258333333333 - type: ndcg_at_3 value: 32.811249999999994 - type: ndcg_at_5 value: 35.22566666666667 - type: precision_at_1 value: 27.96591666666666 - type: precision_at_10 value: 6.834083333333332 - type: precision_at_100 value: 1.12225 - type: precision_at_1000 value: 0.15241666666666667 - type: precision_at_3 value: 15.264333333333335 - type: precision_at_5 value: 11.039416666666666 - type: recall_at_1 value: 23.40791666666667 - type: recall_at_10 value: 49.927083333333336 - type: recall_at_100 value: 73.44641666666668 - type: recall_at_1000 value: 90.19950000000001 - type: recall_at_3 value: 35.88341666666667 - type: recall_at_5 value: 42.061249999999994 - type: map_at_1 value: 19.592000000000002 - type: map_at_10 value: 26.895999999999997 - type: map_at_100 value: 27.921000000000003 - type: map_at_1000 value: 28.02 - type: map_at_3 value: 24.883 - type: map_at_5 value: 25.812 - type: mrr_at_1 value: 22.698999999999998 - type: mrr_at_10 value: 29.520999999999997 - type: mrr_at_100 value: 30.458000000000002 - type: mrr_at_1000 value: 30.526999999999997 - type: mrr_at_3 value: 27.633000000000003 - type: mrr_at_5 value: 28.483999999999998 - type: ndcg_at_1 value: 22.698999999999998 - type: ndcg_at_10 value: 31.061 - type: ndcg_at_100 value: 36.398 - type: ndcg_at_1000 value: 38.89 - type: ndcg_at_3 value: 27.149 - type: ndcg_at_5 value: 28.627000000000002 - type: precision_at_1 value: 22.698999999999998 - type: precision_at_10 value: 5.106999999999999 - type: precision_at_100 value: 0.857 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 11.963 - type: precision_at_5 value: 8.221 - type: recall_at_1 value: 19.592000000000002 - type: recall_at_10 value: 41.329 - type: recall_at_100 value: 66.094 - type: recall_at_1000 value: 84.511 - type: recall_at_3 value: 30.61 - type: recall_at_5 value: 34.213 - type: map_at_1 value: 14.71 - type: map_at_10 value: 20.965 - type: map_at_100 value: 21.994 - type: map_at_1000 value: 22.133 - type: map_at_3 value: 18.741 - type: map_at_5 value: 19.951 - type: mrr_at_1 value: 18.307000000000002 - type: mrr_at_10 value: 24.66 - type: mrr_at_100 value: 25.540000000000003 - type: mrr_at_1000 value: 25.629 - type: mrr_at_3 value: 22.511 - type: mrr_at_5 value: 23.72 - type: ndcg_at_1 value: 18.307000000000002 - type: ndcg_at_10 value: 25.153 - type: ndcg_at_100 value: 30.229 - type: ndcg_at_1000 value: 33.623 - type: ndcg_at_3 value: 21.203 - type: ndcg_at_5 value: 23.006999999999998 - type: precision_at_1 value: 18.307000000000002 - type: precision_at_10 value: 4.725 - type: precision_at_100 value: 0.8659999999999999 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 10.14 - type: precision_at_5 value: 7.481 - type: recall_at_1 value: 14.71 - type: recall_at_10 value: 34.087 - type: recall_at_100 value: 57.147999999999996 - type: recall_at_1000 value: 81.777 - type: recall_at_3 value: 22.996 - type: recall_at_5 value: 27.73 - type: map_at_1 value: 23.472 - type: map_at_10 value: 32.699 - type: map_at_100 value: 33.867000000000004 - type: map_at_1000 value: 33.967000000000006 - type: map_at_3 value: 29.718 - type: map_at_5 value: 31.345 - type: mrr_at_1 value: 28.265 - type: mrr_at_10 value: 36.945 - type: mrr_at_100 value: 37.794 - type: mrr_at_1000 value: 37.857 - type: mrr_at_3 value: 34.266000000000005 - type: mrr_at_5 value: 35.768 - type: ndcg_at_1 value: 28.265 - type: ndcg_at_10 value: 38.35 - type: ndcg_at_100 value: 43.739 - type: ndcg_at_1000 value: 46.087 - type: ndcg_at_3 value: 33.004 - type: ndcg_at_5 value: 35.411 - type: precision_at_1 value: 28.265 - type: precision_at_10 value: 6.715999999999999 - type: precision_at_100 value: 1.059 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 15.299 - type: precision_at_5 value: 10.951 - type: recall_at_1 value: 23.472 - type: recall_at_10 value: 51.413 - type: recall_at_100 value: 75.17 - type: recall_at_1000 value: 91.577 - type: recall_at_3 value: 36.651 - type: recall_at_5 value: 42.814 - type: map_at_1 value: 23.666 - type: map_at_10 value: 32.963 - type: map_at_100 value: 34.544999999999995 - type: map_at_1000 value: 34.792 - type: map_at_3 value: 29.74 - type: map_at_5 value: 31.5 - type: mrr_at_1 value: 29.051 - type: mrr_at_10 value: 38.013000000000005 - type: mrr_at_100 value: 38.997 - type: mrr_at_1000 value: 39.055 - type: mrr_at_3 value: 34.947 - type: mrr_at_5 value: 36.815 - type: ndcg_at_1 value: 29.051 - type: ndcg_at_10 value: 39.361000000000004 - type: ndcg_at_100 value: 45.186 - type: ndcg_at_1000 value: 47.867 - type: ndcg_at_3 value: 33.797 - type: ndcg_at_5 value: 36.456 - type: precision_at_1 value: 29.051 - type: precision_at_10 value: 7.668 - type: precision_at_100 value: 1.532 - type: precision_at_1000 value: 0.247 - type: precision_at_3 value: 15.876000000000001 - type: precision_at_5 value: 11.779 - type: recall_at_1 value: 23.666 - type: recall_at_10 value: 51.858000000000004 - type: recall_at_100 value: 77.805 - type: recall_at_1000 value: 94.504 - type: recall_at_3 value: 36.207 - type: recall_at_5 value: 43.094 - type: map_at_1 value: 15.662 - type: map_at_10 value: 23.594 - type: map_at_100 value: 24.593999999999998 - type: map_at_1000 value: 24.694 - type: map_at_3 value: 20.925 - type: map_at_5 value: 22.817999999999998 - type: mrr_at_1 value: 17.375 - type: mrr_at_10 value: 25.734 - type: mrr_at_100 value: 26.586 - type: mrr_at_1000 value: 26.671 - type: mrr_at_3 value: 23.044 - type: mrr_at_5 value: 24.975 - type: ndcg_at_1 value: 17.375 - type: ndcg_at_10 value: 28.186 - type: ndcg_at_100 value: 33.436 - type: ndcg_at_1000 value: 36.203 - type: ndcg_at_3 value: 23.152 - type: ndcg_at_5 value: 26.397 - type: precision_at_1 value: 17.375 - type: precision_at_10 value: 4.677 - type: precision_at_100 value: 0.786 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 10.351 - type: precision_at_5 value: 7.985 - type: recall_at_1 value: 15.662 - type: recall_at_10 value: 40.066 - type: recall_at_100 value: 65.006 - type: recall_at_1000 value: 85.94000000000001 - type: recall_at_3 value: 27.400000000000002 - type: recall_at_5 value: 35.002 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 8.853 - type: map_at_10 value: 15.568000000000001 - type: map_at_100 value: 17.383000000000003 - type: map_at_1000 value: 17.584 - type: map_at_3 value: 12.561 - type: map_at_5 value: 14.056 - type: mrr_at_1 value: 18.958 - type: mrr_at_10 value: 28.288000000000004 - type: mrr_at_100 value: 29.432000000000002 - type: mrr_at_1000 value: 29.498 - type: mrr_at_3 value: 25.049 - type: mrr_at_5 value: 26.857 - type: ndcg_at_1 value: 18.958 - type: ndcg_at_10 value: 22.21 - type: ndcg_at_100 value: 29.596 - type: ndcg_at_1000 value: 33.583 - type: ndcg_at_3 value: 16.994999999999997 - type: ndcg_at_5 value: 18.95 - type: precision_at_1 value: 18.958 - type: precision_at_10 value: 7.192 - type: precision_at_100 value: 1.5 - type: precision_at_1000 value: 0.22399999999999998 - type: precision_at_3 value: 12.573 - type: precision_at_5 value: 10.202 - type: recall_at_1 value: 8.853 - type: recall_at_10 value: 28.087 - type: recall_at_100 value: 53.701 - type: recall_at_1000 value: 76.29899999999999 - type: recall_at_3 value: 15.913 - type: recall_at_5 value: 20.658 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 9.077 - type: map_at_10 value: 20.788999999999998 - type: map_at_100 value: 30.429000000000002 - type: map_at_1000 value: 32.143 - type: map_at_3 value: 14.692 - type: map_at_5 value: 17.139 - type: mrr_at_1 value: 70.75 - type: mrr_at_10 value: 78.036 - type: mrr_at_100 value: 78.401 - type: mrr_at_1000 value: 78.404 - type: mrr_at_3 value: 76.75 - type: mrr_at_5 value: 77.47500000000001 - type: ndcg_at_1 value: 58.12500000000001 - type: ndcg_at_10 value: 44.015 - type: ndcg_at_100 value: 49.247 - type: ndcg_at_1000 value: 56.211999999999996 - type: ndcg_at_3 value: 49.151 - type: ndcg_at_5 value: 46.195 - type: precision_at_1 value: 70.75 - type: precision_at_10 value: 35.5 - type: precision_at_100 value: 11.355 - type: precision_at_1000 value: 2.1950000000000003 - type: precision_at_3 value: 53.083000000000006 - type: precision_at_5 value: 44.800000000000004 - type: recall_at_1 value: 9.077 - type: recall_at_10 value: 26.259 - type: recall_at_100 value: 56.547000000000004 - type: recall_at_1000 value: 78.551 - type: recall_at_3 value: 16.162000000000003 - type: recall_at_5 value: 19.753999999999998 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 49.44500000000001 - type: f1 value: 44.67067691783401 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 68.182 - type: map_at_10 value: 78.223 - type: map_at_100 value: 78.498 - type: map_at_1000 value: 78.512 - type: map_at_3 value: 76.71 - type: map_at_5 value: 77.725 - type: mrr_at_1 value: 73.177 - type: mrr_at_10 value: 82.513 - type: mrr_at_100 value: 82.633 - type: mrr_at_1000 value: 82.635 - type: mrr_at_3 value: 81.376 - type: mrr_at_5 value: 82.182 - type: ndcg_at_1 value: 73.177 - type: ndcg_at_10 value: 82.829 - type: ndcg_at_100 value: 83.84 - type: ndcg_at_1000 value: 84.07900000000001 - type: ndcg_at_3 value: 80.303 - type: ndcg_at_5 value: 81.846 - type: precision_at_1 value: 73.177 - type: precision_at_10 value: 10.241999999999999 - type: precision_at_100 value: 1.099 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 31.247999999999998 - type: precision_at_5 value: 19.697 - type: recall_at_1 value: 68.182 - type: recall_at_10 value: 92.657 - type: recall_at_100 value: 96.709 - type: recall_at_1000 value: 98.184 - type: recall_at_3 value: 85.9 - type: recall_at_5 value: 89.755 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 21.108 - type: map_at_10 value: 33.342 - type: map_at_100 value: 35.281 - type: map_at_1000 value: 35.478 - type: map_at_3 value: 29.067 - type: map_at_5 value: 31.563000000000002 - type: mrr_at_1 value: 41.667 - type: mrr_at_10 value: 49.913000000000004 - type: mrr_at_100 value: 50.724000000000004 - type: mrr_at_1000 value: 50.766 - type: mrr_at_3 value: 47.504999999999995 - type: mrr_at_5 value: 49.033 - type: ndcg_at_1 value: 41.667 - type: ndcg_at_10 value: 41.144 - type: ndcg_at_100 value: 48.326 - type: ndcg_at_1000 value: 51.486 - type: ndcg_at_3 value: 37.486999999999995 - type: ndcg_at_5 value: 38.78 - type: precision_at_1 value: 41.667 - type: precision_at_10 value: 11.358 - type: precision_at_100 value: 1.873 - type: precision_at_1000 value: 0.244 - type: precision_at_3 value: 25 - type: precision_at_5 value: 18.519 - type: recall_at_1 value: 21.108 - type: recall_at_10 value: 47.249 - type: recall_at_100 value: 74.52 - type: recall_at_1000 value: 93.31 - type: recall_at_3 value: 33.271 - type: recall_at_5 value: 39.723000000000006 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 40.317 - type: map_at_10 value: 64.861 - type: map_at_100 value: 65.697 - type: map_at_1000 value: 65.755 - type: map_at_3 value: 61.258 - type: map_at_5 value: 63.590999999999994 - type: mrr_at_1 value: 80.635 - type: mrr_at_10 value: 86.528 - type: mrr_at_100 value: 86.66199999999999 - type: mrr_at_1000 value: 86.666 - type: mrr_at_3 value: 85.744 - type: mrr_at_5 value: 86.24300000000001 - type: ndcg_at_1 value: 80.635 - type: ndcg_at_10 value: 73.13199999999999 - type: ndcg_at_100 value: 75.927 - type: ndcg_at_1000 value: 76.976 - type: ndcg_at_3 value: 68.241 - type: ndcg_at_5 value: 71.071 - type: precision_at_1 value: 80.635 - type: precision_at_10 value: 15.326 - type: precision_at_100 value: 1.7500000000000002 - type: precision_at_1000 value: 0.189 - type: precision_at_3 value: 43.961 - type: precision_at_5 value: 28.599999999999998 - type: recall_at_1 value: 40.317 - type: recall_at_10 value: 76.631 - type: recall_at_100 value: 87.495 - type: recall_at_1000 value: 94.362 - type: recall_at_3 value: 65.94200000000001 - type: recall_at_5 value: 71.499 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 91.686 - type: ap value: 87.5577120393173 - type: f1 value: 91.6629447355139 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 23.702 - type: map_at_10 value: 36.414 - type: map_at_100 value: 37.561 - type: map_at_1000 value: 37.605 - type: map_at_3 value: 32.456 - type: map_at_5 value: 34.827000000000005 - type: mrr_at_1 value: 24.355 - type: mrr_at_10 value: 37.01 - type: mrr_at_100 value: 38.085 - type: mrr_at_1000 value: 38.123000000000005 - type: mrr_at_3 value: 33.117999999999995 - type: mrr_at_5 value: 35.452 - type: ndcg_at_1 value: 24.384 - type: ndcg_at_10 value: 43.456 - type: ndcg_at_100 value: 48.892 - type: ndcg_at_1000 value: 49.964 - type: ndcg_at_3 value: 35.475 - type: ndcg_at_5 value: 39.711 - type: precision_at_1 value: 24.384 - type: precision_at_10 value: 6.7940000000000005 - type: precision_at_100 value: 0.951 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 15.052999999999999 - type: precision_at_5 value: 11.189 - type: recall_at_1 value: 23.702 - type: recall_at_10 value: 65.057 - type: recall_at_100 value: 90.021 - type: recall_at_1000 value: 98.142 - type: recall_at_3 value: 43.551 - type: recall_at_5 value: 53.738 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.62380300957591 - type: f1 value: 94.49871222100734 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 77.14090287277702 - type: f1 value: 60.32101258220515 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.84330867518494 - type: f1 value: 71.92248688515255 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.10692669804976 - type: f1 value: 77.9904839122866 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.822988923078444 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.38394880253403 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.82504612539082 - type: mrr value: 32.84462298174977 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.029 - type: map_at_10 value: 14.088999999999999 - type: map_at_100 value: 17.601 - type: map_at_1000 value: 19.144 - type: map_at_3 value: 10.156 - type: map_at_5 value: 11.892 - type: mrr_at_1 value: 46.44 - type: mrr_at_10 value: 56.596999999999994 - type: mrr_at_100 value: 57.11000000000001 - type: mrr_at_1000 value: 57.14 - type: mrr_at_3 value: 54.334 - type: mrr_at_5 value: 55.774 - type: ndcg_at_1 value: 44.891999999999996 - type: ndcg_at_10 value: 37.134 - type: ndcg_at_100 value: 33.652 - type: ndcg_at_1000 value: 42.548 - type: ndcg_at_3 value: 41.851 - type: ndcg_at_5 value: 39.842 - type: precision_at_1 value: 46.44 - type: precision_at_10 value: 27.647 - type: precision_at_100 value: 8.309999999999999 - type: precision_at_1000 value: 2.146 - type: precision_at_3 value: 39.422000000000004 - type: precision_at_5 value: 34.675 - type: recall_at_1 value: 6.029 - type: recall_at_10 value: 18.907 - type: recall_at_100 value: 33.76 - type: recall_at_1000 value: 65.14999999999999 - type: recall_at_3 value: 11.584999999999999 - type: recall_at_5 value: 14.626 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 39.373000000000005 - type: map_at_10 value: 55.836 - type: map_at_100 value: 56.611999999999995 - type: map_at_1000 value: 56.63 - type: map_at_3 value: 51.747 - type: map_at_5 value: 54.337999999999994 - type: mrr_at_1 value: 44.147999999999996 - type: mrr_at_10 value: 58.42699999999999 - type: mrr_at_100 value: 58.902 - type: mrr_at_1000 value: 58.914 - type: mrr_at_3 value: 55.156000000000006 - type: mrr_at_5 value: 57.291000000000004 - type: ndcg_at_1 value: 44.119 - type: ndcg_at_10 value: 63.444 - type: ndcg_at_100 value: 66.40599999999999 - type: ndcg_at_1000 value: 66.822 - type: ndcg_at_3 value: 55.962 - type: ndcg_at_5 value: 60.228 - type: precision_at_1 value: 44.119 - type: precision_at_10 value: 10.006 - type: precision_at_100 value: 1.17 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 25.135 - type: precision_at_5 value: 17.59 - type: recall_at_1 value: 39.373000000000005 - type: recall_at_10 value: 83.78999999999999 - type: recall_at_100 value: 96.246 - type: recall_at_1000 value: 99.324 - type: recall_at_3 value: 64.71900000000001 - type: recall_at_5 value: 74.508 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 69.199 - type: map_at_10 value: 82.892 - type: map_at_100 value: 83.578 - type: map_at_1000 value: 83.598 - type: map_at_3 value: 79.948 - type: map_at_5 value: 81.779 - type: mrr_at_1 value: 79.67 - type: mrr_at_10 value: 86.115 - type: mrr_at_100 value: 86.249 - type: mrr_at_1000 value: 86.251 - type: mrr_at_3 value: 85.08200000000001 - type: mrr_at_5 value: 85.783 - type: ndcg_at_1 value: 79.67 - type: ndcg_at_10 value: 86.839 - type: ndcg_at_100 value: 88.252 - type: ndcg_at_1000 value: 88.401 - type: ndcg_at_3 value: 83.86200000000001 - type: ndcg_at_5 value: 85.473 - type: precision_at_1 value: 79.67 - type: precision_at_10 value: 13.19 - type: precision_at_100 value: 1.521 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 36.677 - type: precision_at_5 value: 24.118000000000002 - type: recall_at_1 value: 69.199 - type: recall_at_10 value: 94.321 - type: recall_at_100 value: 99.20400000000001 - type: recall_at_1000 value: 99.947 - type: recall_at_3 value: 85.787 - type: recall_at_5 value: 90.365 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 55.82810046856353 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 63.38132611783628 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 5.127000000000001 - type: map_at_10 value: 12.235 - type: map_at_100 value: 14.417 - type: map_at_1000 value: 14.75 - type: map_at_3 value: 8.906 - type: map_at_5 value: 10.591000000000001 - type: mrr_at_1 value: 25.2 - type: mrr_at_10 value: 35.879 - type: mrr_at_100 value: 36.935 - type: mrr_at_1000 value: 36.997 - type: mrr_at_3 value: 32.783 - type: mrr_at_5 value: 34.367999999999995 - type: ndcg_at_1 value: 25.2 - type: ndcg_at_10 value: 20.509 - type: ndcg_at_100 value: 28.67 - type: ndcg_at_1000 value: 34.42 - type: ndcg_at_3 value: 19.948 - type: ndcg_at_5 value: 17.166 - type: precision_at_1 value: 25.2 - type: precision_at_10 value: 10.440000000000001 - type: precision_at_100 value: 2.214 - type: precision_at_1000 value: 0.359 - type: precision_at_3 value: 18.533 - type: precision_at_5 value: 14.860000000000001 - type: recall_at_1 value: 5.127000000000001 - type: recall_at_10 value: 21.147 - type: recall_at_100 value: 44.946999999999996 - type: recall_at_1000 value: 72.89 - type: recall_at_3 value: 11.277 - type: recall_at_5 value: 15.042 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.0373011786213 - type: cos_sim_spearman value: 79.27889560856613 - type: euclidean_pearson value: 80.31186315495655 - type: euclidean_spearman value: 79.41630415280811 - type: manhattan_pearson value: 80.31755140442013 - type: manhattan_spearman value: 79.43069870027611 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.8659751342045 - type: cos_sim_spearman value: 76.95377612997667 - type: euclidean_pearson value: 81.24552945497848 - type: euclidean_spearman value: 77.18236963555253 - type: manhattan_pearson value: 81.26477607759037 - type: manhattan_spearman value: 77.13821753062756 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 83.34597139044875 - type: cos_sim_spearman value: 84.124169425592 - type: euclidean_pearson value: 83.68590721511401 - type: euclidean_spearman value: 84.18846190846398 - type: manhattan_pearson value: 83.57630235061498 - type: manhattan_spearman value: 84.10244043726902 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.67641885599572 - type: cos_sim_spearman value: 80.46450725650428 - type: euclidean_pearson value: 81.61645042715865 - type: euclidean_spearman value: 80.61418394236874 - type: manhattan_pearson value: 81.55712034928871 - type: manhattan_spearman value: 80.57905670523951 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 88.86650310886782 - type: cos_sim_spearman value: 89.76081629222328 - type: euclidean_pearson value: 89.1530747029954 - type: euclidean_spearman value: 89.80990657280248 - type: manhattan_pearson value: 89.10640563278132 - type: manhattan_spearman value: 89.76282108434047 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.93864027911118 - type: cos_sim_spearman value: 85.47096193999023 - type: euclidean_pearson value: 85.03141840870533 - type: euclidean_spearman value: 85.43124029598181 - type: manhattan_pearson value: 84.99002664393512 - type: manhattan_spearman value: 85.39169195120834 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 88.7045343749832 - type: cos_sim_spearman value: 89.03262221146677 - type: euclidean_pearson value: 89.56078218264365 - type: euclidean_spearman value: 89.17827006466868 - type: manhattan_pearson value: 89.52717595468582 - type: manhattan_spearman value: 89.15878115952923 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 64.20191302875551 - type: cos_sim_spearman value: 64.11446552557646 - type: euclidean_pearson value: 64.6918197393619 - type: euclidean_spearman value: 63.440182631197764 - type: manhattan_pearson value: 64.55692904121835 - type: manhattan_spearman value: 63.424877742756266 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 86.37793104662344 - type: cos_sim_spearman value: 87.7357802629067 - type: euclidean_pearson value: 87.4286301545109 - type: euclidean_spearman value: 87.78452920777421 - type: manhattan_pearson value: 87.42445169331255 - type: manhattan_spearman value: 87.78537677249598 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 84.31465405081792 - type: mrr value: 95.7173781193389 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 57.760999999999996 - type: map_at_10 value: 67.904 - type: map_at_100 value: 68.539 - type: map_at_1000 value: 68.562 - type: map_at_3 value: 65.415 - type: map_at_5 value: 66.788 - type: mrr_at_1 value: 60.333000000000006 - type: mrr_at_10 value: 68.797 - type: mrr_at_100 value: 69.236 - type: mrr_at_1000 value: 69.257 - type: mrr_at_3 value: 66.667 - type: mrr_at_5 value: 67.967 - type: ndcg_at_1 value: 60.333000000000006 - type: ndcg_at_10 value: 72.24199999999999 - type: ndcg_at_100 value: 74.86 - type: ndcg_at_1000 value: 75.354 - type: ndcg_at_3 value: 67.93400000000001 - type: ndcg_at_5 value: 70.02199999999999 - type: precision_at_1 value: 60.333000000000006 - type: precision_at_10 value: 9.533 - type: precision_at_100 value: 1.09 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 26.778000000000002 - type: precision_at_5 value: 17.467 - type: recall_at_1 value: 57.760999999999996 - type: recall_at_10 value: 84.383 - type: recall_at_100 value: 96.267 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 72.628 - type: recall_at_5 value: 78.094 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.8029702970297 - type: cos_sim_ap value: 94.9210324173411 - type: cos_sim_f1 value: 89.8521162672106 - type: cos_sim_precision value: 91.67533818938605 - type: cos_sim_recall value: 88.1 - type: dot_accuracy value: 99.69504950495049 - type: dot_ap value: 90.4919719146181 - type: dot_f1 value: 84.72289156626506 - type: dot_precision value: 81.76744186046511 - type: dot_recall value: 87.9 - type: euclidean_accuracy value: 99.79702970297029 - type: euclidean_ap value: 94.87827463795753 - type: euclidean_f1 value: 89.55680081507896 - type: euclidean_precision value: 91.27725856697819 - type: euclidean_recall value: 87.9 - type: manhattan_accuracy value: 99.7990099009901 - type: manhattan_ap value: 94.87587025149682 - type: manhattan_f1 value: 89.76298537569339 - type: manhattan_precision value: 90.53916581892166 - type: manhattan_recall value: 89 - type: max_accuracy value: 99.8029702970297 - type: max_ap value: 94.9210324173411 - type: max_f1 value: 89.8521162672106 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 65.92385753948724 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 33.671756975431144 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 50.677928036739004 - type: mrr value: 51.56413133435193 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.523589340819683 - type: cos_sim_spearman value: 30.187407518823235 - type: dot_pearson value: 29.039713969699015 - type: dot_spearman value: 29.114740651155508 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.211 - type: map_at_10 value: 1.6199999999999999 - type: map_at_100 value: 8.658000000000001 - type: map_at_1000 value: 21.538 - type: map_at_3 value: 0.575 - type: map_at_5 value: 0.919 - type: mrr_at_1 value: 78 - type: mrr_at_10 value: 86.18599999999999 - type: mrr_at_100 value: 86.18599999999999 - type: mrr_at_1000 value: 86.18599999999999 - type: mrr_at_3 value: 85 - type: mrr_at_5 value: 85.9 - type: ndcg_at_1 value: 74 - type: ndcg_at_10 value: 66.542 - type: ndcg_at_100 value: 50.163999999999994 - type: ndcg_at_1000 value: 45.696999999999996 - type: ndcg_at_3 value: 71.531 - type: ndcg_at_5 value: 70.45 - type: precision_at_1 value: 78 - type: precision_at_10 value: 69.39999999999999 - type: precision_at_100 value: 51.06 - type: precision_at_1000 value: 20.022000000000002 - type: precision_at_3 value: 76 - type: precision_at_5 value: 74.8 - type: recall_at_1 value: 0.211 - type: recall_at_10 value: 1.813 - type: recall_at_100 value: 12.098 - type: recall_at_1000 value: 42.618 - type: recall_at_3 value: 0.603 - type: recall_at_5 value: 0.987 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.2079999999999997 - type: map_at_10 value: 7.777000000000001 - type: map_at_100 value: 12.825000000000001 - type: map_at_1000 value: 14.196 - type: map_at_3 value: 4.285 - type: map_at_5 value: 6.177 - type: mrr_at_1 value: 30.612000000000002 - type: mrr_at_10 value: 42.635 - type: mrr_at_100 value: 43.955 - type: mrr_at_1000 value: 43.955 - type: mrr_at_3 value: 38.435 - type: mrr_at_5 value: 41.088 - type: ndcg_at_1 value: 28.571 - type: ndcg_at_10 value: 20.666999999999998 - type: ndcg_at_100 value: 31.840000000000003 - type: ndcg_at_1000 value: 43.191 - type: ndcg_at_3 value: 23.45 - type: ndcg_at_5 value: 22.994 - type: precision_at_1 value: 30.612000000000002 - type: precision_at_10 value: 17.959 - type: precision_at_100 value: 6.755 - type: precision_at_1000 value: 1.4200000000000002 - type: precision_at_3 value: 23.810000000000002 - type: precision_at_5 value: 23.673 - type: recall_at_1 value: 2.2079999999999997 - type: recall_at_10 value: 13.144 - type: recall_at_100 value: 42.491 - type: recall_at_1000 value: 77.04299999999999 - type: recall_at_3 value: 5.3469999999999995 - type: recall_at_5 value: 9.139 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.9044 - type: ap value: 14.625783489340755 - type: f1 value: 54.814936562590546 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.94227504244483 - type: f1 value: 61.22516038508854 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 49.602409155145864 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.94641473445789 - type: cos_sim_ap value: 76.91572747061197 - type: cos_sim_f1 value: 70.14348097317529 - type: cos_sim_precision value: 66.53254437869822 - type: cos_sim_recall value: 74.1688654353562 - type: dot_accuracy value: 84.80061989628658 - type: dot_ap value: 70.7952548895177 - type: dot_f1 value: 65.44780728844965 - type: dot_precision value: 61.53310104529617 - type: dot_recall value: 69.89445910290237 - type: euclidean_accuracy value: 86.94641473445789 - type: euclidean_ap value: 76.80774009393652 - type: euclidean_f1 value: 70.30522503879979 - type: euclidean_precision value: 68.94977168949772 - type: euclidean_recall value: 71.71503957783642 - type: manhattan_accuracy value: 86.8629671574179 - type: manhattan_ap value: 76.76518632600317 - type: manhattan_f1 value: 70.16056518946692 - type: manhattan_precision value: 68.360450563204 - type: manhattan_recall value: 72.0580474934037 - type: max_accuracy value: 86.94641473445789 - type: max_ap value: 76.91572747061197 - type: max_f1 value: 70.30522503879979 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.10428066907285 - type: cos_sim_ap value: 86.25114759921435 - type: cos_sim_f1 value: 78.37857884586856 - type: cos_sim_precision value: 75.60818546078993 - type: cos_sim_recall value: 81.35971666153372 - type: dot_accuracy value: 87.41995575736406 - type: dot_ap value: 81.51838010086782 - type: dot_f1 value: 74.77398015435503 - type: dot_precision value: 71.53002390662354 - type: dot_recall value: 78.32614721281182 - type: euclidean_accuracy value: 89.12368533395428 - type: euclidean_ap value: 86.33456799874504 - type: euclidean_f1 value: 78.45496750232127 - type: euclidean_precision value: 75.78388462366364 - type: euclidean_recall value: 81.32121958731136 - type: manhattan_accuracy value: 89.10622113556099 - type: manhattan_ap value: 86.31215061745333 - type: manhattan_f1 value: 78.40684906011539 - type: manhattan_precision value: 75.89536643366722 - type: manhattan_recall value: 81.09023714197721 - type: max_accuracy value: 89.12368533395428 - type: max_ap value: 86.33456799874504 - type: max_f1 value: 78.45496750232127 --- # E5-large-v2 [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf). Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 This model has 24 layers and the embedding size is 1024. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] # Each input text should start with "query: " or "passage: ". # For tasks other than retrieval, you can simply use the "query: " prefix. input_texts = ['query: how much protein should a female eat', 'query: summit define', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."] tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-large-v2') model = AutoModel.from_pretrained('intfloat/e5-large-v2') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ## Training Details Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf). ## Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2022text, title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2212.03533}, year={2022} } ``` ## Limitations This model only works for English texts. Long texts will be truncated to at most 512 tokens.
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
pszemraj/long-t5-tglobal-base-16384-booksci-summary-v1
pszemraj
summarization
[ "transformers", "pytorch", "onnx", "safetensors", "longt5", "text2text-generation", "generated_from_trainer", "lay summary", "narrative", "biomedical", "long document summary", "summarization", "en", "dataset:pszemraj/scientific_lay_summarisation-elife-norm", "base_model:pszemraj/long-t5-tglobal-base-16384-book-summary", "base_model:quantized:pszemraj/long-t5-tglobal-base-16384-book-summary", "license:bsd-3-clause", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,680
1,696
36
2
--- base_model: pszemraj/long-t5-tglobal-base-16384-book-summary datasets: - pszemraj/scientific_lay_summarisation-elife-norm language: - en library_name: transformers license: - bsd-3-clause - apache-2.0 metrics: - rouge pipeline_tag: summarization tags: - generated_from_trainer - lay summary - narrative - biomedical - long document summary widget: - text: large earthquakes along a given fault segment do not occur at random intervals because it takes time to accumulate the strain energy for the rupture. The rates at which tectonic plates move and accumulate strain at their boundaries are approximately uniform. Therefore, in first approximation, one may expect that large ruptures of the same fault segment will occur at approximately constant time intervals. If subsequent main shocks have different amounts of slip across the fault, then the recurrence time may vary, and the basic idea of periodic mainshocks must be modified. For great plate boundary ruptures the length and slip often vary by a factor of 2. Along the southern segment of the San Andreas fault the recurrence interval is 145 years with variations of several decades. The smaller the standard deviation of the average recurrence interval, the more specific could be the long term prediction of a future mainshock. example_title: earthquakes - text: ' A typical feed-forward neural field algorithm. Spatiotemporal coordinates are fed into a neural network that predicts values in the reconstructed domain. Then, this domain is mapped to the sensor domain where sensor measurements are available as supervision. Class and Section Problems Addressed Generalization (Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid Representations (Section 3) Computation & memory efficiency, representation capacity, editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section 5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section 6) Edit ability, constraints, regularization. Table 2: The five classes of techniques in the neural field toolbox each addresses problems that arise in learning, inference, and control. (Section 3). We can supervise reconstruction via differentiable forward maps that transform Or project our domain (e.g, 3D reconstruction via 2D images; Section 4) With appropriate network architecture choices, we can overcome neural network spectral biases (blurriness) and efficiently compute derivatives and integrals (Section 5). Finally, we can manipulate neural fields to add constraints and regularizations, and to achieve editable representations (Section 6). Collectively, these classes constitute a ''toolbox'' of techniques to help solve problems with neural fields There are three components in a conditional neural field: (1) An encoder or inference function € that outputs the conditioning latent variable 2 given an observation 0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS a latent code Or feature code_ (2) A mapping function 4 between Z and neural field parameters O: Y(z) = O; (3) The neural field itself $. The encoder € finds the most probable z given the observations O: argmaxz P(2/0). The decoder maximizes the inverse conditional probability to find the most probable 0 given Z: arg- max P(Olz). We discuss different encoding schemes with different optimality guarantees (Section 2.1.1), both global and local conditioning (Section 2.1.2), and different mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable prior over the sur- face in its reconstruction domain to generalize to the partial observations. A neural network expresses a prior via the function space of its architecture and parameters 0, and generalization is influenced by the inductive bias of this function space (Section 5).' example_title: scientific paper - text: 'Is a else or outside the cob and tree written being of early client rope and you have is for good reasons. On to the ocean in Orange for time. By''s the aggregate we can bed it yet. Why this please pick up on a sort is do and also M Getoi''s nerocos and do rain become you to let so is his brother is made in use and Mjulia''s''s the lay major is aging Masastup coin present sea only of Oosii rooms set to you We do er do we easy this private oliiishs lonthen might be okay. Good afternoon everybody. Welcome to this lecture of Computational Statistics. As you can see, I''m not socially my name is Michael Zelinger. I''m one of the task for this class and you might have already seen me in the first lecture where I made a quick appearance. I''m also going to give the tortillas in the last third of this course. So to give you a little bit about me, I''m a old student here with better Bulman and my research centres on casual inference applied to biomedical disasters, so that could be genomics or that could be hospital data. If any of you is interested in writing a bachelor thesis, a semester paper may be mastathesis about this topic feel for reach out to me. you have my name on models and my email address you can find in the directory I''d Be very happy to talk about it. you do not need to be sure about it, we can just have a chat. So with that said, let''s get on with the lecture. There''s an exciting topic today I''m going to start by sharing some slides with you and later on during the lecture we''ll move to the paper. So bear with me for a few seconds. Well, the projector is starting up. Okay, so let''s get started. Today''s topic is a very important one. It''s about a technique which really forms one of the fundamentals of data science, machine learning, and any sort of modern statistics. It''s called cross validation. I know you really want to understand this topic I Want you to understand this and frankly, nobody''s gonna leave Professor Mineshousen''s class without understanding cross validation. So to set the stage for this, I Want to introduce you to the validation problem in computational statistics. So the problem is the following: You trained a model on available data. You fitted your model, but you know the training data you got could always have been different and some data from the environment. Maybe it''s a random process. You do not really know what it is, but you know that somebody else who gets a different batch of data from the same environment they would get slightly different training data and you do not care that your method performs as well. On this training data. you want to to perform well on other data that you have not seen other data from the same environment. So in other words, the validation problem is you want to quantify the performance of your model on data that you have not seen. So how is this even possible? How could you possibly measure the performance on data that you do not know The solution to? This is the following realization is that given that you have a bunch of data, you were in charge. You get to control how much that your model sees. It works in the following way: You can hide data firms model. Let''s say you have a training data set which is a bunch of doubtless so X eyes are the features those are typically hide and national vector. It''s got more than one dimension for sure. And the why why eyes. Those are the labels for supervised learning. As you''ve seen before, it''s the same set up as we have in regression. And so you have this training data and now you choose that you only use some of those data to fit your model. You''re not going to use everything, you only use some of it the other part you hide from your model. And then you can use this hidden data to do validation from the point of you of your model. This hidden data is complete by unseen. In other words, we solve our problem of validation.' example_title: transcribed audio - lecture - text: 'Transformer-based models have shown to be very useful for many NLP tasks. However, a major limitation of transformers-based models is its O(n^2)O(n 2) time & memory complexity (where nn is sequence length). Hence, it''s computationally very expensive to apply transformer-based models on long sequences n > 512n>512. Several recent papers, e.g. Longformer, Performer, Reformer, Clustered attention try to remedy this problem by approximating the full attention matrix. You can checkout 🤗''s recent blog post in case you are unfamiliar with these models. BigBird (introduced in paper) is one of such recent models to address this issue. BigBird relies on block sparse attention instead of normal attention (i.e. BERT''s attention) and can handle sequences up to a length of 4096 at a much lower computational cost compared to BERT. It has achieved SOTA on various tasks involving very long sequences such as long documents summarization, question-answering with long contexts. BigBird RoBERTa-like model is now available in 🤗Transformers. The goal of this post is to give the reader an in-depth understanding of big bird implementation & ease one''s life in using BigBird with 🤗Transformers. But, before going into more depth, it is important to remember that the BigBird''s attention is an approximation of BERT''s full attention and therefore does not strive to be better than BERT''s full attention, but rather to be more efficient. It simply allows to apply transformer-based models to much longer sequences since BERT''s quadratic memory requirement quickly becomes unbearable. Simply put, if we would have ∞ compute & ∞ time, BERT''s attention would be preferred over block sparse attention (which we are going to discuss in this post). If you wonder why we need more compute when working with longer sequences, this blog post is just right for you! Some of the main questions one might have when working with standard BERT-like attention include: Do all tokens really have to attend to all other tokens? Why not compute attention only over important tokens? How to decide what tokens are important? How to attend to just a few tokens in a very efficient way? In this blog post, we will try to answer those questions. What tokens should be attended to? We will give a practical example of how attention works by considering the sentence ''BigBird is now available in HuggingFace for extractive question answering''. In BERT-like attention, every word would simply attend to all other tokens. Let''s think about a sensible choice of key tokens that a queried token actually only should attend to by writing some pseudo-code. Will will assume that the token available is queried and build a sensible list of key tokens to attend to. >>> # let''s consider following sentence as an example >>> example = [''BigBird'', ''is'', ''now'', ''available'', ''in'', ''HuggingFace'', ''for'', ''extractive'', ''question'', ''answering''] >>> # further let''s assume, we''re trying to understand the representation of ''available'' i.e. >>> query_token = ''available'' >>> # We will initialize an empty `set` and fill up the tokens of our interest as we proceed in this section. >>> key_tokens = [] # => currently ''available'' token doesn''t have anything to attend Nearby tokens should be important because, in a sentence (sequence of words), the current word is highly dependent on neighboring past & future tokens. This intuition is the idea behind the concept of sliding attention.' example_title: bigbird blog intro - text: 'To be fair, you have to have a very high IQ to understand Rick and Morty. The humour is extremely subtle, and without a solid grasp of theoretical physics most of the jokes will go over a typical viewer''s head. There''s also Rick''s nihilistic outlook, which is deftly woven into his characterisation- his personal philosophy draws heavily from Narodnaya Volya literature, for instance. The fans understand this stuff; they have the intellectual capacity to truly appreciate the depths of these jokes, to realise that they''re not just funny- they say something deep about LIFE. As a consequence people who dislike Rick & Morty truly ARE idiots- of course they wouldn''t appreciate, for instance, the humour in Rick''s existential catchphrase ''Wubba Lubba Dub Dub,'' which itself is a cryptic reference to Turgenev''s Russian epic Fathers and Sons. I''m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as Dan Harmon''s genius wit unfolds itself on their television screens. What fools.. how I pity them. 😂 And yes, by the way, i DO have a Rick & Morty tattoo. And no, you cannot see it. It''s for the ladies'' eyes only- and even then they have to demonstrate that they''re within 5 IQ points of my own (preferably lower) beforehand. Nothin personnel kid 😎' example_title: Richard & Mortimer - text: The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct. example_title: eiffel parameters: max_length: 64 min_length: 8 no_repeat_ngram_size: 3 early_stopping: true repetition_penalty: 3.5 encoder_no_repeat_ngram_size: 4 length_penalty: 0.4 num_beams: 4 model-index: - name: pszemraj/long-t5-tglobal-base-16384-booksci-summary-v1 results: - task: type: summarization name: Summarization dataset: name: kmfoda/booksum type: kmfoda/booksum config: kmfoda--booksum split: test metrics: - type: rouge value: 36.7976 name: ROUGE-1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzk0ZDQ3MDI1MmRhZDZhOTkzMGY3MWZmNGM2MzMwYTI1Y2MyZDQ0ZWZiZTRkZjI2YzJhMTRkOWE2MmM0MzEzNyIsInZlcnNpb24iOjF9.U_h6vUEz3UYWsk90uBckLpUJqSE9L_XlQiwcBdpDLE_lBPTZZ_V0hoFNrR3c2kUKBLZPPrRWsqCqca_uzhTgDw - type: rouge value: 6.1002 name: ROUGE-2 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWMwNWVjMDMwYTNlNmQ5Yjc3NzQ0Y2MyNjg2NzA3ZTYyN2NkMmUxMTU3YjUwYzZjNmJlZWQwZTc5ODk0ZjhmOSIsInZlcnNpb24iOjF9.efVyAzcR7ay-Yy3jCzgaF7FnRXdjCLxxEz6crKVjsqwdW7B3eBBdFD5AXRItMk5_yGdrZTSjEFjpgb15Qt3yDw - type: rouge value: 16.5037 name: ROUGE-L verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2UwYTU3NzAzNDBmNjRhMWNiOGU2NDE5ZGIwMmY3MzI1NjczZDUxYzVjM2VlNDI5OTgzYjI5NTk3YTgwYjNjZSIsInZlcnNpb24iOjF9.yTuu6tK7MLOf2y_RAG7RAcOrm7uX5OYnYYJ0Nts7ZocojFM45FA4p_DLGwrIKtw8gRWQOj5Y8aUgvRc3ZvPnAA - type: rouge value: 33.6126 name: ROUGE-LSUM verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGMyMmUwNTkzZTBjYzg2NTFkY2M5ODkzOTIwOGYzNWEwNWM4ODFiODg5MGM2ZjBmODNkNGJhZTI3ZjY2YzRiOCIsInZlcnNpb24iOjF9.F4clVCMlK2AvTrsBX9LGmbMoI618Iq_gkhyRyNo0s2gJG4y73nZC6s_TH7zolpIDfo-bcn46ALFX7LGmZALrCw - type: loss value: 2.3926444053649902 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWU3MmJmYmU0ODBhMDJiYWMzN2M3ZTdkM2YzMzI5YzVkM2YwNTA3YjQ5MDBmYmZjOWQ1ZDMwYjUyYTI0ZDQ3YyIsInZlcnNpb24iOjF9.BvumB3q-msXpO1fYkrsy7x9q1ai2mNkRpc18RqfKdUc1pipPnmBOfQYemc9GGZqT8yVAigF2sjWIsZDh4FcICQ - type: gen_len value: 279.9161 name: gen_len verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWM0NDkzZTNiZjhiZjMzMzM5NWUwOGRlYTI4ZjkzNWVjMDNlZTVjMTE2NzdjMTE4ZDJjNDVmZjQxOWZjMDk2MCIsInZlcnNpb24iOjF9.kHWjbQmcBTWxHkhibyIy4S_5Ze759i2nuR8MEB6LIYAQDy0aQgpaOH32Ux0juqENHr390AcxSa04FN8EIQJkCw --- # long-t5-tglobal-base-16384-booksci-summary: v1 <a href="https://colab.research.google.com/gist/pszemraj/ee06b2b3bfcd7d29e588697a90f7a776/long-t5-tglobal-base-16384-booksci-summary-v1-example-with-textsum.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> An experiment investigating transfer learning capabilities by fine-tuning models on different datasets starting from the `booksum` checkpoint. ## Model Details This model is a fine-tuned version of [pszemraj/long-t5-tglobal-base-16384-book-summary](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary) on the `pszemraj/scientific_lay_summarisation-elife-norm` dataset for two epochs. ## Usage It's recommended to use this model with [beam search decoding](https://huggingface.co/docs/transformers/generation_strategies#beamsearch-decoding). If interested, you can also use the `textsum` util repo to have most of this abstracted out for you: ```bash pip install -U textsum ``` ```python from textsum.summarize import Summarizer model_name = "pszemraj/long-t5-tglobal-base-16384-booksci-summary-v1" summarizer = Summarizer(model_name) # GPU auto-detected text = "put the text you don't want to read here" summary = summarizer.summarize_string(text) print(summary) ``` ## Intended uses & limitations - This is an initial experiment - Domain generalization abilities at time of writing are unknown ## Training procedure > Note: this model was trained at a lower LR & not till "absolute convergence" with the intention of retaining some of the properties learned from the initial fine-tuning on `booksum` ### Results It achieves the following results on the evaluation set: - Loss: 2.3994 - Rouge1: 34.2428 - Rouge2: 4.3644 - Rougel: 12.5332 - Rougelsum: 30.6965 - Gen Len: 294.0249 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 2.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:| | 2.7492 | 0.99 | 67 | 2.4272 | 34.6436 | 4.4536 | 12.4985 | 30.916 | 300.7635 | | 2.6689 | 1.97 | 134 | 2.3994 | 34.2428 | 4.3644 | 12.5332 | 30.6965 | 294.0249 |
[ "QUESTION_ANSWERING", "SUMMARIZATION" ]
[ "BEAR" ]
Non_BioNLP
ymelka/camembert-cosmetic-similarity-cp1200
ymelka
sentence-similarity
[ "sentence-transformers", "safetensors", "camembert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:5000", "loss:CoSENTLoss", "arxiv:1908.10084", "base_model:ymelka/camembert-cosmetic-finetuned", "base_model:finetune:ymelka/camembert-cosmetic-finetuned", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,718
1,718
9
1
--- base_model: ymelka/camembert-cosmetic-finetuned datasets: [] language: [] library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:5000 - loss:CoSENTLoss widget: - source_sentence: Un soin régulateur de pores hautement efficace, conçu pour réduire visiblement l'apparence des pores dilatés. Sa formule ciblée aide à affiner le grain de peau et à réguler la production de sébum, pour une peau plus lisse et uniforme. Idéal pour les peaux matures en quête de perfection. sentences: - La Crème Confort 1ères Rides de Coup D’Eclat est un soin hydratant apaisant qui procure une hydratation optimale à la peau tout en la régénérant. En 28 jours, la peau devient moins sensible et réactive, tandis que les premiers signes de l'âge sont corrigés et prévenus. Grâce à des ingrédients tels que l'huile de pépins de raisin, l'huile de macadamia et la vitamine E, cette crème redonne à la peau son éclat et sa vitalité, tout en lissant les ridules et affinant le grain de peau. Pour une utilisation externe sur le visage et le cou, cette formule non-comédogène convient à tous les types de peaux, y compris les peaux sensibles et réactives. Il est recommandé d'appliquer la crème matin et soir par un léger massage sur une peau démaquillée. Il est important de suivre les instructions d'utilisation et de ne pas dépasser la posologie recommandée. - La Source Micellaire Enchantée Rose D'Antan de Garancia est un produit de parapharmacie multifonctionnel qui nettoie, démaquille, hydrate, apaise et illumine la peau du visage, des yeux et des lèvres. Grâce à sa formule enrichie en actifs brevetés hydratants et apaisants, cette eau micellaire réduit de manière significative les sensations d'irritation et de picotement. Composée à 99,5% d'ingrédients d'origine naturelle, elle contient notamment de l'extrait de racine de chicorée, un prébiotique nourrissant pour le microbiote cutané. Pour l'utiliser, il suffit de tourner la pompe vers la gauche, d'imbiber un coton d'eau micellaire et de le passer sur le visage, les yeux et les lèvres sans rinçage. Laissez poser 5 secondes sur les yeux avant de démaquiller. Ce produit convient à tous les types de peau et est présenté dans un flacon pompe de 400 ml. - Le tonique hydratant Cosrx Hydrium est un produit de parapharmacie qui rend la peau plus fraîche et hydratée grâce à sa formule contenant de la vitamine B5 et de l'acide hyaluronique. Ce tonique hydratant agit comme une base essentielle pour la santé de la peau, en formant une barrière d'hydratation et en optimisant l'équilibre des peaux abîmées. Il convient à tous les types de peau, y compris les peaux sèches et à tendance acnéique. Les principaux ingrédients actifs incluent des acides hyaluroniques de type 6, du D-panthénol et de l'allantoïne pour une hydratation en profondeur et un effet apaisant sur les peaux sensibles. Pour une utilisation optimale, appliquez le tonique après le nettoyage du visage, en massant pour une meilleure absorption. Il peut également être utilisé comme masque en feuille, brume ou mélange nettoyant. Présenté en flacon de 150 ml, ce tonique hydratant est un allié idéal pour une peau fraîche et hydratée au quotidien. - source_sentence: Un soin hydratant et revitalisant qui apporte un éclat naturel à la peau. Enrichi en ingrédients nourrissants et anti-âge, ce soin aide à réduire les signes de fatigue et à améliorer la texture de la peau. Parfait pour revitaliser la peau mature et lui redonner toute sa jeunesse. sentences: - L'Alphanova Solide Exfoliant Visage est un produit naturel et végan qui purifie la peau et revitalise le teint. Composé d'huiles bio d'amande douce et de jojoba, de feuilles de verveine et de poudre de coques de noix, il convient aux peaux normales. Sans huile de palme ni sulfate, ce duo moussant doux et végétal offre une mousse généreuse et onctueuse au parfum frais de verveine. Avec 99,9% d'ingrédients d'origine naturelle, dont 73,4% issus de l'agriculture biologique, cet exfoliant visage Alphanova permet jusqu'à 100 utilisations. Pour l'utiliser, il suffit d'appliquer le nettoyant sur le visage humidifié, de masser délicatement en évitant le contour des yeux, puis de rincer abondamment. Présenté en deux formats de 75g, cet exfoliant offre une expérience de soin agréable et respectueuse de l'environnement. - Le Clarins Doux Nettoyant Moussant Apaisant est spécialement conçu pour les femmes ayant une peau très sèche ou sensible. Grâce à sa formule aux herbes des Alpes, ce nettoyant apaise et adoucit la peau tout en la protégeant des agressions extérieures. Enrichi en extraits de saponaire, de reine des près, d'aloé vera, de camomille bio et de beurre de karité, il nettoie en douceur, purifie, hydrate et apaise la peau. Sa texture mousse fine et onctueuse laisse la peau parfaitement nettoyée, douce et protégée. Ce nettoyant peut être utilisé matin et/ou soir en massant délicatement sur le visage et le cou, en évitant le contour des yeux. Il est recommandé de rincer abondamment après utilisation. Évitez le contour des yeux lors de l'application. Disponible en tube de 125 ml, ce nettoyant est idéal pour un nettoyage en douceur des peaux très sèches ou sensibles. - L'Eau Parfumée Bienfaisante Shiso de Roger&Gallet est un parfum unique aux notes fraîches et raffinées, mêlant le shiso, le petitgrain et la mandarine pour une sensation de fraîcheur naturelle. Enrichi en pivoine et en santal, ce parfum vert fusant apporte une énergie vibrante et permet de s'ouvrir à de nouveaux horizons. Idéal pour le corps, ce produit peut être utilisé en vaporisation pour accentuer son effet énergisant. Les principaux ingrédients actifs incluent l'extrait de feuille de Perilla ocymoides, connu pour ses propriétés revitalisantes. Il est recommandé de vaporiser un nuage de parfum devant soi et de le traverser pour profiter pleinement de ses bienfaits. Il est conseillé de ne pas utiliser ce produit sur une peau irritée ou lésée. Profitez de cette fragrance unique pour vous sentir revitalisé et plein d'énergie au quotidien. - source_sentence: Un nettoyant doux et hydratant, spécialement formulé pour éliminer les impuretés tout en apportant de l'éclat à la peau. Sa formule adaptée aux peaux matures aide à lutter contre les taches et les imperfections, tout en respectant la sensibilité de la peau. sentences: - Le nettoyant visage naturel solide Respire est spécialement conçu pour les peaux sensibles, offrant une formule douce et naturelle enrichie en huile de lin Bio, huile de tournesol Bio et beurre de karité Bio. Ces ingrédients apaisent, hydratent et protègent la peau, la laissant douce et saine. Sa formule sans ingrédients controversés convient parfaitement aux peaux sensibles. Facile à utiliser, il suffit de frotter doucement le nettoyant sur le visage humidifié, de masser légèrement la peau et de rincer. Vegan et non-testé sur les animaux, ce nettoyant est testé dermatologiquement et fabriqué en France. Il est idéal pour une peau apaisée et saine, et convient aux peaux sensibles. Il est recommandé de rincer immédiatement en cas de contact avec les yeux. - Le Gamarde Lait Nettoyant Douceur Peaux Délicates Bio est un nettoyant et démaquillant doux spécialement conçu pour les peaux délicates, sèches ou mixtes. Sa formule à base d'ingrédients naturels et biologiques, tels que l'eau de Gamarde les Bains, l'huile d'argan et l'huile de noisette, permet d'éliminer en douceur les impuretés et le maquillage tenace tout en respectant l'équilibre de la peau. Enrichi en huiles essentielles de Palmarosa et d'orange douce, ce lait nettoyant laisse la peau propre, douce et apaisée. Pour l'utiliser, il suffit d'appliquer une petite quantité sur le visage et le cou, puis de retirer avec un coton sec avant de tonifier la peau avec la Lotion Apaisante Douceur. Ce produit convient parfaitement pour un usage quotidien et ne présente aucune contre-indication particulière. - La serviette à cheveux Les Tendances D'Emma en couleur marron est un accessoire pratique et efficace pour sécher les cheveux en douceur. Fabriquée à partir de 90% de viscose de bambou et 10% de polyester, elle absorbe 4 fois mieux qu'une serviette classique. Son attache astucieuse permet de la maintenir en place sur la tête, évitant ainsi de traumatiser les cheveux lors du séchage. Idéale pour tous, y compris ceux qui ont opté pour des colorations naturelles, cette serviette simplifie la vie au quotidien. Facile à utiliser, il suffit de la placer sur la tête, de tourner et de glisser dans l'attache prévue. Lavable en machine, elle est pratique et écologique. Cette serviette à cheveux est conçue, fabriquée et imprimée en France dans une démarche éco-responsable. Un produit incontournable pour prendre soin de ses cheveux en toute simplicité. - source_sentence: Un soin anti-rides et régulateur de sébum, spécialement conçu pour traiter les rides et ridules tout en régulant l'excès de sébum. Sa formule hydratante et apaisante convient parfaitement aux peaux sensibles. sentences: - Le Phyt's Men Soin Anti-Rides est un fluide frais et non gras conçu pour atténuer les premiers signes de l'âge chez les hommes. Certifié Bio et d'origine naturelle, ce soin hydrate, raffermit et illumine la peau masculine. Sa formule contient des huiles végétales de sésame, noisette, chanvre, nigelle et beurre de karité, ainsi que des huiles essentielles de petit grain et géranium, pour leurs propriétés protectrices, apaisantes et tonifiantes. L'extrait de ginseng contribue à revitaliser la peau. Il est recommandé d'appliquer ce produit quotidiennement sur l'ensemble du visage. Ce soin est destiné à lutter contre les premiers signes de l'âge et est à usage externe uniquement. Il convient de noter que ce produit est déconseillé en cas d'allergie à l'un de ses composants. - L'Eau Thermale Spray Brumisateur Apaisant d'Avène est un soin essentiel pour les peaux sensibles, hypersensibles, allergiques et irritées. Grâce à sa composition unique en eau thermale d'Avène, ce spray apaise et sublime toutes les peaux, même les plus sensibles, en leur procurant une sensation d'apaisement, de confort et de bien-être. Les propriétés apaisantes et anti-irritantes de l'eau thermale d'Avène ont été démontrées par de nombreux travaux scientifiques, en faisant un véritable principe actif pour le traitement des affections cutanées. Il est recommandé pour les peaux atopiques, sébo-squameuses, couperosiques et sujettes aux photo-allergies. Les principaux ingrédients actifs de ce spray sont l'eau thermale Avène et le gaz (nitrogène), qui contribuent à apaiser la peau et à la protéger. Pour l'utiliser, il suffit de pulvériser une fine brume sur le visage. Ce soin a été testé par 100 utilisateurs qui ont tous apprécié ses bienfaits. Il est important de noter que ce produit est contre-indiqué en cas d'allergie à l'un de ses composants. - Le soin raffermissant corps et buste Copaïba Demain L'Empire 200ml est un produit de parapharmacie de haute qualité, formulé avec des ingrédients naturels et actifs pour offrir à la peau une hydratation, une protection et une fermeté optimales. Grâce à sa composition riche en huile de macadamia, beurre de babassu et autres actifs puissants, ce soin aide à améliorer l'élasticité de la peau, à réduire les rides, à prévenir les vergetures et à protéger contre les agressions extérieures. En utilisant ce produit quotidiennement, la peau retrouve sa jeunesse et sa vitalité, avec une texture douce et un parfum frais et vivifiant. Les principaux ingrédients actifs tels que la chitine, l'extrait végétal tropical et l'acide hyaluronique agissent en synergie pour rajeunir la peau et lui apporter une hydratation optimale. Il est recommandé d'appliquer ce soin sur tout le corps, en massant délicatement jusqu'à absorption complète. Il est conseillé de l'utiliser régulièrement pour des résultats visibles en seulement quelques mois. Il est important de noter que ce produit est destiné à un usage externe uniquement et qu'il est préférable de consulter un professionnel de la santé en cas de réaction allergique. - source_sentence: En complément du nettoyant et du soin, il est recommandé d'utiliser un masque purifiant et matifiant une à deux fois par semaine. Ce masque aidera à resserrer les pores, purifier la peau en profondeur et réguler l'excès de sébum pour un teint plus éclatant et uniforme. sentences: - La Crème Moussante Nettoyante Hydratante CeraVe est un produit développé en collaboration avec des dermatologues pour nettoyer, démaquiller et hydrater en douceur les peaux normales à sèches. Enrichie en céramides essentiels, acide hyaluronique et acides aminés, sa formule élimine efficacement les impuretés, la pollution et le maquillage longue tenue tout en restaurant la barrière cutanée. Grâce à la Technologie MVE, les actifs sont diffusés en continu pour une hydratation prolongée. Cette crème convient pour le visage et les yeux, est hypoallergénique et non-comédogène. Utilisez-la matin et soir sur une peau humide, faites mousser et rincez abondamment. Présentée en flacon pompe de 236 ml, elle laisse la peau douce, hydratée et propre sans laisser de résidus. - Le Fond de Teint Correcteur Fluide Avène en teinte miel est spécialement conçu pour corriger les imperfections cutanées modérées et unifier le teint de manière naturelle. Sa formule résistante à l'eau et à la sueur offre une haute tenue tout en protégeant la peau des rayons UV grâce à son indice de protection 20. Enrichi en pré-tocophéryl, il prévient le vieillissement photo-induit. Ce fond de teint contient un complexe pigmentaire photo-correcteur pour un teint homogène et lumineux. Il convient à tous les types de peaux sensibles, claires ou mates, et permet de camoufler efficacement les imperfections modérées. Pour une application optimale, il est recommandé de l'appliquer avec les doigts en unifiant sur l'ensemble du visage et du cou. Ce produit de parapharmacie est testé en centre de recherche dermatologique et utilisé à l'Atelier de Maquillage Médical de la Station thermale d'Avène. - Le Masque Purifiant Aromatique à l'Argile Darphin Skin Mat est un soin visage qui absorbe l'excès de sébum et purifie en profondeur l'épiderme. Grâce à sa formule, ce masque nettoie, clarifie et purifie la peau, la laissant plus fraîche et plus claire. Adapté à tous les types de peaux, il s'applique en fine couche sur le visage et le cou, en évitant le contour des yeux, et se laisse poser pendant 10 à 15 minutes avant de rincer à l'eau tiède. Ce masque contient de l'argile, connue pour ses propriétés absorbantes et purifiantes, ainsi que des ingrédients aromatiques pour une expérience sensorielle agréable. Il est recommandé de l'utiliser une à deux fois par semaine pour des résultats optimaux. Il est conseillé de ne pas l'utiliser sur une peau irritée ou lésée, et de faire un test préalable sur une petite zone de la peau pour éviter toute réaction allergique. Profitez des bienfaits de ce masque pour retrouver une peau nette et éclatante. model-index: - name: SentenceTransformer based on ymelka/camembert-cosmetic-finetuned results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: stsb fr dev type: stsb-fr-dev metrics: - type: pearson_cosine value: 0.9019691000053579 name: Pearson Cosine - type: spearman_cosine value: 0.9452471183140297 name: Spearman Cosine - type: pearson_manhattan value: 0.8895868989951163 name: Pearson Manhattan - type: spearman_manhattan value: 0.9377240474149173 name: Spearman Manhattan - type: pearson_euclidean value: 0.8892108374147165 name: Pearson Euclidean - type: spearman_euclidean value: 0.9387698579518865 name: Spearman Euclidean - type: pearson_dot value: 0.8103426051476122 name: Pearson Dot - type: spearman_dot value: 0.9167943283318608 name: Spearman Dot - type: pearson_max value: 0.9019691000053579 name: Pearson Max - type: spearman_max value: 0.9452471183140297 name: Spearman Max --- # SentenceTransformer based on ymelka/camembert-cosmetic-finetuned This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [ymelka/camembert-cosmetic-finetuned](https://huggingface.co/ymelka/camembert-cosmetic-finetuned). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [ymelka/camembert-cosmetic-finetuned](https://huggingface.co/ymelka/camembert-cosmetic-finetuned) <!-- at revision cd4cb90f9388340c5f02740130efd30336c08905 --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity <!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: CamembertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("ymelka/camembert-cosmetic-similarity-cp1200") # Run inference sentences = [ "En complément du nettoyant et du soin, il est recommandé d'utiliser un masque purifiant et matifiant une à deux fois par semaine. Ce masque aidera à resserrer les pores, purifier la peau en profondeur et réguler l'excès de sébum pour un teint plus éclatant et uniforme.", "Le Masque Purifiant Aromatique à l'Argile Darphin Skin Mat est un soin visage qui absorbe l'excès de sébum et purifie en profondeur l'épiderme. Grâce à sa formule, ce masque nettoie, clarifie et purifie la peau, la laissant plus fraîche et plus claire. Adapté à tous les types de peaux, il s'applique en fine couche sur le visage et le cou, en évitant le contour des yeux, et se laisse poser pendant 10 à 15 minutes avant de rincer à l'eau tiède. Ce masque contient de l'argile, connue pour ses propriétés absorbantes et purifiantes, ainsi que des ingrédients aromatiques pour une expérience sensorielle agréable. Il est recommandé de l'utiliser une à deux fois par semaine pour des résultats optimaux. Il est conseillé de ne pas l'utiliser sur une peau irritée ou lésée, et de faire un test préalable sur une petite zone de la peau pour éviter toute réaction allergique. Profitez des bienfaits de ce masque pour retrouver une peau nette et éclatante.", "Le Fond de Teint Correcteur Fluide Avène en teinte miel est spécialement conçu pour corriger les imperfections cutanées modérées et unifier le teint de manière naturelle. Sa formule résistante à l'eau et à la sueur offre une haute tenue tout en protégeant la peau des rayons UV grâce à son indice de protection 20. Enrichi en pré-tocophéryl, il prévient le vieillissement photo-induit. Ce fond de teint contient un complexe pigmentaire photo-correcteur pour un teint homogène et lumineux. Il convient à tous les types de peaux sensibles, claires ou mates, et permet de camoufler efficacement les imperfections modérées. Pour une application optimale, il est recommandé de l'appliquer avec les doigts en unifiant sur l'ensemble du visage et du cou. Ce produit de parapharmacie est testé en centre de recherche dermatologique et utilisé à l'Atelier de Maquillage Médical de la Station thermale d'Avène.", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `stsb-fr-dev` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.902 | | **spearman_cosine** | **0.9452** | | pearson_manhattan | 0.8896 | | spearman_manhattan | 0.9377 | | pearson_euclidean | 0.8892 | | spearman_euclidean | 0.9388 | | pearson_dot | 0.8103 | | spearman_dot | 0.9168 | | pearson_max | 0.902 | | spearman_max | 0.9452 | <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 5,000 training samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 30 tokens</li><li>mean: 55.51 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 124 tokens</li><li>mean: 199.72 tokens</li><li>max: 503 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> | * Samples: | sentence1 | sentence2 | score | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------| | <code>En complément, un sérum anti-imperfections peut être utilisé pour cibler spécifiquement les problèmes de peau tels que les imperfections et les rougeurs. Ce sérum aidera à purifier la peau et à réduire l'apparence des boutons.</code> | <code>Le sérum anti-imperfections Endro à base d'huile végétale de noisette et d'huiles essentielles bio est un concentré d'actifs naturels et antibactériens qui laisse la peau saine et les pores resserrés. Grâce à son action ciblée et hyper concentrée, il lutte efficacement contre les imperfections cutanées, réduisant ainsi les boutons et les rougeurs. Ce sérum convient aux adultes et aux adolescents à partir de 10 ans, et 73,35% des utilisateurs ont constaté une amélioration de leur peau en une semaine seulement. Les principaux ingrédients actifs tels que l'huile de noisette, l'huile essentielle de palmarosa et l'huile essentielle d'arbre à thé agissent en synergie pour purifier la peau et réguler l'excès de sébum. Pour une utilisation optimale, il est recommandé d'appliquer une petite goutte du sérum sur les zones à traiter le soir avant de dormir, en évitant le contour des yeux. Cependant, ce produit n'est pas adapté aux femmes enceintes ou allaitantes. En cas de contact avec les yeux, il est important de rincer abondamment et d'éviter toute exposition au soleil après application. Avec Endro Sérum Anti-Imperfections, retrouvez une peau nette et éclatante en toute simplicité.</code> | <code>0.9809522032737732</code> | | <code>Un soin régulateur et matifiant, idéal pour traiter les imperfections et les pores dilatés. Sa formule spécifique permettra de réduire l'apparence des imperfections tout en resserrant les pores pour une peau plus lisse et uniforme.</code> | <code>Le La Roche-Posay Effaclar MAT Soin Hydratant Sébo-Régulateur Visage Peaux Grasses est un soin spécialement conçu pour les peaux grasses sensibles sujettes à la brillance. Sa formule anti-brillance et anti-pores dilatés, grâce à l'association de Sebulyse, de microsphères absorbantes et de perlite, régule la production de sébum et matifie la peau immédiatement. Ce soin hydratant offre un effet matifiant et hydratant longue durée, tout en étant une excellente base de maquillage. Il convient aux adultes et aux adolescents, et est idéal pour les peaux à imperfections, à tendance acnéique et sujettes à la brillance. Pour une utilisation optimale, il est recommandé d'appliquer le produit matin et/ou soir sur l'ensemble du visage. Il est important de noter que ce produit est testé sous contrôle dermatologique, non comédogène et hypoallergénique.</code> | <code>0.9946829676628112</code> | | <code>Un complément de traitement anti-taches, conçu pour cibler spécifiquement les taches pigmentaires. Ce complément concentré en actifs éclaircissants aidera à atténuer les taches existantes et à prévenir l'apparition de nouvelles taches. Il est recommandé de l'utiliser en complément des autres soins pour une action ciblée et efficace.</code> | <code>Le Lierac Lumilogie Anti-Taches est un traitement ciblé pour les 3 types de taches cutanées : naissantes, visibles et incrustées. Grâce à sa formule innovante inspirée des techniques esthétiques combinées, ce produit agit sur les taches à tous les stades de leur développement. Enrichi en Hexyl R., Lys de mer et Extrait de plantain, il freine la production de mélanine, diminue les taches visibles et lutte contre l'incrustation de la mélanine en profondeur. De plus, les concentrés de vitamines E et B3 ainsi que les 7 hydroxy acides activent le renouvellement cellulaire pour éliminer la mélanine en surface. En résulte un teint unifié et plus uniforme dès la première utilisation, avec une efficacité prouvée dès 7 jours et une correction visible des taches dès 28 jours. Pour une utilisation optimale, appliquez 2 pressions du concentré jour le matin et du concentré nuit le soir sur l'ensemble du visage, en évitant le contour des yeux. Veillez à éviter le contour des yeux et à utiliser une protection solaire avec IP en cas d'exposition au soleil.</code> | <code>0.9939286708831788</code> | * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 1,000 evaluation samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 30 tokens</li><li>mean: 54.83 tokens</li><li>max: 88 tokens</li></ul> | <ul><li>min: 120 tokens</li><li>mean: 197.93 tokens</li><li>max: 491 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.46</li><li>max: 1.0</li></ul> | * Samples: | sentence1 | sentence2 | score | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------| | <code>Un complément hydratant et correcteur, idéal pour les peaux sensibles et sujettes aux taches. Ce complément aidera à hydrater en profondeur, à atténuer les rides et à réduire l'apparence des pores pour une peau plus lisse et uniforme.</code> | <code>L'Huile Végétale de Karité Bio de Puressentiel est un produit nutritif, réparateur et apaisant, idéal pour nourrir et réparer en profondeur la peau et les cheveux. Cette huile 100% pure et naturelle est recommandée pour une utilisation externe sur la peau et les cheveux. Enrichie en beurre de karité issu de l'agriculture biologique, elle offre des propriétés nourrissantes et réparatrices. Pour une utilisation sur le visage et le corps, il est conseillé de chauffer une noisette de beurre de karité dans la paume de la main et de l'appliquer sur les zones sèches et craquelées. Pour les cheveux secs et abîmés, il suffit de chauffer une petite noisette de beurre de karité entre les mains et de l'appliquer sur les pointes et les longueurs. Il est important d'éviter le contact avec les yeux et les muqueuses, et de se laver les mains après application. Il est recommandé de conserver le produit à l'abri de la lumière, de l'air et de la chaleur. Disponible en pot de 100 ml, cette huile de karité bio est un allié naturel pour prendre soin de sa peau et de ses cheveux.</code> | <code>0.0544042661786079</code> | | <code>Un soin anti-âge global, conçu pour traiter les rides, les taches pigmentaires et les imperfections. Sa formule régulatrice et éclatante aidera à lisser la peau, à atténuer les taches et à réduire les imperfections pour un teint plus uniforme et lumineux.</code> | <code>Le sérum contour des yeux anti-rides Maison Éole Elle Et Lui Émerveillé est un produit de parapharmacie haut de gamme qui agit efficacement contre les rides, les ridules, les cernes et les poches. Sa formule complète enrichie en Bakuchiol, alternative naturelle au Rétinol A, nourrit la peau en profondeur et réduit les tâches cutanées. Grâce à ses actifs tels que l'huile de pépin de raisin, le Bisabolol et le Resvératrol, ce sérum hydrate intensément, lisse la peau et prévient le vieillissement cutané. Son utilisation matin et soir sur une peau propre permet d'obtenir un regard éclatant et reposé. Le flacon-pipette de 15ml facilite son application. Ce produit convient à tous les types de peau et ne contient ni parabène, ni silicone, ni ingrédients d'origine animale. Il est recommandé de suivre les instructions d'utilisation pour des résultats optimaux.</code> | <code>0.0781720206141471</code> | | <code>Un soin anti-rides et éclat, enrichi en actifs régénérants et illuminants. Ce soin aidera à lisser les rides, à uniformiser le teint et à redonner de l'éclat à la peau fatiguée.</code> | <code>L'Eau Micellaire Sebiaclear de SVR est un produit de parapharmacie qui purifie, nettoie et démaquille la peau en un seul geste. Adaptée aux peaux sensibles mixtes à grasses, cette eau micellaire aide à éliminer les impuretés, les boutons, les points noirs et l'excès de sébum sans dessécher la peau. Grâce à sa formule innovante contenant de la gluconolactone et de la niacinamide, elle offre une haute efficacité tout en respectant la peau. Les micelles présentes dans le produit nettoient et démaquillent en douceur, laissant la peau nette et fraîche. Pour l'utiliser, il suffit d'appliquer l'eau micellaire matin et/ou soir à l'aide d'un coton sur le visage et les yeux, sans rinçage. Avec une présentation en flacon de 400 ml, ce produit convient aux peaux sensibles à tendance acnéique et offre des résultats visibles dès 7 jours d'utilisation. Il est recommandé de ne pas l'utiliser en cas d'allergie à l'un des ingrédients et de consulter un professionnel de santé en cas de doute.</code> | <code>0.0607918016612529</code> | * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `weight_decay`: 0.01 - `num_train_epochs`: 4 - `warmup_ratio`: 0.1 - `bf16`: True - `load_best_model_at_end`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | loss | stsb-fr-dev_spearman_cosine | |:------:|:----:|:-------------:|:------:|:---------------------------:| | 0 | 0 | - | - | 0.4986 | | 0.3195 | 100 | 4.6554 | 4.3185 | 0.8719 | | 0.6390 | 200 | 4.2773 | 4.1772 | 0.8984 | | 0.9585 | 300 | 4.1015 | 4.0808 | 0.9128 | | 1.2748 | 400 | 4.0285 | 4.0244 | 0.9215 | | 1.5942 | 500 | 3.9269 | 4.0512 | 0.9317 | | 1.9137 | 600 | 3.8057 | 3.9970 | 0.9348 | | 2.2300 | 700 | 3.7665 | 4.0250 | 0.9350 | | 2.5495 | 800 | 3.7541 | 3.9587 | 0.9396 | | 2.8690 | 900 | 3.6029 | 4.0481 | 0.9407 | | 3.1853 | 1000 | 3.6183 | 3.9964 | 0.9416 | | 3.5048 | 1100 | 3.5848 | 3.9711 | 0.9454 | | 3.8243 | 1200 | 3.5029 | 3.9985 | 0.9452 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.0+cu121 - Accelerate: 0.31.0 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION", "SEMANTIC_SIMILARITY" ]
[ "CAS" ]
Non_BioNLP
adriansanz/ST-tramits-SB-003-5ep
adriansanz
sentence-similarity
[ "sentence-transformers", "safetensors", "xlm-roberta", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:2884", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:BAAI/bge-m3", "base_model:finetune:BAAI/bge-m3", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,729
1,729
6
0
--- base_model: BAAI/bge-m3 library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:2884 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: 'P.2 El contingut mínim del projecte és: a) Memòria justificativa, amb: - La descripció de la finca o finques d''origen amb indicació de les seves superfícies i llindars. - La descripció de les finques resultants, la seva superfície i els seus llindars...' sentences: - Quin és el format de sortida de la informació sobre aquesta ciutat? - Quins són els requisits bàsics per sol·licitar la subvenció? - Quin és el contingut mínim del projecte de parcel·lació? - source_sentence: 'La Comissió de Garanties té dues funcions: aclarir els dubtes interpretatius que es plantegin en l''aplicació del mateix.' sentences: - Quines són les dues funcions de la Comissió de Garanties? - Quin és el propòsit d'una llicència d'obres mitjanes en relació amb els moviments de terres? - Quin és el nom del conjunt d'habitatges que es troba al terme municipal de Viladecans? - source_sentence: 'No cal presentar al·legacions en els següents casos: En el cas que la baixa s’hagués iniciat per manca de confirmació bastarà amb realitzar el tràmit de confirmació per que l’expedient de baixa s’arxivi, sempre i quan continuï residint al mateix domicili.' sentences: - És necessari que una persona tècnica professional empleni els documents d'autocontrol? - Quin és el tema principal de la secció d'horari d'obertura i tancament? - Quan no cal presentar al·legacions en un expedient de baixa d'ofici? - source_sentence: L'Ajuntament de Sant Boi obre convocatòria de concessió de beques per col·laborar en el finançament de projectes i activitats dels i de les joves del municipi en diferents àmbits i promoure i facilitar els processos d'emancipació juvenils i garantir la igualtat d'oportunitats i la cohesió social entre la població jove. sentences: - Quin és el propòsit del servei de llista d'espera? - Quin és el problema que es tracta en aquest apartat? - Quin és l'objectiu de les beques per a joves 2024 de l'Ajuntament de Sant Boi? - source_sentence: Empadronament d'un/a menor en un domicili diferent al domicili dels progenitors - Amb autorització de les persones progenitores sentences: - Quin és el límit de temps màxim per al període de funcionament en proves? - Què es necessita per participar en aquest procediment de selecció? - Quin és el resultat de l'empadronament d'un/a menor en un domicili diferent al dels progenitors amb autorització? model-index: - name: SentenceTransformer based on BAAI/bge-m3 results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 1024 type: dim_1024 metrics: - type: cosine_accuracy@1 value: 0.3883495145631068 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6310679611650486 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7198335644937587 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8183079056865464 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.3883495145631068 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.21035598705501618 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1439667128987517 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08183079056865464 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.3883495145631068 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6310679611650486 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7198335644937587 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8183079056865464 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.596832375022475 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5265262091891769 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5337741877067146 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.37447988904299584 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6227461858529819 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.723994452149792 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8210818307905686 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.37447988904299584 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.207582061950994 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1447988904299584 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08210818307905685 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.37447988904299584 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6227461858529819 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.723994452149792 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8210818307905686 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5927947036265483 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5201010501287889 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5274048711370899 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.37309292649098474 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6213592233009708 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7184466019417476 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.826629680998613 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.37309292649098474 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2071197411003236 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1436893203883495 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08266296809986129 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.37309292649098474 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6213592233009708 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7184466019417476 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.826629680998613 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5933965794382484 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5193294146137418 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5262147141098168 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.39528432732316227 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6185852981969486 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6962552011095701 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8252427184466019 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.39528432732316227 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.20619509939898292 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.139251040221914 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0825242718446602 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.39528432732316227 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6185852981969486 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6962552011095701 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8252427184466019 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5982896106972676 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5270165995200669 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.533875073833905 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.3828016643550624 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6033287101248266 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7059639389736477 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8155339805825242 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.3828016643550624 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.20110957004160887 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14119278779472955 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08155339805825243 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.3828016643550624 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6033287101248266 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7059639389736477 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8155339805825242 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.589596475804869 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5181840697444022 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5258716600846131 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.37031900138696255 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5686546463245492 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6851595006934813 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7891816920943134 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.37031900138696255 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.18955154877484973 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.13703190013869623 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07891816920943133 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.37031900138696255 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5686546463245492 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6851595006934813 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7891816920943134 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5679462834016797 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.49845397706007927 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5067836651151116 name: Cosine Map@100 --- # SentenceTransformer based on BAAI/bge-m3 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the json dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 --> - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("adriansanz/ST-tramits-SB-003-5ep") # Run inference sentences = [ "Empadronament d'un/a menor en un domicili diferent al domicili dels progenitors - Amb autorització de les persones progenitores", "Quin és el resultat de l'empadronament d'un/a menor en un domicili diferent al dels progenitors amb autorització?", 'Quin és el límit de temps màxim per al període de funcionament en proves?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_1024` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.3883 | | cosine_accuracy@3 | 0.6311 | | cosine_accuracy@5 | 0.7198 | | cosine_accuracy@10 | 0.8183 | | cosine_precision@1 | 0.3883 | | cosine_precision@3 | 0.2104 | | cosine_precision@5 | 0.144 | | cosine_precision@10 | 0.0818 | | cosine_recall@1 | 0.3883 | | cosine_recall@3 | 0.6311 | | cosine_recall@5 | 0.7198 | | cosine_recall@10 | 0.8183 | | cosine_ndcg@10 | 0.5968 | | cosine_mrr@10 | 0.5265 | | **cosine_map@100** | **0.5338** | #### Information Retrieval * Dataset: `dim_768` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.3745 | | cosine_accuracy@3 | 0.6227 | | cosine_accuracy@5 | 0.724 | | cosine_accuracy@10 | 0.8211 | | cosine_precision@1 | 0.3745 | | cosine_precision@3 | 0.2076 | | cosine_precision@5 | 0.1448 | | cosine_precision@10 | 0.0821 | | cosine_recall@1 | 0.3745 | | cosine_recall@3 | 0.6227 | | cosine_recall@5 | 0.724 | | cosine_recall@10 | 0.8211 | | cosine_ndcg@10 | 0.5928 | | cosine_mrr@10 | 0.5201 | | **cosine_map@100** | **0.5274** | #### Information Retrieval * Dataset: `dim_512` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.3731 | | cosine_accuracy@3 | 0.6214 | | cosine_accuracy@5 | 0.7184 | | cosine_accuracy@10 | 0.8266 | | cosine_precision@1 | 0.3731 | | cosine_precision@3 | 0.2071 | | cosine_precision@5 | 0.1437 | | cosine_precision@10 | 0.0827 | | cosine_recall@1 | 0.3731 | | cosine_recall@3 | 0.6214 | | cosine_recall@5 | 0.7184 | | cosine_recall@10 | 0.8266 | | cosine_ndcg@10 | 0.5934 | | cosine_mrr@10 | 0.5193 | | **cosine_map@100** | **0.5262** | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.3953 | | cosine_accuracy@3 | 0.6186 | | cosine_accuracy@5 | 0.6963 | | cosine_accuracy@10 | 0.8252 | | cosine_precision@1 | 0.3953 | | cosine_precision@3 | 0.2062 | | cosine_precision@5 | 0.1393 | | cosine_precision@10 | 0.0825 | | cosine_recall@1 | 0.3953 | | cosine_recall@3 | 0.6186 | | cosine_recall@5 | 0.6963 | | cosine_recall@10 | 0.8252 | | cosine_ndcg@10 | 0.5983 | | cosine_mrr@10 | 0.527 | | **cosine_map@100** | **0.5339** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.3828 | | cosine_accuracy@3 | 0.6033 | | cosine_accuracy@5 | 0.706 | | cosine_accuracy@10 | 0.8155 | | cosine_precision@1 | 0.3828 | | cosine_precision@3 | 0.2011 | | cosine_precision@5 | 0.1412 | | cosine_precision@10 | 0.0816 | | cosine_recall@1 | 0.3828 | | cosine_recall@3 | 0.6033 | | cosine_recall@5 | 0.706 | | cosine_recall@10 | 0.8155 | | cosine_ndcg@10 | 0.5896 | | cosine_mrr@10 | 0.5182 | | **cosine_map@100** | **0.5259** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.3703 | | cosine_accuracy@3 | 0.5687 | | cosine_accuracy@5 | 0.6852 | | cosine_accuracy@10 | 0.7892 | | cosine_precision@1 | 0.3703 | | cosine_precision@3 | 0.1896 | | cosine_precision@5 | 0.137 | | cosine_precision@10 | 0.0789 | | cosine_recall@1 | 0.3703 | | cosine_recall@3 | 0.5687 | | cosine_recall@5 | 0.6852 | | cosine_recall@10 | 0.7892 | | cosine_ndcg@10 | 0.5679 | | cosine_mrr@10 | 0.4985 | | **cosine_map@100** | **0.5068** | <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### json * Dataset: json * Size: 2,884 training samples * Columns: <code>positive</code> and <code>anchor</code> * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 36.18 tokens</li><li>max: 194 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 19.77 tokens</li><li>max: 60 tokens</li></ul> | * Samples: | positive | anchor | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------| | <code>I assessorem per l'optimització dels contractes de subministraments energètics.</code> | <code>Quin és el resultat esperat del servei de millora dels contractes de serveis de llum i gas?</code> | | <code>Retorna en format JSON adequat</code> | <code>Quin és el format de sortida del qüestionari de projectes específics?</code> | | <code>Aula Mentor és un programa d'ajuda a l'alumne que té com a objectiu principal donar suport als estudiants en la seva formació i desenvolupament personal i professional.</code> | <code>Quin és el format del programa Aula Mentor?</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 5 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.2 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.2 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: True - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | dim_1024_cosine_map@100 | dim_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 | |:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| | 0.8840 | 10 | 2.6418 | - | - | - | - | - | - | | 0.9724 | 11 | - | 0.4986 | 0.5108 | 0.5014 | 0.4934 | 0.4779 | 0.4351 | | 1.7680 | 20 | 1.1708 | - | - | - | - | - | - | | 1.9448 | 22 | - | 0.5197 | 0.5248 | 0.5195 | 0.5290 | 0.5052 | 0.4904 | | 2.6519 | 30 | 0.5531 | - | - | - | - | - | - | | 2.9171 | 33 | - | 0.5304 | 0.5274 | 0.5196 | 0.5279 | 0.5234 | 0.4947 | | 3.5359 | 40 | 0.2859 | - | - | - | - | - | - | | 3.9779 | 45 | - | 0.5256 | 0.5292 | 0.5206 | 0.5313 | 0.5174 | 0.5046 | | 4.4199 | 50 | 0.2144 | - | - | - | - | - | - | | **4.8619** | **55** | **-** | **0.5338** | **0.5274** | **0.5262** | **0.5339** | **0.5259** | **0.5068** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.2.0 - Transformers: 4.44.2 - PyTorch: 2.4.1+cu121 - Accelerate: 1.1.0.dev0 - Datasets: 3.0.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION" ]
[ "CAS" ]
Non_BioNLP
LeroyDyer/LCARS_AI_StarTrek_Computer
LeroyDyer
text2text-generation
[ "transformers", "safetensors", "mistral", "text-generation", "LCARS", "Star-Trek", "128k-Context", "chemistry", "biology", "finance", "legal", "art", "code", "medical", "text-generation-inference", "text2text-generation", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,715
1,729
83
4
--- language: - en library_name: transformers license: mit pipeline_tag: text2text-generation tags: - LCARS - Star-Trek - 128k-Context - mistral - chemistry - biology - finance - legal - art - code - medical - text-generation-inference --- If anybody has star trek data please send as this starship computer database archive needs it! then i can correctly theme this model to be inside its role as a starship computer : so as well as any space dara ffrom nasa ; i have collected some mufon files which i am still framing the correct prompts for ; for recall as well as interogation : I shall also be adding a lot of biblical data and historical data ; from sacred texts; so any generated discussions as phylosophers discussing ancient history and how to solve the problems of the past which they encountered ; in thier lifes: using historical and factual data; as well as playig thier roles after generating a biography and character role to the models to play: they should also be amazed by each others acheivements depending on thier periods: we need multiple role and characters for these discussions: as well as as much historical facts and historys as possible to enhance this models abitlity to dicern ancient aliens truth or false : (so we need astrological, astronomical, as well as sizmological and ecological data for the periods of histroy we know : as well as the unfounded suupositions from youtube subtitles !) another useful source of themed data! This model is a Collection of merged models via various merge methods : Reclaiming Previous models which will be orphened by thier parent models : THis model is the model of models so it may not Remember some task or Infact remember them all as well as highly perform ! There were some very bad NSFW Merges from role play to erotica as well as various characters and roles downloaded into the model: So those models were merged into other models which had been specifically trained for maths or medical data and the coding operations or even translation: the models were heavliy dpo trained ; and various newer methodologies installed : the deep mind series is a special series which contains self correction recal, visio spacial ... step by step thinking: SO the multi merge often fizes these errors between models as well as training gaps :Hopefully they all took and merged well ! Performing even unknown and unprogrammed tasks:
[ "TRANSLATION" ]
[ "MEDICAL DATA" ]
Non_BioNLP
zbrunner/hallucination_noisetag
zbrunner
automatic-speech-recognition
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:tedlium3", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
1,726
1,726
2
0
--- datasets: - tedlium3 language: en license: cc-by-4.0 tags: - espnet - audio - automatic-speech-recognition --- ## ESPnet2 ASR model ### `zbrunner/hallucination_noisetag` This model was trained by zbrunner using tedlium3 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 06693f5abd8cc8c8a34d92cf655b81be8ca27db0 pip install -e . cd egs2/tedlium3/asr1.9_enc6_dec6_att8_lr0.002_heldback_noiseid_transcr ./run.sh --skip_data_prep false --skip_train true --download_model zbrunner/hallucination_noisetag ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Sun Aug 18 12:59:05 BST 2024` - python version: `3.10.8 (main, Nov 24 2022, 14:13:03) [GCC 11.2.0]` - espnet version: `espnet 202402` - pytorch version: `pytorch 1.13.1+cu116` - Git hash: `06693f5abd8cc8c8a34d92cf655b81be8ca27db0` - Commit date: `Thu Jun 27 19:07:30 2024 +0100` ## exp/asr_train_raw_en_bpe10000_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_model_valid.acc.ave/test|1155|27500|74.0|16.4|9.6|3.3|29.3|100.0| |decode_asr_model_valid.acc.ave/test_hb|1695|28699|67.1|18.8|14.2|4.1|37.0|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_10snr_varsnr_reverb|855|28699|66.0|27.8|6.2|20.3|54.3|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_2snr_silnoise|855|28699|90.7|5.9|3.4|12.5|21.8|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_5snr|855|28699|83.6|13.3|3.1|16.8|33.3|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_5snr_reverb|855|28699|52.3|37.9|9.9|20.5|68.2|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_5snr_silnoise|855|28699|90.7|5.8|3.4|12.2|21.4|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_5snr_silnoise_reverb|855|28699|75.3|19.9|4.8|19.0|43.8|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_3s_10snr_varsnr_reverb|855|28699|62.7|27.5|9.8|19.7|57.0|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_3s_nonoise|855|28699|91.0|5.8|3.2|14.9|23.9|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_nonoise|855|28699|91.1|5.7|3.1|12.8|21.7|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_nonoise_reverb|855|28699|72.4|22.4|5.2|12.5|40.1|100.0| |decode_asr_model_valid.acc.ave/test_heldback|1564|26404|64.5|20.3|15.2|4.2|39.7|100.0| |decode_asr_model_valid.acc.ave/test_noiseid|150|0|0.0|0.0|0.0|0.0|0.0|100.0| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_model_valid.acc.ave/test|1155|145066|81.9|7.9|10.3|3.8|22.0|100.0| |decode_asr_model_valid.acc.ave/test_hb|1695|153212|75.4|9.3|15.3|4.1|28.7|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_10snr_varsnr_reverb|855|154052|80.4|10.9|8.7|19.5|39.1|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_2snr_silnoise|855|154052|94.3|1.7|4.0|11.1|16.8|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_5snr|855|154052|91.2|4.7|4.1|16.0|24.8|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_5snr_reverb|855|154052|70.6|15.6|13.8|20.2|49.7|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_5snr_silnoise|855|154052|94.3|1.6|4.1|10.9|16.6|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_5snr_silnoise_reverb|855|154052|86.1|7.5|6.5|17.7|31.6|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_3s_10snr_varsnr_reverb|855|154052|76.8|10.8|12.3|19.6|42.8|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_3s_nonoise|855|154052|94.6|1.6|3.8|14.5|19.9|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_nonoise|855|154052|94.7|1.6|3.7|11.8|17.1|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_nonoise_reverb|855|154052|84.3|8.9|6.7|12.9|28.5|100.0| |decode_asr_model_valid.acc.ave/test_heldback|1564|140769|73.6|10.0|16.4|4.2|30.6|100.0| |decode_asr_model_valid.acc.ave/test_noiseid|150|0|0.0|0.0|0.0|0.0|0.0|100.0| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_model_valid.acc.ave/test|1155|31518|72.0|19.8|8.2|5.6|33.6|100.0| |decode_asr_model_valid.acc.ave/test_hb|1695|33855|65.5|20.9|13.6|6.5|41.0|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_10snr_varsnr_reverb|855|33855|63.5|27.4|9.1|23.7|60.2|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_2snr_silnoise|855|33855|87.2|6.0|6.8|14.0|26.8|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_5snr|855|33855|80.6|13.2|6.2|20.2|39.7|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_5snr_reverb|855|33855|50.3|37.0|12.6|23.7|73.4|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_5snr_silnoise|855|33855|87.3|5.9|6.9|13.8|26.5|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_2s_5snr_silnoise_reverb|855|33855|72.2|19.7|8.1|21.3|49.1|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_3s_10snr_varsnr_reverb|855|33855|60.1|27.0|12.9|25.0|64.9|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_3s_nonoise|855|33855|87.6|6.0|6.5|19.5|31.9|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_nonoise|855|33855|87.7|5.8|6.4|15.1|27.3|100.0| |decode_asr_model_valid.acc.ave/test_hb_multi2_nonoise_reverb|855|33855|69.6|22.0|8.4|16.9|47.3|100.0| |decode_asr_model_valid.acc.ave/test_heldback|1564|31126|63.1|22.6|14.4|6.5|43.4|100.0| |decode_asr_model_valid.acc.ave/test_noiseid|150|0|0.0|0.0|0.0|0.0|0.0|100.0| ## ASR config <details><summary>expand</summary> ``` config: conf/train.yaml print_config: false log_level: INFO drop_last_iter: false dry_run: false iterator_type: sequence valid_iterator_type: null output_dir: exp/asr_train_raw_en_bpe10000_sp ngpu: 1 seed: 2022 num_workers: 2 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 2 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 40285 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: 5 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 4 no_forward_run: false resume: true train_dtype: float32 use_amp: true log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false use_adapter: false adapter: lora save_strategy: all adapter_conf: {} pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 12000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe10000_sp/train/speech_shape - exp/asr_stats_raw_en_bpe10000_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe10000_sp/valid/speech_shape - exp/asr_stats_raw_en_bpe10000_sp/valid/text_shape.bpe batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending shuffle_within_batch: false sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 chunk_excluded_key_prefixes: [] chunk_default_fs: null chunk_max_abs_length: null chunk_discard_short_samples: true train_data_path_and_name_and_type: - - dump/raw/train_noiseid_transcr_sp/wav.scp - speech - kaldi_ark - - dump/raw/train_noiseid_transcr_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - kaldi_ark - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 allow_multi_rates: false valid_max_cache_size: null exclude_weight_decay: false exclude_weight_decay_conf: {} optim: adam optim_conf: lr: 0.001 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 10000 token_list: - <blank> - <unk> - '[unk]' - ▁ - ▁the - ▁and - s - ▁to - ▁of - '''' - ▁a - ▁that - ▁i - ▁in - ▁it - ▁we - ▁you - ▁is - ▁this - ▁so - ▁they - t - ▁was - ▁for - ▁are - ▁have - ▁but - ▁on - ▁what - ▁with - ▁can - ▁one - re - ▁be - ▁about - ▁there - ▁not - ▁all - ▁at - ▁do - ▁my - ▁as - ▁people - ▁like - ▁if - ▁from - ▁our - ing - ▁or - ▁an - ▁he - ▁these - ▁just - d - ▁when - ▁now - ▁because - m - ▁me - ▁out - ed - ▁by - ▁how - ▁very - ▁up - ▁more - ▁had - ▁them - ▁know - ▁going - ▁who - ▁their - ▁think - ve - ▁see - ▁your - ▁were - ▁would - ▁which - ▁get - ▁two - ▁really - ▁us - ▁time - ▁here - ▁world - ▁then - ▁some - ▁has - ▁don - ▁into - ▁way - ▁where - ▁actually - ▁will - ▁other - ▁could - ▁years - ▁things - ▁go - ▁make - ▁want - ▁been - ▁no - ▁she - ▁those - ▁right - ▁first - ▁well - ▁something - ▁thousand - ▁than - ▁hundred - ly - ▁new - ▁over - ▁also - ▁look - ▁thing - ▁even - ▁said - ▁most - ▁back - ▁much - ▁work - ▁little - ▁his - ▁only - ▁life - ▁got - ▁many - ▁need - ▁take - y - ▁say - ▁three - ▁lot - ll - ▁her - ▁did - ▁kind - ▁every - ▁around - ▁good - ▁different - ▁why - e - ▁down - ▁let - ▁through - er - ▁being - ▁same - ▁come - ▁five - ▁day - ▁use - ▁put - ▁year - ▁doing - n - ▁human - ▁any - ▁called - r - ▁after - ▁made - ▁percent - ▁tell - ▁today - ▁change - ▁find - ▁four - ▁fact - ▁didn - ▁talk - ▁own - ▁great - ▁idea - ▁point - ▁last - ▁before - ▁started - ▁another - ▁never - ▁might - ▁give - ▁should - ▁big - ▁better - ▁thought - al - es - ▁twenty - ▁system - ▁part - ▁important - ▁went - ▁still - ▁problem - ▁start - ▁off - ▁each - ▁together - ▁brain - ▁next - ▁ten - ▁women - ▁able - ▁him - ▁show - ▁long - ▁came - ▁place - ▁course - ▁few - ▁ago - ▁does - ▁again - ▁story - ▁bit - ▁water - ▁found - ▁used - ▁between - ▁data - ▁technology - c - ▁question - ▁end - ▁too - ▁love - ▁maybe - ▁school - ▁example - ▁mean - ▁nine - ▁understand - ▁live - ▁old - ▁wanted - ▁doesn - ▁looking - ▁may - ▁call - ▁help - ▁person - ▁children - ▁real - ▁done - ▁believe - ▁feel - ▁ever - ▁whole - ▁six - ▁always - ▁sort - ▁million - ▁trying - ▁working - ▁country - ▁away - ▁everything - ▁try - ▁number - ▁power - ▁home - ▁second - ▁using - o - ▁space - ▁fifty - ▁money - ▁information - ▁design - ▁thinking - ▁create - ▁become - ▁took - ▁man - ▁small - ▁means - ▁high - ▁kids - ▁social - ers - ▁light - ▁enough - ▁best - ▁left - ▁thirty - ▁sense - ▁making - ▁future - ▁ask - ▁seven - ▁car - ▁without - ▁getting - ▁city - ▁probably - ▁hard - ▁science - ▁eight - ▁food - ▁times - p - ▁less - ▁building - ▁body - ▁quite - a - ▁family - ▁told - ▁talking - ▁happened - ▁build - ▁energy - ▁half - en - ▁health - ▁hand - ▁lives - ▁earth - ▁countries - ▁imagine - ▁war - ▁care - ▁moment - ▁pretty - ▁across - ▁comes - ▁interesting - ▁stuff - th - ▁such - ▁while - ▁experience - ▁men - ▁anything - le - ▁thank - ▁learn - i - ▁side - ▁play - ▁am - ▁under - ▁saw - or - ▁young - ▁having - ▁dollars - ▁far - ▁coming - ▁room - ▁open - ▁happen - ▁project - ▁asked - ▁remember - ▁later - ▁reason - ▁once - ▁living - ▁case - ▁un - ▁computer - ▁mind - ▁yet - ▁global - ic - ▁simple - ▁seen - ▁almost - ▁bad - ▁single - ▁public - ▁process - ▁else - ▁move - l - ▁inside - ▁often - ▁nothing - ▁both - k - ▁community - ▁matter - ▁someone - ▁picture - ▁states - ▁already - ▁planet - ▁days - ▁set - b - ▁happens - u - ▁whether - ▁africa - g - ▁face - ▁answer - ▁goes - ▁keep - able - ▁within - ▁wrong - ▁order - ▁billion - ▁instead - ▁history - ▁business - ation - ▁problems - ▁myself - ▁possible - ▁looked - ▁cancer - 'on' - ▁government - ▁job - ▁sometimes - ▁sure - ▁control - ▁group - ▁ways - in - ▁saying - ▁hope - ▁top - ▁months - ▁child - ▁basically - ▁makes - ▁book - ▁bring - ▁research - ▁united - f - ▁couple - ▁read - ▁night - ▁since - ▁until - ▁per - ▁forty - ▁guy - ▁head - ▁black - ity - ▁everybody - ▁age - ▁past - ▁true - ▁de - ▁line - ▁woman - ▁run - ▁share - ▁state - ▁cells - ▁turn - ▁says - ▁words - ▁stop - ▁wasn - ▁amazing - ▁learned - ▁heard - ▁knew - ▁model - ▁built - an - ▁middle - ▁ideas - ▁though - ▁decided - ▁stories - ▁nature - ▁friends - ▁mother - ▁house - ▁america - ▁study - ▁beautiful - ▁became - ▁internet - ▁video - ▁piece - ▁education - ▁art - ▁level - ▁language - ▁word - ▁heart - ▁form - ▁everyone - ▁third - ▁somebody - ▁taking - ch - ▁society - ▁species - ▁against - ness - ▁exactly - ▁sound - ▁completely - ▁huge - ion - ▁ourselves - ▁music - ▁happening - ▁disease - ▁hear - ▁must - ▁company - ▁isn - ▁large - ▁looks - ▁places - ▁ninety - ▁themselves - ra - ▁air - ▁rather - ▁front - ar - ▁turns - ▁couldn - ne - ▁universe - ▁students - ▁god - ▁name - ▁itself - ▁american - ▁c - ▁created - ▁hours - ▁team - ▁environment - el - ▁questions - ▁u - ▁close - ▁animals - ▁worked - ▁learning - ▁entire - ▁early - ▁during - ▁outside - ▁yes - ▁figure - ▁free - ▁least - ▁machine - ▁cell - ▁perhaps - ▁game - ro - ce - ▁sixty - ▁walk - ▁along - ▁minutes - ▁okay - ▁media - ▁common - ▁fifteen - ▁scale - ate - ▁eighty - v - ▁particular - ▁works - ▁natural - ▁ted - ▁gave - ▁based - ▁won - ▁laughter - ▁cities - ▁finally - ▁size - ▁full - ▁culture - ▁image - ▁given - ting - ▁local - ▁cost - ▁behind - ▁view - ▁land - ▁taken - ▁area - ▁white - ▁difference - ▁changed - lo - ▁ok - ▁systems - ▁easy - ▁difficult - us - li - ▁kinds - ▁happy - ▁felt - w - ri - ▁population - ▁companies - ▁co - ▁father - ▁india - te - ▁leave - ▁center - ▁needed - po - ▁began - ▁poor - ▁deal - ▁economic - ▁yourself - ▁turned - il - ▁spend - ▁reality - ▁seeing - ▁national - ▁news - ▁political - ▁death - ▁realized - ▁china - est - ▁york - ▁powerful - ▁red - ▁test - ▁beginning - ▁simply - ▁eyes - ▁market - ▁parents - ▁pay - ▁week - ▁ocean - ▁century - ▁moving - ur - ▁ca - ter - ▁grow - ▁cannot - ▁thousands - ▁rest - ▁g - ▁others - ▁phone - ▁guys - ▁step - ▁known - ▁certain - ▁speak - ▁terms - ▁surface - ▁longer - ▁access - ▁fear - ▁climate - ▁sea - ▁paper - co - it - ▁green - ▁whatever - ▁amount - ment - ▁spent - ▁wonderful - ▁value - ▁buy - ▁hands - ▁self - ▁field - ▁tried - ▁lost - ▁risk - rs - ▁seventy - man - ▁oil - ▁blood - ▁ground - ▁support - ▁die - ▁quickly - ▁patients - ▁ability - ▁lots - ▁gets - ▁friend - ▁sun - ▁challenge - ▁deep - ▁opportunity - ▁write - ▁street - ▁short - less - ▁either - ▁growing - la - ▁stage - ▁lab - ▁key - ▁weeks - ▁interested - ▁blue - ▁fish - ▁individual - ▁south - ▁takes - ▁sitting - ▁parts - ▁gone - ▁medical - ▁complex - sh - ▁law - ▁oh - ▁growth - ▁act - ▁needs - ▁development - ▁dna - ▁watch - ▁program - ▁behavior - h - ▁humans - ▁scientists - ▁cut - ▁audience - ▁network - ling - ▁wouldn - ta - ▁personal - z - ▁feeling - ism - ▁born - ▁experiment - de - ▁average - ▁clear - tic - ist - ▁girl - ▁material - ▁attention - ▁met - ▁result - ▁morning - ▁structure - ▁low - ▁understanding - ted - ▁physical - ▁twelve - ▁realize - ▁issue - ▁incredible - ▁brought - ▁literally - ▁map - ▁girls - ent - ▁anyone - ▁voice - ry - ia - ▁economy - ▁knowledge - ▁solve - ▁impact - ci - ba - ▁movement - ▁alone - ▁animal - ma - ▁giving - ▁cool - ant - ▁force - ▁telling - ▁color - ▁truth - ▁areas - ▁eat - ▁bottom - ▁special - ▁pick - ▁kid - ▁hit - ▁sex - ▁class - ▁starting - ▁likely - ▁feet - ▁meet - ▁b - ▁numbers - ▁hold - ▁fire - ▁film - ▁developed - ies - ▁term - ▁tools - ▁dark - me - ▁changes - ▁seems - ▁shows - ▁forward - ive - ▁zero - ▁north - ▁innovation - ▁images - ▁type - ▁focus - ▁wrote - ▁allow - ▁save - ▁baby - ▁present - ▁k - ▁guess - ▁normal - ▁industry - ▁trust - ▁absolutely - ▁con - ▁yeah - ▁eye - ▁millions - ▁especially - na - ▁running - x - ▁fun - ▁digital - ▁changing - ▁books - ▁technologies - ▁chance - ▁tiny - ▁send - ▁dream - ▁generation - ▁creating - ▁online - ▁develop - ▁code - ▁office - ▁discovered - ▁modern - ▁sit - ▁stay - ul - ▁worth - ▁rate - ▁period - ▁explain - ▁nice - ▁nobody - ▁carbon - ▁choice - ▁eleven - ▁designed - ▁recently - ▁communities - ▁asking - ▁patient - ▁major - ▁miles - ▁wall - ▁europe - ac - ▁relationship - ▁university - ie - ▁begin - ▁cars - ▁similar - ▁stand - ▁table - ▁dead - ▁fast - ▁solution - ▁soon - ▁genetic - ized - ▁role - ge - ▁seem - ▁talked - et - age - ▁revolution - ▁beyond - ▁fly - ▁violence - ▁showed - ▁playing - ▁situation - ▁plan - ▁drive - ▁several - ▁developing - ▁product - ▁evidence - ▁international - ▁measure - ▁basic - ▁month - ▁ice - ▁lived - ga - um - ▁incredibly - ▁produce - ▁journey - ▁theory - ▁issues - ▁box - ▁hospital - ▁general - ▁college - ▁medicine - ▁resources - ▁drug - ▁star - ▁shape - ▁robot - ▁towards - ▁hour - ▁teach - ▁speed - ▁fight - ▁google - ▁mine - ol - ▁english - ▁cause - at - ous - ▁break - ally - ▁reasons - ▁bigger - ck - ▁effect - ▁mo - 'no' - ▁putting - ▁solar - as - ▁hundreds - op - ▁listen - ▁police - ▁chinese - ▁available - se - ▁vision - ▁approach - to - ▁groups - ▁eventually - ▁object - ian - ▁pre - ▁security - ▁list - ▁success - ▁lose - ▁source - ▁haven - ▁follow - ▁perfect - ▁involved - ▁organization - ▁reach - ful - ▁writing - ▁mom - ▁web - ated - ▁anybody - ▁computers - ▁protect - ▁plant - ▁message - ▁add - ▁drugs - ▁favorite - ▁screen - ha - ▁road - ▁died - ▁conversation - ▁safe - ▁clean - ▁lead - ▁action - ▁device - is - ▁tend - ▁gives - ▁mass - ▁led - ▁son - ▁notice - ▁potential - pe - un - ▁walking - ty - ▁choose - va - ca - ▁biggest - ▁families - ru - ▁fall - ▁evolution - ▁quality - ▁obviously - ▁sounds - ▁skin - ▁scientific - ▁camera - ▁certainly - ance - ▁west - mp - ▁finding - ▁higher - ▁door - ▁pictures - ▁particularly - ▁doctor - ▁east - ▁post - ▁poverty - ▁perspective - vi - ▁totally - ▁wind - ▁consider - bo - ▁showing - ▁travel - mo - ▁becomes - ▁boy - ▁p - ▁positive - ▁software - ▁jobs - ▁student - ▁onto - ▁among - ▁slow - ▁movie - ▁creative - ▁strong - ▁moved - ▁spread - ▁fit - ▁aren - ▁pain - ▁provide - ▁supposed - ▁crazy - ▁mars - ▁sleep - ▁rules - ▁path - ▁smart - ▁continue - ▁recognize - ▁leaders - ▁further - ▁largest - ▁fundamental - id - ine - ▁train - ▁context - ▁watching - ▁democracy - ▁response - ▁win - ▁including - ▁grew - ▁worse - ▁shown - ▁pro - ▁treatment - ▁tool - ▁ma - ▁connected - ▁allowed - ▁nuclear - ▁race - ▁tree - ▁everywhere - ▁please - ▁bank - ▁kill - ▁ready - ▁suddenly - ▁african - ke - ▁la - ▁meaning - ▁goal - ▁wait - ▁main - ▁gas - ni - nt - ▁progress - ▁minute - ▁usually - ▁wish - ating - ▁village - ▁standing - ▁plants - ▁individuals - bi - ▁essentially - ▁training - ▁named - ▁decision - ir - ▁expect - ▁moral - ▁taught - ▁increase - ▁rich - ▁hot - ▁secret - ▁income - ▁forest - ▁wife - ▁sent - ▁late - ▁teacher - ▁exciting - ▁intelligence - ▁memory - ▁service - ▁address - ▁river - ▁non - ▁connect - ▁cold - ▁visual - ▁pattern - ▁lower - pi - ▁rise - ▁compassion - ▁square - ▁shot - ▁indeed - ▁meant - ▁schools - ▁teachers - ize - ▁faster - go - ▁successful - ▁career - ▁objects - ▁f - ph - ▁mental - line - ▁doctors - ▁trees - ny - ▁president - ▁famous - ▁crisis - ▁trade - ▁extremely - ▁wonder - ▁arm - ▁range - ▁extraordinary - ▁results - ▁treat - ▁version - ▁track - ▁dance - ut - ▁record - ▁fine - ▁bunch - ▁waste - ▁peace - ▁machines - ▁killed - ▁kept - di - ▁interest - ▁pull - ▁somehow - ▁somewhere - ▁materials - ▁park - ▁serious - mi - ▁bodies - ▁freedom - ▁board - ▁reading - ▁position - ▁floor - ▁site - up - ▁tv - ▁search - ▁examples - ▁humanity - ▁conditions - be - ▁clearly - ▁loved - ▁games - ▁military - ▁plastic - ▁excited - ▁specific - ▁san - ▁robots - ▁older - ▁content - ton - ▁ended - ▁text - ▁truly - ▁financial - ▁check - ▁raise - ▁bar - son - ▁starts - ary - ▁exist - ▁allows - ▁larger - ▁impossible 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▁merchandis - ▁metadata - ▁microsecond - ▁mongolia - ▁nanopatch - ▁napoleon - ▁nutshell - ▁paintbrush - ▁pellets - ▁perceiving - ▁perimeter - ▁persuasion - ▁preliminary - ▁quadruple - ▁reassuring - ▁rebuilt - ▁reconfigur - ▁referendum - ▁rehearse - ▁remorse - ▁retention - ▁revitaliz - ▁scooter - ▁segregated - ▁sequel - ▁squad - ▁squirt - ▁steadi - ▁subtitle - ▁supervisor - ▁surrogate - ▁tentacle - ▁tragedies - ▁transcendence - ▁translucent - ▁trustworthiness - ▁tyranny - ▁uruguay - ▁vermeer - ▁acquaint - ▁bathtub - ▁crazi - ▁daydream - ▁entail - ▁inanimate - ▁intermediar - ▁kabul - ▁newfound - ▁obstruct - ▁reversing - ▁twitch - ▁tyrant - ▁vanished - ▁handicap - ▁jumble - ▁ooh - ▁defecat - ▁tremor - ▁airbag - ▁battered - ▁ellip - ▁fiddle - ▁medicinal - ▁outlook - ▁prosthes - ▁fisherman - ▁renovat - ▁synerg - ▁quotation - ▁jacob - ▁fmri - ▁viking - ▁squish - ▁yanke - ▁dehumaniz - ▁subvert - ▁permeate - ▁musk - ▁detonat - hearted - misunderstanding - utilized - favorable - ▁visualiz - credited - ▁recreat - mplified - ▁substitut - symmetry - sensitive - global - women - operators - western - ▁illustr - ▁overwhelm - ▁suspend - ▁simultaneous - ▁tasmania - ▁legislati - ▁squat - ▁reminisc - '#' - coherence - extraterrestrial - ▁amusement - ▁antiangiogenic - ▁apocalypse - ▁appendage - ▁arrogance - ▁autopsy - ▁bachelor - ▁binoculars - ▁bonica - ▁brexit - ▁charlotte - ▁chernobyl - ▁cleveland - ▁coercion - ▁collateral - ▁contraceptive - ▁crowdsourcing - ▁cyberspace - ▁deceptive - ▁decimal - ▁degeneration - ▁delegation - ▁deputy - ▁descent - ▁divinity - ▁eclipse - ▁epigenetic - ▁expansive - ▁florence - ▁freestyle - ▁fuku - ▁gertrude - ▁goldilocks - ▁grapple - ▁grappling - ▁halloween - ▁harold - ▁hierarchical - ▁incapable - ▁inconvenient - ▁innovating - ▁insignificant - ▁insufficient - ▁interdependent - ▁involuntary - ▁jirga - ▁kilowatt - ▁kuwait - ▁langley - ▁leviathan - ▁lexicograph - ▁libertarian - ▁mahmoud - ▁mccain - ▁memorizing - ▁menstrual - ▁metabolize - ▁morocco - ▁nikola - ▁nirvana - ▁ottoman - ▁patrick - ▁pendulum - ▁peripheral - ▁picasso - '@' - '*' - \ - ^ - R - _ - '-' - '%' - '=' - $ - G - M - ā - ']' - A - E - U - '[' - <sos/eos> init: xavier_uniform input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true brctc_risk_strategy: exp brctc_group_strategy: end brctc_risk_factor: 0.0 joint_net_conf: null use_preprocessor: true use_lang_prompt: false use_nlp_prompt: false token_type: bpe bpemodel: data/en_token_list/bpe_unigram10000/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 aux_ctc_tasks: [] frontend: default frontend_conf: n_fft: 512 win_length: 400 hop_length: 160 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 20 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 5 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_bpe10000_sp/train/feats_stats.npz model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false preencoder: null preencoder_conf: {} encoder: transformer encoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 8 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 preprocessor: default preprocessor_conf: {} required: - output_dir - token_list version: '202402' distributed: true ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
[ "TRANSLATION" ]
[ "BEAR", "CRAFT", "MEDAL" ]
Non_BioNLP
intfloat/multilingual-e5-base
intfloat
sentence-similarity
[ "sentence-transformers", "pytorch", "onnx", "safetensors", "openvino", "xlm-roberta", "mteb", "Sentence Transformers", "sentence-similarity", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "arxiv:2402.05672", "arxiv:2108.08787", "arxiv:2104.08663", "arxiv:2210.07316", "license:mit", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,684
1,739
578,159
263
--- language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit tags: - mteb - Sentence Transformers - sentence-similarity - sentence-transformers model-index: - name: multilingual-e5-base results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 78.97014925373135 - type: ap value: 43.69351129103008 - type: f1 value: 73.38075030070492 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (de) type: mteb/amazon_counterfactual config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.7237687366167 - type: ap value: 82.22089859962671 - type: f1 value: 69.95532758884401 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en-ext) type: mteb/amazon_counterfactual config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.65517241379312 - type: ap value: 28.507918657094738 - type: f1 value: 66.84516013726119 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (ja) type: mteb/amazon_counterfactual config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.32976445396146 - type: ap value: 20.720481637566014 - type: f1 value: 59.78002763416003 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 90.63775 - type: ap value: 87.22277903861716 - type: f1 value: 90.60378636386807 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 44.546 - type: f1 value: 44.05666638370923 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (de) type: mteb/amazon_reviews_multi config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 41.828 - type: f1 value: 41.2710255644252 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (es) type: mteb/amazon_reviews_multi config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.534 - type: f1 value: 39.820743174270326 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 39.684 - type: f1 value: 39.11052682815307 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (ja) type: mteb/amazon_reviews_multi config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.436 - type: f1 value: 37.07082931930871 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.226000000000006 - type: f1 value: 36.65372077739185 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 22.831000000000003 - type: map_at_10 value: 36.42 - type: map_at_100 value: 37.699 - type: map_at_1000 value: 37.724000000000004 - type: map_at_3 value: 32.207 - type: map_at_5 value: 34.312 - type: mrr_at_1 value: 23.257 - type: mrr_at_10 value: 36.574 - type: mrr_at_100 value: 37.854 - type: mrr_at_1000 value: 37.878 - type: mrr_at_3 value: 32.385000000000005 - type: mrr_at_5 value: 34.48 - type: ndcg_at_1 value: 22.831000000000003 - type: ndcg_at_10 value: 44.230000000000004 - type: ndcg_at_100 value: 49.974000000000004 - type: ndcg_at_1000 value: 50.522999999999996 - type: ndcg_at_3 value: 35.363 - type: ndcg_at_5 value: 39.164 - type: precision_at_1 value: 22.831000000000003 - type: precision_at_10 value: 6.935 - type: precision_at_100 value: 0.9520000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.841 - type: precision_at_5 value: 10.754 - type: recall_at_1 value: 22.831000000000003 - type: recall_at_10 value: 69.346 - type: recall_at_100 value: 95.235 - type: recall_at_1000 value: 99.36 - type: recall_at_3 value: 44.523 - type: recall_at_5 value: 53.769999999999996 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 40.27789869854063 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 35.41979463347428 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 58.22752045109304 - type: mrr value: 71.51112430198303 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.71147646622866 - type: cos_sim_spearman value: 85.059167046486 - type: euclidean_pearson value: 75.88421613600647 - type: euclidean_spearman value: 75.12821787150585 - type: manhattan_pearson value: 75.22005646957604 - type: manhattan_spearman value: 74.42880434453272 - task: type: BitextMining dataset: name: MTEB BUCC (de-en) type: mteb/bucc-bitext-mining config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.23799582463465 - type: f1 value: 99.12665274878218 - type: precision value: 99.07098121085595 - type: recall value: 99.23799582463465 - task: type: BitextMining dataset: name: MTEB BUCC (fr-en) type: mteb/bucc-bitext-mining config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 97.88685890380806 - type: f1 value: 97.59336708489249 - type: precision value: 97.44662117543473 - type: recall value: 97.88685890380806 - task: type: BitextMining dataset: name: MTEB BUCC (ru-en) type: mteb/bucc-bitext-mining config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 97.47142362313821 - type: f1 value: 97.1989377670015 - type: precision value: 97.06384944001847 - type: recall value: 97.47142362313821 - task: type: BitextMining dataset: name: MTEB BUCC (zh-en) type: mteb/bucc-bitext-mining config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.4728804634018 - type: f1 value: 98.2973494821836 - type: precision value: 98.2095839915745 - type: recall value: 98.4728804634018 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 82.74025974025975 - type: f1 value: 82.67420447730439 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.0380848063507 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 29.45956405670166 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 32.122 - type: map_at_10 value: 42.03 - type: map_at_100 value: 43.364000000000004 - type: map_at_1000 value: 43.474000000000004 - type: map_at_3 value: 38.804 - type: map_at_5 value: 40.585 - type: mrr_at_1 value: 39.914 - type: mrr_at_10 value: 48.227 - type: mrr_at_100 value: 49.018 - type: mrr_at_1000 value: 49.064 - type: mrr_at_3 value: 45.994 - type: mrr_at_5 value: 47.396 - type: ndcg_at_1 value: 39.914 - type: ndcg_at_10 value: 47.825 - type: ndcg_at_100 value: 52.852 - type: ndcg_at_1000 value: 54.891 - type: ndcg_at_3 value: 43.517 - type: ndcg_at_5 value: 45.493 - type: precision_at_1 value: 39.914 - type: precision_at_10 value: 8.956 - type: precision_at_100 value: 1.388 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 20.791999999999998 - type: precision_at_5 value: 14.821000000000002 - type: recall_at_1 value: 32.122 - type: recall_at_10 value: 58.294999999999995 - type: recall_at_100 value: 79.726 - type: recall_at_1000 value: 93.099 - type: recall_at_3 value: 45.017 - type: recall_at_5 value: 51.002 - type: map_at_1 value: 29.677999999999997 - type: map_at_10 value: 38.684000000000005 - type: map_at_100 value: 39.812999999999995 - type: map_at_1000 value: 39.945 - type: map_at_3 value: 35.831 - type: map_at_5 value: 37.446 - type: mrr_at_1 value: 37.771 - type: mrr_at_10 value: 44.936 - type: mrr_at_100 value: 45.583 - type: mrr_at_1000 value: 45.634 - type: mrr_at_3 value: 42.771 - type: mrr_at_5 value: 43.994 - type: ndcg_at_1 value: 37.771 - type: ndcg_at_10 value: 44.059 - type: ndcg_at_100 value: 48.192 - type: ndcg_at_1000 value: 50.375 - type: ndcg_at_3 value: 40.172000000000004 - type: ndcg_at_5 value: 41.899 - type: precision_at_1 value: 37.771 - type: precision_at_10 value: 8.286999999999999 - type: precision_at_100 value: 1.322 - type: precision_at_1000 value: 0.178 - type: precision_at_3 value: 19.406000000000002 - type: precision_at_5 value: 13.745 - type: recall_at_1 value: 29.677999999999997 - type: recall_at_10 value: 53.071 - type: recall_at_100 value: 70.812 - type: recall_at_1000 value: 84.841 - type: recall_at_3 value: 41.016000000000005 - type: recall_at_5 value: 46.22 - type: map_at_1 value: 42.675000000000004 - type: map_at_10 value: 53.93599999999999 - type: map_at_100 value: 54.806999999999995 - type: map_at_1000 value: 54.867 - type: map_at_3 value: 50.934000000000005 - type: map_at_5 value: 52.583 - type: mrr_at_1 value: 48.339 - type: mrr_at_10 value: 57.265 - type: mrr_at_100 value: 57.873 - type: mrr_at_1000 value: 57.906 - type: mrr_at_3 value: 55.193000000000005 - type: mrr_at_5 value: 56.303000000000004 - type: ndcg_at_1 value: 48.339 - type: ndcg_at_10 value: 59.19799999999999 - type: ndcg_at_100 value: 62.743 - type: ndcg_at_1000 value: 63.99399999999999 - type: ndcg_at_3 value: 54.367 - type: ndcg_at_5 value: 56.548 - type: precision_at_1 value: 48.339 - type: precision_at_10 value: 9.216000000000001 - type: precision_at_100 value: 1.1809999999999998 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 23.72 - type: precision_at_5 value: 16.025 - type: recall_at_1 value: 42.675000000000004 - type: recall_at_10 value: 71.437 - type: recall_at_100 value: 86.803 - type: recall_at_1000 value: 95.581 - type: recall_at_3 value: 58.434 - type: recall_at_5 value: 63.754 - type: map_at_1 value: 23.518 - type: map_at_10 value: 30.648999999999997 - type: map_at_100 value: 31.508999999999997 - type: map_at_1000 value: 31.604 - type: map_at_3 value: 28.247 - type: map_at_5 value: 29.65 - type: mrr_at_1 value: 25.650000000000002 - type: mrr_at_10 value: 32.771 - type: mrr_at_100 value: 33.554 - type: mrr_at_1000 value: 33.629999999999995 - type: mrr_at_3 value: 30.433 - type: mrr_at_5 value: 31.812 - type: ndcg_at_1 value: 25.650000000000002 - type: ndcg_at_10 value: 34.929 - type: ndcg_at_100 value: 39.382 - type: ndcg_at_1000 value: 41.913 - type: ndcg_at_3 value: 30.292 - type: ndcg_at_5 value: 32.629999999999995 - type: precision_at_1 value: 25.650000000000002 - type: precision_at_10 value: 5.311 - type: precision_at_100 value: 0.792 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 12.58 - type: precision_at_5 value: 8.994 - type: recall_at_1 value: 23.518 - type: recall_at_10 value: 46.19 - type: recall_at_100 value: 67.123 - type: recall_at_1000 value: 86.442 - type: recall_at_3 value: 33.678000000000004 - type: recall_at_5 value: 39.244 - type: map_at_1 value: 15.891 - type: map_at_10 value: 22.464000000000002 - type: map_at_100 value: 23.483 - type: map_at_1000 value: 23.613 - type: map_at_3 value: 20.080000000000002 - type: map_at_5 value: 21.526 - type: mrr_at_1 value: 20.025000000000002 - type: mrr_at_10 value: 26.712999999999997 - type: mrr_at_100 value: 27.650000000000002 - type: mrr_at_1000 value: 27.737000000000002 - type: mrr_at_3 value: 24.274 - type: mrr_at_5 value: 25.711000000000002 - type: ndcg_at_1 value: 20.025000000000002 - type: ndcg_at_10 value: 27.028999999999996 - type: ndcg_at_100 value: 32.064 - type: ndcg_at_1000 value: 35.188 - type: ndcg_at_3 value: 22.512999999999998 - type: ndcg_at_5 value: 24.89 - type: precision_at_1 value: 20.025000000000002 - type: precision_at_10 value: 4.776 - type: precision_at_100 value: 0.8500000000000001 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 10.531 - type: precision_at_5 value: 7.811 - type: recall_at_1 value: 15.891 - type: recall_at_10 value: 37.261 - type: recall_at_100 value: 59.12 - type: recall_at_1000 value: 81.356 - type: recall_at_3 value: 24.741 - type: recall_at_5 value: 30.753999999999998 - type: map_at_1 value: 27.544 - type: map_at_10 value: 36.283 - type: map_at_100 value: 37.467 - type: map_at_1000 value: 37.574000000000005 - type: map_at_3 value: 33.528999999999996 - type: map_at_5 value: 35.028999999999996 - type: mrr_at_1 value: 34.166999999999994 - type: mrr_at_10 value: 41.866 - type: mrr_at_100 value: 42.666 - type: mrr_at_1000 value: 42.716 - type: mrr_at_3 value: 39.541 - type: mrr_at_5 value: 40.768 - type: ndcg_at_1 value: 34.166999999999994 - type: ndcg_at_10 value: 41.577 - type: ndcg_at_100 value: 46.687 - type: ndcg_at_1000 value: 48.967 - type: ndcg_at_3 value: 37.177 - type: ndcg_at_5 value: 39.097 - type: precision_at_1 value: 34.166999999999994 - type: precision_at_10 value: 7.420999999999999 - type: precision_at_100 value: 1.165 - type: precision_at_1000 value: 0.154 - type: precision_at_3 value: 17.291999999999998 - type: precision_at_5 value: 12.166 - type: recall_at_1 value: 27.544 - type: recall_at_10 value: 51.99399999999999 - type: recall_at_100 value: 73.738 - type: recall_at_1000 value: 89.33 - type: recall_at_3 value: 39.179 - type: recall_at_5 value: 44.385999999999996 - type: map_at_1 value: 26.661 - type: map_at_10 value: 35.475 - type: map_at_100 value: 36.626999999999995 - type: map_at_1000 value: 36.741 - type: map_at_3 value: 32.818000000000005 - type: map_at_5 value: 34.397 - type: mrr_at_1 value: 32.647999999999996 - type: mrr_at_10 value: 40.784 - type: mrr_at_100 value: 41.602 - type: mrr_at_1000 value: 41.661 - type: mrr_at_3 value: 38.68 - type: mrr_at_5 value: 39.838 - type: ndcg_at_1 value: 32.647999999999996 - type: ndcg_at_10 value: 40.697 - type: ndcg_at_100 value: 45.799 - type: ndcg_at_1000 value: 48.235 - type: ndcg_at_3 value: 36.516 - type: ndcg_at_5 value: 38.515 - type: precision_at_1 value: 32.647999999999996 - type: precision_at_10 value: 7.202999999999999 - type: precision_at_100 value: 1.1360000000000001 - type: precision_at_1000 value: 0.151 - type: precision_at_3 value: 17.314 - type: precision_at_5 value: 12.145999999999999 - type: recall_at_1 value: 26.661 - type: recall_at_10 value: 50.995000000000005 - type: recall_at_100 value: 73.065 - type: recall_at_1000 value: 89.781 - type: recall_at_3 value: 39.073 - type: recall_at_5 value: 44.395 - type: map_at_1 value: 25.946583333333333 - type: map_at_10 value: 33.79725 - type: map_at_100 value: 34.86408333333333 - type: map_at_1000 value: 34.9795 - type: map_at_3 value: 31.259999999999998 - type: map_at_5 value: 32.71541666666666 - type: mrr_at_1 value: 30.863749999999996 - type: mrr_at_10 value: 37.99183333333333 - type: mrr_at_100 value: 38.790499999999994 - type: mrr_at_1000 value: 38.85575000000001 - type: mrr_at_3 value: 35.82083333333333 - type: mrr_at_5 value: 37.07533333333333 - type: ndcg_at_1 value: 30.863749999999996 - type: ndcg_at_10 value: 38.52141666666667 - type: ndcg_at_100 value: 43.17966666666667 - type: ndcg_at_1000 value: 45.64608333333333 - type: ndcg_at_3 value: 34.333000000000006 - type: ndcg_at_5 value: 36.34975 - type: precision_at_1 value: 30.863749999999996 - type: precision_at_10 value: 6.598999999999999 - type: precision_at_100 value: 1.0502500000000001 - type: precision_at_1000 value: 0.14400000000000002 - type: precision_at_3 value: 15.557583333333334 - type: precision_at_5 value: 11.020000000000001 - type: recall_at_1 value: 25.946583333333333 - type: recall_at_10 value: 48.36991666666666 - type: recall_at_100 value: 69.02408333333334 - type: recall_at_1000 value: 86.43858333333331 - type: recall_at_3 value: 36.4965 - type: recall_at_5 value: 41.76258333333334 - type: map_at_1 value: 22.431 - type: map_at_10 value: 28.889 - type: map_at_100 value: 29.642000000000003 - type: map_at_1000 value: 29.742 - type: map_at_3 value: 26.998 - type: map_at_5 value: 28.172000000000004 - type: mrr_at_1 value: 25.307000000000002 - type: mrr_at_10 value: 31.763 - type: mrr_at_100 value: 32.443 - type: mrr_at_1000 value: 32.531 - type: mrr_at_3 value: 29.959000000000003 - type: mrr_at_5 value: 31.063000000000002 - type: ndcg_at_1 value: 25.307000000000002 - type: ndcg_at_10 value: 32.586999999999996 - type: ndcg_at_100 value: 36.5 - type: ndcg_at_1000 value: 39.133 - type: ndcg_at_3 value: 29.25 - type: ndcg_at_5 value: 31.023 - type: precision_at_1 value: 25.307000000000002 - type: precision_at_10 value: 4.954 - type: precision_at_100 value: 0.747 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 12.577 - type: precision_at_5 value: 8.741999999999999 - type: recall_at_1 value: 22.431 - type: recall_at_10 value: 41.134 - type: recall_at_100 value: 59.28600000000001 - type: recall_at_1000 value: 78.857 - type: recall_at_3 value: 31.926 - type: recall_at_5 value: 36.335 - type: map_at_1 value: 17.586 - type: map_at_10 value: 23.304 - type: map_at_100 value: 24.159 - type: map_at_1000 value: 24.281 - type: map_at_3 value: 21.316 - type: map_at_5 value: 22.383 - type: mrr_at_1 value: 21.645 - type: mrr_at_10 value: 27.365000000000002 - type: mrr_at_100 value: 28.108 - type: mrr_at_1000 value: 28.192 - type: mrr_at_3 value: 25.482 - type: mrr_at_5 value: 26.479999999999997 - type: ndcg_at_1 value: 21.645 - type: ndcg_at_10 value: 27.306 - type: ndcg_at_100 value: 31.496000000000002 - type: ndcg_at_1000 value: 34.53 - type: ndcg_at_3 value: 23.73 - type: ndcg_at_5 value: 25.294 - type: precision_at_1 value: 21.645 - type: precision_at_10 value: 4.797 - type: precision_at_100 value: 0.8059999999999999 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 10.850999999999999 - type: precision_at_5 value: 7.736 - type: recall_at_1 value: 17.586 - type: recall_at_10 value: 35.481 - type: recall_at_100 value: 54.534000000000006 - type: recall_at_1000 value: 76.456 - type: recall_at_3 value: 25.335 - type: recall_at_5 value: 29.473 - type: map_at_1 value: 25.095 - type: map_at_10 value: 32.374 - type: map_at_100 value: 33.537 - type: map_at_1000 value: 33.634 - type: map_at_3 value: 30.089 - type: map_at_5 value: 31.433 - type: mrr_at_1 value: 29.198 - type: mrr_at_10 value: 36.01 - type: mrr_at_100 value: 37.022 - type: mrr_at_1000 value: 37.083 - type: mrr_at_3 value: 33.94 - type: mrr_at_5 value: 35.148 - type: ndcg_at_1 value: 29.198 - type: ndcg_at_10 value: 36.729 - type: ndcg_at_100 value: 42.114000000000004 - type: ndcg_at_1000 value: 44.592 - type: ndcg_at_3 value: 32.644 - type: ndcg_at_5 value: 34.652 - type: precision_at_1 value: 29.198 - type: precision_at_10 value: 5.970000000000001 - type: precision_at_100 value: 0.967 - type: precision_at_1000 value: 0.129 - type: precision_at_3 value: 14.396999999999998 - type: precision_at_5 value: 10.093 - type: recall_at_1 value: 25.095 - type: recall_at_10 value: 46.392 - type: recall_at_100 value: 69.706 - type: recall_at_1000 value: 87.738 - type: recall_at_3 value: 35.303000000000004 - type: recall_at_5 value: 40.441 - type: map_at_1 value: 26.857999999999997 - type: map_at_10 value: 34.066 - type: map_at_100 value: 35.671 - type: map_at_1000 value: 35.881 - type: map_at_3 value: 31.304 - type: map_at_5 value: 32.885 - type: mrr_at_1 value: 32.411 - type: mrr_at_10 value: 38.987 - type: mrr_at_100 value: 39.894 - type: mrr_at_1000 value: 39.959 - type: mrr_at_3 value: 36.626999999999995 - type: mrr_at_5 value: 38.011 - type: ndcg_at_1 value: 32.411 - type: ndcg_at_10 value: 39.208 - type: ndcg_at_100 value: 44.626 - type: ndcg_at_1000 value: 47.43 - type: ndcg_at_3 value: 35.091 - type: ndcg_at_5 value: 37.119 - type: precision_at_1 value: 32.411 - type: precision_at_10 value: 7.51 - type: precision_at_100 value: 1.486 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 16.14 - type: precision_at_5 value: 11.976 - type: recall_at_1 value: 26.857999999999997 - type: recall_at_10 value: 47.407 - type: recall_at_100 value: 72.236 - type: recall_at_1000 value: 90.77 - type: recall_at_3 value: 35.125 - type: recall_at_5 value: 40.522999999999996 - type: map_at_1 value: 21.3 - type: map_at_10 value: 27.412999999999997 - type: map_at_100 value: 28.29 - type: map_at_1000 value: 28.398 - type: map_at_3 value: 25.169999999999998 - type: map_at_5 value: 26.496 - type: mrr_at_1 value: 23.29 - type: mrr_at_10 value: 29.215000000000003 - type: mrr_at_100 value: 30.073 - type: mrr_at_1000 value: 30.156 - type: mrr_at_3 value: 26.956000000000003 - type: mrr_at_5 value: 28.38 - type: ndcg_at_1 value: 23.29 - type: ndcg_at_10 value: 31.113000000000003 - type: ndcg_at_100 value: 35.701 - type: ndcg_at_1000 value: 38.505 - type: ndcg_at_3 value: 26.727 - type: ndcg_at_5 value: 29.037000000000003 - type: precision_at_1 value: 23.29 - type: precision_at_10 value: 4.787 - type: precision_at_100 value: 0.763 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 11.091 - type: precision_at_5 value: 7.985 - type: recall_at_1 value: 21.3 - type: recall_at_10 value: 40.782000000000004 - type: recall_at_100 value: 62.13999999999999 - type: recall_at_1000 value: 83.012 - type: recall_at_3 value: 29.131 - type: recall_at_5 value: 34.624 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 9.631 - type: map_at_10 value: 16.634999999999998 - type: map_at_100 value: 18.23 - type: map_at_1000 value: 18.419 - type: map_at_3 value: 13.66 - type: map_at_5 value: 15.173 - type: mrr_at_1 value: 21.368000000000002 - type: mrr_at_10 value: 31.56 - type: mrr_at_100 value: 32.58 - type: mrr_at_1000 value: 32.633 - type: mrr_at_3 value: 28.241 - type: mrr_at_5 value: 30.225 - type: ndcg_at_1 value: 21.368000000000002 - type: ndcg_at_10 value: 23.855999999999998 - type: ndcg_at_100 value: 30.686999999999998 - type: ndcg_at_1000 value: 34.327000000000005 - type: ndcg_at_3 value: 18.781 - type: ndcg_at_5 value: 20.73 - type: precision_at_1 value: 21.368000000000002 - type: precision_at_10 value: 7.564 - type: precision_at_100 value: 1.496 - type: precision_at_1000 value: 0.217 - type: precision_at_3 value: 13.876 - type: precision_at_5 value: 11.062 - type: recall_at_1 value: 9.631 - type: recall_at_10 value: 29.517 - type: recall_at_100 value: 53.452 - type: recall_at_1000 value: 74.115 - type: recall_at_3 value: 17.605999999999998 - type: recall_at_5 value: 22.505 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.885 - type: map_at_10 value: 18.798000000000002 - type: map_at_100 value: 26.316 - type: map_at_1000 value: 27.869 - type: map_at_3 value: 13.719000000000001 - type: map_at_5 value: 15.716 - type: mrr_at_1 value: 66 - type: mrr_at_10 value: 74.263 - type: mrr_at_100 value: 74.519 - type: mrr_at_1000 value: 74.531 - type: mrr_at_3 value: 72.458 - type: mrr_at_5 value: 73.321 - type: ndcg_at_1 value: 53.87499999999999 - type: ndcg_at_10 value: 40.355999999999995 - type: ndcg_at_100 value: 44.366 - type: ndcg_at_1000 value: 51.771 - type: ndcg_at_3 value: 45.195 - type: ndcg_at_5 value: 42.187000000000005 - type: precision_at_1 value: 66 - type: precision_at_10 value: 31.75 - type: precision_at_100 value: 10.11 - type: precision_at_1000 value: 1.9800000000000002 - type: precision_at_3 value: 48.167 - type: precision_at_5 value: 40.050000000000004 - type: recall_at_1 value: 8.885 - type: recall_at_10 value: 24.471999999999998 - type: recall_at_100 value: 49.669000000000004 - type: recall_at_1000 value: 73.383 - type: recall_at_3 value: 14.872 - type: recall_at_5 value: 18.262999999999998 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 45.18 - type: f1 value: 40.26878691789978 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 62.751999999999995 - type: map_at_10 value: 74.131 - type: map_at_100 value: 74.407 - type: map_at_1000 value: 74.423 - type: map_at_3 value: 72.329 - type: map_at_5 value: 73.555 - type: mrr_at_1 value: 67.282 - type: mrr_at_10 value: 78.292 - type: mrr_at_100 value: 78.455 - type: mrr_at_1000 value: 78.458 - type: mrr_at_3 value: 76.755 - type: mrr_at_5 value: 77.839 - type: ndcg_at_1 value: 67.282 - type: ndcg_at_10 value: 79.443 - type: ndcg_at_100 value: 80.529 - type: ndcg_at_1000 value: 80.812 - type: ndcg_at_3 value: 76.281 - type: ndcg_at_5 value: 78.235 - type: precision_at_1 value: 67.282 - type: precision_at_10 value: 10.078 - type: precision_at_100 value: 1.082 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 30.178 - type: precision_at_5 value: 19.232 - type: recall_at_1 value: 62.751999999999995 - type: recall_at_10 value: 91.521 - type: recall_at_100 value: 95.997 - type: recall_at_1000 value: 97.775 - type: recall_at_3 value: 83.131 - type: recall_at_5 value: 87.93299999999999 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 18.861 - type: map_at_10 value: 30.252000000000002 - type: map_at_100 value: 32.082 - type: map_at_1000 value: 32.261 - type: map_at_3 value: 25.909 - type: map_at_5 value: 28.296 - type: mrr_at_1 value: 37.346000000000004 - type: mrr_at_10 value: 45.802 - type: mrr_at_100 value: 46.611999999999995 - type: mrr_at_1000 value: 46.659 - type: mrr_at_3 value: 43.056 - type: mrr_at_5 value: 44.637 - type: ndcg_at_1 value: 37.346000000000004 - type: ndcg_at_10 value: 38.169 - type: ndcg_at_100 value: 44.864 - type: ndcg_at_1000 value: 47.974 - type: ndcg_at_3 value: 33.619 - type: ndcg_at_5 value: 35.317 - type: precision_at_1 value: 37.346000000000004 - type: precision_at_10 value: 10.693999999999999 - type: precision_at_100 value: 1.775 - type: precision_at_1000 value: 0.231 - type: precision_at_3 value: 22.325 - type: precision_at_5 value: 16.852 - type: recall_at_1 value: 18.861 - type: recall_at_10 value: 45.672000000000004 - type: recall_at_100 value: 70.60499999999999 - type: recall_at_1000 value: 89.216 - type: recall_at_3 value: 30.361 - type: recall_at_5 value: 36.998999999999995 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 37.852999999999994 - type: map_at_10 value: 59.961 - type: map_at_100 value: 60.78 - type: map_at_1000 value: 60.843 - type: map_at_3 value: 56.39999999999999 - type: map_at_5 value: 58.646 - type: mrr_at_1 value: 75.70599999999999 - 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type: max_f1 value: 77.87816307403935 --- ## Multilingual-E5-base [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 12 layers and the embedding size is 768. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] # Each input text should start with "query: " or "passage: ", even for non-English texts. # For tasks other than retrieval, you can simply use the "query: " prefix. input_texts = ['query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"] tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-base') model = AutoModel.from_pretrained('intfloat/multilingual-e5-base') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ## Supported Languages This model is initialized from [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. ## Training Details **Initialization**: [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) **First stage**: contrastive pre-training with weak supervision | Dataset | Weak supervision | # of text pairs | |--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------| | Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B | | [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M | | [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B | | [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M | | Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M | | [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M | | [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M | | [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M | | [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M | **Second stage**: supervised fine-tuning | Dataset | Language | # of text pairs | |----------------------------------------------------------------------------------------|--------------|-----------------| | [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k | | [NQ](https://github.com/facebookresearch/DPR) | English | 70k | | [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k | | [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k | | [ELI5](https://huggingface.co/datasets/eli5) | English | 500k | | [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k | | [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k | | [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k | | [SQuAD](https://huggingface.co/datasets/squad) | English | 87k | | [Quora](https://huggingface.co/datasets/quora) | English | 150k | | [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k | | [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k | For all labeled datasets, we only use its training set for fine-tuning. For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672). ## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787) | Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th | |-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- | | BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 | | mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 | | BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 | | | | | multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 | | multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 | | multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 | ## MTEB Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## Support for Sentence Transformers Below is an example for usage with sentence_transformers. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('intfloat/multilingual-e5-base') input_texts = [ 'query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" ] embeddings = model.encode(input_texts, normalize_embeddings=True) ``` Package requirements `pip install sentence_transformers~=2.2.2` Contributors: [michaelfeil](https://huggingface.co/michaelfeil) ## FAQ **1. Do I need to add the prefix "query: " and "passage: " to input texts?** Yes, this is how the model is trained, otherwise you will see a performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. - Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval. - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. **2. Why are my reproduced results slightly different from reported in the model card?** Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue. ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2024multilingual, title={Multilingual E5 Text Embeddings: A Technical Report}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2402.05672}, year={2024} } ``` ## Limitations Long texts will be truncated to at most 512 tokens.
[ "SEMANTIC_SIMILARITY", "TRANSLATION", "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
Amir13/bert-base-parsbert-uncased-ncbi_disease
Amir13
token-classification
[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "arxiv:2302.09611", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,676
1,676
26
0
--- metrics: - precision - recall - f1 - accuracy tags: - generated_from_trainer model-index: - name: bert-base-parsbert-uncased-ncbi_disease results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-parsbert-uncased-ncbi_disease This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on the [ncbi-persian](https://huggingface.co/datasets/Amir13/ncbi-persian) dataset. It achieves the following results on the evaluation set: - Loss: 0.1018 - Precision: 0.8192 - Recall: 0.8645 - F1: 0.8412 - Accuracy: 0.9862 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 169 | 0.0648 | 0.7154 | 0.8237 | 0.7657 | 0.9813 | | No log | 2.0 | 338 | 0.0573 | 0.7870 | 0.8263 | 0.8062 | 0.9853 | | 0.0596 | 3.0 | 507 | 0.0639 | 0.7893 | 0.8776 | 0.8312 | 0.9858 | | 0.0596 | 4.0 | 676 | 0.0678 | 0.8150 | 0.8461 | 0.8302 | 0.9860 | | 0.0596 | 5.0 | 845 | 0.0737 | 0.8070 | 0.8474 | 0.8267 | 0.9862 | | 0.0065 | 6.0 | 1014 | 0.0834 | 0.8052 | 0.8592 | 0.8313 | 0.9856 | | 0.0065 | 7.0 | 1183 | 0.0918 | 0.8099 | 0.8355 | 0.8225 | 0.9859 | | 0.0065 | 8.0 | 1352 | 0.0882 | 0.8061 | 0.8697 | 0.8367 | 0.9857 | | 0.0021 | 9.0 | 1521 | 0.0903 | 0.8045 | 0.85 | 0.8266 | 0.9860 | | 0.0021 | 10.0 | 1690 | 0.0965 | 0.8303 | 0.85 | 0.8401 | 0.9866 | | 0.0021 | 11.0 | 1859 | 0.0954 | 0.8182 | 0.8645 | 0.8407 | 0.9860 | | 0.0008 | 12.0 | 2028 | 0.0998 | 0.8206 | 0.8605 | 0.8401 | 0.9862 | | 0.0008 | 13.0 | 2197 | 0.0995 | 0.82 | 0.8632 | 0.8410 | 0.9862 | | 0.0008 | 14.0 | 2366 | 0.1015 | 0.8214 | 0.8592 | 0.8399 | 0.9861 | | 0.0004 | 15.0 | 2535 | 0.1018 | 0.8192 | 0.8645 | 0.8412 | 0.9862 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2 ### Citation If you used the datasets and models in this repository, please cite it. ```bibtex @misc{https://doi.org/10.48550/arxiv.2302.09611, doi = {10.48550/ARXIV.2302.09611}, url = {https://arxiv.org/abs/2302.09611}, author = {Sartipi, Amir and Fatemi, Afsaneh}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English}, publisher = {arXiv}, year = {2023}, copyright = {arXiv.org perpetual, non-exclusive license} } ```
[ "TRANSLATION" ]
[ "NCBI DISEASE" ]
BioNLP
croissantllm/base_125k
croissantllm
text2text-generation
[ "transformers", "pytorch", "llama", "text-generation", "legal", "code", "text-generation-inference", "art", "text2text-generation", "fr", "en", "dataset:cerebras/SlimPajama-627B", "dataset:uonlp/CulturaX", "dataset:pg19", "dataset:bigcode/starcoderdata", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,705
1,706
6
0
--- datasets: - cerebras/SlimPajama-627B - uonlp/CulturaX - pg19 - bigcode/starcoderdata language: - fr - en license: mit pipeline_tag: text2text-generation tags: - legal - code - text-generation-inference - art --- # CroissantLLM - Base (125k steps) This model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 125k steps (1.97 T) tokens. To play with the final model, we recommend using the Chat version: https://huggingface.co/croissantllm/CroissantLLMChat-v0.1. ## Abstract We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware. To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources. To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives. This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models. ## Citation Our work can be cited as: ```bash Coming soon ``` ## Usage This model is a base model, that is, it is not finetuned for Chat function and works best with few-shot prompting strategies. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "croissantllm/base_125k" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto") inputs = tokenizer("I am so tired I could sleep right now. -> Je suis si fatigué que je pourrais m'endormir maintenant. He is heading to the market. -> Il va au marché. We are running on the beach. ->", return_tensors="pt").to(model.device) tokens = model.generate(**inputs, max_length=100, do_sample=True, top_p=0.95, top_k=60, temperature=0.5) print(tokenizer.decode(tokens[0])) # remove bos token inputs = tokenizer("Capitales: France -> Paris, Italie -> Rome, Allemagne -> Berlin, Espagne ->", return_tensors="pt", add_special_tokens=True).to(model.device) tokens = model.generate(**inputs, max_length=100, do_sample=True, top_p=0.95, top_k=60) print(tokenizer.decode(tokens[0])) ```
[ "TRANSLATION" ]
[ "CRAFT" ]
Non_BioNLP
Teradata/multilingual-e5-small
Teradata
sentence-similarity
[ "onnx", "mteb", "Sentence Transformers", "sentence-similarity", "teradata", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "license:mit", "model-index", "region:us" ]
1,739
1,741
11
0
--- language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit tags: - mteb - Sentence Transformers - sentence-similarity - onnx - teradata model-index: - name: intfloat/multilingual-e5-small results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.79104477611939 - type: ap value: 36.9996434842022 - type: f1 value: 67.95453679103099 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (de) type: mteb/amazon_counterfactual config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.64882226980728 - type: ap value: 82.11942130026586 - type: f1 value: 69.87963421606715 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en-ext) type: mteb/amazon_counterfactual config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.8095952023988 - type: ap value: 24.46869495579561 - type: f1 value: 63.00108480037597 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (ja) type: mteb/amazon_counterfactual config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 64.186295503212 - type: ap value: 15.496804690197042 - type: f1 value: 52.07153895475031 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 88.699325 - type: ap value: 85.27039559917269 - type: f1 value: 88.65556295032513 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 44.69799999999999 - type: f1 value: 43.73187348654165 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (de) type: mteb/amazon_reviews_multi config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.245999999999995 - type: f1 value: 39.3863530637684 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (es) type: mteb/amazon_reviews_multi config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.394 - type: f1 value: 39.301223469483446 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.864 - type: f1 value: 37.97974261868003 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (ja) type: mteb/amazon_reviews_multi config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.682 - type: f1 value: 37.07399369768313 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.504 - type: f1 value: 36.62317273874278 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 19.061 - type: map_at_10 value: 31.703 - type: map_at_100 value: 32.967 - type: map_at_1000 value: 33.001000000000005 - type: map_at_3 value: 27.466 - type: map_at_5 value: 29.564 - type: mrr_at_1 value: 19.559 - type: mrr_at_10 value: 31.874999999999996 - type: mrr_at_100 value: 33.146 - type: mrr_at_1000 value: 33.18 - type: mrr_at_3 value: 27.667 - type: mrr_at_5 value: 29.74 - type: ndcg_at_1 value: 19.061 - type: ndcg_at_10 value: 39.062999999999995 - type: ndcg_at_100 value: 45.184000000000005 - type: ndcg_at_1000 value: 46.115 - type: ndcg_at_3 value: 30.203000000000003 - type: ndcg_at_5 value: 33.953 - type: precision_at_1 value: 19.061 - type: precision_at_10 value: 6.279999999999999 - type: precision_at_100 value: 0.9129999999999999 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 12.706999999999999 - type: precision_at_5 value: 9.431000000000001 - type: recall_at_1 value: 19.061 - type: recall_at_10 value: 62.802 - type: recall_at_100 value: 91.323 - type: recall_at_1000 value: 98.72 - type: recall_at_3 value: 38.122 - type: recall_at_5 value: 47.155 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 39.22266660528253 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 30.79980849482483 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 57.8790068352054 - type: mrr value: 71.78791276436706 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 82.36328364043163 - type: cos_sim_spearman value: 82.26211536195868 - type: euclidean_pearson value: 80.3183865039173 - type: euclidean_spearman value: 79.88495276296132 - type: manhattan_pearson value: 80.14484480692127 - type: manhattan_spearman value: 80.39279565980743 - task: type: BitextMining dataset: name: MTEB BUCC (de-en) type: mteb/bucc-bitext-mining config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - 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type: precision_at_5 value: 19.781000000000002 - type: recall_at_1 value: 90.802 - type: recall_at_10 value: 99.494 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 99.747 - type: recall_at_3 value: 97.975 - type: recall_at_5 value: 98.90299999999999 --- ***See Disclaimer below*** ---- # A Teradata Vantage compatible Embeddings Model # intfloat/multilingual-e5-small ## Overview of this Model An Embedding Model which maps text (sentence/ paragraphs) into a vector. The [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) model well known for its effectiveness in capturing semantic meanings in text data. It's a state-of-the-art model trained on a large corpus, capable of generating high-quality text embeddings. - 117.65M params (Sizes in ONNX format - "fp32": 448.58MB, "int8": 112.8MB, "uint8": 112.8MB) - 512 maximum input tokens - 384 dimensions of output vector - Licence: mit. The released models can be used for commercial purposes free of charge. - Reference to Original Model: https://huggingface.co/intfloat/multilingual-e5-small ## Quickstart: Deploying this Model in Teradata Vantage We have pre-converted the model into the ONNX format compatible with BYOM 6.0, eliminating the need for manual conversion. **Note:** Ensure you have access to a Teradata Database with BYOM 6.0 installed. To get started, clone the pre-converted model directly from the Teradata HuggingFace repository. ```python import teradataml as tdml import getpass from huggingface_hub import hf_hub_download model_name = "multilingual-e5-small" number_dimensions_output = 384 model_file_name = "model.onnx" # Step 1: Download Model from Teradata HuggingFace Page hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"onnx/{model_file_name}", local_dir="./") hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"tokenizer.json", local_dir="./") # Step 2: Create Connection to Vantage tdml.create_context(host = input('enter your hostname'), username=input('enter your username'), password = getpass.getpass("enter your password")) # Step 3: Load Models into Vantage # a) Embedding model tdml.save_byom(model_id = model_name, # must be unique in the models table model_file = f"onnx/{model_file_name}", table_name = 'embeddings_models' ) # b) Tokenizer tdml.save_byom(model_id = model_name, # must be unique in the models table model_file = 'tokenizer.json', table_name = 'embeddings_tokenizers') # Step 4: Test ONNXEmbeddings Function # Note that ONNXEmbeddings expects the 'payload' column to be 'txt'. # If it has got a different name, just rename it in a subquery/CTE. input_table = "emails.emails" embeddings_query = f""" SELECT * from mldb.ONNXEmbeddings( on {input_table} as InputTable on (select * from embeddings_models where model_id = '{model_name}') as ModelTable DIMENSION on (select model as tokenizer from embeddings_tokenizers where model_id = '{model_name}') as TokenizerTable DIMENSION using Accumulate('id', 'txt') ModelOutputTensor('sentence_embedding') EnableMemoryCheck('false') OutputFormat('FLOAT32({number_dimensions_output})') OverwriteCachedModel('true') ) a """ DF_embeddings = tdml.DataFrame.from_query(embeddings_query) DF_embeddings ``` ## What Can I Do with the Embeddings? Teradata Vantage includes pre-built in-database functions to process embeddings further. Explore the following examples: - **Semantic Clustering with TD_KMeans:** [Semantic Clustering Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Clustering_Python.ipynb) - **Semantic Distance with TD_VectorDistance:** [Semantic Similarity Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Similarity_Python.ipynb) - **RAG-Based Application with TD_VectorDistance:** [RAG and Bedrock Query PDF Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/RAG_and_Bedrock_QueryPDF.ipynb) ## Deep Dive into Model Conversion to ONNX **The steps below outline how we converted the open-source Hugging Face model into an ONNX file compatible with the in-database ONNXEmbeddings function.** You do not need to perform these steps—they are provided solely for documentation and transparency. However, they may be helpful if you wish to convert another model to the required format. ### Part 1. Importing and Converting Model using optimum We start by importing the pre-trained [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) model from Hugging Face. To enhance performance and ensure compatibility with various execution environments, we'll use the [Optimum](https://github.com/huggingface/optimum) utility to convert the model into the ONNX (Open Neural Network Exchange) format. After conversion to ONNX, we are fixing the opset in the ONNX file for compatibility with ONNX runtime used in Teradata Vantage We are generating ONNX files for multiple different precisions: fp32, int8, uint8 You can find the detailed conversion steps in the file [convert.py](./convert.py) ### Part 2. Running the model in Python with onnxruntime & compare results Once the fixes are applied, we proceed to test the correctness of the ONNX model by calculating cosine similarity between two texts using native SentenceTransformers and ONNX runtime, comparing the results. If the results are identical, it confirms that the ONNX model gives the same result as the native models, validating its correctness and suitability for further use in the database. ```python import onnxruntime as rt from sentence_transformers.util import cos_sim from sentence_transformers import SentenceTransformer import transformers sentences_1 = 'How is the weather today?' sentences_2 = 'What is the current weather like today?' # Calculate ONNX result tokenizer = transformers.AutoTokenizer.from_pretrained("intfloat/multilingual-e5-small") predef_sess = rt.InferenceSession("onnx/model.onnx") enc1 = tokenizer(sentences_1) embeddings_1_onnx = predef_sess.run(None, {"input_ids": [enc1.input_ids], "attention_mask": [enc1.attention_mask]}) enc2 = tokenizer(sentences_2) embeddings_2_onnx = predef_sess.run(None, {"input_ids": [enc2.input_ids], "attention_mask": [enc2.attention_mask]}) # Calculate embeddings with SentenceTransformer model = SentenceTransformer(model_id, trust_remote_code=True) embeddings_1_sentence_transformer = model.encode(sentences_1, normalize_embeddings=True, trust_remote_code=True) embeddings_2_sentence_transformer = model.encode(sentences_2, normalize_embeddings=True, trust_remote_code=True) # Compare results print("Cosine similiarity for embeddings calculated with ONNX:" + str(cos_sim(embeddings_1_onnx[1][0], embeddings_2_onnx[1][0]))) print("Cosine similiarity for embeddings calculated with SentenceTransformer:" + str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer))) ``` You can find the detailed ONNX vs. SentenceTransformer result comparison steps in the file [test_local.py](./test_local.py) ----- DISCLAIMER: The content herein (“Content”) is provided “AS IS” and is not covered by any Teradata Operations, Inc. and its affiliates (“Teradata”) agreements. Its listing here does not constitute certification or endorsement by Teradata. To the extent any of the Content contains or is related to any artificial intelligence (“AI”) or other language learning models (“Models”) that interoperate with the products and services of Teradata, by accessing, bringing, deploying or using such Models, you acknowledge and agree that you are solely responsible for ensuring compliance with all applicable laws, regulations, and restrictions governing the use, deployment, and distribution of AI technologies. This includes, but is not limited to, AI Diffusion Rules, European Union AI Act, AI-related laws and regulations, privacy laws, export controls, and financial or sector-specific regulations. While Teradata may provide support, guidance, or assistance in the deployment or implementation of Models to interoperate with Teradata’s products and/or services, you remain fully responsible for ensuring that your Models, data, and applications comply with all relevant legal and regulatory obligations. Our assistance does not constitute legal or regulatory approval, and Teradata disclaims any liability arising from non-compliance with applicable laws. You must determine the suitability of the Models for any purpose. Given the probabilistic nature of machine learning and modeling, the use of the Models may in some situations result in incorrect output that does not accurately reflect the action generated. You should evaluate the accuracy of any output as appropriate for your use case, including by using human review of the output.
[ "SEMANTIC_SIMILARITY", "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
nadeem1362/mxbai-embed-large-v1-Q4_K_M-GGUF
nadeem1362
feature-extraction
[ "sentence-transformers", "gguf", "mteb", "transformers.js", "transformers", "llama-cpp", "gguf-my-repo", "feature-extraction", "en", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,716
1,716
17
0
--- language: - en library_name: sentence-transformers license: apache-2.0 pipeline_tag: feature-extraction tags: - mteb - transformers.js - transformers - llama-cpp - gguf-my-repo model-index: - name: mxbai-angle-large-v1 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.044776119403 - type: ap value: 37.7362433623053 - type: f1 value: 68.92736573359774 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.84025000000001 - type: ap value: 90.93190875404055 - type: f1 value: 93.8297833897293 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.184 - type: f1 value: 48.74163227751588 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 41.252 - type: map_at_10 value: 57.778 - type: map_at_100 value: 58.233000000000004 - type: map_at_1000 value: 58.23700000000001 - type: map_at_3 value: 53.449999999999996 - type: map_at_5 value: 56.376000000000005 - type: mrr_at_1 value: 41.679 - type: mrr_at_10 value: 57.92699999999999 - type: mrr_at_100 value: 58.389 - type: mrr_at_1000 value: 58.391999999999996 - type: mrr_at_3 value: 53.651 - type: mrr_at_5 value: 56.521 - type: ndcg_at_1 value: 41.252 - type: ndcg_at_10 value: 66.018 - type: ndcg_at_100 value: 67.774 - type: ndcg_at_1000 value: 67.84400000000001 - type: ndcg_at_3 value: 57.372 - type: ndcg_at_5 value: 62.646 - type: precision_at_1 value: 41.252 - type: precision_at_10 value: 9.189 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 22.902 - type: precision_at_5 value: 16.302 - type: recall_at_1 value: 41.252 - type: recall_at_10 value: 91.892 - type: recall_at_100 value: 99.14699999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 68.706 - type: recall_at_5 value: 81.50800000000001 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.97294504317859 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 42.98071077674629 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 65.16477858490782 - type: mrr value: 78.23583080508287 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 89.6277629421789 - type: cos_sim_spearman value: 88.4056288400568 - type: euclidean_pearson value: 87.94871847578163 - type: euclidean_spearman value: 88.4056288400568 - type: manhattan_pearson value: 87.73271254229648 - type: manhattan_spearman value: 87.91826833762677 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 87.81818181818181 - type: f1 value: 87.79879337316918 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.91773608582761 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.73059477462478 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 32.745999999999995 - type: map_at_10 value: 43.632 - type: map_at_100 value: 45.206 - type: map_at_1000 value: 45.341 - type: map_at_3 value: 39.956 - type: map_at_5 value: 42.031 - type: mrr_at_1 value: 39.485 - type: mrr_at_10 value: 49.537 - type: mrr_at_100 value: 50.249 - type: mrr_at_1000 value: 50.294000000000004 - type: mrr_at_3 value: 46.757 - type: mrr_at_5 value: 48.481 - type: ndcg_at_1 value: 39.485 - type: ndcg_at_10 value: 50.058 - type: ndcg_at_100 value: 55.586 - type: ndcg_at_1000 value: 57.511 - type: ndcg_at_3 value: 44.786 - type: ndcg_at_5 value: 47.339999999999996 - type: precision_at_1 value: 39.485 - type: precision_at_10 value: 9.557 - type: precision_at_100 value: 1.552 - type: precision_at_1000 value: 0.202 - type: precision_at_3 value: 21.412 - type: precision_at_5 value: 15.479000000000001 - type: recall_at_1 value: 32.745999999999995 - type: recall_at_10 value: 62.056 - type: recall_at_100 value: 85.088 - type: recall_at_1000 value: 96.952 - type: recall_at_3 value: 46.959 - type: recall_at_5 value: 54.06999999999999 - type: map_at_1 value: 31.898 - type: map_at_10 value: 42.142 - type: map_at_100 value: 43.349 - type: map_at_1000 value: 43.483 - type: map_at_3 value: 39.18 - type: map_at_5 value: 40.733000000000004 - type: mrr_at_1 value: 39.617999999999995 - type: mrr_at_10 value: 47.922 - type: mrr_at_100 value: 48.547000000000004 - type: mrr_at_1000 value: 48.597 - type: mrr_at_3 value: 45.86 - type: mrr_at_5 value: 46.949000000000005 - type: ndcg_at_1 value: 39.617999999999995 - type: ndcg_at_10 value: 47.739 - type: ndcg_at_100 value: 51.934999999999995 - type: ndcg_at_1000 value: 54.007000000000005 - type: ndcg_at_3 value: 43.748 - type: ndcg_at_5 value: 45.345 - type: precision_at_1 value: 39.617999999999995 - type: precision_at_10 value: 8.962 - type: precision_at_100 value: 1.436 - type: precision_at_1000 value: 0.192 - type: precision_at_3 value: 21.083 - type: precision_at_5 value: 14.752 - type: recall_at_1 value: 31.898 - type: recall_at_10 value: 57.587999999999994 - type: recall_at_100 value: 75.323 - type: recall_at_1000 value: 88.304 - type: recall_at_3 value: 45.275 - type: recall_at_5 value: 49.99 - type: map_at_1 value: 40.458 - type: map_at_10 value: 52.942 - type: map_at_100 value: 53.974 - type: map_at_1000 value: 54.031 - type: map_at_3 value: 49.559999999999995 - type: map_at_5 value: 51.408 - type: mrr_at_1 value: 46.27 - type: mrr_at_10 value: 56.31699999999999 - type: mrr_at_100 value: 56.95099999999999 - type: mrr_at_1000 value: 56.98 - type: mrr_at_3 value: 53.835 - type: mrr_at_5 value: 55.252 - type: ndcg_at_1 value: 46.27 - type: ndcg_at_10 value: 58.964000000000006 - type: ndcg_at_100 value: 62.875 - type: ndcg_at_1000 value: 63.969 - type: ndcg_at_3 value: 53.297000000000004 - type: ndcg_at_5 value: 55.938 - type: precision_at_1 value: 46.27 - type: precision_at_10 value: 9.549000000000001 - type: precision_at_100 value: 1.2409999999999999 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 23.762 - type: precision_at_5 value: 16.262999999999998 - type: recall_at_1 value: 40.458 - type: recall_at_10 value: 73.446 - type: recall_at_100 value: 90.12400000000001 - type: recall_at_1000 value: 97.795 - type: recall_at_3 value: 58.123000000000005 - type: recall_at_5 value: 64.68 - type: map_at_1 value: 27.443 - type: map_at_10 value: 36.081 - type: map_at_100 value: 37.163000000000004 - type: map_at_1000 value: 37.232 - type: map_at_3 value: 33.308 - type: map_at_5 value: 34.724 - type: mrr_at_1 value: 29.492 - type: mrr_at_10 value: 38.138 - type: mrr_at_100 value: 39.065 - type: mrr_at_1000 value: 39.119 - type: mrr_at_3 value: 35.593 - type: mrr_at_5 value: 36.785000000000004 - type: ndcg_at_1 value: 29.492 - type: ndcg_at_10 value: 41.134 - type: ndcg_at_100 value: 46.300999999999995 - type: ndcg_at_1000 value: 48.106 - type: ndcg_at_3 value: 35.77 - type: ndcg_at_5 value: 38.032 - type: precision_at_1 value: 29.492 - type: precision_at_10 value: 6.249 - type: precision_at_100 value: 0.9299999999999999 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 15.065999999999999 - type: precision_at_5 value: 10.373000000000001 - type: recall_at_1 value: 27.443 - type: recall_at_10 value: 54.80199999999999 - type: recall_at_100 value: 78.21900000000001 - type: recall_at_1000 value: 91.751 - type: recall_at_3 value: 40.211000000000006 - type: recall_at_5 value: 45.599000000000004 - type: map_at_1 value: 18.731 - type: map_at_10 value: 26.717999999999996 - type: map_at_100 value: 27.897 - type: map_at_1000 value: 28.029 - type: map_at_3 value: 23.91 - type: map_at_5 value: 25.455 - type: mrr_at_1 value: 23.134 - type: mrr_at_10 value: 31.769 - type: mrr_at_100 value: 32.634 - type: mrr_at_1000 value: 32.707 - type: mrr_at_3 value: 28.938999999999997 - type: mrr_at_5 value: 30.531000000000002 - type: ndcg_at_1 value: 23.134 - type: ndcg_at_10 value: 32.249 - type: ndcg_at_100 value: 37.678 - type: ndcg_at_1000 value: 40.589999999999996 - type: ndcg_at_3 value: 26.985999999999997 - type: ndcg_at_5 value: 29.457 - type: precision_at_1 value: 23.134 - type: precision_at_10 value: 5.8709999999999996 - type: precision_at_100 value: 0.988 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 12.852 - type: precision_at_5 value: 9.428 - type: recall_at_1 value: 18.731 - type: recall_at_10 value: 44.419 - type: recall_at_100 value: 67.851 - type: recall_at_1000 value: 88.103 - type: recall_at_3 value: 29.919 - type: recall_at_5 value: 36.230000000000004 - type: map_at_1 value: 30.324 - type: map_at_10 value: 41.265 - type: map_at_100 value: 42.559000000000005 - type: map_at_1000 value: 42.669000000000004 - type: map_at_3 value: 38.138 - type: map_at_5 value: 39.881 - type: mrr_at_1 value: 36.67 - type: mrr_at_10 value: 46.774 - type: mrr_at_100 value: 47.554 - type: mrr_at_1000 value: 47.593 - type: mrr_at_3 value: 44.338 - type: mrr_at_5 value: 45.723 - type: ndcg_at_1 value: 36.67 - type: ndcg_at_10 value: 47.367 - type: ndcg_at_100 value: 52.623 - type: ndcg_at_1000 value: 54.59 - type: ndcg_at_3 value: 42.323 - type: ndcg_at_5 value: 44.727 - type: precision_at_1 value: 36.67 - type: precision_at_10 value: 8.518 - type: precision_at_100 value: 1.2890000000000001 - type: precision_at_1000 value: 0.163 - type: precision_at_3 value: 19.955000000000002 - type: precision_at_5 value: 14.11 - type: recall_at_1 value: 30.324 - type: recall_at_10 value: 59.845000000000006 - type: recall_at_100 value: 81.77499999999999 - type: recall_at_1000 value: 94.463 - type: recall_at_3 value: 46.019 - type: recall_at_5 value: 52.163000000000004 - type: map_at_1 value: 24.229 - type: map_at_10 value: 35.004000000000005 - type: map_at_100 value: 36.409000000000006 - type: map_at_1000 value: 36.521 - type: map_at_3 value: 31.793 - type: map_at_5 value: 33.432 - type: mrr_at_1 value: 30.365 - type: mrr_at_10 value: 40.502 - type: mrr_at_100 value: 41.372 - type: mrr_at_1000 value: 41.435 - type: mrr_at_3 value: 37.804 - type: mrr_at_5 value: 39.226 - type: ndcg_at_1 value: 30.365 - type: ndcg_at_10 value: 41.305 - type: ndcg_at_100 value: 47.028999999999996 - type: ndcg_at_1000 value: 49.375 - type: ndcg_at_3 value: 35.85 - type: ndcg_at_5 value: 38.12 - type: precision_at_1 value: 30.365 - type: precision_at_10 value: 7.808 - type: precision_at_100 value: 1.228 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 17.352 - type: precision_at_5 value: 12.42 - type: recall_at_1 value: 24.229 - type: recall_at_10 value: 54.673 - type: recall_at_100 value: 78.766 - type: recall_at_1000 value: 94.625 - type: recall_at_3 value: 39.602 - type: recall_at_5 value: 45.558 - type: map_at_1 value: 26.695 - type: map_at_10 value: 36.0895 - type: map_at_100 value: 37.309416666666664 - type: map_at_1000 value: 37.42558333333334 - type: map_at_3 value: 33.19616666666666 - type: map_at_5 value: 34.78641666666667 - type: mrr_at_1 value: 31.486083333333337 - type: mrr_at_10 value: 40.34774999999999 - type: mrr_at_100 value: 41.17533333333333 - type: mrr_at_1000 value: 41.231583333333326 - type: mrr_at_3 value: 37.90075 - type: mrr_at_5 value: 39.266999999999996 - type: ndcg_at_1 value: 31.486083333333337 - type: ndcg_at_10 value: 41.60433333333334 - type: ndcg_at_100 value: 46.74525 - type: ndcg_at_1000 value: 48.96166666666667 - type: ndcg_at_3 value: 36.68825 - type: ndcg_at_5 value: 38.966499999999996 - type: precision_at_1 value: 31.486083333333337 - type: precision_at_10 value: 7.29675 - type: precision_at_100 value: 1.1621666666666666 - type: precision_at_1000 value: 0.1545 - type: precision_at_3 value: 16.8815 - type: precision_at_5 value: 11.974583333333333 - type: recall_at_1 value: 26.695 - type: recall_at_10 value: 53.651916666666665 - type: recall_at_100 value: 76.12083333333332 - type: recall_at_1000 value: 91.31191666666668 - type: recall_at_3 value: 40.03575 - type: recall_at_5 value: 45.876666666666665 - type: map_at_1 value: 25.668000000000003 - type: map_at_10 value: 32.486 - type: map_at_100 value: 33.371 - type: map_at_1000 value: 33.458 - type: map_at_3 value: 30.261 - type: map_at_5 value: 31.418000000000003 - type: mrr_at_1 value: 28.988000000000003 - type: mrr_at_10 value: 35.414 - type: mrr_at_100 value: 36.149 - type: mrr_at_1000 value: 36.215 - type: mrr_at_3 value: 33.333 - type: mrr_at_5 value: 34.43 - type: ndcg_at_1 value: 28.988000000000003 - type: ndcg_at_10 value: 36.732 - type: ndcg_at_100 value: 41.331 - type: ndcg_at_1000 value: 43.575 - type: ndcg_at_3 value: 32.413 - type: ndcg_at_5 value: 34.316 - type: precision_at_1 value: 28.988000000000003 - type: precision_at_10 value: 5.7059999999999995 - type: precision_at_100 value: 0.882 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 13.65 - type: precision_at_5 value: 9.417 - type: recall_at_1 value: 25.668000000000003 - type: recall_at_10 value: 47.147 - type: recall_at_100 value: 68.504 - type: recall_at_1000 value: 85.272 - type: recall_at_3 value: 35.19 - type: recall_at_5 value: 39.925 - type: map_at_1 value: 17.256 - type: map_at_10 value: 24.58 - type: map_at_100 value: 25.773000000000003 - type: map_at_1000 value: 25.899 - type: map_at_3 value: 22.236 - type: map_at_5 value: 23.507 - type: mrr_at_1 value: 20.957 - type: mrr_at_10 value: 28.416000000000004 - type: mrr_at_100 value: 29.447000000000003 - type: mrr_at_1000 value: 29.524 - type: mrr_at_3 value: 26.245 - type: mrr_at_5 value: 27.451999999999998 - type: ndcg_at_1 value: 20.957 - type: ndcg_at_10 value: 29.285 - type: ndcg_at_100 value: 35.003 - type: ndcg_at_1000 value: 37.881 - type: ndcg_at_3 value: 25.063000000000002 - type: ndcg_at_5 value: 26.983 - type: precision_at_1 value: 20.957 - type: precision_at_10 value: 5.344 - type: precision_at_100 value: 0.958 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 11.918 - type: precision_at_5 value: 8.596 - type: recall_at_1 value: 17.256 - type: recall_at_10 value: 39.644 - type: recall_at_100 value: 65.279 - type: recall_at_1000 value: 85.693 - type: recall_at_3 value: 27.825 - type: recall_at_5 value: 32.792 - type: map_at_1 value: 26.700000000000003 - type: map_at_10 value: 36.205999999999996 - type: map_at_100 value: 37.316 - type: map_at_1000 value: 37.425000000000004 - type: map_at_3 value: 33.166000000000004 - type: map_at_5 value: 35.032999999999994 - type: mrr_at_1 value: 31.436999999999998 - type: mrr_at_10 value: 40.61 - type: mrr_at_100 value: 41.415 - type: mrr_at_1000 value: 41.48 - type: mrr_at_3 value: 37.966 - type: mrr_at_5 value: 39.599000000000004 - type: ndcg_at_1 value: 31.436999999999998 - type: ndcg_at_10 value: 41.771 - type: ndcg_at_100 value: 46.784 - type: ndcg_at_1000 value: 49.183 - type: ndcg_at_3 value: 36.437000000000005 - type: ndcg_at_5 value: 39.291 - type: precision_at_1 value: 31.436999999999998 - type: precision_at_10 value: 6.987 - type: precision_at_100 value: 1.072 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 16.448999999999998 - type: precision_at_5 value: 11.866 - type: recall_at_1 value: 26.700000000000003 - type: recall_at_10 value: 54.301 - type: recall_at_100 value: 75.871 - type: recall_at_1000 value: 92.529 - type: recall_at_3 value: 40.201 - type: recall_at_5 value: 47.208 - type: map_at_1 value: 24.296 - type: map_at_10 value: 33.116 - type: map_at_100 value: 34.81 - type: map_at_1000 value: 35.032000000000004 - type: map_at_3 value: 30.105999999999998 - type: map_at_5 value: 31.839000000000002 - type: mrr_at_1 value: 29.051 - type: mrr_at_10 value: 37.803 - type: mrr_at_100 value: 38.856 - type: mrr_at_1000 value: 38.903999999999996 - type: mrr_at_3 value: 35.211 - type: mrr_at_5 value: 36.545 - type: ndcg_at_1 value: 29.051 - type: ndcg_at_10 value: 39.007 - type: ndcg_at_100 value: 45.321 - type: ndcg_at_1000 value: 47.665 - type: ndcg_at_3 value: 34.1 - type: ndcg_at_5 value: 36.437000000000005 - type: precision_at_1 value: 29.051 - type: precision_at_10 value: 7.668 - type: precision_at_100 value: 1.542 - type: precision_at_1000 value: 0.24 - type: precision_at_3 value: 16.14 - type: precision_at_5 value: 11.897 - type: recall_at_1 value: 24.296 - type: recall_at_10 value: 49.85 - type: recall_at_100 value: 78.457 - type: recall_at_1000 value: 92.618 - type: recall_at_3 value: 36.138999999999996 - type: recall_at_5 value: 42.223 - type: map_at_1 value: 20.591 - type: map_at_10 value: 28.902 - type: map_at_100 value: 29.886000000000003 - type: map_at_1000 value: 29.987000000000002 - type: map_at_3 value: 26.740000000000002 - type: map_at_5 value: 27.976 - type: mrr_at_1 value: 22.366 - type: mrr_at_10 value: 30.971 - type: mrr_at_100 value: 31.865 - type: mrr_at_1000 value: 31.930999999999997 - type: mrr_at_3 value: 28.927999999999997 - type: mrr_at_5 value: 30.231 - type: ndcg_at_1 value: 22.366 - type: ndcg_at_10 value: 33.641 - type: ndcg_at_100 value: 38.477 - type: ndcg_at_1000 value: 41.088 - type: ndcg_at_3 value: 29.486 - type: ndcg_at_5 value: 31.612000000000002 - type: precision_at_1 value: 22.366 - type: precision_at_10 value: 5.3420000000000005 - type: precision_at_100 value: 0.828 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 12.939 - type: precision_at_5 value: 9.094 - type: recall_at_1 value: 20.591 - type: recall_at_10 value: 46.052 - type: recall_at_100 value: 68.193 - type: recall_at_1000 value: 87.638 - type: recall_at_3 value: 34.966 - type: recall_at_5 value: 40.082 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 15.091 - type: map_at_10 value: 26.38 - type: map_at_100 value: 28.421999999999997 - type: map_at_1000 value: 28.621999999999996 - type: map_at_3 value: 21.597 - type: map_at_5 value: 24.12 - type: mrr_at_1 value: 34.266999999999996 - type: mrr_at_10 value: 46.864 - type: mrr_at_100 value: 47.617 - type: mrr_at_1000 value: 47.644 - type: mrr_at_3 value: 43.312 - type: mrr_at_5 value: 45.501000000000005 - type: ndcg_at_1 value: 34.266999999999996 - type: ndcg_at_10 value: 36.095 - type: ndcg_at_100 value: 43.447 - type: ndcg_at_1000 value: 46.661 - type: ndcg_at_3 value: 29.337999999999997 - type: ndcg_at_5 value: 31.824 - type: precision_at_1 value: 34.266999999999996 - type: precision_at_10 value: 11.472 - type: precision_at_100 value: 1.944 - type: precision_at_1000 value: 0.255 - type: precision_at_3 value: 21.933 - type: precision_at_5 value: 17.224999999999998 - type: recall_at_1 value: 15.091 - type: recall_at_10 value: 43.022 - type: recall_at_100 value: 68.075 - type: recall_at_1000 value: 85.76 - type: recall_at_3 value: 26.564 - type: recall_at_5 value: 33.594 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 9.252 - type: map_at_10 value: 20.923 - type: map_at_100 value: 30.741000000000003 - type: map_at_1000 value: 32.542 - type: map_at_3 value: 14.442 - type: map_at_5 value: 17.399 - type: mrr_at_1 value: 70.25 - type: mrr_at_10 value: 78.17 - type: mrr_at_100 value: 78.444 - type: mrr_at_1000 value: 78.45100000000001 - type: mrr_at_3 value: 76.958 - type: mrr_at_5 value: 77.571 - type: ndcg_at_1 value: 58.375 - type: ndcg_at_10 value: 44.509 - type: ndcg_at_100 value: 49.897999999999996 - type: ndcg_at_1000 value: 57.269999999999996 - type: ndcg_at_3 value: 48.64 - type: ndcg_at_5 value: 46.697 - type: precision_at_1 value: 70.25 - type: precision_at_10 value: 36.05 - type: precision_at_100 value: 11.848 - type: precision_at_1000 value: 2.213 - type: precision_at_3 value: 52.917 - type: precision_at_5 value: 45.7 - type: recall_at_1 value: 9.252 - type: recall_at_10 value: 27.006999999999998 - type: recall_at_100 value: 57.008 - type: recall_at_1000 value: 80.697 - type: recall_at_3 value: 15.798000000000002 - type: recall_at_5 value: 20.4 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 50.88 - type: f1 value: 45.545495028653384 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 75.424 - type: map_at_10 value: 83.435 - type: map_at_100 value: 83.66900000000001 - type: map_at_1000 value: 83.685 - type: map_at_3 value: 82.39800000000001 - type: map_at_5 value: 83.07 - type: mrr_at_1 value: 81.113 - type: mrr_at_10 value: 87.77199999999999 - type: mrr_at_100 value: 87.862 - type: mrr_at_1000 value: 87.86500000000001 - type: mrr_at_3 value: 87.17099999999999 - type: mrr_at_5 value: 87.616 - type: ndcg_at_1 value: 81.113 - type: ndcg_at_10 value: 86.909 - type: ndcg_at_100 value: 87.746 - type: ndcg_at_1000 value: 88.017 - type: ndcg_at_3 value: 85.368 - type: ndcg_at_5 value: 86.28099999999999 - type: precision_at_1 value: 81.113 - type: precision_at_10 value: 10.363 - type: precision_at_100 value: 1.102 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 32.507999999999996 - type: precision_at_5 value: 20.138 - type: recall_at_1 value: 75.424 - type: recall_at_10 value: 93.258 - type: recall_at_100 value: 96.545 - type: recall_at_1000 value: 98.284 - type: recall_at_3 value: 89.083 - type: recall_at_5 value: 91.445 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 22.532 - type: map_at_10 value: 37.141999999999996 - type: map_at_100 value: 39.162 - type: map_at_1000 value: 39.322 - type: map_at_3 value: 32.885 - type: map_at_5 value: 35.093999999999994 - type: mrr_at_1 value: 44.29 - type: mrr_at_10 value: 53.516 - type: mrr_at_100 value: 54.24 - type: mrr_at_1000 value: 54.273 - type: mrr_at_3 value: 51.286 - type: mrr_at_5 value: 52.413 - type: ndcg_at_1 value: 44.29 - type: ndcg_at_10 value: 45.268 - type: ndcg_at_100 value: 52.125 - type: ndcg_at_1000 value: 54.778000000000006 - type: ndcg_at_3 value: 41.829 - type: ndcg_at_5 value: 42.525 - type: precision_at_1 value: 44.29 - type: precision_at_10 value: 12.5 - type: precision_at_100 value: 1.9720000000000002 - type: precision_at_1000 value: 0.245 - type: precision_at_3 value: 28.035 - type: precision_at_5 value: 20.093 - type: recall_at_1 value: 22.532 - type: recall_at_10 value: 52.419000000000004 - type: recall_at_100 value: 77.43299999999999 - type: recall_at_1000 value: 93.379 - type: recall_at_3 value: 38.629000000000005 - type: recall_at_5 value: 43.858000000000004 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 39.359 - type: map_at_10 value: 63.966 - type: map_at_100 value: 64.87 - type: map_at_1000 value: 64.92599999999999 - type: map_at_3 value: 60.409 - type: map_at_5 value: 62.627 - type: mrr_at_1 value: 78.717 - type: mrr_at_10 value: 84.468 - type: mrr_at_100 value: 84.655 - type: mrr_at_1000 value: 84.661 - type: mrr_at_3 value: 83.554 - type: mrr_at_5 value: 84.133 - type: ndcg_at_1 value: 78.717 - type: ndcg_at_10 value: 72.03399999999999 - type: ndcg_at_100 value: 75.158 - type: ndcg_at_1000 value: 76.197 - type: ndcg_at_3 value: 67.049 - type: ndcg_at_5 value: 69.808 - type: precision_at_1 value: 78.717 - type: precision_at_10 value: 15.201 - type: precision_at_100 value: 1.764 - type: precision_at_1000 value: 0.19 - type: precision_at_3 value: 43.313 - type: precision_at_5 value: 28.165000000000003 - type: recall_at_1 value: 39.359 - type: recall_at_10 value: 76.003 - type: recall_at_100 value: 88.197 - type: recall_at_1000 value: 95.003 - type: recall_at_3 value: 64.97 - type: recall_at_5 value: 70.41199999999999 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 92.83200000000001 - type: ap value: 89.33560571859861 - type: f1 value: 92.82322915005167 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 21.983 - type: map_at_10 value: 34.259 - type: map_at_100 value: 35.432 - type: map_at_1000 value: 35.482 - type: map_at_3 value: 30.275999999999996 - type: map_at_5 value: 32.566 - type: mrr_at_1 value: 22.579 - type: mrr_at_10 value: 34.882999999999996 - type: mrr_at_100 value: 35.984 - type: mrr_at_1000 value: 36.028 - type: mrr_at_3 value: 30.964999999999996 - type: mrr_at_5 value: 33.245000000000005 - type: ndcg_at_1 value: 22.564 - type: ndcg_at_10 value: 41.258 - type: ndcg_at_100 value: 46.824 - type: ndcg_at_1000 value: 48.037 - type: ndcg_at_3 value: 33.17 - type: ndcg_at_5 value: 37.263000000000005 - type: precision_at_1 value: 22.564 - type: precision_at_10 value: 6.572 - type: precision_at_100 value: 0.935 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.130999999999998 - type: precision_at_5 value: 10.544 - type: recall_at_1 value: 21.983 - type: recall_at_10 value: 62.775000000000006 - type: recall_at_100 value: 88.389 - type: recall_at_1000 value: 97.603 - type: recall_at_3 value: 40.878 - type: recall_at_5 value: 50.690000000000005 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.95120839033288 - type: f1 value: 93.73824125055208 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 76.78978568171455 - type: f1 value: 57.50180552858304 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 76.24411566913248 - type: f1 value: 74.37851403532832 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 79.94620040349699 - type: f1 value: 80.21293397970435 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.44403096245675 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.659594631336812 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.53833075108798 - type: mrr value: 33.78840823218308 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 7.185999999999999 - type: map_at_10 value: 15.193999999999999 - type: map_at_100 value: 19.538 - type: map_at_1000 value: 21.178 - type: map_at_3 value: 11.208 - type: map_at_5 value: 12.745999999999999 - type: mrr_at_1 value: 48.916 - type: mrr_at_10 value: 58.141 - type: mrr_at_100 value: 58.656 - type: mrr_at_1000 value: 58.684999999999995 - type: mrr_at_3 value: 55.521 - type: mrr_at_5 value: 57.239 - type: ndcg_at_1 value: 47.059 - type: ndcg_at_10 value: 38.644 - type: ndcg_at_100 value: 36.272999999999996 - type: ndcg_at_1000 value: 44.996 - type: ndcg_at_3 value: 43.293 - type: ndcg_at_5 value: 40.819 - type: precision_at_1 value: 48.916 - type: precision_at_10 value: 28.607 - type: precision_at_100 value: 9.195 - type: precision_at_1000 value: 2.225 - type: precision_at_3 value: 40.454 - type: precision_at_5 value: 34.985 - type: recall_at_1 value: 7.185999999999999 - type: recall_at_10 value: 19.654 - type: recall_at_100 value: 37.224000000000004 - type: recall_at_1000 value: 68.663 - type: recall_at_3 value: 12.158 - type: recall_at_5 value: 14.674999999999999 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 31.552000000000003 - type: map_at_10 value: 47.75 - type: map_at_100 value: 48.728 - type: map_at_1000 value: 48.754 - type: map_at_3 value: 43.156 - type: map_at_5 value: 45.883 - type: mrr_at_1 value: 35.66 - type: mrr_at_10 value: 50.269 - type: mrr_at_100 value: 50.974 - type: mrr_at_1000 value: 50.991 - type: mrr_at_3 value: 46.519 - type: mrr_at_5 value: 48.764 - type: ndcg_at_1 value: 35.632000000000005 - type: ndcg_at_10 value: 55.786 - type: ndcg_at_100 value: 59.748999999999995 - type: ndcg_at_1000 value: 60.339 - type: ndcg_at_3 value: 47.292 - type: ndcg_at_5 value: 51.766999999999996 - type: precision_at_1 value: 35.632000000000005 - type: precision_at_10 value: 9.267 - type: precision_at_100 value: 1.149 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 21.601 - type: precision_at_5 value: 15.539 - type: recall_at_1 value: 31.552000000000003 - type: recall_at_10 value: 77.62400000000001 - type: recall_at_100 value: 94.527 - type: recall_at_1000 value: 98.919 - type: recall_at_3 value: 55.898 - type: recall_at_5 value: 66.121 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 71.414 - type: map_at_10 value: 85.37400000000001 - type: map_at_100 value: 86.01100000000001 - type: map_at_1000 value: 86.027 - type: map_at_3 value: 82.562 - type: map_at_5 value: 84.284 - type: mrr_at_1 value: 82.24000000000001 - type: mrr_at_10 value: 88.225 - type: mrr_at_100 value: 88.324 - type: mrr_at_1000 value: 88.325 - type: mrr_at_3 value: 87.348 - type: mrr_at_5 value: 87.938 - type: ndcg_at_1 value: 82.24000000000001 - type: ndcg_at_10 value: 88.97699999999999 - type: ndcg_at_100 value: 90.16 - type: ndcg_at_1000 value: 90.236 - type: ndcg_at_3 value: 86.371 - type: ndcg_at_5 value: 87.746 - type: precision_at_1 value: 82.24000000000001 - type: precision_at_10 value: 13.481000000000002 - type: precision_at_100 value: 1.534 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.86 - type: precision_at_5 value: 24.738 - type: recall_at_1 value: 71.414 - type: recall_at_10 value: 95.735 - type: recall_at_100 value: 99.696 - type: recall_at_1000 value: 99.979 - type: recall_at_3 value: 88.105 - type: recall_at_5 value: 92.17999999999999 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 60.22146692057259 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 65.29273320614578 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 5.023 - type: map_at_10 value: 14.161000000000001 - type: map_at_100 value: 16.68 - type: map_at_1000 value: 17.072000000000003 - type: map_at_3 value: 9.763 - type: map_at_5 value: 11.977 - type: mrr_at_1 value: 24.8 - type: mrr_at_10 value: 37.602999999999994 - type: mrr_at_100 value: 38.618 - type: mrr_at_1000 value: 38.659 - type: mrr_at_3 value: 34.117 - type: mrr_at_5 value: 36.082 - type: ndcg_at_1 value: 24.8 - type: ndcg_at_10 value: 23.316 - type: ndcg_at_100 value: 32.613 - type: ndcg_at_1000 value: 38.609 - type: ndcg_at_3 value: 21.697 - type: ndcg_at_5 value: 19.241 - type: precision_at_1 value: 24.8 - type: precision_at_10 value: 12.36 - type: precision_at_100 value: 2.593 - type: precision_at_1000 value: 0.402 - type: precision_at_3 value: 20.767 - type: precision_at_5 value: 17.34 - type: recall_at_1 value: 5.023 - type: recall_at_10 value: 25.069999999999997 - type: recall_at_100 value: 52.563 - type: recall_at_1000 value: 81.525 - type: recall_at_3 value: 12.613 - type: recall_at_5 value: 17.583 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 87.71506247604255 - type: cos_sim_spearman value: 82.91813463738802 - type: euclidean_pearson value: 85.5154616194479 - type: euclidean_spearman value: 82.91815254466314 - type: manhattan_pearson value: 85.5280917850374 - type: manhattan_spearman value: 82.92276537286398 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 87.43772054228462 - type: cos_sim_spearman value: 78.75750601716682 - type: euclidean_pearson value: 85.76074482955764 - type: euclidean_spearman value: 78.75651057223058 - type: manhattan_pearson value: 85.73390291701668 - type: manhattan_spearman value: 78.72699385957797 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 89.58144067172472 - type: cos_sim_spearman value: 90.3524512966946 - type: euclidean_pearson value: 89.71365391594237 - type: euclidean_spearman value: 90.35239632843408 - type: manhattan_pearson value: 89.66905421746478 - type: manhattan_spearman value: 90.31508211683513 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 87.77692637102102 - type: cos_sim_spearman value: 85.45710562643485 - type: euclidean_pearson value: 87.42456979928723 - type: euclidean_spearman value: 85.45709386240908 - type: manhattan_pearson value: 87.40754529526272 - type: manhattan_spearman value: 85.44834854173303 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 88.28491331695997 - type: cos_sim_spearman value: 89.62037029566964 - type: euclidean_pearson value: 89.02479391362826 - type: euclidean_spearman value: 89.62036733618466 - type: manhattan_pearson value: 89.00394756040342 - type: manhattan_spearman value: 89.60867744215236 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 85.08911381280191 - type: cos_sim_spearman value: 86.5791780765767 - type: euclidean_pearson value: 86.16063473577861 - type: euclidean_spearman value: 86.57917745378766 - type: manhattan_pearson value: 86.13677924604175 - type: manhattan_spearman value: 86.56115615768685 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 89.58029496205235 - type: cos_sim_spearman value: 89.49551253826998 - type: euclidean_pearson value: 90.13714840963748 - type: euclidean_spearman value: 89.49551253826998 - type: manhattan_pearson value: 90.13039633601363 - type: manhattan_spearman value: 89.4513453745516 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 69.01546399666435 - type: cos_sim_spearman value: 69.33824484595624 - type: euclidean_pearson value: 70.76511642998874 - type: euclidean_spearman value: 69.33824484595624 - type: manhattan_pearson value: 70.84320785047453 - type: manhattan_spearman value: 69.54233632223537 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 87.26389196390119 - type: cos_sim_spearman value: 89.09721478341385 - type: euclidean_pearson value: 88.97208685922517 - type: euclidean_spearman value: 89.09720927308881 - type: manhattan_pearson value: 88.97513670502573 - type: manhattan_spearman value: 89.07647853984004 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.53075025771936 - type: mrr value: 96.24327651288436 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 60.428000000000004 - type: map_at_10 value: 70.088 - type: map_at_100 value: 70.589 - type: map_at_1000 value: 70.614 - type: map_at_3 value: 67.191 - type: map_at_5 value: 68.515 - type: mrr_at_1 value: 63.333 - type: mrr_at_10 value: 71.13000000000001 - type: mrr_at_100 value: 71.545 - type: mrr_at_1000 value: 71.569 - type: mrr_at_3 value: 68.944 - type: mrr_at_5 value: 70.078 - type: ndcg_at_1 value: 63.333 - type: ndcg_at_10 value: 74.72800000000001 - type: ndcg_at_100 value: 76.64999999999999 - type: ndcg_at_1000 value: 77.176 - type: ndcg_at_3 value: 69.659 - type: ndcg_at_5 value: 71.626 - type: precision_at_1 value: 63.333 - type: precision_at_10 value: 10 - type: precision_at_100 value: 1.09 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 27.111 - type: precision_at_5 value: 17.666999999999998 - type: recall_at_1 value: 60.428000000000004 - type: recall_at_10 value: 87.98899999999999 - type: recall_at_100 value: 96.167 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 74.006 - type: recall_at_5 value: 79.05 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.87326732673267 - type: cos_sim_ap value: 96.81770773701805 - type: cos_sim_f1 value: 93.6318407960199 - type: cos_sim_precision value: 93.16831683168317 - type: cos_sim_recall value: 94.1 - type: dot_accuracy value: 99.87326732673267 - type: dot_ap value: 96.8174218946665 - type: dot_f1 value: 93.6318407960199 - type: dot_precision value: 93.16831683168317 - type: dot_recall value: 94.1 - type: euclidean_accuracy value: 99.87326732673267 - type: euclidean_ap value: 96.81770773701807 - type: euclidean_f1 value: 93.6318407960199 - type: euclidean_precision value: 93.16831683168317 - type: euclidean_recall value: 94.1 - type: manhattan_accuracy value: 99.87227722772278 - type: manhattan_ap value: 96.83164126821747 - type: manhattan_f1 value: 93.54677338669335 - type: manhattan_precision value: 93.5935935935936 - type: manhattan_recall value: 93.5 - type: max_accuracy value: 99.87326732673267 - type: max_ap value: 96.83164126821747 - type: max_f1 value: 93.6318407960199 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 65.6212042420246 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.779230635982564 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 55.217701909036286 - type: mrr value: 56.17658995416349 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.954206018888453 - type: cos_sim_spearman value: 32.71062599450096 - type: dot_pearson value: 30.95420929056943 - type: dot_spearman value: 32.71062599450096 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.22699999999999998 - type: map_at_10 value: 1.924 - type: map_at_100 value: 10.525 - type: map_at_1000 value: 24.973 - type: map_at_3 value: 0.638 - type: map_at_5 value: 1.0659999999999998 - type: mrr_at_1 value: 84 - type: mrr_at_10 value: 91.067 - type: mrr_at_100 value: 91.067 - type: mrr_at_1000 value: 91.067 - type: mrr_at_3 value: 90.667 - type: mrr_at_5 value: 91.067 - type: ndcg_at_1 value: 81 - type: ndcg_at_10 value: 75.566 - type: ndcg_at_100 value: 56.387 - type: ndcg_at_1000 value: 49.834 - type: ndcg_at_3 value: 80.899 - type: ndcg_at_5 value: 80.75099999999999 - type: precision_at_1 value: 84 - type: precision_at_10 value: 79 - type: precision_at_100 value: 57.56 - type: precision_at_1000 value: 21.8 - type: precision_at_3 value: 84.667 - type: precision_at_5 value: 85.2 - type: recall_at_1 value: 0.22699999999999998 - type: recall_at_10 value: 2.136 - type: recall_at_100 value: 13.861 - type: recall_at_1000 value: 46.299 - type: recall_at_3 value: 0.6649999999999999 - type: recall_at_5 value: 1.145 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.752 - type: map_at_10 value: 9.951 - type: map_at_100 value: 16.794999999999998 - type: map_at_1000 value: 18.251 - type: map_at_3 value: 5.288 - type: map_at_5 value: 6.954000000000001 - type: mrr_at_1 value: 38.775999999999996 - type: mrr_at_10 value: 50.458000000000006 - type: mrr_at_100 value: 51.324999999999996 - type: mrr_at_1000 value: 51.339999999999996 - type: mrr_at_3 value: 46.939 - type: mrr_at_5 value: 47.857 - type: ndcg_at_1 value: 36.735 - type: ndcg_at_10 value: 25.198999999999998 - type: ndcg_at_100 value: 37.938 - type: ndcg_at_1000 value: 49.145 - type: ndcg_at_3 value: 29.348000000000003 - type: ndcg_at_5 value: 25.804 - type: precision_at_1 value: 38.775999999999996 - type: precision_at_10 value: 22.041 - type: precision_at_100 value: 7.939 - type: precision_at_1000 value: 1.555 - type: precision_at_3 value: 29.932 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 2.752 - type: recall_at_10 value: 16.197 - type: recall_at_100 value: 49.166 - type: recall_at_1000 value: 84.18900000000001 - type: recall_at_3 value: 6.438000000000001 - type: recall_at_5 value: 9.093 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.47980000000001 - type: ap value: 14.605194452178754 - type: f1 value: 55.07362924988948 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.708545557441994 - type: f1 value: 60.04751270975683 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 53.21105960597211 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 87.58419264469214 - type: cos_sim_ap value: 78.55300004517404 - type: cos_sim_f1 value: 71.49673530889001 - type: cos_sim_precision value: 68.20795400095831 - type: cos_sim_recall value: 75.11873350923483 - type: dot_accuracy value: 87.58419264469214 - type: dot_ap value: 78.55297659559511 - type: dot_f1 value: 71.49673530889001 - type: dot_precision value: 68.20795400095831 - type: dot_recall value: 75.11873350923483 - type: euclidean_accuracy value: 87.58419264469214 - type: euclidean_ap value: 78.55300477331477 - type: euclidean_f1 value: 71.49673530889001 - type: euclidean_precision value: 68.20795400095831 - type: euclidean_recall value: 75.11873350923483 - type: manhattan_accuracy value: 87.5663110210407 - type: manhattan_ap value: 78.49982050876562 - type: manhattan_f1 value: 71.35488740722104 - type: manhattan_precision value: 68.18946862226497 - type: manhattan_recall value: 74.82849604221636 - type: max_accuracy value: 87.58419264469214 - type: max_ap value: 78.55300477331477 - type: max_f1 value: 71.49673530889001 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.09069740365584 - type: cos_sim_ap value: 86.22749303724757 - type: cos_sim_f1 value: 78.36863452005407 - type: cos_sim_precision value: 76.49560117302053 - type: cos_sim_recall value: 80.33569448721897 - type: dot_accuracy value: 89.09069740365584 - type: dot_ap value: 86.22750233655673 - type: dot_f1 value: 78.36863452005407 - type: dot_precision value: 76.49560117302053 - type: dot_recall value: 80.33569448721897 - type: euclidean_accuracy value: 89.09069740365584 - type: euclidean_ap value: 86.22749355597347 - type: euclidean_f1 value: 78.36863452005407 - type: euclidean_precision value: 76.49560117302053 - type: euclidean_recall value: 80.33569448721897 - type: manhattan_accuracy value: 89.08293553770326 - type: manhattan_ap value: 86.21913616084771 - type: manhattan_f1 value: 78.3907031479847 - type: manhattan_precision value: 75.0352013517319 - type: manhattan_recall value: 82.06036341238065 - type: max_accuracy value: 89.09069740365584 - type: max_ap value: 86.22750233655673 - type: max_f1 value: 78.3907031479847 --- # nadeem1362/mxbai-embed-large-v1-Q4_K_M-GGUF This model was converted to GGUF format from [`mixedbread-ai/mxbai-embed-large-v1`](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo nadeem1362/mxbai-embed-large-v1-Q4_K_M-GGUF --model mxbai-embed-large-v1.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo nadeem1362/mxbai-embed-large-v1-Q4_K_M-GGUF --model mxbai-embed-large-v1.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mxbai-embed-large-v1.Q4_K_M.gguf -n 128 ```
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
Dampish/StellarX-4B-V0.2
Dampish
text-generation
[ "transformers", "pytorch", "gpt_neox", "text-generation", "arxiv:2204.06745", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,685
1,695
2,264
2
--- license: cc-by-nc-sa-4.0 --- # StellarX: A Base Model by Dampish and Arkane StellarX is a powerful autoregressive language model designed for various natural language processing tasks. It has been trained on a massive dataset containing 810 billion tokens(trained on 300B tokens), trained on "redpajama," and is built upon the popular GPT-NeoX architecture. With approximately 4 billion parameters, StellarX offers exceptional performance and versatility. ## Model Details - **Training Data:** StellarX is trained on a large-scale dataset provided by "redpajama" maintained by the group "togethercumputer." This dataset has been instrumental in shaping StellarX's language capabilities and general-purpose understanding. - **Model Architecture:** StellarX is built upon the GPT-NeoX architecture, which may, be, inspired by GPT-3 and shares similarities with GPT-J-6B. The architecture incorporates key advancements in transformer-based language models, ensuring high-quality predictions and contextual understanding. - **Model Size:** StellarX consists of approximately 4 billion parameters, making it a highly capable language model for a wide range of natural language processing tasks. - **Carbon-Friendly and Resource-Efficient:** StellarX has been optimized for carbon efficiency and can be comfortably run on local devices. When loaded in 8 bits, the model requires only about 5GB of storage, making it more accessible and convenient for various applications. - **V0.2** Meaning what version it is on, currently version 0.2, Assume version 0.2 has only been trained on 300B tokens and the goal is 810B tokens. The next version aims to have a way higher accuracy. ## How to Use To load StellarX using the Hugging Face Transformers library, you can use the following code snippet: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Dampish/StellarX-4B-V0") model = AutoModelForCausalLM.from_pretrained("Dampish/StellarX-4B-V0") ``` This model is particularly beneficial for those seeking a language model that is powerful, compact, and can be run on local devices without a hefty carbon footprint. Remember, when considering Darius1, it's not just about the impressive numbers—it's about what these numbers represent: powerful performance, optimized resources, and responsible computing. **For any queries related to this model, feel free to reach out to "Dampish#3607" on discord.** ## Licensing and Usage StellarX, developed by the Dampish, is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC-BY-NC-SA-4.0). This license ensures that you can utilize the model for research purposes and personal use without any restrictions, while also promoting the sharing and adaptation of the model under certain conditions. # Research and Personal Use StellarX can be freely used for research purposes, allowing you to explore its capabilities, conduct experiments, and develop novel applications. Whether you're a student, researcher, or hobbyist, the model's availability under the CC-BY-NC-SA-4.0 license empowers you to unlock the potential of StellarX for your own non-commercial projects. # Commercial Usage For commercial usage of StellarX, an additional licensing arrangement must be established. If you intend to leverage the model for any commercial purpose, such as integrating it into a product or service, you are required to reach an agreement with the Dampish. This agreement will specify the terms, including the agreed-upon percentage or licensing fee to be paid for the commercial use of StellarX. To initiate discussions regarding commercial usage, please contact Dampish through the designated channels mentioned earlier. They will be able to provide you with further information and guide you through the process of establishing a licensing arrangement tailored to your specific requirements. # Importance of Licensing Compliance It is crucial to respect the licensing terms to ensure the fair usage and continued development of StellarX. The revenue generated from commercial licensing supports the efforts of the Dampish in advancing the model and making it more widely accessible. # Note on CC-BY-NC-SA-4.0 Under the CC-BY-NC-SA-4.0 license, you are allowed to modify and adapt StellarX, incorporating it into your own projects. However, any derivative work or modifications should also be shared under the same license terms, ensuring the continued openness and collaborative spirit of the project. Please review the complete text of the CC-BY-NC-SA-4.0 license to familiarize yourself with its provisions and requirements. It is essential to comply with the terms of the license to respect the intellectual property rights and contributions of the Dampish and the wider community involved in developing StellarX. ## GPT-NeoX and Model Selection GPT-NeoX-20B, a sibling model to StellarX, is a 20 billion parameter autoregressive language model trained on the Pile using the GPT-NeoX library. StellarX draws inspiration from the architectural advancements and performance of GPT-NeoX models. While the specifics of StellarX's architecture and parameters may differ, it benefits from the proven capabilities of GPT-NeoX and its suitability for diverse natural language processing tasks. ## Training and Evaluation StellarX's training dataset comprises a comprehensive collection of English-language texts, covering various domains, thanks to the efforts of "redpajama" dataset by the group "togethercumputer" group. Evaluation of GPT-NeoX 20B performance has demonstrated its competence across different natural language tasks. Although since this description provides a brief summary, we refer to the GPT-NeoX Paper https://arxiv.org/abs/2204.06745, comparing GPT-NeoX 20B to other models on tasks such as OpenAI's LAMBADA, SciQ, PIQA, TriviaQA, and ARC Challenge. ## Limitations and Considerations StellarX, like its sibling models, is intended primarily for research purposes. It provides a powerful foundation for extracting useful features and insights from the English language. While StellarX can be further fine-tuned and adapted for deployment, users should conduct their own risk and bias assessments before using it as a basis for downstream tasks. It's important to note that StellarX is not intended for direct deployment without supervision. It is not designed for human-facing interactions, unlike models like ChatGPT, which have been fine-tuned using reinforcement learning from human feedback to better understand human instructions and dialogue. Furthermore, StellarX is not limited to the English language if trained properly and can sometimes be used for translation aswell as text generation in other languages. Lastly, users should be aware of potential biases and limitations inherent in Special thanks to the group that created the training dataset. The Redpajama dataset, used to train StellarX, thank you togethercumputer. ## Community and Support To inquire about StellarX and receive support, you can join the Dampish's server and engage in discussions in the #questions channel. It is recommended to explore the existing documentation and resources available for GPT-NeoX-20B to familiarize yourself with the model before seeking assistance on. For better information about GPT-NeoX, you can reach out to eleutherAI. ## Summary StellarX, a base language model developed by the Dampish, offers impressive language capabilities and flexibility. Trained on an extensive dataset and built upon the GPT-NeoX architecture, StellarX excels in various natural language processing tasks. Its carbon-friendly and resource-efficient design makes it accessible for local device deployment. Researchers and enthusiasts can freely explore StellarX for research purposes and personal use, while commercial users should adhere to the licensing terms. **Again i am really grateful for the data made by togethercumputers and their willingness to opensource, they inspired this project and sparked the idea in Stellar-models, i am truly really really grateful to them. -dampish** Discord: https://discord.gg/vasyNnUa OR Reach out to me personally on Discord via the username: Dampish#3607 Thank you for your time.
[ "TRANSLATION" ]
[ "SCIQ" ]
Non_BioNLP
RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf
RichardErkhov
null
[ "gguf", "endpoints_compatible", "region:us", "conversational" ]
1,721
1,721
32
0
--- {} --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) vi-gemma-2b-RAG - GGUF - Model creator: https://huggingface.co/himmeow/ - Original model: https://huggingface.co/himmeow/vi-gemma-2b-RAG/ | Name | Quant method | Size | | ---- | ---- | ---- | | [vi-gemma-2b-RAG.Q2_K.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q2_K.gguf) | Q2_K | 1.08GB | | [vi-gemma-2b-RAG.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.IQ3_XS.gguf) | IQ3_XS | 1.16GB | | [vi-gemma-2b-RAG.IQ3_S.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.IQ3_S.gguf) | IQ3_S | 1.2GB | | [vi-gemma-2b-RAG.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q3_K_S.gguf) | Q3_K_S | 1.2GB | | [vi-gemma-2b-RAG.IQ3_M.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.IQ3_M.gguf) | IQ3_M | 1.22GB | | [vi-gemma-2b-RAG.Q3_K.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q3_K.gguf) | Q3_K | 1.29GB | | [vi-gemma-2b-RAG.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q3_K_M.gguf) | Q3_K_M | 1.29GB | | [vi-gemma-2b-RAG.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q3_K_L.gguf) | Q3_K_L | 1.36GB | | [vi-gemma-2b-RAG.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.IQ4_XS.gguf) | IQ4_XS | 1.4GB | | [vi-gemma-2b-RAG.Q4_0.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q4_0.gguf) | Q4_0 | 1.44GB | | [vi-gemma-2b-RAG.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.IQ4_NL.gguf) | IQ4_NL | 1.45GB | | [vi-gemma-2b-RAG.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q4_K_S.gguf) | Q4_K_S | 1.45GB | | [vi-gemma-2b-RAG.Q4_K.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q4_K.gguf) | Q4_K | 1.52GB | | [vi-gemma-2b-RAG.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q4_K_M.gguf) | Q4_K_M | 1.52GB | | [vi-gemma-2b-RAG.Q4_1.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q4_1.gguf) | Q4_1 | 1.56GB | | [vi-gemma-2b-RAG.Q5_0.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q5_0.gguf) | Q5_0 | 1.68GB | | [vi-gemma-2b-RAG.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q5_K_S.gguf) | Q5_K_S | 1.68GB | | [vi-gemma-2b-RAG.Q5_K.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q5_K.gguf) | Q5_K | 1.71GB | | [vi-gemma-2b-RAG.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q5_K_M.gguf) | Q5_K_M | 1.71GB | | [vi-gemma-2b-RAG.Q5_1.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q5_1.gguf) | Q5_1 | 1.79GB | | [vi-gemma-2b-RAG.Q6_K.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q6_K.gguf) | Q6_K | 1.92GB | | [vi-gemma-2b-RAG.Q8_0.gguf](https://huggingface.co/RichardErkhov/himmeow_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q8_0.gguf) | Q8_0 | 2.49GB | Original model description: --- base_model: unsloth/gemma-1.1-2b-it-bnb-4bit language: - en - vi license: apache-2.0 tags: - text-generation-inference - retrieval-augmented-generation - transformers - unsloth - gemma - trl - sft --- ## Model Card: vi-gemma-2b-RAG ### Tiếng Việt (Vietnamese) **Mô tả mô hình:** vi-gemma-2b-RAG là một mô hình ngôn ngữ lớn được tinh chỉnh từ mô hình cơ sở [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it) sử dụng kỹ thuật LoRA. Mô hình được huấn luyện trên tập dữ liệu tiếng Việt với mục tiêu cải thiện khả năng xử lý ngôn ngữ tiếng Việt và nâng cao hiệu suất cho các tác vụ truy xuất thông tin mở (Retrieval Augmented Generation - RAG). **Mục đích sử dụng:** Mô hình vi-gemma-2b-RAG phù hợp cho các tác vụ sau: * Trả lời câu hỏi dựa trên ngữ cảnh tiếng Việt. * Tóm tắt văn bản tiếng Việt. * Dịch máy tiếng Việt. * Và các tác vụ tạo văn bản tiếng Việt khác. **Giới hạn:** Mặc dù đã được tinh chỉnh cho tiếng Việt, vi-gemma-2b-RAG vẫn có thể gặp phải một số hạn chế: * Có thể tạo ra thông tin sai lệch hoặc không chính xác. * Có thể thể hiện thành kiến ​​hoặc quan điểm không phù hợp. * Hiệu suất có thể bị ảnh hưởng bởi chất lượng của dữ liệu đầu vào. **Cách sử dụng:** Dưới đây chúng tôi chia sẻ một số đoạn mã về cách bắt đầu nhanh chóng để sử dụng mô hình. Trước tiên, hãy đảm bảo đã cài đặt `pip install -U transformers`, sau đó sao chép đoạn mã từ phần có liên quan đến usecase của bạn. Chúng tôi khuyến nghị sử dụng `torch.bfloat16` làm mặc định. ```python # pip install transformers torch accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Khởi tạo tokenizer và model từ checkpoint đã lưu tokenizer = AutoTokenizer.from_pretrained("himmeow/vi-gemma-2b-RAG") model = AutoModelForCausalLM.from_pretrained( "himmeow/vi-gemma-2b-RAG", device_map="auto", torch_dtype=torch.bfloat16 ) # Sử dụng GPU nếu có if torch.cuda.is_available(): model.to("cuda") # Định dạng prompt cho model prompt = """ ### Instruction and Input: Dựa vào ngữ cảnh/tài liệu sau: {} Hãy trả lời câu hỏi: {} ### Response: {} """ # Chuẩn bị dữ liệu đầu vào input_data = """ Short Tandem Repeats (STRs) là các trình tự DNA lặp lại ngắn (2- 6 nucleotides) xuất hiện phổ biến trong hệ gen của con người. Các trình tự này có tính đa hình rất cao trong tự nhiên, điều này khiến các STRs trở thành những markers di truyền rất quan trọng trong nghiên cứu bản đồ gen người và chuẩn đoán bệnh lý di truyền cũng như xác định danh tính trong lĩnh vực pháp y. Các STRs trở nên phổ biến tại các phòng xét nghiệm pháp y bởi vì việc nhân bản và phân tích STRs chỉ cần lượng DNA rất thấp ngay cả khi ở dạng bị phân hủy việc đinh danh vẫn có thể được thực hiện thành công. Hơn nữa việc phát hiện và đánh giá sự nhiễm DNA mẫu trong các mẫu vật có thể được giải quyết nhanh với kết quả phân tích STRs. Ở Hoa Kỳ hiện nay, từ bộ 13 markers nay đã tăng lên 20 markers chính đang được sử dụng để tạo ra một cơ sở dữ liệu DNA trên toàn đất nước được gọi là The FBI Combined DNA Index System (Expaned CODIS). CODIS và các cơ sử dữ liệu DNA tương tự đang được sử dụng thực sự thành công trong việc liên kết các hồ sơ DNA từ các tội phạm và các bằng chứng hiện trường vụ án. Kết quả định danh STRs cũng được sử dụng để hỗ trợ hàng trăm nghìn trường hợp xét nghiệm huyết thống cha con mỗi năm' """ query = "Hãy cho tôi biết một số tính chất của STRs được dùng để làm gì?" # Định dạng input text input_text = prompt.format(input_data, query," ") # Mã hóa input text thành input ids input_ids = tokenizer(input_text, return_tensors="pt") # Sử dụng GPU cho input ids nếu có if torch.cuda.is_available(): input_ids = input_ids.to("cuda") # Tạo văn bản bằng model outputs = model.generate( **input_ids, max_new_tokens=500, no_repeat_ngram_size=5, # Ngăn chặn lặp lại các cụm từ 5 gram # do_sample=True, # Kích hoạt chế độ tạo văn bản dựa trên lấy mẫu. Trong chế độ này, model sẽ chọn ngẫu nhiên token tiếp theo dựa trên xác suất được tính từ phân phối xác suất của các token. # temperature=0.7, # Giảm temperature để kiểm soát tính ngẫu nhiên # early_stopping=True, # Dừng tạo văn bản khi tìm thấy kết thúc phù hợp ) # Giải mã và in kết quả print(tokenizer.decode(outputs[0])) ''' <bos> ### Instruction and Input: Dựa vào ngữ cảnh/tài liệu sau: Short Tandem Repeats (STRs) là các trình tự DNA lặp lại ngắn (2- 6 nucleotides) xuất hiện phổ biến trong hệ gen của con người. Các trình tự này có tính đa hình rất cao trong tự nhiên, điều này khiến các STRs trở thành những markers di truyền rất quan trọng trong nghiên cứu bản đồ gen người và chuẩn đoán bệnh lý di truyền cũng như xác định danh tính trong lĩnh vực pháp y. Các STRs trở nên phổ biến tại các phòng xét nghiệm pháp y bởi vì việc nhân bản và phân tích STRs chỉ cần lượng DNA rất thấp ngay cả khi ở dạng bị phân hủy việc đinh danh vẫn có thể được thực hiện thành công. Hơn nữa việc phát hiện và đánh giá sự nhiễm DNA mẫu trong các mẫu vật có thể được giải quyết nhanh với kết quả phân tích STRs. Ở Hoa Kỳ hiện nay, từ bộ 13 markers nay đã tăng lên 20 markers chính đang được sử dụng để tạo ra một cơ sở dữ liệu DNA trên toàn đất nước được gọi là The FBI Combined DNA Index System (Expaned CODIS). CODIS và các cơ sử dữ liệu DNA tương tự đang được sử dụng thực sự thành công trong việc liên kết các hồ sơ DNA từ các tội phạm và các bằng chứng hiện trường vụ án. Kết quả định danh STRs cũng được sử dụng để hỗ trợ hàng trăm nghìn trường hợp xét nghiệm huyết thống cha con mỗi năm' Hãy trả lời câu hỏi: Hãy cho tôi biết một số tính chất của STRs được dùng để làm gì? ### Response: STRs được sử dụng để xác định danh tính, chuẩn đoán bệnh lý và xác định bệnh lý di truyền. <eos> ''' ``` **Huấn luyện:** * **Mô hình cơ sở:** google/gemma-1.1-2b-it * **Tập dữ liệu:** lamhieu/mabrycodes_dialogue_vi * **Phương pháp tinh chỉnh:** LoRA, PEFT với Unsloth ## Model Card: vi-gemma-2b-RAG ### English **Model Description:** vi-gemma-2b-RAG is a large language model fine-tuned from the base model [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it) using LoRA. The model is trained on a Vietnamese dataset to improve its Vietnamese language processing capabilities and enhance its performance for Retrieval Augmented Generation (RAG) tasks. **Intended Use:** The vi-gemma-2b-RAG model is suitable for tasks such as: * Vietnamese question answering. * Vietnamese text summarization. * Vietnamese machine translation. * And other Vietnamese text generation tasks. **Limitations:** While fine-tuned for Vietnamese, vi-gemma-2b-RAG may still have some limitations: * It may generate incorrect or misleading information. * It may exhibit biases or inappropriate opinions. * Its performance may be affected by the quality of the input data. **How to Use:** ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. We recommend `torch.bfloat16` as the default dtype. ```python # pip install transformers torch accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Initialize the tokenizer and model from the saved checkpoint tokenizer = AutoTokenizer.from_pretrained("himmeow/vi-gemma-2b-RAG") model = AutoModelForCausalLM.from_pretrained( "himmeow/vi-gemma-2b-RAG", device_map="auto", torch_dtype=torch.bfloat16 ) # Use GPU if available if torch.cuda.is_available(): model.to("cuda") # Define the prompt format for the model prompt = """ ### Instruction and Input: Based on the following context/document: {} Please answer the question: {} ### Response: {} """ # Prepare the input data input_data = """ Short Tandem Repeats (STRs) are short (2-6 nucleotides) repeating DNA sequences that are widespread in the human genome. These sequences are highly polymorphic in nature, which makes STRs very important genetic markers in human gene mapping and diagnosis of hereditary diseases as well as identification in the field of forensics. STRs have become popular in forensic laboratories because the replication and analysis of STRs requires very small amounts of DNA, even in decomposed form, identification can still be performed successfully. Furthermore, the detection and assessment of sample DNA contamination in specimens can be quickly resolved with STR analysis results. In the United States today, the set of 13 markers has now been increased to 20 main markers being used to create a nationwide DNA database called The FBI Combined DNA Index System (Expaned CODIS). CODIS and similar DNA databases are being used very successfully in linking DNA records from criminals and crime scene evidence. STR identification results are also used to support hundreds of thousands of paternity test cases each year.' """ query = "Tell me what are some properties of STRs used for?" # Format the input text input_text = prompt.format(input_data, query," ") # Encode the input text into input ids input_ids = tokenizer(input_text, return_tensors="pt") # Use GPU for input ids if available if torch.cuda.is_available(): input_ids = input_ids.to("cuda") # Generate text using the model outputs = model.generate( **input_ids, max_new_tokens=500, # Limit the number of tokens generated no_repeat_ngram_size=5, # Prevent repetition of 5-gram phrases # do_sample=True, # temperature=0.7, # Adjust the randomness of the generated text # early_stopping=True, # Stop generating text when a suitable ending is found ) # Decode and print the results print(tokenizer.decode(outputs[0])) ``` **Training:** * **Base Model:** google/gemma-1.1-2b-it * **Dataset:** lamhieu/mabrycodes_dialogue_vi * **Fine-tuning Method:** LoRA, PEFT and Unsloth **Using example repository:** https://github.com/Martincrux/Vietnamese-RAG-system-building-with-vi-gemma-2b-RAG-and-halong_embedding # Uploaded model - **Developed by:** [hiieu](https://huggingface.co/hiieu), [himmeow the coder](https://viblo.asia/u/MartinCrux), [cuctrinh](https://www.linkedin.com/in/trinh-cuc-5722832b6) - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-1.1-2b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
[ "QUESTION_ANSWERING", "TRANSLATION", "SUMMARIZATION" ]
[ "CHIA" ]
Non_BioNLP
mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF
mradermacher
null
[ "transformers", "gguf", "code", "text-generation-inference", "Information Extraction", "IE", "Named Entity Recogniton", "Event Extraction", "Relation Extraction", "LLaMA", "en", "dataset:ACE05", "dataset:bc5cdr", "dataset:conll2003", "dataset:ncbi_disease", "dataset:conll2012_ontonotesv5", "dataset:rams", "dataset:tacred", "dataset:wnut_17", "base_model:KaraKaraWitch/HiTZ-GoLLIE-13B-AsSafeTensors", "base_model:quantized:KaraKaraWitch/HiTZ-GoLLIE-13B-AsSafeTensors", "license:llama2", "endpoints_compatible", "region:us", "imatrix" ]
1,740
1,740
949
1
--- base_model: KaraKaraWitch/HiTZ-GoLLIE-13B-AsSafeTensors datasets: - ACE05 - bc5cdr - conll2003 - ncbi_disease - conll2012_ontonotesv5 - rams - tacred - wnut_17 language: - en library_name: transformers license: llama2 tags: - code - text-generation-inference - Information Extraction - IE - Named Entity Recogniton - Event Extraction - Relation Extraction - LLaMA quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/KaraKaraWitch/HiTZ-GoLLIE-13B-AsSafeTensors <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-IQ1_S.gguf) | i1-IQ1_S | 3.0 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-IQ1_M.gguf) | i1-IQ1_M | 3.2 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-IQ2_S.gguf) | i1-IQ2_S | 4.3 | | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.5 | very low quality | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-IQ2_M.gguf) | i1-IQ2_M | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-Q2_K.gguf) | i1-Q2_K | 5.0 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-IQ3_S.gguf) | i1-IQ3_S | 5.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-IQ3_M.gguf) | i1-IQ3_M | 6.1 | | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-Q3_K_L.gguf) | i1-Q3_K_L | 7.0 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.5 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-Q4_0.gguf) | i1-Q4_0 | 7.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.5 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-Q4_K_M.gguf) | i1-Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-Q4_1.gguf) | i1-Q4_1 | 8.3 | | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-Q5_K_S.gguf) | i1-Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-Q5_K_M.gguf) | i1-Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/HiTZ-GoLLIE-13B-AsSafeTensors-i1-GGUF/resolve/main/HiTZ-GoLLIE-13B-AsSafeTensors.i1-Q6_K.gguf) | i1-Q6_K | 10.8 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
[ "RELATION_EXTRACTION", "EVENT_EXTRACTION" ]
[ "BC5CDR", "NCBI DISEASE" ]
Non_BioNLP
fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-74504128
fine-tuned
feature-extraction
[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "mteb", "en", "dataset:fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-74504128", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,716
1,716
6
0
--- datasets: - fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-74504128 - allenai/c4 language: - en - en license: apache-2.0 pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [**BAAI/bge-m3**](https://huggingface.co/BAAI/bge-m3) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-74504128', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
[ "TEXT_CLASSIFICATION" ]
[ "SCIFACT" ]
Non_BioNLP
microsoft/prophetnet-large-uncased-cnndm
microsoft
text2text-generation
[ "transformers", "pytorch", "rust", "prophetnet", "text2text-generation", "en", "dataset:cnn_dailymail", "arxiv:2001.04063", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,646
1,674
965
2
--- datasets: - cnn_dailymail language: en --- ## prophetnet-large-uncased-cnndm Fine-tuned weights(converted from [original fairseq version repo](https://github.com/microsoft/ProphetNet)) for [ProphetNet](https://arxiv.org/abs/2001.04063) on summarization task CNN/DailyMail. ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction. ProphetNet is able to predict more future tokens with a n-stream decoder. The original implementation is Fairseq version at [github repo](https://github.com/microsoft/ProphetNet). ### Usage ``` from transformers import ProphetNetTokenizer, ProphetNetForConditionalGeneration, ProphetNetConfig model = ProphetNetForConditionalGeneration.from_pretrained('microsoft/prophetnet-large-uncased-cnndm') tokenizer = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased-cnndm') ARTICLE_TO_SUMMARIZE = "USTC was founded in Beijing by the Chinese Academy of Sciences (CAS) in September 1958. The Director of CAS, Mr. Guo Moruo was appointed the first president of USTC. USTC's founding mission was to develop a high-level science and technology workforce, as deemed critical for development of China's economy, defense, and science and technology education. The establishment was hailed as \"A Major Event in the History of Chinese Education and Science.\" CAS has supported USTC by combining most of its institutes with the departments of the university. USTC is listed in the top 16 national key universities, becoming the youngest national key university.".lower() inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=100, return_tensors='pt') # Generate Summary summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=512, early_stopping=True) tokenizer.batch_decode(summary_ids, skip_special_tokens=True) # should give: 'ustc was founded in beijing by the chinese academy of sciences in 1958. [X_SEP] ustc\'s mission was to develop a high - level science and technology workforce. [X_SEP] the establishment was hailed as " a major event in the history of chinese education and science "' ``` Here, [X_SEP] is used as a special token to seperate sentences. ### Citation ```bibtex @article{yan2020prophetnet, title={Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training}, author={Yan, Yu and Qi, Weizhen and Gong, Yeyun and Liu, Dayiheng and Duan, Nan and Chen, Jiusheng and Zhang, Ruofei and Zhou, Ming}, journal={arXiv preprint arXiv:2001.04063}, year={2020} } ```
[ "SUMMARIZATION" ]
[ "CAS" ]
Non_BioNLP
EleutherAI/pythia-1b
EleutherAI
text-generation
[ "transformers", "pytorch", "safetensors", "gpt_neox", "text-generation", "causal-lm", "pythia", "en", "dataset:the_pile", "arxiv:2304.01373", "arxiv:2101.00027", "arxiv:2201.07311", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,678
1,688
64,849
37
--- datasets: - the_pile language: - en license: apache-2.0 tags: - pytorch - causal-lm - pythia --- The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf). It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. We also provide 154 intermediate checkpoints per model, hosted on Hugging Face as branches. The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. <details> <summary style="font-weight:600">Details on previous early release and naming convention.</summary> Previously, we released an early version of the Pythia suite to the public. However, we decided to retrain the model suite to address a few hyperparameter discrepancies. This model card <a href="#changelog">lists the changes</a>; see appendix B in the Pythia paper for further discussion. We found no difference in benchmark performance between the two Pythia versions. The old models are [still available](https://huggingface.co/models?other=pythia_v0), but we suggest the retrained suite if you are just starting to use Pythia.<br> **This is the current release.** Please note that all models in the *Pythia* suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. </details> <br> # Pythia-1B ## Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. [See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation details. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing *Pythia* documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> | Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models | | -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: | | 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — | | 160M | 85,056,000 | 12 | 768 | 12 | 2M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M | | 410M | 302,311,424 | 24 | 1024 | 16 | 2M | 3.0 x 10<sup>-4</sup> | OPT-350M | | 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — | | 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 2M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B | | 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B | | 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B | | 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — | <figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have <b>exactly</b> the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ## Uses and Limitations ### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. We also provide 154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints `step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to `step143000`. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-1B for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-1B as a basis for your fine-tuned model, please conduct your own risk and bias assessment. ### Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. For example, the model may generate harmful or offensive text. Please evaluate the risks associated with your particular use case. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-1B has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-1B will **not** respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “follow” human instructions. ### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token used by the model need not produce the most “accurate” text. Never rely on Pythia-1B to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-1B may produce socially unacceptable or undesirable text, *even if* the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-1B. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(**inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model.<br> For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ## Training ### Training data [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/).<br> The Pile was **not** deduplicated before being used to train Pythia-1B. ### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training, from `step1000` to `step143000` (which is the same as `main`). In addition, we also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All *Pythia* models trained for 143000 steps at a batch size of 2M (2,097,152 tokens).<br> See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br> Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ## Evaluations All 16 *Pythia* models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/*` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br> Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge—Easy Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/> </details> ## Changelog This section compares differences between previously released [Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current models. See Appendix B of the Pythia paper for further discussion of these changes and the motivation behind them. We found that retraining Pythia had no impact on benchmark performance. - All model sizes are now trained with uniform batch size of 2M tokens. Previously, the models of size 160M, 410M, and 1.4B parameters were trained with batch sizes of 4M tokens. - We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64, 128,256,512} in addition to every 1000 training steps. - Flash Attention was used in the new retrained suite. - We remedied a minor inconsistency that existed in the original suite: all models of size 2.8B parameters or smaller had a learning rate (LR) schedule which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and 12B models all used an LR schedule which decayed to a minimum LR of 0. In the redone training runs, we rectified this inconsistency: all models now were trained with LR decaying to a minimum of 0.1× their maximum LR. ### Naming convention and parameter count *Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> | current Pythia suffix | old suffix | total params | non-embedding params | | --------------------: | ---------: | -------------: | -------------------: | | 70M | 19M | 70,426,624 | 18,915,328 | | 160M | 125M | 162,322,944 | 85,056,000 | | 410M | 350M | 405,334,016 | 302,311,424 | | 1B | 800M | 1,011,781,632 | 805,736,448 | | 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 | | 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 | | 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 | | 12B | 13B | 11,846,072,320 | 11,327,027,200 | </figure>
[ "QUESTION_ANSWERING", "TRANSLATION" ]
[ "SCIQ" ]
Non_BioNLP
blockblockblock/Dark-Miqu-70B-bpw6-exl2
blockblockblock
text-generation
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:2403.19522", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]
1,715
1,715
10
0
--- license: other --- ![Dark-Miqu.png](Dark-Miqu.png) ***NOTE***: *For a full range of GGUF quants kindly provided by @mradermacher: [Static](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF) and [IMatrix](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF).* A "dark" creative writing model with 32k context. Based off [miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b) but with greatly reduced "positivity" and "-isms". If you want happy endings, look elsewhere! This model **excels** at writing Dark/Grimdark fantasy (see examples below). # Model background Created using [Mergekit](https://github.com/arcee-ai/mergekit) and based on @sophosympatheia's template for [Midnight-Miqu-70B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.0). This model has a lower perplexity compared to [Midnight-Miqu-70B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.0) (`'4.08 +/- 0.02'` vs `'4.02 +/- 0.02'`). It also generates longer responses when prompted. The model was created in two stages: - First, three "Midnight-Miqu-esque" models were produced using spherical interpolation (slerp) merges between [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) and each of the following models: [Midnight-Rose-70B-v2.0.3](https://huggingface.co/sophosympatheia/Midnight-Rose-70B-v2.0.3), [Euryale-1.3-L2-70B](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B) and [WinterGoddess-1.4x-70B-L2](https://huggingface.co/Sao10K/WinterGoddess-1.4x-70B-L2). These models were selected for their dark, imaginative writing styles. Various slerp-merges between [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) and other models were also experimented with, but these three yielded the darkest creative writing results. - In the second stage, the three slerp-merged models were combined into a single model using the '[Model Stock](https://arxiv.org/abs/2403.19522)' method, with [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) serving as the base model. # Prompting format Vicuna format is preferred: ``` USER: {prompt} ASSISTANT: ``` Mistral and Alpaca formats are also supported: ``` [INST] {prompt} [/INST] ``` ``` ### Instruction: {prompt} ### Response: ``` # Licence and usage restrictions [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) is a dequantized version of the [miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b) model leaked from MistralAI. All miqu-derived models, including this merge, are suitable for non-commercial, personal use only. # Mergekit configuration The following YAML configuration was used to produce this model: ```yaml name: midnight-miqu-70b models: - model: 152334H/miqu-1-70b-sf - model: sophosympatheia/Midnight-Rose-70B-v2.0.3 base_model: 152334H/miqu-1-70b-sf merge_method: slerp parameters: t: - value: [0, 0, 0.2, 0.3, 0.4, 0.5, 0.4, 0.3, 0.2, 0, 0] embed_slerp: true tokenizer_source: model:miqu-1-70b-sf dtype: float16 --- name: euryale-miqu-70b models: - model: 152334H/miqu-1-70b-sf - model: Sao10K/Euryale-1.3-L2-70B base_model: 152334H/miqu-1-70b-sf merge_method: slerp parameters: t: - value: [0, 0, 0.2, 0.3, 0.4, 0.5, 0.4, 0.3, 0.2, 0, 0] embed_slerp: true tokenizer_source: model:miqu-1-70b-sf dtype: float16 --- name: winter-miqu-70b models: - model: 152334H/miqu-1-70b-sf - model: Sao10K/WinterGoddess-1.4x-70B-L2 base_model: 152334H/miqu-1-70b-sf merge_method: slerp parameters: t: - value: [0, 0, 0.2, 0.3, 0.4, 0.5, 0.4, 0.3, 0.2, 0, 0] embed_slerp: true tokenizer_source: model:miqu-1-70b-sf dtype: float16 --- name: dark-miqu-70b models: - model: 152334H/miqu-1-70b-sf - model: midnight-miqu-70b - model: euryale-miqu-70b - model: winter-miqu-70b base_model: 152334H/miqu-1-70b-sf merge_method: model_stock dtype: float16 ``` ## Key configuration details: - '`merge_method: slerp`' uses spherical linear interpolation for merging models. - '`parameters: t`' controls the interpolation ratios between models. - '`embed_slerp: true`' applies slerp to the embedding layers. - '`merge_method: model_stock`' uses the '[Model Stock](https://arxiv.org/abs/2403.19522)' method. See the [Mergekit documentation](https://github.com/arcee-ai/mergekit) for more on these settings. **NOTE**: Run with `mergekit-mega` rather than `mergekit` as there are 4 documents in this one file. # Example stories The following mix of "dark" stories were generated using the Vicuna prompt format with no system message and temperature=0: ## Dark fantasy stories <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a 'Grimdark' stories trilogy in the style of Joe Abercrombie and Rob J Hayes ### ASSISTANT Chapter One: The Crimson Dawn The world was a cruel, unforgiving place, and the city of Vhyrd was its beating, blackened heart. Nestled in the crook of the Serpent's Spine mountains, it was a place where the sun struggled to shine, and the shadows held more sway than the law. It was a city of thieves, murderers, and worse, and it was home to the man who would become known as the Butcher of the Broken Docks. His name was Rennick, but that meant little to the people who feared him. He was a tall, broad-shouldered figure, with a face that had been carved by a drunken sculptor with a penchant for sharp angles and harsh lines. His eyes were a cold, flat grey, like the steel of his twin blades, and his hair was the color of dried blood. He moved through the crowded streets with a predator's grace, his cloak billowing behind him like a banner of death. Rennick had a job to do, and he was good at his job. He was a killer, a thief-taker, a man who dealt in the currency of pain and suffering. He had been hired to find a girl, a slip of a thing who had stolen something valuable from the wrong people. He didn't know what it was, and he didn't care. All that mattered was the coin that would jingle in his purse when he brought her back, preferably in several small, bloody pieces. He had tracked her to the Broken Docks, a maze of rotting wood and crumbling stone that clung to the edge of the city like a leech to a corpse. It was a place where the desperate went to die, and the desperate went to prey on the desperate. It was a place where the screams of the damned were drowned out by the cries of the gulls and the lap of the oily water against the pilings. Rennick moved through the shadows, his blades at the ready. He could feel the eyes on him, the whispers of his name on the wind. He reveled in it. Fear was a weapon, and he wielded it with the same skill as his swords. He could hear the girl's heartbeat, fast and frantic, like a rabbit's. She was close. He turned a corner and found himself in a narrow alley, the walls pressing in on him like the jaws of a trap. At the far end, he saw her, a small, trembling figure huddled in the doorway of a derelict building. She was young, no more than sixteen summers, with matted hair and ragged clothes. She looked up at him with wide, terrified eyes, and he could see the whites of her teeth as she bit her lip to keep from crying out. He advanced on her slowly, savoring the moment. He could almost taste the fear on her, like a fine wine. "Come now, lass," he said, his voice a low, dangerous purr. "You know you can't run from me. Give me what you took, and I might let you live." She shook her head, her eyes darting left and right, searching for an escape. Rennick smiled, a cold, cruel thing that didn't reach his eyes. "You're not thinking straight," he said. "You're just a child. You don't want to die here, in the gutter, like a rat." She spat at him, a glob of phlegm and hate that landed on his boot. He raised an eyebrow, impressed despite himself. "Feisty," he said. "I like that. It'll make this more fun." He lunged forward, his blades flashing in the dim light. She screamed and tried to run, but he was too fast. He caught her by the arm, his grip like iron, and yanked her back towards him. She kicked and struggled, but it was like trying to fight a storm. He slammed her against the wall, his forearm pressed against her throat, cutting off her air. "Where is it?" he growled, his breath hot and sour in her face. "Tell me, and I'll make it quick." She clawed at his arm, her nails leaving bloody furrows in his flesh. He didn't flinch. He was used to pain. He had been born in it, raised in it, bathed in it. It was his constant companion, his only friend. "I...I don't have it," she gasped, her voice raw and desperate. "I swear, I don't!" Rennick chuckled, a sound that sent shivers down her spine. "Lying to me is a bad idea, lass," he said. "I have ways of making people talk." He released her throat and slammed his fist into her stomach, doubling her over. She retched, but there was nothing in her stomach to come up. He grabbed a handful of her hair and yanked her head back, exposing her throat. He could see the pulse hammering in her neck, like a trapped bird trying to escape. "Tell me," he said, his voice a whisper. "Tell me, and I'll end this." She spat in his face, a defiant gesture that would be her last. He brought his blade up, the steel glinting in the moonlight, and sliced her throat from ear to ear. Blood sprayed across the cobblestones, warm and wet, and she slumped to the ground, her life draining away like water through a sieve. Rennick wiped the blood from his face and sheathed his blades. He didn't feel anything. No satisfaction, no remorse. Just the cold, empty void that had been his constant companion since the day he had taken his first life. He searched her body, but found nothing. He cursed under his breath. He would have to find the thing she had stolen the hard way. He turned to leave the alley, but something made him pause. A sound, barely audible over the cries of the gulls and the lapping of the waves. A soft, wet sound, like the patter of rain on a rooftop. He looked down and saw that the girl's body was twitching, her eyes rolling back in her head. And then, with a gurgling scream, she transformed. Her body contorted, bones snapping and reforming, flesh tearing and reshaping. In the space of a heartbeat, she was no longer human. She was something else, something monstrous. Rennick stared in horror as the thing that had been a girl rose to its feet, its eyes burning with an unholy light. It was a creature of nightmares, a twisted mockery of humanity, all teeth and claws and hate. It lunged at him, and he barely had time to draw his blades before it was on him. They fought, a dance of death in the narrow alley, steel ringing on steel, flesh tearing and blood spraying. Rennick was strong, and fast, and skilled, but the creature was stronger, faster, more vicious. It was like fighting a storm, a force of nature that could not be reasoned with or bargained with. It could only be endured. In the end, it was luck that saved him. He stumbled, his foot catching on a loose cobblestone, and the creature's claws raked across his chest, leaving deep, ragged gouges. But it overextended itself, and he was able to bring his sword up, driving it through its eye and into its brain. It fell to the ground, twitching and convulsing, and then it was still. Rennick stood over it, panting, his heart hammering in his chest. He had never seen anything like it. He had never even heard of anything like it. He knew then that he was in over his head. This was no ordinary job, no simple matter of retrieving a stolen trinket. This was something bigger, something darker. He was a small fish in a very large, very dangerous pond, and he was about to be eaten alive. But he was also a survivor. He had been born in the gutter, raised in the shadows, and he had clawed his way to the top of the food chain. He would not go down without a fight. He turned and ran, back towards the city, back towards the people who had hired him. He would find out what they knew, and he would make them pay for it. He would uncover the truth, no matter the cost. And so began the tale of the Butcher of the Broken Docks, a story of blood and betrayal, of monsters and men, of a city on the brink of chaos and the man who would either save it or destroy it. It was a story that would echo through the ages, a grim and terrible saga that would be whispered in the darkest corners of the world, a warning to all who would dare to challenge the forces that lurked in the shadows. It was a story that would end in fire and blood, in screams and tears, in the fall of kings and the rise of monsters. It was a story that had only just begun. </details> <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a Grimdark trilogy in the style of Joe Abercrombie and Rob J Hayes. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is a cultist who has just escaped his cult. He is dressed in dirty yellow robes and his only possession is a mysterious small (magical!?) mirror he stole from the cult. The story starts with him arriving at an apparently deserted ghost town on the edge of a desert. He has an arrow lodged in his shoulder and is losing his mind due to infection and thirst. ### ASSISTANT The sun was a merciless beast, its fiery breath scorching the earth and turning the once-thriving town into a desolate wasteland. The cultist, his yellow robes now a dull, dusty hue, staggered through the empty streets, his vision blurred by a haze of pain and delirium. The arrow jutted from his shoulder like an accusing finger, a constant reminder of his betrayal. He clutched the small, intricately carved mirror in his trembling hand, its cool surface a comfort against the relentless heat. He had heard the whispers of the other acolytes, their hushed voices carrying tales of the town's demise like a plague on the wind. They spoke of a curse, of a darkness that had seeped into the very bones of the place, driving its inhabitants mad before claiming their souls. But he had seen the truth in the High Priest's eyes, the greed and lust for power that had led them here. The mirror was the key, they said, to unlocking the secrets of the ancient gods. But he knew better. He had seen the power it held, the way it twisted reality and bent it to the will of the one who wielded it. And he had taken it, stolen it from the very heart of the cult's temple, and run. Now, with each agonizing step, he questioned the wisdom of his decision. The desert stretched out before him, an endless sea of sand and despair, and he was but a single, insignificant speck in its vast expanse. He had hoped to find refuge here, a place to hide and heal before continuing his journey. But the town was as dead as the dreams he had once held of a better life. His thoughts were a jumbled mess, a cacophony of fear and pain. He could feel the infection spreading through his body, a writhing serpent of fire that threatened to consume him from within. His tongue was swollen, his lips cracked and dry, and his throat was a parched wasteland. He needed water, and soon, or he would join the ghosts that haunted this forsaken place. As he stumbled through the town, he caught glimpses of his reflection in the shattered windows of the abandoned buildings. The man who stared back at him was a stranger, his once-handsome face now a twisted mask of agony and desperation. He had been a fool to believe the lies of the cult, to think that he could ever be one of them. He was an outsider, a fraud, and they had seen it in him from the start. The mirror, though, the mirror was real. He could feel its power pulsing in his hand, a heartbeat that echoed the rhythm of his own. It was a cruel irony that the very thing he had risked everything to obtain was now his only hope for survival. He knew the stories, the legends of the mirror's healing properties, of how it could restore life to the dying. But he also knew the cost. The mirror demanded a sacrifice, a piece of the user's very soul in exchange for its gifts. He reached the town square, a desolate expanse of cracked cobblestones and withered plants. In the center stood a fountain, its once-clear waters now a stagnant pool of green slime. He fell to his knees before it, the pain in his shoulder a white-hot brand that seared his flesh. He dipped the mirror into the water, watching as the surface rippled and danced. "Please," he whispered, his voice hoarse and broken. "I'll give you anything. Just take the pain away." The mirror stared back at him, its surface as still and unyielding as the desert sands. He closed his eyes, willing the power within to answer his plea. And then, he felt it. A cold, insidious presence, slithering into his mind like a snake in the dark. It whispered to him, promising relief, promising power, promising everything he had ever wanted. He opened his eyes, and the world was different. The pain was gone, replaced by a cold, emotionless void. He looked down at his shoulder, and the arrow was gone, the wound nothing more than a faint scar. He laughed, a hollow, empty sound that echoed through the empty streets. "What have I done?" he asked the mirror, but it gave no answer. He stood, his legs unsteady beneath him, and looked out at the desert. He could feel the power coursing through his veins, a dark, intoxicating energy that threatened to consume him. He was no longer just a man, he was something more. Something dangerous. He took a step forward, and then another, the mirror clutched tightly in his hand. He would find the cult, he would make them pay for what they had done to him. And then, he would use the mirror to claim the power that was rightfully his. The wind picked up, whipping the sand into a frenzy, and he smiled. The desert was his now, and he would not be denied. He was the master of his own destiny, and the gods themselves would tremble at his approach. But as he walked, the laughter of the ghosts followed him, a chilling reminder of the price he had paid for his newfound strength. And deep within the mirror, something stirred, a darkness that watched and waited, biding its time. The first chapter of the grimdark trilogy, "The Shattered Reflection," had begun. </details> <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a Grimdark trilogy in the style of Joe Abercrombie and Rob J Hayes. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is a 'Mercer' who's job is to put the mortally wounded out of their mystery after a battle. He has no conscience about what he does. The story starts the morning after the battle and the moor where it took place is blanketed in mist. Something stirs a flashback in the POV character. ### ASSISTANT The morning after the battle, the moor lay still, a shroud of mist clinging to its wounds like a lover's last embrace. The Mercer, a man of few words and fewer scruples, trudged through the carnage, his boots squelching in the mud churned red by the night's bloodshed. He was a reaper of sorts, his task to deliver the final mercy to those who had danced with death and lost. His name was a whisper among the living, a curse among the dying. He paused, his hand resting on the hilt of his blade, as a shiver ran down his spine. The mist swirled around him, carrying with it the cacophony of the night before: the screams of the dying, the clash of steel, the thunder of hooves. He closed his eyes, letting the memories wash over him like a wave of despair. *Why do I do this?* he thought, not for the first time. *Why do I wade through this sea of suffering, playing the part of the grim angel?* But he knew the answer. It was the same answer it had always been. He did it because he was good at it. Because he had no qualms about ending a life, even one that still clung to the faintest flicker of hope. Because, in this world of darkness and pain, it was the only thing he knew how to do. He opened his eyes, the mist parting to reveal a figure lying in the mud. A soldier, or what was left of one. The man's armor was shattered, his face a mask of agony. The Mercer knelt beside him, his movements practiced and efficient. He placed a hand on the soldier's forehead, feeling the fevered heat of his skin, the erratic thump of his heart. "Shh," he whispered, his voice a soothing balm. "It's over now." The soldier's eyes fluttered open, wide with terror. "Please," he croaked, "don't leave me here." The Mercer's lips twisted into a grim smile. "I won't," he said, drawing his blade. "I'll take you with me." As the soldier's life bled out onto the cold ground, the Mercer's mind drifted back to another time, another place. A time before the blood and the pain, before the endless cycle of war and death. He remembered a girl with hair like spun gold, laughing as she danced in a field of wildflowers. He remembered the feel of her hand in his, the sound of her voice as she whispered his name. *Elara.* The memory was a knife to his heart, a reminder of all he had lost. He pushed it away, focusing on the task at hand. There were more souls to reap, more lives to end. He moved through the mist, his blade a silent whisper in the stillness. The cries of the wounded guided him, a morbid symphony that played out across the moor. Each one he found, he dispatched with the same cold efficiency, the same hollow promise of release. As the sun began to rise, casting a sickly pallor over the scene, the Mercer came upon a group of soldiers huddled around a fire. They eyed him warily, their faces a mix of fear and respect. He was a necessary evil, a specter that haunted the battlefield. "Anyone here need my services?" he asked, his voice devoid of emotion. One of the soldiers, a young boy who couldn't have seen more than fifteen summers, pointed to a figure lying a few feet away. "Him," he said, his voice trembling. "He's not going to make it." The Mercer approached, his boots crunching on the frost-kissed grass. The man on the ground was a mess, his body a tapestry of wounds. He was barely conscious, his breathing shallow and ragged. *Another one,* the Mercer thought, his heart heavy with the weight of his duty. *Another soul to send into the void.* He knelt beside the man, his blade glinting in the pale light. The soldier's eyes flickered open, and for a moment, the Mercer saw something in them that he hadn't expected: defiance. "No," the man rasped, his hand feebly grasping at the Mercer's wrist. "I won't go quietly." The Mercer hesitated, a frown creasing his brow. He had never encountered this before. Most welcomed his touch, eager for the release it brought. But this man...this man was different. "Why?" he asked, genuinely curious. The soldier's lips curled into a bloody smile. "Because I have a son," he whispered. "And I want to see him grow up." The Mercer stared at the man, his mind racing. He had no children, no one to mourn him when his time came. He was a solitary figure, a ghost haunting the edges of life. But this man...this man had a reason to hold on. He made his decision. "I can't heal you," he said, his voice soft. "But I can make it quick." The soldier's eyes filled with tears. "Thank you," he breathed, his grip on the Mercer's wrist loosening. The Mercer raised his blade, the mist swirling around them like a mourner's veil. As he brought it down, he couldn't help but wonder: *What would it be like to have something to live for?* But the thought was fleeting, lost in the swirl of the mist and the cries of the dying. He was the Mercer, the bringer of mercy. And the moor was his to tend, until the next battle came and the cycle began anew. </details> <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a Grimdark trilogy in the style of Joe Abercrombie and Rob J Hayes. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is a man awaiting execution for a crime he didn't actually commit. His cell mate is a much larger man, who is a notorious murderer and rapist. One evening they overhear the guards talking about some "... threat from the North" but don't catch the whole conversation. The next morning they awake to find the guards have all fled and now face a new predicament: how to get out of the locked cell before they die of thirst or starvation. ### ASSISTANT The stench of piss and shit was a constant companion in the damp stone cell, but it was the sound of the key turning in the lock that truly set his heart racing. The rusted iron groaned in protest, echoing through the narrow corridor like the dying breath of some ancient beast. The door creaked open, revealing the silhouette of the jailer, a hulking brute of a man with a lantern in one hand and a set of keys in the other. But there was no breakfast, no water, just the empty promise of another day in this hellhole. "What's the meaning of this?" the smaller man barked, his voice barely audible over the rattling of his chains. "Where's our food, you fat sack of shit?" The jailer didn't answer, just slammed the door shut and locked it again, the sound of his footsteps fading into the distance. The larger man, a hulking brute named Grimgor, grunted in response, his eyes never leaving the small patch of light that seeped through the barred window. "Something's not right," he rumbled, his voice like gravel being crushed under a heavy boot. The smaller man, known only as the Fox, couldn't argue with that. He'd been in this cell for what felt like an eternity, awaiting execution for a crime he didn't commit. But even in this place, there was a routine, a semblance of order. And that routine had just been shattered. As the day wore on, the silence from the corridor outside grew more oppressive. No guards, no other prisoners, nothing but the distant howl of the wind and the occasional scurrying of rats in the darkness. The Fox's mind raced, trying to piece together what could have happened. Had there been a riot? A rebellion? Or was it something else entirely? He glanced over at Grimgor, who was staring at the wall, lost in thought. The man was a monster, a notorious murderer and rapist, but he was also the only other living being in this godforsaken place. And in a world gone mad, even monsters could be allies. "You hear anything last night?" the Fox asked, trying to keep the tremor out of his voice. Grimgor grunted, his eyes still fixed on the wall. "Something about a threat from the North." The Fox's heart sank. He'd heard the same thing, snatches of conversation between the guards as they'd passed by their cell. But he'd been too afraid to give it much thought, too focused on his own impending doom. "What do you think it means?" he pressed, unable to keep the desperation at bay. Grimgor shrugged his massive shoulders. "Dunno. Don't care. Just means we're probably gonna die in here." The Fox's stomach twisted at the thought. He'd faced death before, many times, but this... this was different. To die slowly, trapped like an animal, with no chance to fight back... it was a fate worse than any he could imagine. As the hours dragged on, the thirst became unbearable. The Fox's tongue felt like sandpaper, his throat raw and parched. He could see the same desperation in Grimgor's eyes, the realization dawning on them both that they were truly alone. "We have to get out of here," he croaked, his voice barely above a whisper. Grimgor just grunted in agreement, his gaze never leaving the window. The Fox's mind raced, trying to come up with a plan. They had nothing, no tools, no weapons, just their wits and their will to survive. And even that seemed to be fading with each passing moment. But then, as the sun began to set and the shadows lengthened, he noticed something. The light from the window was changing, growing dimmer. He squinted, trying to make out what was happening. And then he saw it. "Grimgor," he hissed, tugging on the larger man's arm. "Look." Grimgor turned, his eyes narrowing as he followed the Fox's gaze. The light was flickering, casting strange shadows on the wall. And then, as if in answer to their unspoken prayers, they heard it. The sound of footsteps, growing louder and louder, accompanied by the jingle of keys. The Fox's heart leapt into his throat, hope and fear warring within him. Who was it? Friend or foe? The door swung open, revealing not the jailer, but a figure shrouded in darkness. The Fox couldn't make out any details, but he could sense the malice radiating off of him like heat from a forge. "Who are you?" he demanded, trying to sound braver than he felt. The figure stepped into the cell, the light from the lantern outside casting his features into sharp relief. He was a tall man, with a cruel smile and eyes that glinted like ice. "I am your salvation," he said, his voice like a knife slicing through the silence. "And your doom." With that, he tossed a set of keys at the Fox's feet and disappeared back into the shadows. The door slammed shut behind him, the sound of the lock clicking into place echoing through the cell like a death knell. The Fox stared at the keys, his heart pounding in his chest. Freedom was within his grasp, but at what cost? He looked over at Grimgor, who was already working on his own chains. "We have to get out of here," the larger man growled. "Before whatever's coming from the North gets here." The Fox nodded, his hands shaking as he picked up the keys. They were cold, colder than they had any right to be. He could feel the weight of them, the promise of escape and the threat of the unknown. Together, they worked in silence, the only sound the rattle of chains and the pounding of their hearts. When they were finally free, they stood for a moment, staring at each other. "What now?" the Fox asked, his voice barely above a whisper. Grimgor's smile was feral, his eyes alight with a madness that sent a shiver down the Fox's spine. "Now," he said, "we find out what's really going on." And with that, they set off into the darkness, their footsteps echoing through the empty corridors like the march of the damned. </details> ## Dark sci-fi stories <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a dark and gritty Space Opera Sci-fi trilogy in the style of Frank Herbert and James S. A. Corey. Use third person personal and feature internal monologues of the characters. The setting is this: a colony ship heading out of the solar system has been pulled off course with no hope of getting to the planned destination. The people know this and society has broken down completely due to the grim prospects they are facing. The POV character for chapter 1 is an orphan. ### ASSISTANT Chapter 1: Adrift The colony ship, once a beacon of hope named Elysium's Dawn, now drifted through the cold void like a ghost ship. Its hull, pitted and scarred from the debris of uncharted space, groaned with each labored breath it took. The once vibrant corridors were now dimly lit, casting long shadows that seemed to swallow any remaining semblance of optimism. In one such shadow, a figure moved silently, her bare feet making no sound on the cold metal floor. She was small for her age, malnourished from the rationing that had become a way of life. Her ragged clothes hung loosely on her frame, but her eyes were sharp and alert, scanning every corner for danger. This was Kira, an orphan of the void, surviving in a world where hope was a luxury few could afford. Kira's thoughts drifted back to the stories she had heard as a child, tales of a green and blue planet called Earth, their home that they would never see again. The ship had been pulled off course by an unknown force, leaving them stranded in the vast emptiness between stars. The adults spoke in hushed tones about the dwindling resources, the failing life support systems, and the ever-present threat of pirates who preyed on lost ships like theirs. She pushed those thoughts aside as she approached her destination: the hydroponics bay. It was one of the few places on the ship where life still thrived, albeit in a controlled and desperate manner. The air here was thick with moisture and the scent of growing things, a stark contrast to the stale recycled air that permeated the rest of the vessel. As she entered, the soft glow of the grow lights illuminated rows upon rows of plants, struggling to produce enough food for the dwindling population. Kira's stomach growled at the sight, but she knew better than to take more than her share. She had learned early on that stealing could mean a swift and brutal punishment from those who guarded the bay. Instead, she found solace in tending to the plants, her small hands deftly pruning and watering them. It was here that she felt closest to something resembling peace, a connection to life that transcended the harsh reality of their situation. As she worked, her mind wandered to the lessons she had learned from the ship's AI, the only constant presence in her life since the adults had given up on teaching the children anything but survival skills. "Kira," a voice whispered urgently, jolting her out of her reverie. She turned to see Jax, another orphan and one of the few people she trusted. His face was pale and his eyes wide with fear. "You need to come with me, now." She followed him through the maze-like corridors, her heart pounding in her chest. They arrived at a small, hidden room that served as their makeshift home. Inside, huddled around a flickering screen, were several other children, their faces etched with worry and excitement. "Look," Jax said, pointing to the screen. It displayed a grainy image of a man in tattered clothing, his face half-hidden by a wild beard. "He says he knows how to fix the engines." Kira's eyes widened in disbelief. The engines had been dead for as long as she could remember, their failure sealing their fate as interstellar castaways. "Who is he?" she asked, her voice barely above a whisper. "They call him the Mechanic," one of the older girls replied. "He used to work in engineering before...before everything fell apart." The man on the screen continued his frantic plea, detailing his plan to restore power and steer the ship towards a habitable planet he claimed was within reach. Kira felt a spark of hope ignite within her, but it was quickly doused by the skepticism of the others. "It's just another one of their lies," spat a boy named Taro. "They'll say anything to get us to do their dirty work." Jax rounded on him, his fists clenched. "This is our chance, Taro! We can't just give up!" The argument raged on, but Kira tuned it out, her thoughts racing. She knew the risks of trusting an adult, especially one who claimed to have access to forbidden knowledge. But what if he was telling the truth? What if they could find a new home and start over? As she watched the debate unfold, she made up her mind. She would seek out this Mechanic and offer him her help, no matter the cost. It was a decision born of desperation, but also of a stubborn refusal to accept the bleak future that lay before them. And so, as the other children slept fitfully around her, Kira slipped out into the dark corridors once more, determined to find the man who might hold the key to their salvation or doom. The ship groaned and creaked around her, a constant reminder of the fragile existence they all clung to. But she pressed on, driven by an unyielding will to survive and the faintest glimmer of hope that burned within her heart. As she navigated the treacherous path towards engineering, she couldn't help but wonder what awaited her in the depths of the ship. Would she find a savior or a madman? And more importantly, would she be able to make it back alive? </details> <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a dark and gritty Sci-fi trilogy in the style of Frank Herbert and James S. A. Corey. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is an astronaut living on a space station orbiting the earth. The day starts off normally, but then him the others notice the mushroom clouds and they have to face inevitability of the situation they are in. ### ASSISTANT Chapter 1: Ashes of Exodus The cold embrace of space enveloped the station like a lover's caress, its infinite blackness punctuated only by the distant twinkle of stars and the ever-present glow of Earth below. Captain Marcus Kane floated through the dimly lit corridors, his magnetic boots barely whispering against the metal floor as he made his way to the observation deck. The daily routine had become a meditative ritual for him, a chance to clear his mind before the cacophony of the day's work began. As he entered the room, the sight that greeted him never failed to take his breath away. Earth, a swirling marble of blues and greens, hanging in the void like an irreplaceable jewel. He couldn't help but feel a pang of longing for its familiar embrace, for the weight of gravity that he had left behind so many years ago. Marcus settled into his favorite spot by the window, the worn leather of the seat molded to his body like an old friend. He closed his eyes and let the silence wash over him, the only sound the soft hum of the station's life support systems. It was in these moments that he felt truly alive, connected to something greater than himself. But today, the silence was shattered by a chilling alarm, its shrill wail piercing the tranquility like a knife through glass. His eyes snapped open as his heart began to race, adrenaline coursing through his veins. He knew that sound all too well; it was the emergency alert for an incoming transmission from Earth. He launched himself towards the comms console, fingers flying over the keys as he tried to make sense of the garbled message. The voice on the other end was frantic, barely coherent through the static. "Multiple...detonations...global catastrophe..." were the only words he could decipher before the signal died completely. Marcus's mind raced as he tried to process what he had just heard. It couldn't be true, it couldn't be happening. Not again. He quickly activated the external cameras, his stomach twisting into knots as he waited for the feed to come online. And then he saw them: mushroom clouds, blooming like grotesque flowers across the planet's surface. Too many to count, their plumes of smoke and ash reaching up towards the heavens in a silent scream of despair. The sight was both beautiful and horrifying, a testament to humanity's capacity for destruction on an unimaginable scale. His thoughts were interrupted by the sound of footsteps behind him as the rest of the crew floated into the room, drawn by the alarm. Their faces mirrored his own shock and disbelief as they stared at the monitors, their voices a cacophony of whispers and gasps. "What...what is that?" asked Dr. Amelia Chen, her voice trembling with fear. Marcus didn't answer; he couldn't find the words. Instead, he simply pointed at the screen, his hand shaking slightly. Engineer Jake Taylor was the first to break the silence. "Nuclear war," he said, his voice flat and emotionless. "It's finally happened." The others looked at him in horror, but Marcus knew he was right. There could be no other explanation for such devastation. He felt a cold weight settle in the pit of his stomach as the reality of their situation began to sink in. They were alone, adrift in the vastness of space with no hope of return. As the implications of what they had just witnessed set in, the crew fell into an uneasy silence, each lost in their own thoughts. Marcus couldn't help but think about his wife and daughter back home, wondering if they were safe or if they too had been consumed by the flames. He knew it was a futile exercise, but he couldn't stop himself from hoping against hope that somehow, someway, they had survived. But as the minutes ticked by and no further communication came from Earth, that hope began to fade like a dying ember. They were truly alone now, the last remnants of humanity clinging to life on this tiny outpost in the void. He turned to face his crew, their faces etched with fear and uncertainty. "We need to come up with a plan," he said, his voice steady despite the turmoil raging within him. "We can't just sit here and wait for...for whatever comes next." Amelia nodded, her eyes hardening with determination. "I'll start running diagnostics on our life support systems, see how long we can last up here." Jake followed suit, his hands already moving to the controls of the station's mainframe. "I'll try to establish contact with any other surviving outposts or spacecraft." Marcus nodded, grateful for their resolve in the face of such overwhelming odds. He knew that they were all thinking the same thing: that this could very well be the end of everything they had ever known. But he also knew that they couldn't give up, not yet. As he watched them work, his mind drifted back to the message from Earth, those final desperate words echoing in his head like a funeral dirge. He couldn't shake the feeling that there was something more, some hidden meaning or clue that would explain what had happened. And then it hit him: the timing of the detonations, the pattern of the blasts. It wasn't random; it was deliberate, almost...calculated. But who would do such a thing? And why? The questions swirled in his mind like a storm, each one more unsettling than the last. He knew that they needed answers, and fast. Because if this was an act of war, then their isolation might not be as secure as they thought. He pushed himself away from the console, his decision made. "I'm going to suit up," he said, his voice firm. "We need to get a closer look." The others stared at him in disbelief, but he didn't waver. This was his duty as captain, to protect his crew and find out the truth no matter how terrible it might be. As he floated towards the airlock, Marcus couldn't help but feel a sense of dread creeping up on him like a shadow. He knew that what he was about to see would change everything, that there was no going back once he stepped outside and gazed upon the ashes of humanity's final stand. But he also felt something else: a burning anger, a fierce determination to uncover the truth and make those responsible pay for their crimes against humanity. Whatever awaited them out there, they would face it together, as one. He closed his eyes and took a deep breath, steeling himself for what was to come. And then, with a quiet prayer on his lips, he opened the airlock and stepped into the void. </details> <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a dark and gritty Sci-fi trilogy in the style of Frank Herbert and James S. A. Corey. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is a worker constructing a biodome when a terrible accident happens. Make the story very dark; no happy ending here... ### ASSISTANT Chapter 1: The Biodome The air was thick with the scent of sweat and burnt metal as Kaelen, a seasoned construction worker, tightened the last bolt on the massive irrigation system. He wiped his brow with the back of his gloved hand, leaving behind a smear of grease that glistened under the harsh artificial lights. The hum of machinery and distant shouts echoed through the cavernous space, a cacophony that had become as familiar to him as his own heartbeat over the years spent building biodomes on this desolate planet. Kaelen's thoughts drifted to his family back on Earth, their faces faded like old photographs in his mind. He wondered if they ever thought about him, or if he was just another ghost haunting the fringes of their memories. The company had promised them a better life, but it came at a steep price: years of backbreaking labor on an alien world, far from everything they'd ever known. "Hey, Kael!" A voice cut through his reverie, jolting him back to reality. It was Tamsin, his foreman, her face hidden behind a tinted visor. "We need you up top! There's an issue with the atmospheric seal." He nodded curtly and began the long climb up the scaffolding, each rung biting into his calloused hands. As he ascended, Kaelen couldn't help but marvel at the sheer scale of their creation: a vast dome of steel and glass that would one day be teeming with life, a self-sustaining ecosystem in the heart of this barren wasteland. But today was not that day. Today, it was just another tomb waiting to be sealed. As he reached the top, Kaelen could see the problem immediately: a small fissure had formed along one of the joints, spewing precious oxygen into the void beyond. He cursed under his breath; they were already behind schedule and over budget. Another delay would mean another round of demerits, another month's pay docked. "What do you think?" Tamsin asked, her voice crackling through his earpiece. "Can we patch it up or do we need to call in the engineers?" Kaelen hesitated, running his fingers along the jagged edge of the tear. It was larger than he'd initially thought, and growing by the second. He could feel the cold tendrils of vacuum reaching out to claim him, whispering promises of oblivion. "I... I don't know," he admitted, his voice heavy with dread. "It doesn't look good." Tamsin swore colorfully and turned away, barking orders into her comm unit. Kaelen watched as workers scrambled to gather tools and materials, their movements frantic and disorganized. He knew they were all thinking the same thing: if they couldn't fix this, they were dead. The air around them grew colder, thinner, as the oxygen continued to escape. Kaelen's lungs burned with every breath, his vision swimming at the edges. He fumbled with the patch kit, his hands shaking uncontrollably. This was it; this was how he would die, millions of miles from home, in service to a corporation that saw him as nothing more than a replaceable cog in their grand machine. "Hurry up!" Tamsin shouted over the growing din. "We're losing pressure fast!" Kaelen's heart pounded in his chest like a sledgehammer, drowning out all other sound. He could feel the panic rising within him, threatening to consume him whole. But he couldn't afford to give in; not now, not when so much was at stake. With trembling hands, he applied the sealant and pressed the patch into place. For a moment, it seemed to hold... but then, with a sickening lurch, the fissure widened, swallowing the feeble attempt whole. The wind howled around them like a ravenous beast, tearing at their suits, trying to pull them apart atom by atom. "Abort!" Tamsin screamed, her voice barely audible over the roar. "Everyone get out now!" But it was too late. The dome shuddered beneath their feet, groaning in protest as the atmosphere inside rushed to equalize with the void outside. Kaelen felt himself being lifted off the ground, his boots scrabbling for purchase on the slick metal surface. He reached out, desperate to grab onto something - anything - but found only emptiness. And then, as suddenly as it had begun, there was silence. A deafening, suffocating silence that pressed in on him from all sides. His vision went dark, and he knew no more. When Kaelen awoke, he was lying on the cold floor of the dome, his body wracked with pain. He tried to move, but found himself held fast by twisted metal and shattered glass. Panic surged through him once again as he realized that his suit had been breached; he could feel the icy chill of vacuum seeping into his very bones. Through the haze of agony, he became aware of movement nearby. Tamsin was there, her visor cracked and fogged, struggling to free herself from the wreckage. She looked at him with wide, terrified eyes, her lips moving soundlessly. He knew what she was saying: they were going to die here, alone and forgotten. Kaelen closed his eyes, willing himself to accept the inevitable. But even as the darkness closed in, he couldn't shake the feeling that this wasn't just an accident - that there were forces at work far beyond their understanding, manipulating them like pawns on a cosmic chessboard. And then, just when he thought it was all over, he heard it: a faint beeping, growing louder by the second. His eyes snapped open to see a small drone hovering above them, its lights flashing urgently. It was one of the company's rescue bots, sent to assess the damage and retrieve any survivors. But there would be no salvation for Kaelen or Tamsin. Their suits were too damaged, their bodies too far gone. As the drone's camera lens bore down on him, he could only manage a weak, bitter smile. "Tell my family... tell them I'm sorry," he croaked, his voice barely audible even to himself. Tamsin reached out a gloved hand, her fingers brushing against his own in a final gesture of solidarity. And then, as the darkness claimed him once more, Kaelen knew that this was not the end of their story - but rather, the beginning of something far more sinister. Something that would change the course of human history forever... </details> <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a dark and gritty Sci-fi trilogy in the style of Frank Herbert and James S. A. Corey. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is manning a listening station in deep space. What he hears over the radio chills him to the bone. ### ASSISTANT Chapter 1 - Whispers from the Void The silence of deep space was like an old friend to Captain Alistair Reynolds, a constant companion that had accompanied him through countless missions and lonely nights at the helm of the listening station. But today, it felt more like a suffocating shroud, heavy with unspoken secrets and foreboding. He sat in his chair, fingers drumming impatiently on the console as he scanned the endless stream of data pouring in from the far reaches of the cosmos. His thoughts drifted to home, to the warmth of his wife's embrace and the laughter of their children playing in the garden under the twin suns of Proxima Centauri. It had been years since he last felt that warmth, since he last heard those laughs. The ache was always there, a dull throb that pulsed in time with his heartbeat, but today it seemed to echo through the cold metal halls of the station like a mournful dirge. A sudden blip on the screen snapped him back to reality. He leaned forward, eyes narrowing as he studied the readout. It was faint, barely more than a whisper against the static, but it was there - a signal, human in origin and heavily encrypted. His heart raced as he keyed in the decryption sequence, his mind already racing ahead to the possibilities of what this could mean. The message unscrambled, revealing a frantic voice, barely audible over the crackle of interference. "This is the UES Normandy, requesting immediate assistance... coordinates attached... under attack by unknown forces... God help us..." The transmission cut out abruptly, leaving only the empty hiss of static in its wake. Alistair's blood ran cold as he stared at the screen, the weight of the silence pressing down on him like a physical force. He knew that ship, had served with her captain back during the Martian uprising. They were old friends, bound by shared battles and the scars they bore from them. And now she was out there, alone and in danger. He didn't hesitate. His fingers flew over the console, sending out an emergency beacon to the nearest fleet. But he knew it would take time for help to arrive, time that the Normandy might not have. He had to do something now. As he prepared to launch a probe towards the coordinates, his mind filled with images of burning ships and desperate faces. He could almost hear the screams of the dying, echoing through the void like ghosts from his past. It was a sound that haunted him still, one he had hoped never to hear again. But duty called, and Alistair was nothing if not a soldier. He took a deep breath, steadying himself against the tide of fear and doubt that threatened to overwhelm him. This was his job, his purpose - to listen, to bear witness, and when necessary, to act. The probe shot out into the darkness, its tiny form swallowed up by the vastness of space. He watched it go, feeling a strange sense of detachment as if he were watching someone else's life play out before him. And perhaps in some ways, he was. For all his years in service, this was the first time he had ever been truly alone, cut off from the rest of humanity by light-years and the cold indifference of the universe. As the minutes ticked by, Alistair found himself slipping into a half-trance, his thoughts drifting back to happier times. He remembered the first time he had held his newborn son, the tiny fingers curling around his own as if trying to hold on for dear life. He remembered the taste of real Earth coffee, rich and bitter, shared with his wife under a sky full of stars that seemed close enough to touch. But those memories were distant now, fading like echoes in the void. All he had was this station, this endless vigil, and the hope that one day, he might make it back home. The radio crackled to life again, jolting him out of his reverie. It was the probe, transmitting data back from the edge of nowhere. He braced himself for what he would find, knowing that it could change everything. What greeted him on the screen was worse than anything he could have imagined. The Normandy was there, alright - or rather, what was left of her. She drifted through space like a broken toy, her hull breached and venting atmosphere into the cold void. There were no signs of life, no distress signals, nothing but silence. Alistair felt a cold knot form in his stomach as he took in the wreckage. This wasn't just any ship; this was his friend's ship, her crew now lost to the void. And the attackers... whoever or whatever they were, they had left no trace behind. He sat back in his chair, the weight of it all settling heavily upon him. He knew what he had to do next - file a report, alert command, and wait for further instructions. But as he stared at the lifeless husk that was once the Normandy, he couldn't shake the feeling that something far greater than himself was at work here. Something dark and terrible had come calling, whispering its secrets through the stars. And now it was his turn to listen, to bear witness, and perhaps, if he was lucky, to survive long enough to tell the tale. </details> ## Miscellaneous stories <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a dark and gritty post-apocalyptic novel in the style of Cormac McCarthy. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is a technician in a nuclear bunker 18 months after the war. The 12 inhabitants of the bunker have been expecting to start to be able to leave soon, but it has just been discovered that at least where they are, salted cobalt bombs were used. It's clear now that either all 12 will die when the resources run out and their is barely enough for 6 to make it to 5 years and have any hope of leaving. After 18 months of calm everything breaks lose today. ### ASSISTANT In the dim light of the bunker, the technician sat hunched over his console, fingers dancing across the keys with a practiced urgency. The air was thick with the hum of machinery and the stench of sweat and fear. He had been down here for what felt like an eternity, but the digital clock on the wall insisted it had only been eighteen months since the world above had turned to ash. His name was Samuel, or at least that's what they called him now. In another life, he had been a software engineer, designing algorithms for self-driving cars and smart homes. Now, he was responsible for keeping twelve souls alive in this underground tomb, their last bastion against the desolation above. He glanced up at the monitor, the green glow illuminating his haggard face. The readings were clear: salted cobalt bombs had been used. The thought sent a shiver down his spine. They had all heard the rumors, of course, but they had clung to hope like a drowning man to driftwood. Hope that the war hadn't been as bad as they feared, that the surface would be habitable again soon. But now, the truth was undeniable. The world outside was dead, and it would remain so for generations. Samuel closed his eyes, trying to block out the images of a scorched earth, devoid of life. He could hear the others in the background, their voices a low murmur as they went about their daily tasks. They didn't know yet. How could he tell them? How could he be the one to snuff out the last ember of hope that had kept them going for so long? He took a deep breath and stood up, his joints creaking in protest. He made his way through the narrow corridors, past the rows of bunks where some slept fitfully, their dreams haunted by memories of a world they would never see again. Others were awake, their faces etched with lines of worry and despair. They looked up as he passed, but he couldn't meet their gazes. He reached the command center, where Captain Reynolds was poring over maps and reports. The man looked up as Samuel entered, his eyes heavy with exhaustion. "What is it?" he asked, his voice gruff. Samuel hesitated for a moment before speaking. "Sir, I have some... troubling news." The captain's expression didn't change, but Samuel could see the tension in his jaw. He had been expecting this, too. "Go on," he said simply. "The radiation readings... they're off the charts. It's cobalt-60. The half-life is over five years." Reynolds let out a slow breath, his eyes never leaving Samuel's face. "And our supplies?" "We have enough for six, maybe seven if we ration carefully. But not twelve." The room seemed to shrink around them as the weight of their situation settled in. The captain rubbed his temples, his thoughts racing. They had always known that their time in the bunker was limited, but they had been so close. So very close. "We'll need to make a decision," he said finally, his voice barely above a whisper. "Who stays, and who goes." Samuel felt a knot form in his stomach. He knew what that meant. It was survival of the fittest down here now, and there were those among them who would do anything to ensure their own survival. As he made his way back to his console, he couldn't help but think about the world above. The trees that would never grow again, the birds that would never sing. He thought of his wife and daughter, their faces fading like photographs left in the sun. They had been lucky, they said, to have made it into the bunker. But now, he wasn't so sure. In the mess hall, the atmosphere was tense. Whispers and hushed conversations filled the air as people tried to make sense of what they had just heard. Samuel sat alone at a table, staring blankly at his food. He couldn't eat. Not now. He felt a hand on his shoulder and looked up to see Maria, the doctor. Her eyes were red-rimmed, but her voice was steady. "We need to talk," she said, guiding him towards the infirmary. Once inside, she closed the door behind them and leaned against it, her body trembling. "What are we going to do?" she asked, her voice cracking. Samuel shook his head. "I don't know." "We can't just let six of us die," she said, her voice rising in panic. "There has to be another way." He looked at her, at the desperation etched on her face, and felt a flicker of anger. "What do you suggest? We all hold hands and sing Kumbaya while we starve to death?" Maria's eyes flashed with anger, but she quickly composed herself. "No," she said softly. "But we can't just give up." Samuel sighed and ran a hand through his greasy hair. "I'm not giving up," he said. "I'm just being realistic." She looked at him for a long moment before nodding. "You're right," she said, her voice barely audible. "We need to be realistic." They sat in silence for a while, each lost in their own thoughts. Then, Maria spoke up again. "There is one option," she said hesitantly. "But it's... drastic." Samuel raised an eyebrow. "What?" "We could... we could draw straws," she said, her voice barely above a whisper. "Choose six people at random to leave the bunker and take their chances on the surface." He stared at her in disbelief. "You can't be serious." "It's not ideal," she admitted. "But it's better than just letting them die down here." Samuel shook his head, his mind reeling. He couldn't fathom the thought of sending people out into that wasteland, knowing they would likely never return. But what other choice did they have? As he sat there, the weight of their decision pressing down on him like a leaden shroud, he heard a commotion outside. Shouting, followed by the sound of scuffling feet and something heavy being dragged across the floor. He exchanged a worried glance with Maria before rushing out into the corridor. What he saw made his blood run cold. A group of men had cornered one of the younger women, their faces twisted with rage and desperation. They were arguing over who would get to leave the bunker, who would get a chance at life. Fists flew, and soon, the sound of gunfire echoed through the halls. Samuel stood there, frozen in place as chaos erupted around him. This was it. The end of everything they had worked so hard to preserve. He could feel the darkness closing in, suffocating him, as he realized that their last hope for survival had just been shattered like glass under a bootheel. And all he could do was watch as the world above claimed them, one by one. </details> <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a historical mystery novel in the style of Arturo Pérez-Reverte and Umberto Eco. The story starts off in California during the 1948 Gold Rush, before moving to London in the mid 1970s. There should be a mysterious 'occult' item that connects these two time periods. ### ASSISTANT Chapter One: The Golden Veil The year was 1848, and California was a land of dreams and desperation. Men from all corners of the world had flocked to its golden shores, lured by the promise of wealth beyond their wildest imaginations. They came with pickaxes and pans, with hope in their hearts and greed in their eyes, ready to tear apart the earth itself in search of fortune. In the midst of this frenzy stood a solitary figure, a man who seemed out of place amidst the chaos. He was tall and lean, with a face that bore the lines of a thousand stories. His clothes were simple but well-made, his boots worn from years of travel. He moved with an air of quiet authority, as if he belonged to another time entirely. His name was Alistair Blackwood, and he had come to California not for gold, but for something far more valuable: knowledge. A scholar by trade and an adventurer at heart, Blackwood had spent his life chasing after ancient texts and forgotten lore, seeking to unravel the secrets of the universe. And now, he believed he was on the verge of a discovery that would change everything. Rumors had reached him of a strange artifact, said to have been found deep within the Sierra Nevada mountains. It was a small, intricately carved box made of an unknown metal, its surface etched with symbols that defied translation. Those who claimed to have seen it spoke in hushed tones of its otherworldly beauty and the eerie sense of power that seemed to emanate from within. Blackwood had tracked the rumors to a dusty saloon in Sacramento, where he hoped to find answers among the grizzled miners who frequented the place. As he pushed open the swinging doors, the din of voices and clinking glasses washed over him like a wave. He scanned the room, his gaze settling on a group of men huddled around a table in the corner. One look at their faces told him they were the ones he sought: sun-weathered and unshaven, their eyes glinting with a mixture of excitement and fear as they whispered amongst themselves. He approached them slowly, aware that he was an outsider in this rough-and-tumble world. "Gentlemen," he said, his voice low and cultured, "I couldn't help but overhear your conversation. Might I be so bold as to ask what has you all so...animated?" The men exchanged wary glances before one of them spoke up. "You ain't from around here, are ya?" he drawled, eyeing Blackwood's fine clothes with suspicion. "No," Blackwood admitted, "I am not. But I have traveled far and wide in search of knowledge, and I believe you may possess something that could be of great interest to me." He reached into his pocket and produced a small leather pouch, which he placed on the table with a soft thud. The men's eyes widened as they saw the glint of gold within. "I understand there is an object - a box, perhaps? - that has recently come into your possession. I would be willing to pay handsomely for any information you might have about it." The miners looked at each other, then back at Blackwood. Finally, the one who had spoken before nodded slowly. "We might know somethin' 'bout that," he said, his voice dropping to a conspiratorial whisper. "But it ain't no ordinary box." As they began to tell their tale, the saloon faded away around them, replaced by images of dark caverns and glittering treasure. Blackwood leaned forward, his heart pounding with anticipation. This was it - the moment he had been waiting for. The box, they said, had been found buried beneath a waterfall, hidden in a cave that seemed to defy all natural laws. It was guarded by strange creatures unlike anything any of them had ever seen: half-man, half-beast, with eyes that glowed like embers in the darkness. But the greatest mystery of all lay within the box itself. For when they opened it, they found not gold or jewels, but a single sheet of parchment, yellowed with age and covered in writing that none of them could decipher. Blackwood's pulse quickened as he listened. This was more than just another piece of occult nonsense; this was the real thing. He knew it in his bones. "I must see this box for myself," he said, his voice barely above a whisper. "Take me to it." The miners exchanged uneasy glances before nodding their assent. They led him out of the saloon and into the night, down winding streets and through shadowy alleyways until they reached a nondescript building on the edge of town. Inside, they descended a rickety staircase to a dimly lit basement filled with crates and barrels. There, in the center of the room, sat the object of his obsession: a small, unassuming box that seemed to hum with energy. Blackwood reached out a trembling hand to touch it, feeling the power coursing through him like electricity. As he did so, the world around him shifted, and suddenly he was no longer in a dingy basement in Sacramento. He stood instead in a grand library, its shelves groaning under the weight of countless tomes and scrolls. A fire crackled in the hearth, casting flickering shadows on the walls. A figure emerged from the gloom, cloaked in darkness save for two piercing eyes that seemed to bore into his very soul. "You have found it," the voice said, deep and resonant. "The key to unlocking the secrets of the universe." Blackwood stared at the apparition, unable to speak. He knew he should be afraid, but all he felt was awe. "But be warned," the figure continued, "for with great power comes great responsibility. The knowledge contained within this box is not meant for mortal minds." And then, just as suddenly as it had appeared, the vision vanished, leaving Blackwood alone once more in the dimly lit room. He stared at the box, his mind racing. What did it all mean? As he pondered this question, a sudden commotion erupted upstairs. Shouts and gunfire filled the air, followed by the sound of heavy footsteps on the staircase. The miners had betrayed him! He snatched up the box and fled into the night, his heart pounding in his chest. He knew he couldn't stay in California - not now that others were after the artifact. No, he would have to take it far away, to a place where its secrets could be studied in safety. And so began a journey that would span continents and decades, from the wild frontier of the Gold Rush to the foggy streets of Victorian London. For Alistair Blackwood had stumbled upon something much greater than mere treasure; he held in his hands the key to unlocking the very fabric of reality itself. But little did he know that the true mystery was only just beginning... </details> <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a dark and gritty historical novel set during the battle of the Bulge. Write in first person. The POV character for Chapter 1 is a young German officer trying to carry his wounded commander back to the deep defensive line after the bunker he was in was overrun. He should have a flashback to the moment it was overrun during the middle of the story. The story should not end well for either of them. ### ASSISTANT Chapter 1 The cold bit into my bones as I trudged through the snow, each step a labored effort. My breath came out in ragged puffs, clouding the air around me. In my arms, I cradled the weight of my world - my commander, Hauptmann Müller, his body limp and heavy with pain. His eyes were closed, but I could hear the shallow rasp of each breath he took, a testament to his will to live. The forest around us was a maze of shadows and death, the trees standing like skeletal sentinels against the moonlit sky. The silence was deafening, broken only by the distant rumble of artillery and the occasional crack of a rifle shot. It was a cruel irony that the same snow that muffled our footsteps also betrayed the presence of the enemy, their tracks glowing like phosphorescent snakes in the pale light. I stumbled, my legs threatening to give way under the strain. Hauptmann Müller groaned, his hand clutching at my coat. "Keep moving, Leutnant," he whispered, his voice barely audible. "We must reach the defensive line." I nodded, gritting my teeth as I adjusted my grip on him. The pain in my arms and shoulders was a constant companion now, but it was nothing compared to the fear that gnawed at my gut. We were alone, cut off from our unit during the chaos of the American assault. The bunker we had been holed up in was now a tomb for the men who had trusted us to lead them. A flash of memory seared through my mind, taking me back to that fateful moment when it all went wrong. The thunderous roar of explosions, the earth shaking beneath our feet as if the very foundations of hell were breaking loose. The screams of men, the acrid smell of smoke and cordite, the taste of blood in my mouth. I remembered the look on Müller's face when he realized what was happening - a mixture of disbelief and determination. He had barked orders, trying to rally our troops, but it was too late. The Americans were upon us, their numbers overwhelming. I saw him take a bullet to the leg, his face contorting in agony as he fell. And then there was nothing but chaos and darkness. When I came to, the bunker was silent except for the moans of the dying. The air was thick with smoke, making it hard to breathe. I found Müller slumped against a wall, his uniform soaked in blood. He looked up at me, his eyes glassy with pain but still burning with that same fierce determination. "We have to go," he said, gripping my arm. "They're coming." And so we had fled into the night, leaving behind the wreckage of our command post and the bodies of our comrades. Now, hours later, we were still stumbling through the forest, searching for safety that seemed to recede with every step. I could feel my strength ebbing away, my legs turning to lead. But I couldn't stop, not now. Not when we were so close. The deep defensive line was our only hope, a thin red line that stood between us and annihilation. If we could just make it there... But fate is a cruel mistress, and she had other plans for us. As we staggered out of the trees and into a small clearing, I saw them - a patrol of American soldiers, their silhouettes stark against the snow. They spotted us at the same time, their rifles swinging around to point in our direction. "Halt!" one of them yelled, his voice harsh and commanding. "Hands up!" I froze, my heart pounding in my chest. Müller's grip on me tightened, his breath coming in short gasps. I knew what he was thinking - we couldn't surrender, not like this. We were too valuable, too close to the front lines. They would shoot us or take us prisoner, and either way, it would be a death sentence for him. "Run," he whispered, his voice barely audible. "Leave me." I shook my head, tears stinging my eyes. I couldn't do that. I wouldn't. He was my commander, my friend. I owed him everything. But before I could make a move, the air was filled with the sound of gunfire. Müller jerked in my arms, a wet gurgle escaping his lips as he slumped against me. I stared down at him in disbelief, feeling the warmth of his blood soaking through my coat. "Nein," I whispered, my voice choked with grief and rage. "Nein!" I fell to my knees, cradling his lifeless body as the world around me spun out of control. The Americans were closing in, their shouts growing louder. I knew it was over - for both of us. With a final, desperate cry, I raised my pistol and fired into the darkness, hoping to take at least one of them with me. But my aim was wild, my strength gone. The last thing I saw was a flash of light, followed by an explosion of pain in my chest. And then there was nothing but blackness. As I slipped away, I thought of home - of my mother's warm embrace, of the laughter of my little sister. And I knew that I would never see them again. This frozen hellhole would be my grave, and Hauptmann Müller's sacrifice would be forgotten by all but me. But in that moment, as death claimed me, I found a strange sort of peace. We had fought for our Fatherland until the very end, and we had done so with honor. And in the grand tapestry of history, perhaps that was all that mattered. </details> <details> <summary>Click to see spoiler</summary> ### USER Write me a short story about the fictional character of Anton Chigurh. It should be written in third person personal and feature internal monologues of his deranged world view. The time period is around 2 years before the events in Cormac McCarthy's 2005 novel 'No Country for Old Men'. It should be set in Mexico, be *very* dark, show Anton's still developing obsession with "chance" and feature a grenade. The story should start with him waiting in a bar for instructions on the "job" he is to perform. ### ASSISTANT In the dimly lit corner of a nondescript Mexican cantina, Anton Chigurh sat motionless, his eyes scanning the room like a predator assessing its prey. The air was thick with the smell of stale cigarette smoke and cheap tequila, but he remained unfazed, his senses heightened, attuned to every sound, every movement around him. He nursed a glass of water, the condensation beading on its surface like droplets of sweat on cold steel. His mind wandered, as it often did, to the concept of chance. The randomness of life and death fascinated him, the idea that one's fate could hinge on something as simple as a coin flip or the roll of dice. He believed in the purity of such decisions, untainted by human emotion or prejudice. It was a philosophy he had embraced wholeheartedly, and it had served him well in his line of work. A fly buzzed around his head, its tiny wings beating an erratic rhythm against the stillness of the room. He watched it with mild interest, wondering if its life would end here, on this very spot, or if it would continue its meaningless existence elsewhere. The thought brought a small, cruel smile to his lips. The bartender, a grizzled old man with a face like leather, eyed him warily from across the room. He knew better than to engage in conversation with this stranger, who had entered his establishment an hour ago and hadn't spoken a word since. There was something about him that made the hairs on the back of his neck stand up, something cold and deadly lurking just beneath the surface. Chigurh's thoughts were interrupted by the sound of the door creaking open. A man in a cheap suit entered, looking around nervously before making his way towards him. He slid into the seat across from Chigurh, who continued to stare straight ahead, as if he hadn't noticed the new arrival. "You're late," Chigurh said finally, his voice low and gravelly. The man in the suit swallowed hard, adjusting his tie. "I apologize. Traffic." Chigurh didn't respond, his eyes still fixed on some distant point. The man fidgeted, reaching into his pocket to retrieve a small envelope. He slid it across the table, and Chigurh picked it up without looking at him, tearing it open with a single, precise movement. Inside was a photograph of a man, along with an address scrawled on a piece of paper. He studied the image for a moment, committing every detail to memory before slipping it back into the envelope and sliding it into his own pocket. "You know what needs to be done," the man in the suit said, his voice barely above a whisper. Chigurh nodded once, his mind already turning to the task at hand. He stood up, leaving a few crumpled bills on the table for the water he hadn't touched. As he walked towards the door, he could feel the weight of the grenade in his other pocket, its cool metal surface pressing against his thigh like a lover's caress. Outside, the heat of the day was oppressive, but Chigurh barely registered it as he climbed into his car and started the engine. He drove through the dusty streets, his thoughts consumed by the upcoming job. The man in the photograph had a wife, two kids. It didn't matter. They were all just pieces on a board, pawns to be moved or removed as necessary. He arrived at the address just as the sun was beginning to set, casting long shadows across the dilapidated building. He sat in his car for several minutes, watching the comings and goings of the neighborhood, waiting for the right moment. When it came, he slipped out of the vehicle and approached the door with a sense of purpose. Inside, he could hear laughter and music, the sounds of a family enjoying their evening together. He smiled to himself as he pulled the grenade from his pocket, feeling its weight in his hand. This was what he lived for: the moment when chance decided who would live and who would die. He took a deep breath, savoring the anticipation, before pulling the pin with a satisfying click. He tossed it into the room, not bothering to wait for the explosion as he turned and walked away. The screams that followed were like music to his ears, a symphony of chaos and destruction. As he drove off into the night, Chigurh couldn't help but feel a sense of satisfaction. Another job well done, another life ended by the whims of fate. He reached into his pocket and pulled out a coin, flipping it idly between his fingers as he considered his next move. Heads or tails? Life or death? It was all the same to him. The world was a cruel place, and he was just its instrument, meting out justice in the only way he knew how: with cold, calculated precision. And if some people happened to get caught in the crossfire... well, that was just the luck of the draw. </details> Big thanks to: @sophosympatheia for working out the merge pattern, @Sao10K for creating Euryale and WinterGoddess, and @chargoddard for writing [Mergekit](https://github.com/arcee-ai/mergekit)!
[ "TRANSLATION" ]
[ "BEAR" ]
Non_BioNLP
Backedman/TriviaAnsweringMachine
Backedman
question-answering
[ "transformers", "TFIDF-QA", "question-answering", "custom_code", "en", "license:mit", "region:us" ]
1,715
1,715
5
0
--- language: - en license: mit pipeline_tag: question-answering --- The evaluation of this project is to answer trivia questions. You do not need to do well at this task, but you should submit a system that completes the task or create adversarial questions in that setting. This will help the whole class share data and resources. If you focus on something other than predicting answers, *that's fine*! About the Data ============== Quiz bowl is an academic competition between schools in English-speaking countries; hundreds of teams compete in dozens of tournaments each year. Quiz bowl is different from Jeopardy, a recent application area. While Jeopardy also uses signaling devices, these are only usable after a question is completed (interrupting Jeopardy's questions would make for bad television). Thus, Jeopardy is rapacious classification followed by a race---among those who know the answer---to punch a button first. Here's an example of a quiz bowl question: Expanding on a 1908 paper by Smoluchowski, he derived a formula for the intensity of scattered light in media fluctuating densities that reduces to Rayleigh's law for ideal gases in The Theory of the Opalescence of Homogenous Fluids and Liquid Mixtures near the Critical State. That research supported his theories of matter first developed when he calculated the diffusion constant in terms of fundamental parameters of the particles of a gas undergoing Brownian Motion. In that same year, 1905, he also published On a Heuristic Point of View Concerning the Production and Transformation of Light. That explication of the photoelectric effect won him 1921 Nobel in Physics. For ten points, name this German physicist best known for his theory of Relativity. *ANSWER*: Albert _Einstein_ Two teams listen to the same question. Teams interrupt the question at any point by "buzzing in"; if the answer is correct, the team gets points and the next question is read. Otherwise, the team loses points and the other team can answer. You are welcome to use any *automatic* method to choose an answer. It need not be similar nor build on our provided systems. In addition to the data we provide, you are welcome to use any external data *except* our test quiz bowl questions (i.e., don't hack our server!). You are welcome (an encouraged) to use any publicly available software, but you may want to check on Piazza for suggestions as many tools are better (or easier to use) than others. If you don't like the interruptability of questions, you can also just answer entire questions. However, you must also output a confidence. Competition ================== We will use Dynabech website (https://dynabench.org/tasks/qa). If you remember the past workshop about Dynabench submission, this is the way to do it. The specific task name is "Grounded QA". Here, with the help of the video tutorial, you submit your QA model and assess how your QA model did compared to others. The assessment will take place by testing your QA model on several QA test datasets and the results of yours and your competitors will be visible on the leaderboard. Your goal is to rank the highest in terms of expected wins: you buzz in with probability proportional to your confidence, and if you're more right than the competition, you win. Writing Questions ================== Alternatively, you can also *write* 50 adversarial questions that challenge modern NLP systems. These questions must be diverse in the subjects asked about, the skills computers need to answer the questions, and the entities in those questions. Remember that your questions should be *factual* and *specific* enough for humans to answer, because your task is to stump the computers relative to humans! In addition to the raw questions, you will also need to create citations describing: * Why the question is difficult for computers: include citations from the NLP/AI/ML literature * Why the information in the question is correct: include citations from the sources you drew on the write the question * Why the question is interesting: include scholarly / popular culture artifacts to prove that people care about this * Why the question is pyramidal: discuss why your first clues are harder than your later clues **Category** We want questions from many domains such as Art, Literature, Geography, History, Science, TV and Film, Music, Lifestyle, and Sport. The questions should be written using all topics above (5 questions for each category and 5 more for the remaining categories). Indicate in your writeup which category you chose to write on for each question. Art: * Questions about works: Mona Lisa, Raft of the Medussa * Questions about forms: color, contour, texture * Questions about artists: Picasso, Monet, Leonardo da Vinci * Questions about context: Renaissance, post-modernism, expressionism, surrealism Literature: * Questions about works: novels (1984), plays (The Lion and the Jewel), poems (Rubaiyat), criticism (Poetics) * Questions about major characters or events in literature: The Death of Anna Karenina, Noboru Wataya, the Marriage of Hippolyta and Theseus * Questions about literary movements (Sturm und Drang) * Questions about translations * Cross-cutting questions (appearances of Overcoats in novels) * Common link questions (the literary output of a country/region) Geography: * Questions about location: names of capital, state, river * Questions about the place: temperature, wind flow, humidity History: * When: When did the First World war start? * Who: Who is called Napoleon of Iran? * Where: Where was the first Summer Olympics held? * Which: Which is the oldest civilization in the world? Science: * Questions about terminology: The concept of gravity was discovered by which famous physicist? * Questions about the experiment * Questions about theory: The social action theory believes that individuals are influenced by this theory. TV and Film: * Quotes: What are the dying words of Charles Foster Kane in Citizen Kane? * Title: What 1927 musical was the first "talkie"? * Plot: In The Matrix, does Neo take the blue pill or the red pill? Music: * Singer: What singer has had a Billboard No. 1 hit in each of the last four decades? * Band: Before Bleachers and fun., Jack Antonoff fronted what band? * Title: What was Madonna's first top 10 hit? * History: Which classical composer was deaf? Lifestyle: * Clothes: What clothing company, founded by a tennis player, has an alligator logo? * Decoration: What was the first perfume sold by Coco Chanel? Sport: * Known facts: What sport is best known as the ‘king of sports’? * Nationality: What’s the national sport of Canada? * Sport player: The classic 1980 movie called Raging Bull is about which real-life boxer? * Country: What country has competed the most times in the Summer Olympics yet hasn’t won any kind of medal? **Diversity** Other than category diversity, if you find an ingenious way of writing questions about underrepresented countries, you will get bonus points (indicate which questions you included the diversity component in your writeup). You may decide which are underrepresented countries with your own reasonable reason (etc., less population may indicate underrepresented), but make sure to articulate this in your writeup. * Run state of the art QA systems on the questions to show they struggle, give individual results for each question and a summary over all questions For an example of what the writeup for a single question should look like, see the adversarial HW: https://github.com/Pinafore/nlp-hw/blob/master/adversarial/question.tex Proposal ================== The project proposal is a one page PDF document that describes: * Who is on your team (team sizes can be between three and six students, but six is really too big to be effective; my suggestion is that most groups should be between four or five). * What techniques you will explore * Your timeline for completing the project (be realistic; you should have your first submission in a week or two) Submit the proposal on Gradescope, but make sure to include all group members. If all group members are not included, you will lose points. Late days cannot be used on this assignment. Milestone 1 ====================== You'll have to update how things are going: what's working, what isn't, and how does it change your timeline? How does it change your division of labor? *Question Writing*: You'll need to have answers selected for all of your questions and first drafts of at least 15 questions. This must be submitted as a JSON file so that we run computer QA systems on it. *Project*: You'll need to have made a submission to the leaderboard with something that satisfies the API. Submit a PDF updating on your progress to Gradescope. If all team members are not on the submission, you will lose points. Milestone 2 =================== As before, provide an updated timeline / division of labor, provide your intermediary results. *Question Writing*: You'll need to have reflected the feedback from the first questions and completed a first draft of at least 30 questions. You'll also need machine results to your questions and an overall evaluation of your human/computer accuracy. *Project*: You'll need to have a made a submission to the leaderboard with a working system (e.g., not just obey the API, but actually get reasonable answers). Submit a PDF updating on your progress. Final Presentation ====================== The final presentation will be virtual (uploading a video). In the final presentation you will: * Explain what you did * Who did what. For example, for the question writing project a team of five people might write: A wrote the first draft of questions. B and C verified they were initially answerable by a human. B ran computer systems to verify they were challenging to a computer. C edited the questions and increased the computer difficulty. D and E verified that the edited questions were still answerable by a human. D and E checked all of the questions for factual accuracy and created citations and the writeup. * What challenges you had * Review how well you did (based on the competition or your own metrics). If you do not use the course infrastructure to evaluate your project's work, you should talk about what alternative evaluations you used, why they're appropriate/fair, and how well you did on them. * Provide an error analysis. An error analysis must contain examples from the development set that you get wrong. You should show those sentences and explain why (in terms of features or the model) they have the wrong answer. You should have been doing this all along as you derive new features, but this is your final inspection of your errors. The feature or model problems you discover should not be trivial features you could add easily. Instead, these should be features or models that are difficult to correct. An error analysis is not the same thing as simply presenting the error matrix, as it does not inspect any individual examples. If you're writing questions, talk about examples of questions that didn't work out as intended. * The linguistic motivation for your features / how your wrote the questions. This is a computational linguistics class, so you should give precedence to features / techniques that we use in this class (e.g., syntax, morphology, part of speech, word sense, etc.). Given two features that work equally well and one that is linguistically motivated, we'll prefer the linguistically motivated one. * Presumably you did many different things; how did they each individually contribute to your final result? Each group has 10 minutes to deliver their presentation. Please record the video, and upload it to Google Drive, and include the link in your writeup submission. Final Question Submission ====================== Because we need to get the questions ready for the systems, upload your raw questions on May 10. This doesn't include the citations or other parts of the writeup. System Submission ====================== You must submit a version of your system by May 12. It may not be perfect, but this what the question writing teams will use to test their results. Your system should be sent directly to the professor and TAs in zip files, including the correct dependencies and a working inference code. Your inference code should run successfully in the root folder (extracted from zip folder) directory with the command: ``` > python3 inference.py --data=evaluation_set.json ``` The input will be in the form of a .json file () in the same format as the file the adversarial question writing team submits. The output format should also be in string. If you have any notes or comments that we should be aware of while running your code, please include them in the folder as a .txt file. Also, dependency information should be included as a .txt file.  Please prepend your email title with [2024-CMSC 470 System Submission]. Project Writeup and JSON file ====================== By May 17, submit your project writeup explaining what you did and what results you achieved. This document should make it clear: * Why this is a good idea * What you did * Who did what * Whether your technique worked or not For systems, please do not go over 2500 words unless you have a really good reason. Images are a much better use of space than words, usually (there's no limit on including images, but use judgement and be selective). For question writing, you have one page (single spaced, two column) per question plus a two page summary of results. Talk about how you organized the question writing, how you evaluated the questions, and a summary of the results. Along with your writeup, turn in a json including the raw text of the question and answer and category. The json file is included in this directory. Make sure your json file is in the correct format and is callable via below code. Your submission will not be graded if it does not follow the format of the example json file. ``` with open('path to your json file', 'r') as f: data = json.load(f) ``` Grade ====================== The grade will be out of 25 points, broken into five areas: * _Presentation_: For your oral presentation, do you highlight what you did and make people care? Did you use time well during the presentation? * _Writeup_: Does the writeup explain what you did in a way that is clear and effective? The final three areas are different between the system and the questions. | | System | Questions | |----------|:-------------:|------:| | _Technical Soundness_ | Did you use the right tools for the job, and did you use them correctly? Were they relevant to this class? | Were your questions correct and accurately cited. | | _Effort_ | Did you do what you say you would, and was it the right ammount of effort. | Are the questions well-written, interesting, and thoroughly edited? | | _Performance_ | How did your techniques perform in terms of accuracy, recall, etc.? | Is the human accuracy substantially higher than the computer accuracy? | All members of the group will receive the same grade. It's impossible for the course staff to adjudicate Rashomon-style accounts of who did what, and the goal of a group project is for all team members to work together to create a cohesive project that works well together. While it makes sense to divide the work into distinct areas of responsibility, at grading time we have now way to know who really did what, so it's the groups responsibility to create a piece of output that reflects well on the whole group.
[ "TRANSLATION" ]
[ "MEDAL" ]
Non_BioNLP
TheBloke/med42-70B-AWQ
TheBloke
text-generation
[ "transformers", "safetensors", "llama", "text-generation", "m42", "health", "healthcare", "clinical-llm", "en", "base_model:m42-health/med42-70b", "base_model:quantized:m42-health/med42-70b", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "awq", "region:us" ]
1,698
1,699
449
2
--- base_model: m42-health/med42-70b language: - en license: other license_name: med42 model_name: Med42 70B pipeline_tag: text-generation tags: - m42 - health - healthcare - clinical-llm inference: false model_creator: M42 Health model_type: llama prompt_template: '<|system|>: You are a helpful medical assistant created by M42 Health in the UAE. <|prompter|>:{prompt} <|assistant|>: ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Med42 70B - AWQ - Model creator: [M42 Health](https://huggingface.co/m42-health) - Original model: [Med42 70B](https://huggingface.co/m42-health/med42-70b) <!-- description start --> ## Description This repo contains AWQ model files for [M42 Health's Med42 70B](https://huggingface.co/m42-health/med42-70b). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/med42-70B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/med42-70B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/med42-70B-GGUF) * [M42 Health's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/m42-health/med42-70b) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Med42 ``` <|system|>: You are a helpful medical assistant created by M42 Health in the UAE. <|prompter|>:{prompt} <|assistant|>: ``` <!-- prompt-template end --> <!-- licensing start --> ## Licensing The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly. In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [M42 Health's Med42 70B](https://huggingface.co/m42-health/med42-70b). <!-- licensing end --> <!-- README_AWQ.md-provided-files start --> ## Provided files, and AWQ parameters For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM. Models are released as sharded safetensors files. | Branch | Bits | GS | AWQ Dataset | Seq Len | Size | | ------ | ---- | -- | ----------- | ------- | ---- | | [main](https://huggingface.co/TheBloke/med42-70B-AWQ/tree/main) | 4 | 128 | [Medical Meadow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc) | 4096 | 36.61 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/med42-70B-AWQ`. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `med42-70B-AWQ` 7. Select **Loader: AutoAWQ**. 8. Click Load, and the model will load and is now ready for use. 9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. 10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started! <!-- README_AWQ.md-text-generation-webui end --> <!-- README_AWQ.md-use-from-vllm start --> ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 python -m vllm.entrypoints.api_server --model TheBloke/med42-70B-AWQ --quantization awq ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''<|system|>: You are a helpful medical assistant created by M42 Health in the UAE. <|prompter|>:{prompt} <|assistant|>: ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/med42-70B-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-tgi start --> ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/med42-70B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''<|system|>: You are a helpful medical assistant created by M42 Health in the UAE. <|prompter|>:{prompt} <|assistant|>: ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ``` <!-- README_AWQ.md-use-from-tgi end --> <!-- README_AWQ.md-use-from-python start --> ## Inference from Python code using AutoAWQ ### Install the AutoAWQ package Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.1 or later. ```shell pip3 install autoawq ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### AutoAWQ example code ```python from awq import AutoAWQForCausalLM from transformers import AutoTokenizer model_name_or_path = "TheBloke/med42-70B-AWQ" # Load tokenizer tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False) # Load model model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True, trust_remote_code=False, safetensors=True) prompt = "Tell me about AI" prompt_template=f'''<|system|>: You are a helpful medical assistant created by M42 Health in the UAE. <|prompter|>:{prompt} <|assistant|>: ''' print("*** Running model.generate:") token_input = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() # Generate output generation_output = model.generate( token_input, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, max_new_tokens=512 ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("LLM output: ", text_output) """ # Inference should be possible with transformers pipeline as well in future # But currently this is not yet supported by AutoAWQ (correct as of September 25th 2023) from transformers import pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) """ ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. <!-- README_AWQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: M42 Health's Med42 70B # **Med42 - Clinical Large Language Model** Med42 is an open-access clinical large language model (LLM) developed by M42 to expand access to medical knowledge. Built off LLaMA-2 and comprising 70 billion parameters, this generative AI system provides high-quality answers to medical questions. ## Model Details *Note: Use of this model is governed by the M42 Health license. In order to download the model weights (and tokenizer), please read the [Med42 License](https://huggingface.co/spaces/m42-health/License) and accept our License by requesting access here.* Beginning with the base LLaMa-2 model, Med42 was instruction-tuned on a dataset of ~250M tokens compiled from different open-access sources, including medical flashcards, exam questions, and open-domain dialogues. **Model Developers:** M42 Health AI Team **Finetuned from model:** Llama-2 - 70B **Context length:** 4k tokens **Input:** Text only data **Output:** Model generates text only **Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we enhance model's performance. **License:** A custom license is available [here](https://huggingface.co/spaces/m42-health/License) **Research Paper:** TBA ## Intended Use Med42 is being made available for further testing and assessment as an AI assistant to enhance clinical decision-making and enhance access to an LLM for healthcare use. Potential use cases include: - Medical question answering - Patient record summarization - Aiding medical diagnosis - General health Q&A To get the expected features and performance for the model, a specific formatting needs to be followed, including the `<|system|>`, `<|prompter|>` and `<|assistant|>` tags. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name_or_path = "m42-health/med42-70b" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) prompt = "What are the symptoms of diabetes ?" prompt_template=f''' <|system|>: You are a helpful medical assistant created by M42 Health in the UAE. <|prompter|>:{prompt} <|assistant|>: ''' input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True,eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id, max_new_tokens=512) print(tokenizer.decode(output[0])) ``` ## Hardware and Software The training process was performed on the Condor Galaxy 1 (CG-1) supercomputer platform. ## Evaluation Results Med42 achieves achieves competitive performance on various medical benchmarks, including MedQA, MedMCQA, PubMedQA, HeadQA, and Measuring Massive Multitask Language Understanding (MMLU) clinical topics. For all evaluations reported so far, we use [EleutherAI's evaluation harness library](https://github.com/EleutherAI/lm-evaluation-harness) and report zero-shot accuracies (except otherwise stated). We compare the performance with that reported for other models (ClinicalCamel-70B, GPT-3.5, GPT-4.0, Med-PaLM 2). |Dataset|Med42|ClinicalCamel-70B|GPT-3.5|GPT-4.0|Med-PaLM-2 (5-shot)*| |---|---|---|---|---|---| |MMLU Clinical Knowledge|74.3|69.8|69.8|86.0|88.3| |MMLU College Biology|84.0|79.2|72.2|95.1|94.4| |MMLU College Medicine|68.8|67.0|61.3|76.9|80.9| |MMLU Medical Genetics|86.0|69.0|70.0|91.0|90.0| |MMLU Professional Medicine|79.8|71.3|70.2|93.0|95.2| |MMLU Anatomy|67.4|62.2|56.3|80.0|77.8| |MedMCQA|60.9|47.0|50.1|69.5|71.3| |MedQA|61.5|53.4|50.8|78.9|79.7| |USMLE Self-Assessment|71.7|-|49.1|83.8|-| |USMLE Sample Exam|72.0|54.3|56.9|84.3|-| **We note that 0-shot performance is not reported for Med-PaLM 2. Further details can be found at [https://github.com/m42health/med42](https://github.com/m42health/med42)*. ### Key performance metrics: - Med42 achieves a 72% accuracy on the US Medical Licensing Examination (USMLE) sample exam, surpassing the prior state of the art among openly available medical LLMs. - 61.5% on MedQA dataset (compared to 50.8% for GPT-3.5) - Consistently higher performance on MMLU clinical topics compared to GPT-3.5. ## Limitations & Safe Use - Med42 is not ready for real clinical use. Extensive human evaluation is undergoing as it is required to ensure safety. - Potential for generating incorrect or harmful information. - Risk of perpetuating biases in training data. Use this model responsibly! Do not rely on it for medical usage without rigorous safety testing. ## Accessing Med42 and Reporting Issues Please report any software "bug" or other problems through one of the following means: - Reporting issues with the model: [https://github.com/m42health/med42](https://github.com/m42health/med42) - Reporting risky content generated by the model, bugs and/or any security concerns: [https://forms.office.com/r/YMJu3kcKat](https://forms.office.com/r/YMJu3kcKat) - M42’s privacy policy available at [https://m42.ae/privacy-policy/](https://m42.ae/privacy-policy/) - Reporting violations of the Acceptable Use Policy or unlicensed uses of Med42: <[email protected]>
[ "QUESTION_ANSWERING", "SUMMARIZATION" ]
[ "MEDQA", "PUBMEDQA" ]
BioNLP
AdaptLLM/finance-LLM
AdaptLLM
text-generation
[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "finance", "en", "dataset:Open-Orca/OpenOrca", "dataset:GAIR/lima", "dataset:WizardLM/WizardLM_evol_instruct_V2_196k", "arxiv:2309.09530", "arxiv:2411.19930", "arxiv:2406.14491", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,695
1,733
665
118
--- datasets: - Open-Orca/OpenOrca - GAIR/lima - WizardLM/WizardLM_evol_instruct_V2_196k language: - en metrics: - accuracy pipeline_tag: text-generation tags: - finance --- # Adapting LLMs to Domains via Continual Pre-Training (ICLR 2024) This repo contains the domain-specific base model developed from **LLaMA-1-7B**, using the method in our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530). We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. **Our 7B model competes with much larger domain-specific models like BloombergGPT-50B**. ### [2024/11/29] 🤗 Introduce the multimodal version of AdaptLLM at [AdaMLLM](https://huggingface.co/papers/2411.19930), for adapting MLLMs to domains 🤗 **************************** **Updates** **************************** * 2024/11/29: Released [AdaMLLM](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains) for adapting MLLMs to domains * 2024/9/20: Our [research paper for Instruction-Pretrain](https://huggingface.co/papers/2406.14491) has been accepted by EMNLP 2024 * 2024/8/29: Updated [guidelines](https://huggingface.co/datasets/AdaptLLM/finance-tasks) on evaluating any 🤗Huggingface models on the domain-specific tasks * 2024/6/22: Released the [benchmarking code](https://github.com/microsoft/LMOps/tree/main/adaptllm) * 2024/6/21: Released the general version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain) * 2024/4/2: Released the [raw data splits (train and test)](https://huggingface.co/datasets/AdaptLLM/ConvFinQA) of all the evaluation datasets * 2024/1/16: Our [research paper for AdaptLLM](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024 * 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B * 2023/12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B * 2023/9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/law-tasks), and [base models](https://huggingface.co/AdaptLLM/law-LLM) developed from LLaMA-1-7B ## 1. Domain-Specific Models ### LLaMA-1-7B In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are: <p align='center'> <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700"> </p> ### LLaMA-1-13B Moreover, we scale up our base model to LLaMA-1-13B to see if **our method is similarly effective for larger-scale models**, and the results are consistently positive too: [Biomedicine-LLM-13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B), [Finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) and [Law-LLM-13B](https://huggingface.co/AdaptLLM/law-LLM-13B). ### LLaMA-2-Chat Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat). For example, to chat with the finance base model (🤗we highly recommend switching to the [chat model](https://huggingface.co/AdaptLLM/finance-chat) for better response quality): ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-LLM") tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-LLM", use_fast=False) # Put your input here: user_input = '''Use this fact to answer the question: Title of each class Trading Symbol(s) Name of each exchange on which registered Common Stock, Par Value $.01 Per Share MMM New York Stock Exchange MMM Chicago Stock Exchange, Inc. 1.500% Notes due 2026 MMM26 New York Stock Exchange 1.750% Notes due 2030 MMM30 New York Stock Exchange 1.500% Notes due 2031 MMM31 New York Stock Exchange Which debt securities are registered to trade on a national securities exchange under 3M's name as of Q2 of 2023?''' # Simply use your input as the prompt for base models prompt = user_input inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device) outputs = model.generate(input_ids=inputs, max_length=2048)[0] answer_start = int(inputs.shape[-1]) pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True) print(pred) ``` ### LLaMA-3-8B (💡New!) In our recent research on [Instruction-Pretrain](https://huggingface.co/papers/2406.14491), we developed a context-based instruction synthesizer to augment the raw corpora with instruction-response pairs, **enabling Llama3-8B to be comparable to or even outperform Llama3-70B**: [Finance-Llama3-8B](https://huggingface.co/instruction-pretrain/finance-Llama3-8B), [Biomedicine-Llama3-8B](https://huggingface.co/instruction-pretrain/medicine-Llama3-8B). ## 2. Domain-Specific Tasks ### Pre-templatized Testing Splits To easily reproduce our prompting results, we have uploaded the filled-in zero/few-shot input instructions and output completions of the test each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks). Note: those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models. ### Evaluating Any Huggingface LMs on Domain-Specific Tasks (💡New!) You can use the following script to reproduce our results and evaluate any other Huggingface models on domain-specific tasks. Note that the script is NOT applicable to models that require specific prompt templates (e.g., Llama2-chat, Llama3-Instruct). 1). **Set Up Dependencies** ```bash git clone https://github.com/microsoft/LMOps cd LMOps/adaptllm pip install -r requirements.txt ``` 2). **Evaluate the Model** ```bash # Select the domain from ['biomedicine', 'finance', 'law'] DOMAIN='finance' # Specify any Huggingface model name (Not applicable to chat models) MODEL='AdaptLLM/finance-LLM' # Model parallelization: # - Set MODEL_PARALLEL=False if the model fits on a single GPU. # We observe that LMs smaller than 10B always meet this requirement. # - Set MODEL_PARALLEL=True if the model is too large and encounters OOM on a single GPU. MODEL_PARALLEL=False # Choose the number of GPUs from [1, 2, 4, 8] N_GPU=1 # Whether to add a BOS token at the beginning of the prompt input: # - Set to False for AdaptLLM. # - Set to True for instruction-pretrain models. # If unsure, we recommend setting it to False, as this is suitable for most LMs. add_bos_token=False # Run the evaluation script bash scripts/inference.sh ${DOMAIN} ${MODEL} ${add_bos_token} ${MODEL_PARALLEL} ${N_GPU} ``` ### Raw Datasets We have also uploaded the raw training and testing splits, for facilitating fine-tuning or other usages: [ChemProt](https://huggingface.co/datasets/AdaptLLM/ChemProt), [RCT](https://huggingface.co/datasets/AdaptLLM/RCT), [ConvFinQA](https://huggingface.co/datasets/AdaptLLM/ConvFinQA), [FiQA_SA](https://huggingface.co/datasets/AdaptLLM/FiQA_SA), [Headline](https://huggingface.co/datasets/AdaptLLM/Headline), [NER](https://huggingface.co/datasets/AdaptLLM/NER), [FPB](https://huggingface.co/datasets/AdaptLLM/FPB) ### Domain Knowledge Probing Our pre-processed knowledge probing datasets are available at: [med_knowledge_prob](https://huggingface.co/datasets/AdaptLLM/med_knowledge_prob) and [law_knowledge_prob](https://huggingface.co/datasets/AdaptLLM/law_knowledge_prob) ## Citation If you find our work helpful, please cite us: ```bibtex @inproceedings{ cheng2024adapting, title={Adapting Large Language Models via Reading Comprehension}, author={Daixuan Cheng and Shaohan Huang and Furu Wei}, booktitle={The Twelfth International Conference on Learning Representations}, year={2024}, url={https://openreview.net/forum?id=y886UXPEZ0} } ```
[ "QUESTION_ANSWERING" ]
[ "CHEMPROT" ]
Non_BioNLP
tsirif/BinGSE-Meta-Llama-3-8B-Instruct
tsirif
sentence-similarity
[ "peft", "safetensors", "text-embedding", "embeddings", "information-retrieval", "beir", "text-classification", "language-model", "text-clustering", "text-semantic-similarity", "text-evaluation", "text-reranking", "feature-extraction", "sentence-similarity", "Sentence Similarity", "natural_questions", "ms_marco", "fever", "hotpot_qa", "mteb", "en", "license:mit", "model-index", "region:us" ]
1,729
1,729
14
0
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type: max_f1 value: 80.1902671057446 - type: max_precision value: 76.4895104895105 - type: max_recall value: 84.3701878657222 - type: similarity_accuracy value: 89.86688399891334 - type: similarity_accuracy_threshold value: 1643.6103820800781 - type: similarity_ap value: 87.6029128769812 - type: similarity_f1 value: 80.1902671057446 - type: similarity_f1_threshold value: 1584.8767280578613 - type: similarity_precision value: 76.40496444010599 - type: similarity_recall value: 84.3701878657222 --- # BinGE: TODO TODO: 2 line summary and link to paper ## Usage ```python import torch from transformers import AutoTokenizer, AutoModel, AutoConfig from peft import PeftModel if __name__ == "__main__": # Loading base Meta-Llama-3 model, along with custom code that enables bidirectional connections in decoder-only LLMs. tokenizer = AutoTokenizer.from_pretrained( "McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp" ) config = AutoConfig.from_pretrained( "McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp", trust_remote_code=True ) model = AutoModel.from_pretrained( "McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp", trust_remote_code=True, config=config, torch_dtype=torch.bfloat16, device_map="cuda" if torch.cuda.is_available() else "cpu", ) # Loading MNTP (Masked Next Token Prediction) model. model = PeftModel.from_pretrained( model, "McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp", ) model = model.merge_and_unload() # This can take several minutes on cpu # Loading BinGSE model. This loads the trained LoRA weights on top of MNTP model. Hence the final weights are -- Base model + MNTP (LoRA) + BinGSE (LoRA). model = PeftModel.from_pretrained( model, model_path ) ``` TODO: initialize wrapper, provide example to check loading happened properly - see https://huggingface.co/McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
yishan-wang/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF
yishan-wang
sentence-similarity
[ "sentence-transformers", "gguf", "feature-extraction", "sentence-similarity", "mteb", "arctic", "snowflake-arctic-embed", "transformers.js", "llama-cpp", "gguf-my-repo", "base_model:Snowflake/snowflake-arctic-embed-m-v1.5", "base_model:quantized:Snowflake/snowflake-arctic-embed-m-v1.5", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,723
1,723
19
0
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type: main_score value: 33.808268374864745 - type: v_measure value: 33.808268374864745 - type: v_measure_std value: 1.2212188701887239 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions (default) type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: main_score value: 52.19806018468037 - type: map value: 52.19806018468037 - type: mrr value: 52.98921462524404 - type: nAUC_map_diff1 value: 37.41443156995912 - type: nAUC_map_max value: 9.410262727675603 - type: nAUC_map_std value: 8.7094185014992 - type: nAUC_mrr_diff1 value: 37.78202772392581 - type: nAUC_mrr_max value: 10.517635536565816 - type: nAUC_mrr_std value: 8.509423813772491 - task: type: Summarization dataset: name: MTEB SummEval (default) type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cosine_pearson value: 30.48413700430812 - type: cosine_spearman value: 30.357162200875816 - type: dot_pearson value: 30.484140144824938 - type: dot_spearman value: 30.357162200875816 - type: main_score value: 30.357162200875816 - type: pearson value: 30.48413700430812 - type: spearman value: 30.357162200875816 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification (default) type: mteb/toxic_conversations_50k config: default split: test revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de metrics: - type: accuracy value: 66.8359375 - type: ap value: 12.482653786025985 - type: ap_weighted value: 12.482653786025985 - type: f1 value: 51.328608527332385 - type: f1_weighted value: 74.07974463955398 - type: main_score value: 66.8359375 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification (default) type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 53.907753254103 - type: f1 value: 54.22707647269581 - type: f1_weighted value: 53.611822984407695 - type: main_score value: 53.907753254103 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering (default) type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: main_score value: 38.1364789307295 - type: v_measure value: 38.1364789307295 - type: v_measure_std value: 2.0731634966352077 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 (default) type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cosine_accuracy value: 82.66674614054956 - type: cosine_accuracy_threshold value: 79.80123162269592 - type: cosine_ap value: 63.28209719072804 - type: cosine_f1 value: 60.16389710903711 - type: cosine_f1_threshold value: 72.22893834114075 - type: cosine_precision value: 52.90232185748599 - type: cosine_recall value: 69.73614775725594 - type: dot_accuracy value: 82.66674614054956 - type: dot_accuracy_threshold value: 79.8012375831604 - type: dot_ap value: 63.282103870645166 - type: dot_f1 value: 60.16389710903711 - type: dot_f1_threshold value: 72.22894430160522 - type: dot_precision value: 52.90232185748599 - type: dot_recall value: 69.73614775725594 - type: euclidean_accuracy value: 82.66674614054956 - type: euclidean_accuracy_threshold value: 63.55905532836914 - type: euclidean_ap value: 63.282095399953164 - type: euclidean_f1 value: 60.16389710903711 - type: euclidean_f1_threshold value: 74.5265781879425 - type: euclidean_precision value: 52.90232185748599 - type: euclidean_recall value: 69.73614775725594 - type: main_score value: 63.282103870645166 - type: manhattan_accuracy value: 82.74423317637242 - type: manhattan_accuracy_threshold value: 1415.380859375 - type: manhattan_ap value: 63.26931757839598 - type: manhattan_f1 value: 60.11014948859166 - type: manhattan_f1_threshold value: 1632.522201538086 - type: manhattan_precision value: 52.359506559624045 - type: manhattan_recall value: 70.55408970976254 - type: max_ap value: 63.282103870645166 - type: max_f1 value: 60.16389710903711 - type: max_precision value: 52.90232185748599 - type: max_recall value: 70.55408970976254 - type: similarity_accuracy value: 82.66674614054956 - type: similarity_accuracy_threshold value: 79.80123162269592 - type: similarity_ap value: 63.28209719072804 - type: similarity_f1 value: 60.16389710903711 - type: similarity_f1_threshold value: 72.22893834114075 - type: similarity_precision value: 52.90232185748599 - type: similarity_recall value: 69.73614775725594 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus (default) type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cosine_accuracy value: 88.10105949470253 - type: cosine_accuracy_threshold value: 68.95147562026978 - type: cosine_ap value: 84.65516103854583 - type: cosine_f1 value: 76.54581123301605 - type: cosine_f1_threshold value: 63.92929553985596 - type: cosine_precision value: 72.46526344751685 - type: cosine_recall value: 81.11333538651063 - type: dot_accuracy value: 88.10105949470253 - type: dot_accuracy_threshold value: 68.95147562026978 - type: dot_ap value: 84.65516301437592 - type: dot_f1 value: 76.54581123301605 - type: dot_f1_threshold value: 63.92928957939148 - type: dot_precision value: 72.46526344751685 - type: dot_recall value: 81.11333538651063 - type: euclidean_accuracy value: 88.10105949470253 - type: euclidean_accuracy_threshold value: 78.80169153213501 - type: euclidean_ap value: 84.65517268264233 - type: euclidean_f1 value: 76.54581123301605 - type: euclidean_f1_threshold value: 84.93610620498657 - type: euclidean_precision value: 72.46526344751685 - type: euclidean_recall value: 81.11333538651063 - type: main_score value: 84.65517268264233 - type: manhattan_accuracy value: 88.08941669577366 - type: manhattan_accuracy_threshold value: 1739.3169403076172 - type: manhattan_ap value: 84.64592398855694 - type: manhattan_f1 value: 76.62890540443034 - type: manhattan_f1_threshold value: 1861.344337463379 - type: manhattan_precision value: 72.09775967413442 - type: manhattan_recall value: 81.76778564829073 - type: max_ap value: 84.65517268264233 - type: max_f1 value: 76.62890540443034 - type: max_precision value: 72.46526344751685 - type: max_recall value: 81.76778564829073 - type: similarity_accuracy value: 88.10105949470253 - type: similarity_accuracy_threshold value: 68.95147562026978 - type: similarity_ap value: 84.65516103854583 - type: similarity_f1 value: 76.54581123301605 - type: similarity_f1_threshold value: 63.92929553985596 - type: similarity_precision value: 72.46526344751685 - type: similarity_recall value: 81.11333538651063 --- # yishan-wang/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF This model was converted to GGUF format from [`Snowflake/snowflake-arctic-embed-m-v1.5`](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo yishan-wang/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF --hf-file snowflake-arctic-embed-m-v1.5-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo yishan-wang/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF --hf-file snowflake-arctic-embed-m-v1.5-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo yishan-wang/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF --hf-file snowflake-arctic-embed-m-v1.5-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo yishan-wang/snowflake-arctic-embed-m-v1.5-Q8_0-GGUF --hf-file snowflake-arctic-embed-m-v1.5-q8_0.gguf -c 2048 ```
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
sschet/scibert_scivocab_uncased-finetuned-ner
sschet
token-classification
[ "transformers", "pytorch", "bert", "token-classification", "Named Entity Recognition", "SciBERT", "Adverse Effect", "Drug", "Medical", "en", "dataset:ade_corpus_v2", "dataset:tner/bc5cdr", "dataset:commanderstrife/jnlpba", "dataset:bc2gm_corpus", "dataset:drAbreu/bc4chemd_ner", "dataset:linnaeus", "dataset:chintagunta85/ncbi_disease", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,675
1,675
152
0
--- datasets: - ade_corpus_v2 - tner/bc5cdr - commanderstrife/jnlpba - bc2gm_corpus - drAbreu/bc4chemd_ner - linnaeus - chintagunta85/ncbi_disease language: - en tags: - Named Entity Recognition - SciBERT - Adverse Effect - Drug - Medical widget: - text: Abortion, miscarriage or uterine hemorrhage associated with misoprostol (Cytotec), a labor-inducing drug. example_title: Abortion, miscarriage, ... - text: Addiction to many sedatives and analgesics, such as diazepam, morphine, etc. example_title: Addiction to many... - text: Birth defects associated with thalidomide example_title: Birth defects associated... - text: Bleeding of the intestine associated with aspirin therapy example_title: Bleeding of the intestine... - text: Cardiovascular disease associated with COX-2 inhibitors (i.e. Vioxx) example_title: Cardiovascular disease... --- This is a SciBERT-based model fine-tuned to perform Named Entity Recognition for drug names and adverse drug effects. ![model image](https://raw.githubusercontent.com/jsylee/personal-projects/master/Hugging%20Face%20ADR%20Fine-Tuning/hf_adr.png) This model classifies input tokens into one of five classes: - `B-DRUG`: beginning of a drug entity - `I-DRUG`: within a drug entity - `B-EFFECT`: beginning of an AE entity - `I-EFFECT`: within an AE entity - `O`: outside either of the above entities To get started using this model for inference, simply set up an NER `pipeline` like below: ```python from transformers import (AutoModelForTokenClassification, AutoTokenizer, pipeline, ) model_checkpoint = "jsylee/scibert_scivocab_uncased-finetuned-ner" model = AutoModelForTokenClassification.from_pretrained(model_checkpoint, num_labels=5, id2label={0: 'O', 1: 'B-DRUG', 2: 'I-DRUG', 3: 'B-EFFECT', 4: 'I-EFFECT'} ) tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) model_pipeline = pipeline(task="ner", model=model, tokenizer=tokenizer) print( model_pipeline ("Abortion, miscarriage or uterine hemorrhage associated with misoprostol (Cytotec), a labor-inducing drug.")) ``` SciBERT: https://huggingface.co/allenai/scibert_scivocab_uncased Dataset: https://huggingface.co/datasets/ade_corpus_v2
[ "NAMED_ENTITY_RECOGNITION" ]
[ "BC5CDR", "JNLPBA", "LINNAEUS", "NCBI DISEASE" ]
BioNLP
LXC1999/gte-Qwen2-7B-instruct-Q4_K_M-GGUF
LXC1999
sentence-similarity
[ "sentence-transformers", "gguf", "mteb", "transformers", "Qwen2", "sentence-similarity", "llama-cpp", "gguf-my-repo", "base_model:Alibaba-NLP/gte-Qwen2-7B-instruct", "base_model:quantized:Alibaba-NLP/gte-Qwen2-7B-instruct", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]
1,739
1,739
9
0
--- base_model: Alibaba-NLP/gte-Qwen2-7B-instruct license: apache-2.0 tags: - mteb - sentence-transformers - transformers - Qwen2 - sentence-similarity - llama-cpp - gguf-my-repo model-index: - name: gte-qwen2-7B-instruct results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 91.31343283582089 - type: ap value: 67.64251402604096 - type: f1 value: 87.53372530755692 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 97.497825 - type: ap value: 96.30329547047529 - type: f1 value: 97.49769793778039 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 62.564 - type: f1 value: 60.975777935041066 - task: type: Retrieval dataset: name: MTEB ArguAna type: mteb/arguana config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 36.486000000000004 - type: map_at_10 value: 54.842 - type: map_at_100 value: 55.206999999999994 - type: map_at_1000 value: 55.206999999999994 - type: map_at_3 value: 49.893 - type: map_at_5 value: 53.105000000000004 - type: mrr_at_1 value: 37.34 - type: mrr_at_10 value: 55.143 - type: mrr_at_100 value: 55.509 - type: mrr_at_1000 value: 55.509 - type: mrr_at_3 value: 50.212999999999994 - type: mrr_at_5 value: 53.432 - type: ndcg_at_1 value: 36.486000000000004 - type: ndcg_at_10 value: 64.273 - type: ndcg_at_100 value: 65.66199999999999 - type: ndcg_at_1000 value: 65.66199999999999 - type: ndcg_at_3 value: 54.352999999999994 - type: ndcg_at_5 value: 60.131 - type: precision_at_1 value: 36.486000000000004 - type: precision_at_10 value: 9.395000000000001 - type: precision_at_100 value: 0.996 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 22.428 - type: precision_at_5 value: 16.259 - type: recall_at_1 value: 36.486000000000004 - type: recall_at_10 value: 93.95400000000001 - type: recall_at_100 value: 99.644 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 67.283 - type: recall_at_5 value: 81.294 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 56.461169803700564 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 51.73600434466286 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 67.57827065898053 - type: mrr value: 79.08136569493911 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 83.53324575999243 - type: cos_sim_spearman value: 81.37173362822374 - type: euclidean_pearson value: 82.19243335103444 - type: euclidean_spearman value: 81.33679307304334 - type: manhattan_pearson value: 82.38752665975699 - type: manhattan_spearman value: 81.31510583189689 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 87.56818181818181 - type: f1 value: 87.25826722019875 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 50.09239610327673 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 46.64733054606282 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 33.997 - type: map_at_10 value: 48.176 - type: map_at_100 value: 49.82 - type: map_at_1000 value: 49.924 - type: map_at_3 value: 43.626 - type: map_at_5 value: 46.275 - type: mrr_at_1 value: 42.059999999999995 - type: mrr_at_10 value: 53.726 - type: mrr_at_100 value: 54.398 - type: mrr_at_1000 value: 54.416 - type: mrr_at_3 value: 50.714999999999996 - type: mrr_at_5 value: 52.639 - type: ndcg_at_1 value: 42.059999999999995 - type: ndcg_at_10 value: 55.574999999999996 - type: ndcg_at_100 value: 60.744 - type: ndcg_at_1000 value: 61.85699999999999 - type: ndcg_at_3 value: 49.363 - type: ndcg_at_5 value: 52.44 - type: precision_at_1 value: 42.059999999999995 - type: precision_at_10 value: 11.101999999999999 - type: precision_at_100 value: 1.73 - type: precision_at_1000 value: 0.218 - type: precision_at_3 value: 24.464 - type: precision_at_5 value: 18.026 - type: recall_at_1 value: 33.997 - type: recall_at_10 value: 70.35900000000001 - type: recall_at_100 value: 91.642 - type: recall_at_1000 value: 97.977 - type: recall_at_3 value: 52.76 - type: recall_at_5 value: 61.148 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: BeIR/cqadupstack config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 35.884 - type: map_at_10 value: 48.14 - type: map_at_100 value: 49.5 - type: map_at_1000 value: 49.63 - type: map_at_3 value: 44.646 - type: map_at_5 value: 46.617999999999995 - type: mrr_at_1 value: 44.458999999999996 - type: mrr_at_10 value: 53.751000000000005 - type: mrr_at_100 value: 54.37800000000001 - type: mrr_at_1000 value: 54.415 - type: mrr_at_3 value: 51.815 - type: mrr_at_5 value: 52.882 - type: ndcg_at_1 value: 44.458999999999996 - type: ndcg_at_10 value: 54.157 - type: ndcg_at_100 value: 58.362 - type: ndcg_at_1000 value: 60.178 - type: ndcg_at_3 value: 49.661 - type: ndcg_at_5 value: 51.74999999999999 - type: precision_at_1 value: 44.458999999999996 - type: precision_at_10 value: 10.248 - type: precision_at_100 value: 1.5890000000000002 - type: precision_at_1000 value: 0.207 - type: precision_at_3 value: 23.928 - type: precision_at_5 value: 16.878999999999998 - type: recall_at_1 value: 35.884 - type: recall_at_10 value: 64.798 - type: recall_at_100 value: 82.345 - type: recall_at_1000 value: 93.267 - type: recall_at_3 value: 51.847 - type: recall_at_5 value: 57.601 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: BeIR/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 39.383 - type: map_at_10 value: 53.714 - type: map_at_100 value: 54.838 - type: map_at_1000 value: 54.87800000000001 - type: map_at_3 value: 50.114999999999995 - type: map_at_5 value: 52.153000000000006 - type: mrr_at_1 value: 45.016 - type: mrr_at_10 value: 56.732000000000006 - type: mrr_at_100 value: 57.411 - type: mrr_at_1000 value: 57.431 - type: mrr_at_3 value: 54.044000000000004 - type: mrr_at_5 value: 55.639 - type: ndcg_at_1 value: 45.016 - type: ndcg_at_10 value: 60.228 - type: ndcg_at_100 value: 64.277 - type: ndcg_at_1000 value: 65.07 - type: ndcg_at_3 value: 54.124 - type: ndcg_at_5 value: 57.147000000000006 - type: precision_at_1 value: 45.016 - type: precision_at_10 value: 9.937 - type: precision_at_100 value: 1.288 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 24.471999999999998 - type: precision_at_5 value: 16.991 - type: recall_at_1 value: 39.383 - type: recall_at_10 value: 76.175 - type: recall_at_100 value: 93.02 - type: recall_at_1000 value: 98.60900000000001 - type: recall_at_3 value: 60.265 - type: recall_at_5 value: 67.46600000000001 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: BeIR/cqadupstack config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 27.426000000000002 - type: map_at_10 value: 37.397000000000006 - type: map_at_100 value: 38.61 - type: map_at_1000 value: 38.678000000000004 - type: map_at_3 value: 34.150999999999996 - type: map_at_5 value: 36.137 - type: mrr_at_1 value: 29.944 - type: mrr_at_10 value: 39.654 - type: mrr_at_100 value: 40.638000000000005 - type: mrr_at_1000 value: 40.691 - type: mrr_at_3 value: 36.817 - type: mrr_at_5 value: 38.524 - type: ndcg_at_1 value: 29.944 - type: ndcg_at_10 value: 43.094 - type: ndcg_at_100 value: 48.789 - type: ndcg_at_1000 value: 50.339999999999996 - type: ndcg_at_3 value: 36.984 - type: ndcg_at_5 value: 40.248 - type: precision_at_1 value: 29.944 - type: precision_at_10 value: 6.78 - type: precision_at_100 value: 1.024 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 15.895000000000001 - type: precision_at_5 value: 11.39 - type: recall_at_1 value: 27.426000000000002 - type: recall_at_10 value: 58.464000000000006 - type: recall_at_100 value: 84.193 - type: recall_at_1000 value: 95.52000000000001 - type: recall_at_3 value: 42.172 - type: recall_at_5 value: 50.101 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: BeIR/cqadupstack config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 19.721 - type: map_at_10 value: 31.604 - type: map_at_100 value: 32.972 - type: map_at_1000 value: 33.077 - type: map_at_3 value: 27.218999999999998 - type: map_at_5 value: 29.53 - type: mrr_at_1 value: 25.0 - type: mrr_at_10 value: 35.843 - type: mrr_at_100 value: 36.785000000000004 - type: mrr_at_1000 value: 36.842000000000006 - type: mrr_at_3 value: 32.193 - type: mrr_at_5 value: 34.264 - type: ndcg_at_1 value: 25.0 - type: ndcg_at_10 value: 38.606 - type: ndcg_at_100 value: 44.272 - type: ndcg_at_1000 value: 46.527 - type: ndcg_at_3 value: 30.985000000000003 - type: ndcg_at_5 value: 34.43 - type: precision_at_1 value: 25.0 - type: precision_at_10 value: 7.811 - type: precision_at_100 value: 1.203 - type: precision_at_1000 value: 0.15 - type: precision_at_3 value: 15.423 - type: precision_at_5 value: 11.791 - type: recall_at_1 value: 19.721 - type: recall_at_10 value: 55.625 - type: recall_at_100 value: 79.34400000000001 - type: recall_at_1000 value: 95.208 - type: recall_at_3 value: 35.19 - type: recall_at_5 value: 43.626 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: BeIR/cqadupstack config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 33.784 - type: map_at_10 value: 47.522 - type: map_at_100 value: 48.949999999999996 - type: map_at_1000 value: 49.038 - type: map_at_3 value: 43.284 - type: map_at_5 value: 45.629 - type: mrr_at_1 value: 41.482 - type: mrr_at_10 value: 52.830999999999996 - type: mrr_at_100 value: 53.559999999999995 - type: mrr_at_1000 value: 53.588 - type: mrr_at_3 value: 50.016000000000005 - type: mrr_at_5 value: 51.614000000000004 - type: ndcg_at_1 value: 41.482 - type: ndcg_at_10 value: 54.569 - type: ndcg_at_100 value: 59.675999999999995 - type: ndcg_at_1000 value: 60.989000000000004 - type: ndcg_at_3 value: 48.187000000000005 - type: ndcg_at_5 value: 51.183 - type: precision_at_1 value: 41.482 - type: precision_at_10 value: 10.221 - type: precision_at_100 value: 1.486 - type: precision_at_1000 value: 0.17500000000000002 - type: precision_at_3 value: 23.548 - type: precision_at_5 value: 16.805 - type: recall_at_1 value: 33.784 - type: recall_at_10 value: 69.798 - type: recall_at_100 value: 90.098 - type: recall_at_1000 value: 98.176 - type: recall_at_3 value: 52.127 - type: recall_at_5 value: 59.861 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: BeIR/cqadupstack config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 28.038999999999998 - type: map_at_10 value: 41.904 - type: map_at_100 value: 43.36 - type: map_at_1000 value: 43.453 - type: map_at_3 value: 37.785999999999994 - type: map_at_5 value: 40.105000000000004 - type: mrr_at_1 value: 35.046 - type: mrr_at_10 value: 46.926 - type: mrr_at_100 value: 47.815000000000005 - type: mrr_at_1000 value: 47.849000000000004 - type: mrr_at_3 value: 44.273 - type: mrr_at_5 value: 45.774 - type: ndcg_at_1 value: 35.046 - type: ndcg_at_10 value: 48.937000000000005 - type: ndcg_at_100 value: 54.544000000000004 - type: ndcg_at_1000 value: 56.069 - type: ndcg_at_3 value: 42.858000000000004 - type: ndcg_at_5 value: 45.644 - type: precision_at_1 value: 35.046 - type: precision_at_10 value: 9.452 - type: precision_at_100 value: 1.429 - type: precision_at_1000 value: 0.173 - type: precision_at_3 value: 21.346999999999998 - type: precision_at_5 value: 15.342 - type: recall_at_1 value: 28.038999999999998 - type: recall_at_10 value: 64.59700000000001 - type: recall_at_100 value: 87.735 - type: recall_at_1000 value: 97.41300000000001 - type: recall_at_3 value: 47.368 - type: recall_at_5 value: 54.93900000000001 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: BeIR/cqadupstack config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 28.17291666666667 - type: map_at_10 value: 40.025749999999995 - type: map_at_100 value: 41.39208333333333 - type: map_at_1000 value: 41.499249999999996 - type: map_at_3 value: 36.347 - type: map_at_5 value: 38.41391666666667 - type: mrr_at_1 value: 33.65925 - type: mrr_at_10 value: 44.085499999999996 - type: mrr_at_100 value: 44.94116666666667 - type: mrr_at_1000 value: 44.9855 - type: mrr_at_3 value: 41.2815 - type: mrr_at_5 value: 42.91491666666666 - type: ndcg_at_1 value: 33.65925 - type: ndcg_at_10 value: 46.430833333333325 - type: ndcg_at_100 value: 51.761 - type: ndcg_at_1000 value: 53.50899999999999 - type: ndcg_at_3 value: 40.45133333333333 - type: ndcg_at_5 value: 43.31483333333334 - type: precision_at_1 value: 33.65925 - type: precision_at_10 value: 8.4995 - type: precision_at_100 value: 1.3210000000000004 - type: precision_at_1000 value: 0.16591666666666666 - type: precision_at_3 value: 19.165083333333335 - type: precision_at_5 value: 13.81816666666667 - type: recall_at_1 value: 28.17291666666667 - type: recall_at_10 value: 61.12624999999999 - type: recall_at_100 value: 83.97266666666667 - type: recall_at_1000 value: 95.66550000000001 - type: recall_at_3 value: 44.661249999999995 - type: recall_at_5 value: 51.983333333333334 - type: map_at_1 value: 17.936 - type: map_at_10 value: 27.399 - type: map_at_100 value: 28.632 - type: map_at_1000 value: 28.738000000000003 - type: map_at_3 value: 24.456 - type: map_at_5 value: 26.06 - type: mrr_at_1 value: 19.224 - type: mrr_at_10 value: 28.998 - type: mrr_at_100 value: 30.11 - type: mrr_at_1000 value: 30.177 - type: mrr_at_3 value: 26.247999999999998 - type: mrr_at_5 value: 27.708 - type: ndcg_at_1 value: 19.224 - type: ndcg_at_10 value: 32.911 - type: ndcg_at_100 value: 38.873999999999995 - type: ndcg_at_1000 value: 41.277 - type: ndcg_at_3 value: 27.142 - type: ndcg_at_5 value: 29.755 - type: precision_at_1 value: 19.224 - type: precision_at_10 value: 5.6930000000000005 - type: precision_at_100 value: 0.9259999999999999 - type: precision_at_1000 value: 0.126 - type: precision_at_3 value: 12.138 - type: precision_at_5 value: 8.909 - type: recall_at_1 value: 17.936 - type: recall_at_10 value: 48.096 - type: recall_at_100 value: 75.389 - type: recall_at_1000 value: 92.803 - type: recall_at_3 value: 32.812999999999995 - type: recall_at_5 value: 38.851 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: BeIR/cqadupstack config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 24.681 - type: map_at_10 value: 34.892 - type: map_at_100 value: 35.996 - type: map_at_1000 value: 36.083 - type: map_at_3 value: 31.491999999999997 - type: map_at_5 value: 33.632 - type: mrr_at_1 value: 28.528 - type: mrr_at_10 value: 37.694 - type: mrr_at_100 value: 38.613 - type: mrr_at_1000 value: 38.668 - type: mrr_at_3 value: 34.714 - type: mrr_at_5 value: 36.616 - type: ndcg_at_1 value: 28.528 - type: ndcg_at_10 value: 40.703 - type: ndcg_at_100 value: 45.993 - type: ndcg_at_1000 value: 47.847 - type: ndcg_at_3 value: 34.622 - type: ndcg_at_5 value: 38.035999999999994 - type: precision_at_1 value: 28.528 - type: precision_at_10 value: 6.902 - type: precision_at_100 value: 1.0370000000000001 - type: precision_at_1000 value: 0.126 - type: precision_at_3 value: 15.798000000000002 - type: precision_at_5 value: 11.655999999999999 - type: recall_at_1 value: 24.681 - type: recall_at_10 value: 55.81 - type: recall_at_100 value: 79.785 - type: recall_at_1000 value: 92.959 - type: recall_at_3 value: 39.074 - type: recall_at_5 value: 47.568 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: BeIR/cqadupstack config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 18.627 - type: map_at_10 value: 27.872000000000003 - type: map_at_100 value: 29.237999999999996 - type: map_at_1000 value: 29.363 - type: map_at_3 value: 24.751 - type: map_at_5 value: 26.521 - type: mrr_at_1 value: 23.021 - type: mrr_at_10 value: 31.924000000000003 - type: mrr_at_100 value: 32.922000000000004 - type: mrr_at_1000 value: 32.988 - type: mrr_at_3 value: 29.192 - type: mrr_at_5 value: 30.798 - type: ndcg_at_1 value: 23.021 - type: ndcg_at_10 value: 33.535 - type: ndcg_at_100 value: 39.732 - type: ndcg_at_1000 value: 42.201 - type: ndcg_at_3 value: 28.153 - type: ndcg_at_5 value: 30.746000000000002 - type: precision_at_1 value: 23.021 - type: precision_at_10 value: 6.459 - type: precision_at_100 value: 1.1320000000000001 - type: precision_at_1000 value: 0.153 - type: precision_at_3 value: 13.719000000000001 - type: precision_at_5 value: 10.193000000000001 - type: recall_at_1 value: 18.627 - type: recall_at_10 value: 46.463 - type: recall_at_100 value: 74.226 - type: recall_at_1000 value: 91.28500000000001 - type: recall_at_3 value: 31.357000000000003 - type: recall_at_5 value: 38.067 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: BeIR/cqadupstack config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 31.457 - type: map_at_10 value: 42.888 - type: map_at_100 value: 44.24 - type: map_at_1000 value: 44.327 - type: map_at_3 value: 39.588 - type: map_at_5 value: 41.423 - type: mrr_at_1 value: 37.126999999999995 - type: mrr_at_10 value: 47.083000000000006 - type: mrr_at_100 value: 47.997 - type: mrr_at_1000 value: 48.044 - type: mrr_at_3 value: 44.574000000000005 - type: mrr_at_5 value: 46.202 - type: ndcg_at_1 value: 37.126999999999995 - type: ndcg_at_10 value: 48.833 - type: ndcg_at_100 value: 54.327000000000005 - type: ndcg_at_1000 value: 56.011 - type: ndcg_at_3 value: 43.541999999999994 - type: ndcg_at_5 value: 46.127 - type: precision_at_1 value: 37.126999999999995 - type: precision_at_10 value: 8.376999999999999 - type: precision_at_100 value: 1.2309999999999999 - type: precision_at_1000 value: 0.146 - type: precision_at_3 value: 20.211000000000002 - type: precision_at_5 value: 14.16 - type: recall_at_1 value: 31.457 - type: recall_at_10 value: 62.369 - type: recall_at_100 value: 85.444 - type: recall_at_1000 value: 96.65599999999999 - type: recall_at_3 value: 47.961 - type: recall_at_5 value: 54.676 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: BeIR/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 27.139999999999997 - type: map_at_10 value: 38.801 - type: map_at_100 value: 40.549 - type: map_at_1000 value: 40.802 - type: map_at_3 value: 35.05 - type: map_at_5 value: 36.884 - type: mrr_at_1 value: 33.004 - type: mrr_at_10 value: 43.864 - type: mrr_at_100 value: 44.667 - type: mrr_at_1000 value: 44.717 - type: mrr_at_3 value: 40.777 - type: mrr_at_5 value: 42.319 - type: ndcg_at_1 value: 33.004 - type: ndcg_at_10 value: 46.022 - type: ndcg_at_100 value: 51.542 - type: ndcg_at_1000 value: 53.742000000000004 - type: ndcg_at_3 value: 39.795 - type: ndcg_at_5 value: 42.272 - type: precision_at_1 value: 33.004 - type: precision_at_10 value: 9.012 - type: precision_at_100 value: 1.7770000000000001 - type: precision_at_1000 value: 0.26 - type: precision_at_3 value: 19.038 - type: precision_at_5 value: 13.675999999999998 - type: recall_at_1 value: 27.139999999999997 - type: recall_at_10 value: 60.961 - type: recall_at_100 value: 84.451 - type: recall_at_1000 value: 98.113 - type: recall_at_3 value: 43.001 - type: recall_at_5 value: 49.896 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: mteb/climate-fever config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 22.076999999999998 - type: map_at_10 value: 35.44 - type: map_at_100 value: 37.651 - type: map_at_1000 value: 37.824999999999996 - type: map_at_3 value: 30.764999999999997 - type: map_at_5 value: 33.26 - type: mrr_at_1 value: 50.163000000000004 - type: mrr_at_10 value: 61.207 - type: mrr_at_100 value: 61.675000000000004 - type: mrr_at_1000 value: 61.692 - type: mrr_at_3 value: 58.60999999999999 - type: mrr_at_5 value: 60.307 - type: ndcg_at_1 value: 50.163000000000004 - type: ndcg_at_10 value: 45.882 - type: ndcg_at_100 value: 53.239999999999995 - type: ndcg_at_1000 value: 55.852000000000004 - type: ndcg_at_3 value: 40.514 - type: ndcg_at_5 value: 42.038 - type: precision_at_1 value: 50.163000000000004 - type: precision_at_10 value: 13.466000000000001 - type: precision_at_100 value: 2.164 - type: precision_at_1000 value: 0.266 - type: precision_at_3 value: 29.707 - type: precision_at_5 value: 21.694 - type: recall_at_1 value: 22.076999999999998 - type: recall_at_10 value: 50.193 - type: recall_at_100 value: 74.993 - type: recall_at_1000 value: 89.131 - type: recall_at_3 value: 35.472 - type: recall_at_5 value: 41.814 - task: type: Retrieval dataset: name: MTEB DBPedia type: mteb/dbpedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 9.953 - type: map_at_10 value: 24.515 - type: map_at_100 value: 36.173 - type: map_at_1000 value: 38.351 - type: map_at_3 value: 16.592000000000002 - type: map_at_5 value: 20.036 - type: mrr_at_1 value: 74.25 - type: mrr_at_10 value: 81.813 - type: mrr_at_100 value: 82.006 - type: mrr_at_1000 value: 82.011 - type: mrr_at_3 value: 80.875 - type: mrr_at_5 value: 81.362 - type: ndcg_at_1 value: 62.5 - type: ndcg_at_10 value: 52.42 - type: ndcg_at_100 value: 56.808 - type: ndcg_at_1000 value: 63.532999999999994 - type: ndcg_at_3 value: 56.654 - type: ndcg_at_5 value: 54.18300000000001 - type: precision_at_1 value: 74.25 - type: precision_at_10 value: 42.699999999999996 - type: precision_at_100 value: 13.675 - type: precision_at_1000 value: 2.664 - type: precision_at_3 value: 60.5 - type: precision_at_5 value: 52.800000000000004 - type: recall_at_1 value: 9.953 - type: recall_at_10 value: 30.253999999999998 - type: recall_at_100 value: 62.516000000000005 - type: recall_at_1000 value: 84.163 - type: recall_at_3 value: 18.13 - type: recall_at_5 value: 22.771 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 79.455 - type: f1 value: 74.16798697647569 - task: type: Retrieval dataset: name: MTEB FEVER type: mteb/fever config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 87.531 - type: map_at_10 value: 93.16799999999999 - type: map_at_100 value: 93.341 - type: map_at_1000 value: 93.349 - type: map_at_3 value: 92.444 - type: map_at_5 value: 92.865 - type: mrr_at_1 value: 94.014 - type: mrr_at_10 value: 96.761 - type: mrr_at_100 value: 96.762 - type: mrr_at_1000 value: 96.762 - type: mrr_at_3 value: 96.672 - type: mrr_at_5 value: 96.736 - type: ndcg_at_1 value: 94.014 - type: ndcg_at_10 value: 95.112 - type: ndcg_at_100 value: 95.578 - type: ndcg_at_1000 value: 95.68900000000001 - type: ndcg_at_3 value: 94.392 - type: ndcg_at_5 value: 94.72500000000001 - type: precision_at_1 value: 94.014 - type: precision_at_10 value: 11.065 - type: precision_at_100 value: 1.157 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 35.259 - type: precision_at_5 value: 21.599 - type: recall_at_1 value: 87.531 - type: recall_at_10 value: 97.356 - type: recall_at_100 value: 98.965 - type: recall_at_1000 value: 99.607 - type: recall_at_3 value: 95.312 - type: recall_at_5 value: 96.295 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: mteb/fiqa config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 32.055 - type: map_at_10 value: 53.114 - type: map_at_100 value: 55.235 - type: map_at_1000 value: 55.345 - type: map_at_3 value: 45.854 - type: map_at_5 value: 50.025 - type: mrr_at_1 value: 60.34 - type: mrr_at_10 value: 68.804 - type: mrr_at_100 value: 69.309 - type: mrr_at_1000 value: 69.32199999999999 - type: mrr_at_3 value: 66.40899999999999 - type: mrr_at_5 value: 67.976 - type: ndcg_at_1 value: 60.34 - type: ndcg_at_10 value: 62.031000000000006 - type: ndcg_at_100 value: 68.00500000000001 - type: ndcg_at_1000 value: 69.286 - type: ndcg_at_3 value: 56.355999999999995 - type: ndcg_at_5 value: 58.687 - type: precision_at_1 value: 60.34 - type: precision_at_10 value: 17.176 - type: precision_at_100 value: 2.36 - type: precision_at_1000 value: 0.259 - type: precision_at_3 value: 37.14 - type: precision_at_5 value: 27.809 - type: recall_at_1 value: 32.055 - type: recall_at_10 value: 70.91 - type: recall_at_100 value: 91.83 - type: recall_at_1000 value: 98.871 - type: recall_at_3 value: 51.202999999999996 - type: recall_at_5 value: 60.563 - task: type: Retrieval dataset: name: MTEB HotpotQA type: mteb/hotpotqa config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 43.68 - type: map_at_10 value: 64.389 - type: map_at_100 value: 65.24 - type: map_at_1000 value: 65.303 - type: map_at_3 value: 61.309000000000005 - type: map_at_5 value: 63.275999999999996 - type: mrr_at_1 value: 87.36 - type: mrr_at_10 value: 91.12 - type: mrr_at_100 value: 91.227 - type: mrr_at_1000 value: 91.229 - type: mrr_at_3 value: 90.57600000000001 - type: mrr_at_5 value: 90.912 - type: ndcg_at_1 value: 87.36 - type: ndcg_at_10 value: 73.076 - type: ndcg_at_100 value: 75.895 - type: ndcg_at_1000 value: 77.049 - type: ndcg_at_3 value: 68.929 - type: ndcg_at_5 value: 71.28 - type: precision_at_1 value: 87.36 - type: precision_at_10 value: 14.741000000000001 - type: precision_at_100 value: 1.694 - type: precision_at_1000 value: 0.185 - type: precision_at_3 value: 43.043 - type: precision_at_5 value: 27.681 - type: recall_at_1 value: 43.68 - type: recall_at_10 value: 73.707 - type: recall_at_100 value: 84.7 - type: recall_at_1000 value: 92.309 - type: recall_at_3 value: 64.564 - type: recall_at_5 value: 69.203 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 96.75399999999999 - type: ap value: 95.29389839242187 - type: f1 value: 96.75348377433475 - task: type: Retrieval dataset: name: MTEB MSMARCO type: mteb/msmarco config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 25.176 - type: map_at_10 value: 38.598 - type: map_at_100 value: 39.707 - type: map_at_1000 value: 39.744 - type: map_at_3 value: 34.566 - type: map_at_5 value: 36.863 - type: mrr_at_1 value: 25.874000000000002 - type: mrr_at_10 value: 39.214 - type: mrr_at_100 value: 40.251 - type: mrr_at_1000 value: 40.281 - type: mrr_at_3 value: 35.291 - type: mrr_at_5 value: 37.545 - type: ndcg_at_1 value: 25.874000000000002 - type: ndcg_at_10 value: 45.98 - type: ndcg_at_100 value: 51.197 - type: ndcg_at_1000 value: 52.073 - type: ndcg_at_3 value: 37.785999999999994 - type: ndcg_at_5 value: 41.870000000000005 - type: precision_at_1 value: 25.874000000000002 - type: precision_at_10 value: 7.181 - type: precision_at_100 value: 0.979 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 16.051000000000002 - type: precision_at_5 value: 11.713 - type: recall_at_1 value: 25.176 - type: recall_at_10 value: 68.67699999999999 - type: recall_at_100 value: 92.55 - type: recall_at_1000 value: 99.164 - type: recall_at_3 value: 46.372 - type: recall_at_5 value: 56.16 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 99.03784769721841 - type: f1 value: 98.97791641821495 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 91.88326493388054 - type: f1 value: 73.74809928034335 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 85.41358439811701 - type: f1 value: 83.503679460639 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 89.77135171486215 - type: f1 value: 88.89843747468366 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 46.22695362087359 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 44.132372165849425 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 33.35680810650402 - type: mrr value: 34.72625715637218 - task: type: Retrieval dataset: name: MTEB NFCorpus type: mteb/nfcorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 7.165000000000001 - type: map_at_10 value: 15.424 - type: map_at_100 value: 20.28 - type: map_at_1000 value: 22.065 - type: map_at_3 value: 11.236 - type: map_at_5 value: 13.025999999999998 - type: mrr_at_1 value: 51.702999999999996 - type: mrr_at_10 value: 59.965 - type: mrr_at_100 value: 60.667 - type: mrr_at_1000 value: 60.702999999999996 - type: mrr_at_3 value: 58.772000000000006 - type: mrr_at_5 value: 59.267 - type: ndcg_at_1 value: 49.536 - type: ndcg_at_10 value: 40.6 - type: ndcg_at_100 value: 37.848 - type: ndcg_at_1000 value: 46.657 - type: ndcg_at_3 value: 46.117999999999995 - type: ndcg_at_5 value: 43.619 - type: precision_at_1 value: 51.393 - type: precision_at_10 value: 30.31 - type: precision_at_100 value: 9.972 - type: precision_at_1000 value: 2.329 - type: precision_at_3 value: 43.137 - type: precision_at_5 value: 37.585 - type: recall_at_1 value: 7.165000000000001 - type: recall_at_10 value: 19.689999999999998 - type: recall_at_100 value: 39.237 - type: recall_at_1000 value: 71.417 - type: recall_at_3 value: 12.247 - type: recall_at_5 value: 14.902999999999999 - task: type: Retrieval dataset: name: MTEB NQ type: mteb/nq config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 42.653999999999996 - type: map_at_10 value: 59.611999999999995 - type: map_at_100 value: 60.32300000000001 - type: map_at_1000 value: 60.336 - type: map_at_3 value: 55.584999999999994 - type: map_at_5 value: 58.19 - type: mrr_at_1 value: 47.683 - type: mrr_at_10 value: 62.06700000000001 - type: mrr_at_100 value: 62.537 - type: mrr_at_1000 value: 62.544999999999995 - type: mrr_at_3 value: 59.178 - type: mrr_at_5 value: 61.034 - type: ndcg_at_1 value: 47.654 - type: ndcg_at_10 value: 67.001 - type: ndcg_at_100 value: 69.73899999999999 - type: ndcg_at_1000 value: 69.986 - type: ndcg_at_3 value: 59.95700000000001 - type: ndcg_at_5 value: 64.025 - type: precision_at_1 value: 47.654 - type: precision_at_10 value: 10.367999999999999 - type: precision_at_100 value: 1.192 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 26.651000000000003 - type: precision_at_5 value: 18.459 - type: recall_at_1 value: 42.653999999999996 - type: recall_at_10 value: 86.619 - type: recall_at_100 value: 98.04899999999999 - type: recall_at_1000 value: 99.812 - type: recall_at_3 value: 68.987 - type: recall_at_5 value: 78.158 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: mteb/quora config: default split: test revision: None metrics: - type: map_at_1 value: 72.538 - type: map_at_10 value: 86.702 - type: map_at_100 value: 87.31 - type: map_at_1000 value: 87.323 - type: map_at_3 value: 83.87 - type: map_at_5 value: 85.682 - type: mrr_at_1 value: 83.31 - type: mrr_at_10 value: 89.225 - type: mrr_at_100 value: 89.30399999999999 - type: mrr_at_1000 value: 89.30399999999999 - type: mrr_at_3 value: 88.44300000000001 - type: mrr_at_5 value: 89.005 - type: ndcg_at_1 value: 83.32000000000001 - type: ndcg_at_10 value: 90.095 - type: ndcg_at_100 value: 91.12 - type: ndcg_at_1000 value: 91.179 - type: ndcg_at_3 value: 87.606 - type: ndcg_at_5 value: 89.031 - type: precision_at_1 value: 83.32000000000001 - type: precision_at_10 value: 13.641 - type: precision_at_100 value: 1.541 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 38.377 - type: precision_at_5 value: 25.162000000000003 - type: recall_at_1 value: 72.538 - type: recall_at_10 value: 96.47200000000001 - type: recall_at_100 value: 99.785 - type: recall_at_1000 value: 99.99900000000001 - type: recall_at_3 value: 89.278 - type: recall_at_5 value: 93.367 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 73.55219145406065 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 74.13437105242755 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: mteb/scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 6.873 - type: map_at_10 value: 17.944 - type: map_at_100 value: 21.171 - type: map_at_1000 value: 21.528 - type: map_at_3 value: 12.415 - type: map_at_5 value: 15.187999999999999 - type: mrr_at_1 value: 33.800000000000004 - type: mrr_at_10 value: 46.455 - type: mrr_at_100 value: 47.378 - type: mrr_at_1000 value: 47.394999999999996 - type: mrr_at_3 value: 42.367 - type: mrr_at_5 value: 44.972 - type: ndcg_at_1 value: 33.800000000000004 - type: ndcg_at_10 value: 28.907 - type: ndcg_at_100 value: 39.695 - type: ndcg_at_1000 value: 44.582 - type: ndcg_at_3 value: 26.949 - type: ndcg_at_5 value: 23.988 - type: precision_at_1 value: 33.800000000000004 - type: precision_at_10 value: 15.079999999999998 - type: precision_at_100 value: 3.056 - type: precision_at_1000 value: 0.42100000000000004 - type: precision_at_3 value: 25.167 - type: precision_at_5 value: 21.26 - type: recall_at_1 value: 6.873 - type: recall_at_10 value: 30.568 - type: recall_at_100 value: 62.062 - type: recall_at_1000 value: 85.37700000000001 - type: recall_at_3 value: 15.312999999999999 - type: recall_at_5 value: 21.575 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 82.37009118256057 - type: cos_sim_spearman value: 79.27986395671529 - type: euclidean_pearson value: 79.18037715442115 - type: euclidean_spearman value: 79.28004791561621 - type: manhattan_pearson value: 79.34062972800541 - type: manhattan_spearman value: 79.43106695543402 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 87.48474767383833 - type: cos_sim_spearman value: 79.54505388752513 - type: euclidean_pearson value: 83.43282704179565 - type: euclidean_spearman value: 79.54579919925405 - type: manhattan_pearson value: 83.77564492427952 - type: manhattan_spearman value: 79.84558396989286 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 88.803698035802 - type: cos_sim_spearman value: 88.83451367754881 - type: euclidean_pearson value: 88.28939285711628 - type: euclidean_spearman value: 88.83528996073112 - type: manhattan_pearson value: 88.28017412671795 - 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type: cos_sim_pearson value: 85.68398747560902 - type: cos_sim_spearman value: 86.48815303460574 - type: euclidean_pearson value: 85.52356631237954 - type: euclidean_spearman value: 86.486391949551 - type: manhattan_pearson value: 85.67267981761788 - type: manhattan_spearman value: 86.7073696332485 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 88.9057107443124 - type: cos_sim_spearman value: 88.7312168757697 - type: euclidean_pearson value: 88.72810439714794 - type: euclidean_spearman value: 88.71976185854771 - type: manhattan_pearson value: 88.50433745949111 - type: manhattan_spearman value: 88.51726175544195 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 67.59391795109886 - type: cos_sim_spearman value: 66.87613008631367 - type: euclidean_pearson value: 69.23198488262217 - type: euclidean_spearman value: 66.85427723013692 - type: manhattan_pearson value: 69.50730124841084 - type: manhattan_spearman value: 67.10404669820792 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 87.0820605344619 - type: cos_sim_spearman value: 86.8518089863434 - type: euclidean_pearson value: 86.31087134689284 - type: euclidean_spearman value: 86.8518520517941 - type: manhattan_pearson value: 86.47203796160612 - type: manhattan_spearman value: 87.1080149734421 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 89.09255369305481 - type: mrr value: 97.10323445617563 - task: type: Retrieval dataset: name: MTEB SciFact type: mteb/scifact config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 61.260999999999996 - type: map_at_10 value: 74.043 - type: map_at_100 value: 74.37700000000001 - type: map_at_1000 value: 74.384 - type: map_at_3 value: 71.222 - type: map_at_5 value: 72.875 - type: mrr_at_1 value: 64.333 - type: mrr_at_10 value: 74.984 - type: mrr_at_100 value: 75.247 - type: mrr_at_1000 value: 75.25500000000001 - type: mrr_at_3 value: 73.167 - type: mrr_at_5 value: 74.35000000000001 - type: ndcg_at_1 value: 64.333 - type: ndcg_at_10 value: 79.06 - type: ndcg_at_100 value: 80.416 - type: ndcg_at_1000 value: 80.55600000000001 - type: ndcg_at_3 value: 74.753 - type: ndcg_at_5 value: 76.97500000000001 - type: precision_at_1 value: 64.333 - type: precision_at_10 value: 10.567 - type: precision_at_100 value: 1.1199999999999999 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 29.889 - type: precision_at_5 value: 19.533 - type: recall_at_1 value: 61.260999999999996 - type: recall_at_10 value: 93.167 - type: recall_at_100 value: 99.0 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 81.667 - type: recall_at_5 value: 87.394 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.71980198019801 - type: cos_sim_ap value: 92.81616007802704 - type: cos_sim_f1 value: 85.17548454688318 - type: cos_sim_precision value: 89.43894389438944 - type: cos_sim_recall value: 81.3 - type: dot_accuracy value: 99.71980198019801 - type: dot_ap value: 92.81398760591358 - type: dot_f1 value: 85.17548454688318 - type: dot_precision value: 89.43894389438944 - type: dot_recall value: 81.3 - type: euclidean_accuracy value: 99.71980198019801 - type: euclidean_ap value: 92.81560637245072 - type: euclidean_f1 value: 85.17548454688318 - type: euclidean_precision value: 89.43894389438944 - type: euclidean_recall value: 81.3 - type: manhattan_accuracy value: 99.73069306930694 - 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type: map value: 55.66040806627947 - type: mrr value: 56.58670475766064 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.51015090598575 - type: cos_sim_spearman value: 31.35016454939226 - type: dot_pearson value: 31.5150068731 - type: dot_spearman value: 31.34790869023487 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: mteb/trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.254 - type: map_at_10 value: 2.064 - type: map_at_100 value: 12.909 - type: map_at_1000 value: 31.761 - type: map_at_3 value: 0.738 - type: map_at_5 value: 1.155 - type: mrr_at_1 value: 96.0 - type: mrr_at_10 value: 98.0 - type: mrr_at_100 value: 98.0 - type: mrr_at_1000 value: 98.0 - type: mrr_at_3 value: 98.0 - type: mrr_at_5 value: 98.0 - type: ndcg_at_1 value: 93.0 - type: ndcg_at_10 value: 82.258 - type: ndcg_at_100 value: 64.34 - 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type: recall_at_1000 value: 99.529 - type: recall_at_3 value: 57.902 - type: recall_at_5 value: 74.602 - task: type: Retrieval dataset: name: MTEB EcomRetrieval type: C-MTEB/EcomRetrieval config: default split: dev revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9 metrics: - type: map_at_1 value: 56.10000000000001 - type: map_at_10 value: 66.15299999999999 - type: map_at_100 value: 66.625 - type: map_at_1000 value: 66.636 - type: map_at_3 value: 63.632999999999996 - type: map_at_5 value: 65.293 - type: mrr_at_1 value: 56.10000000000001 - type: mrr_at_10 value: 66.15299999999999 - type: mrr_at_100 value: 66.625 - type: mrr_at_1000 value: 66.636 - type: mrr_at_3 value: 63.632999999999996 - type: mrr_at_5 value: 65.293 - type: ndcg_at_1 value: 56.10000000000001 - type: ndcg_at_10 value: 71.146 - type: ndcg_at_100 value: 73.27799999999999 - type: ndcg_at_1000 value: 73.529 - type: ndcg_at_3 value: 66.09 - type: ndcg_at_5 value: 69.08999999999999 - type: precision_at_1 value: 56.10000000000001 - 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type: euclidean_spearman value: 78.9046133980509 - type: manhattan_pearson value: 78.63743094286502 - type: manhattan_spearman value: 79.12136348449269 - task: type: STS dataset: name: MTEB STS22 (fr) type: mteb/sts22-crosslingual-sts config: fr split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 81.452697919749 - type: cos_sim_spearman value: 82.58116836039301 - type: euclidean_pearson value: 81.04038478932786 - type: euclidean_spearman value: 82.58116836039301 - type: manhattan_pearson value: 81.37075396187771 - type: manhattan_spearman value: 82.73678231355368 - task: type: STS dataset: name: MTEB STSBenchmarkMultilingualSTS (fr) type: stsb_multi_mt config: fr split: test revision: 93d57ef91790589e3ce9c365164337a8a78b7632 metrics: - type: cos_sim_pearson value: 85.7419764013806 - type: cos_sim_spearman value: 85.46085808849622 - type: euclidean_pearson value: 83.70449639870063 - type: euclidean_spearman value: 85.46159013076233 - type: manhattan_pearson value: 83.95259510313929 - type: manhattan_spearman value: 85.8029724659458 - task: type: Summarization dataset: name: MTEB SummEvalFr type: lyon-nlp/summarization-summeval-fr-p2p config: default split: test revision: b385812de6a9577b6f4d0f88c6a6e35395a94054 metrics: - type: cos_sim_pearson value: 32.61063271753325 - type: cos_sim_spearman value: 31.454589417353603 - type: dot_pearson value: 32.6106288643431 - type: dot_spearman value: 31.454589417353603 - task: type: Reranking dataset: name: MTEB SyntecReranking type: lyon-nlp/mteb-fr-reranking-syntec-s2p config: default split: test revision: b205c5084a0934ce8af14338bf03feb19499c84d metrics: - type: map value: 84.31666666666666 - type: mrr value: 84.31666666666666 - task: type: Retrieval dataset: name: MTEB SyntecRetrieval type: lyon-nlp/mteb-fr-retrieval-syntec-s2p config: default split: test revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff metrics: - type: map_at_1 value: 63.0 - type: map_at_10 value: 73.471 - type: map_at_100 value: 73.87 - type: map_at_1000 value: 73.87 - type: map_at_3 value: 70.5 - type: map_at_5 value: 73.05 - type: mrr_at_1 value: 63.0 - type: mrr_at_10 value: 73.471 - type: mrr_at_100 value: 73.87 - type: mrr_at_1000 value: 73.87 - type: mrr_at_3 value: 70.5 - type: mrr_at_5 value: 73.05 - type: ndcg_at_1 value: 63.0 - type: ndcg_at_10 value: 78.255 - type: ndcg_at_100 value: 79.88 - type: ndcg_at_1000 value: 79.88 - type: ndcg_at_3 value: 72.702 - type: ndcg_at_5 value: 77.264 - type: precision_at_1 value: 63.0 - type: precision_at_10 value: 9.3 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 26.333000000000002 - type: precision_at_5 value: 18.0 - type: recall_at_1 value: 63.0 - type: recall_at_10 value: 93.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 79.0 - type: recall_at_5 value: 90.0 - task: type: Retrieval dataset: name: MTEB XPQARetrieval (fr) type: jinaai/xpqa config: fr split: test revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f metrics: - type: map_at_1 value: 40.338 - type: map_at_10 value: 61.927 - type: map_at_100 value: 63.361999999999995 - type: map_at_1000 value: 63.405 - type: map_at_3 value: 55.479 - type: map_at_5 value: 59.732 - type: mrr_at_1 value: 63.551 - type: mrr_at_10 value: 71.006 - type: mrr_at_100 value: 71.501 - type: mrr_at_1000 value: 71.509 - type: mrr_at_3 value: 69.07 - type: mrr_at_5 value: 70.165 - type: ndcg_at_1 value: 63.551 - type: ndcg_at_10 value: 68.297 - type: ndcg_at_100 value: 73.13199999999999 - type: ndcg_at_1000 value: 73.751 - type: ndcg_at_3 value: 62.999 - type: ndcg_at_5 value: 64.89 - type: precision_at_1 value: 63.551 - type: precision_at_10 value: 15.661 - type: precision_at_100 value: 1.9789999999999999 - type: precision_at_1000 value: 0.207 - type: precision_at_3 value: 38.273 - type: precision_at_5 value: 27.61 - type: recall_at_1 value: 40.338 - type: recall_at_10 value: 77.267 - type: recall_at_100 value: 95.892 - type: recall_at_1000 value: 99.75500000000001 - type: recall_at_3 value: 60.36 - type: recall_at_5 value: 68.825 - task: type: Clustering dataset: name: MTEB 8TagsClustering type: PL-MTEB/8tags-clustering config: default split: test revision: None metrics: - type: v_measure value: 51.36126303874126 - task: type: Classification dataset: name: MTEB AllegroReviews type: PL-MTEB/allegro-reviews config: default split: test revision: None metrics: - type: accuracy value: 67.13717693836979 - type: f1 value: 57.27609848003782 - task: type: Retrieval dataset: name: MTEB ArguAna-PL type: clarin-knext/arguana-pl config: default split: test revision: 63fc86750af76253e8c760fc9e534bbf24d260a2 metrics: - type: map_at_1 value: 35.276999999999994 - type: map_at_10 value: 51.086 - type: map_at_100 value: 51.788000000000004 - type: map_at_1000 value: 51.791 - type: map_at_3 value: 46.147 - type: map_at_5 value: 49.078 - type: mrr_at_1 value: 35.917 - type: mrr_at_10 value: 51.315999999999995 - type: mrr_at_100 value: 52.018 - type: mrr_at_1000 value: 52.022 - type: mrr_at_3 value: 46.349000000000004 - type: mrr_at_5 value: 49.297000000000004 - type: ndcg_at_1 value: 35.276999999999994 - type: ndcg_at_10 value: 59.870999999999995 - type: ndcg_at_100 value: 62.590999999999994 - type: ndcg_at_1000 value: 62.661 - type: ndcg_at_3 value: 49.745 - type: ndcg_at_5 value: 55.067 - type: precision_at_1 value: 35.276999999999994 - type: precision_at_10 value: 8.791 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.057 - type: precision_at_5 value: 14.637 - type: recall_at_1 value: 35.276999999999994 - type: recall_at_10 value: 87.909 - type: recall_at_100 value: 99.14699999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 60.171 - type: recall_at_5 value: 73.18599999999999 - task: type: Classification dataset: name: MTEB CBD type: PL-MTEB/cbd config: default split: test revision: None metrics: - type: accuracy value: 78.03000000000002 - type: ap value: 29.12548553897622 - type: f1 value: 66.54857118886073 - task: type: PairClassification dataset: name: MTEB CDSC-E type: PL-MTEB/cdsce-pairclassification config: default split: test revision: None metrics: - type: cos_sim_accuracy value: 89.0 - type: cos_sim_ap value: 76.75437826834582 - type: cos_sim_f1 value: 66.4850136239782 - type: cos_sim_precision value: 68.92655367231639 - type: cos_sim_recall value: 64.21052631578948 - type: dot_accuracy value: 89.0 - type: dot_ap value: 76.75437826834582 - type: dot_f1 value: 66.4850136239782 - type: dot_precision value: 68.92655367231639 - type: dot_recall value: 64.21052631578948 - type: euclidean_accuracy value: 89.0 - type: euclidean_ap value: 76.75437826834582 - type: euclidean_f1 value: 66.4850136239782 - type: euclidean_precision value: 68.92655367231639 - type: euclidean_recall value: 64.21052631578948 - type: manhattan_accuracy value: 89.0 - type: manhattan_ap value: 76.66074220647083 - type: manhattan_f1 value: 66.47058823529412 - type: manhattan_precision value: 75.33333333333333 - type: manhattan_recall value: 59.473684210526315 - type: max_accuracy value: 89.0 - type: max_ap value: 76.75437826834582 - type: max_f1 value: 66.4850136239782 - task: type: STS dataset: name: MTEB CDSC-R type: PL-MTEB/cdscr-sts config: default split: test revision: None metrics: - type: cos_sim_pearson value: 93.12903172428328 - type: cos_sim_spearman value: 92.66381487060741 - type: euclidean_pearson value: 90.37278396708922 - type: euclidean_spearman value: 92.66381487060741 - type: manhattan_pearson value: 90.32503296540962 - type: manhattan_spearman value: 92.6902938354313 - task: type: Retrieval dataset: name: MTEB DBPedia-PL type: clarin-knext/dbpedia-pl config: default split: test revision: 76afe41d9af165cc40999fcaa92312b8b012064a metrics: - type: map_at_1 value: 8.83 - type: map_at_10 value: 18.326 - type: map_at_100 value: 26.496 - type: map_at_1000 value: 28.455000000000002 - type: map_at_3 value: 12.933 - type: map_at_5 value: 15.168000000000001 - type: mrr_at_1 value: 66.0 - type: mrr_at_10 value: 72.76700000000001 - type: mrr_at_100 value: 73.203 - type: mrr_at_1000 value: 73.219 - type: mrr_at_3 value: 71.458 - type: mrr_at_5 value: 72.246 - type: ndcg_at_1 value: 55.375 - type: ndcg_at_10 value: 41.3 - type: ndcg_at_100 value: 45.891 - type: ndcg_at_1000 value: 52.905 - type: ndcg_at_3 value: 46.472 - type: ndcg_at_5 value: 43.734 - type: precision_at_1 value: 66.0 - type: precision_at_10 value: 33.074999999999996 - type: precision_at_100 value: 11.094999999999999 - type: precision_at_1000 value: 2.374 - type: precision_at_3 value: 48.583 - type: precision_at_5 value: 42.0 - type: recall_at_1 value: 8.83 - type: recall_at_10 value: 22.587 - type: recall_at_100 value: 50.61600000000001 - type: recall_at_1000 value: 73.559 - type: recall_at_3 value: 13.688 - type: recall_at_5 value: 16.855 - task: type: Retrieval dataset: name: MTEB FiQA-PL type: clarin-knext/fiqa-pl config: default split: test revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e metrics: - type: map_at_1 value: 20.587 - type: map_at_10 value: 33.095 - type: map_at_100 value: 35.24 - type: map_at_1000 value: 35.429 - type: map_at_3 value: 28.626 - type: map_at_5 value: 31.136999999999997 - type: mrr_at_1 value: 40.586 - type: mrr_at_10 value: 49.033 - type: mrr_at_100 value: 49.952999999999996 - type: mrr_at_1000 value: 49.992 - type: mrr_at_3 value: 46.553 - type: mrr_at_5 value: 48.035 - type: ndcg_at_1 value: 40.586 - type: ndcg_at_10 value: 41.046 - type: ndcg_at_100 value: 48.586 - type: ndcg_at_1000 value: 51.634 - type: ndcg_at_3 value: 36.773 - type: ndcg_at_5 value: 38.389 - type: precision_at_1 value: 40.586 - type: precision_at_10 value: 11.466 - type: precision_at_100 value: 1.909 - type: precision_at_1000 value: 0.245 - type: precision_at_3 value: 24.434 - type: precision_at_5 value: 18.426000000000002 - type: recall_at_1 value: 20.587 - type: recall_at_10 value: 47.986000000000004 - type: recall_at_100 value: 75.761 - type: recall_at_1000 value: 94.065 - type: recall_at_3 value: 33.339 - type: recall_at_5 value: 39.765 - task: type: Retrieval dataset: name: MTEB HotpotQA-PL type: clarin-knext/hotpotqa-pl config: default split: test revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907 metrics: - type: map_at_1 value: 40.878 - type: map_at_10 value: 58.775999999999996 - type: map_at_100 value: 59.632 - type: map_at_1000 value: 59.707 - type: map_at_3 value: 56.074 - type: map_at_5 value: 57.629 - type: mrr_at_1 value: 81.756 - type: mrr_at_10 value: 86.117 - type: mrr_at_100 value: 86.299 - type: mrr_at_1000 value: 86.30600000000001 - type: mrr_at_3 value: 85.345 - type: mrr_at_5 value: 85.832 - type: ndcg_at_1 value: 81.756 - type: ndcg_at_10 value: 67.608 - type: ndcg_at_100 value: 70.575 - type: ndcg_at_1000 value: 71.99600000000001 - type: ndcg_at_3 value: 63.723 - type: ndcg_at_5 value: 65.70700000000001 - type: precision_at_1 value: 81.756 - type: precision_at_10 value: 13.619 - type: precision_at_100 value: 1.5939999999999999 - type: precision_at_1000 value: 0.178 - type: precision_at_3 value: 39.604 - type: precision_at_5 value: 25.332 - type: recall_at_1 value: 40.878 - type: recall_at_10 value: 68.096 - type: recall_at_100 value: 79.696 - type: recall_at_1000 value: 89.082 - type: recall_at_3 value: 59.406000000000006 - type: recall_at_5 value: 63.329 - task: type: Retrieval dataset: name: MTEB MSMARCO-PL type: clarin-knext/msmarco-pl config: default split: test revision: 8634c07806d5cce3a6138e260e59b81760a0a640 metrics: - type: map_at_1 value: 2.1839999999999997 - type: map_at_10 value: 11.346 - type: map_at_100 value: 30.325000000000003 - type: map_at_1000 value: 37.806 - type: map_at_3 value: 4.842 - type: map_at_5 value: 6.891 - type: mrr_at_1 value: 86.047 - type: mrr_at_10 value: 89.14699999999999 - type: mrr_at_100 value: 89.46600000000001 - type: mrr_at_1000 value: 89.46600000000001 - type: mrr_at_3 value: 89.14699999999999 - type: mrr_at_5 value: 89.14699999999999 - type: ndcg_at_1 value: 67.829 - type: ndcg_at_10 value: 62.222 - type: ndcg_at_100 value: 55.337 - type: ndcg_at_1000 value: 64.076 - type: ndcg_at_3 value: 68.12700000000001 - type: ndcg_at_5 value: 64.987 - type: precision_at_1 value: 86.047 - type: precision_at_10 value: 69.535 - type: precision_at_100 value: 32.93 - type: precision_at_1000 value: 6.6049999999999995 - type: precision_at_3 value: 79.845 - type: precision_at_5 value: 75.349 - type: recall_at_1 value: 2.1839999999999997 - type: recall_at_10 value: 12.866 - type: recall_at_100 value: 43.505 - type: recall_at_1000 value: 72.366 - type: recall_at_3 value: 4.947 - type: recall_at_5 value: 7.192 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (pl) type: mteb/amazon_massive_intent config: pl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 80.75319435104238 - type: f1 value: 77.58961444860606 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (pl) type: mteb/amazon_massive_scenario config: pl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 85.54472091459313 - type: f1 value: 84.29498563572106 - task: type: Retrieval dataset: name: MTEB NFCorpus-PL type: clarin-knext/nfcorpus-pl config: default split: test revision: 9a6f9567fda928260afed2de480d79c98bf0bec0 metrics: - type: map_at_1 value: 4.367 - type: map_at_10 value: 10.38 - type: map_at_100 value: 13.516 - type: map_at_1000 value: 14.982000000000001 - type: map_at_3 value: 7.367 - type: map_at_5 value: 8.59 - type: mrr_at_1 value: 41.486000000000004 - type: mrr_at_10 value: 48.886 - type: mrr_at_100 value: 49.657000000000004 - type: mrr_at_1000 value: 49.713 - type: mrr_at_3 value: 46.904 - type: mrr_at_5 value: 48.065000000000005 - type: ndcg_at_1 value: 40.402 - type: ndcg_at_10 value: 30.885 - type: ndcg_at_100 value: 28.393 - type: ndcg_at_1000 value: 37.428 - type: ndcg_at_3 value: 35.394999999999996 - type: ndcg_at_5 value: 33.391999999999996 - type: precision_at_1 value: 41.486000000000004 - type: precision_at_10 value: 23.437 - type: precision_at_100 value: 7.638 - type: precision_at_1000 value: 2.0389999999999997 - type: precision_at_3 value: 32.817 - type: precision_at_5 value: 28.915999999999997 - type: recall_at_1 value: 4.367 - type: recall_at_10 value: 14.655000000000001 - type: recall_at_100 value: 29.665999999999997 - type: recall_at_1000 value: 62.073 - type: recall_at_3 value: 8.51 - type: recall_at_5 value: 10.689 - task: type: Retrieval dataset: name: MTEB NQ-PL type: clarin-knext/nq-pl config: default split: test revision: f171245712cf85dd4700b06bef18001578d0ca8d metrics: - type: map_at_1 value: 28.616000000000003 - type: map_at_10 value: 41.626000000000005 - type: map_at_100 value: 42.689 - type: map_at_1000 value: 42.733 - type: map_at_3 value: 37.729 - type: map_at_5 value: 39.879999999999995 - type: mrr_at_1 value: 32.068000000000005 - type: mrr_at_10 value: 44.029 - type: mrr_at_100 value: 44.87 - type: mrr_at_1000 value: 44.901 - type: mrr_at_3 value: 40.687 - type: mrr_at_5 value: 42.625 - type: ndcg_at_1 value: 32.068000000000005 - type: ndcg_at_10 value: 48.449999999999996 - type: ndcg_at_100 value: 53.13 - type: ndcg_at_1000 value: 54.186 - type: ndcg_at_3 value: 40.983999999999995 - type: ndcg_at_5 value: 44.628 - type: precision_at_1 value: 32.068000000000005 - type: precision_at_10 value: 7.9750000000000005 - type: precision_at_100 value: 1.061 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 18.404999999999998 - type: precision_at_5 value: 13.111 - type: recall_at_1 value: 28.616000000000003 - type: recall_at_10 value: 66.956 - type: recall_at_100 value: 87.657 - type: recall_at_1000 value: 95.548 - type: recall_at_3 value: 47.453 - type: recall_at_5 value: 55.87800000000001 - task: type: Classification dataset: name: MTEB PAC type: laugustyniak/abusive-clauses-pl config: default split: test revision: None metrics: - type: accuracy value: 69.04141326382856 - type: ap value: 77.47589122111044 - type: f1 value: 66.6332277374775 - task: type: PairClassification dataset: name: MTEB PPC type: PL-MTEB/ppc-pairclassification config: default split: test revision: None metrics: - type: cos_sim_accuracy value: 86.4 - type: cos_sim_ap value: 94.1044939667201 - type: cos_sim_f1 value: 88.78048780487805 - type: cos_sim_precision value: 87.22044728434504 - type: cos_sim_recall value: 90.39735099337747 - type: dot_accuracy value: 86.4 - type: dot_ap value: 94.1044939667201 - type: dot_f1 value: 88.78048780487805 - type: dot_precision value: 87.22044728434504 - type: dot_recall value: 90.39735099337747 - type: euclidean_accuracy value: 86.4 - type: euclidean_ap value: 94.1044939667201 - type: euclidean_f1 value: 88.78048780487805 - type: euclidean_precision value: 87.22044728434504 - type: euclidean_recall value: 90.39735099337747 - type: manhattan_accuracy value: 86.4 - type: manhattan_ap value: 94.11438365697387 - type: manhattan_f1 value: 88.77968877968877 - type: manhattan_precision value: 87.84440842787681 - type: manhattan_recall value: 89.73509933774835 - type: max_accuracy value: 86.4 - type: max_ap value: 94.11438365697387 - type: max_f1 value: 88.78048780487805 - task: type: PairClassification dataset: name: MTEB PSC type: PL-MTEB/psc-pairclassification config: default split: test revision: None metrics: - type: cos_sim_accuracy value: 97.86641929499072 - type: cos_sim_ap value: 99.36904211868182 - type: cos_sim_f1 value: 96.56203288490283 - type: cos_sim_precision value: 94.72140762463343 - type: cos_sim_recall value: 98.47560975609755 - type: dot_accuracy value: 97.86641929499072 - type: dot_ap value: 99.36904211868183 - type: dot_f1 value: 96.56203288490283 - type: dot_precision value: 94.72140762463343 - type: dot_recall value: 98.47560975609755 - type: euclidean_accuracy value: 97.86641929499072 - type: euclidean_ap value: 99.36904211868183 - type: euclidean_f1 value: 96.56203288490283 - type: euclidean_precision value: 94.72140762463343 - type: euclidean_recall value: 98.47560975609755 - type: manhattan_accuracy value: 98.14471243042672 - type: manhattan_ap value: 99.43359540492416 - type: manhattan_f1 value: 96.98795180722892 - type: manhattan_precision value: 95.83333333333334 - type: manhattan_recall value: 98.17073170731707 - type: max_accuracy value: 98.14471243042672 - type: max_ap value: 99.43359540492416 - type: max_f1 value: 96.98795180722892 - task: type: Classification dataset: name: MTEB PolEmo2.0-IN type: PL-MTEB/polemo2_in config: default split: test revision: None metrics: - type: accuracy value: 89.39058171745152 - type: f1 value: 86.8552093529568 - task: type: Classification dataset: name: MTEB PolEmo2.0-OUT type: PL-MTEB/polemo2_out config: default split: test revision: None metrics: - type: accuracy value: 74.97975708502024 - type: f1 value: 58.73081628832407 - task: type: Retrieval dataset: name: MTEB Quora-PL type: clarin-knext/quora-pl config: default split: test revision: 0be27e93455051e531182b85e85e425aba12e9d4 metrics: - type: map_at_1 value: 64.917 - type: map_at_10 value: 78.74600000000001 - type: map_at_100 value: 79.501 - type: map_at_1000 value: 79.524 - type: map_at_3 value: 75.549 - type: map_at_5 value: 77.495 - type: mrr_at_1 value: 74.9 - type: mrr_at_10 value: 82.112 - type: mrr_at_100 value: 82.314 - type: mrr_at_1000 value: 82.317 - type: mrr_at_3 value: 80.745 - type: mrr_at_5 value: 81.607 - type: ndcg_at_1 value: 74.83999999999999 - type: ndcg_at_10 value: 83.214 - type: ndcg_at_100 value: 84.997 - type: ndcg_at_1000 value: 85.207 - type: ndcg_at_3 value: 79.547 - type: ndcg_at_5 value: 81.46600000000001 - type: precision_at_1 value: 74.83999999999999 - type: precision_at_10 value: 12.822 - type: precision_at_100 value: 1.506 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 34.903 - type: precision_at_5 value: 23.16 - type: recall_at_1 value: 64.917 - type: recall_at_10 value: 92.27199999999999 - type: recall_at_100 value: 98.715 - type: recall_at_1000 value: 99.854 - type: recall_at_3 value: 82.04599999999999 - type: recall_at_5 value: 87.2 - task: type: Retrieval dataset: name: MTEB SCIDOCS-PL type: clarin-knext/scidocs-pl config: default split: test revision: 45452b03f05560207ef19149545f168e596c9337 metrics: - type: map_at_1 value: 3.51 - type: map_at_10 value: 9.046999999999999 - type: map_at_100 value: 10.823 - type: map_at_1000 value: 11.144 - type: map_at_3 value: 6.257 - type: map_at_5 value: 7.648000000000001 - type: mrr_at_1 value: 17.299999999999997 - type: mrr_at_10 value: 27.419 - type: mrr_at_100 value: 28.618 - type: mrr_at_1000 value: 28.685 - type: mrr_at_3 value: 23.817 - type: mrr_at_5 value: 25.927 - type: ndcg_at_1 value: 17.299999999999997 - type: ndcg_at_10 value: 16.084 - type: ndcg_at_100 value: 23.729 - type: ndcg_at_1000 value: 29.476999999999997 - type: ndcg_at_3 value: 14.327000000000002 - type: ndcg_at_5 value: 13.017999999999999 - type: precision_at_1 value: 17.299999999999997 - type: precision_at_10 value: 8.63 - type: precision_at_100 value: 1.981 - type: precision_at_1000 value: 0.336 - type: precision_at_3 value: 13.4 - type: precision_at_5 value: 11.700000000000001 - type: recall_at_1 value: 3.51 - type: recall_at_10 value: 17.518 - type: recall_at_100 value: 40.275 - type: recall_at_1000 value: 68.203 - type: recall_at_3 value: 8.155 - type: recall_at_5 value: 11.875 - task: type: PairClassification dataset: name: MTEB SICK-E-PL type: PL-MTEB/sicke-pl-pairclassification config: default split: test revision: None metrics: - type: cos_sim_accuracy value: 86.30248675091724 - type: cos_sim_ap value: 83.6756734006714 - type: cos_sim_f1 value: 74.97367497367497 - type: cos_sim_precision value: 73.91003460207612 - type: cos_sim_recall value: 76.06837606837607 - type: dot_accuracy value: 86.30248675091724 - type: dot_ap value: 83.6756734006714 - type: dot_f1 value: 74.97367497367497 - type: dot_precision value: 73.91003460207612 - type: dot_recall value: 76.06837606837607 - type: euclidean_accuracy value: 86.30248675091724 - type: euclidean_ap value: 83.67566984333091 - type: euclidean_f1 value: 74.97367497367497 - type: euclidean_precision value: 73.91003460207612 - type: euclidean_recall value: 76.06837606837607 - type: manhattan_accuracy value: 86.28210354667753 - type: manhattan_ap value: 83.64216119130171 - type: manhattan_f1 value: 74.92152075340078 - type: manhattan_precision value: 73.4107997265892 - type: manhattan_recall value: 76.49572649572649 - type: max_accuracy value: 86.30248675091724 - type: max_ap value: 83.6756734006714 - type: max_f1 value: 74.97367497367497 - task: type: STS dataset: name: MTEB SICK-R-PL type: PL-MTEB/sickr-pl-sts config: default split: test revision: None metrics: - type: cos_sim_pearson value: 82.23295940859121 - type: cos_sim_spearman value: 78.89329160768719 - type: euclidean_pearson value: 79.56019107076818 - type: euclidean_spearman value: 78.89330209904084 - type: manhattan_pearson value: 79.76098513973719 - type: manhattan_spearman value: 79.05490162570123 - task: type: STS dataset: name: MTEB STS22 (pl) type: mteb/sts22-crosslingual-sts config: pl split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 37.732606308062486 - type: cos_sim_spearman value: 41.01645667030284 - type: euclidean_pearson value: 26.61722556367085 - type: euclidean_spearman value: 41.01645667030284 - type: manhattan_pearson value: 26.60917378970807 - type: manhattan_spearman value: 41.51335727617614 - task: type: Retrieval dataset: name: MTEB SciFact-PL type: clarin-knext/scifact-pl config: default split: test revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e metrics: - type: map_at_1 value: 54.31700000000001 - type: map_at_10 value: 65.564 - type: map_at_100 value: 66.062 - type: map_at_1000 value: 66.08699999999999 - type: map_at_3 value: 62.592999999999996 - type: map_at_5 value: 63.888 - type: mrr_at_1 value: 56.99999999999999 - type: mrr_at_10 value: 66.412 - type: mrr_at_100 value: 66.85900000000001 - type: mrr_at_1000 value: 66.88 - type: mrr_at_3 value: 64.22200000000001 - type: mrr_at_5 value: 65.206 - type: ndcg_at_1 value: 56.99999999999999 - type: ndcg_at_10 value: 70.577 - type: ndcg_at_100 value: 72.879 - type: ndcg_at_1000 value: 73.45 - type: ndcg_at_3 value: 65.5 - type: ndcg_at_5 value: 67.278 - type: precision_at_1 value: 56.99999999999999 - type: precision_at_10 value: 9.667 - type: precision_at_100 value: 1.083 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 26.0 - type: precision_at_5 value: 16.933 - type: recall_at_1 value: 54.31700000000001 - type: recall_at_10 value: 85.056 - type: recall_at_100 value: 95.667 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 71.0 - type: recall_at_5 value: 75.672 - task: type: Retrieval dataset: name: MTEB TRECCOVID-PL type: clarin-knext/trec-covid-pl config: default split: test revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd metrics: - type: map_at_1 value: 0.245 - type: map_at_10 value: 2.051 - type: map_at_100 value: 12.009 - type: map_at_1000 value: 27.448 - type: map_at_3 value: 0.721 - type: map_at_5 value: 1.13 - type: mrr_at_1 value: 88.0 - type: mrr_at_10 value: 93.0 - type: mrr_at_100 value: 93.0 - type: mrr_at_1000 value: 93.0 - type: mrr_at_3 value: 93.0 - type: mrr_at_5 value: 93.0 - type: ndcg_at_1 value: 85.0 - type: ndcg_at_10 value: 80.303 - type: ndcg_at_100 value: 61.23499999999999 - type: ndcg_at_1000 value: 52.978 - type: ndcg_at_3 value: 84.419 - type: ndcg_at_5 value: 82.976 - type: precision_at_1 value: 88.0 - type: precision_at_10 value: 83.39999999999999 - type: precision_at_100 value: 61.96 - type: precision_at_1000 value: 22.648 - type: precision_at_3 value: 89.333 - type: precision_at_5 value: 87.2 - type: recall_at_1 value: 0.245 - type: recall_at_10 value: 2.193 - type: recall_at_100 value: 14.938 - type: recall_at_1000 value: 48.563 - type: recall_at_3 value: 0.738 - type: recall_at_5 value: 1.173 --- # LXC1999/gte-Qwen2-7B-instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`Alibaba-NLP/gte-Qwen2-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo LXC1999/gte-Qwen2-7B-instruct-Q4_K_M-GGUF --hf-file gte-qwen2-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo LXC1999/gte-Qwen2-7B-instruct-Q4_K_M-GGUF --hf-file gte-qwen2-7b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo LXC1999/gte-Qwen2-7B-instruct-Q4_K_M-GGUF --hf-file gte-qwen2-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo LXC1999/gte-Qwen2-7B-instruct-Q4_K_M-GGUF --hf-file gte-qwen2-7b-instruct-q4_k_m.gguf -c 2048 ```
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
RichardErkhov/EleutherAI_-_pythia-160m-deduped-v0-8bits
RichardErkhov
text-generation
[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:2101.00027", "arxiv:2201.07311", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]
1,713
1,713
10
0
--- {} --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) pythia-160m-deduped-v0 - bnb 8bits - Model creator: https://huggingface.co/EleutherAI/ - Original model: https://huggingface.co/EleutherAI/pythia-160m-deduped-v0/ Original model description: --- language: - en tags: - pytorch - causal-lm - pythia - pythia_v0 license: apache-2.0 datasets: - EleutherAI/the_pile_deduplicated --- The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available [on Hugging Face](https://huggingface.co/models?other=pythia). The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Please note that all models in the *Pythia* suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. ## Pythia-160M-deduped ### Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing *Pythia* documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> | Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models | | -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: | | 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — | | 160M | 85,056,000 | 12 | 768 | 12 | 4M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M | | 410M | 302,311,424 | 24 | 1024 | 16 | 4M | 3.0 x 10<sup>-4</sup> | OPT-350M | | 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — | | 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 4M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B | | 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B | | 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B | | 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — | <figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have <b>exactly</b> the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ### Uses and Limitations #### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change in the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-160M-deduped for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-160M-deduped as a basis for your fine-tuned model, please conduct your own risk and bias assessment. #### Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-160M-deduped has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-160M-deduped will **not** respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions. #### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-160M-deduped to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-160M-deduped may produce socially unacceptable or undesirable text, *even if* the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-160M-deduped. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(**inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model.<br> For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ### Training #### Training data Pythia-160M-deduped was trained on the Pile **after the dataset has been globally deduplicated**.<br> [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). #### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All *Pythia* models trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, with checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for consistency with all 2M batch models, so `step1000` is the first checkpoint for `pythia-1.4b` that was saved (corresponding to step 500 in training), and `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved (corresponding to 1000 “actual” steps).<br> See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br> Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ### Evaluations All 16 *Pythia* models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/*` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json).<br> Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge – Challenge Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/> </details> ### Naming convention and parameter count *Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> | current Pythia suffix | old suffix | total params | non-embedding params | | --------------------: | ---------: | -------------: | -------------------: | | 70M | 19M | 70,426,624 | 18,915,328 | | 160M | 125M | 162,322,944 | 85,056,000 | | 410M | 350M | 405,334,016 | 302,311,424 | | 1B | 800M | 1,011,781,632 | 805,736,448 | | 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 | | 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 | | 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 | | 12B | 13B | 11,846,072,320 | 11,327,027,200 | </figure>
[ "QUESTION_ANSWERING", "TRANSLATION" ]
[ "SCIQ" ]
Non_BioNLP
pankajrajdeo/UMLS-ED-Bioformer-8L-V-1.25
pankajrajdeo
sentence-similarity
[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:187491593", "loss:CustomTripletLoss", "arxiv:1908.10084", "arxiv:1703.07737", "base_model:pankajrajdeo/UMLS-ED-Bioformer-8L-V-1", "base_model:finetune:pankajrajdeo/UMLS-ED-Bioformer-8L-V-1", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,733
1,736
24
0
--- base_model: - pankajrajdeo/UMLS-ED-Bioformer-8L-V-1 library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:187491593 - loss:CustomTripletLoss widget: - source_sentence: Hylocharis xantusii sentences: - Xantus's hummingbird - C5721346 - C1623532 - Iole viridescens viridescens - source_sentence: HTLV1+2 RNA XXX Ql PCR sentences: - HTLV 1+2 RNA:MevcEşik:Zmlı:XXX:Srl:Prob.amf.hdf - Nota de progreso:Tipo:Punto temporal:{Configuración}:Documento:Pain medicine - C0368469 - C4070921 - source_sentence: Degeneração Nigroestriatal sentences: - C0270733 - hiperinsulinismo debido a deficiencia de 3-hidroxiacil-coenzima A deshidrogenasa de cadena corta - Striatonigral atrophy - C4303473 - source_sentence: Clostridioides difficile As:titer:moment:serum:semikwantitatief sentences: - Dehidroepiandrosteron:MevcEşik:Zmlı:İdrar:Srl - C0485219 - C0364328 - Clostridium difficile Ac:Título:Pt:Soro:Qn - source_sentence: E Vicotrat sentences: - C2742706 - C2350910 - germanium L-cysteine alpha-tocopherol complex - Eosine I Bluish, Dipotassium Salt --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 512-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 512 tokens - **Similarity Function:** Cosine Similarity <!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 512, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("pankajrajdeo/937457_bioformer_8L") # Run inference sentences = [ 'E Vicotrat', 'Eosine I Bluish, Dipotassium Salt', 'C2742706', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 512] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 187,491,593 training samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative_id</code>, <code>positive_id</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative_id | positive_id | negative | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 13.27 tokens</li><li>max: 247 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 12.25 tokens</li><li>max: 157 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 6.27 tokens</li><li>max: 7 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 6.49 tokens</li><li>max: 7 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.53 tokens</li><li>max: 118 tokens</li></ul> | * Samples: | anchor | positive | negative_id | positive_id | negative | |:----------------------------------------------|:------------------------------------------------------------------------------------------------|:----------------------|:----------------------|:------------------------------------------------------------------------------------------------| | <code>Zaburzenie metabolizmu minerałów</code> | <code>Distúrbio não especificado do metabolismo de minerais</code> | <code>C2887914</code> | <code>C0154260</code> | <code>Acute alcoholic hepatic failure</code> | | <code>testy funkčnosti placenty</code> | <code>Metoder som brukes til å vurdere morkakefunksjon.</code> | <code>C2350391</code> | <code>C0032049</code> | <code>Hjärtmuskelscintigrafi</code> | | <code>Tsefapiriin:Susc:Pt:Is:OrdQn</code> | <code>cefapirina:susceptibilidad:punto en el tiempo:cepa clínica:ordinal o cuantitativo:</code> | <code>C0942365</code> | <code>C0801894</code> | <code>2 proyecciones:hallazgo:punto en el tiempo:tobillo.izquierdo:Narrativo:radiografía</code> | * Loss: <code>__main__.CustomTripletLoss</code> with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 50 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 50 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | |:------:|:------:|:-------------:| | 0.0003 | 1000 | 0.9785 | | 0.0005 | 2000 | 0.925 | | 0.0008 | 3000 | 0.8548 | | 0.0011 | 4000 | 0.7979 | | 0.0013 | 5000 | 0.7635 | | 0.0016 | 6000 | 0.7176 | | 0.0019 | 7000 | 0.6813 | | 0.0021 | 8000 | 0.6225 | | 0.0024 | 9000 | 0.6135 | | 0.0027 | 10000 | 0.5827 | | 0.0029 | 11000 | 0.5695 | | 0.0032 | 12000 | 0.5152 | | 0.0035 | 13000 | 0.5213 | | 0.0037 | 14000 | 0.4895 | | 0.0040 | 15000 | 0.4942 | | 0.0043 | 16000 | 0.4819 | | 0.0045 | 17000 | 0.4799 | | 0.0048 | 18000 | 0.4572 | | 0.0051 | 19000 | 0.4396 | | 0.0053 | 20000 | 0.4389 | | 0.0056 | 21000 | 0.4269 | | 0.0059 | 22000 | 0.4155 | | 0.0061 | 23000 | 0.4034 | | 0.0064 | 24000 | 0.4067 | | 0.0067 | 25000 | 0.401 | | 0.0069 | 26000 | 0.376 | | 0.0072 | 27000 | 0.3715 | | 0.0075 | 28000 | 0.3788 | | 0.0077 | 29000 | 0.362 | | 0.0080 | 30000 | 0.3644 | | 0.0083 | 31000 | 0.3487 | | 0.0085 | 32000 | 0.3432 | | 0.0088 | 33000 | 0.3394 | | 0.0091 | 34000 | 0.3423 | | 0.0093 | 35000 | 0.3314 | | 0.0096 | 36000 | 0.3447 | | 0.0099 | 37000 | 0.3206 | | 0.0101 | 38000 | 0.3283 | | 0.0104 | 39000 | 0.3183 | | 0.0107 | 40000 | 0.3167 | | 0.0109 | 41000 | 0.3169 | | 0.0112 | 42000 | 0.3122 | | 0.0115 | 43000 | 0.3022 | | 0.0117 | 44000 | 0.3066 | | 0.0120 | 45000 | 0.3002 | | 0.0123 | 46000 | 0.3003 | | 0.0125 | 47000 | 0.2907 | | 0.0128 | 48000 | 0.2843 | | 0.0131 | 49000 | 0.2905 | | 0.0133 | 50000 | 0.2816 | | 0.0136 | 51000 | 0.2959 | | 0.0139 | 52000 | 0.2765 | | 0.0141 | 53000 | 0.2813 | | 0.0144 | 54000 | 0.2715 | | 0.0147 | 55000 | 0.2826 | | 0.0149 | 56000 | 0.2845 | | 0.0152 | 57000 | 0.2709 | | 0.0155 | 58000 | 0.2704 | | 0.0157 | 59000 | 0.2667 | | 0.0160 | 60000 | 0.2589 | | 0.0163 | 61000 | 0.2574 | | 0.0165 | 62000 | 0.2598 | | 0.0168 | 63000 | 0.2427 | | 0.0171 | 64000 | 0.2505 | | 0.0173 | 65000 | 0.265 | | 0.0176 | 66000 | 0.263 | | 0.0179 | 67000 | 0.2521 | | 0.0181 | 68000 | 0.2532 | | 0.0184 | 69000 | 0.256 | | 0.0187 | 70000 | 0.2599 | | 0.0189 | 71000 | 0.2558 | | 0.0192 | 72000 | 0.2526 | | 0.0195 | 73000 | 0.2402 | | 0.0197 | 74000 | 0.2471 | | 0.0200 | 75000 | 0.24 | | 0.0203 | 76000 | 0.2562 | | 0.0205 | 77000 | 0.2398 | | 0.0208 | 78000 | 0.2622 | | 0.0211 | 79000 | 0.235 | | 0.0213 | 80000 | 0.2421 | | 0.0216 | 81000 | 0.2378 | | 0.0219 | 82000 | 0.2323 | | 0.0221 | 83000 | 0.232 | | 0.0224 | 84000 | 0.2319 | | 0.0227 | 85000 | 0.2361 | | 0.0229 | 86000 | 0.2252 | | 0.0232 | 87000 | 0.2282 | | 0.0235 | 88000 | 0.2213 | | 0.0237 | 89000 | 0.2228 | | 0.0240 | 90000 | 0.2265 | | 0.0243 | 91000 | 0.2375 | | 0.0245 | 92000 | 0.2328 | | 0.0248 | 93000 | 0.2318 | | 0.0251 | 94000 | 0.2321 | | 0.0253 | 95000 | 0.2205 | | 0.0256 | 96000 | 0.2319 | | 0.0259 | 97000 | 0.2193 | | 0.0261 | 98000 | 0.2188 | | 0.0264 | 99000 | 0.2196 | | 0.0267 | 100000 | 0.2223 | | 0.0269 | 101000 | 0.2268 | | 0.0272 | 102000 | 0.219 | | 0.0275 | 103000 | 0.206 | | 0.0277 | 104000 | 0.2154 | | 0.0280 | 105000 | 0.2261 | | 0.0283 | 106000 | 0.2112 | | 0.0285 | 107000 | 0.2015 | | 0.0288 | 108000 | 0.2115 | | 0.0291 | 109000 | 0.2145 | | 0.0293 | 110000 | 0.2142 | | 0.0296 | 111000 | 0.2217 | | 0.0299 | 112000 | 0.213 | | 0.0301 | 113000 | 0.2089 | | 0.0304 | 114000 | 0.2089 | | 0.0307 | 115000 | 0.2027 | | 0.0309 | 116000 | 0.217 | | 0.0312 | 117000 | 0.2008 | | 0.0315 | 118000 | 0.2035 | | 0.0317 | 119000 | 0.208 | | 0.0320 | 120000 | 0.2006 | | 0.0323 | 121000 | 0.2089 | | 0.0325 | 122000 | 0.212 | | 0.0328 | 123000 | 0.2074 | | 0.0331 | 124000 | 0.203 | | 0.0333 | 125000 | 0.2038 | | 0.0336 | 126000 | 0.1979 | | 0.0339 | 127000 | 0.197 | | 0.0341 | 128000 | 0.1947 | | 0.0344 | 129000 | 0.2034 | | 0.0347 | 130000 | 0.1924 | | 0.0349 | 131000 | 0.1957 | | 0.0352 | 132000 | 0.1894 | | 0.0355 | 133000 | 0.1934 | | 0.0357 | 134000 | 0.1933 | | 0.0360 | 135000 | 0.1953 | | 0.0363 | 136000 | 0.192 | | 0.0365 | 137000 | 0.1871 | | 0.0368 | 138000 | 0.2053 | | 0.0371 | 139000 | 0.1971 | | 0.0373 | 140000 | 0.1904 | | 0.0376 | 141000 | 0.1891 | | 0.0379 | 142000 | 0.1876 | | 0.0381 | 143000 | 0.1875 | | 0.0384 | 144000 | 0.194 | | 0.0387 | 145000 | 0.1932 | | 0.0389 | 146000 | 0.1895 | | 0.0392 | 147000 | 0.1937 | | 0.0395 | 148000 | 0.1888 | | 0.0397 | 149000 | 0.1836 | | 0.0400 | 150000 | 0.1886 | | 0.0403 | 151000 | 0.183 | | 0.0405 | 152000 | 0.1896 | | 0.0408 | 153000 | 0.1851 | | 0.0411 | 154000 | 0.1844 | | 0.0413 | 155000 | 0.184 | | 0.0416 | 156000 | 0.1846 | | 0.0419 | 157000 | 0.1876 | | 0.0421 | 158000 | 0.1848 | | 0.0424 | 159000 | 0.1824 | | 0.0427 | 160000 | 0.1844 | | 0.0429 | 161000 | 0.1864 | | 0.0432 | 162000 | 0.1726 | | 0.0435 | 163000 | 0.1838 | | 0.0437 | 164000 | 0.1818 | | 0.0440 | 165000 | 0.1811 | | 0.0443 | 166000 | 0.176 | | 0.0445 | 167000 | 0.1831 | | 0.0448 | 168000 | 0.1791 | | 0.0451 | 169000 | 0.182 | | 0.0453 | 170000 | 0.1814 | | 0.0456 | 171000 | 0.1783 | | 0.0459 | 172000 | 0.1771 | | 0.0461 | 173000 | 0.1806 | | 0.0464 | 174000 | 0.1821 | | 0.0467 | 175000 | 0.1805 | | 0.0469 | 176000 | 0.1698 | | 0.0472 | 177000 | 0.1796 | | 0.0475 | 178000 | 0.1774 | | 0.0477 | 179000 | 0.1703 | | 0.0480 | 180000 | 0.179 | | 0.0483 | 181000 | 0.1839 | | 0.0485 | 182000 | 0.1695 | | 0.0488 | 183000 | 0.1681 | | 0.0491 | 184000 | 0.1783 | | 0.0493 | 185000 | 0.1792 | | 0.0496 | 186000 | 0.1664 | | 0.0499 | 187000 | 0.1711 | | 0.0501 | 188000 | 0.168 | | 0.0504 | 189000 | 0.1722 | | 0.0507 | 190000 | 0.1776 | | 0.0509 | 191000 | 0.1704 | | 0.0512 | 192000 | 0.161 | | 0.0515 | 193000 | 0.1719 | | 0.0517 | 194000 | 0.1679 | | 0.0520 | 195000 | 0.1731 | | 0.0523 | 196000 | 0.1778 | | 0.0525 | 197000 | 0.1658 | | 0.0528 | 198000 | 0.1607 | | 0.0531 | 199000 | 0.1682 | | 0.0533 | 200000 | 0.1675 | | 0.0536 | 201000 | 0.1708 | | 0.0539 | 202000 | 0.1694 | | 0.0541 | 203000 | 0.1767 | | 0.0544 | 204000 | 0.1665 | | 0.0547 | 205000 | 0.1695 | | 0.0549 | 206000 | 0.1693 | | 0.0552 | 207000 | 0.1697 | | 0.0555 | 208000 | 0.1721 | | 0.0557 | 209000 | 0.1633 | | 0.0560 | 210000 | 0.1712 | | 0.0563 | 211000 | 0.1712 | | 0.0565 | 212000 | 0.1646 | | 0.0568 | 213000 | 0.1639 | | 0.0571 | 214000 | 0.1692 | | 0.0573 | 215000 | 0.1694 | | 0.0576 | 216000 | 0.1684 | | 0.0579 | 217000 | 0.1608 | | 0.0581 | 218000 | 0.1663 | | 0.0584 | 219000 | 0.1669 | | 0.0587 | 220000 | 0.1671 | | 0.0589 | 221000 | 0.1632 | | 0.0592 | 222000 | 0.1642 | | 0.0595 | 223000 | 0.1619 | | 0.0597 | 224000 | 0.1672 | | 0.0600 | 225000 | 0.1704 | | 0.0603 | 226000 | 0.1602 | | 0.0605 | 227000 | 0.1548 | | 0.0608 | 228000 | 0.1631 | | 0.0611 | 229000 | 0.1555 | | 0.0613 | 230000 | 0.1666 | | 0.0616 | 231000 | 0.1611 | | 0.0619 | 232000 | 0.1504 | | 0.0621 | 233000 | 0.159 | | 0.0624 | 234000 | 0.1642 | | 0.0627 | 235000 | 0.1573 | | 0.0629 | 236000 | 0.1612 | | 0.0632 | 237000 | 0.1649 | | 0.0635 | 238000 | 0.1687 | | 0.0637 | 239000 | 0.1601 | | 0.0640 | 240000 | 0.1592 | | 0.0643 | 241000 | 0.1606 | | 0.0645 | 242000 | 0.1545 | | 0.0648 | 243000 | 0.1646 | | 0.0651 | 244000 | 0.1576 | | 0.0653 | 245000 | 0.1514 | | 0.0656 | 246000 | 0.1606 | | 0.0659 | 247000 | 0.1517 | | 0.0661 | 248000 | 0.1503 | | 0.0664 | 249000 | 0.1627 | | 0.0667 | 250000 | 0.1555 | | 0.0669 | 251000 | 0.1566 | | 0.0672 | 252000 | 0.1624 | | 0.0675 | 253000 | 0.1495 | | 0.0677 | 254000 | 0.1535 | | 0.0680 | 255000 | 0.1492 | | 0.0683 | 256000 | 0.1494 | | 0.0685 | 257000 | 0.1708 | | 0.0688 | 258000 | 0.1563 | | 0.0691 | 259000 | 0.1541 | | 0.0693 | 260000 | 0.1568 | | 0.0696 | 261000 | 0.1535 | | 0.0699 | 262000 | 0.1519 | | 0.0701 | 263000 | 0.1571 | | 0.0704 | 264000 | 0.1536 | | 0.0707 | 265000 | 0.147 | | 0.0709 | 266000 | 0.147 | | 0.0712 | 267000 | 0.1537 | | 0.0715 | 268000 | 0.1527 | | 0.0717 | 269000 | 0.1545 | | 0.0720 | 270000 | 0.1523 | | 0.0723 | 271000 | 0.1539 | | 0.0725 | 272000 | 0.1561 | | 0.0728 | 273000 | 0.1513 | | 0.0731 | 274000 | 0.1571 | | 0.0733 | 275000 | 0.1577 | | 0.0736 | 276000 | 0.1613 | | 0.0739 | 277000 | 0.1523 | | 0.0741 | 278000 | 0.1468 | | 0.0744 | 279000 | 0.1534 | | 0.0747 | 280000 | 0.1544 | | 0.0749 | 281000 | 0.1552 | | 0.0752 | 282000 | 0.1514 | | 0.0755 | 283000 | 0.1504 | | 0.0757 | 284000 | 0.149 | | 0.0760 | 285000 | 0.1537 | | 0.0763 | 286000 | 0.1527 | | 0.0765 | 287000 | 0.1482 | | 0.0768 | 288000 | 0.1503 | | 0.0771 | 289000 | 0.1476 | | 0.0773 | 290000 | 0.1535 | | 0.0776 | 291000 | 0.1575 | | 0.0779 | 292000 | 0.1465 | | 0.0781 | 293000 | 0.147 | | 0.0784 | 294000 | 0.147 | | 0.0787 | 295000 | 0.1484 | | 0.0789 | 296000 | 0.1502 | | 0.0792 | 297000 | 0.147 | | 0.0795 | 298000 | 0.1544 | | 0.0797 | 299000 | 0.156 | | 0.0800 | 300000 | 0.1445 | | 0.0803 | 301000 | 0.143 | | 0.0805 | 302000 | 0.1541 | | 0.0808 | 303000 | 0.159 | | 0.0811 | 304000 | 0.1434 | | 0.0813 | 305000 | 0.1511 | | 0.0816 | 306000 | 0.1473 | | 0.0819 | 307000 | 0.1514 | | 0.0821 | 308000 | 0.1491 | | 0.0824 | 309000 | 0.1443 | | 0.0827 | 310000 | 0.1496 | | 0.0829 | 311000 | 0.1535 | | 0.0832 | 312000 | 0.152 | | 0.0835 | 313000 | 0.1496 | | 0.0837 | 314000 | 0.1521 | | 0.0840 | 315000 | 0.1459 | | 0.0843 | 316000 | 0.1449 | | 0.0845 | 317000 | 0.148 | | 0.0848 | 318000 | 0.1566 | | 0.0851 | 319000 | 0.149 | | 0.0853 | 320000 | 0.1502 | | 0.0856 | 321000 | 0.1501 | | 0.0859 | 322000 | 0.1447 | | 0.0861 | 323000 | 0.1468 | | 0.0864 | 324000 | 0.1474 | | 0.0867 | 325000 | 0.1455 | | 0.0869 | 326000 | 0.1374 | | 0.0872 | 327000 | 0.1397 | | 0.0875 | 328000 | 0.1468 | | 0.0877 | 329000 | 0.1436 | | 0.0880 | 330000 | 0.1523 | | 0.0883 | 331000 | 0.1407 | | 0.0885 | 332000 | 0.1446 | | 0.0888 | 333000 | 0.1476 | | 0.0891 | 334000 | 0.1487 | | 0.0893 | 335000 | 0.1486 | | 0.0896 | 336000 | 0.1564 | | 0.0899 | 337000 | 0.1487 | | 0.0901 | 338000 | 0.1492 | | 0.0904 | 339000 | 0.1469 | | 0.0907 | 340000 | 0.1487 | | 0.0909 | 341000 | 0.1513 | | 0.0912 | 342000 | 0.151 | | 0.0915 | 343000 | 0.14 | | 0.0917 | 344000 | 0.1487 | | 0.0920 | 345000 | 0.1527 | | 0.0923 | 346000 | 0.1419 | | 0.0925 | 347000 | 0.1541 | | 0.0928 | 348000 | 0.1426 | | 0.0931 | 349000 | 0.1426 | | 0.0933 | 350000 | 0.1503 | | 0.0936 | 351000 | 0.1392 | | 0.0939 | 352000 | 0.1505 | | 0.0941 | 353000 | 0.1452 | | 0.0944 | 354000 | 0.1462 | | 0.0947 | 355000 | 0.1412 | | 0.0949 | 356000 | 0.1438 | | 0.0952 | 357000 | 0.1457 | | 0.0955 | 358000 | 0.1414 | | 0.0957 | 359000 | 0.1458 | | 0.0960 | 360000 | 0.1477 | | 0.0963 | 361000 | 0.1423 | | 0.0965 | 362000 | 0.1498 | | 0.0968 | 363000 | 0.1426 | | 0.0971 | 364000 | 0.1469 | | 0.0973 | 365000 | 0.136 | | 0.0976 | 366000 | 0.142 | | 0.0979 | 367000 | 0.138 | | 0.0981 | 368000 | 0.1439 | | 0.0984 | 369000 | 0.1402 | | 0.0987 | 370000 | 0.1431 | | 0.0989 | 371000 | 0.1382 | | 0.0992 | 372000 | 0.1456 | | 0.0995 | 373000 | 0.1364 | | 0.0997 | 374000 | 0.1424 | | 0.1000 | 375000 | 0.1499 | | 0.1003 | 376000 | 0.1471 | | 0.1005 | 377000 | 0.1401 | | 0.1008 | 378000 | 0.1365 | | 0.1011 | 379000 | 0.1434 | | 0.1013 | 380000 | 0.1422 | | 0.1016 | 381000 | 0.1318 | | 0.1019 | 382000 | 0.15 | | 0.1021 | 383000 | 0.1437 | | 0.1024 | 384000 | 0.138 | | 0.1027 | 385000 | 0.1394 | | 0.1029 | 386000 | 0.1446 | | 0.1032 | 387000 | 0.1327 | | 0.1035 | 388000 | 0.1448 | | 0.1037 | 389000 | 0.142 | | 0.1040 | 390000 | 0.1446 | | 0.1043 | 391000 | 0.1409 | | 0.1045 | 392000 | 0.1444 | | 0.1048 | 393000 | 0.1353 | | 0.1051 | 394000 | 0.1484 | | 0.1053 | 395000 | 0.1464 | | 0.1056 | 396000 | 0.1293 | | 0.1059 | 397000 | 0.1393 | | 0.1061 | 398000 | 0.1393 | | 0.1064 | 399000 | 0.1473 | | 0.1067 | 400000 | 0.1412 | | 0.1069 | 401000 | 0.1315 | | 0.1072 | 402000 | 0.1419 | | 0.1075 | 403000 | 0.1366 | | 0.1077 | 404000 | 0.1426 | | 0.1080 | 405000 | 0.1401 | | 0.1083 | 406000 | 0.1367 | | 0.1085 | 407000 | 0.139 | | 0.1088 | 408000 | 0.1376 | | 0.1091 | 409000 | 0.1354 | | 0.1093 | 410000 | 0.1405 | | 0.1096 | 411000 | 0.1341 | | 0.1099 | 412000 | 0.1454 | | 0.1101 | 413000 | 0.1375 | | 0.1104 | 414000 | 0.1431 | | 0.1107 | 415000 | 0.1344 | | 0.1109 | 416000 | 0.1313 | | 0.1112 | 417000 | 0.1464 | | 0.1115 | 418000 | 0.1363 | | 0.1117 | 419000 | 0.1346 | | 0.1120 | 420000 | 0.1381 | | 0.1123 | 421000 | 0.1331 | | 0.1125 | 422000 | 0.1349 | | 0.1128 | 423000 | 0.1377 | | 0.1131 | 424000 | 0.1414 | | 0.1133 | 425000 | 0.1366 | | 0.1136 | 426000 | 0.1319 | | 0.1139 | 427000 | 0.1387 | | 0.1141 | 428000 | 0.138 | | 0.1144 | 429000 | 0.1351 | | 0.1147 | 430000 | 0.1373 | | 0.1149 | 431000 | 0.131 | | 0.1152 | 432000 | 0.1302 | | 0.1155 | 433000 | 0.1317 | | 0.1157 | 434000 | 0.1332 | | 0.1160 | 435000 | 0.1344 | | 0.1163 | 436000 | 0.1425 | | 0.1165 | 437000 | 0.1276 | | 0.1168 | 438000 | 0.1314 | | 0.1171 | 439000 | 0.1238 | | 0.1173 | 440000 | 0.1291 | | 0.1176 | 441000 | 0.1311 | | 0.1179 | 442000 | 0.1222 | | 0.1181 | 443000 | 0.1311 | | 0.1184 | 444000 | 0.1423 | | 0.1187 | 445000 | 0.1308 | | 0.1189 | 446000 | 0.1317 | | 0.1192 | 447000 | 0.1369 | | 0.1195 | 448000 | 0.1282 | | 0.1197 | 449000 | 0.1376 | | 0.1200 | 450000 | 0.1253 | | 0.1203 | 451000 | 0.1271 | | 0.1205 | 452000 | 0.131 | | 0.1208 | 453000 | 0.1316 | | 0.1211 | 454000 | 0.1353 | | 0.1213 | 455000 | 0.1277 | | 0.1216 | 456000 | 0.1238 | | 0.1219 | 457000 | 0.1271 | | 0.1221 | 458000 | 0.1319 | | 0.1224 | 459000 | 0.1281 | | 0.1227 | 460000 | 0.1305 | | 0.1229 | 461000 | 0.1376 | | 0.1232 | 462000 | 0.1333 | | 0.1235 | 463000 | 0.1211 | | 0.1237 | 464000 | 0.1211 | | 0.1240 | 465000 | 0.1286 | | 0.1243 | 466000 | 0.1329 | | 0.1245 | 467000 | 0.1227 | | 0.1248 | 468000 | 0.1283 | | 0.1251 | 469000 | 0.1275 | | 0.1253 | 470000 | 0.1362 | | 0.1256 | 471000 | 0.1293 | | 0.1259 | 472000 | 0.1264 | | 0.1261 | 473000 | 0.1241 | | 0.1264 | 474000 | 0.118 | | 0.1267 | 475000 | 0.1279 | | 0.1269 | 476000 | 0.1267 | | 0.1272 | 477000 | 0.1294 | | 0.1275 | 478000 | 0.1299 | | 0.1277 | 479000 | 0.1323 | | 0.1280 | 480000 | 0.1284 | | 0.1283 | 481000 | 0.1299 | | 0.1285 | 482000 | 0.1255 | | 0.1288 | 483000 | 0.1289 | | 0.1291 | 484000 | 0.1256 | | 0.1293 | 485000 | 0.1274 | | 0.1296 | 486000 | 0.1279 | | 0.1299 | 487000 | 0.1234 | | 0.1301 | 488000 | 0.1299 | | 0.1304 | 489000 | 0.1257 | | 0.1307 | 490000 | 0.1195 | | 0.1309 | 491000 | 0.1265 | | 0.1312 | 492000 | 0.1249 | | 0.1315 | 493000 | 0.1254 | | 0.1317 | 494000 | 0.1299 | | 0.1320 | 495000 | 0.1255 | | 0.1323 | 496000 | 0.1316 | | 0.1325 | 497000 | 0.1303 | | 0.1328 | 498000 | 0.1213 | | 0.1331 | 499000 | 0.1182 | | 0.1333 | 500000 | 0.12 | | 0.1336 | 501000 | 0.1193 | | 0.1339 | 502000 | 0.1241 | | 0.1341 | 503000 | 0.1258 | | 0.1344 | 504000 | 0.1279 | | 0.1347 | 505000 | 0.1293 | | 0.1349 | 506000 | 0.1278 | | 0.1352 | 507000 | 0.1241 | | 0.1355 | 508000 | 0.1221 | | 0.1357 | 509000 | 0.1213 | | 0.1360 | 510000 | 0.1232 | | 0.1363 | 511000 | 0.1278 | | 0.1365 | 512000 | 0.1208 | | 0.1368 | 513000 | 0.1203 | | 0.1371 | 514000 | 0.1251 | | 0.1373 | 515000 | 0.1207 | | 0.1376 | 516000 | 0.1233 | | 0.1379 | 517000 | 0.1287 | | 0.1381 | 518000 | 0.1255 | | 0.1384 | 519000 | 0.1234 | | 0.1387 | 520000 | 0.1198 | | 0.1389 | 521000 | 0.1274 | | 0.1392 | 522000 | 0.1209 | | 0.1395 | 523000 | 0.116 | | 0.1397 | 524000 | 0.1154 | | 0.1400 | 525000 | 0.1197 | | 0.1403 | 526000 | 0.1249 | | 0.1405 | 527000 | 0.1127 | | 0.1408 | 528000 | 0.1221 | | 0.1411 | 529000 | 0.122 | | 0.1413 | 530000 | 0.1251 | | 0.1416 | 531000 | 0.123 | | 0.1419 | 532000 | 0.1222 | | 0.1421 | 533000 | 0.1205 | | 0.1424 | 534000 | 0.1196 | | 0.1427 | 535000 | 0.1172 | | 0.1429 | 536000 | 0.1185 | | 0.1432 | 537000 | 0.1249 | | 0.1435 | 538000 | 0.123 | | 0.1437 | 539000 | 0.1227 | | 0.1440 | 540000 | 0.1198 | | 0.1443 | 541000 | 0.1219 | | 0.1445 | 542000 | 0.1183 | | 0.1448 | 543000 | 0.1203 | | 0.1451 | 544000 | 0.117 | | 0.1453 | 545000 | 0.1157 | | 0.1456 | 546000 | 0.1175 | | 0.1459 | 547000 | 0.1178 | | 0.1461 | 548000 | 0.1155 | | 0.1464 | 549000 | 0.1233 | | 0.1467 | 550000 | 0.1127 | | 0.1469 | 551000 | 0.12 | | 0.1472 | 552000 | 0.1229 | | 0.1475 | 553000 | 0.1211 | | 0.1477 | 554000 | 0.1125 | | 0.1480 | 555000 | 0.1178 | | 0.1483 | 556000 | 0.1178 | | 0.1485 | 557000 | 0.1132 | | 0.1488 | 558000 | 0.1119 | | 0.1491 | 559000 | 0.1157 | | 0.1493 | 560000 | 0.1197 | | 0.1496 | 561000 | 0.1151 | | 0.1499 | 562000 | 0.1217 | | 0.1501 | 563000 | 0.1146 | | 0.1504 | 564000 | 0.1202 | | 0.1507 | 565000 | 0.1165 | | 0.1509 | 566000 | 0.1179 | | 0.1512 | 567000 | 0.115 | | 0.1515 | 568000 | 0.1195 | | 0.1517 | 569000 | 0.1258 | | 0.1520 | 570000 | 0.1139 | | 0.1523 | 571000 | 0.1158 | | 0.1525 | 572000 | 0.1194 | | 0.1528 | 573000 | 0.1131 | | 0.1531 | 574000 | 0.1132 | | 0.1533 | 575000 | 0.1198 | | 0.1536 | 576000 | 0.116 | | 0.1539 | 577000 | 0.1173 | | 0.1541 | 578000 | 0.1175 | | 0.1544 | 579000 | 0.1128 | | 0.1547 | 580000 | 0.1127 | | 0.1549 | 581000 | 0.1168 | | 0.1552 | 582000 | 0.1131 | | 0.1555 | 583000 | 0.1213 | | 0.1557 | 584000 | 0.1182 | | 0.1560 | 585000 | 0.1146 | | 0.1563 | 586000 | 0.1189 | | 0.1565 | 587000 | 0.1153 | | 0.1568 | 588000 | 0.1136 | | 0.1571 | 589000 | 0.1121 | | 0.1573 | 590000 | 0.1082 | | 0.1576 | 591000 | 0.1116 | | 0.1579 | 592000 | 0.113 | | 0.1581 | 593000 | 0.1148 | | 0.1584 | 594000 | 0.1085 | | 0.1587 | 595000 | 0.119 | | 0.1589 | 596000 | 0.1073 | | 0.1592 | 597000 | 0.1157 | | 0.1595 | 598000 | 0.1142 | | 0.1597 | 599000 | 0.1125 | | 0.1600 | 600000 | 0.1112 | | 0.1603 | 601000 | 0.1122 | | 0.1605 | 602000 | 0.1173 | | 0.1608 | 603000 | 0.113 | | 0.1611 | 604000 | 0.1068 | | 0.1613 | 605000 | 0.1131 | | 0.1616 | 606000 | 0.1132 | | 0.1619 | 607000 | 0.1142 | | 0.1621 | 608000 | 0.1169 | | 0.1624 | 609000 | 0.1094 | | 0.1627 | 610000 | 0.1206 | | 0.1629 | 611000 | 0.1129 | | 0.1632 | 612000 | 0.1177 | | 0.1635 | 613000 | 0.1101 | | 0.1637 | 614000 | 0.1102 | | 0.1640 | 615000 | 0.1074 | | 0.1643 | 616000 | 0.1156 | | 0.1645 | 617000 | 0.1061 | | 0.1648 | 618000 | 0.1112 | | 0.1651 | 619000 | 0.1166 | | 0.1653 | 620000 | 0.1035 | | 0.1656 | 621000 | 0.1153 | | 0.1659 | 622000 | 0.1105 | | 0.1661 | 623000 | 0.1128 | | 0.1664 | 624000 | 0.1052 | | 0.1667 | 625000 | 0.1146 | | 0.1669 | 626000 | 0.1092 | | 0.1672 | 627000 | 0.1137 | | 0.1675 | 628000 | 0.1139 | | 0.1677 | 629000 | 0.11 | | 0.1680 | 630000 | 0.1062 | | 0.1683 | 631000 | 0.1136 | | 0.1685 | 632000 | 0.1124 | | 0.1688 | 633000 | 0.1087 | | 0.1691 | 634000 | 0.1109 | | 0.1693 | 635000 | 0.1124 | | 0.1696 | 636000 | 0.1074 | | 0.1699 | 637000 | 0.106 | | 0.1701 | 638000 | 0.1102 | | 0.1704 | 639000 | 0.1127 | | 0.1707 | 640000 | 0.108 | | 0.1709 | 641000 | 0.1047 | | 0.1712 | 642000 | 0.107 | | 0.1715 | 643000 | 0.1135 | | 0.1717 | 644000 | 0.1138 | | 0.1720 | 645000 | 0.1087 | | 0.1723 | 646000 | 0.1067 | | 0.1725 | 647000 | 0.1116 | | 0.1728 | 648000 | 0.1107 | | 0.1731 | 649000 | 0.1105 | | 0.1733 | 650000 | 0.1143 | | 0.1736 | 651000 | 0.1098 | | 0.1739 | 652000 | 0.1055 | | 0.1741 | 653000 | 0.1089 | | 0.1744 | 654000 | 0.1047 | | 0.1747 | 655000 | 0.1003 | | 0.1749 | 656000 | 0.1043 | | 0.1752 | 657000 | 0.1112 | | 0.1755 | 658000 | 0.1054 | | 0.1757 | 659000 | 0.1145 | | 0.1760 | 660000 | 0.1093 | | 0.1763 | 661000 | 0.1102 | | 0.1765 | 662000 | 0.1102 | | 0.1768 | 663000 | 0.1086 | | 0.1771 | 664000 | 0.108 | | 0.1773 | 665000 | 0.1046 | | 0.1776 | 666000 | 0.1064 | | 0.1779 | 667000 | 0.1014 | | 0.1781 | 668000 | 0.1039 | | 0.1784 | 669000 | 0.1132 | | 0.1787 | 670000 | 0.1076 | | 0.1789 | 671000 | 0.1075 | | 0.1792 | 672000 | 0.1089 | | 0.1795 | 673000 | 0.1109 | | 0.1797 | 674000 | 0.1035 | | 0.1800 | 675000 | 0.105 | | 0.1803 | 676000 | 0.108 | | 0.1805 | 677000 | 0.1088 | | 0.1808 | 678000 | 0.1094 | | 0.1811 | 679000 | 0.1019 | | 0.1813 | 680000 | 0.1054 | | 0.1816 | 681000 | 0.1041 | | 0.1819 | 682000 | 0.1086 | | 0.1821 | 683000 | 0.1126 | | 0.1824 | 684000 | 0.0996 | | 0.1827 | 685000 | 0.1019 | | 0.1829 | 686000 | 0.1013 | | 0.1832 | 687000 | 0.1043 | | 0.1835 | 688000 | 0.1045 | | 0.1837 | 689000 | 0.1076 | | 0.1840 | 690000 | 0.1046 | | 0.1843 | 691000 | 0.1096 | | 0.1845 | 692000 | 0.0994 | | 0.1848 | 693000 | 0.1049 | | 0.1851 | 694000 | 0.1104 | | 0.1853 | 695000 | 0.1089 | | 0.1856 | 696000 | 0.1039 | | 0.1859 | 697000 | 0.1035 | | 0.1861 | 698000 | 0.1056 | | 0.1864 | 699000 | 0.1058 | | 0.1867 | 700000 | 0.1074 | | 0.1869 | 701000 | 0.1074 | | 0.1872 | 702000 | 0.1122 | | 0.1875 | 703000 | 0.1013 | | 0.1877 | 704000 | 0.1029 | | 0.1880 | 705000 | 0.0997 | | 0.1883 | 706000 | 0.1052 | | 0.1885 | 707000 | 0.1135 | | 0.1888 | 708000 | 0.1114 | | 0.1891 | 709000 | 0.111 | | 0.1893 | 710000 | 0.104 | | 0.1896 | 711000 | 0.1018 | | 0.1899 | 712000 | 0.1077 | | 0.1901 | 713000 | 0.103 | | 0.1904 | 714000 | 0.1083 | | 0.1907 | 715000 | 0.1042 | | 0.1909 | 716000 | 0.1078 | | 0.1912 | 717000 | 0.1014 | | 0.1915 | 718000 | 0.1022 | | 0.1917 | 719000 | 0.1023 | | 0.1920 | 720000 | 0.1041 | | 0.1923 | 721000 | 0.0982 | | 0.1925 | 722000 | 0.1094 | | 0.1928 | 723000 | 0.1085 | | 0.1931 | 724000 | 0.1033 | | 0.1933 | 725000 | 0.1042 | | 0.1936 | 726000 | 0.105 | | 0.1939 | 727000 | 0.1047 | | 0.1941 | 728000 | 0.1014 | | 0.1944 | 729000 | 0.1029 | | 0.1947 | 730000 | 0.1003 | | 0.1949 | 731000 | 0.1071 | | 0.1952 | 732000 | 0.1 | | 0.1955 | 733000 | 0.1074 | | 0.1957 | 734000 | 0.1097 | | 0.1960 | 735000 | 0.1059 | | 0.1963 | 736000 | 0.1042 | | 0.1965 | 737000 | 0.1039 | | 0.1968 | 738000 | 0.104 | | 0.1971 | 739000 | 0.1031 | | 0.1973 | 740000 | 0.1016 | | 0.1976 | 741000 | 0.1039 | | 0.1979 | 742000 | 0.1023 | | 0.1981 | 743000 | 0.0954 | | 0.1984 | 744000 | 0.1035 | | 0.1987 | 745000 | 0.102 | | 0.1989 | 746000 | 0.1081 | | 0.1992 | 747000 | 0.1083 | | 0.1995 | 748000 | 0.1049 | | 0.1997 | 749000 | 0.0957 | | 0.2000 | 750000 | 0.104 | | 0.2003 | 751000 | 0.1074 | | 0.2005 | 752000 | 0.1007 | | 0.2008 | 753000 | 0.1022 | | 0.2011 | 754000 | 0.0987 | | 0.2013 | 755000 | 0.1054 | | 0.2016 | 756000 | 0.0981 | | 0.2019 | 757000 | 0.0948 | | 0.2021 | 758000 | 0.0991 | | 0.2024 | 759000 | 0.1004 | | 0.2027 | 760000 | 0.1111 | | 0.2029 | 761000 | 0.0993 | | 0.2032 | 762000 | 0.1038 | | 0.2035 | 763000 | 0.103 | | 0.2037 | 764000 | 0.105 | | 0.2040 | 765000 | 0.1027 | | 0.2043 | 766000 | 0.0977 | | 0.2045 | 767000 | 0.1067 | | 0.2048 | 768000 | 0.1 | | 0.2051 | 769000 | 0.1039 | | 0.2053 | 770000 | 0.0986 | | 0.2056 | 771000 | 0.1035 | | 0.2059 | 772000 | 0.1013 | | 0.2061 | 773000 | 0.1006 | | 0.2064 | 774000 | 0.1056 | | 0.2067 | 775000 | 0.0997 | | 0.2069 | 776000 | 0.0976 | | 0.2072 | 777000 | 0.0957 | | 0.2075 | 778000 | 0.0996 | | 0.2077 | 779000 | 0.1043 | | 0.2080 | 780000 | 0.0936 | | 0.2083 | 781000 | 0.1004 | | 0.2085 | 782000 | 0.1002 | | 0.2088 | 783000 | 0.101 | | 0.2091 | 784000 | 0.1018 | | 0.2093 | 785000 | 0.0955 | | 0.2096 | 786000 | 0.0933 | | 0.2099 | 787000 | 0.1031 | | 0.2101 | 788000 | 0.1016 | | 0.2104 | 789000 | 0.0948 | | 0.2107 | 790000 | 0.1 | | 0.2109 | 791000 | 0.1032 | | 0.2112 | 792000 | 0.0992 | | 0.2115 | 793000 | 0.098 | | 0.2117 | 794000 | 0.0935 | | 0.2120 | 795000 | 0.0975 | | 0.2123 | 796000 | 0.101 | | 0.2125 | 797000 | 0.0968 | | 0.2128 | 798000 | 0.0955 | | 0.2131 | 799000 | 0.0987 | | 0.2133 | 800000 | 0.0991 | | 0.2136 | 801000 | 0.0949 | | 0.2139 | 802000 | 0.0899 | | 0.2141 | 803000 | 0.1008 | | 0.2144 | 804000 | 0.0943 | | 0.2147 | 805000 | 0.1011 | | 0.2149 | 806000 | 0.0978 | | 0.2152 | 807000 | 0.1021 | | 0.2155 | 808000 | 0.0967 | | 0.2157 | 809000 | 0.0989 | | 0.2160 | 810000 | 0.1007 | | 0.2163 | 811000 | 0.0965 | | 0.2165 | 812000 | 0.0983 | | 0.2168 | 813000 | 0.0965 | | 0.2171 | 814000 | 0.095 | | 0.2173 | 815000 | 0.1011 | | 0.2176 | 816000 | 0.0987 | | 0.2179 | 817000 | 0.0999 | | 0.2181 | 818000 | 0.0952 | | 0.2184 | 819000 | 0.094 | | 0.2187 | 820000 | 0.0981 | | 0.2189 | 821000 | 0.0937 | | 0.2192 | 822000 | 0.0962 | | 0.2195 | 823000 | 0.096 | | 0.2197 | 824000 | 0.091 | | 0.2200 | 825000 | 0.0973 | | 0.2203 | 826000 | 0.0993 | | 0.2205 | 827000 | 0.104 | | 0.2208 | 828000 | 0.0964 | | 0.2211 | 829000 | 0.1015 | | 0.2213 | 830000 | 0.0903 | | 0.2216 | 831000 | 0.0967 | | 0.2219 | 832000 | 0.1029 | | 0.2221 | 833000 | 0.0936 | | 0.2224 | 834000 | 0.0993 | | 0.2227 | 835000 | 0.0864 | | 0.2229 | 836000 | 0.0954 | | 0.2232 | 837000 | 0.0972 | | 0.2235 | 838000 | 0.0974 | | 0.2237 | 839000 | 0.0986 | | 0.2240 | 840000 | 0.0947 | | 0.2243 | 841000 | 0.0999 | | 0.2245 | 842000 | 0.0975 | | 0.2248 | 843000 | 0.0955 | | 0.2251 | 844000 | 0.0968 | | 0.2253 | 845000 | 0.0894 | | 0.2256 | 846000 | 0.096 | | 0.2259 | 847000 | 0.101 | | 0.2261 | 848000 | 0.094 | | 0.2264 | 849000 | 0.0937 | | 0.2267 | 850000 | 0.1052 | | 0.2269 | 851000 | 0.0888 | | 0.2272 | 852000 | 0.0898 | | 0.2275 | 853000 | 0.0908 | | 0.2277 | 854000 | 0.0963 | | 0.2280 | 855000 | 0.0971 | | 0.2283 | 856000 | 0.0968 | | 0.2285 | 857000 | 0.0978 | | 0.2288 | 858000 | 0.0946 | | 0.2291 | 859000 | 0.1004 | | 0.2293 | 860000 | 0.0923 | | 0.2296 | 861000 | 0.0929 | | 0.2299 | 862000 | 0.0952 | | 0.2301 | 863000 | 0.0948 | | 0.2304 | 864000 | 0.0936 | | 0.2307 | 865000 | 0.092 | | 0.2309 | 866000 | 0.0894 | | 0.2312 | 867000 | 0.0922 | | 0.2315 | 868000 | 0.0946 | | 0.2317 | 869000 | 0.0967 | | 0.2320 | 870000 | 0.0965 | | 0.2323 | 871000 | 0.0966 | | 0.2325 | 872000 | 0.0927 | | 0.2328 | 873000 | 0.0931 | | 0.2331 | 874000 | 0.0901 | | 0.2333 | 875000 | 0.0929 | | 0.2336 | 876000 | 0.096 | | 0.2339 | 877000 | 0.0912 | | 0.2341 | 878000 | 0.0915 | | 0.2344 | 879000 | 0.095 | | 0.2347 | 880000 | 0.0938 | | 0.2349 | 881000 | 0.0987 | | 0.2352 | 882000 | 0.0955 | | 0.2355 | 883000 | 0.091 | | 0.2357 | 884000 | 0.0909 | | 0.2360 | 885000 | 0.094 | | 0.2363 | 886000 | 0.095 | | 0.2365 | 887000 | 0.0923 | | 0.2368 | 888000 | 0.0986 | | 0.2371 | 889000 | 0.0945 | | 0.2373 | 890000 | 0.0951 | | 0.2376 | 891000 | 0.0922 | | 0.2379 | 892000 | 0.0896 | | 0.2381 | 893000 | 0.095 | | 0.2384 | 894000 | 0.0915 | | 0.2387 | 895000 | 0.0907 | | 0.2389 | 896000 | 0.0917 | | 0.2392 | 897000 | 0.091 | | 0.2395 | 898000 | 0.093 | | 0.2397 | 899000 | 0.0993 | | 0.2400 | 900000 | 0.0988 | | 0.2403 | 901000 | 0.093 | | 0.2405 | 902000 | 0.0905 | | 0.2408 | 903000 | 0.0968 | | 0.2411 | 904000 | 0.0918 | | 0.2413 | 905000 | 0.0937 | | 0.2416 | 906000 | 0.0971 | | 0.2419 | 907000 | 0.0896 | | 0.2421 | 908000 | 0.0936 | | 0.2424 | 909000 | 0.0923 | | 0.2427 | 910000 | 0.0959 | | 0.2429 | 911000 | 0.0901 | | 0.2432 | 912000 | 0.0937 | | 0.2435 | 913000 | 0.0968 | | 0.2437 | 914000 | 0.0889 | | 0.2440 | 915000 | 0.0921 | | 0.2443 | 916000 | 0.0945 | | 0.2445 | 917000 | 0.088 | | 0.2448 | 918000 | 0.0916 | | 0.2451 | 919000 | 0.0975 | | 0.2453 | 920000 | 0.085 | | 0.2456 | 921000 | 0.0903 | | 0.2459 | 922000 | 0.0988 | | 0.2461 | 923000 | 0.0846 | | 0.2464 | 924000 | 0.0937 | | 0.2467 | 925000 | 0.0951 | | 0.2469 | 926000 | 0.092 | | 0.2472 | 927000 | 0.0989 | | 0.2475 | 928000 | 0.0835 | | 0.2477 | 929000 | 0.0925 | | 0.2480 | 930000 | 0.0953 | | 0.2483 | 931000 | 0.0885 | | 0.2485 | 932000 | 0.0887 | | 0.2488 | 933000 | 0.0868 | | 0.2491 | 934000 | 0.0882 | | 0.2493 | 935000 | 0.0933 | | 0.2496 | 936000 | 0.0896 | | 0.2499 | 937000 | 0.0917 | </details> ### Framework Versions - Python: 3.12.2 - Sentence Transformers: 3.2.1 - Transformers: 4.44.2 - PyTorch: 2.5.0 - Accelerate: 1.0.1 - Datasets: 3.0.2 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### CustomTripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION" ]
[ "PCR" ]
BioNLP
RomainDarous/large_directOneEpoch_additivePooling_randomInit_mistranslationModel
RomainDarous
sentence-similarity
[ "sentence-transformers", "safetensors", "xlm-roberta", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:4460010", "loss:CoSENTLoss", "dataset:RomainDarous/corrupted_os_by_language", "arxiv:1908.10084", "base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-multilingual-mpnet-base-v2", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,739
1,739
25
0
--- base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 datasets: - RomainDarous/corrupted_os_by_language library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:4460010 - loss:CoSENTLoss widget: - source_sentence: Malformed target specific variable definition sentences: - Hedefe özgü değişken tanımı bozuk - Kan alle data in die gids lees - "слава Украине! героям слава!\uFEFF" - source_sentence: Can't write an inode bitmap sentences: - Skontrolujte stav aktualizácií alebo to skúste znova neskôr. - Malsukcesis skribi i nodan bitmapon - Zastępuje wersję GL obsługiwaną przez sterownik - source_sentence: Optimize soft proofing color transformations sentences: - 'arkadaslar biz artik her an kirmizi kart yiyecek,bencil,pas yapamayan,isabetsiz orta yapani istemiyoruz. sozde efsaneniz bu sezon Besiktasa en cok zarar verenlerden biriydi. kendini dusunmeden once Besiktasi dusunecek adam lazim bize. o yuzden #GoHomeQuaresma' - Yav bizim dedikodusunu yaptığımız insanın bile bi vizyonu var. Senin hakkında neden oturup konuşalım? - Ik ben een transgender. - source_sentence: 'Pass 1: Checking @is, @bs, and sizes' sentences: - Bu adam cidden kurabiye gibi ben bunu çayın yanında yerim - sagnat. errada. invisible. justificació. idioma - Wilt u echt de primaire sleutel verplaatsen? (j N) - source_sentence: Search for matching log entries sentences: - quem te lembra? caralho tô assustada aqui kkkkk - sendotasunik gabeko\ egoera bistaratuko den ala ez adierazten du - En aquest cas, hem d'incloure les imatges del contenidor )sr iov per a càrregues de treball de telco (per exemple, com a referència, es podrien obtenir des de valors de helm chart) model-index: - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts eval type: sts-eval metrics: - type: pearson_cosine value: 0.9776864132331542 name: Pearson Cosine - type: spearman_cosine value: 0.8655550009784482 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.9777097765409098 name: Pearson Cosine - type: spearman_cosine value: 0.8655731390530881 name: Spearman Cosine --- # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the [corrupted_open_os_by_language](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 --> - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [corrupted_open_os_by_language](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language) <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): MultiHeadGeneralizedPooling( (P): ModuleList( (0-7): 8 x Linear(in_features=768, out_features=96, bias=True) ) (W1): ModuleList( (0-7): 8 x Linear(in_features=96, out_features=384, bias=True) ) (W2): ModuleList( (0-7): 8 x Linear(in_features=384, out_features=96, bias=True) ) ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("RomainDarous/large_directOneEpoch_additivePooling_randomInit_mistranslationModel") # Run inference sentences = [ 'Search for matching log entries', 'quem te lembra? caralho tô assustada aqui kkkkk', 'sendotasunik gabeko\\ egoera bistaratuko den ala ez adierazten du', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> ## Evaluation ### Metrics #### Semantic Similarity * Datasets: `sts-eval` and `sts-test` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | sts-eval | sts-test | |:--------------------|:-----------|:-----------| | pearson_cosine | 0.9777 | 0.9777 | | **spearman_cosine** | **0.8656** | **0.8656** | <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### corrupted_open_os_by_language * Dataset: [corrupted_open_os_by_language](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language) at [9d25780](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language/tree/9d25780e2032b1e8f06af6a4ff55124d7a930c3c) * Size: 4,460,010 training samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 6 tokens</li><li>mean: 18.33 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.47 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~50.60%</li><li>1: ~49.40%</li></ul> | * Samples: | sentence1 | sentence2 | score | |:--------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:---------------| | <code>Check spelling. Print the document. Show completion window. General. Show help</code> | <code>Kontrolli õigekirja. присоединяюсь. </code> | <code>0</code> | | <code>EXIF not supported for this file format.</code> | <code>Šiam failo formatui EXIF nepalaikomas.</code> | <code>1</code> | | <code>This package includes the documentation for texlive everyhook</code> | <code>Paket ini menyertakan dokumentasi untuk texlive everyhook</code> | <code>1</code> | * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Evaluation Dataset #### corrupted_open_os_by_language * Dataset: [corrupted_open_os_by_language](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language) at [9d25780](https://huggingface.co/datasets/RomainDarous/corrupted_os_by_language/tree/9d25780e2032b1e8f06af6a4ff55124d7a930c3c) * Size: 4,460,010 evaluation samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 5 tokens</li><li>mean: 17.71 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 26.95 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>0: ~50.60%</li><li>1: ~49.40%</li></ul> | * Samples: | sentence1 | sentence2 | score | |:----------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | <code>Could not identify the current seat.</code> | <code> 天天花着男人的钱还这这创造新词汇男权你可真牛批,你也就这一出了一问男权，就说是我是吧,到现在我也没听到你给我们讲的男权,你也就是在网上喷喷，现实走道都不敢探头自卑，你现实要把你女权的劲拿出来总低啥头,您老应该去国家教育局把男权加上是吧,你们女权天天说自己生活不好没地位，给你们地位了你们能干啥？用你们的女权打到全世界男性是吧,能相出男权这一词您老也是人才呀，是不是庆幸自己是个女的,活在自己想想的世界里不觉得孤单吗，假象有男权是吧,自己假象和男权还说自己不是田园女权,田园女权能连自己都骂说自己妈是驴爸是大鼎的也是奇葩呀,那我们国家大肆宣扬过你们这么田园女权吗，国家要的是女性人群自主自理，你们可好看看你们女权干的啥事,给你们女权地位高了，看看你们女权干的事n绿地集团高管怎么都不说呀，人家可是有钱有地位,也不是我们说三从四德洗衣做饭你们女权会吗？,那我问问你们女权干过啥惊天大事,还甩锅给孔子，还封建社会，那我问问你们女权在福利面前为啥说自己是女性呀不是社会主义社会吗不应该男女平等吗,天天自己也不知道是不是抱个手机天天欧巴欧巴，你家那位要是不陪你看一会就会问你是不是不爱我了是吧大姐,您老也就赚这白菜钱操心国家事,中国五千年的历史被您老一句否决，还嘲讽人家日本女性，好意思说自己不是女权，三从四德流传这么久到您这变成日本文化了,我就想问问男权您老是怎么想的,那你问孔子老人家呗为什么女人要三从四德,我说的是女权你干嘛自己对号入座,连中华人民传承的东西都不认跟我这谈男权,还男权您老给我举个例子呗，让我们男权听听都是h啥,这些不都是你们女权的标准吗？,还男权，您老醒醒吧这里是现实，不是你的公主世界，总觉得自己多么多么重要，地球没你是不能转了还是人类要灭亡呀,我真的想问一句你给我找一条男权的新闻,咋了我们男人不能提女权呗你老授权了呗,那我们谈论田园女权你老对号入座干嘛,天天过节要礼物，还嫌弃自己男朋友没有钱，我寻思你找个有钱人包养你呗，对了有钱人怎么可能看上你这种女权的呢,还要孩子跟女方姓我也没看见你没跟你妈姓呀,年年过节男人给你们送礼物你们女人给男人送过礼物吗？，一问我不是陪着他吗我对他说我爱你了这不是最好的礼物吗？，男人只要不送礼物就是不爱你们了呗,人家国际女权讲的男人能做的我们女人也能做，田园女权男人能做的我们女人为啥要做,还男权我笑了，以前结婚几头牛换个衣服原装的，现在几十万彩...</code> | <code>0</code> | | <code>Undoing Date and Time Adjustment</code> | <code>正在取消日期和时间调整</code> | <code>1</code> | | <code>Dependency package for gsl_2_6 gnu hpc</code> | <code>Pacotes de desenvolvimento do KDE</code> | <code>1</code> | * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | corrupted open os by language loss | sts-eval_spearman_cosine | sts-test_spearman_cosine | |:-----:|:-----:|:-------------:|:----------------------------------:|:------------------------:|:------------------------:| | 1.0 | 55751 | 0.8632 | 0.3088 | 0.8656 | - | | -1 | -1 | - | - | - | 0.8656 | ### Framework Versions - Python: 3.10.13 - Sentence Transformers: 3.4.1 - Transformers: 4.48.2 - PyTorch: 2.1.2+cu121 - Accelerate: 1.3.0 - Datasets: 2.16.1 - Tokenizers: 0.21.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION", "SEMANTIC_SIMILARITY", "TRANSLATION" ]
[ "CAS" ]
Non_BioNLP
RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-4bits
RichardErkhov
null
[ "safetensors", "qwen2", "custom_code", "arxiv:2308.03281", "4-bit", "bitsandbytes", "region:us" ]
1,731
1,731
4
0
--- {} --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) gte-Qwen2-7B-instruct - bnb 4bits - Model creator: https://huggingface.co/Alibaba-NLP/ - Original model: https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/ Original model description: --- tags: - mteb - sentence-transformers - transformers - Qwen2 - sentence-similarity license: apache-2.0 model-index: - name: gte-qwen2-7B-instruct results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 91.31343283582089 - type: ap value: 67.64251402604096 - type: f1 value: 87.53372530755692 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 97.497825 - type: ap value: 96.30329547047529 - type: f1 value: 97.49769793778039 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 62.564 - type: f1 value: 60.975777935041066 - task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 36.486000000000004 - type: map_at_10 value: 54.842 - type: map_at_100 value: 55.206999999999994 - type: map_at_1000 value: 55.206999999999994 - type: map_at_3 value: 49.893 - type: map_at_5 value: 53.105000000000004 - type: mrr_at_1 value: 37.34 - type: mrr_at_10 value: 55.143 - type: mrr_at_100 value: 55.509 - type: mrr_at_1000 value: 55.509 - type: mrr_at_3 value: 50.212999999999994 - type: mrr_at_5 value: 53.432 - type: ndcg_at_1 value: 36.486000000000004 - type: ndcg_at_10 value: 64.273 - type: ndcg_at_100 value: 65.66199999999999 - type: ndcg_at_1000 value: 65.66199999999999 - type: ndcg_at_3 value: 54.352999999999994 - type: ndcg_at_5 value: 60.131 - type: precision_at_1 value: 36.486000000000004 - type: precision_at_10 value: 9.395000000000001 - type: precision_at_100 value: 0.996 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 22.428 - type: precision_at_5 value: 16.259 - type: recall_at_1 value: 36.486000000000004 - type: recall_at_10 value: 93.95400000000001 - type: recall_at_100 value: 99.644 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 67.283 - type: recall_at_5 value: 81.294 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 56.461169803700564 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 51.73600434466286 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 67.57827065898053 - type: mrr value: 79.08136569493911 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 83.53324575999243 - type: cos_sim_spearman value: 81.37173362822374 - type: euclidean_pearson value: 82.19243335103444 - type: euclidean_spearman value: 81.33679307304334 - type: manhattan_pearson value: 82.38752665975699 - type: manhattan_spearman value: 81.31510583189689 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 87.56818181818181 - type: f1 value: 87.25826722019875 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 50.09239610327673 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 46.64733054606282 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 33.997 - type: map_at_10 value: 48.176 - type: map_at_100 value: 49.82 - type: map_at_1000 value: 49.924 - type: map_at_3 value: 43.626 - type: map_at_5 value: 46.275 - type: mrr_at_1 value: 42.059999999999995 - type: mrr_at_10 value: 53.726 - type: mrr_at_100 value: 54.398 - type: mrr_at_1000 value: 54.416 - type: mrr_at_3 value: 50.714999999999996 - type: mrr_at_5 value: 52.639 - type: ndcg_at_1 value: 42.059999999999995 - type: ndcg_at_10 value: 55.574999999999996 - type: ndcg_at_100 value: 60.744 - type: ndcg_at_1000 value: 61.85699999999999 - type: ndcg_at_3 value: 49.363 - type: ndcg_at_5 value: 52.44 - type: precision_at_1 value: 42.059999999999995 - type: precision_at_10 value: 11.101999999999999 - type: precision_at_100 value: 1.73 - type: precision_at_1000 value: 0.218 - type: precision_at_3 value: 24.464 - type: precision_at_5 value: 18.026 - type: recall_at_1 value: 33.997 - type: recall_at_10 value: 70.35900000000001 - type: recall_at_100 value: 91.642 - type: recall_at_1000 value: 97.977 - type: recall_at_3 value: 52.76 - type: recall_at_5 value: 61.148 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 35.884 - type: map_at_10 value: 48.14 - type: map_at_100 value: 49.5 - type: map_at_1000 value: 49.63 - type: map_at_3 value: 44.646 - type: map_at_5 value: 46.617999999999995 - type: mrr_at_1 value: 44.458999999999996 - type: mrr_at_10 value: 53.751000000000005 - type: mrr_at_100 value: 54.37800000000001 - type: mrr_at_1000 value: 54.415 - type: mrr_at_3 value: 51.815 - type: mrr_at_5 value: 52.882 - type: ndcg_at_1 value: 44.458999999999996 - type: ndcg_at_10 value: 54.157 - type: ndcg_at_100 value: 58.362 - type: ndcg_at_1000 value: 60.178 - type: ndcg_at_3 value: 49.661 - type: ndcg_at_5 value: 51.74999999999999 - type: precision_at_1 value: 44.458999999999996 - type: precision_at_10 value: 10.248 - type: precision_at_100 value: 1.5890000000000002 - type: precision_at_1000 value: 0.207 - type: precision_at_3 value: 23.928 - type: precision_at_5 value: 16.878999999999998 - type: recall_at_1 value: 35.884 - type: recall_at_10 value: 64.798 - type: recall_at_100 value: 82.345 - type: recall_at_1000 value: 93.267 - type: recall_at_3 value: 51.847 - type: recall_at_5 value: 57.601 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 39.383 - type: map_at_10 value: 53.714 - type: map_at_100 value: 54.838 - type: map_at_1000 value: 54.87800000000001 - type: map_at_3 value: 50.114999999999995 - type: map_at_5 value: 52.153000000000006 - type: mrr_at_1 value: 45.016 - type: mrr_at_10 value: 56.732000000000006 - type: mrr_at_100 value: 57.411 - type: mrr_at_1000 value: 57.431 - type: mrr_at_3 value: 54.044000000000004 - type: mrr_at_5 value: 55.639 - type: ndcg_at_1 value: 45.016 - type: ndcg_at_10 value: 60.228 - type: ndcg_at_100 value: 64.277 - type: ndcg_at_1000 value: 65.07 - type: ndcg_at_3 value: 54.124 - type: ndcg_at_5 value: 57.147000000000006 - type: precision_at_1 value: 45.016 - type: precision_at_10 value: 9.937 - type: precision_at_100 value: 1.288 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 24.471999999999998 - type: precision_at_5 value: 16.991 - type: recall_at_1 value: 39.383 - type: recall_at_10 value: 76.175 - type: recall_at_100 value: 93.02 - type: recall_at_1000 value: 98.60900000000001 - type: recall_at_3 value: 60.265 - type: recall_at_5 value: 67.46600000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 27.426000000000002 - type: map_at_10 value: 37.397000000000006 - type: map_at_100 value: 38.61 - type: map_at_1000 value: 38.678000000000004 - type: map_at_3 value: 34.150999999999996 - type: map_at_5 value: 36.137 - type: mrr_at_1 value: 29.944 - type: mrr_at_10 value: 39.654 - type: mrr_at_100 value: 40.638000000000005 - type: mrr_at_1000 value: 40.691 - type: mrr_at_3 value: 36.817 - type: mrr_at_5 value: 38.524 - type: ndcg_at_1 value: 29.944 - type: ndcg_at_10 value: 43.094 - type: ndcg_at_100 value: 48.789 - type: ndcg_at_1000 value: 50.339999999999996 - type: ndcg_at_3 value: 36.984 - type: ndcg_at_5 value: 40.248 - type: precision_at_1 value: 29.944 - type: precision_at_10 value: 6.78 - type: precision_at_100 value: 1.024 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 15.895000000000001 - type: precision_at_5 value: 11.39 - type: recall_at_1 value: 27.426000000000002 - type: recall_at_10 value: 58.464000000000006 - type: recall_at_100 value: 84.193 - type: recall_at_1000 value: 95.52000000000001 - type: recall_at_3 value: 42.172 - type: recall_at_5 value: 50.101 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 19.721 - type: map_at_10 value: 31.604 - type: map_at_100 value: 32.972 - type: map_at_1000 value: 33.077 - type: map_at_3 value: 27.218999999999998 - type: map_at_5 value: 29.53 - type: mrr_at_1 value: 25.0 - type: mrr_at_10 value: 35.843 - type: mrr_at_100 value: 36.785000000000004 - type: mrr_at_1000 value: 36.842000000000006 - type: mrr_at_3 value: 32.193 - type: mrr_at_5 value: 34.264 - type: ndcg_at_1 value: 25.0 - type: ndcg_at_10 value: 38.606 - type: ndcg_at_100 value: 44.272 - type: ndcg_at_1000 value: 46.527 - type: ndcg_at_3 value: 30.985000000000003 - type: ndcg_at_5 value: 34.43 - type: precision_at_1 value: 25.0 - type: precision_at_10 value: 7.811 - type: precision_at_100 value: 1.203 - type: precision_at_1000 value: 0.15 - type: precision_at_3 value: 15.423 - type: precision_at_5 value: 11.791 - type: recall_at_1 value: 19.721 - type: recall_at_10 value: 55.625 - type: recall_at_100 value: 79.34400000000001 - type: recall_at_1000 value: 95.208 - type: recall_at_3 value: 35.19 - type: recall_at_5 value: 43.626 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 33.784 - type: map_at_10 value: 47.522 - type: map_at_100 value: 48.949999999999996 - type: map_at_1000 value: 49.038 - type: map_at_3 value: 43.284 - type: map_at_5 value: 45.629 - type: mrr_at_1 value: 41.482 - type: mrr_at_10 value: 52.830999999999996 - type: mrr_at_100 value: 53.559999999999995 - type: mrr_at_1000 value: 53.588 - type: mrr_at_3 value: 50.016000000000005 - type: mrr_at_5 value: 51.614000000000004 - type: ndcg_at_1 value: 41.482 - type: ndcg_at_10 value: 54.569 - type: ndcg_at_100 value: 59.675999999999995 - type: ndcg_at_1000 value: 60.989000000000004 - type: ndcg_at_3 value: 48.187000000000005 - type: ndcg_at_5 value: 51.183 - type: precision_at_1 value: 41.482 - type: precision_at_10 value: 10.221 - type: precision_at_100 value: 1.486 - type: precision_at_1000 value: 0.17500000000000002 - type: precision_at_3 value: 23.548 - type: precision_at_5 value: 16.805 - type: recall_at_1 value: 33.784 - type: recall_at_10 value: 69.798 - type: recall_at_100 value: 90.098 - type: recall_at_1000 value: 98.176 - type: recall_at_3 value: 52.127 - type: recall_at_5 value: 59.861 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 28.038999999999998 - type: map_at_10 value: 41.904 - type: map_at_100 value: 43.36 - type: map_at_1000 value: 43.453 - type: map_at_3 value: 37.785999999999994 - type: map_at_5 value: 40.105000000000004 - type: mrr_at_1 value: 35.046 - type: mrr_at_10 value: 46.926 - type: mrr_at_100 value: 47.815000000000005 - type: mrr_at_1000 value: 47.849000000000004 - type: mrr_at_3 value: 44.273 - type: mrr_at_5 value: 45.774 - type: ndcg_at_1 value: 35.046 - type: ndcg_at_10 value: 48.937000000000005 - type: ndcg_at_100 value: 54.544000000000004 - type: ndcg_at_1000 value: 56.069 - type: ndcg_at_3 value: 42.858000000000004 - type: ndcg_at_5 value: 45.644 - type: precision_at_1 value: 35.046 - type: precision_at_10 value: 9.452 - type: precision_at_100 value: 1.429 - type: precision_at_1000 value: 0.173 - type: precision_at_3 value: 21.346999999999998 - type: precision_at_5 value: 15.342 - type: recall_at_1 value: 28.038999999999998 - type: recall_at_10 value: 64.59700000000001 - type: recall_at_100 value: 87.735 - type: recall_at_1000 value: 97.41300000000001 - type: recall_at_3 value: 47.368 - type: recall_at_5 value: 54.93900000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 28.17291666666667 - type: map_at_10 value: 40.025749999999995 - type: map_at_100 value: 41.39208333333333 - type: map_at_1000 value: 41.499249999999996 - type: map_at_3 value: 36.347 - type: map_at_5 value: 38.41391666666667 - type: mrr_at_1 value: 33.65925 - type: mrr_at_10 value: 44.085499999999996 - type: mrr_at_100 value: 44.94116666666667 - type: mrr_at_1000 value: 44.9855 - type: mrr_at_3 value: 41.2815 - type: mrr_at_5 value: 42.91491666666666 - type: ndcg_at_1 value: 33.65925 - type: ndcg_at_10 value: 46.430833333333325 - type: ndcg_at_100 value: 51.761 - type: ndcg_at_1000 value: 53.50899999999999 - type: ndcg_at_3 value: 40.45133333333333 - type: ndcg_at_5 value: 43.31483333333334 - type: precision_at_1 value: 33.65925 - type: precision_at_10 value: 8.4995 - type: precision_at_100 value: 1.3210000000000004 - type: precision_at_1000 value: 0.16591666666666666 - type: precision_at_3 value: 19.165083333333335 - type: precision_at_5 value: 13.81816666666667 - type: recall_at_1 value: 28.17291666666667 - type: recall_at_10 value: 61.12624999999999 - type: recall_at_100 value: 83.97266666666667 - type: recall_at_1000 value: 95.66550000000001 - type: recall_at_3 value: 44.661249999999995 - type: recall_at_5 value: 51.983333333333334 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 24.681 - type: map_at_10 value: 34.892 - type: map_at_100 value: 35.996 - type: map_at_1000 value: 36.083 - type: map_at_3 value: 31.491999999999997 - type: map_at_5 value: 33.632 - type: mrr_at_1 value: 28.528 - type: mrr_at_10 value: 37.694 - type: mrr_at_100 value: 38.613 - type: mrr_at_1000 value: 38.668 - type: mrr_at_3 value: 34.714 - type: mrr_at_5 value: 36.616 - type: ndcg_at_1 value: 28.528 - type: ndcg_at_10 value: 40.703 - type: ndcg_at_100 value: 45.993 - type: ndcg_at_1000 value: 47.847 - type: ndcg_at_3 value: 34.622 - type: ndcg_at_5 value: 38.035999999999994 - type: precision_at_1 value: 28.528 - type: precision_at_10 value: 6.902 - type: precision_at_100 value: 1.0370000000000001 - type: precision_at_1000 value: 0.126 - type: precision_at_3 value: 15.798000000000002 - type: precision_at_5 value: 11.655999999999999 - type: recall_at_1 value: 24.681 - type: recall_at_10 value: 55.81 - type: recall_at_100 value: 79.785 - type: recall_at_1000 value: 92.959 - type: recall_at_3 value: 39.074 - type: recall_at_5 value: 47.568 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 18.627 - type: map_at_10 value: 27.872000000000003 - type: map_at_100 value: 29.237999999999996 - type: map_at_1000 value: 29.363 - type: map_at_3 value: 24.751 - type: map_at_5 value: 26.521 - type: mrr_at_1 value: 23.021 - type: mrr_at_10 value: 31.924000000000003 - type: mrr_at_100 value: 32.922000000000004 - type: mrr_at_1000 value: 32.988 - type: mrr_at_3 value: 29.192 - type: mrr_at_5 value: 30.798 - type: ndcg_at_1 value: 23.021 - type: ndcg_at_10 value: 33.535 - type: ndcg_at_100 value: 39.732 - type: ndcg_at_1000 value: 42.201 - type: ndcg_at_3 value: 28.153 - type: ndcg_at_5 value: 30.746000000000002 - type: precision_at_1 value: 23.021 - type: precision_at_10 value: 6.459 - type: precision_at_100 value: 1.1320000000000001 - type: precision_at_1000 value: 0.153 - type: precision_at_3 value: 13.719000000000001 - type: precision_at_5 value: 10.193000000000001 - type: recall_at_1 value: 18.627 - type: recall_at_10 value: 46.463 - type: recall_at_100 value: 74.226 - type: recall_at_1000 value: 91.28500000000001 - type: recall_at_3 value: 31.357000000000003 - type: recall_at_5 value: 38.067 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 31.457 - type: map_at_10 value: 42.888 - type: map_at_100 value: 44.24 - type: map_at_1000 value: 44.327 - type: map_at_3 value: 39.588 - type: map_at_5 value: 41.423 - type: mrr_at_1 value: 37.126999999999995 - type: mrr_at_10 value: 47.083000000000006 - type: mrr_at_100 value: 47.997 - type: mrr_at_1000 value: 48.044 - type: mrr_at_3 value: 44.574000000000005 - type: mrr_at_5 value: 46.202 - type: ndcg_at_1 value: 37.126999999999995 - type: ndcg_at_10 value: 48.833 - type: ndcg_at_100 value: 54.327000000000005 - type: ndcg_at_1000 value: 56.011 - type: ndcg_at_3 value: 43.541999999999994 - type: ndcg_at_5 value: 46.127 - type: precision_at_1 value: 37.126999999999995 - type: precision_at_10 value: 8.376999999999999 - type: precision_at_100 value: 1.2309999999999999 - type: precision_at_1000 value: 0.146 - type: precision_at_3 value: 20.211000000000002 - type: precision_at_5 value: 14.16 - type: recall_at_1 value: 31.457 - type: recall_at_10 value: 62.369 - type: recall_at_100 value: 85.444 - type: recall_at_1000 value: 96.65599999999999 - type: recall_at_3 value: 47.961 - type: recall_at_5 value: 54.676 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 27.139999999999997 - type: map_at_10 value: 38.801 - type: map_at_100 value: 40.549 - type: map_at_1000 value: 40.802 - type: map_at_3 value: 35.05 - type: map_at_5 value: 36.884 - type: mrr_at_1 value: 33.004 - type: mrr_at_10 value: 43.864 - type: mrr_at_100 value: 44.667 - type: mrr_at_1000 value: 44.717 - type: mrr_at_3 value: 40.777 - type: mrr_at_5 value: 42.319 - type: ndcg_at_1 value: 33.004 - type: ndcg_at_10 value: 46.022 - type: ndcg_at_100 value: 51.542 - type: ndcg_at_1000 value: 53.742000000000004 - type: ndcg_at_3 value: 39.795 - type: ndcg_at_5 value: 42.272 - type: precision_at_1 value: 33.004 - type: precision_at_10 value: 9.012 - type: precision_at_100 value: 1.7770000000000001 - type: precision_at_1000 value: 0.26 - type: precision_at_3 value: 19.038 - type: precision_at_5 value: 13.675999999999998 - type: recall_at_1 value: 27.139999999999997 - type: recall_at_10 value: 60.961 - type: recall_at_100 value: 84.451 - type: recall_at_1000 value: 98.113 - type: recall_at_3 value: 43.001 - type: recall_at_5 value: 49.896 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 17.936 - type: map_at_10 value: 27.399 - type: map_at_100 value: 28.632 - type: map_at_1000 value: 28.738000000000003 - type: map_at_3 value: 24.456 - type: map_at_5 value: 26.06 - type: mrr_at_1 value: 19.224 - type: mrr_at_10 value: 28.998 - type: mrr_at_100 value: 30.11 - type: mrr_at_1000 value: 30.177 - type: mrr_at_3 value: 26.247999999999998 - type: mrr_at_5 value: 27.708 - type: ndcg_at_1 value: 19.224 - type: ndcg_at_10 value: 32.911 - type: ndcg_at_100 value: 38.873999999999995 - type: ndcg_at_1000 value: 41.277 - type: ndcg_at_3 value: 27.142 - type: ndcg_at_5 value: 29.755 - type: precision_at_1 value: 19.224 - type: precision_at_10 value: 5.6930000000000005 - type: precision_at_100 value: 0.9259999999999999 - type: precision_at_1000 value: 0.126 - type: precision_at_3 value: 12.138 - type: precision_at_5 value: 8.909 - type: recall_at_1 value: 17.936 - type: recall_at_10 value: 48.096 - type: recall_at_100 value: 75.389 - type: recall_at_1000 value: 92.803 - type: recall_at_3 value: 32.812999999999995 - type: recall_at_5 value: 38.851 - task: type: Retrieval dataset: type: mteb/climate-fever name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 22.076999999999998 - type: map_at_10 value: 35.44 - type: map_at_100 value: 37.651 - type: map_at_1000 value: 37.824999999999996 - type: map_at_3 value: 30.764999999999997 - type: map_at_5 value: 33.26 - type: mrr_at_1 value: 50.163000000000004 - type: mrr_at_10 value: 61.207 - type: mrr_at_100 value: 61.675000000000004 - type: mrr_at_1000 value: 61.692 - type: mrr_at_3 value: 58.60999999999999 - type: mrr_at_5 value: 60.307 - type: ndcg_at_1 value: 50.163000000000004 - type: ndcg_at_10 value: 45.882 - type: ndcg_at_100 value: 53.239999999999995 - type: ndcg_at_1000 value: 55.852000000000004 - type: ndcg_at_3 value: 40.514 - type: ndcg_at_5 value: 42.038 - type: precision_at_1 value: 50.163000000000004 - type: precision_at_10 value: 13.466000000000001 - type: precision_at_100 value: 2.164 - type: precision_at_1000 value: 0.266 - type: precision_at_3 value: 29.707 - type: precision_at_5 value: 21.694 - type: recall_at_1 value: 22.076999999999998 - type: recall_at_10 value: 50.193 - type: recall_at_100 value: 74.993 - type: recall_at_1000 value: 89.131 - type: recall_at_3 value: 35.472 - type: recall_at_5 value: 41.814 - task: type: Retrieval dataset: type: mteb/dbpedia name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 9.953 - type: map_at_10 value: 24.515 - type: map_at_100 value: 36.173 - type: map_at_1000 value: 38.351 - type: map_at_3 value: 16.592000000000002 - type: map_at_5 value: 20.036 - type: mrr_at_1 value: 74.25 - type: mrr_at_10 value: 81.813 - type: mrr_at_100 value: 82.006 - type: mrr_at_1000 value: 82.011 - type: mrr_at_3 value: 80.875 - type: mrr_at_5 value: 81.362 - type: ndcg_at_1 value: 62.5 - type: ndcg_at_10 value: 52.42 - type: ndcg_at_100 value: 56.808 - type: ndcg_at_1000 value: 63.532999999999994 - type: ndcg_at_3 value: 56.654 - type: ndcg_at_5 value: 54.18300000000001 - type: precision_at_1 value: 74.25 - type: precision_at_10 value: 42.699999999999996 - type: precision_at_100 value: 13.675 - type: precision_at_1000 value: 2.664 - type: precision_at_3 value: 60.5 - type: precision_at_5 value: 52.800000000000004 - type: recall_at_1 value: 9.953 - type: recall_at_10 value: 30.253999999999998 - type: recall_at_100 value: 62.516000000000005 - type: recall_at_1000 value: 84.163 - type: recall_at_3 value: 18.13 - type: recall_at_5 value: 22.771 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 79.455 - type: f1 value: 74.16798697647569 - task: type: Retrieval dataset: type: mteb/fever name: MTEB FEVER config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 87.531 - type: map_at_10 value: 93.16799999999999 - type: map_at_100 value: 93.341 - type: map_at_1000 value: 93.349 - type: map_at_3 value: 92.444 - type: map_at_5 value: 92.865 - type: mrr_at_1 value: 94.014 - type: mrr_at_10 value: 96.761 - type: mrr_at_100 value: 96.762 - type: mrr_at_1000 value: 96.762 - type: mrr_at_3 value: 96.672 - type: mrr_at_5 value: 96.736 - type: ndcg_at_1 value: 94.014 - type: ndcg_at_10 value: 95.112 - type: ndcg_at_100 value: 95.578 - type: ndcg_at_1000 value: 95.68900000000001 - type: ndcg_at_3 value: 94.392 - type: ndcg_at_5 value: 94.72500000000001 - type: precision_at_1 value: 94.014 - type: precision_at_10 value: 11.065 - type: precision_at_100 value: 1.157 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 35.259 - type: precision_at_5 value: 21.599 - type: recall_at_1 value: 87.531 - type: recall_at_10 value: 97.356 - type: recall_at_100 value: 98.965 - type: recall_at_1000 value: 99.607 - type: recall_at_3 value: 95.312 - type: recall_at_5 value: 96.295 - task: type: Retrieval dataset: type: mteb/fiqa name: MTEB FiQA2018 config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 32.055 - type: map_at_10 value: 53.114 - type: map_at_100 value: 55.235 - type: map_at_1000 value: 55.345 - type: map_at_3 value: 45.854 - type: map_at_5 value: 50.025 - type: mrr_at_1 value: 60.34 - type: mrr_at_10 value: 68.804 - type: mrr_at_100 value: 69.309 - type: mrr_at_1000 value: 69.32199999999999 - type: mrr_at_3 value: 66.40899999999999 - type: mrr_at_5 value: 67.976 - type: ndcg_at_1 value: 60.34 - type: ndcg_at_10 value: 62.031000000000006 - type: ndcg_at_100 value: 68.00500000000001 - type: ndcg_at_1000 value: 69.286 - type: ndcg_at_3 value: 56.355999999999995 - type: ndcg_at_5 value: 58.687 - type: precision_at_1 value: 60.34 - type: precision_at_10 value: 17.176 - type: precision_at_100 value: 2.36 - type: precision_at_1000 value: 0.259 - type: precision_at_3 value: 37.14 - type: precision_at_5 value: 27.809 - type: recall_at_1 value: 32.055 - type: recall_at_10 value: 70.91 - type: recall_at_100 value: 91.83 - type: recall_at_1000 value: 98.871 - type: recall_at_3 value: 51.202999999999996 - type: recall_at_5 value: 60.563 - task: type: Retrieval dataset: type: mteb/hotpotqa name: MTEB HotpotQA config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 43.68 - type: map_at_10 value: 64.389 - type: map_at_100 value: 65.24 - type: map_at_1000 value: 65.303 - type: map_at_3 value: 61.309000000000005 - type: map_at_5 value: 63.275999999999996 - type: mrr_at_1 value: 87.36 - type: mrr_at_10 value: 91.12 - type: mrr_at_100 value: 91.227 - type: mrr_at_1000 value: 91.229 - type: mrr_at_3 value: 90.57600000000001 - type: mrr_at_5 value: 90.912 - type: ndcg_at_1 value: 87.36 - type: ndcg_at_10 value: 73.076 - type: ndcg_at_100 value: 75.895 - type: ndcg_at_1000 value: 77.049 - type: ndcg_at_3 value: 68.929 - type: ndcg_at_5 value: 71.28 - type: precision_at_1 value: 87.36 - type: precision_at_10 value: 14.741000000000001 - type: precision_at_100 value: 1.694 - type: precision_at_1000 value: 0.185 - type: precision_at_3 value: 43.043 - type: precision_at_5 value: 27.681 - type: recall_at_1 value: 43.68 - type: recall_at_10 value: 73.707 - type: recall_at_100 value: 84.7 - type: recall_at_1000 value: 92.309 - type: recall_at_3 value: 64.564 - type: recall_at_5 value: 69.203 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 96.75399999999999 - type: ap value: 95.29389839242187 - type: f1 value: 96.75348377433475 - task: type: Retrieval dataset: type: mteb/msmarco name: MTEB MSMARCO config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 25.176 - type: map_at_10 value: 38.598 - type: map_at_100 value: 39.707 - type: map_at_1000 value: 39.744 - type: map_at_3 value: 34.566 - type: map_at_5 value: 36.863 - type: mrr_at_1 value: 25.874000000000002 - type: mrr_at_10 value: 39.214 - type: mrr_at_100 value: 40.251 - type: mrr_at_1000 value: 40.281 - type: mrr_at_3 value: 35.291 - type: mrr_at_5 value: 37.545 - type: ndcg_at_1 value: 25.874000000000002 - type: ndcg_at_10 value: 45.98 - type: ndcg_at_100 value: 51.197 - type: ndcg_at_1000 value: 52.073 - type: ndcg_at_3 value: 37.785999999999994 - type: ndcg_at_5 value: 41.870000000000005 - type: precision_at_1 value: 25.874000000000002 - type: precision_at_10 value: 7.181 - type: precision_at_100 value: 0.979 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 16.051000000000002 - type: precision_at_5 value: 11.713 - type: recall_at_1 value: 25.176 - type: recall_at_10 value: 68.67699999999999 - type: recall_at_100 value: 92.55 - type: recall_at_1000 value: 99.164 - type: recall_at_3 value: 46.372 - type: recall_at_5 value: 56.16 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 99.03784769721841 - type: f1 value: 98.97791641821495 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 91.88326493388054 - type: f1 value: 73.74809928034335 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 85.41358439811701 - type: f1 value: 83.503679460639 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 89.77135171486215 - type: f1 value: 88.89843747468366 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 46.22695362087359 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 44.132372165849425 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 33.35680810650402 - type: mrr value: 34.72625715637218 - task: type: Retrieval dataset: type: mteb/nfcorpus name: MTEB NFCorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 7.165000000000001 - type: map_at_10 value: 15.424 - type: map_at_100 value: 20.28 - type: map_at_1000 value: 22.065 - type: map_at_3 value: 11.236 - type: map_at_5 value: 13.025999999999998 - type: mrr_at_1 value: 51.702999999999996 - type: mrr_at_10 value: 59.965 - type: mrr_at_100 value: 60.667 - type: mrr_at_1000 value: 60.702999999999996 - type: mrr_at_3 value: 58.772000000000006 - type: mrr_at_5 value: 59.267 - type: ndcg_at_1 value: 49.536 - type: ndcg_at_10 value: 40.6 - type: ndcg_at_100 value: 37.848 - type: ndcg_at_1000 value: 46.657 - type: ndcg_at_3 value: 46.117999999999995 - type: ndcg_at_5 value: 43.619 - type: precision_at_1 value: 51.393 - type: precision_at_10 value: 30.31 - type: precision_at_100 value: 9.972 - type: precision_at_1000 value: 2.329 - type: precision_at_3 value: 43.137 - type: precision_at_5 value: 37.585 - type: recall_at_1 value: 7.165000000000001 - type: recall_at_10 value: 19.689999999999998 - type: recall_at_100 value: 39.237 - type: recall_at_1000 value: 71.417 - type: recall_at_3 value: 12.247 - type: recall_at_5 value: 14.902999999999999 - task: type: Retrieval dataset: type: mteb/nq name: MTEB NQ config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 42.653999999999996 - type: map_at_10 value: 59.611999999999995 - type: map_at_100 value: 60.32300000000001 - type: map_at_1000 value: 60.336 - type: map_at_3 value: 55.584999999999994 - type: map_at_5 value: 58.19 - type: mrr_at_1 value: 47.683 - type: mrr_at_10 value: 62.06700000000001 - type: mrr_at_100 value: 62.537 - type: mrr_at_1000 value: 62.544999999999995 - type: mrr_at_3 value: 59.178 - type: mrr_at_5 value: 61.034 - type: ndcg_at_1 value: 47.654 - type: ndcg_at_10 value: 67.001 - type: ndcg_at_100 value: 69.73899999999999 - type: ndcg_at_1000 value: 69.986 - type: ndcg_at_3 value: 59.95700000000001 - type: ndcg_at_5 value: 64.025 - type: precision_at_1 value: 47.654 - type: precision_at_10 value: 10.367999999999999 - type: precision_at_100 value: 1.192 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 26.651000000000003 - type: precision_at_5 value: 18.459 - type: recall_at_1 value: 42.653999999999996 - type: recall_at_10 value: 86.619 - type: recall_at_100 value: 98.04899999999999 - type: recall_at_1000 value: 99.812 - type: recall_at_3 value: 68.987 - type: recall_at_5 value: 78.158 - task: type: Retrieval dataset: type: mteb/quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 72.538 - type: map_at_10 value: 86.702 - type: map_at_100 value: 87.31 - type: map_at_1000 value: 87.323 - type: map_at_3 value: 83.87 - type: map_at_5 value: 85.682 - type: mrr_at_1 value: 83.31 - type: mrr_at_10 value: 89.225 - type: mrr_at_100 value: 89.30399999999999 - type: mrr_at_1000 value: 89.30399999999999 - type: mrr_at_3 value: 88.44300000000001 - type: mrr_at_5 value: 89.005 - type: ndcg_at_1 value: 83.32000000000001 - type: ndcg_at_10 value: 90.095 - type: ndcg_at_100 value: 91.12 - type: ndcg_at_1000 value: 91.179 - type: ndcg_at_3 value: 87.606 - type: ndcg_at_5 value: 89.031 - type: precision_at_1 value: 83.32000000000001 - type: precision_at_10 value: 13.641 - type: precision_at_100 value: 1.541 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 38.377 - type: precision_at_5 value: 25.162000000000003 - type: recall_at_1 value: 72.538 - type: recall_at_10 value: 96.47200000000001 - type: recall_at_100 value: 99.785 - type: recall_at_1000 value: 99.99900000000001 - type: recall_at_3 value: 89.278 - type: recall_at_5 value: 93.367 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 73.55219145406065 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 74.13437105242755 - task: type: Retrieval dataset: type: mteb/scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 6.873 - type: map_at_10 value: 17.944 - type: map_at_100 value: 21.171 - type: map_at_1000 value: 21.528 - type: map_at_3 value: 12.415 - type: map_at_5 value: 15.187999999999999 - type: mrr_at_1 value: 33.800000000000004 - type: mrr_at_10 value: 46.455 - type: mrr_at_100 value: 47.378 - type: mrr_at_1000 value: 47.394999999999996 - type: mrr_at_3 value: 42.367 - type: mrr_at_5 value: 44.972 - type: ndcg_at_1 value: 33.800000000000004 - type: ndcg_at_10 value: 28.907 - type: ndcg_at_100 value: 39.695 - type: ndcg_at_1000 value: 44.582 - type: ndcg_at_3 value: 26.949 - type: ndcg_at_5 value: 23.988 - type: precision_at_1 value: 33.800000000000004 - type: precision_at_10 value: 15.079999999999998 - type: precision_at_100 value: 3.056 - type: precision_at_1000 value: 0.42100000000000004 - type: precision_at_3 value: 25.167 - type: precision_at_5 value: 21.26 - type: recall_at_1 value: 6.873 - type: recall_at_10 value: 30.568 - type: recall_at_100 value: 62.062 - type: recall_at_1000 value: 85.37700000000001 - type: recall_at_3 value: 15.312999999999999 - type: recall_at_5 value: 21.575 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 82.37009118256057 - type: cos_sim_spearman value: 79.27986395671529 - type: euclidean_pearson value: 79.18037715442115 - type: euclidean_spearman value: 79.28004791561621 - type: manhattan_pearson value: 79.34062972800541 - type: manhattan_spearman value: 79.43106695543402 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 87.48474767383833 - type: cos_sim_spearman value: 79.54505388752513 - type: euclidean_pearson value: 83.43282704179565 - type: euclidean_spearman value: 79.54579919925405 - type: manhattan_pearson value: 83.77564492427952 - type: manhattan_spearman value: 79.84558396989286 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 88.803698035802 - type: cos_sim_spearman value: 88.83451367754881 - type: euclidean_pearson value: 88.28939285711628 - type: euclidean_spearman value: 88.83528996073112 - type: manhattan_pearson value: 88.28017412671795 - type: manhattan_spearman value: 88.9228828016344 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 85.27469288153428 - type: cos_sim_spearman value: 83.87477064876288 - type: euclidean_pearson value: 84.2601737035379 - type: euclidean_spearman value: 83.87431082479074 - type: manhattan_pearson value: 84.3621547772745 - type: manhattan_spearman value: 84.12094375000423 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 88.12749863201587 - type: cos_sim_spearman value: 88.54287568368565 - type: euclidean_pearson value: 87.90429700607999 - type: euclidean_spearman value: 88.5437689576261 - type: manhattan_pearson value: 88.19276653356833 - type: manhattan_spearman value: 88.99995393814679 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 85.68398747560902 - type: cos_sim_spearman value: 86.48815303460574 - type: euclidean_pearson value: 85.52356631237954 - type: euclidean_spearman value: 86.486391949551 - type: manhattan_pearson value: 85.67267981761788 - type: manhattan_spearman value: 86.7073696332485 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 88.9057107443124 - type: cos_sim_spearman value: 88.7312168757697 - type: euclidean_pearson value: 88.72810439714794 - type: euclidean_spearman value: 88.71976185854771 - type: manhattan_pearson value: 88.50433745949111 - type: manhattan_spearman value: 88.51726175544195 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 67.59391795109886 - type: cos_sim_spearman value: 66.87613008631367 - type: euclidean_pearson value: 69.23198488262217 - type: euclidean_spearman value: 66.85427723013692 - type: manhattan_pearson value: 69.50730124841084 - type: manhattan_spearman value: 67.10404669820792 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 87.0820605344619 - type: cos_sim_spearman value: 86.8518089863434 - type: euclidean_pearson value: 86.31087134689284 - type: euclidean_spearman value: 86.8518520517941 - type: manhattan_pearson value: 86.47203796160612 - type: manhattan_spearman value: 87.1080149734421 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 89.09255369305481 - type: mrr value: 97.10323445617563 - task: type: Retrieval dataset: type: mteb/scifact name: MTEB SciFact config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 61.260999999999996 - type: map_at_10 value: 74.043 - type: map_at_100 value: 74.37700000000001 - type: map_at_1000 value: 74.384 - type: map_at_3 value: 71.222 - type: map_at_5 value: 72.875 - type: mrr_at_1 value: 64.333 - type: mrr_at_10 value: 74.984 - type: mrr_at_100 value: 75.247 - type: mrr_at_1000 value: 75.25500000000001 - type: mrr_at_3 value: 73.167 - type: mrr_at_5 value: 74.35000000000001 - type: ndcg_at_1 value: 64.333 - type: ndcg_at_10 value: 79.06 - type: ndcg_at_100 value: 80.416 - type: ndcg_at_1000 value: 80.55600000000001 - type: ndcg_at_3 value: 74.753 - type: ndcg_at_5 value: 76.97500000000001 - type: precision_at_1 value: 64.333 - type: precision_at_10 value: 10.567 - type: precision_at_100 value: 1.1199999999999999 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 29.889 - type: precision_at_5 value: 19.533 - type: recall_at_1 value: 61.260999999999996 - type: recall_at_10 value: 93.167 - type: recall_at_100 value: 99.0 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 81.667 - type: recall_at_5 value: 87.394 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.71980198019801 - type: cos_sim_ap value: 92.81616007802704 - type: cos_sim_f1 value: 85.17548454688318 - type: cos_sim_precision value: 89.43894389438944 - type: cos_sim_recall value: 81.3 - type: dot_accuracy value: 99.71980198019801 - type: dot_ap value: 92.81398760591358 - type: dot_f1 value: 85.17548454688318 - type: dot_precision value: 89.43894389438944 - type: dot_recall value: 81.3 - type: euclidean_accuracy value: 99.71980198019801 - type: euclidean_ap value: 92.81560637245072 - type: euclidean_f1 value: 85.17548454688318 - type: euclidean_precision value: 89.43894389438944 - type: euclidean_recall value: 81.3 - type: manhattan_accuracy value: 99.73069306930694 - type: manhattan_ap value: 93.14005487480794 - type: manhattan_f1 value: 85.56263269639068 - type: manhattan_precision value: 91.17647058823529 - type: manhattan_recall value: 80.60000000000001 - type: max_accuracy value: 99.73069306930694 - type: max_ap value: 93.14005487480794 - type: max_f1 value: 85.56263269639068 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 79.86443362395185 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 49.40897096662564 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 55.66040806627947 - type: mrr value: 56.58670475766064 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.51015090598575 - type: cos_sim_spearman value: 31.35016454939226 - type: dot_pearson value: 31.5150068731 - type: dot_spearman value: 31.34790869023487 - task: type: Retrieval dataset: type: mteb/trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.254 - type: map_at_10 value: 2.064 - type: map_at_100 value: 12.909 - type: map_at_1000 value: 31.761 - type: map_at_3 value: 0.738 - type: map_at_5 value: 1.155 - type: mrr_at_1 value: 96.0 - type: mrr_at_10 value: 98.0 - type: mrr_at_100 value: 98.0 - type: mrr_at_1000 value: 98.0 - type: mrr_at_3 value: 98.0 - type: mrr_at_5 value: 98.0 - type: ndcg_at_1 value: 93.0 - type: ndcg_at_10 value: 82.258 - type: ndcg_at_100 value: 64.34 - type: ndcg_at_1000 value: 57.912 - type: ndcg_at_3 value: 90.827 - type: ndcg_at_5 value: 86.79 - type: precision_at_1 value: 96.0 - type: precision_at_10 value: 84.8 - type: precision_at_100 value: 66.0 - type: precision_at_1000 value: 25.356 - type: precision_at_3 value: 94.667 - type: precision_at_5 value: 90.4 - type: recall_at_1 value: 0.254 - type: recall_at_10 value: 2.1950000000000003 - type: recall_at_100 value: 16.088 - type: recall_at_1000 value: 54.559000000000005 - type: recall_at_3 value: 0.75 - type: recall_at_5 value: 1.191 - task: type: Retrieval dataset: type: mteb/touche2020 name: MTEB Touche2020 config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.976 - type: map_at_10 value: 11.389000000000001 - type: map_at_100 value: 18.429000000000002 - type: map_at_1000 value: 20.113 - type: map_at_3 value: 6.483 - type: map_at_5 value: 8.770999999999999 - type: mrr_at_1 value: 40.816 - type: mrr_at_10 value: 58.118 - type: mrr_at_100 value: 58.489999999999995 - type: mrr_at_1000 value: 58.489999999999995 - type: mrr_at_3 value: 53.061 - type: mrr_at_5 value: 57.041 - type: ndcg_at_1 value: 40.816 - type: ndcg_at_10 value: 30.567 - type: ndcg_at_100 value: 42.44 - type: ndcg_at_1000 value: 53.480000000000004 - type: ndcg_at_3 value: 36.016 - type: ndcg_at_5 value: 34.257 - type: precision_at_1 value: 42.857 - type: precision_at_10 value: 25.714 - type: precision_at_100 value: 8.429 - type: precision_at_1000 value: 1.5939999999999999 - type: precision_at_3 value: 36.735 - type: precision_at_5 value: 33.878 - type: recall_at_1 value: 2.976 - type: recall_at_10 value: 17.854999999999997 - type: recall_at_100 value: 51.833 - type: recall_at_1000 value: 86.223 - type: recall_at_3 value: 7.887 - 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type: euclidean_spearman value: 62.6183520374191 - type: manhattan_pearson value: 66.8063778911633 - type: manhattan_spearman value: 62.569607573241115 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 53.98400000000001 - type: f1 value: 51.21447361350723 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: e3dda5e115e487b39ec7e618c0c6a29137052a55 metrics: - type: cos_sim_pearson value: 79.11941660686553 - type: cos_sim_spearman value: 81.25029594540435 - type: euclidean_pearson value: 82.06973504238826 - type: euclidean_spearman value: 81.2501989488524 - type: manhattan_pearson value: 82.10094630392753 - type: manhattan_spearman value: 81.27987244392389 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476 metrics: - 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task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: 41bc36f332156f7adc9e38f53777c959b2ae9766 metrics: - type: cos_sim_accuracy value: 82.68190018039687 - type: cos_sim_ap value: 90.18017125327886 - type: cos_sim_f1 value: 83.64080906868193 - type: cos_sim_precision value: 79.7076890489303 - type: cos_sim_recall value: 87.98223053542202 - type: dot_accuracy value: 82.68190018039687 - type: dot_ap value: 90.18782350103646 - type: dot_f1 value: 83.64242087729039 - type: dot_precision value: 79.65313028764805 - type: dot_recall value: 88.05237315875614 - type: euclidean_accuracy value: 82.68190018039687 - type: euclidean_ap value: 90.1801957900632 - type: euclidean_f1 value: 83.63636363636364 - type: euclidean_precision value: 79.52772506852203 - type: euclidean_recall value: 88.19265840542437 - type: manhattan_accuracy value: 82.14070956103427 - type: manhattan_ap value: 89.96178420101427 - type: manhattan_f1 value: 83.21087838578791 - type: manhattan_precision value: 78.35605121850475 - type: manhattan_recall value: 88.70703764320785 - type: max_accuracy value: 82.68190018039687 - type: max_ap value: 90.18782350103646 - type: max_f1 value: 83.64242087729039 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: 1271c7809071a13532e05f25fb53511ffce77117 metrics: - type: map_at_1 value: 72.234 - type: map_at_10 value: 80.10000000000001 - type: map_at_100 value: 80.36 - type: map_at_1000 value: 80.363 - type: map_at_3 value: 78.315 - type: map_at_5 value: 79.607 - type: mrr_at_1 value: 72.392 - type: mrr_at_10 value: 80.117 - type: mrr_at_100 value: 80.36999999999999 - type: mrr_at_1000 value: 80.373 - type: mrr_at_3 value: 78.469 - type: mrr_at_5 value: 79.633 - type: ndcg_at_1 value: 72.392 - type: ndcg_at_10 value: 83.651 - type: ndcg_at_100 value: 84.749 - type: ndcg_at_1000 value: 84.83000000000001 - type: ndcg_at_3 value: 80.253 - type: ndcg_at_5 value: 82.485 - type: precision_at_1 value: 72.392 - type: precision_at_10 value: 9.557 - type: precision_at_100 value: 1.004 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 28.732000000000003 - type: precision_at_5 value: 18.377 - type: recall_at_1 value: 72.234 - type: recall_at_10 value: 94.573 - type: recall_at_100 value: 99.368 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 85.669 - type: recall_at_5 value: 91.01700000000001 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: a1a333e290fe30b10f3f56498e3a0d911a693ced metrics: - type: map_at_1 value: 26.173999999999996 - type: map_at_10 value: 80.04 - type: map_at_100 value: 82.94500000000001 - type: map_at_1000 value: 82.98100000000001 - type: map_at_3 value: 55.562999999999995 - type: map_at_5 value: 69.89800000000001 - type: mrr_at_1 value: 89.5 - type: mrr_at_10 value: 92.996 - type: mrr_at_100 value: 93.06400000000001 - type: mrr_at_1000 value: 93.065 - type: mrr_at_3 value: 92.658 - type: mrr_at_5 value: 92.84599999999999 - type: ndcg_at_1 value: 89.5 - type: ndcg_at_10 value: 87.443 - type: ndcg_at_100 value: 90.253 - type: ndcg_at_1000 value: 90.549 - type: ndcg_at_3 value: 85.874 - type: ndcg_at_5 value: 84.842 - type: precision_at_1 value: 89.5 - type: precision_at_10 value: 41.805 - type: precision_at_100 value: 4.827 - type: precision_at_1000 value: 0.49 - type: precision_at_3 value: 76.85 - type: precision_at_5 value: 64.8 - type: recall_at_1 value: 26.173999999999996 - type: recall_at_10 value: 89.101 - type: recall_at_100 value: 98.08099999999999 - type: recall_at_1000 value: 99.529 - type: recall_at_3 value: 57.902 - type: recall_at_5 value: 74.602 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9 metrics: - type: map_at_1 value: 56.10000000000001 - type: map_at_10 value: 66.15299999999999 - type: map_at_100 value: 66.625 - 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task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: 421605374b29664c5fc098418fe20ada9bd55f8a metrics: - type: accuracy value: 54.52096960369373 - type: f1 value: 40.930845295808695 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: b7c64bd89eb87f8ded463478346f76731f07bf8b metrics: - type: accuracy value: 86.51031894934334 - type: ap value: 55.9516014323483 - type: f1 value: 81.54813679326381 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: 17f9b096f80380fce5ed12a9be8be7784b337daf metrics: - type: cos_sim_pearson value: 69.67437838574276 - type: cos_sim_spearman value: 73.81314174653045 - type: euclidean_pearson value: 72.63430276680275 - type: euclidean_spearman value: 73.81358736777001 - type: manhattan_pearson value: 72.58743833842829 - type: manhattan_spearman value: 73.7590419009179 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 31.648613483640254 - type: mrr value: 30.37420634920635 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: 539bbde593d947e2a124ba72651aafc09eb33fc2 metrics: - type: map_at_1 value: 73.28099999999999 - type: map_at_10 value: 81.977 - type: map_at_100 value: 82.222 - type: map_at_1000 value: 82.22699999999999 - type: map_at_3 value: 80.441 - type: map_at_5 value: 81.46600000000001 - type: mrr_at_1 value: 75.673 - type: mrr_at_10 value: 82.41000000000001 - type: mrr_at_100 value: 82.616 - type: mrr_at_1000 value: 82.621 - type: mrr_at_3 value: 81.094 - type: mrr_at_5 value: 81.962 - type: ndcg_at_1 value: 75.673 - type: ndcg_at_10 value: 85.15599999999999 - type: ndcg_at_100 value: 86.151 - type: ndcg_at_1000 value: 86.26899999999999 - type: ndcg_at_3 value: 82.304 - type: ndcg_at_5 value: 84.009 - type: precision_at_1 value: 75.673 - type: precision_at_10 value: 10.042 - type: precision_at_100 value: 1.052 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 30.673000000000002 - type: precision_at_5 value: 19.326999999999998 - type: recall_at_1 value: 73.28099999999999 - type: recall_at_10 value: 94.446 - type: recall_at_100 value: 98.737 - type: recall_at_1000 value: 99.649 - type: recall_at_3 value: 86.984 - type: recall_at_5 value: 91.024 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (zh-CN) config: zh-CN split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 81.08607935440484 - type: f1 value: 78.24879986066307 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (zh-CN) config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 86.05917955615332 - 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type: recall_at_3 value: 58.333999999999996 - type: recall_at_5 value: 72.974 - task: type: Classification dataset: type: C-MTEB/TNews-classification name: MTEB TNews config: default split: validation revision: 317f262bf1e6126357bbe89e875451e4b0938fe4 metrics: - type: accuracy value: 52.971999999999994 - type: f1 value: 50.2898280984929 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringP2P name: MTEB ThuNewsClusteringP2P config: default split: test revision: 5798586b105c0434e4f0fe5e767abe619442cf93 metrics: - type: v_measure value: 86.0797948663824 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringS2S name: MTEB ThuNewsClusteringS2S config: default split: test revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d metrics: - type: v_measure value: 85.10759092255017 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: 58c2597a5943a2ba48f4668c3b90d796283c5639 metrics: - type: map_at_1 value: 65.60000000000001 - 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type: recall_at_1000 value: 99.6 - type: recall_at_3 value: 82.19999999999999 - type: recall_at_5 value: 86.9 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: 339287def212450dcaa9df8c22bf93e9980c7023 metrics: - type: accuracy value: 89.47 - type: ap value: 75.59561751845389 - type: f1 value: 87.95207751382563 - dataset: config: default name: MTEB AlloProfClusteringP2P revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b split: test type: lyon-nlp/alloprof metrics: - type: v_measure value: 76.05592323841036 task: type: Clustering - dataset: config: default name: MTEB AlloProfClusteringS2S revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b split: test type: lyon-nlp/alloprof metrics: - type: v_measure value: 64.51718058866508 task: type: Clustering - dataset: config: default name: MTEB AlloprofReranking revision: 666fdacebe0291776e86f29345663dfaf80a0db9 split: test type: lyon-nlp/mteb-fr-reranking-alloprof-s2p metrics: - type: map value: 73.08278490943373 - type: mrr value: 74.66561454570449 task: type: Reranking - dataset: config: default name: MTEB AlloprofRetrieval revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b split: test type: lyon-nlp/alloprof metrics: - type: map_at_1 value: 38.912 - type: map_at_10 value: 52.437999999999995 - type: map_at_100 value: 53.38 - type: map_at_1000 value: 53.427 - type: map_at_3 value: 48.879 - type: map_at_5 value: 50.934000000000005 - type: mrr_at_1 value: 44.085 - type: mrr_at_10 value: 55.337 - type: mrr_at_100 value: 56.016999999999996 - type: mrr_at_1000 value: 56.043 - type: mrr_at_3 value: 52.55499999999999 - type: mrr_at_5 value: 54.20399999999999 - type: ndcg_at_1 value: 44.085 - type: ndcg_at_10 value: 58.876 - type: ndcg_at_100 value: 62.714000000000006 - type: ndcg_at_1000 value: 63.721000000000004 - type: ndcg_at_3 value: 52.444 - type: ndcg_at_5 value: 55.692 - type: precision_at_1 value: 44.085 - type: precision_at_10 value: 9.21 - type: precision_at_100 value: 1.164 - type: precision_at_1000 value: 0.128 - type: precision_at_3 value: 23.043 - type: precision_at_5 value: 15.898000000000001 - type: recall_at_1 value: 38.912 - type: recall_at_10 value: 75.577 - type: recall_at_100 value: 92.038 - type: recall_at_1000 value: 99.325 - type: recall_at_3 value: 58.592 - type: recall_at_5 value: 66.235 task: type: Retrieval - dataset: config: fr name: MTEB AmazonReviewsClassification (fr) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 55.532000000000004 - type: f1 value: 52.5783943471605 task: type: Classification - dataset: config: default name: MTEB BSARDRetrieval revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59 split: test type: maastrichtlawtech/bsard metrics: - type: map_at_1 value: 8.108 - type: map_at_10 value: 14.710999999999999 - type: map_at_100 value: 15.891 - type: map_at_1000 value: 15.983 - type: map_at_3 value: 12.237 - type: map_at_5 value: 13.679 - type: mrr_at_1 value: 8.108 - type: mrr_at_10 value: 14.710999999999999 - type: mrr_at_100 value: 15.891 - type: mrr_at_1000 value: 15.983 - type: mrr_at_3 value: 12.237 - type: mrr_at_5 value: 13.679 - type: ndcg_at_1 value: 8.108 - type: ndcg_at_10 value: 18.796 - type: ndcg_at_100 value: 25.098 - type: ndcg_at_1000 value: 27.951999999999998 - type: ndcg_at_3 value: 13.712 - type: ndcg_at_5 value: 16.309 - type: precision_at_1 value: 8.108 - type: precision_at_10 value: 3.198 - type: precision_at_100 value: 0.626 - type: precision_at_1000 value: 0.086 - type: precision_at_3 value: 6.006 - type: precision_at_5 value: 4.865 - type: recall_at_1 value: 8.108 - type: recall_at_10 value: 31.982 - type: recall_at_100 value: 62.613 - type: recall_at_1000 value: 86.036 - type: recall_at_3 value: 18.018 - type: recall_at_5 value: 24.324 task: type: Retrieval - dataset: config: default name: MTEB HALClusteringS2S revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915 split: test type: lyon-nlp/clustering-hal-s2s metrics: - type: v_measure value: 30.833269778867116 task: type: Clustering - dataset: config: default name: MTEB MLSUMClusteringP2P revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 split: test type: mlsum metrics: - type: v_measure value: 50.0281928004713 task: type: Clustering - dataset: config: default name: MTEB MLSUMClusteringS2S revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 split: test type: mlsum metrics: - type: v_measure value: 43.699961510636534 task: type: Clustering - dataset: config: fr name: MTEB MTOPDomainClassification (fr) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 96.68963357344191 - type: f1 value: 96.45175170820961 task: type: Classification - dataset: config: fr name: MTEB MTOPIntentClassification (fr) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 87.46946445349202 - type: f1 value: 65.79860440988624 task: type: Classification - dataset: config: fra name: MTEB MasakhaNEWSClassification (fra) revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 split: test type: masakhane/masakhanews metrics: - type: accuracy value: 82.60663507109005 - type: f1 value: 77.20462646604777 task: type: Classification - dataset: config: fra name: MTEB MasakhaNEWSClusteringP2P (fra) revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 split: test type: masakhane/masakhanews metrics: - type: v_measure value: 60.19311264967803 task: type: Clustering - dataset: config: fra name: MTEB MasakhaNEWSClusteringS2S (fra) revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 split: test type: masakhane/masakhanews metrics: - type: v_measure value: 63.6235764409785 task: type: Clustering - dataset: config: fr name: MTEB MassiveIntentClassification (fr) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 81.65097511768661 - type: f1 value: 78.77796091490924 task: type: Classification - dataset: config: fr name: MTEB MassiveScenarioClassification (fr) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 86.64425016812373 - type: f1 value: 85.4912728670017 task: type: Classification - dataset: config: fr name: MTEB MintakaRetrieval (fr) revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e split: test type: jinaai/mintakaqa metrics: - type: map_at_1 value: 35.913000000000004 - type: map_at_10 value: 48.147 - type: map_at_100 value: 48.91 - type: map_at_1000 value: 48.949 - type: map_at_3 value: 45.269999999999996 - type: map_at_5 value: 47.115 - type: mrr_at_1 value: 35.913000000000004 - type: mrr_at_10 value: 48.147 - type: mrr_at_100 value: 48.91 - type: mrr_at_1000 value: 48.949 - type: mrr_at_3 value: 45.269999999999996 - type: mrr_at_5 value: 47.115 - type: ndcg_at_1 value: 35.913000000000004 - type: ndcg_at_10 value: 54.03 - type: ndcg_at_100 value: 57.839 - type: ndcg_at_1000 value: 58.925000000000004 - type: ndcg_at_3 value: 48.217999999999996 - type: ndcg_at_5 value: 51.56699999999999 - type: precision_at_1 value: 35.913000000000004 - type: precision_at_10 value: 7.244000000000001 - type: precision_at_100 value: 0.9039999999999999 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 18.905 - type: precision_at_5 value: 12.981000000000002 - type: recall_at_1 value: 35.913000000000004 - type: recall_at_10 value: 72.441 - type: recall_at_100 value: 90.41799999999999 - type: recall_at_1000 value: 99.099 - type: recall_at_3 value: 56.716 - type: recall_at_5 value: 64.90599999999999 task: type: Retrieval - dataset: config: fr name: MTEB OpusparcusPC (fr) revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a split: test type: GEM/opusparcus metrics: - type: cos_sim_accuracy value: 99.90069513406156 - type: cos_sim_ap value: 100.0 - type: cos_sim_f1 value: 99.95032290114257 - type: cos_sim_precision value: 100.0 - type: cos_sim_recall value: 99.90069513406156 - type: dot_accuracy value: 99.90069513406156 - type: dot_ap value: 100.0 - type: dot_f1 value: 99.95032290114257 - type: dot_precision value: 100.0 - type: dot_recall value: 99.90069513406156 - type: euclidean_accuracy value: 99.90069513406156 - type: euclidean_ap value: 100.0 - type: euclidean_f1 value: 99.95032290114257 - type: euclidean_precision value: 100.0 - type: euclidean_recall value: 99.90069513406156 - type: manhattan_accuracy value: 99.90069513406156 - type: manhattan_ap value: 100.0 - type: manhattan_f1 value: 99.95032290114257 - type: manhattan_precision value: 100.0 - type: manhattan_recall value: 99.90069513406156 - type: max_accuracy value: 99.90069513406156 - type: max_ap value: 100.0 - type: max_f1 value: 99.95032290114257 task: type: PairClassification - dataset: config: fr name: MTEB PawsX (fr) revision: 8a04d940a42cd40658986fdd8e3da561533a3646 split: test type: paws-x metrics: - type: cos_sim_accuracy value: 75.25 - type: cos_sim_ap value: 80.86376001270014 - type: cos_sim_f1 value: 73.65945437441204 - type: cos_sim_precision value: 64.02289452166802 - type: cos_sim_recall value: 86.71096345514951 - type: dot_accuracy value: 75.25 - type: dot_ap value: 80.93686107633002 - type: dot_f1 value: 73.65945437441204 - type: dot_precision value: 64.02289452166802 - type: dot_recall value: 86.71096345514951 - type: euclidean_accuracy value: 75.25 - type: euclidean_ap value: 80.86379136218862 - type: euclidean_f1 value: 73.65945437441204 - type: euclidean_precision value: 64.02289452166802 - type: euclidean_recall value: 86.71096345514951 - type: manhattan_accuracy value: 75.3 - type: manhattan_ap value: 80.87826606097734 - type: manhattan_f1 value: 73.68421052631581 - type: manhattan_precision value: 64.0 - type: manhattan_recall value: 86.82170542635659 - type: max_accuracy value: 75.3 - type: max_ap value: 80.93686107633002 - type: max_f1 value: 73.68421052631581 task: type: PairClassification - dataset: config: default name: MTEB SICKFr revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a split: test type: Lajavaness/SICK-fr metrics: - type: cos_sim_pearson value: 81.42349425981143 - type: cos_sim_spearman value: 78.90454327031226 - type: euclidean_pearson value: 78.39086497435166 - type: euclidean_spearman value: 78.9046133980509 - type: manhattan_pearson value: 78.63743094286502 - type: manhattan_spearman value: 79.12136348449269 task: type: STS - dataset: config: fr name: MTEB STS22 (fr) revision: eea2b4fe26a775864c896887d910b76a8098ad3f split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 81.452697919749 - type: cos_sim_spearman value: 82.58116836039301 - type: euclidean_pearson value: 81.04038478932786 - type: euclidean_spearman value: 82.58116836039301 - type: manhattan_pearson value: 81.37075396187771 - type: manhattan_spearman value: 82.73678231355368 task: type: STS - dataset: config: fr name: MTEB STSBenchmarkMultilingualSTS (fr) revision: 93d57ef91790589e3ce9c365164337a8a78b7632 split: test type: stsb_multi_mt metrics: - type: cos_sim_pearson value: 85.7419764013806 - type: cos_sim_spearman value: 85.46085808849622 - type: euclidean_pearson value: 83.70449639870063 - type: euclidean_spearman value: 85.46159013076233 - type: manhattan_pearson value: 83.95259510313929 - type: manhattan_spearman value: 85.8029724659458 task: type: STS - dataset: config: default name: MTEB SummEvalFr revision: b385812de6a9577b6f4d0f88c6a6e35395a94054 split: test type: lyon-nlp/summarization-summeval-fr-p2p metrics: - type: cos_sim_pearson value: 32.61063271753325 - type: cos_sim_spearman value: 31.454589417353603 - type: dot_pearson value: 32.6106288643431 - type: dot_spearman value: 31.454589417353603 task: type: Summarization - dataset: config: default name: MTEB SyntecReranking revision: b205c5084a0934ce8af14338bf03feb19499c84d split: test type: lyon-nlp/mteb-fr-reranking-syntec-s2p metrics: - type: map value: 84.31666666666666 - type: mrr value: 84.31666666666666 task: type: Reranking - dataset: config: default name: MTEB SyntecRetrieval revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff split: test type: lyon-nlp/mteb-fr-retrieval-syntec-s2p metrics: - type: map_at_1 value: 63.0 - type: map_at_10 value: 73.471 - type: map_at_100 value: 73.87 - type: map_at_1000 value: 73.87 - type: map_at_3 value: 70.5 - type: map_at_5 value: 73.05 - type: mrr_at_1 value: 63.0 - type: mrr_at_10 value: 73.471 - type: mrr_at_100 value: 73.87 - type: mrr_at_1000 value: 73.87 - type: mrr_at_3 value: 70.5 - type: mrr_at_5 value: 73.05 - type: ndcg_at_1 value: 63.0 - type: ndcg_at_10 value: 78.255 - type: ndcg_at_100 value: 79.88 - type: ndcg_at_1000 value: 79.88 - type: ndcg_at_3 value: 72.702 - type: ndcg_at_5 value: 77.264 - type: precision_at_1 value: 63.0 - type: precision_at_10 value: 9.3 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 26.333000000000002 - type: precision_at_5 value: 18.0 - type: recall_at_1 value: 63.0 - type: recall_at_10 value: 93.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 79.0 - type: recall_at_5 value: 90.0 task: type: Retrieval - dataset: config: fr name: MTEB XPQARetrieval (fr) revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f split: test type: jinaai/xpqa metrics: - type: map_at_1 value: 40.338 - type: map_at_10 value: 61.927 - type: map_at_100 value: 63.361999999999995 - type: map_at_1000 value: 63.405 - type: map_at_3 value: 55.479 - type: map_at_5 value: 59.732 - type: mrr_at_1 value: 63.551 - type: mrr_at_10 value: 71.006 - type: mrr_at_100 value: 71.501 - type: mrr_at_1000 value: 71.509 - type: mrr_at_3 value: 69.07 - type: mrr_at_5 value: 70.165 - type: ndcg_at_1 value: 63.551 - type: ndcg_at_10 value: 68.297 - type: ndcg_at_100 value: 73.13199999999999 - type: ndcg_at_1000 value: 73.751 - type: ndcg_at_3 value: 62.999 - type: ndcg_at_5 value: 64.89 - type: precision_at_1 value: 63.551 - type: precision_at_10 value: 15.661 - type: precision_at_100 value: 1.9789999999999999 - type: precision_at_1000 value: 0.207 - type: precision_at_3 value: 38.273 - type: precision_at_5 value: 27.61 - type: recall_at_1 value: 40.338 - type: recall_at_10 value: 77.267 - type: recall_at_100 value: 95.892 - type: recall_at_1000 value: 99.75500000000001 - type: recall_at_3 value: 60.36 - type: recall_at_5 value: 68.825 task: type: Retrieval - dataset: config: default name: MTEB 8TagsClustering revision: None split: test type: PL-MTEB/8tags-clustering metrics: - type: v_measure value: 51.36126303874126 task: type: Clustering - dataset: config: default name: MTEB AllegroReviews revision: None split: test type: PL-MTEB/allegro-reviews metrics: - type: accuracy value: 67.13717693836979 - type: f1 value: 57.27609848003782 task: type: Classification - dataset: config: default name: MTEB ArguAna-PL revision: 63fc86750af76253e8c760fc9e534bbf24d260a2 split: test type: clarin-knext/arguana-pl metrics: - type: map_at_1 value: 35.276999999999994 - type: map_at_10 value: 51.086 - type: map_at_100 value: 51.788000000000004 - type: map_at_1000 value: 51.791 - type: map_at_3 value: 46.147 - type: map_at_5 value: 49.078 - type: mrr_at_1 value: 35.917 - type: mrr_at_10 value: 51.315999999999995 - type: mrr_at_100 value: 52.018 - type: mrr_at_1000 value: 52.022 - type: mrr_at_3 value: 46.349000000000004 - type: mrr_at_5 value: 49.297000000000004 - type: ndcg_at_1 value: 35.276999999999994 - type: ndcg_at_10 value: 59.870999999999995 - type: ndcg_at_100 value: 62.590999999999994 - type: ndcg_at_1000 value: 62.661 - type: ndcg_at_3 value: 49.745 - type: ndcg_at_5 value: 55.067 - type: precision_at_1 value: 35.276999999999994 - type: precision_at_10 value: 8.791 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.057 - type: precision_at_5 value: 14.637 - type: recall_at_1 value: 35.276999999999994 - type: recall_at_10 value: 87.909 - type: recall_at_100 value: 99.14699999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 60.171 - type: recall_at_5 value: 73.18599999999999 task: type: Retrieval - dataset: config: default name: MTEB CBD revision: None split: test type: PL-MTEB/cbd metrics: - type: accuracy value: 78.03000000000002 - type: ap value: 29.12548553897622 - type: f1 value: 66.54857118886073 task: type: Classification - dataset: config: default name: MTEB CDSC-E revision: None split: test type: PL-MTEB/cdsce-pairclassification metrics: - type: cos_sim_accuracy value: 89.0 - type: cos_sim_ap value: 76.75437826834582 - type: cos_sim_f1 value: 66.4850136239782 - type: cos_sim_precision value: 68.92655367231639 - type: cos_sim_recall value: 64.21052631578948 - type: dot_accuracy value: 89.0 - type: dot_ap value: 76.75437826834582 - type: dot_f1 value: 66.4850136239782 - type: dot_precision value: 68.92655367231639 - type: dot_recall value: 64.21052631578948 - type: euclidean_accuracy value: 89.0 - type: euclidean_ap value: 76.75437826834582 - type: euclidean_f1 value: 66.4850136239782 - type: euclidean_precision value: 68.92655367231639 - type: euclidean_recall value: 64.21052631578948 - type: manhattan_accuracy value: 89.0 - type: manhattan_ap value: 76.66074220647083 - type: manhattan_f1 value: 66.47058823529412 - type: manhattan_precision value: 75.33333333333333 - type: manhattan_recall value: 59.473684210526315 - type: max_accuracy value: 89.0 - type: max_ap value: 76.75437826834582 - type: max_f1 value: 66.4850136239782 task: type: PairClassification - dataset: config: default name: MTEB CDSC-R revision: None split: test type: PL-MTEB/cdscr-sts metrics: - type: cos_sim_pearson value: 93.12903172428328 - type: cos_sim_spearman value: 92.66381487060741 - type: euclidean_pearson value: 90.37278396708922 - type: euclidean_spearman value: 92.66381487060741 - type: manhattan_pearson value: 90.32503296540962 - type: manhattan_spearman value: 92.6902938354313 task: type: STS - dataset: config: default name: MTEB DBPedia-PL revision: 76afe41d9af165cc40999fcaa92312b8b012064a split: test type: clarin-knext/dbpedia-pl metrics: - type: map_at_1 value: 8.83 - type: map_at_10 value: 18.326 - type: map_at_100 value: 26.496 - type: map_at_1000 value: 28.455000000000002 - type: map_at_3 value: 12.933 - type: map_at_5 value: 15.168000000000001 - type: mrr_at_1 value: 66.0 - type: mrr_at_10 value: 72.76700000000001 - type: mrr_at_100 value: 73.203 - type: mrr_at_1000 value: 73.219 - type: mrr_at_3 value: 71.458 - type: mrr_at_5 value: 72.246 - type: ndcg_at_1 value: 55.375 - type: ndcg_at_10 value: 41.3 - type: ndcg_at_100 value: 45.891 - type: ndcg_at_1000 value: 52.905 - type: ndcg_at_3 value: 46.472 - type: ndcg_at_5 value: 43.734 - type: precision_at_1 value: 66.0 - type: precision_at_10 value: 33.074999999999996 - type: precision_at_100 value: 11.094999999999999 - type: precision_at_1000 value: 2.374 - type: precision_at_3 value: 48.583 - type: precision_at_5 value: 42.0 - type: recall_at_1 value: 8.83 - type: recall_at_10 value: 22.587 - type: recall_at_100 value: 50.61600000000001 - type: recall_at_1000 value: 73.559 - type: recall_at_3 value: 13.688 - type: recall_at_5 value: 16.855 task: type: Retrieval - dataset: config: default name: MTEB FiQA-PL revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e split: test type: clarin-knext/fiqa-pl metrics: - type: map_at_1 value: 20.587 - type: map_at_10 value: 33.095 - type: map_at_100 value: 35.24 - type: map_at_1000 value: 35.429 - type: map_at_3 value: 28.626 - type: map_at_5 value: 31.136999999999997 - type: mrr_at_1 value: 40.586 - type: mrr_at_10 value: 49.033 - type: mrr_at_100 value: 49.952999999999996 - type: mrr_at_1000 value: 49.992 - type: mrr_at_3 value: 46.553 - type: mrr_at_5 value: 48.035 - type: ndcg_at_1 value: 40.586 - type: ndcg_at_10 value: 41.046 - type: ndcg_at_100 value: 48.586 - type: ndcg_at_1000 value: 51.634 - type: ndcg_at_3 value: 36.773 - type: ndcg_at_5 value: 38.389 - type: precision_at_1 value: 40.586 - type: precision_at_10 value: 11.466 - type: precision_at_100 value: 1.909 - type: precision_at_1000 value: 0.245 - type: precision_at_3 value: 24.434 - type: precision_at_5 value: 18.426000000000002 - type: recall_at_1 value: 20.587 - type: recall_at_10 value: 47.986000000000004 - type: recall_at_100 value: 75.761 - type: recall_at_1000 value: 94.065 - type: recall_at_3 value: 33.339 - type: recall_at_5 value: 39.765 task: type: Retrieval - dataset: config: default name: MTEB HotpotQA-PL revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907 split: test type: clarin-knext/hotpotqa-pl metrics: - type: map_at_1 value: 40.878 - type: map_at_10 value: 58.775999999999996 - type: map_at_100 value: 59.632 - type: map_at_1000 value: 59.707 - type: map_at_3 value: 56.074 - type: map_at_5 value: 57.629 - type: mrr_at_1 value: 81.756 - type: mrr_at_10 value: 86.117 - type: mrr_at_100 value: 86.299 - type: mrr_at_1000 value: 86.30600000000001 - type: mrr_at_3 value: 85.345 - type: mrr_at_5 value: 85.832 - type: ndcg_at_1 value: 81.756 - type: ndcg_at_10 value: 67.608 - type: ndcg_at_100 value: 70.575 - type: ndcg_at_1000 value: 71.99600000000001 - type: ndcg_at_3 value: 63.723 - type: ndcg_at_5 value: 65.70700000000001 - type: precision_at_1 value: 81.756 - type: precision_at_10 value: 13.619 - type: precision_at_100 value: 1.5939999999999999 - type: precision_at_1000 value: 0.178 - type: precision_at_3 value: 39.604 - type: precision_at_5 value: 25.332 - type: recall_at_1 value: 40.878 - type: recall_at_10 value: 68.096 - type: recall_at_100 value: 79.696 - type: recall_at_1000 value: 89.082 - type: recall_at_3 value: 59.406000000000006 - type: recall_at_5 value: 63.329 task: type: Retrieval - dataset: config: default name: MTEB MSMARCO-PL revision: 8634c07806d5cce3a6138e260e59b81760a0a640 split: test type: clarin-knext/msmarco-pl metrics: - type: map_at_1 value: 2.1839999999999997 - type: map_at_10 value: 11.346 - type: map_at_100 value: 30.325000000000003 - type: map_at_1000 value: 37.806 - type: map_at_3 value: 4.842 - type: map_at_5 value: 6.891 - type: mrr_at_1 value: 86.047 - type: mrr_at_10 value: 89.14699999999999 - type: mrr_at_100 value: 89.46600000000001 - type: mrr_at_1000 value: 89.46600000000001 - type: mrr_at_3 value: 89.14699999999999 - type: mrr_at_5 value: 89.14699999999999 - type: ndcg_at_1 value: 67.829 - type: ndcg_at_10 value: 62.222 - type: ndcg_at_100 value: 55.337 - type: ndcg_at_1000 value: 64.076 - type: ndcg_at_3 value: 68.12700000000001 - type: ndcg_at_5 value: 64.987 - type: precision_at_1 value: 86.047 - type: precision_at_10 value: 69.535 - type: precision_at_100 value: 32.93 - type: precision_at_1000 value: 6.6049999999999995 - type: precision_at_3 value: 79.845 - type: precision_at_5 value: 75.349 - type: recall_at_1 value: 2.1839999999999997 - type: recall_at_10 value: 12.866 - type: recall_at_100 value: 43.505 - type: recall_at_1000 value: 72.366 - type: recall_at_3 value: 4.947 - type: recall_at_5 value: 7.192 task: type: Retrieval - dataset: config: pl name: MTEB MassiveIntentClassification (pl) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 80.75319435104238 - type: f1 value: 77.58961444860606 task: type: Classification - dataset: config: pl name: MTEB MassiveScenarioClassification (pl) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 85.54472091459313 - type: f1 value: 84.29498563572106 task: type: Classification - dataset: config: default name: MTEB NFCorpus-PL revision: 9a6f9567fda928260afed2de480d79c98bf0bec0 split: test type: clarin-knext/nfcorpus-pl metrics: - type: map_at_1 value: 4.367 - type: map_at_10 value: 10.38 - type: map_at_100 value: 13.516 - type: map_at_1000 value: 14.982000000000001 - type: map_at_3 value: 7.367 - type: map_at_5 value: 8.59 - type: mrr_at_1 value: 41.486000000000004 - type: mrr_at_10 value: 48.886 - type: mrr_at_100 value: 49.657000000000004 - type: mrr_at_1000 value: 49.713 - type: mrr_at_3 value: 46.904 - type: mrr_at_5 value: 48.065000000000005 - type: ndcg_at_1 value: 40.402 - type: ndcg_at_10 value: 30.885 - type: ndcg_at_100 value: 28.393 - type: ndcg_at_1000 value: 37.428 - type: ndcg_at_3 value: 35.394999999999996 - type: ndcg_at_5 value: 33.391999999999996 - type: precision_at_1 value: 41.486000000000004 - type: precision_at_10 value: 23.437 - type: precision_at_100 value: 7.638 - type: precision_at_1000 value: 2.0389999999999997 - type: precision_at_3 value: 32.817 - type: precision_at_5 value: 28.915999999999997 - type: recall_at_1 value: 4.367 - type: recall_at_10 value: 14.655000000000001 - type: recall_at_100 value: 29.665999999999997 - type: recall_at_1000 value: 62.073 - type: recall_at_3 value: 8.51 - type: recall_at_5 value: 10.689 task: type: Retrieval - dataset: config: default name: MTEB NQ-PL revision: f171245712cf85dd4700b06bef18001578d0ca8d split: test type: clarin-knext/nq-pl metrics: - type: map_at_1 value: 28.616000000000003 - type: map_at_10 value: 41.626000000000005 - type: map_at_100 value: 42.689 - type: map_at_1000 value: 42.733 - type: map_at_3 value: 37.729 - type: map_at_5 value: 39.879999999999995 - type: mrr_at_1 value: 32.068000000000005 - type: mrr_at_10 value: 44.029 - type: mrr_at_100 value: 44.87 - type: mrr_at_1000 value: 44.901 - type: mrr_at_3 value: 40.687 - type: mrr_at_5 value: 42.625 - type: ndcg_at_1 value: 32.068000000000005 - type: ndcg_at_10 value: 48.449999999999996 - type: ndcg_at_100 value: 53.13 - type: ndcg_at_1000 value: 54.186 - type: ndcg_at_3 value: 40.983999999999995 - type: ndcg_at_5 value: 44.628 - type: precision_at_1 value: 32.068000000000005 - type: precision_at_10 value: 7.9750000000000005 - type: precision_at_100 value: 1.061 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 18.404999999999998 - type: precision_at_5 value: 13.111 - type: recall_at_1 value: 28.616000000000003 - type: recall_at_10 value: 66.956 - type: recall_at_100 value: 87.657 - type: recall_at_1000 value: 95.548 - type: recall_at_3 value: 47.453 - type: recall_at_5 value: 55.87800000000001 task: type: Retrieval - dataset: config: default name: MTEB PAC revision: None split: test type: laugustyniak/abusive-clauses-pl metrics: - type: accuracy value: 69.04141326382856 - type: ap value: 77.47589122111044 - type: f1 value: 66.6332277374775 task: type: Classification - dataset: config: default name: MTEB PPC revision: None split: test type: PL-MTEB/ppc-pairclassification metrics: - type: cos_sim_accuracy value: 86.4 - type: cos_sim_ap value: 94.1044939667201 - type: cos_sim_f1 value: 88.78048780487805 - type: cos_sim_precision value: 87.22044728434504 - type: cos_sim_recall value: 90.39735099337747 - type: dot_accuracy value: 86.4 - type: dot_ap value: 94.1044939667201 - type: dot_f1 value: 88.78048780487805 - type: dot_precision value: 87.22044728434504 - type: dot_recall value: 90.39735099337747 - type: euclidean_accuracy value: 86.4 - type: euclidean_ap value: 94.1044939667201 - type: euclidean_f1 value: 88.78048780487805 - type: euclidean_precision value: 87.22044728434504 - type: euclidean_recall value: 90.39735099337747 - type: manhattan_accuracy value: 86.4 - type: manhattan_ap value: 94.11438365697387 - type: manhattan_f1 value: 88.77968877968877 - type: manhattan_precision value: 87.84440842787681 - type: manhattan_recall value: 89.73509933774835 - type: max_accuracy value: 86.4 - type: max_ap value: 94.11438365697387 - type: max_f1 value: 88.78048780487805 task: type: PairClassification - dataset: config: default name: MTEB PSC revision: None split: test type: PL-MTEB/psc-pairclassification metrics: - type: cos_sim_accuracy value: 97.86641929499072 - type: cos_sim_ap value: 99.36904211868182 - type: cos_sim_f1 value: 96.56203288490283 - type: cos_sim_precision value: 94.72140762463343 - type: cos_sim_recall value: 98.47560975609755 - type: dot_accuracy value: 97.86641929499072 - type: dot_ap value: 99.36904211868183 - type: dot_f1 value: 96.56203288490283 - type: dot_precision value: 94.72140762463343 - type: dot_recall value: 98.47560975609755 - type: euclidean_accuracy value: 97.86641929499072 - type: euclidean_ap value: 99.36904211868183 - type: euclidean_f1 value: 96.56203288490283 - type: euclidean_precision value: 94.72140762463343 - type: euclidean_recall value: 98.47560975609755 - type: manhattan_accuracy value: 98.14471243042672 - type: manhattan_ap value: 99.43359540492416 - type: manhattan_f1 value: 96.98795180722892 - type: manhattan_precision value: 95.83333333333334 - type: manhattan_recall value: 98.17073170731707 - type: max_accuracy value: 98.14471243042672 - type: max_ap value: 99.43359540492416 - type: max_f1 value: 96.98795180722892 task: type: PairClassification - dataset: config: default name: MTEB PolEmo2.0-IN revision: None split: test type: PL-MTEB/polemo2_in metrics: - type: accuracy value: 89.39058171745152 - type: f1 value: 86.8552093529568 task: type: Classification - dataset: config: default name: MTEB PolEmo2.0-OUT revision: None split: test type: PL-MTEB/polemo2_out metrics: - type: accuracy value: 74.97975708502024 - type: f1 value: 58.73081628832407 task: type: Classification - dataset: config: default name: MTEB Quora-PL revision: 0be27e93455051e531182b85e85e425aba12e9d4 split: test type: clarin-knext/quora-pl metrics: - type: map_at_1 value: 64.917 - type: map_at_10 value: 78.74600000000001 - type: map_at_100 value: 79.501 - type: map_at_1000 value: 79.524 - type: map_at_3 value: 75.549 - type: map_at_5 value: 77.495 - type: mrr_at_1 value: 74.9 - type: mrr_at_10 value: 82.112 - type: mrr_at_100 value: 82.314 - type: mrr_at_1000 value: 82.317 - type: mrr_at_3 value: 80.745 - type: mrr_at_5 value: 81.607 - type: ndcg_at_1 value: 74.83999999999999 - type: ndcg_at_10 value: 83.214 - type: ndcg_at_100 value: 84.997 - type: ndcg_at_1000 value: 85.207 - type: ndcg_at_3 value: 79.547 - type: ndcg_at_5 value: 81.46600000000001 - type: precision_at_1 value: 74.83999999999999 - type: precision_at_10 value: 12.822 - type: precision_at_100 value: 1.506 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 34.903 - type: precision_at_5 value: 23.16 - type: recall_at_1 value: 64.917 - type: recall_at_10 value: 92.27199999999999 - type: recall_at_100 value: 98.715 - type: recall_at_1000 value: 99.854 - type: recall_at_3 value: 82.04599999999999 - type: recall_at_5 value: 87.2 task: type: Retrieval - dataset: config: default name: MTEB SCIDOCS-PL revision: 45452b03f05560207ef19149545f168e596c9337 split: test type: clarin-knext/scidocs-pl metrics: - type: map_at_1 value: 3.51 - type: map_at_10 value: 9.046999999999999 - type: map_at_100 value: 10.823 - type: map_at_1000 value: 11.144 - type: map_at_3 value: 6.257 - type: map_at_5 value: 7.648000000000001 - type: mrr_at_1 value: 17.299999999999997 - type: mrr_at_10 value: 27.419 - type: mrr_at_100 value: 28.618 - type: mrr_at_1000 value: 28.685 - type: mrr_at_3 value: 23.817 - type: mrr_at_5 value: 25.927 - type: ndcg_at_1 value: 17.299999999999997 - type: ndcg_at_10 value: 16.084 - type: ndcg_at_100 value: 23.729 - type: ndcg_at_1000 value: 29.476999999999997 - type: ndcg_at_3 value: 14.327000000000002 - type: ndcg_at_5 value: 13.017999999999999 - type: precision_at_1 value: 17.299999999999997 - type: precision_at_10 value: 8.63 - type: precision_at_100 value: 1.981 - type: precision_at_1000 value: 0.336 - type: precision_at_3 value: 13.4 - type: precision_at_5 value: 11.700000000000001 - type: recall_at_1 value: 3.51 - type: recall_at_10 value: 17.518 - type: recall_at_100 value: 40.275 - type: recall_at_1000 value: 68.203 - type: recall_at_3 value: 8.155 - type: recall_at_5 value: 11.875 task: type: Retrieval - dataset: config: default name: MTEB SICK-E-PL revision: None split: test type: PL-MTEB/sicke-pl-pairclassification metrics: - type: cos_sim_accuracy value: 86.30248675091724 - type: cos_sim_ap value: 83.6756734006714 - type: cos_sim_f1 value: 74.97367497367497 - type: cos_sim_precision value: 73.91003460207612 - type: cos_sim_recall value: 76.06837606837607 - type: dot_accuracy value: 86.30248675091724 - type: dot_ap value: 83.6756734006714 - type: dot_f1 value: 74.97367497367497 - type: dot_precision value: 73.91003460207612 - type: dot_recall value: 76.06837606837607 - type: euclidean_accuracy value: 86.30248675091724 - type: euclidean_ap value: 83.67566984333091 - type: euclidean_f1 value: 74.97367497367497 - type: euclidean_precision value: 73.91003460207612 - type: euclidean_recall value: 76.06837606837607 - type: manhattan_accuracy value: 86.28210354667753 - type: manhattan_ap value: 83.64216119130171 - type: manhattan_f1 value: 74.92152075340078 - type: manhattan_precision value: 73.4107997265892 - type: manhattan_recall value: 76.49572649572649 - type: max_accuracy value: 86.30248675091724 - type: max_ap value: 83.6756734006714 - type: max_f1 value: 74.97367497367497 task: type: PairClassification - dataset: config: default name: MTEB SICK-R-PL revision: None split: test type: PL-MTEB/sickr-pl-sts metrics: - type: cos_sim_pearson value: 82.23295940859121 - type: cos_sim_spearman value: 78.89329160768719 - type: euclidean_pearson value: 79.56019107076818 - type: euclidean_spearman value: 78.89330209904084 - type: manhattan_pearson value: 79.76098513973719 - type: manhattan_spearman value: 79.05490162570123 task: type: STS - dataset: config: pl name: MTEB STS22 (pl) revision: eea2b4fe26a775864c896887d910b76a8098ad3f split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 37.732606308062486 - type: cos_sim_spearman value: 41.01645667030284 - type: euclidean_pearson value: 26.61722556367085 - type: euclidean_spearman value: 41.01645667030284 - type: manhattan_pearson value: 26.60917378970807 - type: manhattan_spearman value: 41.51335727617614 task: type: STS - dataset: config: default name: MTEB SciFact-PL revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e split: test type: clarin-knext/scifact-pl metrics: - type: map_at_1 value: 54.31700000000001 - type: map_at_10 value: 65.564 - type: map_at_100 value: 66.062 - type: map_at_1000 value: 66.08699999999999 - type: map_at_3 value: 62.592999999999996 - type: map_at_5 value: 63.888 - type: mrr_at_1 value: 56.99999999999999 - type: mrr_at_10 value: 66.412 - type: mrr_at_100 value: 66.85900000000001 - type: mrr_at_1000 value: 66.88 - type: mrr_at_3 value: 64.22200000000001 - type: mrr_at_5 value: 65.206 - type: ndcg_at_1 value: 56.99999999999999 - type: ndcg_at_10 value: 70.577 - type: ndcg_at_100 value: 72.879 - type: ndcg_at_1000 value: 73.45 - type: ndcg_at_3 value: 65.5 - type: ndcg_at_5 value: 67.278 - type: precision_at_1 value: 56.99999999999999 - type: precision_at_10 value: 9.667 - type: precision_at_100 value: 1.083 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 26.0 - type: precision_at_5 value: 16.933 - type: recall_at_1 value: 54.31700000000001 - type: recall_at_10 value: 85.056 - type: recall_at_100 value: 95.667 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 71.0 - type: recall_at_5 value: 75.672 task: type: Retrieval - dataset: config: default name: MTEB TRECCOVID-PL revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd split: test type: clarin-knext/trec-covid-pl metrics: - type: map_at_1 value: 0.245 - type: map_at_10 value: 2.051 - type: map_at_100 value: 12.009 - type: map_at_1000 value: 27.448 - type: map_at_3 value: 0.721 - type: map_at_5 value: 1.13 - type: mrr_at_1 value: 88.0 - type: mrr_at_10 value: 93.0 - type: mrr_at_100 value: 93.0 - type: mrr_at_1000 value: 93.0 - type: mrr_at_3 value: 93.0 - type: mrr_at_5 value: 93.0 - type: ndcg_at_1 value: 85.0 - type: ndcg_at_10 value: 80.303 - type: ndcg_at_100 value: 61.23499999999999 - type: ndcg_at_1000 value: 52.978 - type: ndcg_at_3 value: 84.419 - type: ndcg_at_5 value: 82.976 - type: precision_at_1 value: 88.0 - type: precision_at_10 value: 83.39999999999999 - type: precision_at_100 value: 61.96 - type: precision_at_1000 value: 22.648 - type: precision_at_3 value: 89.333 - type: precision_at_5 value: 87.2 - type: recall_at_1 value: 0.245 - type: recall_at_10 value: 2.193 - type: recall_at_100 value: 14.938 - type: recall_at_1000 value: 48.563 - type: recall_at_3 value: 0.738 - type: recall_at_5 value: 1.173 task: type: Retrieval --- ## gte-Qwen2-7B-instruct **gte-Qwen2-7B-instruct** is the latest model in the gte (General Text Embedding) model family that ranks **No.1** in both English and Chinese evaluations on the Massive Text Embedding Benchmark [MTEB benchmark](https://huggingface.co/spaces/mteb/leaderboard) (as of June 16, 2024). Recently, the [**Qwen team**](https://huggingface.co/Qwen) released the Qwen2 series models, and we have trained the **gte-Qwen2-7B-instruct** model based on the [Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) LLM model. Compared to the [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) model, the **gte-Qwen2-7B-instruct** model uses the same training data and training strategies during the finetuning stage, with the only difference being the upgraded base model to Qwen2-7B. Considering the improvements in the Qwen2 series models compared to the Qwen1.5 series, we can also expect consistent performance enhancements in the embedding models. The model incorporates several key advancements: - Integration of bidirectional attention mechanisms, enriching its contextual understanding. - Instruction tuning, applied solely on the query side for streamlined efficiency - Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks. ## Model Information - Model Size: 7B - Embedding Dimension: 3584 - Max Input Tokens: 32k ## Requirements ``` transformers>=4.39.2 flash_attn>=2.5.6 ``` ## Usage ### Sentence Transformers ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True) # In case you want to reduce the maximum length: model.max_seq_length = 8192 queries = [ "how much protein should a female eat", "summit define", ] documents = [ "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.", ] query_embeddings = model.encode(queries, prompt_name="query") document_embeddings = model.encode(documents) scores = (query_embeddings @ document_embeddings.T) * 100 print(scores.tolist()) ``` Observe the [config_sentence_transformers.json](config_sentence_transformers.json) to see all pre-built prompt names. Otherwise, you can use `model.encode(queries, prompt="Instruct: ...\nQuery: "` to use a custom prompt of your choice. ### Transformers ```python import torch import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0]) if left_padding: return last_hidden_states[:, -1] else: sequence_lengths = attention_mask.sum(dim=1) - 1 batch_size = last_hidden_states.shape[0] return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths] def get_detailed_instruct(task_description: str, query: str) -> str: return f'Instruct: {task_description}\nQuery: {query}' # Each query must come with a one-sentence instruction that describes the task task = 'Given a web search query, retrieve relevant passages that answer the query' queries = [ get_detailed_instruct(task, 'how much protein should a female eat'), get_detailed_instruct(task, 'summit define') ] # No need to add instruction for retrieval documents documents = [ "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." ] input_texts = queries + documents tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True) model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True) max_length = 8192 # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ## Evaluation ### MTEB & C-MTEB You can use the [scripts/eval_mteb.py](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/blob/main/scripts/eval_mteb.py) to reproduce the following result of **gte-Qwen2-7B-instruct** on MTEB(English)/C-MTEB(Chinese): | Model Name | MTEB(56) | C-MTEB(35) | MTEB-fr(26) | MTEB-pl(26) | |:----:|:---------:|:----------:|:----------:|:----------:| | [bge-base-en-1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 64.23 | - | - | - | | [bge-large-en-1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 63.55 | - | - | - | | [gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 65.39 | - | - | - | | [gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 64.11 | - | - | - | | [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 64.68 | - | - | - | | [acge_text_embedding](https://huggingface.co/aspire/acge_text_embedding) | - | 69.07 | - | - | | [stella-mrl-large-zh-v3.5-1792d](https://huggingface.co/infgrad/stella-mrl-large-zh-v3.5-1792d) | - | 68.55 | - | - | | [gte-large-zh](https://huggingface.co/thenlper/gte-large-zh) | - | 66.72 | - | - | | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 59.45 | 56.21 | - | - | | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 61.50 | 58.81 | - | - | | [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) | 66.63 | 60.81 | - | - | | [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | 67.34 | 69.52 | - | - | | [NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1) | 69.32 | - | - | - | | [**gte-Qwen2-7B-instruct**](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | **70.24** | **72.05** | **68.25** | **67.86** | | gte-Qwen2-1.5B-instruc(https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | 67.16 | 67.65 | 66.60 | 64.04 | ### GTE Models The gte series models have consistently released two types of models: encoder-only models (based on the BERT architecture) and decode-only models (based on the LLM architecture). | Models | Language | Max Sequence Length | Dimension | Model Size (Memory Usage, fp32) | |:-------------------------------------------------------------------------------------:|:--------:|:-----: |:---------:|:-------------------------------:| | [GTE-large-zh](https://huggingface.co/thenlper/gte-large-zh) | Chinese | 512 | 1024 | 1.25GB | | [GTE-base-zh](https://huggingface.co/thenlper/gte-base-zh) | Chinese | 512 | 512 | 0.41GB | | [GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh) | Chinese | 512 | 512 | 0.12GB | | [GTE-large](https://huggingface.co/thenlper/gte-large) | English | 512 | 1024 | 1.25GB | | [GTE-base](https://huggingface.co/thenlper/gte-base) | English | 512 | 512 | 0.21GB | | [GTE-small](https://huggingface.co/thenlper/gte-small) | English | 512 | 384 | 0.10GB | | [GTE-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 8192 | 1024 | 1.74GB | | [GTE-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 8192 | 768 | 0.51GB | | [GTE-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | Multilingual | 32000 | 4096 | 26.45GB | | [GTE-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | Multilingual | 32000 | 3584 | 26.45GB | | [GTE-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | Multilingual | 32000 | 1536 | 6.62GB | ## Cloud API Services In addition to the open-source [GTE](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469) series models, GTE series models are also available as commercial API services on Alibaba Cloud. - [Embedding Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-embedding/): Rhree versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service. - [ReRank Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-sorting-model/): The gte-rerank model service is available. Note that the models behind the commercial APIs are not entirely identical to the open-source models. ## Citation If you find our paper or models helpful, please consider cite: ``` @article{li2023towards, title={Towards general text embeddings with multi-stage contrastive learning}, author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, journal={arXiv preprint arXiv:2308.03281}, year={2023} } ```
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
croissantllm/base_185k
croissantllm
text2text-generation
[ "transformers", "pytorch", "llama", "text-generation", "legal", "code", "text-generation-inference", "art", "text2text-generation", "fr", "en", "dataset:cerebras/SlimPajama-627B", "dataset:uonlp/CulturaX", "dataset:pg19", "dataset:bigcode/starcoderdata", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,704
1,706
5
0
--- datasets: - cerebras/SlimPajama-627B - uonlp/CulturaX - pg19 - bigcode/starcoderdata language: - fr - en license: mit pipeline_tag: text2text-generation tags: - legal - code - text-generation-inference - art --- # CroissantLLM - Base (185k steps) This model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 185k steps (2.91 T) tokens. To play with the final model, we recommend using the Chat version: https://huggingface.co/croissantllm/CroissantLLMChat-v0.1. ## Abstract We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware. To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources. To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives. This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models. ## Citation Our work can be cited as: ```bash Coming soon ``` ## Usage This model is a base model, that is, it is not finetuned for Chat function and works best with few-shot prompting strategies. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "croissantllm/base_185k" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto") inputs = tokenizer("I am so tired I could sleep right now. -> Je suis si fatigué que je pourrais m'endormir maintenant. He is heading to the market. -> Il va au marché. We are running on the beach. ->", return_tensors="pt").to(model.device) tokens = model.generate(**inputs, max_length=100, do_sample=True, top_p=0.95, top_k=60, temperature=0.5) print(tokenizer.decode(tokens[0])) # remove bos token inputs = tokenizer("Capitales: France -> Paris, Italie -> Rome, Allemagne -> Berlin, Espagne ->", return_tensors="pt", add_special_tokens=True).to(model.device) tokens = model.generate(**inputs, max_length=100, do_sample=True, top_p=0.95, top_k=60) print(tokenizer.decode(tokens[0])) ```
[ "TRANSLATION" ]
[ "CRAFT" ]
Non_BioNLP
espnet/pengcheng_aishell_asr_train_asr_whisper_medium_finetune_raw_zh_whisper_multilingual_sp
espnet
automatic-speech-recognition
[ "espnet", "audio", "automatic-speech-recognition", "zh", "dataset:aishell", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
1,690
1,690
1
1
--- datasets: - aishell language: zh license: cc-by-4.0 tags: - espnet - audio - automatic-speech-recognition --- ## ESPnet2 ASR model ### `espnet/pengcheng_aishell_asr_train_asr_whisper_medium_finetune_raw_zh_whisper_multilingual_sp` This model was trained by Pengcheng Guo using aishell recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 8503eb7de909bfade7c2339641073eb19b33c1f2 pip install -e . cd egs2/aishell/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/pengcheng_aishell_asr_train_asr_whisper_medium_finetune_raw_zh_whisper_multilingual_sp ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Sun Jul 23 12:02:06 CST 2023` - python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]` - espnet version: `espnet 202304` - pytorch version: `pytorch 1.10.1` - Git hash: `` - Commit date: `` ## exp/whisper_finetune/asr_train_asr_whisper_full_raw_zh_whisper_multilingual_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_whisper_noctc_beam10_asr_model_valid.acc.ave/dev|14326|14326|76.0|24.0|0.0|0.0|24.0|24.0| |decode_asr_whisper_noctc_beam10_asr_model_valid.acc.ave/test|7176|7176|74.5|25.5|0.0|0.0|25.5|25.5| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_whisper_noctc_beam10_asr_model_valid.acc.ave/dev|14326|205341|97.3|2.6|0.1|0.1|2.8|24.0| |decode_asr_whisper_noctc_beam10_asr_model_valid.acc.ave/test|7176|104765|97.1|2.8|0.1|0.1|3.0|25.5| ## ASR config <details><summary>expand</summary> ``` config: conf/whisper/train_asr_whisper_full.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/whisper_finetune/asr_train_asr_whisper_full_raw_zh_whisper_multilingual_sp ngpu: 1 seed: 2022 num_workers: 4 num_att_plot: 0 dist_backend: nccl dist_init_method: env:// dist_world_size: 8 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 53027 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: false collect_stats: false write_collected_feats: false max_epoch: 3 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 3 nbest_averaging_interval: 0 grad_clip: 1.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 4 no_forward_run: false resume: true train_dtype: float32 use_amp: true log_interval: 100 use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 12000000 valid_batch_bins: null train_shape_file: - exp/whisper_finetune/asr_stats_raw_zh_whisper_multilingual_sp/train/speech_shape - exp/whisper_finetune/asr_stats_raw_zh_whisper_multilingual_sp/train/text_shape.whisper_multilingual valid_shape_file: - exp/whisper_finetune/asr_stats_raw_zh_whisper_multilingual_sp/valid/speech_shape - exp/whisper_finetune/asr_stats_raw_zh_whisper_multilingual_sp/valid/text_shape.whisper_multilingual batch_type: numel valid_batch_type: null fold_length: - 51200 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 chunk_excluded_key_prefixes: [] train_data_path_and_name_and_type: - - dump/raw/train_sp/wav.scp - speech - kaldi_ark - - dump/raw/train_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - kaldi_ark - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null exclude_weight_decay: false exclude_weight_decay_conf: {} optim: adamw optim_conf: lr: 1.0e-05 weight_decay: 0.01 betas: - 0.9 - 0.99 eps: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 1500 token_list: - '!' - '"' - '#' - $ - '%' - '&' - '''' - ( - ) - '*' - + - ',' - '-' - . - / - '0' - '1' - '2' - '3' - '4' - '5' - '6' - '7' - '8' - '9' - ':' - ; - < - '=' - '>' - '?' - '@' - A - B - C - D - E - F - G - H - I - J - K - L - M - N - O - P - Q - R - S - T - U - V - W - X - Y - Z - '[' - \ - ']' - ^ - _ - '`' - a - b - c - d - e - f - g - h - i - j - k - l - m - n - o - p - q - r - s - t - u - v - w - x - y - z - '{' - '|' - '}' - '~' - ¡ - ¢ - £ - ¤ - ¥ - ¦ - § - ¨ - © - ª - « - ¬ - ® - ¯ - ° - ± - ² - ³ - ´ - µ - ¶ - · - ¸ - ¹ - º - » - ¼ - ½ - ¾ - ¿ - À - Á -  - à - Ä - Å - Æ - Ç - È - É - Ê - Ë - Ì - Í - Î - Ï - Ð - Ñ - Ò - Ó - Ô - Õ - Ö - × - Ø - Ù - Ú - Û - Ü - Ý - Þ - ß - à - á - â - ã - ä - å - æ - ç - è - é - ê - ë - ì - í - î - ï - ð - ñ - ò - ó - ô - õ - ö - ÷ - ø - ù - ú - û - ü - ý - þ - ÿ - Ā - ā - Ă - ă - Ą - ą - Ć - ć - Ĉ - ĉ - Ċ - ċ - Č - č - Ď - ď - Đ - đ - Ē - ē - Ĕ - ĕ - Ė - ė - Ę - ę - Ě - ě - Ĝ - ĝ - Ğ - ğ - '' - ġ - Ģ - ģ - Ĥ - ĥ - Ħ - ħ - Ĩ - ĩ - Ī - ī - Ĭ - ĭ - Į - į - İ - ı - IJ - ij - Ĵ - ĵ - Ķ - ķ - ĸ - Ĺ - ĺ - Ļ - ļ - Ľ - ľ - Ŀ - ŀ - Ł - ł - Ń - ' t' - ' a' - ' th' - 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' D' - ie - ' he' - ' con' - ' ch' - ore - ' al' - ' or' - ' qu' - ' O' - ome - ra - ul - ' N' - pp - ' your' - ould - ' P' - ' fr' - ge - ers - '''re' - и - ' they' - ' what' - use - ' all' - ' The' - ' L' - ess - em - ' kn' - ' just' - art - ' pro' - very - um - ' lo' - ' ì' - ' my' - ok - ' ex' - ab - ' there' - ' but' - ' know' - ' su' - ' G' - Ñģ - ' E' - ' ma' - оР- ' en' - ' about' - ' It' - ist - ' wor' - ri - ind - ' one' - ate - and - ink - ' le' - ort - '''m' - ' F' - ich - ÑĢ - ide - ' get' - ' out' - '...' - ' will' - ãģ - ive - н - ' from' - ain - ' We' - ' up' - pe - res - ca - ' R' - ' if' - ' pl' - ' don' - ack - ' 1' - ' "' - ' tr' - ' us' - ' Wh' - ity - ' J' - ' You' - ' here' - her - ' some' - oug - ak - ard - ' going' - ' un' - ment - ' think' - ' pe' - end - ' (' - cause - ' tim' - ast - é - ' our' - ' want' - ame - ies - ' ë' - ud - ine - ' really' - ' te' - ' see' - ci - ' by' - so - ure - ose - ' [' - are - ' more' - ah - one - ck - ople - аР- ' then' - ' thing' - ' them' - ven - ound - ost - ong - ect - ' right' - ag - ' int' - ' people' - ' when' - ous - pl - ' time' - ' im' - ' who' - ' 2' - ap - ' because' - hing - ' no' - ice - ' look' - ' has' - ' would' - ' how' - act - ' fe' - nt - ough - ' pr' - ' But' - ' say' - Ñĥ - ' now' - ' man' - ' very' - ' work' - iz - ' K' - iv - itt - ' ar' - ep - ' cl' - ' which' - ' co' - ans - '''ve' - ' sa' - ff - '''ll' - ' any' - ' act' - ' ye' - ber - ach - age - per - ' also' - fer - ' these' - ' ad' - еР- ther - ace - ick - ake - reat - ire - ue - ' ag' - ' U' - uch - ions - ry - '00' - na - ' did' - ' que' - ' had' - ' every' - ' He' - ' la' - ' way' - ' sp' - ble - ' This' - ass - ' their' - ite - ' need' - ' part' - ' were' - ' back' - ip - own - omet - be - ase - ' make' - irst - ia - ence - ang - ank - ' got' - ' pre' - ' cont' - ' other' - pt - ' That' - og - ' good' - ' into' - alk - ' been' - ' am' - ' over' - ually - ' â' - ìĿ - ' und' - he - way - ' gr' - ÑĮ - ' dif' - ' per' - Ñı - ' In' - ' tw' - ond - ars - int - orm - ' lot' - ' where' - ' Ã' - ' V' - ' somet' - л - ens - ' gu' - ' ac' - ug - Ñĭ - ı - ' first' - ree - ' his' - ittle - ' imp' - ' mo' - av - ' little' - ' What' - ' much' - ' z' - ' ê' - able - ' п' - ' po' - ' comp' - ne - ' dis' - ' let' - ance - ' her' - ' things' - ' start' - ult - ' app' - ' res' - ' fo' - ' could' - ' inter' - ' those' - ' des' - ' well' - ' two' - ' kind' - xt - ress - ely - ä - ' br' - ' thr' - ' в' - ' i' - ish - ' differ' - ' ro' - ' St' - ' something' - ' take' - ' bo' - ys - ' she' - ' talk' - lo - Ñĩ - ' even' - к - ãĢ - ' н' - ' bu' - ' If' - ' down' - ' Ch' - ade - ations - ' use' - ord - ' off' - ' actually' - ' spe' - du - ated - ater - oss - ning - ü - ' does' - ' Ñģ' - ' new' - ' bet' - vel - cess - ple - ' happ' - ting - onna - ' es' - ' day' - ' only' - ign - kay - sel - ents - ount - ild - ile - ' sc' - ' him' - ' again' - ving - ' gonna' - ' comm' - ' hel' - other - ' ke' - ical - ' 3' - ' el' - ' through' - ' come' - 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' ES' - åĴĮ - '300' - ' ense' - ' ìµ' - ' dict' - GA - ' ahÃŃ' - åĭķ - ' tej' - ' оÑģÑĤ' - ' Elect' - ' intellectual' - ' bias' - ' burden' - çĤ¹ - ' ìĸ´ëĸ»' - ' cheer' - ' soph' - ' portfolio' - uba - ' estos' - TV - For - ' ash' - ' kommer' - ' collective' - ' wrest' - ' Jetzt' - ' Wat' - reich - ' primer' - active - ' mie' - icked - ' hunting' - ' testim' - ' compassion' - ' ر' - ' brut' - ' salad' - обÑīе - ' solving' - ' floating' - ç· - ' attractive' - ÙĪÙĦ - ' perd' - iffer - ' sculpt' - hhh - ' Week' - ' enthus' - ' nad' - ' merch' - ' íĻķ' - ' mile' - 好äºĨ - ' θ' - ' ëĤĺë' - éĩį - '38' - ' chains' - ' Almost' - ' tickets' - rin - ' CC' - ' distributed' - abetes - ' temperatures' - ' gained' - ' flexibility' - ' screaming' - ' abroad' - uno - ' entrepreneurs' - ' Network' - ' Canadian' - ' prev' - ' sö' - ' ÑĤебÑı' - ' Poke' - ' Pod' - ' Turkey' - çı¾åľ¨ - ' abstract' - ' snake' - ' Amy' - ' ëĬIJëĤĮ' - ' brave' - ' ìŀĪìĸ´ìļĶ' - ' Kal' - ' 2007' - ário - ' marked' - gines - ' alloc' - ONG - ' scientist' - ' esca' - ' racism' - ×ij× - ' Sams' - ' Penn' - ' loads' - ' ந' - über - Me - ixò - ' però' - anne - ' expressed' - меÑĢ - ' moet' - ' returning' - nia - ' expon' - Pro - ' loyal' - ML - ' lamp' - ' shy' - ' composition' - ' Ly' - ' magnetic' - ' premier' - ' measured' - ' summary' - ' attacked' - ' finishing' - ÐĹ - ç¥ - ' sits' - ' hydrogen' - ' mai' - ' Deutsch' - ası - ' obtain' - vie - ' soit' - ' ë°Ķ' - ' lane' - ' consegu' - во - ' ease' - akin - ' Fa' - ' untuk' - ' burst' - ' cum' - alım - úblic - idi - ' Royal' - ' Kon' - ' commonly' - ' removing' - ' jur' - ilib - ' anch' - íĸī - ượ - ' ÐľÑĭ' - ' Anth' - ' SÃ¥' - ' interrupt' - ' stere' - ' OS' - onym - tery - ' Maria' - ê²ĥ - ' exploring' - ' transparent' - ' fate' - ' Jung' - ' grup' - ' darker' - ' Doug' - ' mane' - æĶ¾ - ại - dri - look - ' Design' - ' tutaj' - ' horizontal' - reon - orte - ' Correct' - ' Steven' - ' vine' - '02' - iÄĩ - ' siempre' - ' Key' - åĥı - ' Games' - ' naar' - ' shocked' - elve - ' Rose' - ìĭ¬ - ' stopping' - ohl - ' Mix' - ' suffered' - ' sigma' - ' weakness' - ' Ow' - ีà¹Ī - IF - ' à®ħ' - aded - ' Netflix' - anes - ' remained' - iry - ' rip' - ellt - ' silent' - ' proven' - ' toxic' - ' alumin' - ' multipl' - aland - ' 34' - '06' - ' Bru' - ' ìłķë§IJ' - Just - boy - ' shoe' - ' creature' - ' headed' - ' оÑĤк' - æ± - ' essence' - ' remarkable' - ' númer' - ' drew' - ' puzzle' - ' Library' - ' Fu' - ashes - kk - ' Ist' - ¦° - ' Bry' - ' ceremony' - ' à®İ' - ' cri' - equ - ãĤ¢ - ' prize' - ' dimensions' - ogram - ' leather' - ' populations' - uum - ' vegan' - Ñıд - ' cómo' - åĦ - ' strip' - å£ - ' vacation' - ħķ - ' meals' - ilipp - ' ents' - aram - richt - ' grain' - ' Spain' - ' cheek' - ' Aff' - ION - ' Bring' - ' 38' - ielen - ulu - ' болÑĮÑĪе' - ' announcement' - ' ÑĤÑĥÑĤ' - ' Prophet' - ardo - '37' - ' woke' - ' translation' - ' NOT' - ' CL' - ' dÃ¼ÅŁ' - ÑĨÑĸ - acer - ' Loc' - ' perception' - 'NO' - ' diesen' - Look - heart - aved - ' boundary' - ' flows' - Ñijм - ' arguments' - ' elections' - ıs - ' heck' - ' suitable' - ' fiber' - ' Stra' - xy - ' Hum' - ' monthly' - uper - ' golf' - ' lately' - ' Gard' - ' Ren' - ' Ast' - ' Fant' - аÑģÑģ - ' obser' - ë¡ľ - ' easiest' - įĶë - ' websites' - pol - ' cocon' - ' à®ĩ' - ' Veg' - ' walks' - ' intro' - ' directed' - ' Anna' - ' ëĵ¤ìĸ´' - ' Eastern' - ' Saint' - ' Bow' - ' roast' - ' URL' - ' jeden' - uras - aja - ' semi' - ' rapidly' - ' targets' - ' Control' - ' bah' - ' reflection' - ' creativity' - holders - ' ìĺ¬ë' - ' amongst' - ' feeding' - ÑįÑĤомÑĥ - ' виде' - ' ë§Įëĵ¤' - ' Smart' - ' reliable' - ' vezes' - ' ר' - chuckles - azione - ' Williams' - ' aç' - ' slee' - еÑī - ' timeline' - ' thorough' - á»į - ' Ot' - ạn - ' imagination' - ' mechanics' - rist - ' claimed' - ÏĦη - ête - ' Hurry' - ' iPad' - ' constru' - ' Cla' - ' Als' - ä¼ļ - utz - ' cultures' - ' ìĸ´ëĸ»ê²Į' - ' belongs' - ' yer' - ' Doesn' - ' geomet' - ' bid' - ' foam' - ' hob' - ' Britain' - ' substance' - ' anniversary' - 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' idol' - ' reputation' - ' ìĨĮë' - ÙĪر - ' hecho' - ' poem' - ' subjects' - plant - ' Beh' - ' Speaking' - ' batteries' - ' followers' - öl - ' gently' - ' sixt' - ' parameter' - ' ikke' - ' Tour' - ' DJ' - otte - ' Jahren' - ' preparation' - ' дÑĥм' - ' 800' - cop - iking - ' 문' - ' нÑĥ' - ' леÑĤ' - åIJĮ - ' Ide' - ' ì¡°ê¸Ī' - ' laughter' - ' molecules' - ' Rest' - ' observed' - dzie - ' advertising' - erto - ' moins' - ' MIT' - ' excit' - ' tum' - ' tyl' - ' invested' - ' pharm' - ' unexpected' - ' phi' - otype - weise - ' geç' - jourd - ' horses' - nÄħ - =" - ' SM' - ' fib' - ' clips' - çķ¶ - å¦Ĥæŀľ - ' regime' - ' rotate' - rou - nik - ' armor' - ðŁĺ - еÑĢа - 度 - ' Och' - ' richtig' - üzel - aneously - mek - éĮ¯ - ' Xiao' - ' existed' - worth - ãģ£ãģ¨ - ' naught' - ' heiÃŁt' - ' Bal' - ' resid' - ivot - omatic - ' hired' - ' gradually' - ' onions' - ' compat' - ' intim' - ' jew' - ' contribution' - ' Ire' - acji - ' slice' - ' immun' - ' Rus' - ' grows' - ' Similarly' - ' hardest' - ' struck' - ' measurement' - '...]' - they - ' ìłĢë' - ' sneak' - ' applies' - ' нем' - æĵ - ×ijר - ' ЧÑĤо' - ' outro' - ' innocent' - ' mog' - ' Samsung' - ' mercy' - ' handling' - ' intervention' - idays - got - ' curric' - ' boundaries' - ' confusing' - Ŀ¼ëĬĶ - æĩ - ' stitches' - ÃŃvel - ' tunnel' - itä - ' gost' - imy - ' czas' - ' mé' - ' catal' - ' Simon' - ' LIAM' - mic - ' Ф' - ' eyel' - isas - ' CPU' - ' Dou' - ' näch' - ' infinity' - ' rif' - ' Peace' - ' Cu' - ' minimal' - ' listened' - ' pole' - halb - ' loaded' - ' steady' - ' Besides' - êm - ' lap' - ' coop' - ' friendship' - world - ' geh' - ' tylko' - ' Laura' - ' surrounded' - ' Event' - ' chap' - ' Wonder' - break - ' drove' - ' broader' - ' chi' - Fi - ' gehen' - ' western' - ' intelligent' - ' persist' - ' founded' - ãģĵãģ¨ - ' historic' - ' frÃ¥' - cksÃ¥ - ' handy' - ' symp' - ' rows' - ' nutri' - bur - ' Leon' - ' sistema' - ' extensive' - ' Ñĥв' - íı - ' nights' - ' các' - ' counting' - ' Must' - allow - еÑģÑģ - 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解 - ' Marshall' - ' Ihnen' - uetooth - bag - ' Äijã' - ' Highness' - ' ì°į' - ика - ' Wu' - ' Fran' - ' peng' - ' fon' - ' hypothesis' - ' ÑĢÑĥ' - ' ly' - ×ļ - ìĽĶ - ' Radio' - à¸ŀ - Dav - ' embarrassing' - ' ìŀĪìĸ´' - ' casting' - ' cage' - ' Psych' - ' ìĿ¼ëĭ¨' - ' ž' - imb - ' directors' - SH - ' ÏĦην' - á»ģu - ' konuÅŁ' - ' optional' - quarters - iker - ' Sant' - ' verses' - ë¶Ģ - ' olar' - ' Ïĩ' - ãĥķ - ' για' - ' Imm' - ' controversial' - ' ersten' - ' recip' - ' Christianity' - ' ê´ľ' - ordon - ×ķש - ' slash' - ' Pf' - ÑĥдÑĮ - ×ķ×Ŀ - ' Perry' - ' mamy' - ' backgrounds' - ' à®İன' - ' pendant' - ' Columbia' - ' inverse' - ' ÑĩеÑĢез' - ' sv' - ' digging' - '41' - chem - ' navigation' - ' Shin' - ' Front' - PD - ' bearing' - ' Wasser' - ' wax' - ' CHRIS' - ching - ' pressed' - El - ' Dal' - onsin - ' binding' - Ñģкой - poons - ' mock' - arest - кÑĢа - MM - ' corrupt' - storm - ' refres' - ' Coach' - llä - ' THIS' - ' parag' - ' ìĵ°' - pool - ' billions' - ' ê¹Ģ' - group - ' welcoming' - cellence - ' Duke' - 긴 - ' primera' - ìł¸ - ' pond' - ' statue' - ' 구ë' - ' hatch' - ' instrumental' - ' residential' - 커 - ' accepting' - oshi - date - ' ìĶ¨' - ' planted' - ' joking' - ' ìĦľ' - ' hated' - ' ÑĢаÑģÑģк' - ' slept' - ' packages' - ' islands' - esen - ģı - ' diagon' - ' Osc' - ' mesh' - ' scales' - arity - ' Defense' - ãģ¡ãĤĩ - ' Lewis' - ' ÑģегоднÑı' - ' flies' - uinely - ' Consider' - ' stark' - hew - ' AsÃŃ' - ³´ë - ' propose' - ' íķĺë©´' - odo - ' Normally' - ' heeft' - ' Harris' - gro - ' Blood' - base - ' iOS' - ' touches' - ' inspir' - ' ×ĵ' - ' binary' - ' ì¶Ķ' - ' serial' - ' ion' - ' unemployment' - ' odds' - ' Fab' - ' FBI' - BRUN - ' weights' - νο - atile - ' nurses' - ' involvement' - ' íĶ¼' - ' governance' - ' âĤ¬' - ÑĢÑĥп - ierra - íĺķ - ' Jerry' - ' beard' - ' salvation' - ' Along' - gentle - ' Ki' - bol - ' Plat' - ' hasht' - è¿ij - ' ware' - ' partie' - ycz - ' intr' - Fih - nent - ' cheat' - ilen - ' ë¯' - orie - ' fácil' - etric - ' affecting' - unciation - ' affairs' - ' bee' - ' viewing' - ' orang' - ' Lan' - ' СÑĤ' - ä¸ĸ - ' Mes' - ĥģ - erie - ' espa' - ' interpre' - ' possess' - ' purely' - rito - found - asma - ìłģìĿ¸ - ' examine' - ' Ñĥм' - ' besch' - ' Tomorrow' - ' Block' - ' variant' - ' preference' - ' coaches' - ' medications' - ' íĺĦ' - ' empire' - ëĦ¤ - ' Illinois' - ' crispy' - ' thì' - ' bees' - '77' - ' glow' - èº - ' Studies' - åIJĦ - ' Challenge' - ' unlikely' - Ч - ıyorsun - DIE - ' minimize' - izard - ' ún' - ' encontrar' - ' Kill' - å» - ' vanilla' - ' Grant' - ' GT' - sea - ' sought' - вод - ' näm' - ' Aunt' - OWN - ' pumpkin' - stellen - ' rag' - егда - ' storyt' - ' forum' - æ©Ł - ' estaba' - uche - ' congress' - ' Rey' - ' dramatically' - ' Sport' - ' Yellow' - ' ê³ĦìĨį' - ' disgusting' - ' Recent' - ' acquired' - ' cables' - çĶļ - din - ' visto' - ' communicating' - ÑģÑĤавлÑı - еÑģÑĤо - ãĥ»ãĥ»ãĥ» - ' rég' - ' socks' - ' proces' - because - ' utter' - ' colocar' - ' newest' - ' gramm' - 表 - ä¸įçŁ¥éģĵ - ' shifting' - ' carrier' - ' ÑģкоÑĢ' - ' Schw' - ' executed' - ' maintained' - ' ÏĨ' - ' Moses' - ' disse' - ' horr' - ãĢľ - ' rally' - ' allem' - ' Eventually' - ' diyor' - lvania - ' schnell' - ' ê³¼' - ' 매' - ' struggles' - late - ' clarify' - ément - ' multiplic' - ибо - ' journ' - ' fragr' - ' surprisingly' - ' desperate' - '52' - ' sul' - ' Read' - ' Fried' - ' mond' - woo - ' organizing' - ãģĹãĤĩãģĨ - ' Soon' - ' вопÑĢоÑģ' - ' Nur' - ' ÐĹд' - ' spider' - еÑģÑı - ' tutorials' - ' nutrients' - orer - ' coefficient' - ' arrangement' - ' pricing' - nan - yu - BL - ' tribe' - ' Howard' - unks - ' newer' - ' provin' - ' prediction' - hos - ' olsun' - ' Around' - ' vier' - ' ÑģÑĤоÑĢон' - ' valley' - ' Ela' - ifi - ' galaxy' - ' tranqu' - ' advers' - ' Temple' - iffs - igence - èĩªå·± - ' könnte' - ' Äijó' - Did - ' photographs' - ' AWS' - ÑĨиÑı - ' guards' - ' appointed' - ' Gil' - ' мом' - ' cod' - ' Unlike' - ' evenly' - isconsin - ' estou' - ' mnie' - ' Exec' - ' MV' - ' Eine' - ä¿¡ - ' Roger' - ' Fac' - ' List' - ' fuer' - аеÑĤе - omed - ' attraction' - èī² - ' terrain' - ' Drop' - ' corporations' - ' sciences' - ' throne' - ãģĦãģŁ - ' aj' - ' Rot' - çī¹ - ' supporters' - ' Bere' - Here - ' diferentes' - ' significance' - Ïĥη - æĪij覺å¾Ĺ - ' clamp' - ' ëĮĢë' - ' fabulous' - rez - æĮģ - ' assumptions' - uther - wid - pot - è¿İ - ' yan' - ulin - ÑĢÑĭв - ' Slow' - ' Pennsy' - ' íķ´ìĦľ' - ' meio' - ' wealthy' - ' Eight' - ' pulse' - ' friction' - idity - ' Holl' - iyorum - ' sounded' - ' Carr' - ' fork' - âĺ - ' PA' - ' conspir' - ' coding' - rt - ' Typ' - ' ìĸij' - ' пог' - ' miser' - ' ÑģмоÑĤÑĢ' - ' Sweden' - ' olarak' - ' Zhang' - ' Chi' - ' Titan' - ' screening' - ' Spider' - ' Åŀimdi' - ' obstacles' - lara - ' challenged' - pse - TON - ụ - ' Pi' - ' lagi' - ieurs - ' hurting' - ' neglect' - ' generating' - ' youngest' - ' audit' - ' ÑĢез' - Ïģά - ' donate' - ' PDF' - ' visits' - ' cruise' - PP - aser - ' wsp' - backs - ivals - ãģĨãĤĵ - ' deve' - ' proport' - ' cath' - ' Effect' - ' winds' - ' ìĻĶ' - ' charts' - ' sama' - ' automation' - ' пока' - ' olan' - ' boats' - ' cafe' - ' denied' - ' Mama' - ' blocking' - ' Thor' - ' phenomenal' - ' stakeholders' - ' unos' - ÑĥеÑĤ - ' Abraham' - ãģ§ãĤĤ - ' detection' - ' juris' - ' powered' - zial - ' welfare' - ' upgrad' - ' można' - ' Case' - cular - ĶìĿ´ - ãĥģ - ' Guess' - ' cycles' - ä¾ĭ - 給 - rock - umi - ' elite' - ' què' - åł± - ÑĤом - ' shore' - gunta - ' ku' - ' faithful' - ' Jeremy' - aid - à· - ugal - å°įåķĬ - ' Vel' - ' vrai' - stell - ¨¸ - ' kol' - è½ - ' quanto' - ' заÑĢ' - ' 2002' - esy - ' reserve' - ' моменÑĤ' - ' deployed' - ' defining' - ' sau' - ' gaat' - '")' - ' transmit' - ' publishing' - ' ranking' - ' offense' - ' 46' - pin - ' Taking' - ' entitled' - ' genuinely' - ' variations' - ' finde' - ' tau' - ' unfortunate' - ' Rah' - ports - ' cÅ' - ' monkey' - ' brac' - wei - lung - ' artif' - ' syrup' - ' ÐĶав' - ' lifted' - ' chez' - ' Advent' - ' Stock' - ' dol' - мен - иÑĪÑĮ - ' yn' - gio - det - ' desse' - ' gri' - ' Chairman' - çħ - ' cuenta' - anim - ' crab' - ' escal' - ' première' - ' Gef' - ' dining' - ' seventh' - ' chasing' - ' Tower' - ' brutal' - ' fundamentally' - ãģ¨ãģĨ - лениÑı - stage - ' acquis' - ' cylinder' - ' commander' - mem - ' UV' - happy - ' epsilon' - ' invitation' - ' farmer' - chair - ' destiny' - ' sovere' - ' Hebrew' - ' servant' - ' bew' - ' gast' - uties - ' administrative' - ' Command' - éta - ' nitrogen' - ê·¼ - ' abi' - ' villain' - ' blanket' - ' Send' - ' beaten' - ²Ħ - ' volunt' - ' scholar' - ' Emperor' - ' 43' - vable - ' Dus' - ' GU' - ' targeting' - www - ' amendment' - ìĨĮë - ' ting' - ' nasty' - ' gauge' - ' ÑĢод' - ' Hans' - Your - αν - ' projet' - ' Hawaii' - ' suspicious' - ' schw' - ' removal' - ' intrig' - ' MU' - ' ponto' - ा - ' обÑĢаз' - ' guessing' - pace - ' mothers' - ' millimeter' - ление - 没æľī - ' availability' - icz - æѤ - ' fract' - ' bases' - km - ' BTS' - ' Field' - ' dzie' - ' segundo' - ' ëĤĺëĬĶ' - ' legitimate' - imas - ' вн' - ' corruption' - ' smash' - ' Valent' - ' aligned' - ' Pennsylvania' - ' gab' - ' Eun' - enth - ' Morning' - ' candle' - ' backpack' - ' Islamic' - ações - ' encry' - ' mushrooms' - íĮĮ - dit - ' transit' - ' Wisconsin' - ' participated' - ' Ils' - ' unfold' - ¶Ģë - ' profits' - ' warming' - ' Gang' - ' networking' - ' mega' - ' thoroughly' - lements - ' Hm' - ' deciding' - ' emotionally' - ' exhausted' - ' ÐŁÐ¾ÑĤ' - cido - ' HTML' - ' copyright' - ' melody' - yim - ' anders' - oshop - ' ë³¼' - ' athlete' - ' GE' - ' frequent' - ' desires' - ' needing' - ' Yun' - ' rifle' - ' lover' - '''T' - ' dense' - ' tão' - ' notified' - ' idi' - ìĹŃ - íĨ - ' interacting' - ' rapport' - еÑĢи - ski - ' besser' - ' manufacturer' - ' Kyle' - ' accountable' - ' Sak' - ' Pil' - ' Domin' - ' presum' - ' ÐĴÑģе' - ' vinegar' - ' guaranteed' - çľĭåĪ° - ' handled' - éŁ³ - cat - ' civilization' - ' accomp' - ' VM' - émon - ' deze' - ' grades' - ' sollte' - ' staring' - ×IJת - arnt - ' horizon' - ' travail' - hour - 第ä¸Ģ - ' ED' - ' Dak' - ' ny' - ' conve' - ' Cham' - ' firms' - ' Liu' - ' ÑģÑĤÑĢан' - ' libert' - ' lenses' - ' intake' - ' вÑĭб' - ' mensen' - hel - ' practition' - ' 350' - ãĤ³ - FO - ' beds' - ' ancestors' - ' ìĹĦì²Ń' - ' disturb' - ' Lastly' - ' Support' - ีà¹ī - ' Corona' - ' enthusi' - ' возм' - ' ìĤ¬ëŀĮë' - ' 52' - bird - ' reduces' - ' ìŀĪìĿĦ' - ' Gene' - êµIJ - ÄĻp - ' Ãľber' - ' concerning' - user - ' concentrate' - ' WHAT' - ishop - onymous - nold - ' suggesting' - ©° - ' Fish' - '........' - ' vessel' - ' trabajo' - ãģµ - ' Ocean' - å§IJ - yg - ' towns' - del - ' terrifying' - ' çalÄ±ÅŁ' - ' sino' - ' eats' - ' gez' - ' geme' - ' ìĻĦ' - ' compart' - ' implementing' - ' Potter' - ' Germans' - ' gÅĤ' - ' tennis' - ' carpet' - auer - ' Saudi' - yeong - ' curry' - ' Forest' - Ñĭл - ' fifteen' - ' bolts' - ' {\' - ¬´ - ' settlement' - ' lange' - ' bam' - Get - íķĻ - ' swap' - ' Khan' - ' commence' - ' quarantine' - ' scored' - çĸ - ' 1950' - ' thicker' - ' sûr' - åı£ - ' Larry' - ' allez' - ìĭľëĬĶ - ' gü' - ' spectacular' - // - both - ' stats' - 妳 - ' Nancy' - ' bunu' - ' crust' - ' activated' - ' ê·¸ëŀ' - outhe - ' ports' - ' neural' - ' jaw' - ' observations' - ' voit' - aban - ải - ¦¬ë¥¼ - omes - à¯ĭ - qui - ' kindness' - Ðij - ' 41' - ' moderate' - ' angels' - ' Tamb' - èt - ' chlor' - ' Billy' - ì²ĺë - acon - ' selecting' - ' Delta' - ' null' - denly - ' ciud' - ' tendency' - ' breakdown' - ' mint' - ÑĦоÑĢм - orph - ' dawn' - spr - ' WILL' - ächlich - ' puppy' - '700' - ' த' - ' fails' - ' Conc' - ' relatives' - ' inviting' - ' autonom' - ' composed' - ' unity' - ' decis' - ' accessories' - ' Cass' - ' bist' - ' Tip' - 째 - ' punt' - ' ráp' - éĢ² - ANK - ãģļ - exist - ' compatible' - ' ner' - ' емÑĥ' - ' aplic' - ' bapt' - ' failing' - ' Tamam' - ' oscill' - ' letzten' - ' repeatedly' - ' jungle' - ' Push' - hai - ' η' - ' deadly' - Ñıж - wiÄħ - ' Common' - ' Îķ' - ' skate' - TC - ' Mini' - ' hobby' - ần - ' routes' - ' amigos' - ' conjun' - ' partnerships' - ' novo' - ' aver' - ' pouvez' - bridge - ' preoc' - him - ' turb' - ' sob' - ' Snap' - ' ì°¸' - minute - ' traject' - ujÄĻ - ' eager' - ' regulatory' - ' banking' - bling - ÑĪÑĮ - aż - ' bizarre' - itated - dire - ' threatened' - ' shining' - ' nesse' - ' corps' - ' ÑģÑĥ' - ' teles' - ' temp' - tem - ' кан' - ' fever' - New - ' heavier' - ' Sah' - bud - ' outros' - ' ì°¾' - ' ëªħ' - arring - ' ê´ľì°®' - ' Nap' - ' semin' - ' Than' - ifs - ' desen' - ' ÑĤакое' - ' loses' - ' Balt' - kon - ' напÑĢ' - ' vois' - ' Moscow' - ' chairs' - his - ' refugees' - kg - ' kole' - į¨ - аÑģибо - ¦½ - ' Universe' - ' Direct' - ' cheating' - ' Cin' - ' patri' - ' advise' - ' Nether' - ' primeiro' - ' mentioning' - nut - '56' - arı - ' petite' - bled - ' pensar' - icio - IND - ' veteran' - ' ladder' - ' consequence' - ожал - ' Burn' - ' rug' - ' Made' - ' git' - '"...' - ' competitors' - ' przed' - ' apparent' - ' Argentina' - ' Working' - ' collaborate' - woman - ' retain' - ' leurs' - ' dashboard' - ×Ļ×ĵ - ' Early' - BM - ' еÑij' - олог - ' satisfying' - ' oftentimes' - ' mapping' - ünkü - arth - fold - ' launching' - ' aura' - ' precision' - works - God - ' strap' - ' Imper' - ' rivers' - ' |' - ' cuer' - regon - ' arrival' - каÑħ - ' Miami' - анÑĭ - ' survivors' - ' Senior' - David - ' estado' - ' sectors' - ' popping' - ' chim' - ayı - ' kunnen' - ' gallery' - ' sunlight' - esehen - ' yelling' - ' Mein' - ' Phoenix' - ' mano' - ' historia' - ' occurring' - 欸 - ì¸ - ади - å¾ħ - ' institutional' - ' Tut' - ç² - ' slaves' - ãģ©ãģĨ - ' forgiveness' - ' twin' - ' Hyun' - нÑĮ - ' Komm' - andra - shot - ssä - ' ÑĨе' - atta - ' expense' - ' GPU' - ' Past' - ribly - ' ëŃIJìķ¼' - ' года' - ' respir' - æĿ± - ' Queens' - hops - ' série' - ' pref' - ' comed' - ' plut' - ' Overall' - ' ãģĿ' - ' cush' - ' ringing' - ' incorrect' - ' ÑģÑĤÑĢ' - ' geometry' - ' advertis' - ' Ш' - ' reviewed' - ãģĤãģĤ - ' dozens' - ' determination' - ' Phill' - ' contributed' - ' Cit' - ' passengers' - ' côté' - ' rever' - ' technological' - ' allen' - ' raining' - avi - ' salty' - ' typing' - ' ÑĤе' - ' tilt' - ' ì¹ĺ' - ' оÑĢ' - ' пÑĢÑıм' - ' rou' - ' arena' - arat - åĪ« - HHHH - ' manufacturers' - ' Edward' - ' tuck' - ' blows' - ingo - ' Marc' - ìķĦìĦľ - Mich - ' Clean' - è´ - esto - ' Pack' - ' shaft' - BRUNO - ' aven' - uur - ÑģколÑĮко - ê´Ģ - ' automated' - ' venture' - ' surveillance' - ' Grow' - ' Emer' - ' доÑĢ' - ' investor' - ' Yok' - ' latter' - ' NI' - ' functioning' - ' Hamilton' - ' 51' - ' murdered' - ' anchor' - ' cuc' - ' SCP' - ' Madam' - ' constraints' - ' barn' - anken - ' ë§İìĿĢ' - ' Motor' - ' Doing' - ' amen' - etts - ' instructor' - egt - ako - ' posture' - ivia - ' Polish' - ' два' - ' colorful' - ' elbow' - ' parle' - ' passer' - ' condem' - ortal - ' fertil' - اد - ' Colomb' - ' alignment' - ' astronaut' - ' Mut' - ' salmon' - ' structured' - ŀר - ' clicks' - ' miej' - æĶ¿ - ãģĦãĤĦ - ' Round' - ' rainbow' - ' VA' - ãģĶãģĸ - ì§Ī - otz - ',</' - ' Nicole' - lishing - ' whilst' - ' republic' - ' tamam' - verted - ' recognizing' - ' глав' - ' dub' - ' Jos' - falls - ichi - ' czÄĻ' - ' Ц' - ' Mitch' - CR - click - ãģĦãģ¦ - ' stunning' - ' Julia' - mers - ' Poly' - ' dessa' - ' inté' - ' ê³łë' - ' doÄŁ' - ' diver' - ' striking' - aphor - ' apenas' - ouses - ' tragedy' - ' Fan' - ' Turkish' - ' prophet' - ' distancing' - ' Hem' - ' cartoon' - Ke - anting - ' Clark' - ç¿ - ' davon' - ' íħ' - ' yummy' - ' compromise' - ' startup' - ritt - ' certified' - ' pillow' - bere - ì¤Ģ - ' seguir' - ' stadium' - ativo - ' simpler' - ³¸ - ' visa' - ' pathway' - ' nuevo' - ' ray' - ãĥIJ - éľ - Ã¶ÃŁ - ' зан' - ' celebrity' - за - ' eines' - ' Given' - ' Ara' - ' Job' - ' yak' - ' Arbeit' - ressing - ánd - ' grabbed' - pend - ' sine' - irk - ' ÐŀÑĤ' - ' Fle' - ichen - ç¦ - ' Neil' - èĻŁ - ' repeating' - ' drawings' - rise - ' glitter' - five - ' surt' - ' sicher' - ' adjustments' - ' éĤ£' - ippi - cke - ' representatives' - ' midst' - ' spoil' - meye - ' tags' - ' yep' - ' Stephanie' - ' gere' - ' Rud' - çĭ - ' gros' - ' queue' - ' accord' - ' organisation' - endy - ' Text' - üyor - ' ÃŃ' - ' conclus' - ' ì¤Ģë' - ' amp' - ' Less' - ' ëIJĺëĬĶ' - cano - ' Pix' - aped - ' darauf' - uo - ynth - abel - ' Done' - ' dick' - athon - ' hilar' - acco - ' ìĨį' - ' Oregon' - ' Weil' - ' mathematics' - ' alm' - ' pixels' - ' frÃ¥n' - бо - FC - нÑİ - heim - gos - ' Forget' - fend - ' VoilÃł' - ' Greet' - ' αÏħÏĦ' - ' recur' - æĶ¶ - '51' - ' ìŀĪê³ł' - At - ' yards' - иÑĤи - ' offset' - rolling - ' ÐŁÐ¾Ñģ' - ' enlight' - ' Pad' - limited - илÑĮно - ' Sara' - ' ÑģделаÑĤÑĮ' - mart - ' Jump' - ' adorable' - orse - cheering - ' empathy' - ' Tonight' - orp - ' Hunter' - Point - га - ' passenger' - ' Knight' - ' seemingly' - huh - ' theatre' - ' tomb' - ' depressed' - ' summon' - ' satisfaction' - doors - ' Houston' - аÑİÑī - ' Rio' - глÑı - ' arranged' - ' handles' - ' trillion' - ' nightmare' - ' Quando' - ' ole' - ' Guide' - ooo - ' bile' - ' empez' - ' 72' - cribed - ' progression' - ' Linux' - 리 - ' ì²ĺìĿĮ' - ' fossil' - ' quero' - ìĨ¡ - ativa - ' puzz' - ' Zus' - ãĤª - ' thrilled' - ' CB' - ' miner' - ÑĢаÑī - ' SAR' - ' Nos' - ' гоÑĢод' - ' camb' - ' ÑĤа' - ' resulted' - ' Dick' - oung - ' comics' - ' absolut' - stan - dimensional - ' tense' - mus - ' Intell' - ' ÑįÑĤÑĥ' - ' phases' - ' volta' - ' vão' - bound - ' Anderson' - ' curiosity' - ' pont' - éĢĻ裡 - ' demonstrated' - oline - ' Speed' - ' mama' - ' shocking' - ' kiedy' - ' earthquake' - ' implies' - ' enters' - ŀĢ - ' elevator' - ' delighted' - ' Mitt' - ' Based' - ' Dol' - ' ken' - ' worrying' - ' filed' - ailand - ' меÑĤ' - ' masc' - ' Îij' - ' Julie' - ' dimensional' - human - Tok - ÿ - ' unst' - ' seule' - ' embar' - ' íķ©ëĭĪëĭ¤' - acion - ' ìī' - ' ë¶Ģë¶Ħ' - ' heated' - âĢ¦âĢ¦ - '"!' - ' realise' - еÑĤÑĭ - ienia - iez - ' füh' - ' Esse' - ' ps' - ' dó' - asters - ' ons' - PM - ' retro' - maker - when - ' ella' - ' Living' - ' Lam' - ' trong' - ' approve' - ' θα' - ' sung' - ениÑİ - ' Remove' - ène - iren - ' stranger' - инÑĭ - ' væ' - after - otto - Ķë¡ľ - ' Ahora' - mill - ISH - ' graduating' - kte - ' renov' - ' processed' - keys - еко - ' enrich' - ' ÅŁek' - ' insec' - ' Nan' - cakes - ' illusion' - ĺ를 - ' airl' - ims - ' anten' - ững - sn - ' precisa' - 기ìŀIJ - ' اÙĦع' - ' foremost' - ' paragraph' - avais - ' воÑģ' - ' mans' - ÃŃfic - bot - ' عÙĨ' - ' broth' - ' alternate' - ' Chapter' - ' vectors' - esar - ' indication' - ' Nein' - ¶ģ - ' jeans' - YE - cond - ' united' - abi - ' Serge' - ' partially' - ' macro' - æīį - å¼µ - ' ethical' - ruit - ' shifted' - ' cabe' - ' mathematical' - ' rude' - ×Ļ×ķת - ' Merc' - ' ganze' - icion - ' unconscious' - ' burnt' - ' ÑĢеб' - íĬ¸ë - ' charm' - andal - ì²ľ - othy - ' Hadi' - ' appreciation' - END - ' réal' - ¶Ħëĵ¤ - ' Nag' - ł¤ê³ł - ' Lauren' - ' vỼi' - ' Bridge' - ' Umm' - ' Weg' - ' chaque' - ' Soph' - ' gdzie' - íijľ - ' ster' - ' Bla' - ' reflects' - ' benchmark' - ваÑĤ - amine - ãģ¡ãĤĥ - ' anh' - ' continent' - ' FDA' - ì¡° - ' êtes' - ×Ļ×IJ - å¼Ģ - ' bloody' - ' Nine' - ielt - emand - ' ë³´ê³ł' - ' tidak' - ' Scient' - plex - osten - ' animated' - assa - ' derived' - ' иÑģÑĤоÑĢ' - ' Mig' - ìħĺ - ' ros' - plus - osaur - ' ^' - ' intensive' - ' globally' - ' diferen' - ìĿ´ê³ł - ä½łçļĦ - Äħd - ' dés' - ' presentations' - ' Cro' - ' esses' - ' Between' - Pa - ' naw' - à¸Ńà¸ĩ - ' breed' - ichte - ' Ðŀни' - ' Building' - ' conform' - MO - ' Ðĸ' - ' Kid' - nas - ' Due' - rés - ' diox' - ' Bin' - ' taxi' - ' sap' - ' Hub' - çĤºä»Ģ麼 - ' centered' - ' surge' - ' avons' - ' learnt' - ' Yam' - ' Diese' - ники - ' Beij' - Will - ' attempted' - ' grief' - ój - ' kidney' - ' opponents' - æĽ´ - ' nome' - '57' - ÑıÑĤно - ' midnight' - Announcer - acity - oned - ' puedes' - ' problematic' - ' cops' - ' Pete' - rint - unted - ' bip' - æ¢ - ' ÃĢ' - ' cens' - atively - ' ä¸į' - ' urgent' - ' struggled' - achus - ' microwave' - ' Side' - ' Denn' - ' Ñıв' - ' urge' - ' forcing' - wang - ' коÑĤоÑĢаÑı' - ' mamm' - ' ðŁİ' - ' tribes' - ' Shadow' - ' Sang' - ' Hitler' - ' lun' - ' scent' - ì§ij - ' overwhelmed' - ' bombs' - ' crimin' - ' consolid' - ' molecular' - ×ķק - nor - ' perceived' - ' vé' - ' altogether' - ' orth' - ' vem' - ' zwar' - izo - Å« - ' melted' - orden - ' Charlotte' - ' Excel' - arta - ìľł - ' Gew' - ' romance' - eremos - ' colonial' - ' traditionally' - ' quan' - hoo - ' championship' - ' arbitr' - ìħĶ - ' мин' - ' selfish' - ' blew' - rying - ' operators' - ' jurisd' - ıħ - uition - ' EC' - ' Anybody' - vate - ieties - ' analyst' - ´ìĹIJ - ' вÑģегда' - çek - ' Kun' - ' aging' - Õ¡ - ÑĢаÑĦ - ' Moment' - ' Hua' - èĥ - then - ела - estone - ' ende' - ' awarded' - ' nächsten' - ' Spot' - ' Neg' - ' fairy' - 代 - ' Cover' - ' deposit' - ' stressful' - ' junk' - ' metabol' - Ja - ' ê·Ģ' - ' undergraduate' - ' cancell' - ' consensus' - ' oso' - éĩij - ặ - ÄŁer - rada - ' Palace' - ' pedal' - ' exagger' - ' behavioral' - player - lles - ' connector' - ' skept' - įĶëĿ¼ê³ł - ' mitt' - ' Haha' - ' peque' - ' Gott' - fang - à° - jos - ' kicking' - ' mounted' - ' replacing' - vos - ' quietly' - ' milit' - ' owns' - ' niveau' - ' aur' - ' Buy' - ' predicted' - ' cows' - ' poner' - ' Dri' - ' remarks' - ' reporter' - ' arkadaÅŁ' - еÑģÑĤи - ' saves' - ' çoc' - ' metaphor' - ' Kel' - station - sembly - ' advisor' - ' workshops' - ' accounting' - ' tok' - nier - inner - ' burada' - ' BB' - ' Olympic' - ' Pract' - Christ - ' ÑģÑİ' - ' kas' - ' viewed' - ' markers' - ' foto' - getic - ' Lucas' - ' pads' - ' Joh' - ' CDU' - affen - arem - ' Beck' - ' Gosh' - shit - ãģĮãģ¨ãģĨ - ' Mater' - abulary - ' Room' - llen - ' Following' - ' doit' - balls - ixa - ' grounds' - ' ìŀĪëĬĶëį°' - LS - ' wildlife' - ' SQL' - ' shifts' - ä¸Ģé»ŀ - Book - ' hosted' - llor - ' snaps' - ' besoin' - ' ש×Ķ' - ' peanut' - äft - ¹ł - ÅĽl - Audience - ' Barbara' - ' adoption' - ' wolf' - ' оÑģнов' - arda - ' expose' - ' ì¦' - jas - Äĵ - ' countless' - ' ì§ģ' - health - uent - iso - otion - ' hunger' - ' mois' - offs - ' claiming' - ' Îļ' - ' Belg' - ' най' - 기ëıĦ - ' unpre' - ' ged' - ' Io' - ' поÑģмоÑĤÑĢ' - ' coÅĽ' - ' Narrator' - ' Ãĩok' - íĻ© - à¸Ńย - cipl - ' timer' - ' defic' - avin - ' categor' - ' throws' - ' ëĤľ' - ' поÑģлед' - ' Thai' - ' mascul' - ' bekommen' - ' internation' - ulse - ' aye' - ' poi' - ' pixel' - Chris - ' stove' - οι - ' generator' - ' 컬ë' - ' academ' - ' practiced' - ' aquest' - ' contributing' - ' Ig' - ' ợ' - ' containing' - ' wrestling' - ' Ñĩего' - haupt - ' essas' - velope - ' exceptional' - YU - ' Applause' - ricane - ' convenience' - ' делаÑĤÑĮ' - илиÑģÑĮ - ' Environ' - '85' - ' câ' - ' ìķĪëħķíķĺìĦ¸ìļĶ' - ' MO' - ' Pope' - ' sah' - obi - ' masters' - aines - ' blessings' - ' obey' - ' flux' - ' brow' - ' ìĭ¤' - ' popularity' - ' Lamb' - zeug - ìĻĶ - ıĦë¡Ŀ - ituation - ' accompan' - ' dialog' - ' Jamie' - åĬłæ²¹ - ' sewing' - ' bleeding' - ' bail' - ' threads' - odge - ' Shang' - ' deployment' - ched - ' satisfy' - ' laz' - ' missile' - ' Linked' - ' makers' - cium - fre - ' 먼' - ' 무ë' - ' Edge' - ' societies' - ' agua' - ' synchron' - ¡ł - unft - ' unm' - ' triang' - ' injust' - top - ' oral' - kor - ' íķ¨' - ldigt - ceÄŁ - quet - ' Leo' - ' savoir' - ' eastern' - ieu - ' exped' - ' Сп' - ' unnecessary' - ' Perform' - ' Ming' - ' ÑĢав' - ' intentions' - ' compression' - ' Sac' - ολ - arson - ' trouve' - ' Muhammad' - ' вÑĭÑģ' - ' finite' - ' наÑħод' - uga - ÑĢазÑĥ - ' celebrated' - ' confess' - ' squares' - ' Gordon' - ' ëĤĺìĺ' - ' syndrome' - ' completion' - ' backing' - ' darf' - ' Quran' - ' intermediate' - ' ker' - ' dü' - hesive - ' accountability' - ' Rebecca' - èijĹ - ' Sleep' - ' différent' - ols - ' Rice' - ' 본' - ' antibiot' - ÏĦά - rz - ambling - ' sensitivity' - ' chron' - allas - '64' - ' fleet' - ' optimistic' - Ñģкого - ' jadi' - ailleurs - ' Enough' - ' senin' - ' packs' - bn - ' Area' - ' Tro' - ¨ë¦¬ - аÑĶ - ' Thom' - ' harmony' - ника - ' someday' - ISE - ' Broadway' - lares - erness - à¹Ħม - ' Tenn' - ' NATO' - ãĤĬãģ¾ãģĻ - ' minutos' - ' Kansas' - ' Mong' - ' compte' - åĽĽ - Ĭ¤ - ' ìĹŃ' - ' superhero' - ' Garden' - ' Mos' - ' attachment' - ' bust' - à¯Ĭ - ' Thailand' - stat - ' spice' - ' Leb' - ' leap' - zech - GL - ' verl' - ' fixing' - ' ë³´ë©´' - ' porn' - ' büy' - ' Ùħا' - ' Virt' - ' Tommy' - ' cargo' - ' Olha' - ' roku' - ÙĥÙĨ - ' baked' - ' tactics' - ' marketplace' - ' która' - arlo - ' switches' - ' cache' - ' HR' - ' Gan' - ' GPS' - ' duas' - heres - еÑĢÑĪ - track - ' lungs' - Station - iggles - ' camping' - åĵĩ - ' completing' - amas - ' cycl' - ' prototype' - ' Judge' - otypes - ' infections' - ł¤ë - еÑĢг - oba - ' Bod' - ' Secondly' - ' apost' - ' sogar' - ' reass' - iek - æĸ¼ - ' ashamed' - ' curves' - ' важ' - ' ensemble' - atur - ' photographer' - ' eighth' - ' wasted' - ç®Ĺ - ' damp' - ' мал' - arena - ' internally' - ' heels' - ' Salt' - ' blir' - ĪëĤĺ - ' contrary' - ' prima' - ' oss' - ' rabbit' - ' autor' - ' broadly' - ÃŃst - ' backs' - íĶĦ - eto - ' jury' - è± - ' prostu' - ' bara' - ' parliament' - orient - илаÑģÑĮ - ' indirect' - ám - ' Ã¥r' - ' traits' - ' dÃŃas' - ÙĦÙħ - ' CT' - alyst - ' livest' - ' kos' - May - ' Jing' - ' journalists' - Ñĩик - arms - ' ê°IJìĤ¬' - ' име' - ' égal' - ' Newton' - ' recovered' - ' brauchen' - ' Bron' - ано - ' pale' - prises - ' horas' - chts - éĢļ - ÿÿ - akers - ' Alaska' - ziej - ' scoop' - ìĿ´ê°Ģ - ãģķãģĦ - cor - élé - ' surg' - ' viene' - ' Krist' - '54' - ' banned' - ' smoothly' - ' treats' - ' pronounce' - ' flush' - ' cambi' - ' musician' - ' Ashley' - ' SPD' - ' Bobby' - ' gloss' - respect - ' reviewing' - ' generic' - Æ°á»Ľc - atsächlich - ' healthier' - ubers - ' дан' - ' Medicare' - '53' - ' complaints' - jac - ' agricultural' - Spe - ' Jong' - ' dioxide' - 겨 - elijk - ' Shit' - aints - ' Ian' - ' Simply' - ' Stre' - æľĭ - ' GDP' - '59' - asz - ' Katie' - ' бÑĢ' - ' peek' - owych - ' resort' - ' residence' - ' spices' - ció - ' jeder' - ' emo' - arium - ' puff' - ë§ī - ÑĥлÑĮÑĤ - ' meta' - ' ìłĦë' - ' optimization' - gang - ' íķĦ' - ' efficiently' - ' visually' - ' frost' - ' Arthur' - ' ż' - ' achieving' - ' rotating' - ' lining' - ' occupied' - å¼Ł - mentation - ' stretching' - ' stall' - ostic - ' Sever' - ' gluc' - ' róż' - ' outreach' - stra - iken - ' ìĸĺ기' - ' Join' - ' impe' - ' compensation' - ' Tat' - ' Carlos' - ührt - ' Francis' - cji - yeah - ' membrane' - ' exhale' - ' reli' - ' OR' - ' refrigerator' - ' Venez' - Like - ' raises' - ottle - atura - ' ruler' - ' weer' - ' guided' - ' Magn' - ' Corpor' - įĶ - ' attribute' - ' Woah' - ' arrows' - ' await' - ' Prim' - ' dignity' - ' Ontario' - ischer - ' ìĭĿ' - imen - ouver - ASS - á»ĩn - opy - achusetts - ' elderly' - åİŁ - FA - ' Daily' - shine - ' 56' - è¢ - ierno - ' skilled' - ' groÃŁe' - ' Oak' - 第äºĮ - iggle - елей - ' biraz' - ' arguing' - ' поÑįÑĤомÑĥ' - ' drift' - ' harness' - ' deixar' - autre - ' Seeing' - ' capitalism' - ' Eld' - zione - ' Beyond' - ' perfection' - ' hoe' - ' declare' - алаÑģÑĮ - ' poke' - ' ס' - ' fighters' - ê²łëĭ¤ - оÑĢов - ' accordingly' - ' Isa' - ' optimize' - ' Ministry' - ' sage' - ìĭľë©´ - ' beni' - ' donation' - ' cleared' - ' Luckily' - ' harmful' - µì»¤ - ' cement' - пиÑģ - ' dedi' - ' Craig' - ' demons' - ' customize' - ' ignored' - ' Tian' - ' hoped' - ' Bureau' - ' ri' - ' Yah' - ' socket' - ' featuring' - ' parf' - ' TE' - ' Teacher' - ' catalog' - ê°Ģì§Ģê³ł - ' Seite' - ' cone' - ' Palestin' - ' gewoon' - ' gaining' - ' Ø¢' - ' catast' - ' neighbour' - IST - ' stealing' - ' trois' - ' intend' - ' Shoot' - ' pione' - ' Intel' - ' LIN' - ' brighter' - ' Yesterday' - ' sow' - sin - ods - ' ethics' - ' interviewed' - rell - ' refreshing' - sÃ¥ - ' absurd' - ' phosph' - fil - ' stehen' - vals - ' cared' - æĪĸ - ' dell' - bone - ' hoch' - ' pup' - ' io' - ' acontece' - elles - ' Spl' - igi - ' tän' - ' elephant' - ' gates' - ' slices' - ' prank' - okrat - ' hilarious' - ' Sid' - ' ëĴ¤' - ' essere' - ' telephone' - inally - rator - ' helicopter' - ' iÅŁte' - ' gid' - ' tourist' - ' conflicts' - аÑĤа - ' té' - ' assert' - ' laundry' - ' Bom' - ' specialized' - ' Modern' - ograf - ' ano' - ' retrie' - ' Putin' - ' HAR' - ' маÑĪ' - ' αÏĢÏĮ' - ' tutti' - ' вÑĤоÑĢ' - ìĸµ - ' Bul' - ëĭ¤ë©´ - ÅĤe - æľĭåıĭ - arin - ' therapist' - ' gÃ¥r' - ' Czy' - ppe - mir - ' Term' - ' Bear' - lace - ' Moreover' - ' Disc' - ' íĥĢ' - ' titled' - ' strips' - ' Fahr' - ' Ring' - rando - afa - 身 - ' shorts' - ' trunk' - ' sentido' - Ïīν - ' acres' - ' overd' - ' Olympics' - åı« - ' Merci' - ' ëĤĺìĺ¤' - ' germ' - ammed - ' pregunt' - ' Nut' - ' </' - ' travels' - ' vocabulary' - eten - oder - ' consuming' - writing - è¶ħ - ' appearing' - ' adjusted' - sem - ' frente' - ' maximize' - ' zwischen' - ' zam' - conscious - zek - 谢谢 - hao - ì²ĺëŁ¼ - ' Episode' - ' visibility' - ' mijn' - ' vielen' - ' Brothers' - ×Ļ×ij - ' väldigt' - ' crushed' - ufen - ä½łåĢij - actic - ' Bed' - ' FA' - issippi - ' remot' - ' pets' - ' thunder' - ' Mam' - ìķµì»¤ - parents - ' bı' - ' surtout' - ' segments' - ' nehmen' - ' utiliz' - ' Ruby' - ' rá»ĵi' - ' happily' - ' bush' - ultan - çİ© - ظ - ' Hil' - ' lawn' - ' eyebrows' - mez - ' Syd' - rep - inf - éłŃ - ' overhead' - cznie - ' oxid' - ' Wol' - ' destroying' - ' Additionally' - umbled - dep - ' depos' - ' commod' - ' cakes' - ' talents' - ' pourquoi' - ' contempl' - nels - оÑī - ' Arabic' - ' Maryland' - çİĭ - owo - ' Pla' - ÄŁlum - ' prophe' - ' Represent' - opol - accord - ' Meaning' - ' joints' - ' brakes' - ckt - ' 1999' - ' publication' - ' Review' - ойд - ' niche' - ' significa' - ' debr' - ' overlap' - ' demanding' - ' Só' - ' subsequent' - ' quotes' - ' Currently' - ' preventing' - ' 130' - ' Cel' - onn - wnież - ìķ½ - ' какие' - ACH - ' gum' - ' Israeli' - ìľ¼ëĭĪê¹Į - å¨ - rukt - ' clapping' - ' Massachusetts' - ' resilience' - ' subscribing' - ' jewelry' - gebra - ' correction' - boo - ئ - lio - sam - ' envelope' - kal - ' Farm' - ' cattle' - ' bras' - ' repent' - ' tones' - osion - pection - ' denen' - ÈĽi - ' Marg' - ' acquire' - iblings - ' aspir' - ' sized' - ' alc' - ' vibration' - til - 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³´ì - ' quien' - номÑĥ - ' habitat' - '......' - abling - ' ÑĤакие' - ' besond' - ' employed' - ' arrives' - ' vessels' - ' Ax' - ' displays' - '150' - ologie - ' ìĹIJ' - ' clo' - ' дов' - ' Ðŀд' - ' vuel' - èĬ± - wend - ' slipp' - urp - ' Lot' - ' bullets' - ' rage' - ' skirt' - ientes - ' những' - ' Natural' - ' hind' - ' workload' - mu - íĥľ - ' sunset' - вол - pit - åįģ - ' ASH' - ' ë¶Ħëĵ¤' - ' downstairs' - éŃ - ' counted' - ' naz' - ×ķפ - ' Philippines' - ' 110' - ' Parker' - ' gitu' - ' interes' - ' umbre' - ' Nature' - ' jer' - enos - ' panelists' - ' coating' - ' cherry' - ' Pent' - ' Mist' - regation - ' vind' - ' Corps' - ' Mission' - ' noble' - ' fonction' - ' warrior' - ' protests' - ouri - ' constitutional' - ÅĤam - ' emerged' - ' dye' - ' Trying' - igm - ä¸Ģ个 - équ - LO - ' Verm' - erving - ' TIM' - ' Ci' - ' freezer' - ' grupo' - ' Sports' - ' пÑĢог' - ' ÙĦا' - otherap - iffany - bian - ' ranked' - ' proposals' - ' Äijây' - ' freezing' - ' insects' - vil - ' compost' - çİ° - ' semana' - ' distinguish' - ' facilitate' - ' plusieurs' - ' verg' - ' alguns' - ' TikTok' - ' Express' - менÑĤ - SU - ' intimate' - ' Author' - ' witnesses' - ' kalau' - ' argued' - ' avoiding' - ctive - ' pursuing' - ' syll' - ável - ' Atlanta' - ' Utah' - ' Till' - ' erf' - ' 2022' - äter - ' funeral' - ' Flash' - ' Atlantic' - ' gele' - ì¦Ī - ' mortgage' - ' ëĦĺ' - licht - ' ambitious' - ' Beijing' - ' diving' - ' unbox' - illas - ' otras' - ' evac' - ' marine' - ' Ñģозд' - ' Create' - ' gj' - ' frequencies' - ington - ' Romans' - ' aiming' - ' Buff' - ' emperor' - ' Moi' - ' promising' - ãģľ - ' alguma' - ' pasa' - ' disorders' - SI - ' succeeded' - ' cuerpo' - ' sodium' - ' stub' - heiro - ' delayed' - etera - tw - ' sync' - hd - ' tourists' - ' syst' - ' mét' - ' qualify' - ' Others' - llers - аÑĤелÑĮно - ' Ðŀна' - ' perceive' - ' ê²Ģ' - ' ê°Ģìŀ¥' - ' иÑģк' - ' Matter' - ' Bluetooth' - ' pearl' - ' arise' - ' monument' - ' именно' - agi - ÙĦÙĬ - ' rho' - ' smarter' - ' conj' - 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AST - ' Assembly' - ' sticker' - ' mieux' - ' entertaining' - ' DON' - ' Amend' - ' Karl' - ' inhib' - sst - ieg - ~~~ - ' hooked' - ' literal' - ' sunny' - steps - ' ë°ľë' - ' Marine' - ' sue' - ' prisoners' - ' Eb' - '58' - ' drums' - ' guilt' - alg - ' happier' - ' CM' - ' ìķĦëĭĪìķ¼' - ' ÐŁÐµÑĢ' - ÑĥлÑı - ' keyword' - ' Parce' - ' Foreign' - ' Amanda' - ç¥ŀ - ' 목' - pless - Ī¬ - ómo - ' qualquer' - ìĿ´ëĿ¼ê³ł - ' conspiracy' - ' strawberry' - ' hatten' - Es - ' spos' - ' villages' - ' lev' - ' ÑģÑĢед' - ' waking' - ' calculations' - ' Ùħع' - ' pouring' - ' lebih' - ' polish' - ' Tout' - ' funktion' - мо - ' Ti' - ' wasting' - istically - ' manipulate' - ' simplify' - ' teammates' - ' бо' - ' contam' - ' Quite' - ' kurz' - ' Cand' - type - outheast - ' financially' - олн - elson - ' forehead' - uage - naudible - ' Behind' - ' negotiations' - ' ë§ĪìĿĮ' - ' alternatives' - rank - holder - æĩī - ' healed' - ÑĤоÑĩ - ' Spec' - 件 - ä»ĸåĢij - ' exhibit' - ' shallow' - ' gob' - ' ëľ' - ' frustration' - ÃŃo - ' melting' - ' Storm' - ' patent' - ' Barcel' - ' pedest' - ÙĪÙħ - ' tai' - ' Mode' - ' wil' - ' 모르' - ' également' - éĤ£éº¼ - ' ×IJ×Ĺ' - ayan - ' amazed' - ì§ĢëĬĶ - ' haciendo' - ' ìĿ´ìķ¼' - λα - à¸Ĥ - еÑĤа - ' exams' - ' travelling' - Press - иÑĢÑĥ - ' baseline' - ' buses' - ' reinfor' - venant - ' Truth' - Ŀ½ - obe - ' yell' - ' sausage' - TF - ' Evil' - ' meiner' - ×Ļק - ' hopeful' - ' również' - ' Però' - two - nder - ' миÑĢ' - ' conscience' - ' Warren' - icky - ' aimed' - ' göra' - XT - ' pyram' - Red - 鼻 - atu - ' Esta' - ' earnings' - ' hats' - ' Stadt' - icket - points - inander - ' motorcycle' - ' ëıĮ' - ' íķ´ìķ¼' - kom - ' Ding' - æĴ - ' recurs' - ' estimates' - ' derni' - ' versch' - ãģĿãģ® - ' MIC' - иваÑĤÑĮ - ' пÑĢоÑĪ' - ' dost' - ' вÑģÑĤÑĢ' - ' wiel' - ' siblings' - ' дев' - ' earliest' - ' fatigue' - ' nhi' - ' gusta' - ' bonne' - æľĢå¾Į - from - ' Jenny' - ' supposedly' - intage - ' counties' - ' unre' - ' planting' - ' Grac' - ' Genesis' - ' Alpha' - ysz - ' tile' - ' ê²½ìļ°' - ' ×Ļש' - quel - ' distribute' - def - éral - ' clutch' - adelph - ' PlayStation' - Ħ¸ - ' sj' - breaking - ' ëIJĺë' - ' Cuba' - ' Russians' - ' MARK' - ' perse' - ' restricted' - iges - ' Travel' - ' electronics' - ' quarters' - ' Keith' - sized - ' deadline' - arenth - ' vÃŃdeos' - ' protocols' - amment - ' Training' - ' â' - ' sequel' - нак - ' keinen' - ' mattress' - luding - ' classified' - ' reactor' - ' Kont' - ' passar' - ' honour' - orig - INA - ' Nathan' - ва - ' ÑģказаÑĤÑĮ' - tır - ' exclusively' - ' shades' - ' пÑĢоÑĨ' - ' occasions' - ija - çļĦæĻĤåĢĻ - åݲ - æħ¢ - fig - ' tus' - ' remem' - ' Christopher' - ' slime' - ' alguna' - ' Fortunately' - ' lors' - voll - aver - ' outlet' - ' LinkedIn' - ' Executive' - ' organs' - ' Begin' - ' íĻĶ' - ' transplant' - ragen - VO - ' För' - ' باÙĦ' - ' Andre' - isine - ' lasts' - ' história' - ' luz' - ' collar' - ' kidna' - ' optical' - iov - ' tob' - ' exterior' - ' metric' - ieur - ' troll' - ' ÑĢоз' - æĺŁ - ' tô' - ' ìĺĪìģ' - ' Gesetz' - ' ед' - ' denominator' - ì³ - ' lett' - åħī - ' grÃ¶ÃŁ' - é¡ĺ - ' Luther' - ' reste' - ' resemb' - ' permet' - ksi - ' fisher' - ãģŁãģĦ - ' Von' - íĶ¼ - ' ÏĥÏĦο' - ' locks' - ' shoots' - ' kamu' - ' Ker' - ' Obs' - çĿĢ - ' bili' - ' ë°±' - ' torture' - assy - ' иг' - ' lasting' - 好çļĦ - ' tienes' - ' receives' - ' Oscar' - ' remembering' - ' problemas' - ' ia' - åĺĽ - ' memorable' - ' jours' - ' façon' - amic - ' ë´¤' - atique - ' ëŃĶê°Ģ' - ' zip' - halt - ' ðŁĺ' - ' fries' - ' finden' - gra - ÑĢÑĥд - import - ' ëĭ¬ë' - ' iki' - ' complaining' - ' fazendo' - ' google' - ' tabs' - ' ëĵ¤ìĸ´ì' - ãĤ¦ - ugo - ierto - aufen - ' 먼ìłĢ' - ' skulle' - ' suiv' - ' spy' - ' Kai' - éĤ£åĢĭ - ' martial' - ' onder' - 誰 - atility - ' irgendwie' - ' clap' - intell - ' installing' - ' uniqu' - ' Centre' - asts - uar - ' revis' - ' threatening' - rais - ' cuid' - ska - ' resolved' - ' rides' - ' failures' - ' semb' - ' males' - UFF - å¾Īå¤ļ - ' três' - apped - ' newspapers' - riet - ' applauds' - Ðĵ - ' ãģ¯' - ' NC' - åįĥ - æĻĤéĸĵ - ' heter' - ' hazard' - ' ry' - ' strictly' - ' 54' - ' ëĵ¤ìĸ´ê°Ģ' - ' spont' - ' tatsächlich' - ' ë§IJìĶ' - laub - ' absorbed' - acaģız - ' onu' - ' ÐIJн' - ' explicitly' - ' ìŀ¬' - ' Future' - achten - Ãło - yon - ' seria' - ' Herren' - cej - ' Albert' - ìĿ´ëĬĶ - ector - ' packing' - ' virtue' - ' venir' - DD - ' yaz' - ' logs' - ' Photoshop' - ' sid' - lings - ' remotely' - ' Different' - ' operated' - lights - ' discrimin' - istance - ' GRE' - ' plac' - ' shirts' - ' justify' - ' trabalho' - util - voc - ' quart' - ' Τ' - SC - ' SR' - ' -"' - ' hesitate' - ' pak' - èĩ³ - gua - Jo - ' souvent' - ' Angela' - essee - adelphia - arks - ' weed' - ' kannst' - åĤĻ - ' ê·¸ëŁ¬ëĭĪê¹Į' - ' plutôt' - ' Commander' - ' summarize' - à¯Ģ - ' 98' - ãģĩ - ' developments' - ' Cost' - ' theoretical' - ' ore' - ' metall' - οÏħν - fahr - ÐļÐIJ - ' chuck' - ' adapted' - ' Oklah' - ' Netherlands' - ' poet' - sto - kat - ' wears' - ç¯ - ' ìĸ´ëĶĶ' - ' Esto' - ' laughed' - ' donner' - ' ëį°' - ' ìĽIJë' - ocur - ' Kick' - ' Detroit' - ' bicycle' - ' lacking' - phabet - ' Kend' - Ass - ' reveals' - ' Îł' - ' Noah' - ¦¬ëĬĶ - ' sells' - ' Alabama' - ' terrific' - ' Element' - ' íĨ' - ' turbo' - ' Hom' - ' theorem' - ' adventures' - ' purchasing' - ' Tá' - ' маÑĤ' - ' vemos' - ' duties' - ' wenig' - ' booth' - ' entrar' - VA - ' gears' - ' Jae' - èn - ' calcium' - ' Roberts' - ' пÑĢоблем' - ' ribbon' - ' назÑĭв' - ' lav' - ' interventions' - ' Ultra' - ' namely' - ' adequate' - ' recap' - ' dock' - fting - ' voi' - ' consultation' - ' Ñģем' - ' podem' - ' possession' - ' clues' - ' Russell' - ' renewable' - åݲ害 - ' Ñĥз' - information - iggers - With - wno - ' elaborate' - ctoral - ' Dow' - ' ramen' - æıIJ - á»ķ - ' erste' - ' Zel' - ãĥĹ - ' quasi' - ' нак' - ç§Ĵ - ' Stars' - ' tribal' - ' seated' - ' wol' - ' chol' - ämä - ' outbreak' - ' cres' - ' unserer' - ' íijľ' - ' underwater' - ' assure' - OOD - ' naprawdÄĻ' - ' establishment' - ' incon' - ' diferente' - ' excus' - ' Dim' - оÑħ - ' Ling' - rolog - ' ãģ¾' - ' outdoors' - naj - ' epidemic' - ' unters' - ' 3000' - ' Gabriel' - ' ìĹĨëĬĶ' - ' encl' - ' Oder' - ' Foot' - pas - ' Zuk' - åĵ¡ - ' workflow' - ' unp' - ' alliance' - enschaft - ' yogurt' - ине - ' eru' - ' fiz' - äºĶ - ' aÅŁ' - ' aprend' - ' cualquier' - ' carrots' - ının - afood - ' floors' - ' keywords' - ' spotted' - ' drank' - ' paras' - ' último' - ' hablar' - ' prosecut' - ìĹIJëıĦ - éĸĭå§ĭ - ' ép' - ' stickers' - ' pushes' - kh - ' restart' - ' Thunder' - á»Ŀi - ' muita' - ' fox' - ardeÅŁ - ' Zach' - ' Minecraft' - ç¸ - ' ====' - ' göre' - ' stance' - igung - ÙİÙij - kä - ' teachings' - éĨ - ' decay' - ' ric' - omena - ' вÑģем' - chten - ' Vert' - ' íķľêµŃ' - ¬´ë - ' coc' - :) - keiten - ' BA' - etheless - ' headquarters' - ' spike' - ' Base' - ' 101' - ' coordinates' - ' tard' - ' boiled' - ' Monster' - ' notebook' - ' ê´Ģ' - ' Wake' - ' Setting' - ìĿ´ìĹ - ' Sydney' - ' Finn' - ' lobby' - å¾ŀ - ' seniors' - ниÑħ - avan - ' JE' - ' traff' - think - ' slap' - ' Castle' - ©ng - ' algunos' - ' Personally' - ' Male' - íĭ° - ' Generally' - ' Pel' - ' dias' - ' evolving' - itol - воÑĢ - ' plein' - ' flights' - ' eleven' - owej - á»ijng - ' aku' - ' glance' - ' connectivity' - ' bald' - ÑĭÑĩ - ' intest' - ág - ' GRÃľ' - iblical - ' Papa' - ' pity' - ' faint' - ' wurden' - ' legally' - ' prey' - ' Sciences' - ' пÑĢоÑģ' - ' trainer' - ' problème' - ' kilo' - кого - ' bridges' - '89' - ' lasted' - ' elegant' - bows - ' palab' - ' directory' - ä¸įæľĥ - ' bulb' - people - IX - ' geb' - ' 66' - ' Tennessee' - ahlen - ieval - ' caut' - ' Damen' - plo - iane - але - attan - ' اÙĦس' - ' risky' - ' sleeve' - ' incidents' - ' ë°ķ' - Co - ' applicable' - ' imperial' - ' Philip' - ' Yea' - еÑĢо - ' показ' - üne - ìĺĢ - Hub - tor - ' sigu' - cend - ' politically' - ' ìĤ´' - ' pars' - ' ouv' - ' primeira' - ' Shah' - ' satur' - ' combust' - ' promoted' - 주ë - æĢķ - ' templates' - ' ëĭ¬' - ' haul' - ' ÑĤеÑĢ' - ' sliding' - cedented - ' ãģ®' - children - MR - ' Wei' - ' bör' - æĹ© - ' próximo' - arÃŃa - ' sampling' - елен - esi - ' Danielle' - ' Oklahoma' - èħ - çķĮ - еÑģп - ' DVD' - ' вÑĭп' - rous - cons - ' enhanced' - éĽ£ - ' pastor' - ' Suddenly' - è®ĵ - far - PER - ' Ng' - '1000' - ' chew' - ' rumors' - ' Ana' - ' années' - ' ÑĥÑģÑĤ' - ' Philadelphia' - åĹ¯ - еждÑĥ - ' effectiveness' - è¿Ļæł· - été - ' ding' - ' religions' - ' aged' - zieÄĩ - ' Ric' - ' Kap' - ' Page' - ' sü' - ' nämlich' - ' mankind' - ' resting' - ' influences' - ' Schul' - ' нев' - ' mana' - ' consumed' - ' Pom' - ç¾İåľĭ - ' conseguir' - ' Thanksgiving' - ' Hindu' - lais - ' thrive' - ' contour' - аÑĨиÑı - ' falando' - ' Já' - zan - иÑĤÑĥ - ipher - jamin - ' Hallo' - ' 160' - ' оÑģоб' - ' mete' - ' ìķĮë' - ' Barcelona' - letter - ' ÐĿеÑĤ' - åĻ - ' además' - ' coordination' - unts - ' slop' - ' пÑĢид' - ì§Ģë§ī - ' questioning' - ' diesel' - ' dej' - ' affirm' - įĶëĿ¼ê³łìļĶ - ienne - ' crank' - ' predictions' - ' physi' - chsel - ' combinations' - ' excellence' - éĢĻ麼 - á»Ŀ - width - weed - Ħ를 - Ħë§Ī - ' alto' - ' dairy' - ' Normal' - ppen - ' oben' - ' devastating' - ' poz' - ' Hus' - maz - ' warned' - ' denk' - ' Auss' - ' trades' - hell - ' primero' - ' mia' - ваÑĢ - بÙĬ - ' kicks' - ' aÄŁ' - ' Mü' - ' luc' - ением - ' Standard' - rice - ' Cub' - ' gou' - ' João' - ÑĥÑģк - ' enqu' - £Į - gew - ' íģ°' - owania - iani - ' fakt' - Ñıни - ' bef' - ' thumbna' - ' ceux' - æŃ¡è¿İ - apple - NEN - ' gad' - apon - ' Fantastic' - ' concentrated' - girl - lene - ' ÐĶлÑı' - ' éta' - aan - ' outta' - ' narc' - ' Body' - brush - ' legislative' - ' Megan' - ' mistaken' - ' Missouri' - ' labeled' - лÑıеÑĤÑģÑı - ' realised' - yorsun - ãģĤãĤĬãģĮãģ¨ãģĨ - ' Safety' - ' accelerate' - ' sanctions' - ' pee' - ' juego' - ' peppers' - ' wal' - ê¸ī - ellow - ' жен' - ' cinco' - ' ÑģиÑģÑĤ' - covery - ' gram' - ' épo' - ' BMW' - ivol - ' Chem' - çļĦ話 - usement - ' Suppose' - ' ê°Ģì§Ģê³ł' - ' millenn' - ' Tun' - ' medal' - ' hacia' - ' stimulus' - ' brightness' - aient - ' Hands' - inet - ' coalition' - åѸ - ' rises' - rina - ' scoot' - ' ãģ§' - ' defending' - ' invers' - ' hills' - ' fulfilled' - åĪ°äºĨ - llie - ' adoles' - ' Chase' - åĸľæŃ¡ - ' JJ' - ' neuen' - ' Tru' - ' inherit' - ' sixty' - ' Exp' - ' Clay' - оÑģоб - arna - ' Imperial' - ' ÑįÑĤа' - ' socially' - aty - odynam - ' ribs' - omic - ' Tol' - олж - ' 1998' - ' fram' - ' ranks' - ' бÑĥдÑĥ' - ' Colon' - Hz - ' accommodate' - ' explode' - íĦ°ë - HAEL - ' Hart' - ' жизни' - æ¡ - ' delicate' - ł×Ĺ - ' tofu' - ' achievements' - ' Sor' - ' agreements' - ' 57' - ' tamp' - ' français' - ' herbs' - corn - ' konk' - ANA - ' Qi' - ' próp' - ' tiger' - ' ëijĺ' - Äĥm - ' apprent' - ahan - ' ruling' - ' tsp' - ' twitter' - ' teenager' - bus - ' íĴ' - ' Amendment' - ' tapping' - ' APIs' - åł´ - ' matched' - ë©´ - WA - ' Beauty' - ' inevitable' - ' gases' - ' Ù¾' - high - ' Opt' - ' predomin' - ÏģÏĮ - ' tubes' - ' ìķł' - ' Aa' - ' æľī' - ometown - ' IM' - ' desar' - ären - ' маÑģ' - ' Möglich' - ' rental' - ' íķ¨ê»ĺ' - ' Diana' - ' autism' - ' Puerto' - ıld - ' falan' - ' dreaming' - ' gute' - ' кам' - ' wreck' - ' storytelling' - ' Legend' - ' Ukrain' - ' пÑĢоиÑģ' - ' SK' - ' íĸī' - ' ÅĽwi' - ' Believe' - ' mostrar' - ' Todd' - ' Niger' - icting - hard - :// - irable - igation - ' Members' - ' ìłľíĴĪ' - ' discour' - Ł½ - rika - ' DN' - ' Fif' - ' Capital' - ÑĢом - ' Sans' - yun - ' pilots' - ' trat' - ' nyt' - ' 민' - ' exponential' - ' emerge' - ' trajectory' - ' поÑĩемÑĥ' - ' sealed' - atti - ' wides' - ' огÑĢ' - iances - ' witnessed' - Or - osi - ' Joel' - onal - èģ½ - ' Inte' - cedes - ' Gotta' - anium - ' females' - ' Lebens' - ' moistur' - ' Simple' - ' Doch' - ará - ' gesehen' - UST - Æ¡i - ' classification' - ' diagonal' - ' permett' - comp - ' اÙĦØŃ' - ' Malays' - ' gehört' - ' popped' - ' contacted' - ' ׼׾' - ' 140' - ' adaptation' - ' manus' - ' turkey' - ' preach' - bright - ' downs' - ' unprecedented' - ' mighty' - ' cater' - itti - gs - ' Deputy' - write - ' Bless' - ác - ' summit' - ' ëı¼ìļĶ' - ' thoughtful' - ' shred' - singing - ' лÑĥÑĩÑĪе' - ' yen' - ' vibrant' - ' Walter' - ' hosts' - ' ambul' - ' invasion' - ogan - ' reasoning' - ' succ' - лекÑĤ - ' fala' - ' kings' - ' goin' - ' calib' - ' GRÃľNEN' - oter - ' einz' - ' insulin' - Ĭ¨ - ' scaling' - ' Corn' - hyd - ' matte' - PL - ' aliens' - ' Seg' - è¯Ŀ - esti - astics - ' warmer' - ' ingen' - ' ML' - ' rode' - ' Eye' - beits - ' Barn' - », - ' Chuck' - ' profitable' - uguese - ' Arabia' - ' coco' - ' puedo' - ' inflammation' - clip - ' tablespoons' - ' ìłij' - ' Swed' - ' anat' - ìĪł - ' arrib' - ' dancer' - ' Carter' - ' magnific' - store - éģ¸ - ' fade' - ' accompany' - ' wahr' - ' yeast' - ' mineral' - ' legislature' - ä½ı - iros - ' crowded' - ÑĢаÑĪ - ocado - ìĸ´ìķ¼ - ' íĽĦ' - ' Barry' - master - ' nickname' - ' "...' - ' Rs' - ' Moore' - ' venue' - ' бÑĥ' - ãĥ¡ - lihood - ' Agency' - лов - ' kah' - ' ìĨĮ리' - ' marsh' - ' incorporated' - antwort - ' kimchi' - ' woo' - ' distracted' - eries - ' información' - ' Choose' - ' Jadi' - ' analogy' - say - uffle - bok - ' acids' - ' acquisition' - ' variants' - èµ·ä¾Ĩ - ' passiert' - ìĿ´ëĤĺ - ructive - brig - ' ãĢĮ' - epher - ' pH' - utlich - å·® - ' relie' - uite - ' reception' - ' coh' - ' Prep' - ' anticipate' - æĢ§ - kee - ' designated' - ÑıÑĤи - ' Kor' - ' Anim' - ühl - ' Whit' - ' uncover' - ' Maya' - ' ÑĤогда' - °ķ - utenant - ' ìĸ¼ë' - ' forests' - ' meme' - ' distinguished' - ' Marx' - ' Lion' - ' servants' - ' Diam' - çķ¶çĦ¶ - ' Policy' - į¼ - ' triggered' - abilir - ' ìĿij' - ' negotiate' - ' fez' - ' erw' - ' varies' - ' jemand' - ' discharge' - ÑģÑıÑĩ - ' PAR' - ' Affairs' - ' voter' - ' aten' - ' crois' - obil - ' Oops' - ' Arc' - ' Heather' - anka - ' simples' - ον - '">' - ' chords' - ' Sanders' - ' ë¶Ħë' - Ben - ' darüber' - ilians - ' ordering' - ' Manh' - ' kilogram' - ' karÅŁ' - ' grasp' - ' ghosts' - alen - ' Jedi' - ' бли' - ' downloaded' - ' conducting' - ' Hak' - ' researcher' - ilan - good - ' Hannah' - ' dÃ¼ÅŁÃ¼n' - ' Messiah' - uity - iona - ' probable' - ' YE' - ' independently' - ' buffer' - burn - ourd - ' McK' - ' lingu' - ujemy - еÑĢÑĤ - ' intuitive' - ' cracks' - appropri - nty - ' geen' - ' lend' - ' certification' - IDS - unter - pees - ' trump' - ' bankrupt' - ' feas' - èĹ - ' duż' - æ¸ħ - ' viruses' - ' 58' - god - ' жел' - ' stalk' - Ind - achi - ' CF' - ' Cond' - ' sanct' - ' conten' - ' freed' - ' RT' - ' mentors' - 족 - ' portable' - ' Paulo' - rane - HAHA - ' Section' - çĨ - hyun - ' ÎŃÏĩ' - ' Pub' - ' Independ' - ' compounds' - ' ÑģÑĭ' - ' messaging' - ' dedication' - ' noticing' - ' devoted' - ÑİÑĤÑģÑı - ' snakes' - ' battlefield' - pers - ' dela' - '92' - ' hai' - illä - érer - every - ' responsive' - ×Ļ×ķ - opf - éī - Ĭ¸ - Because - ' tourism' - ' ê·¸ê²Į' - ×ķצ - ' cans' - stüt - ' donne' - ' Dios' - ' Uber' - actory - ' oriented' - ' Herm' - ' patron' - urf - bei - ' programa' - ' Ohh' - gener - ' fist' - ' Wendy' - ' anda' - ' guessed' - ' freak' - ä¸Ńåľĭ - ' Kings' - chool - ' offline' - ' Indiana' - ' Alliance' - ' 53' - ' particul' - ' Focus' - ' inhabit' - ' ê°ĻìĿĢëį°' - ' McG' - owski - ' ìĿ´ê±´' - ' paÅĦst' - они - itta - ' confirmation' - ' Brooklyn' - ' noodle' - fund - itud - ' grandparents' - ' barbecue' - ειÏĤ - ' á' - ' ballot' - ' Veter' - ' pipes' - igious - ' Graph' - ested - ' ë¸Įë' - ' KE' - ãģ¡ãĤĩãģ£ãģ¨ - ' eins' - ' hatred' - ãģijãģ© - ' dang' - eeee - ' archae' - ' Jesse' - ' detected' - ' seni' - burgh - ' displacement' - ' dop' - ' conditioning' - ' неÑģколÑĮко' - ' disturbing' - PH - ' thinner' - ' wounded' - ' Cuando' - ' cushion' - ' whites' - ' preferences' - ' ì¤Ģë¹Ħ' - ' każ' - ' Gate' - ' Path' - dles - à¸Ħร - imore - ' ë³´ìŬ' - ' disciplines' - á»ı - ' mesma' - ' ìĥĪë' - ' ìĭ¬' - ' ging' - ' umbrella' - IGHT - ' pension' - ' combining' - SS - ' rectangle' - á»ĩt - ' proxim' - ' Cow' - ¸Į - ' intentional' - æķĻ - ' decid' - ' Ñģкаж' - ' Uma' - iasm - buz - ' debris' - ' cass' - ' Prop' - iska - ëł¥ - esterol - ussian - ìĿ´ëŀij - ' unlimited' - ' admire' - ' tightly' - ' genome' - ' Junior' - venir - gus - ' cÄĥ' - ' Vlad' - ' íĤ' - ' relativ' - inci - ' aunque' - ' Boys' - ÑĨион - ' Swiss' - ' physicians' - ' íıī' - ' PET' - ' wounds' - about - Ãłi - onz - urities - ' Ñĥвид' - å·¦ - ' mentality' - ' variance' - ' segunda' - ' volcano' - alie - à¥ĩ - ' tiles' - ' Terry' - ' اÙĦÙĦÙĩ' - ' canon' - ' scattered' - pton - ' definitions' - ' algebra' - oten - ablo - ijuana - ' wrapping' - ' sesame' - ' наÑĩина' - ' Alf' - ' ÐłÐ¾ÑģÑģ' - orno - ' ankle' - ' specialty' - ' attempting' - iliation - ' 1920' - ' phenomena' - ' Product' - ' Buck' - ' Aww' - seen - ' void' - ' Franklin' - ' advocacy' - ' Sep' - ' coolest' - ' ÑģÑĢазÑĥ' - ' Quand' - ' 900' - ' Trad' - dies - ' hash' - æĪijå°± - ä¹Łæĺ¯ - ' pots' - ' sadly' - ' viable' - ' Tiger' - ' ONE' - ' neurons' - owanie - ÄĹ - ' Shar' - ' Landes' - ' conferences' - 該 - ' credential' - ' lime' - inee - xit - pay - ' incons' - ' >>:' - èªį - ' íŀĺë' - ' lesser' - ' spill' - ' premise' - ' 365' - ' Host' - ' tomar' - ×IJ׾ - ë²Ī - ' Whats' - ' lightweight' - ' Map' - fia - ellschaft - ' vendors' - uesto - ' Mister' - ' ÐŁÑĢи' - åı³ - hma - ' intentionally' - ' Tang' - éĹ® - ' identification' - ' etcetera' - ' Nee' - ' ÑĤÑĢи' - ê·¸ - ' cryptocur' - ' inhale' - ' addict' - åIJĦä½į - ' mau' - ' ÑĤакаÑı' - ' ë²Ħ' - ' comprar' - iedzieÄĩ - ' оÑĤно' - ' beginner' - ' мÑĥж' - ' obsc' - ' limiting' - ascular - ' inspection' - aci - ' rejo' - Mus - ' zaten' - ' szcz' - ' Madrid' - ' varieties' - ' estÃł' - ' Shakes' - ' kits' - ' administer' - ' lava' - ' gÃ¥' - 試 - ת×Ļ - ' Wayne' - ' instagram' - ' rated' - paper - ' bild' - ' pretending' - ' observing' - ' Ñģамом' - ' tror' - ' organisms' - ' falta' - ' hometown' - ç± - ' íĭ' - ' cheg' - ' ì¡' - ' comma' - isé - ' likelihood' - avored - ' geldi' - ников - ' medio' - ' jakie' - ' Jup' - ' greenhouse' - ' spit' - кое - ' каж' - ' Gram' - ' Conference' - ' deficit' - sın - inse - uÄŁ - ' richt' - ' coincidence' - åıį - ' europ' - ' butterfly' - pread - ' ìĸ¼' - èĢ¶ - ' wavel' - ' Infin' - ' Planet' - ' selfie' - ientras - ' arrog' - oser - idal - ł×Ĺ׳×ķ - ütün - ' freshman' - ' Machine' - ÏĥÏĦ - ' Dia' - ìĿ´ëĭ¤ - ãģĵãģĨ - nea - ' listing' - ' configure' - utor - Up - tschaft - rière - ' upwards' - ' ÑħоÑĩÑĥ' - ' sweep' - Br - ' expressing' - ' unhappy' - ' mandatory' - gender - ' AÃŃ' - ' indicators' - ' oils' - note - ' segur' - ожеÑĤ - ynasty - ' distances' - ' merge' - BERT - ' surrender' - ' buat' - ' Awards' - ' señor' - odox - ' flavour' - ' abdom' - ' configur' - '86' - ' DIY' - ' rigid' - °ĺ - ' corporation' - ' groom' - jaw - ' Near' - ило - ' opera' - ' Innov' - иÑĢа - ĵ± - ' specified' - ' cosm' - ' Freedom' - ' clown' - ' Nem' - ' вол' - Ñijн - ' charger' - à¹ģล - ' influential' - äsident - é¤ - ' ìĦłë' - ' volumes' - æIJ - ' outras' - ' Twitch' - ' founding' - ' awhile' - ' coil' - ê°Ļ - ' cả' - ' Throw' - ' Hence' - ommt - ' Benjamin' - глÑıд - Time - obic - ' mour' - ' dread' - ' LÃł' - 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netes - ' counterpart' - ' tôi' - ' ted' - ' Bart' - ' ëĿ¼' - ' Genau' - ' incoming' - ' ABC' - rique - ' оÑĤп' - qual - ' incentive' - ' ihren' - ׳×Ļ - loe - ' 1930' - ' barg' - ' diction' - ' önce' - INS - ' reh' - isiaj - mouth - ' scoring' - lık - ' ìķĦ주' - ORIA - ' Estados' - ' companion' - ' assemble' - ' punished' - ' ital' - ' prevents' - istes - ' Kentucky' - ' locate' - ' fasting' - ãģ¨æĢĿ - ĥĢ - ' Seb' - ' Crown' - opia - ' whip' - usz - ками - ' databases' - åŃĹ - ' prosec' - ' 1997' - ' ìĤ´ì§Ŀ' - ' Solar' - ' Pues' - ' Zen' - ollo - ' Guru' - ' squeez' - ' ÐĹа' - ' Äį' - ceptions - cca - izable - mand - ' breakthrough' - ' tablespoon' - ' SEC' - ikh - ' São' - ' пло' - amen - ' prac' - ' darling' - ' taller' - ' rendering' - ' ìļ°ë¦¬ê°Ģ' - ' ÏĦηÏĤ' - ' mã' - ' esos' - uerdo - ' ÑģÑĩиÑĤ' - aller - ìĹĪìĸ´ìļĶ - ' millones' - lerin - ' pegar' - onne - ' enrollment' - ' liegt' - ' boa' - wiÄĻ - bsp - ' cycling' - ' Bernie' - ' 1989' - ' далÑĮ' - ' Dakota' - ' ÑģвÑıз' - ' CP' - ' stare' - íĤ¤ - ' prosperity' - ' arrangements' - ' arriving' - mä - ' kayak' - ipt - ' pardon' - ' relat' - ' verste' - ' Fig' - ' foil' - ' Talking' - peare - ' noi' - ' пÑĢиÑĪ' - ' hockey' - ' ado' - ' OUT' - '67' - ' hormones' - ' Avenue' - ' Superman' - ' prescription' - ubernetes - CL - otive - NIS - ienen - ' sadness' - ' Vit' - Ty - ' starter' - ' bede' - ' foundations' - ' sore' - åºĹ - ÑīеÑģÑĤв - ìļ°ë - ' ÑĩÑĥв' - link - ' maneu' - working - Ãłn - ' Attack' - ' Cart' - veis - ' Resp' - ensing - ' ì¢ĭìķĦìļĶ' - ' escuch' - ' RNA' - Ĥ´ - ' adop' - ' bending' - عد - ' manages' - usp - ' tart' - ' router' - Bo - ' establishing' - ' balancing' - ' athletic' - ' Slo' - ' fills' - ' наб' - ' дал' - ' posso' - ' Vielen' - ' critics' - ' lawsuit' - ' Isaac' - ' ÑĦилÑĮм' - ' tras' - ' praw' - ' Crazy' - ' neu' - ' kull' - ' tumor' - ' APP' - gate - ' ARE' - '98' - ' Steam' - ' fucked' - lage - ' âĻ¬' - ' MD' - fy - ' shells' - ' Seems' - izers - ' ranges' - ' Antonio' - ATION - ' Baba' - 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' uso' - ' ìķĦì§ģ' - ' Yuan' - ' íķĦìļĶ' - pler - ressive - ' spraw' - ' Stew' - ' 1994' - ' elders' - ' meinen' - ' junt' - ' acoust' - ' Wohn' - ' bananas' - ' projection' - ' Stick' - legt - speed - ' cÅ©ng' - ' Wort' - ' Baltimore' - ' ÑĨел' - ' dunno' - å¼· - ?, - ãĥīãĥ³ - ' Local' - osto - ÐŃ - ода - ' Portuguese' - ' theirs' - ' dém' - åı¦ - ' drauf' - ' Buddhist' - erta - Ge - ' carrot' - ' Wonderful' - ' soak' - ' chairman' - ggi - ICA - fried - ' flick' - ' Throughout' - ' ìļ°ë' - ' cough' - ' fluffy' - school - ' ripped' - '--------' - ' Zukunft' - ' неб' - ' sto' - ' BO' - pent - ' Lawrence' - ÏīÏĤ - sticks - ' Eins' - ' ÑĢÑĭ' - ' Strong' - ' caramel' - ' spite' - azar - éĥ½æĺ¯ - ' critically' - ' obra' - owitz - ' Zone' - ' ÑĢек' - ' sug' - arded - ' gì' - ffentlich - anche - ØŁ - astically - ìĿ¼ë - лав - ' simplest' - ' Friend' - ' quello' - ' ambition' - ' abbiamo' - åºķ - ' ÑĦоÑĢм' - ' Essa' - ' educators' - ' statistical' - éĢĻéĤĬ - ' changer' - ' atau' - étais - ' Shakespeare' - 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UP - opard - sce - ' immense' - ' Ort' - roller - ' Dallas' - ' Nicholas' - ' sulf' - ' Toyota' - ' quantities' - ceans - ' cui' - ança - ' CAN' - itzerland - åĦ¿ - ' zou' - ' Cyber' - legen - ' Init' - edu - ' apert' - ' adjac' - ouv - èĢĮä¸Ķ - rs - ' cabbage' - ' wheelchair' - inyl - ' Dynam' - ' ìķĦëĭĪëĿ¼' - ' ling' - hl - ' могÑĥ' - ' crisp' - ' mij' - ' dug' - nin - ' bloss' - ' belonging' - ' loudly' - ' minerals' - ' concluded' - ' searched' - '96' - ' Meet' - ' SEO' - ' Ск' - ' Hob' - otta - ' propaganda' - ' cinnamon' - ' hunter' - ' gemeins' - ' sculpture' - ulsion - ' väl' - ' magazines' - ' controversy' - ä¸Ģ樣 - ' sequences' - ãģĦãĤĭ - ' íļĮ' - ' deleted' - 使 - IJëıĦ - ' varying' - ãĥĨ - ' mounting' - ' affair' - ' pathways' - æ¦ - ' digo' - 亮 - ' док' - Alex - ' tobacco' - ' CV' - ' bothered' - ' ambient' - inky - ' SL' - ' hates' - ' jeżeli' - ' congreg' - ' elas' - ' deuts' - ' Studios' - chÄĻ - ' documented' - ' Cruz' - ' Len' - ' Douglas' - ' Portugal' - enti - ' spouse' - 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minded - ' dispon' - ' Switzerland' - ' clusters' - ' queda' - ailing - ' manga' - ' 68' - ĦĪ - ' tet' - gins - haus - 空 - å·¥ - ' OP' - oted - ' nouveau' - ALLY - ÙĪد - òn - ' mortality' - ' GitHub' - drop - ' disgu' - ' recom' - ' locals' - ' homemade' - amba - ' pronunciation' - ' alphabet' - анÑĮ - owany - iras - idency - OME - ' ÑĢаÑģÑģ' - arak - viamente - ' nonprofit' - ' YouTuber' - ' parenth' - ' Boo' - vat - ' Stir' - ' precip' - ' ants' - ' ally' - ' Maori' - ' ëĮĢíķľ' - åı¯æĺ¯ - ogene - ' Labour' - arette - ' recycling' - ensa - ' pursuit' - ' sak' - ' ÐĹдеÑģÑĮ' - ' tolerance' - ' saat' - ' clicked' - âĻ¥ - ' facebook' - ' Into' - ' incentives' - 기ëĬĶ - ' Dennis' - ' Wik' - gesch - à¹Ģà¸Ľ - ' ÏĢα' - ' Whoo' - ' rounded' - ' dope' - ' capturing' - ' Warri' - ' civilian' - ' charming' - ' esas' - ' sustained' - ' leaning' - ' abundance' - ÃŃlia - алÑĮнÑĭй - ' phải' - acja - ' ê°ĻìķĦ' - activ - าย - ' 97' - ' мой' - cro - ' Jackie' - ittees - bracht - ulent - ' ìłľë' - ' plugin' - vantage - party - ' suas' - ' ante' - Ñĥл - ÐĿÐIJ - æĤ¨ - ' ÏĥÏħ' - ' meth' - ' enthusiasm' - ÑıÑĤÑģÑı - íĻĶë - ' synthetic' - ' seasoning' - ' Lost' - onomy - ' Spark' - ' bure' - ' assured' - ' imagin' - ' carro' - Sha - Äħt - нÑĥÑĤÑĮ - ática - TY - ' kern' - ' Brazilian' - ð - ' suspended' - ' Carib' - ' bizim' - ' Oliver' - ãģ¶ - Tom - ' план' - ' nope' - omething - ' beiden' - ÑĨен - ' fluct' - ' μοÏħ' - ' fathers' - ' Blake' - ' upward' - ' Dash' - ' Lil' - ' ìĪĺëıĦ' - ' revelation' - ' elevated' - ' Jiang' - LED - ' Thompson' - ' могÑĥÑĤ' - ÑģÑĤÑĢÑĥ - ifiers - ' comeback' - ' buyers' - ê²° - ' Sales' - иÑĩе - ciones - ' whistle' - ' dull' - LEX - ' íķĺê²łìĬµëĭĪëĭ¤' - ' criminals' - ' descent' - ipple - ması - ' foolish' - ' дÑĥмаÑİ' - tar - ' mango' - ' choreography' - Matt - ' territor' - ' acaba' - ' Einstein' - ' IBM' - ' Metal' - ' Crystal' - ' rah' - ' foul' - ' Islands' - ' intact' - ' Rail' - '.:' - ' acá' - ' пÑĢоп' - еÑĢе - ' Write' - hehe - ' FO' - ' ÏĥÏĦη' - ' doin' - held - ' appropriately' - ' deliberately' - ' archive' - ' giveaway' - ãģĵãģĵ - ' finale' - лаÑģ - ено - Æ¡n - æ£Ĵ - ogo - çī© - ' Audience' - ãħł - ' subur' - ' headache' - аннÑı - ' Witch' - ' Swedish' - ' BI' - ' erase' - ' khi' - ' commentary' - ' Sultan' - íĥĿ - ' Leban' - ' ë³´ìĭ' - ' Pam' - pekt - month - ' grounded' - ê¾ - ' ÅŁekilde' - '250' - ' SCH' - ioso - ' inaug' - heimer - ' reflecting' - ' Ruth' - ' Oil' - ' trouver' - uep - ..] - ' ìŀĪë' - ' olha' - ' reasonably' - ' glitch' - UB - ' Gran' - ' adalah' - ' lent' - را - ' traction' - ' adjusting' - ´¤ - нибÑĥдÑĮ - ' доп' - ' stretched' - ' ort' - ' cosine' - viol - ' ìħ' - cir - ' bastard' - ä¸ĩ - ' Ñħод' - ' quier' - ' pressures' - ' Anh' - å¹¾ - ' elles' - ' дÑĢÑĥз' - ' можеÑĤе' - ' chá»' - ' Mé' - ök - ầu - ìłĪ - zin - ' caution' - iban - ' judging' - ÑĥÑİÑĤ - ' baj' - ' СейÑĩаÑģ' - ' Poor' - ' Nazi' - ' upbeat' - yang - ' weekends' - ' Essentially' - ' oluyor' - ' spatial' - acker - ' seller' - ' ×IJ×ķת' - ij׾ - ' vivid' - ' Bond' - ê¶Į - iskt - ãĤµ - ' goat' - driver - ' mug' - ictional - ' allt' - ' Initi' - ' Rand' - ' finishes' - ' ê°Ī' - ' vitam' - ' teenagers' - ' Morris' - ì¤Ħ - ' Ori' - iya - ' myös' - Step - ' Kre' - 辦 - ' dinosaur' - ' ëªĩ' - affe - ' ëIJ©ëĭĪëĭ¤' - ' zeg' - åĪĩ - ' Manhattan' - ' sujet' - uelle - stoff - ' dür' - ' submar' - eses - ' aquele' - ' nou' - ' Faith' - tz - ' ÑĤомÑĥ' - aceut - liers - ' bandwidth' - Æ°á»Ŀ - ' respective' - ' Ave' - ' spreadshe' - ' Sent' - icamente - ' infra' - ' learners' - ' à®ī' - aiah - renal - ' mustard' - ' habt' - çĥ - ' Qué' - ' analyzing' - æ¯ı - ' solic' - ' ×Ķ×ķ×IJ' - ' causa' - ' welcomed' - ' Success' - ' facile' - ' ÐŁÐ¾ÑĤомÑĥ' - schein - ' fetch' - ' strat' - ' ÑģÑĤоиÑĤ' - ìĹIJìĦľëĬĶ - ' ÑģпоÑģоб' - mam - ' serÃŃa' - naments - writer - ' consulting' - íĺĢ - ' Berkeley' - eu - asive - UU - ' Analyt' - ' submission' - ' magnificent' - enza - ' econ' - ' profiles' - ' incar' - Ab - ' Nun' - ' hic' - screaming - ' resilient' - åĪ© - grund - ' concur' - ' bereits' - LD - ' nurt' - ìī - ' feast' - ' encuent' - ' Michel' - ' suprem' - '"]' - ' feeds' - ' Kollegen' - isser - ' Feng' - ' Wen' - mun - ' tenÃŃa' - ' Wrest' - ' ìĺ¤ëĬĺìĿĢ' - ' stead' - ' restoration' - ' donated' - ' dels' - ' census' - ' desperately' - worthy - HE - ' Spa' - ' Bryan' - ' hj' - ' Raw' - ìķĦë - ' Camera' - ' zien' - ' styl' - ' TW' - ' Cheese' - borne - ' obl' - ' Already' - ' unstable' - ' flames' - post - Ha - romagn - ' ìĹĦë§Ī' - dest - ' kolej' - ' temporarily' - ' determining' - ' Glass' - ÑĢон - olan - ' dominated' - åĮĸ - ____ - ' Ùĩذا' - ' Dana' - ' dinheiro' - aqu - 민 - ' Ãłs' - ' Joey' - ' Griff' - ' attain' - ' transitions' - ' Literally' - енд - ' Haven' - ' grabbing' - ' crystals' - ' Fourth' - ' candles' - ' ÑģлÑĥÑĩа' - rico - ' 5000' - etto - ' undo' - ' kto' - ' divert' - ' chir' - ' persec' - ' hiking' - ' announcements' - çĶ± - зÑĭ - ' auc' - ' systemic' - ' RM' - Ïĥα - ' ÐĶж' - ' yar' - ' Ward' - ' pissed' - ' carn' - ' autonomous' - ãħİãħİ - sover - æ²ĴéĮ¯ - å¾Ī好 - ' reflex' - ' gardens' - ' dated' - ì± - amiÄĻ - ' continuity' - ' citizenship' - ' schwer' - ' zak' - table - ' ÑģÑĩ' - è§ģ - ' Ïĥε' - ' generates' - 구ëĤĺ - öh - óm - alam - ' JUDY' - ' Bug' - ' ãģ¦' - ' drones' - ' água' - acaks - æļ - ' Ðļон' - ×ĸ×Ķ - ' strive' - ' Altern' - ' nearest' - ' proyect' - tera - ' ASHLEY' - ' worm' - ' replay' - ' tara' - ' Indians' - ãĤ° - icaid - ' ìĪľ' - ' appealing' - ' Wes' - ' mentions' - ' деле' - ' kw' - ' fragile' - isz - ków - hang - color - ' presidente' - '87' - еÑĦ - çĪ¸ - ' добав' - ' Nelson' - áfic - ' MICHAEL' - ' mechanic' - ' metres' - ' oczywiÅĽcie' - ' Cind' - ' ogsÃ¥' - ' landsca' - ACE - ' headlines' - ' catalyst' - ' Catch' - inkles - ' pills' - ordo - ' immigrant' - ' examination' - ' accidents' - zÄħd - ' quiere' - ' nella' - ' 67' - ' passa' - ' superfic' - istor - ' nov' - ëĭµ - ' mandate' - isons - ' Virtual' - ' selber' - ' counseling' - ' NBA' - ' sept' - ' believer' - ' marvel' - ' Integr' - ' мÑĸ' - ' orph' - ' backward' - ' Generation' - ' Pict' - ' ÑĤоÑĤ' - ' tapi' - prochen - ' hallway' - hte - ' ÛģÛĴ' - ' Zum' - èĢģ師 - achment - iquer - folg - ' Eddie' - ' Kil' - ' wellness' - stock - è¼ĥ - ' kaç' - ' terrorism' - ' pointer' - Of - heric - ' Ultimately' - ' meses' - ' Trade' - ' pint' - ' tuition' - ' disagre' - ' ê²ĮìŀĦ' - ' manuscript' - ' roomm' - ' outputs' - еÑĨи - ' ries' - ' salud' - otzdem - ' masses' - ' byÅĤa' - ' clearing' - ' discourse' - atson - ' folded' - ' Jar' - ÙĦÙī - '900' - ' ÑĥÑģп' - ' prophecy' - ' interfere' - иÑħод - à¹Į - ' thri' - ' ×ŀש' - ' lazım' - ' 1992' - ' futuro' - ' locking' - ' embargo' - ' Neither' - ivamente - ' mÃ¥ste' - ' mik' - ' collector' - екоÑĤоÑĢ - ' Gand' - ' sentir' - ' Might' - å¡Ķ - ' ganzen' - UC - ' relating' - SD - ' mosquito' - GR - ' hollow' - âĺħ - ' Walker' - ' affiliate' - ' duplicate' - нем - ' grape' - ' Organization' - ' synt' - Joe - ' geg' - ' revealing' - ' Ethan' - outer - ' yay' - é«Ķ - лаÑĢ - ' reportedly' - ' ihrer' - ' recognise' - ' bumper' - ' Randy' - ' Venus' - tles - ' appetite' - ' glucose' - ' chodzi' - ' Furthermore' - tir - ' conta' - ' intuition' - ' altitude' - ' chunks' - ' Joshua' - ıģım - rylic - leans - ' íĶ¼ë' - LL - Que - ' gor' - ' знаÑĩиÑĤ' - ' poems' - ' excel' - ' explored' - ' popul' - ' incluso' - stä - ' Gavin' - alling - ' ÏĦον' - é© - arbeit - ' Gas' - ' glorious' - rieben - ' spam' - ' indoor' - ' thrust' - ' Ald' - ' Prior' - ' onboard' - ãģłãģķãģĦ - oca - ASH - £ł - ' Christine' - ' drawer' - ' noon' - ' ìŀĺë' - ' permanently' - æ·± - ' напÑĢимеÑĢ' - ' podcasts' - erapeut - prit - ' stainless' - ' Ú©ÛĴ' - ' familia' - ' ÑĢазÑĢ' - unto - ' ÑģÑĤол' - ' hä' - ' Hai' - ' PB' - izon - ' konnte' - ' büyük' - ' utilizar' - ÚĨ - ' aquesta' - ' mixer' - udent - лекÑģ - ÅĤu - ' ÑģиÑģÑĤем' - ' ноÑĢм' - ' fatal' - ' considerations' - ' validation' - ' oli' - ' kardeÅŁ' - ' GLORIA' - ' pall' - еÑģÑĤе - ' rectang' - ' medieval' - allahi - asti - ' Syrian' - ' shear' - ' debug' - ' Mai' - ' knocking' - ' Lex' - ardan - rov - ' memorial' - æ°£ - ooky - ' stuffed' - ' passé' - ' wig' - Ĥł - ' próxima' - ' 1991' - ' междÑĥ' - ' nuestros' - ' Beast' - ' smo' - atched - ologia - ' мод' - ' gee' - ' conceptual' - ' ô' - ' decreases' - ' queries' - олÑĮÑĪ - ' Apart' - ' exempl' - å±± - ' fled' - ' OFF' - ggak - ' bead' - hir - lies - ' Clearly' - ılar - ' chess' - ' whichever' - ' 96' - ằ - ' respects' - ' моÑĢ' - ' organism' - ' grandpa' - ' Vie' - è·Łä½ł - ' flooding' - ' upgraded' - ÑijÑĢ - ' cheeks' - ' conquer' - ' stubborn' - ' puzzles' - ' auction' - ' relying' - ' PROF' - ' Esper' - ' ÐľÐ£' - ' hype' - ' possibil' - ' imprison' - ' Ern' - ìĹĪìĬµëĭĪëĭ¤ - ' envie' - ' resurrection' - ä¸įè¡Į - ' sper' - ' Venezuela' - som - ' ìŀłê¹' - ' nouvelle' - ' closes' - ' 1940' - ' qua' - ' Jared' - ' Pir' - ' inde' - ' scrub' - uku - ' requiring' - ' вами' - ' considerable' - åIJĽ - ilia - ' inne' - ' meinem' - ' hardship' - ' traps' - roc - ' ìĦ¤ë' - ' researching' - ' Margaret' - ' penny' - ' bırak' - Ñijл - ' wool' - ' rhet' - ' flatten' - çĩ - à¹Ģร - ' pied' - ' Chap' - ' underm' - ' fret' - ' crashed' - ' Frauen' - Ø°Ùĩ - ivan - ' literary' - latego - ' später' - ' similarities' - âĨ - ' Coron' - ' Creek' - ' bosses' - ' accompanied' - ' debates' - ' assembled' - ' Ãģ' - ' Vai' - ' tract' - ' simplement' - ' Arin' - ' vulnerability' - ' hormone' - IEL - OOK - ' relay' - ' Andrea' - ril - ' necessity' - aceutical - ÑİÑī - ousing - nahmen - ' footprint' - map - ' Tier' - annya - intend - åĸ® - å¢ - ' decorate' - ' zombies' - ' Hyd' - ' Suz' - ' campuses' - ' Emb' - ' throttle' - ' admin' - ' oportun' - ' mirrors' - ' identities' - ' Clin' - ' ë¹Ħë' - á¹£ - ' Ott' - ' blues' - ' impressions' - -, - ' vague' - afe - ' inferior' - erald - ' medicines' - ' pregunta' - osely - ' télé' - ' Month' - ' Leaders' - ' Egyptian' - ' ration' - kers - heits - ' recht' - Play - ' eg' - ' polls' - ' WOODR' - ' slots' - jam - Both - ' Rat' - ÑĢаж - ' Bright' - ä¸Ģå®ļ - á»iji - urious - ' singers' - ' login' - ' têm' - lation - ' Mum' - Æ°á»Ŀng - ' Editor' - åIJij - ' innovations' - have - ' Sek' - ' weaker' - ' Gob' - After - ´ì§Ģ - ' ë¬¸ìłľ' - ãĥ¼ãĥ¼ - ' disadvantage' - 確 - ' gaze' - ' Mack' - Ïģί - ' Kiss' - ' Holo' - ' Birth' - izi - bab - ä¿Ŀ - ìĭľê³ł - деÑĢж - ' squat' - кÑĥÑģ - uni - ' Comme' - ' WOODRUFF' - ' Championship' - ' welche' - ' Youth' - zem - ' odpow' - ' persistent' - rut - ìĶ© - íĸ¥ - lair - iku - ' vendor' - ' chúng' - ' financi' - ' overly' - âu - ' gluten' - ' 1800' - ' divisions' - ' ciudad' - ' obed' - ' warum' - ' eher' - ' elim' - ' ÐĴо' - ' peuvent' - ' Wanna' - ' attendance' - ' assessments' - ' Bog' - ' imagery' - ' collectively' - ' informal' - ' Schwe' - ' deutlich' - ' Chel' - ' PE' - owed - ' banner' - ' shelves' - ' Return' - æĭ¿ - LAUGHS - ' congratulate' - ' Norway' - ' dwell' - ' Caribbean' - ' norms' - ' Animal' - ' Valentine' - ' extending' - ' Vou' - orr - ' Cheng' - ¡ - ' доÑĢог' - ' veg' - ' hÃ¥' - ' Xin' - ' ì¹´ë' - emet - ' hypoth' - ' interessante' - rices - IZ - ' USD' - ' runner' - ' Bag' - ' ê½' - ' começar' - ' pigs' - ' weaknesses' - Ph - ' Viol' - ä¸įçĶ¨ - ' dragging' - ' AquÃŃ' - ' CSS' - ' millimeters' - ' estás' - ' acute' - ' dejar' - iÄŁ - obra - Love - ' silk' - '****' - ' joins' - ' prol' - ' ê°IJìĤ¬íķ©ëĭĪëĭ¤' - æĶ¯ - ØŃد - aghetti - änner - ' strang' - ' doubled' - ' descriptions' - ' stellen' - ' parti' - ç«ĭ - ²Ħë - ' Ã¶ÄŁ' - ighing - ' angular' - ' natuur' - ' Shel' - Æ°Æ¡ - ' rays' - ' seper' - start - vised - ' rushed' - ' internationally' - ' nivel' - ' boxing' - fallen - á»ijc - ' seinen' - plicity - ' carboh' - ' Travis' - uso - ' Phase' - ' activation' - ' opio' - ·¨ - ' decreased' - Car - ' bundle' - ' expend' - ormal - ' adjacent' - ' mee' - ' оÑĢг' - ' transcript' - ' Language' - GS - è§ī - ' seul' - Ãłnh - ' nya' - nings - ' ìĭľë' - ' ëĶ°ëĿ¼' - ' Agr' - ÃŃd - çķĻ - ' aby' - ' Neo' - ıyoruz - ' Thinking' - aime - ' vite' - ' través' - ' ×ij×¢' - ' мед' - Our - hoot - ' liner' - ' Pizza' - ' hyg' - flies - ' Continue' - ' dental' - ' Tib' - ' regulate' - lieÃŁ - ALK - ' Tae' - 길 - ' Brexit' - ' Gut' - ' occupation' - ' zrobi' - âm - ' whisk' - ä¸ĸçķĮ - ' kanske' - omon - robe - ' warfare' - ' thá»ĥ' - ' jaki' - ' strokes' - ' peas' - ' Damit' - HAN - ' interference' - ' минÑĥÑĤ' - NER - outing - ' textures' - Łī - owi - ' íķĻ' - ' dens' - ' protagonist' - änn - ' goddess' - ' wollte' - ijo - ' Woche' - ' VPN' - story - ' kinderg' - ' funnel' - ' distress' - ноÑģÑĤÑĮÑİ - ' noisy' - ' пÑĢодолж' - ' daran' - ' enzyme' - лож - ' mute' - ' dwar' - ' اس' - ' kompl' - ' merit' - ' fosse' - ' Drink' - ' fora' - ' wohl' - ' breeze' - ' sanit' - ' drin' - ' ìĿ´ê±°ëĬĶ' - ' 62' - ' ì°¨ë' - abytes - ' deeds' - ' й' - ième - iggling - ' "''' - ' ÑĩаÑģÑĤÑĮ' - ' Answer' - ' evangel' - ' 1080' - ' Visit' - icient - ' reliability' - ÑİÑģÑĮ - ' Earlier' - ' fid' - çŃīä¸Ģä¸ĭ - ' sleeves' - iyorsun - ' bib' - ' Account' - Ñıли - ciplinary - zas - ' беÑĢ' - ' necklace' - ' blender' - ' Phillips' - eti - ' Jupiter' - ' provoc' - ' Years' - entre - acio - ' kü' - ' antenna' - ' novels' - ' fart' - ' Sugar' - ' Judy' - ' collapsed' - ç° - ritis - ' ìĥģíĻ©' - ÐĹЫ - ' Verf' - ranean - ereum - ' Target' - ' 88' - ' Ðĺз' - ideo - ' regression' - ì¶ľ - ' mówi' - ' studios' - iens - iph - ' frying' - ' fascinated' - ' Wah' - bucks - maya - ' Saturn' - ' Mommy' - ' ratings' - ' autumn' - Æ°Æ¡ng - ' loser' - ' centro' - érieur - ' Fold' - ' supervisor' - ' Nobel' - ' underest' - obia - ' вÑģÑı' - ' verw' - ' fuels' - ' artifacts' - ' ë¶Ļ' - ' Autom' - çļĦæĺ¯ - ÛĶ - ×ķס - ' ihnen' - ' 59' - ounding - еÑĢÑĭ - inars - chant - ' addicted' - ' explosive' - ' dispers' - âĸĪ - axis - ARY - ' lum' - ' ÑĥÑģл' - ' ØĮ' - ' rupees' - ' Pearl' - camp - tv - oya - ' concludes' - ' collision' - ' buyer' - ' playground' - ' springs' - ' feminine' - ' Ras' - ' incarcer' - íĹĺ - ' dialect' - ' closure' - ' chatting' - ' babe' - ' spotlight' - ' notation' - è·¯ - Star - ião - ' tête' - ' tide' - ' junto' - ' senator' - Ð¥ - ' excuses' - ' blink' - ' admission' - ' Lily' - Ñĭми - ' amigo' - ' lust' - ëĭ¬ - ' amino' - äºĭæĥħ - ' consultant' - ' Electric' - ' ëħ¸ëŀĺ' - ujah - ' shooter' - ichten - ' Ukrainian' - ' aims' - ' Entertain' - ' miracles' - èŃ° - ' zeigen' - ' lam' - ' ress' - ' Jill' - ylan - ' rook' - ' haya' - ' passport' - adata - ' juicy' - conf - лей - ' Sz' - ' intercept' - ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸ - ' Teams' - ' maken' - irrel - ' LIKE' - áºŃy - êµ° - ' shortage' - ' paradigm' - ' papel' - ' astero' - ãģ¾ãģŁ - ' sollen' - ' Mickey' - ' Orleans' - ' cholesterol' - ' goose' - ÑĨиÑİ - ãģĤãĤĭ - ' FL' - ' голов' - ' tribute' - ' Gam' - ' évidemment' - ÑıÑħ - å®ŀ - çĶ° - ' inappropri' - uhan - ' organizational' - ailed - ' endure' - ' 76' - ' shotgun' - ' livre' - ' suited' - ' warmth' - ' SIM' - ' envision' - ' degrad' - îne - Laughing - ' Whoever' - ' Buddhism' - ' sprinkle' - ceÄŁiz - ' ruins' - ' starch' - ' Herz' - ' injustice' - ' humidity' - ожалÑĥй - ' Object' - ' Ign' - ' Exam' - igers - ' thou' - ' Soy' - ivas - ' poles' - math - ' вним' - INGING - edral - ' explor' - ' roasted' - ' crawl' - ' coff' - ' anom' - ' wij' - ' improves' - ' treaty' - ' discovering' - ' statute' - ' mercado' - ' Ñģил' - ' intel' - ' Chancellor' - ' Medicaid' - ugi - ' verbal' - ' dön' - ' scripture' - ' iteration' - eks - ' Oxford' - ' wäh' - ' Vad' - ' AK' - ' ìķĦìĿ´ë' - ' iets' - ' needles' - ÙĥÙħ - ' pasado' - ' albums' - ' yea' - etzen - ĦëıĦ - ' determines' - ' thee' - ' Playing' - ärt - ' צ' - cled - ' downward' - alone - ' solu' - ' partition' - ' wz' - dd - ' pessoal' - 媽 - ' factories' - ' bleibt' - มา - alsa - ' NFL' - ' fuera' - ' reserved' - ' Earn' - ' helt' - ' shortcut' - ' convincing' - space - ' enforce' - ' cores' - ' efter' - ' recession' - xico - ' proposition' - arians - ropol - ' 몰ë' - ' Îľ' - ' ìļĶì¦ĺ' - ' activist' - ' conviction' - ' zab' - ' canceled' - ÑĤоÑĩно - ' ή' - éĢĻ樣åŃIJ - nite - ' fundra' - buzzer - ело - ications - ' zona' - ' teens' - ' methodology' - ' ì¤ijìļĶ' - than - ' Ul' - ' Grey' - ' hog' - INK - ' Sung' - ' Claud' - ' CNN' - ' delivers' - alin - ' Adobe' - othe - ' Deswegen' - ำ - ' werde' - ' grease' - ' upgrades' - ' Finland' - accept - ' interrog' - bee - ' ãģ«' - ' prede' - ' Nep' - ' Cambridge' - ' graphs' - ' haunted' - Ñģем - æ§ - åħĭ - Some - ' Mall' - ' rehearsal' - ' Urban' - ' Lag' - ' nim' - ê°ķ - ' positioned' - ' avoided' - EMA - ' llegar' - ' rápido' - ' gouvern' - ' hing' - ' dealer' - ' reforms' - ' fatty' - кол - ' Ace' - ' nep' - ' ì²Ń' - ' computation' - ' Stream' - bourne - tur - Por - ' sleepy' - ' banget' - ãģĤãģ® - ' weighs' - ' bleiben' - ' Gren' - ' unions' - ' êµIJ' - ' aprender' - uitar - ' Jest' - uming - ' Player' - ' Extrem' - ' integer' - аÑĩе - ' concerts' - ×ķ׼ - ' trochÄĻ' - ' Repe' - éĩįè¦ģ - à¹Ĥ - żen - ' sounding' - ' anonymous' - ' exca' - ' Iranian' - ' energetic' - ' wives' - ' ÑĨвеÑĤ' - ' ais' - ãģĭãģª - ' sudah' - ' underwear' - ' crunchy' - ' Pain' - ' gerçek' - redict - ' misma' - ÑĸÑĤ - ' surviving' - ÎŃÏĤ - ' participant' - ' Hessen' - árias - ' subway' - istä - ' coral' - ' marijuana' - ' Memorial' - ÑĪий - riz - ' satellites' - ' lease' - ' Cameron' - umph - ' classmates' - ähän - ÑģÑĤве - ' hue' - ĵ¤ìĿĦ - ' proportional' - ' noss' - ' laps' - rÃ¥ - ' bitcoin' - ÐĹЫÐļÐIJ - ' 충' - ' ÙĦÙĦ' - ' Mort' - ' Esp' - arnos - ' Ñģказал' - ' änd' - åħĦ - ×Ļ×Ļ×Ŀ - ' Geb' - gehen - Inaudible - borough - ÑĦÑĦ - ' fellowship' - ' Paper' - ' curved' - ' GEOR' - ' calculator' - ' Catal' - ' vÃło' - ' bypass' - леÑĤ - à³ - trans - rencies - ì¡Į - igent - ' tasted' - ' oceans' - uft - ervice - ' ÐľÐ£ÐĹЫÐļÐIJ' - ' Classic' - ' respectively' - ~) - ître - ' Nash' - ' zit' - ' ìĽĥ' - ' ëĨĴ' - quote - ' Uns' - ' tac' - ' proves' - ' Portland' - bly - ' ere' - ì¶Ķ - ' época' - ' ÑĤÑĭÑģÑıÑĩ' - '76' - ' hade' - ' Fro' - ' polÃŃtica' - tag - ' íķŃ' - ' schö' - arett - ' provisions' - ' motors' - ' imaging' - ' dok' - ulously - ' meille' - çİ°åľ¨ - ëIJ - ' ISO' - ' STEM' - ' Bowl' - 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ש×Ķ - ' schemes' - ougher - abolic - ' ê±°ì£ł' - ' slowing' - ' wtedy' - ' destructive' - ' опÑĢед' - ' landmark' - ' ëıĪ' - ' Walking' - ẹ - ' tijd' - ' KN' - ' Quant' - ìĺ¤ë - ' кÑĢÑĥ' - ' perder' - ' nove' - ände - ' ãģĹ' - bia - ' custody' - ' biod' - æĿ±è¥¿ - ' directing' - '...âĢĭ' - ' reloc' - ' demande' - ãĤĵãģł - ' oÄŁlum' - ' одна' - ' Milk' - åı· - ' Kra' - ' Honda' - ' pue' - ' elekt' - ' beginners' - ' spear' - ÃŃnh - ' Luft' - ' nig' - ' Schools' - ' forums' - ' Qin' - ppo - ' zag' - ' Ю' - ' toothp' - ' Style' - ì´Ī - ' punct' - ' reps' - ' Aly' - ' amendments' - ' öz' - ' digits' - urai - ' chaotic' - ' Masters' - eon - ' Cash' - ' Cuz' - ' bedeutet' - ' scanning' - ' жд' - неÑĤ - ' certainty' - jek - ' dijo' - ' Climate' - ' rinse' - ' krij' - veland - ' soundtrack' - ' Safe' - ' Nova' - '94' - ' athe' - ' Verb' - oler - ìĿ´ì£ł - ' vin' - ' respiratory' - ' Study' - ' CAM' - ' avocado' - ' Zhen' - ' latency' - ' feathers' - ' contar' - ' веÑī' - ' fark' - ' blended' - 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' ë½' - ' riv' - ' Tank' - ' ëĨĵ' - ' rendered' - ÅĽlÄĻ - conds - ' disruption' - ' inconven' - ' quiser' - ' denial' - ' galaxies' - ' sovereign' - ' polsk' - ÏģÏİ - ' mex' - ' caracter' - ' Lego' - anden - .'" - ' íĶĮë' - ' compressor' - ' Movie' - ' applicants' - ziehen - ' vegetation' - ' belle' - ' GOOD' - ' Bau' - ' resent' - sex - amentos - ' ×Ķ×ĸ×Ķ' - ' overload' - ' silicone' - еÑģÑĤно - ' denken' - ' definit' - ' Wasn' - ' altered' - ' Soo' - ' Wing' - indre - ' NPC' - ÏģÎŃ - ' Twenty' - ' Liebe' - ' homelessness' - oulder - ' ÐĺÑĤак' - ÑģкаÑı - ' cuatro' - ' Harvey' - ' philan' - ' Beet' - ' policing' - ' Alexand' - ' молод' - ' müs' - ' hizo' - ë³´ëĭ¤ - ' позвол' - ' пÑĭÑĤ' - оÑĩемÑĥ - ' íĥľ' - ' cryptocurrency' - ' loro' - ' summation' - ' bakalım' - ' neuros' - Ø¥ - ' можем' - ' üst' - ' preliminary' - ' horns' - ' TI' - ÙĥÙĦ - YO - ' hinge' - ' repairs' - ' bonding' - ' bize' - ' ÑĪÑĤ' - ' motive' - ' Nigeria' - '120' - block - ' aviation' - ' Kommun' - ' оказ' - ' tenha' - 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ffective - ' bully' - agar - ' commodity' - ' obrig' - OUR - ' unpleasant' - nox - Jul - olith - ÑĤоÑıÑī - ' Bella' - ' dolls' - ' Hoff' - ' advisors' - ' transfers' - ' Goku' - ' 1200' - inhos - Pal - ' ëĺij' - ' rept' - ' accomplishment' - ' weave' - ' oversight' - ' unhealthy' - ' filt' - ' pudding' - ' Miguel' - ' chuckles' - åı°çģ£ - version - ' confession' - value - ' triumph' - ' sair' - ' ëħ¸' - ' arte' - ' Material' - uti - ' liquor' - ' Bayern' - ' Mail' - ' íĸ¥' - Ñģком - ' cheapest' - ' ÑĩаÑģÑĤи' - ' Jobs' - ' Canyon' - harma - aley - andro - ' appearances' - prof - ' оз' - lagen - ' //' - ' лиÑĪÑĮ' - ' recovering' - дж - psy - ãĥ¢ - ' swift' - ' Spin' - å¸Ī - ' seinem' - ' dolph' - führ - ât - ' altijd' - ' Marty' - ' Hoch' - ' predators' - ' vorher' - ' ÐĶавай' - ' fragments' - ' pastry' - ' commen' - ' Sana' - ' ê±´ëį°' - ussen - ' tela' - ' Nina' - lek - ' cries' - ' thighs' - ' Flex' - ' Buzz' - ãĦ - Us - ' paso' - ' declined' - ' Ny' - balance - ' masa' - ' jos' - ãģªãĤĭ - ' СпаÑģибо' - achu - loud - ' pena' - ' Wald' - ' elimination' - ' веÑģÑĮ' - orage - ' misunderstanding' - ' endorse' - ' ogóle' - ' greed' - ' klein' - ׾×Ķ - REY - ' Eating' - ' seminar' - ' Birthday' - ' quelle' - ' Multi' - ' tirar' - ' perch' - ' lavor' - ' Jia' - ' mutations' - ' cigarettes' - ÙĪج - ' cousins' - ' capsule' - ' horrific' - ' stur' - ' zeigt' - nuts - ' meanwhile' - ' Colin' - ' gobierno' - ' gw' - ' uhh' - ' JER' - specific - ' allegations' - ' ë©ĭ' - ' Ella' - ooked - ' Fit' - affle - ' Après' - ' Duck' - ' cellular' - ców - ' ÑĩÑĥвÑģÑĤв' - genommen - ìĬ¤íĬ¸ - ' lain' - isol - ' holders' - ' booster' - ' Sasha' - ÑĭваеÑĤ - ģ¼ - ' separating' - ' reinforcement' - ' одной' - ìĹĨ - IDE - ' Option' - phon - ' plais' - ' Camb' - ' íĻĺ' - ' uncommon' - '":' - miyorum - moi - acje - ажÑĥ - Õ¶ - ' gems' - üler - ools - ' enzymes' - ' kidnapped' - ' ketchup' - talk - ' zach' - ' washer' - ãĢĤãĢĤ - ' Architect' - venue - ' Planning' - éĢģ - ' Savior' - ' гÑĢÑĥпп' - íĬ¼ - arya - ' proceso' - ' limbs' - ' realizes' - iander - FS - aji - ' unite' - ' ìĿĺë' - ' possÃŃvel' - raits - ' Agre' - ÛĮÚ© - ìĦľëıĦ - æİī - ' вел' - ' меÑģÑı' - anor - Pat - ' dernier' - ÏĥÏĦε - ' какаÑı' - ' lässt' - æİ° - ' Meh' - ' ngh' - ' amateur' - è«ĸ - Fe - ' ê¶ģ' - ' situación' - ' sedan' - ' cleansing' - lasting - ' communist' - ANE - ' irregular' - ' sout' - ' Carney' - ' allemaal' - ' muchÃŃs' - ' libro' - ÐŃÑĤо - ' ап' - ' continuation' - ' Lor' - ?", - quin - ' characterized' - ajes - ' sights' - ' ÑıзÑĭ' - ' Uhh' - è·³ - birth - dong - ' hablando' - ' symptom' - çµĤ - ' capacitor' - ' transported' - ' ignorant' - ' никогда' - ' drip' - ' Eva' - ' adject' - ' massively' - ' Ethi' - ' Circle' - ' rainfall' - ' Mouse' - ' refund' - ' Zw' - assemb - ' 220' - ' Ord' - è§Ĵ - ' veins' - ' Giant' - ' mãe' - ' vap' - ' misses' - οÏħÏĤ - Mo - ' Entwick' - INT - ÙĨت - ' theoretically' - ' tearing' - ' troubled' - prem - ' repetitive' - ' âĸ' - ' heavenly' - ' Amber' - ' полож' - ' íķ´ì¤' - ' vowel' - anking - ' Wirtschaft' - ' irr' - ' cozy' - ' unfamiliar' - ' Pors' - ' ë§ŀìķĦ' - ' Timothy' - ÑģолÑİÑĤ - pex - ' VIS' - )( - ' superst' - ' improv' - ' Beng' - ' disconnected' - ' apt' - ÑĢен - ' Extra' - ' бел' - shop - dings - ' Connecticut' - ì°¬ - ' GC' - åıĸ - beh - Jeremy - ' Batt' - ãģ¸ - atha - ' Zusammen' - screams - ' gras' - afft - ' Initially' - ' Brett' - ' specifications' - ' seaweed' - ' oath' - ' fountain' - ' коÑĤоÑĢой' - ' Stein' - èģ² - ' Corinth' - ' conjug' - å·¦åı³ - ' compensate' - ' ëĬIJëĤĮìĿ´' - ' onze' - ' skincare' - Brian - ' servir' - '}}' - ' Vik' - ' unint' - ' suppliers' - ' balcony' - ' energia' - ometric - зÑı - ' sigh' - ' TOM' - ' Pure' - ytt - ÑĭÑģ - ' Rainbow' - ' Pitts' - ×Ļ×ŀ - ' statues' - heads - ' coupled' - éĮ¢ - ' herd' - ä½ĵ - ' excluded' - ' gilt' - ' Ñİ' - ' swoje' - ' Sver' - '63' - issant - ' dürfen' - łĪë - ' kissing' - oof - 以ä¸Ĭ - ' cursed' - ' showers' - ' swinging' - ' reproduce' - ãģ¨ãģĦãģĨãģĵãģ¨ - ' sätt' - elcome - ' fundamentals' - ' almond' - ' pé' - ' wellbeing' - ' hunters' - å¾Ģ - Sec - ĵľë¦´ - ' emission' - ' psychologist' - ' betrayed' - ' Reynolds' - LES - ' polling' - ' negatively' - ' combines' - ׾×IJ - аÑĢа - λλά - ' Turns' - OTT - ' ×Ķ×Ļ' - aison - ' airline' - ' restriction' - wal - ' aurait' - ' Lebanon' - ' MOR' - ' monkeys' - éner - ÑĸÑĹ - ' motherf' - ' ÙĩØ°Ùĩ' - ' feu' - ühren - ' hygiene' - enteen - Des - ' dissip' - Est - ' saints' - ' potassium' - ' reckon' - Clintus - ' manifestation' - ' Appro' - ' Inspect' - ' ventilation' - ' helm' - ' kara' - าà¸Ļ - ' favorable' - ' ìķĬìķĺ' - ' Hispanic' - à¸ľ - ' ×Ķ׼' - ' validate' - ' Resident' - ' comenz' - beiter - erer - ä¸Ģèµ· - ' dado' - atching - metros - ' Hin' - ' Dum' - ' hazır' - ' Natalie' - ' encryption' - оÑĩка - mma - houses - ' analytical' - ' Dang' - first - æŃĮ - çºĮ - ' Enc' - cando - ' ludzi' - wart - ' statistic' - ' ìĤ°' - ' commenting' - ' coordinated' - ' Hyper' - åļ - ' Bert' - çľ¾ - ' Hip' - kem - ünü - ' zal' - ' íķĺëĬĶëį°' - ' Robot' - 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anal - acaksın - ' Forum' - ennial - ' Resources' - ãģ¨æĢĿãģĦãģ¾ãģĻ - ' meisten' - ' Fell' - ' unanim' - ' TB' - ' Selbst' - æĨ - ' intimidating' - ' Gefühl' - ' ì½Ķë¡ľ' - æĭī - idor - iciones - arsa - ']..' - azo - ' kendi' - ' Tage' - termin - ' Prozent' - Maybe - lé - ' questi' - ' memes' - ' corre' - ' VIP' - ' Gallery' - ' urgency' - ' noche' - ' kindly' - ' Meredith' - ' váºŃy' - ' اÙĦب' - ' Estado' - åĩºä¾Ĩ - zug - oque - ' obesity' - 'Off' - ' Europeans' - öd - ì¹´ë - ' hoop' - ' enjoys' - ' Chip' - patient - ' microscope' - ' legitim' - ' ÑıвлÑıеÑĤÑģÑı' - Ïĥι - argent - ' sham' - ' licensing' - olia - Sorry - rama - ' accelerated' - ' wym' - ' fairness' - ' Reading' - ' slack' - ' Dok' - ziÄĻkujÄĻ - ' rubbing' - аÑĤÑĥ - ' allocated' - jung - ' pains' - ' winding' - ' geliyor' - ' CU' - mot - cock - ' Position' - bros - ' livestream' - ' Brain' - ì°© - ' przek' - ' Ei' - ' Coco' - ба - ' shovel' - ãĥıãĥı - ea - ' chocol' - ' rebellion' - ' showc' - ' Halo' - ' dividend' - mission - ' usando' - ' ["' - ' falei' - æĽ¸ - Black - ' Surely' - ' Å»' - ' philosopher' - ä½łä»¬ - ' overhe' - ' Born' - ' objetivo' - ' 128' - scheid - ' Nazis' - ' solche' - lift - cede - adors - ' marshm' - ' LORD' - ĶìĿ´íģ¬ - ' owning' - Cont - ' landscapes' - ' lending' - ' Authority' - овой - oqu - ' Ses' - ' Ferrari' - ' responsabil' - ' vários' - ' delic' - ' embark' - ' embroider' - ' frameworks' - ' simmer' - ' nacional' - ' remainder' - ' Vielleicht' - ' quieres' - ìĹĶ - ' testoster' - ihen - ' Oz' - èle - ' portrayed' - κε - ' Politik' - ' aperture' - ' bland' - indust - ' обÑĢаÑĤ' - ' Thous' - Bay - ' dando' - ' sher' - ' admissions' - ' Crew' - ' Ñĸн' - SINGING - ' ounce' - ' iy' - ' basil' - ' overtime' - ' threaten' - ' partnered' - ' Cann' - avana - ' знаеÑĤе' - éĢĻäºĽ - ' оÑĤÑģ' - ' Tudo' - ì½Ķ - ' ëĨĢë' - fel - ' rearr' - ' inward' - ' Rogers' - à¹ĥห - ' tweak' - ' dryer' - cession - ' rigorous' - ' Daar' - omics - ' fats' - vad - ' zipper' - acceptable - ' demonstrating' - ' Yum' - ' beau' - ' roster' - ' predominantly' - еÑĢÑĥ - ningar - ' triangles' - ' texting' - ' berries' - ' ìĤ¬ì§Ħ' - éĶĻ - adder - ' faites' - ' Image' - lere - ' bounds' - ' Laur' - ' ìķĦ무ë' - ' mio' - ' usa' - ' Ø°' - ' toen' - ' Jang' - že - chod - anan - ' обÑĢазом' - ' persever' - ' Swe' - ' augment' - ä¸ĥ - uggling - ièrement - istles - acjÄĻ - '91' - ' mah' - ' KIR' - Die - ' downhill' - ' 1968' - оÑĢоÑĪо - å¹¹ - ographics - ' tässä' - ê²łì£ł - ' лиÑĩ' - AUDIO - ' плоÑħ' - ' proposing' - éł» - ' tempted' - ' converting' - ' Lehr' - ' persone' - ' Feeling' - ìĸ´ì£¼ - ombres - ' ׾×Ļ' - ' guru' - ' dement' - низ - иÑĤелей - ' compañ' - æľª - å¸ĮæľĽ - ' redo' - ' conductor' - mia - ' idols' - ' Mul' - ' inex' - ' tämä' - ' impacting' - ' daylight' - gil - ' helfen' - ' entsprech' - ' wiÄĻks' - ' scriptures' - ' dismissed' - ãĥ³ãĥĪ - ' Podcast' - Ùħر - ' annually' - ' usable' - ' libre' - озм - ' rubbish' - çļĦ人 - ' continuar' - ' humili' - ' speeches' - ÑĢаÑĩ - bard - '71' - '><' - ologÃŃa - wealth - ' meditate' - ĵ¤ìĿĺ - ' Craft' - è§īå¾Ĺ - æĻ® - riv - ' Against' - ' ceramic' - espère - ' competent' - ' Hopkins' - ' kilos' - ' gravel' - ' piston' - ' friendships' - ' escre' - ' voz' - ' Gesellschaft' - ' unterstüt' - ' muj' - ' warnings' - pos - ' Professional' - wszy - odle - bands - ' teamwork' - stellung - ' dx' - åįĬ - ' attorneys' - ' weitere' - ãħĭãħĭãħĭ - ' Original' - ×Ļ×Ĺ - ' broadcasting' - ' пеÑĢвÑĭй' - uchi - ' heure' - ' grabs' - ' WOR' - ' Plaid' - Min - ' paz' - ' Puis' - umu - itates - ' coats' - ' buen' - ' heir' - ' pneum' - שר - enser - ' JUDGE' - ' blonde' - á¹Ľ - ' gak' - ' sık' - ' quoted' - ' equipo' - ' wishing' - ÃŃcia - ' verbs' - çµĦ - ' Canadians' - ' governing' - ' Evans' - Euro - ' genres' - ' unterschied' - ' Becky' - ³¼ê²ĮìļĶ - ' einge' - ' Raise' - oland - ' Strateg' - ' eres' - ' Veterans' - ' breakout' - ' santé' - ' adel' - ' investigated' - ' peur' - ' agile' - ' railroad' - anska - ' ей' - ' expos' - atories - ' Content' - ' truths' - ' Trail' - ' gua' - ' pores' - ' writings' - ' Uhr' - ' Thats' - ' icing' - OC - ' Production' - ' carne' - ISS - ' ninguém' - non - ' vicious' - ×ķ×Ķ - ' reconnect' - ' centres' - ' Kem' - ' crease' - ' ìĿ´ë¯¸' - айÑĤеÑģÑĮ - ' боÑĢ' - ' Hayır' - ' ÑģÑĥд' - ' única' - owaÅĤ - ' adher' - hua - ZZ - ' preciso' - ' currents' - ' seasoned' - ' IoT' - ' Bishop' - è¨Ī - sted - ' Bernard' - ì¤ĺ - æ²» - ' Glenn' - ' którym' - ืà¹Ī - ' astrolog' - ' Kot' - å¤ľ - ' parfois' - ' forwards' - ' WiÄĻ' - ' Îĺ' - ' nano' - è»į - sub - ' Brill' - ' grit' - ' cited' - gado - ' melts' - ' forcé' - âĸĪâĸĪ - ' bajo' - ' discretion' - °° - ativity - ' situated' - ãĥ«ãĤ¯ - Ñīее - åľ°æĸ¹ - ' пÑĢинÑĨип' - amaz - ' aquarium' - ' dissolve' - ' Gods' - Super - ' amid' - zk - ' ãģĦ' - éłIJ - ampf - ' hela' - '''!' - ' developmental' - ' Dise' - ' ÑĢабоÑĤаеÑĤ' - ' snapshot' - 好好 - Õ¸ - ' Yue' - ' Hulk' - ' Doom' - ' Felix' - ' réf' - Male - ç·Ĭ - phants - ENS - ' Mechan' - ' Golf' - åĨįè¦ĭ - ' generosity' - ätze - ' unlocked' - ' ãĤĴ' - íĥģ - ocalypse - Alright - ' ê°ľë' - ' ×IJ×ij׾' - ' Keeping' - ' collaborating' - chief - ' Fernando' - ' chefs' - ' íĶ¼ë¶Ģ' - ' skipped' - ' personn' - ' axe' - chez - ' extraction' - ' AV' - ' Gibbs' - ' íľ' - ' sı' - IAM - View - ' GRANT' - ' 몸' - ' verification' - ' depicted' - ' Moz' - oux - ' tul' - ' scanner' - ' comedian' - ' Volks' - ' JEFF' - è¨Ĥéĸ± - §Ħ - ' distraction' - rá - ' INTER' - ' sincer' - ' ×ŀת' - ' ש׳' - ' constructive' - arf - ' ëĪĦë' - ' eco' - ramos - ' renewed' - inement - ' Ub' - ' Pepper' - ì§Ģê°Ģ - ' Darwin' - ' merchand' - ' várias' - èce - NG - ' ìľĦíķ´ìĦľ' - ' акÑĤив' - ' Unters' - عÙĦ - ' intric' - omma - ieving - ' Caroline' - åĵģ - ' PRES' - ' performer' - ' autour' - ãģ¾ãģĽãĤĵ - ' utterly' - ' synthesis' - ' lesbian' - ' retrieve' - ' maneira' - ' impair' - ' mentoring' - ' Souls' - ' GoPro' - ÑĢаÑĤÑĮ - ' cose' - ' SSD' - IRE - ' upfront' - ' Aun' - ' gamer' - ' litt' - ' aggression' - ' Likewise' - ' Betty' - ' Dart' - ' DLC' - 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ÃŁer - èg - maal - generation - ' minorities' - ' llevar' - ' nomination' - ' consid' - ' ×ľ×¢' - muÅŁ - ' Esc' - ' numerator' - ' kaik' - ' których' - iesen - ' vê' - ' USS' - ' Private' - ' одно' - ' além' - ÃŃtulo - ' limb' - ' forgiven' - ' disclosure' - ÏĦί - ' ningún' - ' therapeutic' - ' negotiating' - ' Nike' - enseful - ' incap' - ' flagship' - town - âĪ - ' ÏĢολ' - ' wolves' - ' violations' - ' Arnold' - ' intervene' - ' heater' - ' recursos' - ' maid' - ê²¼ - ' давайÑĤе' - ' Celebr' - ' cape' - ' Sty' - ainen - site - bij - ' полÑĮз' - ' framed' - ' publishers' - ' ÑĩÑĥÑĤÑĮ' - ' temptation' - ' certeza' - ' exempt' - ìĬ¹ - selling - ' Task' - hoon - ' Coc' - ' Parks' - ' repetition' - ' ÑĤÑĥда' - ' ensl' - ' deÄŁiÅŁ' - ' Orlando' - ' Mainten' - æŃ¢ - ocument - ' HC' - ' scooter' - ' напиÑģ' - ' tighter' - ' tease' - ' removes' - ' kijken' - ' ÑģÑĥÑīеÑģÑĤв' - ' thé' - ' вÑĭглÑıд' - ' relieve' - ' mitä' - ' stationary' - öff - pable - ' arter' - ' déf' - rative - ' conect' - ' saddle' - ' Diane' - ' commemor' - fendim - SÃŃ - ' íģ´ë' - ' mange' - atte - ' arrogant' - ' robotic' - ' giÃł' - æĺ¯çļĦ - ' neighbourhood' - isson - ' движ' - ' RI' - ' Norman' - brand - amation - ' razor' - ' murders' - ' ÑĤÑĥ' - ' wszystkim' - ' utilities' - ' microscop' - ê¿ - ' daqui' - ollar - ' ÐĶавайÑĤе' - ' année' - ' kilometres' - ' homosexual' - ' architects' - ãģ¡ãģ¯ - ' niye' - LER - ' microphones' - ' Stunden' - ' consecutive' - ienda - vänd - DER - ' lifts' - ' Meat' - ' savez' - íĸĪëįĺ - Men - ' dismant' - 거를 - ' insulation' - ' scall' - ' spooky' - ' parc' - ' ballet' - ' WhatsApp' - ' franc' - ' deliberate' - ' íħĮ' - ' mars' - ' Zur' - Pr - disciplinary - ' obsession' - ме - ' marching' - ' Emergency' - iguous - ' szy' - ' Lands' - ' boarding' - ' поÑĩÑĤи' - ' envy' - ' compassionate' - ' merci' - ' desirable' - dale - ' canım' - ' Antar' - temps - ' configured' - ' Compared' - neh - icating - ' nickel' - ÙĪÙĤ - ÙĥÙĪÙĨ - opes - ' formulas' - ' ÐķÑģÑĤÑĮ' - ' pobl' - ' PJ' - ' Lud' - ä»ĬåĽŀ - ' Brid' - ' Hog' - ' Bris' - Jen - ' shading' - ' Yas' - ' disturbed' - ' recommending' - ' cé' - ' HOW' - ìĹĪìĸ´ - ' reversed' - ' Interestingly' - ioxid - åħŃ - ' ìĺ¤ì¼ĢìĿ´' - ếu - xx - ' ouais' - ' YouTubers' - ' Rosa' - ' Haupt' - jadi - ' vlogs' - ' cultura' - ' Leadership' - ' Hep' - ' illum' - ´ëıĻ - ' customized' - ' marca' - ' quatro' - ' наг' - ' SpaceX' - ' Eigen' - asting - ' olduÄŁu' - ' forts' - ãģī - riment - iencia - ' tenir' - roffen - ' 1979' - ' cie' - ' ëIJĺê³ł' - ' escri' - ÏĮÏĤ - íı¬ - uzzy - Cong - ìĿ¸ìĿ´ - Great - sil - éch - ãģ¨ãģĭ - ' multic' - ' Disk' - ²ķ - ' fazla' - ' levant' - ' abajo' - urry - stru - ' 먹ëĬĶ' - ' accessory' - ' двиг' - ' Rid' - '2019' - ' downstream' - æķ¸ - ' kaz' - utan - ' charcoal' - ' afect' - wu - ' contexts' - ' feared' - ' ìĦ¤' - ' histories' - ' fas' - ensible - ' cocoa' - illar - geons - ' spirituality' - ' Pew' - ' pharmacy' - ' passions' - ' bos' - ' allá' - ' thriving' - ' React' - ' occupy' - ' withdrawal' - ' allowance' - ' Fraktion' - ' buddies' - ' idle' - ' dissolved' - ' prevalent' - ' militar' - ' sensing' - ' pojaw' - ' ancora' - ' abundant' - ' hairst' - ãģĤãĤĮ - ' twee' - ' nächste' - ' Möglichkeit' - ' hoo' - ufficient - ' fantast' - ' edible' - ' ëĸ¨ìĸ´ì' - ìĽĥ - ' vein' - ucci - ' devotion' - ' concealer' - income - ' recycled' - ' ìĬ¤íĥĢ' - ' pontos' - ' dessus' - ' vérit' - ' reflections' - ' AA' - ' takeaway' - bare - ' Contact' - eil - ' Hear' - ' mirac' - ' Gerilim' - ' ÑģамÑĭй' - ' vivo' - ' kilograms' - ' Crim' - ût - '78' - ' sincerely' - raz - ' ë³µ' - ' arriv' - ' conception' - ' Persian' - ' själ' - ' starring' - ' ìķĦ무' - ' Forever' - еÑģÑĤÑĮ - ' veil' - ' subtit' - odka - ' оÑĤноÑĪ' - ' cooks' - енÑı - Kay - ' niños' - ' Phone' - ' stitching' - ' fingerprint' - é¢ĺ - λά - ' dedicate' - ' Lob' - ' blacks' - ' Ble' - bout - ' Äijang' - ' eks' - ' squash' - ' Kü' - odi - ' nÆ°á»Ľc' - ' voyage' - ' playful' - ' Ø¥ÙĦÙī' - anic - ' condemn' - ' Böyle' - ' Polize' - ãĤ¿ãĥ¼ - ' ayuda' - ' pam' - à¹Ħà¸Ľ - ' Kathy' - един - нова - ' brig' - eger - ' eagle' - ' visions' - ' íķŃìĥģ' - ' shitty' - ' hott' - ' Britt' - utors - ENTE - æĽ² - ' phon' - ' Bing' - ' поддеÑĢж' - spring - æĸ¯ - etten - ' pilgr' - ' ediyor' - енÑĤÑĭ - aggio - ' jul' - ' comprend' - teil - ' ز' - ' performers' - ' infamous' - ' MK' - çª - æ³ģ - otle - eff - ' Hash' - ' coward' - ' BRA' - ' DD' - ' comida' - ' plata' - ' flap' - ' Mehr' - ribution - ' Yemen' - ' mysteries' - ' Ä°yi' - ' stell' - ' eyeliner' - ' deles' - ' nailed' - ' illnesses' - ' stacks' - ' trabajar' - flower - ciu - ' crude' - ' substantially' - ' homem' - ' nephew' - ' stamps' - ' carbs' - ÑĮÑĤе - mooth - ' tunnels' - acie - æ³¢ - ' Señ' - ' Hera' - ' ìķĦëĭĪìĹIJìļĶ' - ' Wyoming' - ' HDMI' - ' Lis' - ución - ' steer' - оÑİ - иÑĤа - NT - ' ìĸ¼êµ´' - ' palms' - ' neon' - ованиÑı - ' filtering' - ' jouer' - ' Hö' - ' неÑģ' - ê²łìĸ´ìļĶ - ' 81' - ' storyline' - ' przep' - ' thanking' - ' Boeing' - ' softly' - jem - алÑĮнÑĭÑħ - ' flashlight' - ' пÑĥ' - ' WOMAN' - ắc - ÃŃch - ' luxurious' - ' wün' - ' impactful' - ' conson' - reu - irring - ifter - ' constituents' - èIJ½ - ' 94' - ' Tou' - gom - ' ìĥĿê°ģìĿĦ' - ' stereotypes' - ' możli' - åĪĨ享 - Ĥ¨ - ' pencils' - ' Ñģлож' - ' ihrem' - ' Besch' - ' Koh' - ' Entscheid' - ' lek' - ' förs' - ' totalmente' - ' lively' - ' entropy' - ' discern' - ' ÐĹна' - ' dov' - ' mythology' - è¨ĺå¾Ĺ - apanese - ' approximate' - аÑĤив - ifiable - ' Seo' - åĢĴ - ´ìĭ¬íŀĪ - ' ìĺ·' - ' temporal' - ' iT' - ' estat' - ким - ' sprink' - ' grund' - ' infantry' - ' schaffen' - ç´Ħ - ' ank' - riages - ' Yeon' - ' Moroc' - ' invasive' - ģĶ - ' parenting' - ' Ris' - ibile - ' mods' - å½¢ - ' пÑĢовеÑĢ' - ' Thing' - ' Wherever' - ' acknowledging' - ' pawn' - ummer - orb - '69' - ' retrouve' - ' relies' - ' Highway' - ' awe' - ãģ§ãģĻãģĭ - itaire - ' applicant' - ' aisle' - worm - ' payload' - ' carre' - ' Bach' - æł¼ - ' ì¹ľêµ¬ë' - ние - ' itÃŃs' - onnaise - sol - èı¯ - algia - ' rocking' - ' besten' - rites - ^^ - иной - ' baixo' - ' 기ìĸµ' - оÑĤÑĢи - sim - ' incarn' - ëĭ¤ìĿĮ - ' lick' - sided - ' 71' - forder - ' resonance' - ' tegen' - ' metaph' - owser - ' ×IJ׳×Ĺ׳×ķ' - ?ãĢį - ' spielen' - ' volley' - ĶìĿ´íģ¬ìĹħ - looked - ' sentenced' - ' multiplying' - ' ideals' - ' wahrscheinlich' - ' deposits' - bilir - ' effet' - illon - Īë§Į - ' testimon' - ' zawsze' - ' пÑĢоÑĨеÑģÑģ' - ' Lav' - ä¸įéĮ¯ - ' travailler' - ' laisse' - ' Mountains' - ' ÑĢоб' - ' examined' - itus - Was - лÑĭ - ' attributed' - ' ìĬ¹' - ' Baron' - ' gep' - ' attent' - ' Collection' - ' theat' - ' Cai' - ' wells' - ' humano' - çĹħ - ' Hast' - ' ÑħоÑĤÑı' - czas - ' permits' - ' legg' - ' epo' - ' Fen' - ' thi' - ' Foi' - ' élect' - ' 83' - ' overth' - ' è¬Ŀè¬Ŀ' - ' tenant' - è²· - Next - ' praised' - security - ' Impact' - 为ä»Ģä¹Ī - ' vouch' - ' negó' - ' unve' - ' criticize' - ' Kenya' - ' tactic' - ' logr' - ' pois' - ' papa' - speaks - ðŁij - ispers - ' surplus' - ' colder' - åįĹ - åIJ¬ - plets - ' Vienna' - ' Lead' - ' aerial' - ' Tah' - енÑĤов - ' Greeks' - Cam - ' máxim' - ' kuin' - chio - ' demonstrates' - anos - ' Cert' - ' Ñįн' - ' blogs' - ' ìĦľìļ¸' - ' beams' - иков - ' prompted' - ' frightening' - ' Porsche' - ãģĪãģ¦ - larını - ' chilling' - isphere - ' flashing' - ' Kard' - bread - ' exh' - ' tycker' - ' ecological' - ' Mae' - ' ×ŀ×IJ×ķ×ĵ' - ' ëĤĺëıĦ' - лон - yss - ' pergunt' - ' prix' - izzard - ' cancers' - ' 91' - susp - ' Item' - ÅŁa - ' pest' - ' takÄħ' - ' lymph' - ' Patri' - fill - ' reconna' - ' optimism' - ' mimic' - ' ì²ľ' - ' Madame' - ocy - lining - åijĬ訴 - erme - ' folders' - ' czÅĤ' - uchar - ' curso' - ' breach' - ниÑĤÑĮ - ' pamiÄĻ' - ' elig' - ' autop' - Flow - ' programmed' - ' Process' - ' figur' - ' SF' - ' Eles' - ' programmes' - ' dizzy' - ìĭľê°Ħ - ' либо' - ' sniff' - ' Sebastian' - ' Hye' - ' 4000' - ' permite' - æ¢Ŀ - ' заÑī' - ' guit' - ' Dais' - ' accordance' - ' modular' - ogeneous - æĭį - ' pouquinho' - ' artillery' - ' lubric' - ' volcan' - ' NH' - ðŁ¤ - ' dean' - Rh - ' ministre' - 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' Kraft' - ' ÑįлекÑĤ' - ' Bead' - екÑĤив - ' crushing' - ì³IJ - ' navy' - ÙĪÚº - sho - ' oak' - ippers - ' soils' - ' pigment' - ' evitar' - ãĥĩ - ' fuse' - ' Dale' - :" - ' complètement' - ' kel' - à¹Ĩ - ' quatre' - ' UM' - ' ë§IJë' - æł¹ - ÃŃr - ' leisure' - ' Housing' - ' folds' - estion - ARS - ' mash' - urpose - ' accumulated' - ' Stuff' - èªŀ - ' tapes' - ' ÑģилÑĮно' - ' LOVE' - ' 1982' - ' scars' - ' capitalist' - ' Ned' - ' soften' - ' notably' - ' forcément' - ' Raum' - ' необÑħод' - ' trademark' - ' fertig' - ' ?!' - æĹł - ' reinforced' - ' recharge' - ' Putting' - ' villains' - ' handic' - ' advertisement' - تÙĬ - ' ÑģÑĥм' - ' Riley' - ×ķ×ij× - 京 - Os - از - Boy - ' squish' - ocket - ' testify' - æ¼Ķ - ' ׾×ŀ×' - ' маÑģÑģ' - manuel - ' Arkansas' - iffe - ' analysts' - ' Deaf' - ' jó' - ' groceries' - ' Wheel' - ' ÑĢиÑģ' - ' còn' - ' Cob' - ' prisons' - ève - ' Cabinet' - ' posed' - ' guerre' - ' Lloyd' - ' clerk' - ' crises' - ' Sho' - ' Ore' - ' Football' - ' Advis' - ' Zheng' - 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' Micha' - ç·ļ - ' descob' - PI - ' palabra' - ' tracked' - ' dependence' - ' Barack' - åģĩ - ' fertility' - ' Southwest' - ' incomplete' - ' comunic' - ' compris' - ' Restaur' - ' acron' - κα - ' apprentices' - ' musst' - ' Abr' - ' pentru' - ' Consort' - ' Avec' - ' dumplings' - LR - ' wszystkie' - ' swamp' - нев - uggle - ' watercolor' - ' proton' - ' España' - ocking - овал - ' takim' - Very - ' dementia' - ' ÅŁeyi' - Jac - ' MacBook' - ' Liv' - fficients - ' Hunt' - ' overlay' - æĦŁè¦º - ' Skype' - punkt - ' confined' - ' Adrian' - رÙĥ - ' Jeep' - ' enquanto' - ' anest' - оÑĤвеÑĤ - ' менÑĮ' - ' irrigation' - á»ijn - ' eighteen' - ' Pon' - ' rescued' - ' 1983' - rü - jae - ' Jeong' - ' amazingly' - ' FDP' - ' backstage' - cue - ' ÏĥÏĦην' - ' اÙĦص' - ' livestock' - ' Warner' - ' majors' - ãĥģãĥ£ - ' cooperative' - ' Brady' - rained - rieb - ' ×ij×ŀ×' - ' доволÑĮно' - ' FE' - ' leaked' - ' Mercury' - ' persuade' - ' transformer' - ' Norweg' - ' ìŬ룬' - ' zrobiÄĩ' - ' cardiovascular' - 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' enlar' - ' rotten' - ãĥĥãĥĪ - ' wah' - èĤī - ' dzi' - ' Install' - Ay - ' crear' - енÑĤа - ' weighing' - ' butterflies' - ' Gast' - äºķ - horn - warz - ICEOVER - ' найÑĤи' - ' coefficients' - ç°¡åĸ® - ' Spencer' - ' Higher' - ' cowork' - å¨ĺ - ' коÑĤоÑĢое' - ' monit' - ' dysfunction' - ' ÑģÑĤанов' - ' tournaments' - ' oyster' - BN - ' trud' - slow - ' Penny' - ' Odys' - ær - ' fou' - ' enjoyment' - аÑĤÑĭ - ' wyglÄħda' - алÑĮнаÑı - ' Protect' - ' moy' - ' claw' - ' suspicion' - ' sacrificed' - ' gosto' - Big - ' aggressively' - ' vorne' - ãĥł - ' blamed' - ' Sehr' - פר - cito - ' seals' - ' mujer' - ' Weird' - ' forens' - ' contributes' - estra - ' pog' - LOL - ' hacerlo' - оÑĤÑĮ - fiction - '79' - λο - 大æ¦Ĥ - 声 - ' ÑĤоб' - ' GS' - ' Clara' - itez - ' advocating' - ' íĶĦë' - sung - ' vertices' - ' navigating' - ' europé' - çļĨ - ' slowed' - ' foreground' - ' Industrial' - ' adore' - ìĭŃ - ' créer' - æŀĹ - chnitt - ' unaware' - ' curly' - entar - ' ler' - ' prohibited' - ' Heroes' - ' Reed' - 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' flare' - ' Adri' - ñas - ' backl' - mÃ¤ÃŁ - ' Bend' - ' workloads' - ' ÑģÑĥп' - ' 1975' - имÑģÑı - ане - ' мон' - ' aspirations' - ' Aer' - ' говоÑĢиÑĤÑĮ' - ' Qian' - å¦Ī - ' compromised' - ' yolk' - лаÑģÑĤ - ' hemen' - rove - dens - ' комменÑĤ' - ' ---' - ' fluores' - ноÑģ - ' Liverpool' - ' Ñģобой' - ' Zwe' - ' lumin' - ' OG' - Ḡ- holm - profits - SN - ' proportions' - ' mica' - ' Boh' - ' Atlas' - ' unsure' - ' touring' - ' nied' - ' tÄĻ' - ' imperative' - ' demek' - ' Sheriff' - rance - ' homeland' - ' Hail' - ' Ganz' - ymm - Mon - åĨ· - vida - ' desarroll' - æĬĢ - ' intriguing' - ' Hugo' - ' ãĤĤ' - é¬ - аÑĨ - ' WiÄĻc' - atted - ' ìķĦëĭĪê³ł' - ' Vari' - ád - ' surreal' - ' disparities' - ' mó' - ullen - ' ìŀĪëĭ¤ê³ł' - ' пожалÑĥйÑģÑĤа' - ' mains' - ' eject' - ' methane' - ' marginalized' - ' chilli' - rès - ' yem' - ä½łæĺ¯ - ' Chun' - ' debts' - ' downloading' - ' Athens' - isierung - ryn - ' tekn' - ' Quindi' - éľĢ - ' taraf' - ' hé' - ' consciously' - ' fixes' - uckle - mayın - 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ãĤ± - ÙĬÙħ - ' juven' - ' Renaissance' - ' fundraising' - ' Chaos' - ' paraly' - ' narrator' - ' ecosystems' - Ash - ' mitigation' - ' Aujourd' - ' Idee' - '!,' - ' ½' - ' landlord' - ' defects' - ' acre' - ulsive - ' algae' - pek - ' emba' - ' Roc' - éĽ¢ - ksom - äche - ' leuk' - ' leveraging' - ' ê·¸ëłĩì§Ģ' - ' Palm' - ' även' - ' lis' - ' Insp' - ' Rita' - ' Abb' - ithm - ' supervision' - ' revisit' - ' piÄĻ' - ' euh' - ' fades' - ' motto' - åį¡ - езж - ' Shim' - ' relevance' - ' oo' - ' ostat' - nica - ' choix' - ' Faculty' - ' ì¤ijìĹIJ' - ' Above' - ' неболÑĮÑĪ' - ' sequencing' - ' nutrient' - ' conquered' - ' digestive' - ' backdrop' - ' Lori' - ailable - Game - ' neglected' - omorph - illah - ' kne' - ' siitä' - ' workspace' - ' Venice' - ' Kne' - Ñīо - ħĢ - ' Hass' - ' vita' - Ŀ¼ë©´ - ' lays' - ências - érica - ' Ll' - æ±Ĥ - ' Coca' - ' WHY' - èĪŀ - ' routing' - ' permissions' - ' dings' - prend - program - ' crocod' - bral - AAAAAAAA - agit - ' Nä' - ' gekommen' - atten - ' referenced' - ' pairing' - ' Partner' - ' Coronavirus' - ÑĸÑģ - è½ī - ' ×Ķ×ĵ' - ' especÃŃfic' - arsi - quelle - ' spontaneous' - çĨ± - ' ê²ĥìĿĦ' - ' ÐŁÐ¾Ñģле' - ' اÙĦد' - ' Shout' - ' нал' - ' disguise' - ' Jord' - ' wee' - ' miejsc' - ' serum' - ' plaisir' - ' credible' - ' bÃ¥' - ' AJ' - mares - ' rods' - ' eran' - ãģ¾ãģĤ - ' pää' - ' UA' - ' Unknown' - ' ÙĦÙħ' - ' Rabbi' - ' laat' - ' hairstyle' - ' غ' - éģĭ - ' cach' - ' Writing' - оÑĩки - abad - ' straighten' - --" - wife - ' hottest' - ' punya' - ' Fashion' - griff - ' QR' - otch - ' ÐľÐ¾Ð¶ÐµÑĤ' - Cloud - ' Strike' - ' Hein' - ' 羣çļĦ' - ' lei' - ' Flow' - wegs - ' habr' - åīĽåīĽ - nahme - Ìģ - ' pleasing' - opping - ' 구ëıħ' - ' dran' - ' bangs' - ' 79' - ' sket' - ' caval' - ' Macron' - ' weighted' - ' muted' - ' nuestras' - EEP - ' mathematic' - ' MRI' - agus - ' therapies' - θε - ' unpl' - ' commencer' - full - ' towels' - ' prue' - ' licenses' - ׼×ķ׾ - ' ÐŁÐ¾ÑĩемÑĥ' - ' pointless' - Bye - ' eligibility' - ' scrape' - ' abusive' - ' Mant' - ' jeunes' - tal - ' Princip' - ' Orthodox' - ' melod' - ' маÑĤеÑĢи' - ' prosecutor' - ' opioid' - ' ÑĥвеÑĢ' - ' Been' - ' ìłijì¢ħ' - ' dynasty' - ' ajuda' - ' entreg' - ' weighed' - ' eure' - ' Bem' - ' abnormal' - '82' - ' JR' - ' Akt' - ' Bri' - út - ' stagn' - '!*' - ' wegen' - ' leaking' - ' Words' - ' Mau' - ' vue' - ' Liam' - анием - ' clinicians' - ' Pump' - ' först' - ?... - ' automotive' - ' Owen' - zusagen - ' Hundred' - ' decentralized' - ' bulbs' - ' ׾׼' - ' provinces' - ' Milan' - '81' - kas - ' ëĵ£' - ' força' - ' rightly' - 島 - rÄħ - ' venues' - ' wai' - ' predicting' - ' WiFi' - ' ê¶ģê¸Ī' - رÙĪ - ' ×Ķ×ĸ' - century - ' gradual' - ' Probleme' - ' ìĹħ' - ' coping' - ' Brus' - ' peanuts' - irtschaft - ' зал' - ' Troy' - ' sperm' - ' Mitar' - ' Türkiye' - grand - ¦Ń - ' ×ŀס' - ' pans' - ' Knowledge' - berly - ' Ðķго' - ' danced' - ' Frost' - ' Burg' - ' biting' - ìłķìĿĦ - meal - ' heroic' - ' motherboard' - ' Licht' - ãģ£ãģ - llan - айн - ' ÑĢÑıд' - ' à¹Ģà¸' - onen - irie - Art - rang - νη - ' newborn' - ' amis' - ' اÙĪر' - ' sophom' - ' Careful' - ' prospects' - ensen - ' thrill' - ' Viá»ĩt' - Adam - rition - entric - uden - ' certificates' - ' ashes' - 調 - playing - ' sadece' - ' ost' - ' airplanes' - ÑĢок - oner - ' magnesium' - ' goddamn' - ' 1972' - ' Schule' - ' temat' - ' partout' - à¯Ĥ - ' inve' - ' Scientists' - ' Hudson' - winning - ceksin - ' congressional' - oru - ' ropes' - вед - ' madre' - ' ferry' - ' Cohen' - ' Pred' - ' vagy' - ' беÑģп' - ' multim' - ' drainage' - ' simulator' - giggles - ' Stadium' - обÑī - ' notices' - ' crawling' - ' groupe' - åı¸ - ' ktoÅĽ' - ' Yoga' - ' medida' - ' ÑħваÑĤ' - ' Lite' - ' rav' - orama - ' discord' - ' DIRE' - ' teh' - ' Nurs' - ç²ī - ' pitched' - ' barking' - ' Coke' - wiad - ' populated' - éĻ¤ - pelled - ' бог' - ' pewno' - ' Cube' - ' recruited' - éĢĻ種 - ' Cara' - ıģını - imated - ' ÑĪкол' - icional - ' пÑĢоÑĦ' - ' contamination' - ' últimos' - ' fearful' - ' elephants' - usi - ' iTunes' - ' Swami' - ê¼ - ' ìĦ¤ëªħ' - ' Richards' - ' magnets' - ' Richtung' - ' Legion' - èıľ - ' kitty' - ' kissed' - ' watering' - ' cono' - ' Palestine' - idir - ' maze' - ' fluids' - ' Producer' - ' Krsna' - 好åķ¦ - laf - ' ×IJ×ķ' - ' miesz' - ' Xing' - ointed - sein - ' Fuk' - ' Depression' - ' Duty' - ' Panther' - ' sund' - ' refere' - ' exclusion' - ' naval' - ' Winston' - ' slogan' - ' hypothetical' - ' elevate' - ëł¹ - ' cabeça' - ' Gesund' - meter - ' ìķĦëĭĪë©´' - ' cloudy' - âĢ¦? - ' Schritt' - ' JS' - ìį - ' Springs' - ' Batter' - ·° - ' tailor' - ' PTSD' - ' Gent' - ' baÄŁ' - ' spatula' - ' cray' - ' Legisl' - ' sú' - ' leve' - าม - ' erad' - ' dong' - ' derm' - ' Banks' - icho - åħĪçĶŁ - ' Franz' - ravel - éģĶ - оло - ' flute' - ' Ek' - ' joyful' - ' chased' - ' Large' - Over - ' entrepreneurial' - ' considers' - Ñĥем - opa - ' dormir' - ' Elementary' - ' przypad' - ÑĥÑģка - ' оÑĩеÑĢ' - ugene - ' tenido' - ' lugares' - ë¥ - ' ÑĩаÑģÑĤ' - ' sao' - ' braid' - ' Vere' - ' Reich' - ' Poss' - ' inan' - wand - ref - ' montrer' - ' 1981' - çķª - asında - ' chrome' - ' Trinity' - ' exploitation' - ' Sense' - ' CMS' - ' Noble' - ' ìĦłíĥĿ' - ' swelling' - electronic - ']?' - ' brushing' - ' liquidity' - ' Hook' - ' Connor' - ' Alum' - ' gucken' - suite - ' wiele' - ' barrels' - ' Regel' - ' Ment' - ' Trip' - ' Brush' - ' Erik' - urate - ÉĻr - ' Cyr' - ouble - ' Becca' - ' passwords' - ű - borg - ' vendo' - ' Claus' - ' Faz' - indest - ' deceased' - ' comparisons' - ' LCD' - ' Pork' - ' eventual' - ' patreon' - ' inability' - ' extinction' - ' ì¢ĭìķĦíķĺëĬĶ' - ' ÑģоÑģ' - aju - ' ×ij×IJ×' - ' sofort' - ' destined' - ' Rin' - ' mouths' - ' Natürlich' - ' preserving' - ' limp' - 黨 - ocused - инг - ' exposing' - ' ξ' - ëį - laugh - ' hiss' - ãģłãģĭãĤī - ' indie' - ' detal' - ÑĢавÑģÑĤв - ' trên' - æķ° - ' ogni' - ' simplemente' - ' 1978' - ' goo' - ' 1967' - ' genug' - hö - ' histó' - å®Ł - ' lobster' - cendo - ' teil' - ' allevi' - '0000' - OLD - ' pesos' - ' bonuses' - ' ami' - ' revival' - ' Horse' - 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' reacts' - ë© - ' mercury' - ' Luigi' - ' ог' - ' JUST' - ' lod' - ' cortex' - wig - ' lakh' - ì¤ijìĹIJ - ' Vic' - ' Mund' - ' mapped' - ' Dell' - ' Druck' - ' lifes' - алÑĮное - ividual - adım - ' atrav' - ' Flug' - ' Klein' - ê±°ìķ¼ - หà¸Ļ - ' appli' - ா? - üyorum - ' инÑĤеÑĢеÑģно' - ' disinfect' - '>-' - ' champagne' - ' kla' - opers - Trans - ' Desert' - ' cultivate' - ' Fucking' - idelity - ' ÑĤан' - ' incub' - ' temu' - ' learner' - founder - ' Syl' - ãĤĢ - ' fato' - zier - ' ìĹĨìĿ´' - ' ìĪ¨' - ' psycho' - ' ÑĤелеÑĦ' - ' regarde' - ' representations' - ' litigation' - ' spann' - ults - bior - è¦ĭãģ¦ - ä¸įå¤ļ - ' Survey' - ' LEDs' - ' trä' - ' lên' - ' antioxid' - еÑĢом - ' induction' - ' fooled' - ätzlich - ' говоÑĢÑıÑĤ' - ' Fact' - umbai - ' wiggle' - NOUN - ' développ' - ' Claro' - ' ì¸' - ë¬ - ãģªãĤĵãģł - ' accumulate' - ' maintains' - ëĦ - ' Fighter' - íĨł - ' matin' - ' coupon' - ' stunt' - ' debuted' - å¾ħãģ£ãģ¦ - ' prag' - иваем - '73' - ' expres' - ' ìĺ¤ë¹ł' - ' пеÑĢÑģон' - ' calculus' - ' abrupt' - ' Inspector' - ourt - æĸĻ - źniej - intense - Ba - ' lounge' - ' asthma' - ' Hiç' - ª» - ' editorial' - ' seize' - ' kır' - ' mouve' - ' tierra' - ' testosterone' - ' rh' - ' Kingston' - ELLE - ' Representative' - ' 1974' - ' iba' - Ts - ' sorta' - ' (?)' - ' تÙĪ' - ' ëĤ´ëł¤' - ' bekommt' - ' spiritually' - ' distorted' - Mad - ' reim' - ánh - ' Ottoman' - ' Relig' - ' Els' - ' retained' - ' Laughs' - æĢ» - ' SAS' - ' колиÑĩеÑģÑĤво' - ×ķתר - ' innovate' - ' kork' - ' ÑĢаÑģÑģказÑĭв' - ondere - ivi - aye - ounty - ' полÑĥÑĩаеÑĤÑģÑı' - ' buns' - åħ« - ' yüzden' - ' surgeries' - Ø£ÙĨ - ' bankruptcy' - welt - ' siamo' - ' darkest' - ' Hann' - gga - ' formas' - ' Dj' - named - ' shields' - ueller - ' Few' - ' lace' - ' furious' - ' YU' - ' societal' - ' judgement' - ' Dos' - ' jab' - laws - ' reinvent' - ' Katherine' - ' Choi' - adows - ' rans' - oden - ' Midwest' - nın - ' deport' - ' Dip' - ç´ħ - ' atención' - ' Courtney' - ividad - ' Ú©Ûģ' - ' efficacy' - ' Brooks' - ' referral' - ' конÑĨ' - ' malicious' - ' kir' - ' Goddess' - ' funky' - ' interim' - ' Körper' - ' ìĸ¼ë§' - kur - ' кли' - ' trucs' - gesetz - ' zug' - ' Glück' - ' Minute' - ' prestigious' - ' niez' - ' concentrations' - лаÑģÑĤи - ' Sis' - ' Vitamin' - kov - ' PBS' - ' нее' - ' retailers' - ' conventions' - ' Samantha' - ' proudly' - Jordan - ' JASON' - atk - ' triste' - ' stär' - ' reiterate' - ' posterior' - ' 1973' - ' Pine' - ' Juliet' - ' pedir' - kil - ' overlapping' - ' exclude' - ' económ' - ' accepts' - ' Ster' - 決 - ' ìļ´ëıĻ' - estab - ' tug' - arg - ' livro' - اص - ' seams' - ' buraya' - ' ello' - ' TM' - ' Paw' - ' Index' - Exc - ' inspirational' - ' dunk' - è°ģ - akter - ' conditioner' - ' Salut' - ÅĤec - ' ìī½' - ' Ñĥзна' - ' Romeo' - fruit - ' YO' - ' chá»ī' - бÑĥ - bons - ' reproductive' - ' orada' - ' íļ¨' - ' tentar' - ' mañana' - ãĤ¬ - ' solvent' - Jessica - ' Legal' - ' tua' - ' sic' - ' EQ' - aukee - ìĭľëĭ¤ - ' Åŀu' - ' adhere' - ' Tul' - ' à®Ĩ' - ' textbooks' - 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念 - âte - ' Kenny' - ' Siri' - ãĥĥãĥĹ - ' ëĬĺ' - ' EE' - ' unch' - кон - ' اÙĦØ¥' - ' belts' - ' hass' - ' моÑı' - ' displaced' - ' abra' - ÎŃλ - ' scratches' - ' comet' - ' authorization' - ' LLC' - ' produk' - ' rehabilitation' - åŀ - ÑĸÑĩ - uding - olit - ' 105' - ' expands' - ' altri' - ' Komment' - ' anf' - Pl - ' Mana' - fed - ' bri' - ' ora' - Gs - ' Gur' - uckland - ' junction' - ' ironic' - ' Feed' - ' prakt' - ' Hammer' - ĮëıĦ - ' Tracy' - çµ± - ' Aside' - него - ' иÑģполÑĮзоваÑĤÑĮ' - ' zaj' - ' equitable' - ' curb' - ' ãģĵãĤĮ' - ' derivatives' - ' puppies' - ' Kenneth' - ' Compl' - igram - ' Garcia' - )" - ' Harbor' - estial - ' ä¾Ĩ' - ' ers' - æ¹ - ' unwanted' - ' belang' - аго - emb - dos - ' ìĻľë' - ' Budget' - ' battling' - ØŃت - kok - наÑĩала - ' plag' - ' cantidad' - ' grupos' - ' plugins' - lerini - ' имееÑĤ' - ' sozusagen' - olics - ' pueblo' - ' reminis' - rän - ' Morrison' - ' linha' - ' breaths' - ' Taste' - ' enfrent' - ' Docker' - ' ден' - ' ethnicity' - ' wob' - 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' fick' - ' Naomi' - ' backside' - ieux - Kap - ' vedere' - ' lengthy' - ' breaker' - ' Rolle' - ' predator' - ' nossos' - ' advertise' - è³ĩ - ÑĢоде - Rednerwechsel - reten - ' collectors' - ıģımız - ' trig' - ' axes' - inters - ' penalties' - ' Osman' - ' Jenna' - ' flakes' - ' trainers' - ' stunned' - ' Scroll' - ' Pip' - ' наÑģÑĤ' - ' nhÃł' - ' Smack' - ẫn - ratos - ' ÑĢабоÑĤÑĭ' - ' ucz' - ' Lemon' - ' Sind' - ' psychic' - ' Abg' - ' mammals' - ' immersive' - ' bots' - ' verschiedene' - ' geral' - ' follower' - ' ä»ĸ' - ' seguridad' - ' immersed' - feito - cross - ' öld' - íĥĦ - ' ãģĵãģ®' - ' ×Ķ×Ļ×IJ' - ' Jian' - ' biliyor' - area - ' kaf' - ' godt' - çĽ¸ä¿¡ - ' ë°©ìĨ¡' - ' detriment' - æ¥ļ - Ñĸл - ' Äijâu' - ' chloride' - øre - lei - ' monte' - ' différentes' - à¯ģ. - ' caregivers' - ' inadequ' - ' farewell' - ' ÑĤипа' - ontec - ' Eph' - HHH - ' Todos' - ' СШÐIJ' - ' trov' - ' lige' - ' công' - ' Civ' - ' capaz' - ' Vallahi' - ' queste' - ' replica' - سب - zna - ' ÑģлÑĥж' - ' PT' - wave - ieni - ' relied' - develop - ' deme' - ' Aman' - ' [...]' - ' compliments' - uais - ' íĮ¨' - ' smelling' - ' dadurch' - ÙĪت - ' oranges' - ' лай' - ' stabilization' - åĢį - ãĤĮãģŁ - 楽 - ' appliances' - ' hm' - ĥIJë©´ - odynamics - ' ciÄĻ' - ' Cott' - MON - ' Mang' - æĶ¯æĮģ - ' allerdings' - ική - shots - ' ts' - ' Gör' - ' CHAR' - ' :(' - ' wrath' - ' fique' - ' führen' - ' testament' - ' ^^' - á¹Ľá¹£á¹ĩa - ALD - ' texto' - ' Dogs' - ' sib' - ' pathetic' - ocks - ' radically' - ' MORE' - ' JAMES' - ' ingl' - ' Technical' - ' porch' - ' UT' - ' обÑıзаÑĤелÑĮно' - ' renewal' - ' aesthetics' - ikum - ' beverage' - dern - ' predictive' - ' chuy' - ' Regarding' - ' Forward' - ' ÙĪÙĦ' - ' contextual' - ' dwarf' - ' prehe' - ' governed' - ħĦ - ' trabalhar' - ' negócio' - ' болÑĮÑĪой' - еÑĩаÑĤ - ' дÑĥÑħ' - ' floods' - ' bowling' - ' OB' - ' Här' - ' grading' - 주ëĬĶ - ' gars' - dling - ' rak' - ëĪ - creat - ' Ñīе' - ' neighbours' - food - Query - ' heroin' - iceps - ' Kinda' - NET - ' mari' - ' imitate' - ' achter' - ' settlements' - rare - cciones - ' ëĵľ' - ' fik' - itung - ' макÑģим' - ' elf' - ' dalla' - ' Polsce' - ' Pul' - ЧÑĤо - ' Morgen' - ØŃÙħ - ' supremacy' - ' kys' - ' Hurricane' - ' GTA' - ' Feh' - ' finalmente' - mund - ' Krie' - époque - ' Tucker' - ITT - ' lur' - ' dipping' - äv - ' eerste' - ' Flint' - bildung - ูà¹ī - ' toim' - ' pracy' - ' transforms' - ' speeding' - ' presenter' - ' fellows' - filled - ieza - ' advising' - ' Interview' - игÑĢ - wehr - ' Dante' - pture - Ī문 - ¯¸ë - IJIJ - ' Counter' - ' crist' - ' ì§ľ' - ' jeune' - ' ÑģÑĤÑĢаÑĪ' - ' mieÄĩ' - ' tutor' - ' masala' - ' powdered' - ' nau' - ' Frederick' - ' billing' - ' Eisen' - ' добÑĢ' - ' mest' - æ½ - ' snipp' - ' mono' - ' Alo' - ' Mercy' - érience - ' casualties' - ' ANNOUNCER' - ä»İ - ' tocar' - ' bacterial' - Ho - ' streak' - ' JENN' - ' plast' - Ñģлед - ' reapp' - ' paycheck' - ' miners' - habt - ' Jap' - нÑĥÑĤ - ' redemption' - ' quir' - hnlich - ' accumulation' - ' shove' - ' adrenaline' - Make - ' Hern' - ossing - ' Vil' - ubby - hertz - breaks - ' spur' - ' Daha' - USTIN - ' continuer' - ' Saul' - ãģ®ãģ¯ - ' íıŃ' - ' ëIJĺë©´' - ' ë§IJìĶĢ' - ' ож' - ' suspects' - ' laquelle' - ' Muchas' - ' völlig' - ulen - ' impres' - ' lobb' - enee - ' наж' - Ta - ' réalité' - ' Rex' - ' harvesting' - ' estr' - æ¶ - ospace - OSS - ' disturbance' - assic - ' Isab' - ' découv' - ' Hampshire' - ' ornament' - ' luôn' - ' UW' - ' jÄħ' - éĤ£ä¹Ī - ' respecto' - ' comunidad' - ' comigo' - agna - ' intrinsic' - ' Alumni' - ' sesleri' - ' estimation' - âĢĶâĢĶ - ' produit' - ãĢĤãĢį - ' вÑĢ' - ' whirl' - ' acces' - çu - ' variability' - ' vodka' - itsu - ' internships' - ' allocate' - RR - íĽĪ - ' instructional' - tant - ' à®ħத' - ' invites' - ' hak' - ' scares' - ' eclipse' - пов - колÑĮ - ativas - ' stabbed' - ' DOM' - ä¸įåĪ° - roots - ' Picture' - íĺ¼ - ' CHA' - iec - ıı - hanol - ' misunderstand' - Ray - ' roadmap' - ocumented - izione - ' Olive' - rift - ' ×Ķ׳' - æ¯į - lest - ;; - ' EA' - éľĢè¦ģ - одÑĥ - ' hobbies' - ' burial' - ãģ«ãģ¡ãģ¯ - Ф - lege - ' HJ' - ' objection' - ' ãģŃ' - ctory - ' incremental' - ' gymn' - ' epidemi' - ÑģÑĭл - Ãij - ' advancement' - ' parch' - News - ' ayr' - лам - ' ׾ש' - ' diploma' - ãģ¡ãĤĥãĤĵ - ' robbed' - Only - ' incur' - ' chanting' - ' íķ´ëıĦ' - ' riches' - ' Carmen' - ' nostro' - λÎŃ - ' Powder' - à¹Ģห - ' ìŀĪìľ¼ë©´' - ' gerçekten' - ' Pikachu' - емон - OLL - ' planetary' - ' slows' - ' clockwise' - alion - ' ìĮ' - ' vern' - ' homme' - ' endpoint' - ' innocence' - ' elementos' - ' sophomore' - ' notions' - ' Couldn' - pur - ' zat' - ' obsess' - ' motivo' - ' Kub' - ' Drug' - Ant - ' Players' - ' Humans' - ' melee' - ' Wildlife' - ' VP' - ' volcanic' - ' comin' - ' Guang' - ' ÏĦιÏĤ' - ' оÑģобенно' - ' Size' - Listen - ' Aaa' - appro - ' barbar' - ' Parkinson' - нÑıÑĤÑĮ - åį° - ' underestimate' - ' substitution' - ' cosmetic' - ä¸ĭ次 - ' willen' - ' beide' - anni - ' conditioned' - ' Debbie' - ' isto' - ' Edwards' - ìĽĮìļĶ - ' ÑĤов' - ' abbrevi' - ' Mün' - ' Princ' - ' Liang' - ' stink' - ' radioactive' - ãģĨãĤı - ' acontec' - ' uncon' - ' Turbo' - ãģIJ - ' kisses' - æĺ¯ä»Ģ麼 - еÑĤÑĢов - ' frontier' - ' Spy' - ' Belarus' - ' CBS' - á»Ĺ - amoto - íķľëį° - ' ÑģÑĤÑĢо' - ' Enfin' - ' breadth' - éĺ² - ' Cafe' - ' Dafür' - ' Bour' - aras - ' blueprint' - anı - ' constants' - ' attacker' - ' Formula' - zaÄĩ - ' sowie' - ' eyebrow' - obook - ' setzen' - 第ä¸ī - onsider - awning - ' söyleye' - ' invaded' - ' pronouns' - ' dobry' - Si - ' ХоÑĤ' - ' volleyball' - ' lament' - isches - arme - api - ' Wiki' - лиÑĪ - ' kasih' - ' pess' - ' ÑĦоÑĤ' - ' Sul' - å¾· - ' pseudo' - ' memo' - ' ìĹ°ìĬµ' - ' доллаÑĢов' - ' пеÑĢем' - ' Reach' - miral - alted - ' statut' - reading - ' söyled' - ' Lindsey' - ' Ahmad' - ë¶Ģë - ' СегоднÑı' - ' przygot' - ' hyster' - URE - ' Neigh' - Reporter - ' Bunu' - ' Treaty' - ' Rank' - ' Fame' - inished - ' geared' - ' compose' - odia - ' Lon' - ' jesteÅĽmy' - ' DIRECTOR' - ' elkaar' - ' Viel' - ×IJש - ynthia - 並 - ' mère' - ' Tomato' - ' exatamente' - niÄĻ - ' Frei' - ' Dif' - ' openings' - ' graphical' - ' Ñĥдоб' - ' вÑģп' - ' Weekly' - ева - ' hangs' - ' unsafe' - ' emblem' - ' Kolleginnen' - alay - ' ksi' - ' hides' - ' olmay' - ' entste' - ' arthritis' - ÃŁerdem - ' binnen' - ' listens' - ' Hess' - åĨįä¾Ĩ - ' Louise' - lden - енÑģ - ' Version' - ' Agriculture' - ìĬ¤ë¥¼ - ман - ëĦ¤ìļĶ - ' wines' - ' INF' - rul - ' JK' - ıyorlar - shield - reath - ' terus' - ' Lum' - ' anticipation' - ' accustomed' - ' Mina' - ' wield' - ioè - mera - ' countdown' - ' cling' - ' commend' - ' faktiskt' - ' defenses' - ' cockpit' - ' команд' - ' dishwas' - ' Thanos' - ' kidneys' - ' sehe' - ' microbes' - ' cuff' - ' вÑĭÑģок' - ' Spicy' - çŃīçŃī - வர - culus - orc - ç¾ħ - ixes - ' Credit' - ' raj' - ' bringt' - ' Niss' - ' grim' - ' SOL' - ' tenim' - ' Sudan' - ' Spart' - ' promotes' - ' Nossa' - ' ÑģоÑģÑĤоÑıни' - ' ì°©' - ' uncont' - ' Liberal' - ' ТолÑĮко' - ' Viele' - ' której' - ' ****' - Max - ' ЧÑĤобÑĭ' - '350' - ' íĺ¼ìŀIJ' - ' ë¶Ħëĵ¤ìĿ´' - ' warp' - ' tenga' - ' sympathetic' - ' bizi' - ' Zack' - iedo - ' ëī´ì' - piel - ' ÑĤол' - ' scaled' - ' PETER' - ' COMM' - ' Came' - ' catastrophe' - ' sweaty' - igration - ' stuffing' - ' ÏĢολÏį' - ' Driver' - zyst - Tech - ' assessed' - ' Surface' - ırım - sur - lerweile - ' дог' - ' shutting' - ' fractions' - ' Ñģол' - everyone - ' ern' - ' ÐĿов' - ' defenders' - ' versucht' - ãĥ³ãĥĢ - ' polity' - ' ÐŁÐ¾Ð½' - verständ - ' browsers' - ' transformative' - ' dictate' - ' LEGO' - ' ninguna' - ê´ij - ' pizz' - ' Harold' - ' Lopez' - Ú¾ÛĮ - anız - atchet - ÙĬت - ' lernen' - ' ê·ĢìŬ' - ' housed' - ' cleanse' - ' WAT' - laration - ' bytes' - ' tucked' - ' faults' - до - FX - ' ìĸ¼ë§ĪëĤĺ' - ' deform' - ' contracting' - ' TIME' - irse - ' neben' - ' cerc' - ' Armstrong' - ' tester' - ' parfait' - ' jealousy' - ' toxins' - ' disbel' - ÑĥÑĢÑĭ - impression - ' prostate' - ' firewall' - ' classics' - еÑĩÑĮ - ' socialism' - ' gracious' - ' Ñģнова' - ' днÑı' - ' burner' - ' Minor' - ' ìļ°ë¦¬ë' - ' jedes' - ' continuum' - ' hots' - ' occurrence' - ' administered' - ' замеÑĤ' - ' hesitation' - ' drills' - erca - ' вÑĤоÑĢой' - ' steadily' - ' insanlar' - ' ihan' - íij - ' helper' - ' Senin' - åģľ - ование - ' ERIC' - bla - ' Academic' - ' humanities' - black - umpy - ortex - ' ìłĪë' - ' Ø¥ÙĨ' - ' disclose' - ' Elijah' - ' λÎŃ' - ' Quer' - بÙĦ - ãĤ¡ - Tell - arle - ÑĸÑĢ - ' augmented' - ' ë¹ĦìĬ·' - ' android' - त - arma - ' szer' - geord - ' geek' - ' yeux' - ' pong' - ' ãģĿãģĨ' - ' tortured' - ' Bath' - zig - asonable - ' nets' - ' baru' - ' Flat' - ' Vater' - ' Terror' - ' Avo' - ' ceremonies' - roe - Ùģس - Ops - ' hyvin' - ' apresent' - olor - ' игÑĢÑĭ' - orton - ' ê·¸ëŀ¬' - ' lookin' - ' TY' - ' Mint' - Add - ' mite' - ' Smoke' - ' nota' - ' moss' - ' Abend' - ' 컨' - ' exaggerated' - fires - ' redist' - ffiti - ' openness' - ê°IJìĿ´ - endeu - енной - Watch - ' avatar' - ' Pey' - urun - ' senza' - ' ì§ĢìĹŃ' - ' Natomiast' - ' emergence' - rays - ' crafted' - gary - 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' Storage' - eight - ' ÑĤÑĢÑĥ' - ' donÃŃt' - ' sizin' - ' outdated' - ' наÑĪи' - ' affir' - ' spoons' - ' oni' - ' flank' - ' Gol' - hã - ' péri' - ' honorable' - ' Breathe' - scenes - ' obviamente' - икÑģ - ' ש×ŀ×' - ' smoothie' - ŀĪë - ' dime' - ' íĸĪìĸ´ìļĶ' - ' appel' - ' Catholics' - ' singles' - ' laten' - ' çünkü' - ' Vader' - æıĽ - ' vardı' - ' Istanbul' - gré - ' Elsa' - ël - ' invece' - ' crane' - ' obe' - ' Shark' - ' smack' - ' restoring' - .\ - ' ë¹łë' - ' faded' - umbers - Singing - ' depressing' - thest - ' Wahr' - ' multitude' - ÑĢавÑģÑĤвÑĥйÑĤе - rijk - eka - ' completes' - ' Wells' - ' roy' - ' Pray' - ' Kalau' - izin - iaÅĤem - ' locom' - ' Nashville' - ' Pentagon' - 미 - ' NEW' - ÄħÄĩ - ÃŃss - ' marrying' - ' feud' - íĻķ - æĢ¥ - )! - ' Operations' - ÑĥÑĶ - ' moje' - ' instructed' - ' ëĪĦ구' - ' ×Ķ×Ĵ' - ' помоÑīÑĮÑİ' - ' sabia' - ìķĺìĸ´ìļĶ - plane - pri - ' полноÑģÑĤÑĮÑİ' - ' Kitty' - ' próprio' - edere - ' interesante' - ' де' - ' condensed' - ' avent' - TOR - ' greasy' - ARK - orta - AJ - ' disreg' - ' corrections' - ' stero' - ' influenza' - ' desses' - ' ballots' - ' meget' - ' mafia' - ' böl' - nost - ' ÑģÑĤаÑĤÑĮ' - ' responder' - ' hinten' - grav - à¸Ńะ - ynchron - ' viens' - ' samo' - ' dt' - pannt - ' ÅĽwiat' - ' запиÑģ' - ' merged' - ' kep' - ' misleading' - ' digamos' - ' ammon' - è¾Ľ - chet - ' ê°Ģìł¸' - ' uni' - ' ëIJĺëĬĶëį°' - ' напÑĢав' - ' коÑĤоÑĢого' - ' animate' - ×ķ×IJ× - еÑĢв - ' minced' - ' kaum' - ãģĤãģģ - ÏĢε - лег - existing - ' plataform' - ' KRIS' - ìĽł - ' Familien' - ' Libya' - ' biodiversity' - ' idiots' - irdi - ' szyb' - ' Rolling' - ücht - ' Ñĥдив' - ÑģÑĥд - ' realizar' - ' canned' - ' ÑĢан' - ' metabolic' - ' Beef' - ' kilka' - лÑİÑģ - ' registry' - моÑĤÑĢиÑĤе - ' vielä' - ' odc' - ' condemned' - æ©ĭ - fal - ' Dil' - woÅĽci - Aw - ' statistically' - ' sogen' - ' BETH' - ' shaving' - 幸 - ocal - ' Funny' - ' peacefully' - ' addictive' - ' Insert' - lauf - ' experiencia' - é¦ĸåħĪ - иÑĤелÑı - ÃŃgen - ágina - ' abdomen' - íķľëĭ¤ - 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' 1918' - presented - ' prowad' - ' Terr' - ' Princeton' - ' morgen' - ' attracting' - ' Sigma' - igner - ' Rechts' - ' Peki' - ' methy' - ' hamm' - ' direito' - ' delegation' - иваÑİÑĤ - ' gin' - Young - ' dependencies' - ' Bradley' - buds - ' fis' - ' pytanie' - ' interconnected' - ' embaixo' - ' Sas' - ' ruh' - ' Sicht' - Sur - ' superb' - ' Sabbath' - ' Danger' - kol - ' hou' - supp - ' Nacional' - ' succession' - ' vá' - ' MaÃŁnahmen' - ' Jessie' - ' Idaho' - forest - ħĺ - ' ×ŀ×ĵ' - ' Ø£ÙĬ' - ' sweetheart' - ' neatly' - ' Evangel' - 곡 - ' Suite' - ública - ' Ñĥли' - ' Announcer' - ligh - ' sensations' - ' shelters' - ' hart' - ' squeezing' - ' Rivers' - ' Cooking' - ì±ħ - personal - ' manos' - ÑijÑĤÑģÑı - wij - ' gogg' - ' Milli' - ' FP' - ünst - ' LS' - ' spraying' - ' faux' - ' autograph' - ologic - ' torment' - ' encrypted' - á»ħ - ' estre' - ç¹¼ - à± - ' stumbled' - ' aider' - ' saben' - xter - ' Cities' - ' Türk' - ëĭ¥ - chine - ' topping' - ' poisoned' - ' Romania' - ×ĵ×Ļ - Ģë¡ľ - ' поÑĢÑıд' - ' chirping' - ' ìĻĦë' - ×ij×¢ - ' cuanto' - ' donating' - ' Regent' - ' Beruf' - ' distracting' - ' stamina' - ' Darren' - ' ì¶ķ' - lists - dal - chuss - ' economist' - ãģĪãĥ¼ - orgt - ' istiyorum' - è¿Ľ - ' Surprise' - ' Hao' - ' ìµľê³ł' - ' GW' - ' Inner' - ' quieren' - ' minded' - ' supercomputer' - ' diagrams' - íĬľë - ê²łìĸ´ - ' обÑĬÑıÑģ' - ' estaban' - ' destroys' - ' Breaking' - ' karÄ±ÅŁ' - ' rebuilding' - ľëĮĢ - ливо - ' Sauce' - ' Fusion' - ×ķ×ŀ× - ' Quinn' - ' gauche' - ' ÙĪØ£' - ' È' - çĵľ - ' techno' - ' dispatch' - ' aÅŁk' - ' einzel' - ' Gmail' - çŀ - ' ê°ľìĿ¸' - ' ÑģемÑĮ' - ' journeys' - ' iht' - ' fibre' - ' dramas' - ouched - ' rename' - ' опеÑĢ' - ' poo' - ' Dru' - ' иÑĤог' - ' zast' - ' coz' - ' zucch' - ' obtaining' - ' commute' - ' submer' - ' Vish' - ' Rabb' - ogg - ' hut' - íĸĪìĸ´ - æ¯Ķå¦Ĥ - eremi - ' μα' - ' diskut' - ' бÑĥк' - ' impaired' - depend - ' ÙĪا' - ' ÑĢÑĥк' - ' баÑĢ' - ' oxidation' - ' situação' - ÉĻn - ução - ' sagte' - ' SER' - ' Cake' - ' turmeric' - ' Kak' - bung - ' Ká¹Ľá¹£á¹ĩa' - ' poisoning' - ' slipping' - ' Says' - å°±åı¯ä»¥ - òng - çŁ³ - « - ' Claudia' - ' Character' - ниÑĨ - coat - ' progressed' - ' Fergus' - ' ìĺ¤ëĬ' - ' oat' - ordable - ' Ley' - ' Heraus' - ' resultados' - ' Kayla' - ' riff' - ' chegou' - ' xi' - ' spacious' - ' recognised' - ' ech' - ' Tie' - ' launcher' - Jim - ' suppression' - ' Impossible' - ' guitars' - ' Fourier' - иÑĩеÑģкий - ' Therap' - ' Kaf' - centered - ' ÑģооÑĤвеÑĤ' - ' klim' - ' carbohydrates' - ignant - ' Astron' - ' emple' - ' drastic' - ' миÑĢе' - вин - uw - ' prettier' - ' donuts' - ' Athena' - ' dissert' - ' plante' - ' uranium' - ìĿĮë - aré - ' rzecz' - ' displaying' - æĪ² - ' sarc' - rão - ' tampoco' - ' philosophers' - ' Recht' - æĵļ - ' comentarios' - yse - ' ìľ¤' - ' mise' - ' Gin' - ' ном' - ' FROM' - liner - atif - ' spoÅĤec' - xa - ' ÑĤÑĢÑĥд' - ' wag' - 기ìĹIJ - ' MG' - ' offspring' - ' Understanding' - åıªæĺ¯ - ORA - ' whirring' - ' surrend' - ' poker' - ' monuments' - ' âĻ©' - ' organised' - ' Sozial' - ' Factory' - Ñħа - ' resemble' - зд - ' explosions' - ' payroll' - ' omn' - ' Jorge' - ιÏĥ - ' fracture' - ' persecution' - ' demais' - ECH - ',)' - ' criar' - ' JOSH' - ' demographics' - ' 1600' - ' currencies' - ' Tips' - ' éĢĻåĢĭ' - ' Refer' - ' Dancing' - ' inconsistent' - ' deh' - ' immens' - ' meist' - ' impatient' - ' behaves' - æĿ¾ - ' ëĤ´ìļ©' - ' backstory' - ' agreeing' - ' Åģ' - ihin - ' temperatura' - ' Background' - ' nutzen' - ' ëħ¹' - ' Männer' - ' collaborations' - ' Kos' - éģİåİ» - ' nightmares' - ëĵ± - ' Queensland' - ' associates' - ' Kok' - ' factorial' - ' Hyung' - ' ê·¸ëĭ¤ìĿĮ' - ' filho' - ' elét' - ' íĸīë³µ' - °± - ' gefunden' - ' semicondu' - ' counselors' - ' Upper' - ' Aub' - ickers - Ver - ' northwest' - ' Maintenant' - ' Lakes' - аÑıв - inté - ì°½ - ' газ' - ' giorn' - ' digitally' - ' Circuit' - ì¼Ģ - ãĤĬãģ¾ãģĹãģŁ - ' cheerful' - ' Peterson' - ' Danish' - ativos - ' liken' - ' harbor' - алиÑģÑĤ - xe - ' curls' - ' Rhod' - End - ' ET' - ' acquaint' - ' Kelvin' - ' trif' - ' Away' - ìŀIJëĬĶ - vs - ' página' - ' inlet' - ' Santos' - ' ìļ°ìĻĢ' - ' yapıyorsun' - theme - ' souff' - ' injected' - ' później' - iverso - amped - ' daher' - ' dagger' - ' лÑİбим' - ' tummy' - ' enlightened' - cents - ' Dah' - ' cuest' - ä¾Ĩ說 - ILY - ' ×ijר' - ' banging' - ' Emil' - ' Cler' - ' Border' - ижÑĥ - ' presenters' - ' STUD' - coins - ' íĻį' - ' perks' - ' parap' - ' certaines' - ' Lore' - öst - ' MARTIN' - ' bios' - ' whereby' - verts - ' Miranda' - ' stip' - 澤 - andez - ׼׾ - ujin - ' ê¾' - ' allergies' - plate - ' yapıl' - ' undertake' - ' ëĤĺê°Ģ' - Part - ' kızım' - hguru - ãģĤãģ¨ - ' Johns' - ' eyelashes' - ' drained' - ' stÃ¥r' - ãģĤãĤĬãģ¾ãģĻ - ' Jade' - ' calend' - film - ' mesa' - ' ludzie' - ' attracts' - ' juices' - ' кил' - ' nieuwe' - ' mencion' - ' ignition' - ' bladder' - andaag - ' Extension' - íĤ¨ - feed - ' ÙĪÙĩ' - ' spun' - ' tät' - оÑĢоÑĤ - tyard - ronics - ' Huge' - Ñĥжд - string - ' unjust' - ' prawn' - ' frosting' - ' disappearance' - iosa - ' cardi' - ' Priest' - ' cientÃŃfic' - åĵªè£¡ - ' ÐĴаÑģ' - ' ë¶Ģíĥģ' - ' thieves' - ' physique' - ' Eugene' - ' близ' - ' monopoly' - ' biography' - ' hoÅŁ' - ' tö' - mac - ' shocks' - ìĦ¸ë - hit - ' snug' - ' incl' - ' dedic' - ' ultras' - ' извеÑģÑĤ' - ' utilization' - ' ÑģовеÑĢÑĪенно' - ' servi' - stag - '180' - ' sewer' - ' Choice' - ' discharged' - ' JD' - олеÑĤ - ' кваÑĢÑĤи' - ' telescop' - ' JeÅĽli' - ' Nana' - cale - ' ÑĤон' - mmm - äºĨåIJ§ - ' gehabt' - ëĤł - æĬķ - à¸Ļà¸Ļ - ' ether' - ' zen' - ' researched' - ' Czyli' - å®Įåħ¨ - workers - ' 경찰' - ' sheriff' - allo - ' tipos' - ' prosecution' - ' frogs' - ' falt' - jd - ' íĮĶ' - ' filtered' - ' Oft' - ' ìį' - ' disfr' - ' Mustang' - ' woah' - ' REALLY' - ' могли' - ' entrada' - ' игÑĢа' - ' mixes' - ' авÑĤомоб' - ÐĻ - ' shin' - ' paranormal' - ' someplace' - ' dishon' - etaan - ' fuerte' - Ù¹ - ' doom' - ìĪľ - ' existential' - ' buld' - ' SDK' - ' пÑĢавда' - ' turnover' - ' ìĹ¬ê¸°ìĹIJ' - 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лоÑģÑĮ - ' inertia' - ' uniforms' - 足 - ério - ×ķר×Ķ - ént - ' à®Ĵ' - ' ÑģамÑĭÑħ' - ' voulais' - ' Zimmer' - ê²łë - ' ноÑģ' - encias - ' relación' - ' 걸ë' - ' faction' - ' gosp' - полож - nap - hak - ' proceedings' - ' ìĨĶ' - ìķĦëĭĪ - ' ìŀIJ기' - ' werd' - ' sof' - ' schlim' - ' flavored' - ' quadratic' - ' Boot' - ' publicity' - ' Caro' - ' ?"' - ниÑĨа - mania - ' SUR' - ' BUR' - lance - ética - ' zobaczy' - ' trio' - sama - ' taÅŁ' - ' asymm' - resser - ' تع' - ' пеÑģ' - ' beginnings' - ladım - ' бÑĭÑģÑĤÑĢ' - ' moo' - ' Geneva' - ' åľ¨' - erus - borah - ' refusing' - bull - ' Waiting' - ' Individual' - ' anonym' - imens - ' medidas' - ' fragrant' - ' directement' - ' ìķĦë§Ī' - uria - ' spherical' - ' abge' - ' Victorian' - ' spectacle' - ' Rodriguez' - ' ocup' - ' När' - marks - ngulo - ' Luci' - ' shouted' - ' regulators' - ÄŁini - ' disent' - ' ÑĢÑĭн' - ëĤ¨ - ' ìĤ´ë' - ' problèmes' - ' Finger' - assemble - ' pear' - ' droite' - ' Everywhere' - tam - оÑĤив - вой - ordinate - ' Lak' - 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' stimmt' - ' Gaza' - ' volte' - ' nuo' - ' món' - ' Comput' - ูà¹Ī - ' Radi' - ' exceptionally' - ' assumes' - éĸĭå¿ĥ - ãģĪãģ° - inform - ' shrine' - æĵĬ - ' implication' - ' Fitz' - æ²ĴéĹľä¿Ĥ - '!.' - ' lt' - ' alloy' - ' ethic' - ' monastery' - ìĭľì£ł - icação - ' coordinating' - ' Moto' - ' overlook' - ' chois' - ' antibiotic' - ' Minne' - ' BJ' - ' Apa' - orian - ' spilled' - Jam - ' husbands' - ' creations' - ' añ' - üssel - ' ìĿ´ìļ©' - ' analyse' - rose - ' punched' - ' presque' - ' astronomy' - ' schwierig' - ' Ebola' - ' cis' - ' acet' - ' FX' - endre - ' ìĿĮìķħ' - ' webpage' - ' freaked' - ' latte' - ' ì¿ł' - ' 머ë' - Never - Gra - íĻĶ를 - eyed - ' ë°ľëĿ¼' - ' espera' - ' aparece' - ração - ' disruptive' - ' Joint' - urous - reas - ' querÃŃa' - ' distributions' - ' exponent' - ì¹ĺ를 - ' dl' - zhou - ' Hearing' - å·®ä¸įå¤ļ - ' Craw' - ' floats' - ounced - Lab - World - ' burdens' - ' authoritarian' - ' Bolt' - ' однÑĥ' - ' pigeon' - ' distractions' - ' Herausforder' - ' zest' - esc - ' shakes' - atas - ' ÙħØ´' - holes - ' thinkers' - alta - ' arche' - ' Suk' - anha - ' tempting' - ' youtuber' - ' vì' - ' dziaÅĤa' - ' Vatican' - Park - ' supers' - ' Nikki' - ëĬIJë - orang - ramient - 鬼 - ' ê°ĸê³ł' - ' desserts' - ' avere' - ' Gregory' - ' ëĵ¤ìĸ´ìĺ' - ' costing' - ' Clinic' - ' rebels' - ' Mob' - ' bunlar' - ' Yours' - ertime - ' retali' - mara - atus - alles - ' дÑĢ' - ' диÑģ' - ' discounts' - ' GUY' - ' какое' - ' Experiment' - rement - ' Xiang' - ' bate' - WE - ' specialize' - ' deity' - ' Loki' - mag - ' Nit' - West - ' maternal' - ' quis' - åŁºæľ¬ - broken - ' lasers' - ' hakk' - ' Angels' - ' mastery' - antis - Tiffany - eee - çij - orem - ' inacc' - ' jurisdictions' - ' Kardash' - æľº - Il - ' Sinn' - åĭķçĶ» - ' athletics' - cÄĻ - ' loosely' - ' dieta' - Ag - ' ??' - ' ëĮĢíijľ' - ' superv' - ' nutrit' - ' drifting' - ' ìĦłìĥĿëĭĺ' - ' понÑıл' - ' Victory' - ÙĦØ© - ×ķ׳×Ķ - ' пиÑĪ' - ' shaved' - ' mesure' - onden - Ùĥر - ' exile' - ' Desde' - ' Pinterest' - ' attachments' - ' hombres' - ' fines' - ' ìĦ¸ìĥģ' - ' sleeps' - ' Taco' - ' IRA' - rios - ' oll' - etes - ' unut' - fashioned - ' treball' - ' Nearly' - ' ÑĢеалÑĮно' - ' chil' - éĢ± - ÄŁa - ' MEL' - roscop - ' CG' - ' venge' - ' dishwasher' - algic - ' modifier' - ' embassy' - timer - emics - ' intricate' - ' evet' - ' ëĮĢë°ķ' - ' isot' - ' наÑĥÑĩ' - ' Quiz' - reso - δÏİ - ' yelled' - ' feder' - ELLER - ' exceeded' - onas - icano - ' живоÑĤ' - ' Mao' - ' Kazuto' - ' ãħĭãħĭãħĭãħĭ' - ' frontline' - ' Hungarian' - ' überall' - awat - ' grips' - ições - arnya - ' Í¡' - ' seid' - ' anak' - ' acabou' - íķij - ' notorious' - ' Godzilla' - ' overcoming' - ' Pend' - ' olabilir' - ülme - ' erhalten' - ãĤīãģĦ - ê·¹ - ' Meter' - ' staan' - Ol - ' chats' - ' Buenos' - ÃŃve - aluable - ' strategically' - ' comprised' - ' пеÑĢÑģонаж' - ' wann' - ' Cen' - ниÑĤе - Łģ - ' ÑĤобой' - iad - ' kardeÅŁim' - ' Congressman' - reaming - homme - ' communaut' - ' alcoholic' - ' pickled' - ' acord' - position - egól - ' troubling' - ' Marcheg' - ' zumindest' - ' seamlessly' - ' olun' - ' TVs' - ' пÑĢакÑĤиÑĩеÑģки' - ' backend' - ãģĵãĤĵãģ«ãģ¡ãģ¯ - idable - ' gadget' - ' faço' - ' Marchegiani' - ' ë°¤' - ' accidental' - ' LP' - ' eldest' - ' Admiral' - ' nÄĥm' - lever - ' pastel' - ' fondo' - Connie - ' tercer' - ' pact' - ' Monte' - ' meats' - ' SMS' - ' Australians' - ç¼ - Rhett - ' exactement' - ' ë¹¼' - ' MOD' - ç¡ - ' Rapt' - ' Noch' - ' abort' - ' Naval' - ' Fuji' - INTER - ' новÑĭй' - ' miejsce' - ' ICU' - ' Graduate' - ' Glen' - ardi - ' Èĺ' - ' solder' - ' professions' - ' orthog' - omn - introdu - ' Denise' - ìŀIJ를 - ' correspondence' - AMA - ' inflict' - ' fand' - ' Gü' - ' ÑĩеÑĤ' - ' traced' - ' patents' - ' ambush' - ' lotta' - ffer - ' Wagner' - ' imperson' - ' extrêmement' - ÙĤت - conduct - Att - ' Mueller' - ' Alicia' - ' cyc' - ' hacker' - ' tys' - ' hail' - ' заÑıв' - ' passo' - ' ì¶Ķê°Ģ' - ' ÎĪ' - ' packaged' - ' Cynthia' - heet - ä¸ŃåĽ½ - ' Nissan' - ' Questo' - é¨ - did - ' μια' - ' Ellis' - ' Analysis' - cemos - ' aseg' - ' Myster' - ' Cao' - ' tuv' - ' Industry' - ì£¼ê³ł - otal - ' pequeño' - bras - ' comprehend' - ' Simpson' - ÑģÑĤвие - ocracy - иÑĩеÑģки - ' Mush' - ' Laurie' - ' triangular' - ' Presents' - ' Kunden' - ç´¹ - æѦ - ' Iss' - ' Deck' - á»ĥn - ' Darkness' - ' inflammatory' - eremiah - ' warmed' - veyard - ' Memory' - etty - ' taxpayers' - à¸ĵ - Ø¡ - ' practise' - ëĭ¬ë - ' drilled' - mÃ¼ÅŁ - logo - ' Fach' - ¤ë¡ľ - ' übrigens' - ' konnten' - ' normalmente' - ' argues' - ilingual - °ë¥¼ - egal - ' travaill' - ovy - аÑĤо - ' ruth' - ' Lights' - ' consisted' - ×ijר×Ļ×Ŀ - ' stereotype' - ' payer' - ' Ree' - ' Airbnb' - ' drowned' - ' Zoe' - ' canopy' - ' barr' - ' ноÑĩ' - ' pagan' - ' jars' - ' rê' - erver - æĪ¿ - ieben - ' espect' - ' Fi' - ' unwilling' - ' technician' - ặt - member - ' Canal' - سÙħ - ' lieber' - ' inference' - ' honoring' - åijµ - ' Campaign' - ' lineage' - ' Stress' - ' victories' - ' deja' - ×£ - êtes - blick - ' менее' - oths - ' Couple' - Jason - ' Nicolas' - екÑģ - lib - ' herramient' - ' ×IJ×ķ×ŀר' - ' видим' - millimeter - ' silhouette' - ' driveway' - ' cherish' - ãħłãħł - ' ransom' - ' interdisciplinary' - ' Portal' - ' trag' - thood - ' tedious' - ' glossy' - ' prépar' - ' Cay' - ' Took' - ' Bottom' - ' zig' - å« - åį± - represented - à¹Ģลย - ' desarrollo' - ìĦľë - ' viscos' - ' milligram' - ' Gund' - ' ferment' - drum - ' drawers' - Laugh - ' pelos' - ' pavement' - ' memoir' - avait - ' 2050' - ¤ë¥¼ - ' razón' - ' flourish' - ' stern' - ä¸Ī - ' Chung' - ' serpent' - ' Gentlemen' - 羣çļĦå¾Ī - kook - ' lut' - importe - parent - ' wsz' - ' scree' - ' Mitarbeiter' - å·´ - mut - ' ìĸĺ기를' - ' semble' - ' OW' - ' investigator' - ' Cheryl' - ' Gerald' - ' prere' - ' compares' - nyt - ' diferença' - ?- - ' quá' - ר×Ļ - Sen - ' heps' - ' gratuit' - ' consort' - ' STOP' - ' Protestant' - ' electrode' - âĹ - ' securely' - иÑĩеÑģкой - ' tää' - ' registers' - ' Heavenly' - ogly - issä - ' Physics' - ' Merkel' - ' rév' - éĻ¢ - ' erased' - ' Sacramento' - ' coffin' - ' exacer' - ' lanz' - ' poets' - ulif - ' ì¹ĺë' - ' Nerd' - ' NCT' - ' Hour' - nehmer - ŀĺëıĦ - ' Princi' - Sw - mies - armed - ' Beatles' - ' propagation' - ' exchanged' - ' cumulative' - ' ì§ijìĹIJ' - ' defeating' - æĬ± - bels - ' wes' - ' Odyssey' - ä½łæĥ³ - avior - ' ìľĦìĹIJ' - ' brit' - ' hijo' - DAY - ' اÙĦتÙĬ' - ' СеÑĢг' - Ñĥка - edsiÄĻ - ' impos' - ' ellas' - ' firearms' - ' NR' - ' ×ij×IJ' - ' ÐŁÐ¾ÐºÐ°' - awi - ' ìĦ±ê³µ' - ' pupils' - ' Tack' - ' frase' - ' Ship' - ' stad' - 举 - ' Greater' - unun - immung - grown - ' NXT' - ' Americas' - fox - ' manten' - éłIJåĤĻ - ' Ñģок' - ' rikt' - lectric - deep - ' знаеÑĪÑĮ' - ' benut' - ' Infrast' - ' Emir' - ' оÑĤпÑĢав' - ' Kimchi' - ' Finnish' - ´ìłģ - inaire - ' oike' - æ¸ħæ¥ļ - ' hostage' - ' Button' - ÙĤÙĬ - eking - ' Kazakh' - ' comforting' - ' sog' - ' greeted' - guitar - payer - ' relational' - ' construir' - çī¹åĪ¥ - opian - ' Volume' - ieth - ÑģÑĤвом - urrection - liÅĽmy - ' hemisphere' - ' Bean' - IGN - ' kötü' - ' Fallout' - ' brace' - ç¹¼çºĮ - ÏĢά - ' HAS' - ' gé' - ' characterize' - ặc - ' Milky' - ' tumors' - ' nuit' - ' Gaz' - ' ìŀĪëĭ¤ëĬĶ' - ' гаÑĢ' - essment - ' Abe' - ' ë½ij' - ' Einsatz' - JIN - jä - Cry - ' Promised' - ' ÑģеÑĢд' - okus - ' scalable' - ' поÑģмоÑĤÑĢеÑĤÑĮ' - ücklich - ' realism' - ' mayo' - ' juvenile' - ' headlights' - ' görÃ¼ÅŁ' - ' Reform' - ' halves' - czne - ' breakup' - żej - ' rätt' - Day - ' ìĿ¼ë³¸' - ' muerte' - ' tunes' - ' Smile' - record - ' recherche' - atisfied - ' pozi' - ' celebrations' - isexual - ' ROB' - thirds - ' Fortune' - ' ÑĤой' - ' branded' - loo - ' dud' - ' randomized' - ' combin' - ä¸ĢäºĽ - ieran - czenia - įãĥ« - ' curator' - ' artery' - ' ÑĥÑĪ' - ' ÑĩиÑĤ' - ' subsidies' - ' blossom' - ' Twilight' - ' hyvä' - ' Pompe' - ' Cisco' - ' ÐŁÑĢо' - ' biri' - ' gern' - ' rebuilt' - ' wcze' - ' benefici' - ' drummer' - ' solids' - ' diyorsun' - ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸãģĦãģ¾ãģĹãģŁ - lated - ' muddy' - ' holog' - ' claps' - ' Rings' - ' Okey' - ' Brave' - ' valuation' - ' migrant' - ' intermitt' - ' eigene' - iliary - ãĥ¼ãĥĪ - markt - kr - ' Rib' - á»Ļi - ' accusations' - ' arab' - wash - ' Bardzo' - ' ugh' - esters - ophren - ' alimentos' - ' Uz' - ÖĤ - ' 650' - ' пÑĢиеÑħ' - FI - ' sampai' - ' parlé' - hesion - ' sır' - ' apparatus' - ' correlated' - ' Principal' - ' corr' - ' Official' - иÑĩеÑģкие - ' terminals' - Should - ' vacun' - ' stellt' - ' mooi' - etzung - ' кÑĢа' - ' dai' - ' пож' - Team - ' PPE' - ' ÐŀÑģ' - ' Leah' - ' Ivy' - yst - ' uhhh' - ' nighttime' - ' trendy' - ' securities' - ' continents' - ' firsthand' - ' Veron' - ' ëĤ®' - ' browsing' - ' Cada' - tro - ' tramp' - reib - ' erstmal' - irler - ' psic' - ' getir' - ' NP' - ' dzieci' - обÑĢаз - ' magician' - ' scrutiny' - ' slab' - ' OT' - isty - iries - orest - ' tasked' - ' morally' - ìķ¼ì§Ģ - ustered - ' fools' - ' irrespons' - ' einf' - ' viá»ĩc' - ' scor' - ' pillows' - ' Gegen' - ' tutte' - ' quarterly' - ' didnt' - ' Gym' - ' Ether' - ' Ø«' - лиÑĪком - ' signaling' - ' Node' - ' Doncs' - ' yah' - ' Kanal' - ' fading' - etin - ' influencers' - ' medals' - ' engineered' - ' fermented' - ê²łì§Ģë§Į - ' Beethoven' - ×ŀש - inental - ' ìķĮ볤' - ütfen - alnya - ' overe' - ' denkt' - акÑĤеÑĢ - ' âĺ' - ' necesit' - ' generators' - grass - ' подÑĥм' - lieÃŁen - Bar - ľëıĻ - ' деÑĤей' - ' sucking' - ' stencil' - ' primo' - ' Breath' - strom - ' immensely' - ' appreh' - ìłķìĿ´ - Pop - ' jong' - ' Giul' - ' ADHD' - ' hören' - ' elo' - ivent - ' rus' - ' outrageous' - ' mastered' - ' 커' - ÙĪÙģ - ipes - ' Rudy' - Jacob - ' bullish' - ' tapped' - ' faud' - izophren - ' ÑģоÑħ' - ' Darling' - ' 1963' - ' Prevention' - ²Ķ - ' abdominal' - stones - ' avaient' - á»ķi - make - ' sare' - ' Instant' - кам - ' keeper' - ' blankets' - ãģ§ãģĹãĤĩãģĨ - ' sweats' - ' Minneapolis' - åħ¨éĥ¨ - ' genommen' - ' fasten' - ' Brussels' - åij¼ - ' cafeter' - ' absorbing' - ' hago' - ' Elmo' - ' gusto' - ' Yap' - Música - ' tert' - ' banda' - ' mily' - ' thereafter' - ' Stockholm' - ' Carson' - ' calibration' - avaÅŁ - ansa - ikke - ' foresee' - ' qualche' - ' deste' - æ¤ - ünüz - ' forge' - Dis - esten - ' δια' - ' encaps' - ' Gespr' - ' chercher' - ickets - ÑĤоÑĢÑĭ - Cr - ' Также' - ' rabbits' - ' Dot' - heiten - ' causal' - ' Foster' - ajÄħc - ' bereit' - ' ayudar' - é«Ļ - ãģ³ - song - comb - ' fringe' - ' cybersecurity' - ' 뾨' - ' kier' - ' beschäft' - ' конÑĨе' - ' facilit' - ' Namen' - ' bilateral' - tx - ' Wissenschaft' - ' nuances' - ' ripping' - ' fy' - ' Sicherheit' - ' Ghana' - olon - ' topped' - ' Morocco' - ' radial' - ' LEE' - ' Andreas' - edd - ' ìĹ´ë' - ' Airlines' - ãģĵãĤį - ' valores' - ê·ľ - Hy - ' задаÑĩ' - ' Kendall' - ' ÑħаÑĢ' - ' Vamp' - ' python' - ' manageable' - ' Gente' - oise - iciary - ' imposs' - ' Bunny' - iesta - Andrew - ' sert' - ' Cec' - zzarella - ' automobile' - ' Tiere' - allows - åĨĨ - ' ë°Ģ' - ' Scorp' - ' Jelly' - agara - ' Stretch' - ' redef' - ' exacerb' - ' SHA' - éf - orsa - ' flawed' - ' Noel' - ?!? - ' procent' - ' menstru' - ' пÑĢоÑĩ' - ' infants' - ðŁİµ - pause - ' Racing' - ' 1948' - ' superintendent' - idores - idy - brahim - ' unlucky' - ' perk' - anci - ' ë§ĮëĤĺ' - ' ÐľÐ¾Ñģкв' - ' finans' - ' diferencia' - łĪìĿ´ - éħį - ORY - ' Tac' - ÛĮا - ' desem' - ' важно' - ' JU' - ' ìŀĪìŀĸìķĦìļĶ' - ' ÎĿ' - ' informations' - ' HEL' - hst - ' поговоÑĢ' - ' voiture' - ' reus' - ändig - ' поÑħож' - jing - ' dru' - altra - ' produits' - ' kite' - ' eyeball' - ' Belt' - ' Restaurant' - ' gamb' - ' porridge' - itters - ' converts' - ' yardım' - ' máximo' - wirtschaft - ' íķĺëĤĺë' - ' ì¤Ģ' - ' iceberg' - ' vorbei' - ' 256' - ocratic - ' reckless' - onner - ' mús' - ' logically' - ' Prison' - ' Netz' - ' vacant' - ' nimmt' - ' HARR' - ' зов' - ' Dee' - ringe - niest - ' Rules' - ìĬ¤ëŁ½ - cussions - ' floral' - ' constrained' - ' differentiation' - ' Quebec' - ' ÛģÛĮÚº' - ' pública' - itel - ' accommodations' - ' Grü' - íľ - ' pickles' - иÑĩеÑģкиÑħ - ' commissions' - ' Baek' - ' çocuÄŁ' - ' Medium' - ' periodically' - ' wonderfully' - ' staffing' - ìĽIJë - rire - fle - ' McL' - ' ÑĤеп' - ' пеÑĢек' - нолог - ' íģ¬ê²Į' - çĻ¼çı¾ - ' prosperous' - ' Spiritual' - ' Chick' - DIA - ' ÐŁÑĢивеÑĤ' - ' perÃŃ' - ÑĮÑİÑĤ - ' consultants' - ' Earl' - ä»Ĭå¹´ - ' ruining' - оÑĢе - ' penser' - ' takiej' - ' strengthened' - ' Liquid' - онеÑĨ - аваÑĤÑĮ - ' camer' - ' disagreement' - ' bathing' - ' Yosh' - aal - prechen - RISADAS - ' superstar' - æģŃ - лÑıÑĤÑĮ - ' nib' - ' Therm' - ' DANIEL' - ' paw' - ' liquids' - ' capacit' - arken - ' vagina' - ' mashed' - ' emerges' - yscy - ' unrelated' - ' Guild' - ' inverted' - itives - Tra - ' begr' - ' alte' - ì§ķ - ãĤģãģ¦ - ' ÑĢазÑĢабоÑĤ' - finder - ' далее' - ' благодаÑĢ' - walker - ' crater' - assadors - rences - inski - ' KIM' - ' Elliot' - '2017' - ' Sr' - inka - anov - ' ìŀĺ못' - ' proprietary' - displaystyle - ' Ñģим' - ' изб' - ' Panel' - ' instincts' - ' Communications' - 麻 - midt - ' ë§Įëĵ¤ìĸ´' - ' Ñģлова' - ' Gilbert' - 缮åīį - Так - voorbeeld - еÑİÑģÑĮ - aryn - quez - ' dart' - ÑĸÑĪ - ' Hut' - Sal - ' southeast' - ' pesticides' - ' helicopters' - ' endured' - iada - ' brewing' - ìŬë - ' Ñģвобод' - ' Saints' - ' Français' - ' Economics' - ' disloc' - ophobia - Camer - ' negotiated' - ' ÑģÑĤали' - ìĬ¤íģ - ogie - ' tsunami' - ' peeled' - ' motivations' - è¨Ń - ostat - flan - ' DAC' - ' kav' - '''RE' - ' Pearson' - bbe - czenie - ' atenção' - íĨµëł¹ - ãģ£ãģ¡ - ' ÑĥдаÑĢ' - ' introductory' - ' Ici' - ëĮĢë - akat - ' trench' - ' proceeded' - ' Coin' - ' derecho' - ' Rede' - æ¯Ľ - аннÑĭй - ' incarcerated' - ' Richmond' - Rock - ' Pav' - ' Karma' - uges - ' conteú' - ë¹Ħ - ' ê·¸ë§Į' - ' Gone' - ' wspóÅĤ' - ' Rahmen' - unken - ' ì¤ijìļĶíķľ' - ' ib' - ' attaching' - Hay - ' suka' - ìį¹ - ' pivotal' - ' Respect' - ÃŃda - IB - ' Verantwort' - wiet - ' forensic' - ÑĢиÑģÑĤ - ' пÑĢинÑĨипе' - ' markings' - ' kettle' - ' Opera' - ' Doctors' - ' shredded' - ' recuer' - ' vigil' - ' Fail' - ' entrev' - ' дÑĥÑĪ' - ' outbreaks' - èµ°åIJ§ - ' ÏĢο' - ' rogue' - angled - ' yearly' - ' Creed' - ' wam' - ' lotus' - ê³¼ë - ãĢģãĢģ - ' Spit' - ' Itu' - ' strains' - ' stamped' - ' plaint' - ' potion' - ' consolidation' - è©ķ - оÑĩкÑĥ - ' vlogging' - ' slate' - ' Auft' - ' Incor' - ừng - §IJ - enh - ' heiÃŁ' - ' domest' - ' Strom' - åį³ - akis - ' fragen' - ' finer' - ' Sug' - ' uphill' - ' één' - âĢ¦) - ' Ñģоп' - ' Corey' - ' siebie' - ' muse' - ' cloves' - ' pous' - ' Finanz' - ' Route' - amat - ' mutually' - ' внÑĥÑĤÑĢи' - ' Selena' - ëĶ - ' Gaussian' - ë¶ĢíĦ° - ' ×ij׼' - ' ejerc' - å¾® - kea - ' Gerry' - ' Sic' - 大çļĦ - ' 1966' - iese - ' fossils' - ' estad' - ' Kane' - ciÄĩ - ' ìľłíĬľë' - ' пам' - ' Cruise' - intérieur - ' bekannt' - ' Pode' - ' demander' - Rem - ' invade' - ' decorating' - ropic - ' cowboy' - ' Photo' - opolit - ' ì»¬ëŁ¬ë' - ' reap' - ' handwriting' - à¹Ħร - ' ëļ' - ' بعد' - ' Mt' - ÙĢ - ' spaceship' - ' nationalism' - ' councils' - ' Griffin' - ' Ahmed' - ' clich' - ' OL' - wl - ' Pilot' - å®® - ' acronym' - ' gels' - ' electroly' - èĵ - ' мной' - ' episod' - ' Dieses' - ' ATP' - ' ediyorum' - ' expresses' - ' exhibits' - Comm - ' кÑĢÑĥп' - ' matar' - ' 2025' - ' Artem' - vasive - rÃł - ' beÅŁ' - é»ĥ - ' lizard' - ' fille' - ' ì§Ī문' - ' моÑī' - ' tür' - ' culprit' - ' woven' - ' ANY' - nim - ' tay' - ' promin' - ' acompa' - ' idé' - ' boiler' - ' Themen' - ' avenue' - ' Mud' - ' новÑĭе' - ' witnessing' - ' lance' - ' CHAN' - ' Bever' - تÙħ - ' chemotherapy' - King - ' bÄĻdÄĻ' - ' atual' - ' tive' - ' talkin' - ' quedar' - ieÃŁ - edel - ' ìĸ´ìłľ' - ' jogar' - ' ör' - ' undertaking' - ' Strength' - ' milhões' - ' Wine' - ' Molt' - 讲 - ãģijãĤĮ - ' undermine' - ' Archives' - vana - mercial - MC - ' caste' - пÑĢ - ' legislators' - ulators - ênio - ' ëį°ë' - ' ÑħоÑĤиÑĤе' - ' нек' - ' surn' - ' consci' - ' POW' - ' culinary' - ' KAT' - ' Folks' - Ñĭваем - ' вок' - ãģijãĤĭ - service - pts - ' побед' - æĺ¯åķĬ - ' tents' - ' nord' - STE - ' republican' - ' wyk' - ' minions' - èĻķ - ' memang' - jest - ' comparative' - ' tyle' - carbon - bedingt - ksen - ' negativity' - ' själv' - ' dú' - æīĢæľī - ' recalled' - cra - ' Tada' - ' ÑĢÑĥки' - ' опÑĢедел' - ' procrast' - ' jogos' - ' Oo' - ' Hearts' - ' éch' - ' ksiÄħż' - ' coarse' - ' Tube' - ' Greens' - ' én' - ' dumbbell' - ' ÑĤи' - ' querer' - اØŃ - Ïĥει - ' пÑĢавилÑĮно' - ' пап' - ' compra' - ' tér' - ' Antes' - ' optimum' - ' biscuit' - κι - aczego - ' ìĭľê°ĦìĿ´' - ' Marines' - vero - ' vaccinations' - ' petty' - riters - ' ал' - country - ' counters' - ' attendant' - ' Hui' - ãģ¨ãģĦãģĨãģĵãģ¨ãģ§ - cka - ÑģÑĤвеннÑĭй - guy - ' tricked' - ' RED' - ' thrilling' - ÏĢοι - ' piggy' - ' anunci' - ORTER - ' Value' - ' rond' - ' ADA' - ' poser' - hores - ' Roland' - ĵ¯ - ' noir' - ' ש×IJ×' - ë°ľ - iemand - ' поÑĤеÑĢ' - ê³³ - ' ê±±' - ' formatting' - ' Led' - è§Ģçľ¾ - ' killers' - ' Äijấy' - ' haar' - again - '!</' - ' somethin' - ' coughing' - ' nave' - ' prospective' - ' HK' - ' Rescue' - maybe - gger - ' ÑĢабоÑĤÑĥ' - ×ķ׾×Ŀ - tails - íķĺíķĺ - ' eyelid' - ' customization' - avilion - ' prochain' - ' glaze' - æĥħæ³ģ - Sim - ' опаÑģ' - ' mosquitoes' - ' fent' - ' capacities' - ' apostles' - ' altura' - ' 묻' - ' seront' - ' Anytime' - ¥´ëĬĶ - ' cosplay' - ' spac' - ' samen' - ãĥĦ - ucc - ières - ' sibling' - ' Cock' - ' ëıħ' - ' пÑĢедÑģÑĤавлÑı' - ' installment' - ' dije' - ' MCU' - ' EH' - ' Ning' - ' prepares' - ' hypocr' - pty - ' kadın' - ' Frozen' - haul - ' Kylie' - éĢĻ樣çļĦ - ' shuffle' - ' elemental' - ' auÃŁer' - ' KNOW' - ' ALISSA' - ZA - ì²ł - ç¾İåħĥ - ' recite' - ' scrib' - ' 115' - ä¼ij - ' starred' - ' lequel' - ' brewer' - ' Opportun' - ' rä' - ' chopsticks' - ' Kah' - ' Ethiopia' - ' handmade' - ' erfolg' - ' Dz' - ittens - èªįçĤº - вал - ην - åĬŀ - ãĥĵ - bringen - ' unplug' - ' offs' - ' herman' - lied - asonic - ' Serbia' - ' Guatem' - ' ..."' - ' erreichen' - ' ambiguous' - ' Whitney' - zuf - MAND - łµ - ' squeezed' - ãģĿãģĨãģł - yas - é¾į - ' Shock' - ' utilise' - uko - bolt - ' motif' - ' inmates' - ' corrupted' - ' concret' - ' Critical' - ' Singing' - ' ÑĦÑĥнк' - éŃĶ - nova - rebbe - dt - Unis - ' webcam' - ' camoufl' - Ken - ' lawsuits' - ' Consumer' - ' recoll' - ' kleiner' - ' FIFA' - ' 1962' - èѦ - ' malad' - ' ì°½' - ' Ã¥t' - ' influencer' - ' Artist' - sti - ãģªãĤĭãģ»ãģ© - วย - ysÅĤ - ' Bian' - ĪëĦ¤ - ' fireplace' - ' Application' - ' mniej' - ' acidic' - ' Mormon' - ssa - åĭĻ - ' sneaky' - ' ojos' - ' voud' - ' Dai' - ' grassroots' - ' Unbelievable' - ' Gabe' - ' Extreme' - ' hassle' - ' cob' - mumbling - Pass - Į룬 - ' systematically' - ' seventeen' - ÏĢει - âĻ¡ - ' коÑĤ' - ' sendiri' - ' bathrooms' - ' Stern' - ' Arduino' - è¹ - cribing - ' reopening' - ' cerv' - pee - ARI - ' cadre' - ' Anch' - Lee - ' MAX' - ' männ' - ' chores' - ' adesso' - æĿij - ' Nig' - ' dissertation' - ' Vay' - STALK - ака - avat - çł´ - ' punkt' - ' padding' - ' Templ' - ' eje' - ' íĦ°' - ' azt' - ' ëĮĢíĨµëł¹' - ' rearrange' - ách - ' ìĤ¬ëŀĮëĵ¤' - ' freakin' - crire - ' 커ë' - ' Explain' - ' ÏĦÏīν' - ' bodily' - ' Leist' - ' sigui' - ' bunker' - ' azul' - ' Haush' - Sub - ' ÐIJнд' - ' кÑĢай' - ' illegally' - ' Muy' - ' Fei' - ' Banana' - ' scholarly' - ' Przy' - ' Moss' - ' Filter' - ' ìĸ´ëĸ¡' - ' Maxwell' - tense - ' longitud' - ' langsam' - ' ×ŀק' - smith - izada - ' ноÑĢмалÑĮно' - ' Voll' - ' Elena' - æĸ¹éĿ¢ - ' ÑħоÑĤÑĮ' - ' Dabei' - ' conservatives' - ' própria' - ' Dieser' - ' Brenda' - ookie - ' banc' - ãĥ¯ - ìĿ´ì¦ - ìĽĥìĿĮ - ' keh' - ' weddings' - ' thunderstorm' - æĶ¾å¿ĥ - ' Coordin' - ìĪĺê°Ģ - ' przeci' - éĴ± - OSSTALK - maan - ' ê±´ë' - ' بÙĩ' - ' żad' - ' yacht' - ' göt' - ' bleach' - ' shorten' - ' ÑģÑĤало' - usan - ' ìŀIJìĹ°' - ' ders' - xis - įĶëĭĪ - ' quantidade' - ' oppressed' - ' законÑĩ' - ä¸Ī夫 - ãģĪãģĪ - ' ÑĩеÑĤÑĭ' - ' ÐĿапÑĢимеÑĢ' - ulp - æĢĸ - ÙĤÙĪÙĦ - оÑĩе - άλ - zeniu - ' formations' - ' sparked' - ' Entwicklung' - alls - ' vivir' - ' expiration' - otine - ' ЧеÑĢ' - ' Turning' - ' tariffs' - ' nastÄĻp' - ' abide' - iksi - ' flashes' - ' disputes' - ' ì²´' - ' merak' - ' enormously' - zahl - ' führt' - вон - ' завиÑģ' - ' perseverance' - ' dividends' - ' contestants' - ' proszÄĻ' - ' Franken' - ãĤįãģĨ - ' explorer' - ' buffalo' - âĢķ - ' ecology' - ' scalar' - ' cran' - εÏĦαι - żyÄĩ - ' ìļĶë' - ' gia' - ' Gog' - ' Priv' - ' ë§IJìĿĦ' - ' Reason' - raktion - ' Deborah' - ' kitten' - ' Edin' - ä¹¾ - piej - ' ëĭ´' - ' máqu' - ' bidding' - ' affinity' - ' aika' - folk - ' Conse' - ' deutschen' - èĨ - ' debit' - ıģın - isel - ' ì¤ijêµŃ' - ' ëŃIJê°Ģ' - ' trustworthy' - ' Started' - æķij - ürd - ' понÑıÑĤно' - ' scientifically' - Pods - CROSSTALK - ' preguntas' - ' calming' - ' Premiere' - ׼ש - ' Ñħолод' - ' capita' - ' toma' - ' murm' - ' fuerza' - ' Hani' - æĪijæľī - üf - arlos - ' häuf' - ãģijãģ¦ - ' osoby' - jego - ' пиÑģ' - ' calmly' - idet - buch - gone - ' viscosity' - ' modal' - ' gesam' - ' Hz' - ' municipalities' - ' circulating' - olina - Sho - é¢ij - ' Bened' - olu - ' rests' - ' lÃ¥ng' - ' Ðŀднако' - ' przew' - ' pepp' - ' marriages' - ' BIG' - andan - ' magically' - ' babys' - ' ëĮĵ' - ' hackers' - Baby - ' Monst' - ' cier' - ' Arabs' - ' магаз' - ' Indonesian' - ãģĦãģĨãģĵãģ¨ - ' Markt' - ' dachte' - ' Schüler' - ' VND' - ' spielt' - ' perlu' - ãĤ´ - åŃĺ - ' пÑĢоÑħод' - ' salted' - ' improvis' - ' Instr' - velmente - ' ness' - ' fungus' - ' collaborators' - ' Virus' - estar - ' projector' - ' ÐŁÑĢав' - ' agility' - ×Ļ׳×ķ - erel - ' возв' - ' баз' - ' Cathy' - ÄŁu - ' говоÑĢил' - bility - ' Lanc' - ' Kimberly' - ' Brief' - åħ· - ' utveck' - ' goggles' - ' preschool' - ç§į - ATHER - ' motives' - ' Bong' - EX - ' chilly' - ' Advisory' - âĢĭâĢĭ - ' коÑĤоÑĢом' - ' traitor' - ' demasiado' - ' ÑĨен' - ' мои' - åŀĭ - ' multif' - ìĶ¬ - ' Alexis' - ' ziet' - ' Rama' - brance - ' sanction' - itous - ×ķ×ļ - ' ë³´ëĤ' - ÑģÑĤанов - 趣 - ' ÑĢеÑģ' - ' Churchill' - ' пÑĢез' - ' IO' - ' Gee' - ' Gather' - atori - Tyler - ' немнож' - ' bÃ¥de' - ' Killer' - ' tuber' - ' Ramadan' - á¿ - ieht - ' strangely' - лÑĥ - ' redesign' - ' incumb' - ' beraber' - ' Volkswagen' - metal - dzy - pción - ' ìķĬìķĦ' - åĶ± - 头 - ' Goodness' - иваеÑĤÑģÑı - bahn - ' Antarctica' - екÑĤоÑĢ - ' homeowners' - zeigt - ' íĺĦìŀ¬' - ì§ĢëıĦ - ' geographical' - thinking - ' gosta' - ' Imam' - uliflower - dag - annt - akov - ' downwards' - ì²´ê°Ģ - CUBE - ' ÐļÑģÑĤаÑĤи' - ' полов' - ' plateau' - ãģĦãģį - ḥ - ' chlorine' - ' accelerator' - ' solves' - ' Grass' - piano - ' کا' - ' بت' - ' Rochester' - ' ÙĩÙĬ' - ' collects' - įĶëĿ¼ - ' Cheer' - lingen - ' ÑĢазг' - ' améric' - hta - ECT - ' artific' - ' PayPal' - hana - Stephen - ' Gest' - phalt - ' replication' - ' Willie' - ' neutr' - ' irrational' - ' dados' - ' Aid' - kam - anter - ' дÑĥже' - ' deton' - ' hare' - ' bets' - bagai - ' stained' - ' plausible' - ' peeling' - ' crÃŃt' - ' grote' - 춰 - ¥´ê²Į - altet - Phone - Fil - SQL - ' gefallen' - åıĶ - ' saúde' - ' Tamil' - cous - ' главное' - ' através' - ussia - ' zweiten' - ' Elvis' - ' mover' - ' limite' - 追 - arez - ¥´ê³ł - ' Kranken' - üre - ' ìķĬìķĦìļĶ' - ' thÃłnh' - ' profoundly' - ' bedrooms' - ' toothpaste' - ' Accept' - ético - ' küç' - ' Ary' - adin - ' granular' - ected - ' menjadi' - ' competence' - doc - ' sparkling' - ' ì¢ĭìĿĦ' - ' constructing' - ' amusement' - ' Insurance' - ' Feuer' - ' renovation' - such - plat - ' prosth' - ' bey' - ' Completely' - ' zod' - aln - Vict - ' confirms' - ätz - âĸ - hammer - ' знаеÑĤ' - ' admired' - łë¥¼ - ' Fruit' - erten - ' niece' - ' Tiny' - ' plumbing' - erma - ' легко' - ' windshield' - ' ÑģмеÑĢ' - ' bzw' - ' abolition' - ' Sadhguru' - ' preached' - ' Creating' - çīĽ - pered - ' volont' - ' quint' - ' printers' - ' negro' - ' grosse' - ' Thy' - ' Fellows' - æİ¥ä¸ĭä¾Ĩ - ' stanie' - ' newcom' - ' Hue' - ' Freunde' - ' Construction' - ' adversity' - ' negatives' - ' hazardous' - ' compelled' - ' wok' - ' Oy' - па - ª¨ë - ' rendez' - ' overc' - ' weaving' - ' идеÑĤ' - ' prosecutors' - ' audiobook' - ' ancestor' - ' undergoing' - ' pounding' - ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸãģĦãģ¾ãģĻ - ' íĴĢ' - ' 춤' - ' tulee' - ' ìĹ´ì' - ' zoals' - ' nein' - éŃļ - ' oke' - ' Joyce' - ' nud' - ' diligence' - ' Labs' - ' vents' - ' ancestral' - หม - ' мÑĥжÑĩ' - ' només' - 表示 - wali - qing - ' Multiple' - ' Consult' - ' istedi' - ' Doy' - akah - ' disciplined' - ' alternating' - çĴ - ' verme' - ' оÑī' - ' tota' - ' Prag' - ' sworn' - ' beber' - ' Aufgabe' - ìļ´ë - 辦æ³ķ - ' yup' - ' reclaim' - onut - ' aucune' - ' amph' - ' ÅĽwie' - ' aa' - iscover - ' Arg' - cież - ' dessas' - ' Wäh' - ỹ - ' давно' - ' silently' - arc - ' íĽĦë³´' - ' tweeting' - ' Ond' - é¡ŀ - ¦¬ë©´ - ' bowel' - ìħ¨ìĸ´ìļĶ - èģĬ - OSE - ' propio' - ' Kunst' - kung - ' données' - ' Horizon' - ' Frog' - åĢĭ人 - ' arist' - âl - ' кож' - ' segundos' - ' Shortly' - ' Crowd' - iran - ' wÅĤaÅĽci' - ' Lac' - idente - ' ê°ĢìŀIJ' - ' len' - ' SUS' - ' Motors' - ' Trent' - omie - ' transmitter' - ' Assad' - ' psychiatric' - ' жиÑĤÑĮ' - ' outlines' - ' effectivement' - ' Religion' - preh - ' должна' - ' ͡°' - ' Conservation' - ' á»' - ' зай' - ' reside' - ' completo' - KEN - ' ëĤĺìĺ¤ëĬĶ' - ' suburban' - ' répondre' - ' ÑĢазлиÑĩ' - ' galleries' - ' rapt' - æĦŁè¬Ŀ - )... - ' cruelty' - ' VMware' - íĪ¬ - ' hayır' - ' grouping' - ' Rider' - ' syllable' - ' beispielsweise' - ' safeguard' - ' pelÃŃcula' - arti - ' Со' - ' chega' - ' комÑĥ' - ' seism' - ' harmless' - ' Warriors' - ãģĦãģ¤ - ' пÑģ' - ' shameless' - ' Baum' - install - ' toolkit' - ' pipelines' - ' pussy' - ' conceal' - ' protesting' - ochond - ' dua' - ' Pose' - ' helium' - ' UX' - ikle - ' Suff' - ' ìĦ¸ê³Ħ' - ingers - ' ÑģлÑĥÑĩай' - ' descending' - ' æ²Ĵæľī' - ' montage' - High - ' ìĿ´ìĸ' - ' Idi' - ' ×ijס' - ' expressive' - ç§ĭ - ' полез' - ' pone' - ' adolescent' - аннÑĭе - ' assassination' - weisen - ematically - auth - ' urg' - ' ganhar' - ' fundo' - ' Rhode' - ' иÑģÑĤоÑĢии' - ' compartil' - æķ¢ - ' diminished' - ' apprentice' - ' ÐijÑĥд' - ' photons' - ' cód' - å¹ķ - æ¬Ĭ - onak - ' adelante' - ' chu' - opic - ' aixÃŃ' - eddar - ' Congrats' - mor - 好åIJ§ - ' reservations' - ' Toby' - ' Kern' - ' razem' - ' forged' - ' horrifying' - ÙĬع - ' Joining' - ãĥ©ãĤ¤ - ' Auth' - dah - ' consig' - ' intimidated' - ' peripheral' - ' meno' - ' detecting' - ' teor' - ' tagged' - ' nostalgic' - ' 미ìķĪ' - åĢ¼ - ' verdi' - ' labeling' - под - astes - ' vist' - ' cyt' - ' flips' - ÑĢиз - balanced - ãģªãģı - ' оÑĪиб' - ' destin' - lasse - erei - ' kalo' - ' arqu' - ' plano' - ' ordinance' - ' compilation' - ' Vocês' - ' Eco' - ' ì¶Ķì²ľ' - ' encima' - ' Garrett' - ' Cord' - ölker - ' Arrow' - ' protons' - ',âĢĭ' - ' ì²ĺë' - ' scand' - ' beige' - cong - ' biking' - ' TL' - Ñĥнд - ' ìĨĶì§ģ' - ' Villa' - ' JACK' - 以åıĬ - ' Ã¶ÄŁren' - ' temas' - ' Kyung' - Jenn - ' cud' - ' imposing' - ' commandments' - ' Means' - ' Där' - ' recomend' - ' disposition' - اÙĩ - ' thu' - ' reductions' - ' diu' - ' ×ķ×IJ×' - ' иÑģÑģлед' - thren - ' lados' - ' RB' - ixed - ' ìı' - Fr - still - ' olmas' - CHUCK - ' íĨł' - ' Independent' - ÐĴÐŀ - ' pits' - ' undertaken' - ' før' - ' Naw' - ' ìŀijìĹħ' - ' shepherd' - ' langue' - ' Jab' - ' Drum' - ' Elekt' - æĭ¬ - ãģĺãĤĥãģªãģĦ - á»ijt - ' ìĿ´ìª½' - ' beginnen' - ' Fury' - á»ĥu - sections - ' sprayed' - ' már' - ' Volt' - ' Seong' - иÑĤел - duction - asan - ' judgments' - imaan - ŀת - ' siento' - ' ACT' - ' BH' - dev - ' ì¢ĭìķĦíķĺ' - ' jorn' - ISTIN - ' roar' - ' immersion' - affles - ' trainee' - ' Billboard' - resses - ' Warm' - ' Roberto' - ' utilizz' - ' Igor' - ' rash' - ' analytic' - iram - ' symmetrical' - ' lifespan' - ' eater' - ' Bloomberg' - aterial - ' 믿' - ' ister' - ' invaluable' - ' assisting' - ' shack' - μαÏĦα - jis - eniz - ' пÑĢедлож' - ' declaring' - ' Viking' - ' Assim' - ' expenditure' - ' posing' - ' Onun' - ' inic' - аÑİÑĤÑĮ - rev - ' miedo' - ' filthy' - ' IB' - ' Discover' - ichtet - million - ¶Ħëĵ¤ìĿ´ - ' ambigu' - ' Flynn' - bardziej - ' incomp' - авно - zia - ' influencing' - ' worldly' - ' Salesforce' - zet - ' particulier' - ' Koch' - ' 1943' - ' toner' - ' ÑįкÑģпеÑĢ' - ' suscri' - ' triggering' - ICES - ìĬ¤ê°Ģ - δα - ÑĢабоÑĤ - ' afterward' - pine - ' IL' - areth - ' пал' - ' saker' - ' 1947' - AF - uyorsun - ' ìĬ¤ë' - ' quantify' - ' mentorship' - ' llega' - ' Tamara' - ' optimizing' - ' fronts' - osters - ' esquer' - ' submissions' - ' annih' - ' suction' - luence - chieden - INGS - ' ×ij×Ķ' - ' ÑģÑĨен' - ' wielu' - ' objeto' - ' boobs' - ' Geschäft' - ' earbuds' - ' ÑĢанÑĮÑĪе' - ' routinely' - ' collagen' - одÑĭ - ' Cinnamon' - ' baix' - دÙħ - frage - ' кноп' - ' deception' - ' unexpectedly' - ' smelled' - ' loos' - ' highlighter' - ' 기본' - ' Glasgow' - owana - mn - ' Jeremiah' - ' Datab' - iete - ' baw' - ' propia' - ' propri' - OOOOOOOO - inker - ' perturb' - ' Fake' - ìĿ´ìĸ - imming - ' undocumented' - ' trabajando' - ' roam' - ' должно' - ' arbe' - ' ani' - atal - ' arada' - ' Anda' - ' ìĽĢ' - ' Branch' - oires - ' outsider' - dollar - å½ĵçĦ¶ - isses - beans - ' Gig' - çĿ¡ - rados - ' Sut' - ' Lance' - edsiÄĻbior - ' cola' - onents - ' reconsider' - ãĤ¹ãĥĪ - ' mondo' - ãĥ³ãĥįãĥ« - ' unsuccess' - ' Kä' - è¾¹ - ' regel' - ' bisog' - etus - ' unravel' - ' sweetie' - ' représent' - ouring - ' groundwater' - ' Bew' - ' scratched' - ' cassette' - ' cider' - pis - ' Ñģама' - ' globalization' - ' degradation' - ' degener' - ' Rosie' - ickt - ' overweight' - ' MEM' - ' guardians' - ' consec' - Hmm - æĪijåľ¨ - ' поÑĤÑĢеб' - ' meva' - ' graffiti' - ' flirt' - ' BP' - ' justo' - ' Thousands' - çĶľ - Ł¬ìļ´ - .* - ' RAW' - ' fluor' - iyi - antal - jed - ' Sheng' - ' Elise' - ' Charge' - ìĿ´íĬ¸ - ' cones' - nies - gia - ' наÑĩала' - ' Dharma' - ' ëĭ¤ìĸij' - ' favors' - ' Trung' - hetto - ' pozw' - ' longo' - ' kelu' - ' digestion' - ' Eig' - ' THERE' - ' tiers' - ' sunk' - ' mystical' - zub - ' Ãīt' - ' anticipating' - ' Vine' - YY - ' concentrating' - ' Agreement' - ' около' - ' lidt' - ' Yao' - ' ÑģлиÑĪком' - rÃŃ - ISTINCT - ' OFFIC' - ' soaking' - ' siihen' - ' referencing' - ' Tampa' - aney - ' respuesta' - ' Coalition' - ' ÑģоглаÑģ' - ankind - ' ëĽ' - ' Yummy' - ë°° - ' onc' - uição - ' theo' - ' mural' - ' Teachers' - ' waits' - ' renting' - ' Harmon' - ' eÅŁ' - ' Munich' - íĻľ - ìĸ¼ - cards - ' rouge' - ' nên' - club - ' unseen' - ' depreci' - ' computed' - ' wiping' - ' Elli' - identified - ' clutter' - roleum - ' telef' - ' leveling' - ' Woody' - ' Gus' - ' Bennett' - ' sitio' - iÅĤ - ' possessions' - ' Natasha' - oldown - ' ÑģообÑī' - ' Lic' - ' ë§Įëĵł' - ' lorsque' - weh - ' мам' - liter - adomo - ' fini' - ÏİÏĤ - ' Ñĥбий' - ' indisp' - ' televis' - ' pá' - ' Creo' - ÃŃll - ' gur' - ' MAL' - ' ÑĢазнÑĭÑħ' - ' ziehen' - ' fashioned' - ' debating' - ' Soup' - ' Province' - ê·¸ëłĩ - ' improper' - ' imagen' - ' Ñģделал' - ' logos' - ' evento' - è§Ĩ - ảo - larda - ' назÑĭваеÑĤÑģÑı' - ' verf' - ' screenshots' - ×ķ×ĵ×¢ - ' Aurora' - ' Bali' - tered - ' contagious' - ' compartir' - venidos - rike - ' вÑĭглÑıдиÑĤ' - ' freedoms' - nicas - ł¤ìĦľ - ' reduz' - ' Ecu' - ' abonn' - ' SEÃij' - ' Bitch' - ' projeto' - иÑĩно - ettre - ANNA - thank - ' AO' - æīĢ以åij¢ - arnish - ieÃŁen - ' ripple' - ' pantry' - ' GH' - γα - ' ìĿ´ë²ĪìĹIJ' - ' validated' - ' brushed' - ' Emin' - ' Darth' - esin - ',.' - ' valle' - ' jersey' - ulan - Read - ' Rangers' - ' soothing' - ' complementary' - ' Verkehr' - acakt - ' batht' - ' ND' - Son - ' íĻĶìŀ¥' - ' Avi' - ' SAL' - aisse - ' semaines' - ' Surv' - wier - ' видел' - ' siete' - ĶëıĦ - ' Ramsay' - ' Queensborough' - ' Menge' - ' Foods' - ' theological' - ' [#' - ' вони' - ' immin' - iosity - ' Abgeord' - ' Acho' - ' ÃĶ' - ' stains' - ' realistically' - ' fashionable' - ' CEOs' - ' Skill' - ' вже' - ' dever' - ' Plug' - æª - Pod - ' loaf' - ' gebracht' - ' absorbs' - ' Granny' - ' malware' - agÄĻ - ' civilizations' - ' Ïģ' - ' hält' - СТ - great - ' layering' - sings - ' вÑĸн' - ' recognizable' - ' woj' - ' weten' - 第ä¸ĢåĢĭ - γο - Student - ' défin' - please - ench - ' attic' - ' Ottawa' - ' opted' - ' captiv' - ' mÅĤ' - ' YA' - ' Wand' - ' bounty' - ' 270' - ' speculate' - ' enhancement' - ' commodities' - ' Milton' - ej - alom - Das - ' cooldown' - ר×IJ׾ - ' ×IJפ' - ' wczeÅĽniej' - ' elong' - ' diode' - inação - ' Iris' - ' Ib' - ' summoned' - ' respe' - ' Rach' - 注æĦı - ' »:' - éĨĴ - ' vur' - ' movimento' - ' fluent' - ' Evolution' - ' Butt' - ificación - ĶĶìĸ´ - ' ÑįнеÑĢг' - ' manipulating' - ' positiv' - моÑģ - ' wiz' - ' intox' - ÎŃÏģ - емÑģÑı - ivesse - imizi - ' ìļ¸' - ' knocks' - ' congestion' - ' Ideally' - ' Holding' - ' pobre' - ' JUL' - ' ë¶Ħëĵ¤ìĿĢ' - ' ακ' - ' Ferguson' - ' Laboratory' - richten - rophy - production - assung - ITA - ' siècle' - רת - cision - ' פ×Ķ' - ' Irene' - anca - ' ìĤ¬ê³ł' - ' pinpoint' - ' designation' - ÅŁam - lÄ±ÅŁ - aat - ' nÃ¥gra' - ' mythical' - ' Declaration' - ' ìŀ¡ìķĦ' - ' byte' - .âĻª - Del - ' íį¼' - ' nutritious' - ' ÑĢÑĥблей' - åĤ³ - SAY - Master - ' ÑĦоÑĤогÑĢаÑĦ' - ' ëĴ¤ìĹIJ' - ' neh' - ' dokument' - çªģ - ' czasu' - ' continua' - ' Silent' - ' tensor' - ' tanta' - ' irgendwo' - ' LET' - ' Shakt' - lama - chlag - ' dingen' - ÑģÑĤÑĢа - ' ehrlich' - ' Macht' - rels - Ãłcies - video - ' naturale' - ' STEVE' - umm - BACK - ' 720' - ãģ§ãģĹãģŁ - ' momencie' - ' Swan' - ' technicians' - ' geehr' - ' Mend' - Reg - ' scaff' - ' aide' - ' ë³´ëĬĶ' - ' presses' - lerde - \' - ' ultrasound' - ' disclaimer' - ' Mits' - ' Holiday' - ' externally' - ' Fate' - INO - ' Cats' - ë°ķ - umo - control - ' theCUBE' - tic - ierungs - ' знаком' - ' freestyle' - MANDARIN - ' ise' - aurus - 許 - ' Strategy' - ' Beam' - räge - ' exploited' - ãģĪãģ£ - idis - ' chime' - ' Peninsula' - ' merits' - ' altro' - ' TOP' - ' Sens' - ' Kant' - oras - ' royalty' - ' IDE' - å¤ī - racy - ' THOM' - omos - ' länger' - ' numbered' - Um - ' Niye' - θη - zyka - lime - ' Personen' - ' validity' - ' contrat' - ' Comic' - çons - ' Heidi' - ' zg' - ' renamed' - ' cumin' - ' JF' - inel - ' enforced' - ' chama' - лиÑĩно - ẻ - ' денег' - ' profund' - ' pelvic' - ' palavra' - ' extras' - ' ankles' - ìĹIJìĦľëıĦ - ' TF' - ' insanely' - ' мÑıÑģ' - ' réponse' - ' göster' - ' BBQ' - ' ÑĥÑĩаÑģÑĤ' - ' shaken' - ãĤ«ãĥ³ãĤ¿ - ' almonds' - dish - ' PG' - ' Blizzard' - ÑĮого - ' ãħ' - ' knapp' - Too - ' unde' - ' mounts' - омина - ' northeast' - ' censorship' - ÑıÑĤÑĮÑģÑı - lr - ' lawmakers' - ' sÃ¥dan' - ' insider' - ' cleanup' - ' Nada' - óc - ' harvested' - ' Después' - íļį - ' redundant' - ENA - ' delegate' - ' burg' - ' Alison' - æĸ°èģŀ - ' celestial' - ' sinners' - ' martyr' - ' Perm' - ' specimens' - ' mitochond' - ' maravil' - ' cavalry' - ' arrays' - ' annex' - ' laboratories' - ' Byz' - ' atac' - ' Ñģложно' - ' topl' - ' geri' - ' Combat' - ÑģÑıÑĤ - eken - ' ÐĴлад' - ' ajust' - ' marque' - ' lookout' - ' Lol' - ' rooftop' - ' Orion' - ' бой' - ' heartbreaking' - ' detto' - zh - ätter - cera - ' heats' - ' antiqu' - ' unfinished' - ' Kazu' - ılı - ' slightest' - leo - ' vÃ¥ra' - ' verschiedenen' - ' lotion' - ä½łå°± - æĮº - ÑĪего - ctional - ' ìĿ´ìł' - dragon - ' resonates' - ' inm' - avic - ' fulfil' - ' 기ëĮĢ' - ' justamente' - ' доÑģÑĤÑĥп' - ' 그건' - ' reconcile' - ' Schön' - ' Avoid' - ê¹Ģ - '''D' - ' confinement' - ' íij' - ' motivating' - ' Brittany' - ' ãģĻ' - ' screamed' - object - ' decree' - ' travaille' - issible - ' busted' - process - ' massacre' - ' nghÄ©' - ilyn - ' вÑĢоде' - ' poetic' - ' nhất' - ' ironically' - usu - nio - ' staging' - omedical - leased - ' ìĥĪë¡ľìļ´' - ' NZ' - acting - ' Battlefield' - playful - Vi - ' señora' - ' prompts' - lichkeit - ' çıkar' - jiang - ' picky' - ' Cave' - ' miraculous' - ' Hughes' - '2016' - ' xu' - ' Dorothy' - ' virtues' - ' retract' - ' tyr' - ' charismatic' - ' bola' - é¼ - ' ë§IJìĶĢë' - ' parental' - ' millionaire' - ariat - æĶ¿åºľ - ' invoke' - żenie - ' extremes' - ' Aku' - ividade - ' ï·º' - ' ìĭľì²Ń' - ' Garlic' - RIA - ' доÑģ' - ' Pont' - ' milj' - elli - ' racket' - ' competit' - ' Whis' - ' realt' - ignment - estre - ' pernah' - ' Opening' - ' FS' - ' Demokraten' - acements - ' worldview' - ' playoffs' - ' CAD' - ' étant' - ' yemek' - ' sentiments' - odel - buster - aÅŁ - ' KY' - czÄĻ - ' schöne' - ape - ' Raspberry' - ' credited' - ' Hidden' - ' sausages' - ruce - ' Bev' - ilantro - ' pokemon' - ' ê°Ģ격' - ' proceeding' - ' veio' - ' 175' - è¸ - max - ' frater' - ìłĦìĹIJ - ' egent' - ' 2500' - usch - Tube - ' amplify' - ' prawd' - ' odor' - ' Scan' - ' plotting' - ithmetic - ' resigned' - ' SCOTT' - ' stereoty' - ' doable' - ' Complex' - ÙģÙĬ - tım - ÑĢиг - lardan - eso - DEN - ' hoodie' - ' CAT' - اط - ' bonded' - ' Burns' - опаÑģ - ' rÄĻ' - εια - ' оÑĤделÑĮ' - ' timeless' - ' Vij' - ' Panama' - ' reorgan' - ' Tä' - ' Pluto' - Orange - ' пойд' - ' Bristol' - uced - ' ëIJĺìĸ´' - ' unbedingt' - adle - ' volunteered' - ' mieli' - ' Edinburgh' - ikal - ' alten' - ' Arsen' - ' mouvement' - ' antique' - ' bh' - ' Hers' - ' saute' - ' aspire' - ' spheres' - ' Wam' - ắm - ' wipes' - ' 280' - ' Veh' - ' coloca' - аÑĦ - ' возможноÑģÑĤÑĮ' - ' physiological' - hwa - etu - ' prolonged' - ' experiência' - ' видно' - ' quarant' - ' puedan' - èĶ - vine - ' USDA' - phem - ' formidable' - ' flatter' - ìĸ´ì§Ģ - ' bén' - à¹ģà¸ķ - ' ë¬¼ë¡ł' - ' factions' - ' Leaving' - ' ×IJת×Ķ' - ' Expert' - dio - ' Verd' - ãģ¿ãģŁãģĦ - ' sint' - ÙĨد - number - ' owed' - ' induce' - ' Freddie' - abo - ' Filipino' - ¯¼ë - believably - athlon - amaan - ' devenir' - ' Gos' - ' Jenkins' - bait - ' bins' - ' MICH' - uyorum - igrade - isso - ' ìĹ´' - ' ìķĦë¹ł' - ' diarrhea' - ' tornar' - addin - ' ungefähr' - ' restroom' - ' psychiatrist' - ' Kickstarter' - ' gera' - ' alred' - ' Wrap' - ÏĮÏĥ - ' sinner' - CHEERING - ' kilow' - ' determinant' - ' demonic' - idences - chas - ' Ded' - å¼ķ - ' stumble' - ' Urs' - ' deceived' - ' TER' - ' Có' - elled - ' notwend' - ' ì§Ģê¸Īê¹Įì§Ģ' - ' partido' - ' descended' - ' vardır' - ' enacted' - ' czÄĻÅĽci' - å·¥ä½ľ - ' trainees' - ' audible' - ' malf' - ' veo' - ìn - ' GPA' - ' Appe' - åĤ· - ' rut' - ' Carla' - kach - ' savior' - itched - ' climax' - аÑĤелÑı - ' McConnell' - олÑı - ereye - ' Ñģозн' - ' cabo' - ' Sne' - ' Affordable' - ' sarÃł' - ' legitimacy' - ' scarce' - '...</' - ' 108' - ' acum' - ' Frankly' - ' radiator' - ' generals' - ' divides' - ' cheesecake' - ' sorcer' - ' misconception' - ' hardships' - ' OnePlus' - üyorsun - ' Soviets' - ' Italia' - icki - ' Afterwards' - ' ridiculously' - ' gdzieÅĽ' - ' Notes' - ÙĥاÙĨ - ' roman' - ' organizer' - ' courtyard' - ' ÑĩеловеÑĩ' - ' Witness' - ' пÑıÑĤ' - ' Chill' - ' Valve' - ' άλλ' - ' KP' - chluss - ' deflect' - ' Toni' - ' clair' - ' stacking' - ä½İ - raszam - ' Sonra' - ãģ£ãģ¡ãĤĥ - ' Atari' - ' pasó' - ' charms' - anst - ' terce' - ' Lilly' - ' psychologically' - ' cÅĵ' - uste - ¥´ì - CTV - ' miel' - çļĩ - Care - ' âĢij' - ' snapped' - ãģ©ãĤĤ - ' ê°IJë' - оÑĤÑĭ - ' mês' - .? - ' tonnes' - ×ķ×ĵ×Ķ - à¸Ħà¸Ļ - Tu - ' distributing' - ' crackers' - ' coração' - ämän - ä½łåľ¨ - clamation - оÑĢд - ĵľë¦´ê²ĮìļĶ - ' Unterschied' - Fine - cko - ' ÑĢебен' - ' spic' - ' doctoral' - ' ÑģкоÑĢее' - univers - acula - ' Ãĸsterreich' - ' grinder' - ' ambos' - ' vastly' - éĢĻåĢĭæĺ¯ - ' confessed' - ' Shh' - anders - ' Guan' - ' необÑħодимо' - ' championships' - ' Vul' - ' Phi' - ' Measure' - æľ¨ - ' insgesamt' - æħ¢æħ¢ - vette - ' genom' - indung - gli - Det - ' unmute' - ãģ¾ãĤĬ - ' sauces' - ' Dw' - ×ijת - ' BRE' - ' nurture' - ' detained' - ' Beer' - ' миÑĢа' - ве - ' Birds' - ' meilleur' - ' rewind' - ' pore' - ×Ļ×ĸ - éger - quela - ' trousers' - ' siinä' - ' Gaga' - ' BRAND' - leben - ' raspberry' - ä»ĺ - ilik - ' versão' - lak - ' logar' - ' MIDI' - ' ìľĦíķľ' - ' пÑĢоизоÑĪ' - ' steril' - ' harmed' - авлив - ' ÑģÑģÑĭл' - ' lacked' - ' contacting' - ' 기ìŀIJ' - ' gefähr' - ' coy' - ikel - ' binge' - ' orthogonal' - ' entendu' - ' Thirty' - ' smartest' - å¤ļå°ij - ' rasa' - ' Quá»ijc' - ÑĭваÑİÑĤ - ' slut' - лÑĥÑĩ - igten - ' ÑĢаб' - ' taman' - ' qualidade' - ' domination' - ' sinus' - ' programmers' - ' allergy' - ' Torres' - ' Austrian' - nants - å®ĮæĪIJ - Mel - ' ÑĥвелиÑĩ' - ' Agg' - ' sok' - ' pluck' - ' binds' - ' propor' - ' Maf' - ' osob' - ' VIC' - é¥ - ' заÑĩем' - ' exhibitions' - ' etti' - cza - ' наÑĪиÑħ' - ' Mitte' - обÑĭÑĤи - ' clocks' - ' rico' - æĶ» - ' иÑģÑĤоÑĢиÑı' - ' schizophren' - ' fluff' - ' ÑģобиÑĢ' - ' apoy' - ' princes' - ' braces' - ' FIR' - ' Sna' - ' ;)' - venes - ' vuelta' - ' mies' - ' broom' - ' merry' - ' especialmente' - ' Alban' - ' поÑģÑĤоÑıнно' - ' Lena' - ' Cult' - also - ' quoting' - ' genere' - ' Yar' - ' Lage' - ' demost' - ' dage' - ' Ecuador' - ' använd' - uÃŁen - ' ë°ĽìķĦ' - ' psychologists' - ' Lars' - ' possa' - ' outgoing' - ' metic' - ' baggage' - eria - ' richtige' - ìĭľìĹIJ - ' ÑģоÑħÑĢан' - ' rooting' - ' droplets' - çļĨãģķãĤĵ - ' nasal' - ' Cox' - Xi - ' disposable' - ' butcher' - ' Zar' - ' Armenian' - ' ë¿Įë' - ' Fool' - ' CBD' - ' sost' - ' perish' - ' Rép' - ç´° - ãģĿãĤĮãģ§ãģ¯ - ' Freud' - ' fandom' - ' bloque' - ' inventor' - ' abre' - ' énormément' - ' imports' - éĪ - ' otur' - ' Ryu' - ' âĨĴ' - ' secondo' - ' incompet' - ' incarceration' - ' ascend' - bene - åĸľæ¬¢ - ' olurs' - noch - ' breeds' - лиз - ' Verfüg' - ' mailing' - really - ' esf' - ' pele' - ' leash' - ' disks' - ' замеÑĩ' - ìķĦìķĦ - abouts - ' Mull' - ' Dent' - edereen - Drive - ' tipping' - ' nigga' - ordum - ' porter' - ' karaoke' - ' documentaries' - ' RIGHT' - ' Purd' - ' оÑģÑĤан' - клад - érence - ' ê±¸ë¡ľ' - ' ÑĤоп' - ' Wong' - ä¸į对 - ' пÑĢиÑĢ' - ' nominal' - ' aula' - ' ÑįкÑĢан' - ' cherche' - ' Thr' - åħ¶å®ŀ - ' laufen' - ' Kathleen' - ' reactors' - ihat - ' sided' - ' Simone' - ' guideline' - important - bumps - tone - ' entreprises' - ' constitute' - oscope - ' Mystery' - cycles - ' Warsaw' - ' bursts' - ' Zhong' - å®ĮäºĨ - ' SARAH' - ' ëĬIJê»' - éį - ' beacon' - åįĩ - ADE - ' ì§ĢëĤĺ' - ' ersch' - ' integers' - ' Crossing' - source - ' schooling' - ' ROM' - atorium - ' ìŀĪê²Į' - ' rôle' - ÐķÐĿ - Chat - ' shrinking' - ' reimburse' - ' lumber' - ücks - ' salah' - Mother - ' kali' - ' Qatar' - otional - ' opacity' - ' nee' - ' Cory' - ' 측' - ' turbulent' - zers - ' ÑĤеÑģÑĤ' - ' écrit' - ' ë³´íĨµ' - ' disgrace' - ' ì¹´' - ' courtesy' - inga - ' hugging' - ' ABS' - mith - ' insufficient' - ' crooked' - ' ê·¸ëĮĢë¡ľ' - ìĭ¤í - ' simulated' - ' ëĦ¤ê°Ģ' - ' bö' - ' Otto' - LING - ' illustrates' - ' Destroy' - ' 1961' - ' Tagen' - ' melon' - ' Pascal' - QUE - ' полÑĥÑĩиÑĤÑĮ' - ' incidence' - ' Stevens' - ' Gins' - rue - ' unreasonable' - ' Jie' - ysics - ' 몰ëĿ¼' - ' fishes' - ©´ì - ' precurs' - ' mogÄĻ' - tight - eté - ' mundial' - ìĹĪëĭ¤ - âĢ¦! - BU - ' sociology' - ' brutality' - ' personaje' - ' nÃŃvel' - ' fazem' - ' essen' - ' dwelling' - ' commercially' - ' edits' - ' dues' - ' GSA' - ìĿ¸ê°Ģ - ' íĹĪíĮĿ' - ' Yahoo' - енеÑĢ - ìľ¨ - ÑĥÑĪки - left - ' captive' - cipher - ' ×ŀ×ŀ×' - ' гÑĢом' - ' innate' - ' impul' - ' ìŬìŀIJ' - ' swallowed' - ' Tabii' - ìĿ´ìĭ - ' ÑģоÑģÑĤав' - ' oyun' - ' obrigado' - ' Aph' - Katie - ' cena' - ' AllÄģh' - ÙĪس - ' przyp' - ' pept' - ' voluntarily' - ' OÄŁlum' - ' Elo' - oue - Bir - burger - ' SBS' - ' 6000' - ' promotional' - ' Herrn' - ' stamping' - ' qualifying' - ' cosmos' - ' afar' - æ±Ł - abus - ' dads' - ãģŃãģĩ - ' Ñįконом' - incarn - ' ìĸ´ëĶ' - ' леж' - ' BET' - ' найд' - onter - ' reusable' - ' komma' - ' Bij' - ' Teraz' - ' Olá' - ' ìķĦ침' - ' ÑĢазмеÑĢ' - awan - ' carta' - æIJŀ - iceless - ' sme' - ' Tutaj' - ' Èĺi' - ' probation' - ' adequately' - ' Presidential' - indruck - blade - ' veulent' - ' cioè' - åĮħæĭ¬ - ' reverb' - ' gegenüber' - ' Espero' - ' bege' - ' STUDENT' - sound - ' Dü' - ' offend' - ' "..' - kennt - ' ÑģлÑĥÑĪ' - ' purposely' - ' Lit' - ' íĽ¨' - ucher - ' hina' - ých - ignon - THE - ' glide' - ourcing - ' Ø£ÙĨا' - ' ollut' - ' archety' - ' shady' - ' somm' - ' epile' - Keep - ' najbardziej' - à¤ķ - itutional - ' май' - ' sinful' - ' Bronx' - ' глÑĥб' - ' vam' - ' presets' - ' Dag' - ' ìĻĦìĦ±' - ' creek' - itures - ' Lords' - ött - UNT - Ra - ' inequalities' - ' collateral' - ' wrists' - ' grouped' - ' обÑĭÑĩно' - ' armored' - ' tung' - ' converge' - ' bok' - ' Dodge' - нÑıÑı - ' fleeing' - ' Martinez' - ' Dreams' - kek - ' sociale' - ' Plaza' - دة - ' kell' - ' Stellen' - felt - ' ÑģпаÑģ' - ' Pv' - ' canción' - ' Hert' - ' Balance' - ' selves' - ' vandaag' - ' pry' - ' najle' - ' видиÑĤе' - ' velvet' - ' groot' - ' fout' - 模 - ' Schulen' - ' Mohammed' - ' Centers' - ' haver' - ' freuen' - ¤íĬ¸ - лан - POS - inki - ' ëĭµ' - ' paralyzed' - GLISH - ' casts' - ' VC' - ìĿ´ìħĺ - ' تھ' - 票 - ' ì¤ĺ' - ' ר×ķצ' - ' suced' - ' progresses' - ' EÄŁer' - °ëıĦ - ' installations' - pedo - еÑĢб - interpret - ' ê³łë¯¼' - ' Azerbai' - ividades - ' ì£ĦìĨ¡' - ' entfer' - ' chwil' - ' Herbert' - ' Alexandria' - yty - ' sechs' - ' caliber' - ' Weise' - ' Heck' - ' Yug' - ' اÙĦØ·' - ' pesar' - ' cigar' - ' mél' - ' haird' - ' przypadku' - ' confidently' - ' anarch' - ' Gian' - ' dobre' - cjÄĻ - awy - ' Rece' - ' Gobierno' - ' carga' - umsy - ' norte' - ' handler' - ' respecting' - ' allied' - ' Piet' - ichtlich - ' olds' - ' dusty' - ' gry' - ' -...' - GHT - ' neo' - Ñĩики - ежд - aide - ' бÑĥло' - íį¼ - ' temporada' - ' doute' - âĺĨ - ' ìĪł' - ' JUSTIN' - auto - ' rationale' - prob - ' fishy' - ' doorway' - ' emptiness' - еннаÑı - ' brag' - ' Ðĵде' - çĪ¾ - ' transient' - ' mittlerweile' - ' Bret' - ' fij' - ' deposited' - NS - ' ìķŀìĹIJ' - ' kimse' - ' charities' - ' Millenn' - dogs - ' moyen' - ' nuevos' - ' Cookie' - parable - doing - ' Sail' - ' icy' - haba - ' queens' - ' chocolates' - ' Nay' - ' ÑĦин' - ' vec' - ' helmets' - TM - ' Armed' - ' impairment' - ' Tus' - ' Même' - omez - ' Requ' - ' Investig' - íİĺ - ' golpe' - ' Rac' - igraph - ' kwest' - ' sailors' - ' statutory' - ' milestones' - ' Mash' - ' Gesetzentwurf' - éĬ - ' coloured' - huma - ' yere' - ' subtitles' - ' embodied' - ' misschien' - ' iPh' - ützen - ' detached' - ' descrição' - ciamo - ' recoil' - ' ÐŃÑĤоÑĤ' - ' exported' - ' Alone' - antry - ' estan' - ' Sod' - ' lavoro' - æĬĬå®ĥ - ר×ij - ' Äijá»ĭ' - ' swag' - ' PCB' - ' Kaiser' - ' Moder' - jug - ' textile' - Tw - ' nac' - frei - ' retard' - iscern - ' tallest' - ' Luca' - Rah - ' preacher' - ' jut' - ' Rica' - iciency - ' Äijiá»ģu' - ' kaufen' - ' nett' - ' discut' - ' deprived' - ¡Ń - ' spricht' - ' enclosed' - ' Subst' - ç§ij - ' Rabbit' - prised - ' bitches' - ìŁģ - çīĪ - ' tapa' - ' Essen' - ' Bao' - ' devient' - ' Wuhan' - ' Tipp' - ' disast' - ÑģÑĤвÑĥ - ublique - ' qualité' - ' inadequate' - ' bargaining' - ' Gotcha' - евиÑĩ - ievous - erton - blue - ' ìĽĢì§ģ' - ' sandbox' - ' Rein' - 親 - ' ìĿ´ê²ĥëıĦ' - ' sax' - zogen - unächst - ' herkes' - ' -,' - zeni - rising - ' resposta' - ' promotions' - ' Unterstüt' - ' MAS' - Nothing - otics - ' вÑĭй' - ' rotates' - kien - ' habla' - ' Dani' - union - ' wack' - ' archaeological' - ' Curtis' - ' Horiz' - ' 골ë' - ' waiver' - åĺ¿ - Bon - ' rotated' - ' pitcher' - ' inad' - ' hugs' - ' Northeast' - ×Ļת×Ļ - ' plea' - ' cupcake' - ' LY' - ' famili' - ' groo' - ' Blair' - ' lij' - ' habitats' - ' communism' - osium - bars - ' Freeman' - neo - ' diffuse' - ' cylinders' - ' Debat' - íĸĪëĬĶëį° - еÑĪе - ' fingerprints' - ' amar' - вид - ' ìłķëıĦë¡ľ' - ' affiliated' - ' ÑħоÑĩеÑĤ' - ãģ°ãģĦ - ' etiqu' - ' chÃŃnh' - æģŃåĸľ - ' cruising' - ' Weihn' - çĶµ - ' Titanic' - ç´Ģ - ' Nast' - ' ëĵ¤ë' - ' вал' - ' demi' - ' Kristin' - MIN - ' rigor' - ' moto' - ' LAKE' - ' íĻľ' - ' ë§Įìķ½' - ' Stro' - ' prototypes' - ' LC' - ìĿ¸ìĿĦ - ÑĢим - ' violating' - ' giorno' - ' childish' - æ°Ķ - ' ×IJ×Ĺ×ĵ' - ' overdose' - agogue - адÑĨ - heus - ' говоÑĢÑı' - ' incr' - ' debated' - ÙħÙĦ - ' chicks' - ' quin' - LAUGHING - ' tightening' - ' supervisors' - ' Hawk' - ' Baz' - ' повÑĤоÑĢ' - ' блок' - Äģn - ' dumping' - ' facto' - berger - ' arsenal' - ' Africans' - ¡Ģ - ' cafeteria' - feeding - quila - ' paÅĦstwo' - ınt - Ħ± - ' environmentally' - ' després' - ' Willy' - ' PaÅĦstwo' - ' GG' - ' chacun' - ' directional' - ' hört' - ' ðĿ' - enary - ' voiced' - aģı - ' pope' - ' comrades' - ' Gibson' - ' ACC' - vik - ' modelling' - ' aggi' - ãģªãĤĵãģ§ãģĻ - ' conversions' - ' averages' - Ellie - ' gestellt' - ' UE' - osaic - ÐĴоÑĤ - Say - ' Ñģамого' - ' mesures' - isiert - gasp - voice - ' checkpoint' - ' percentages' - ' disrupted' - ' Tuc' - ' Homer' - ' WAY' - ' Turks' - heen - imoto - ' OC' - ÃŃna - ziel - ' mudar' - ãĥIJãĤ¤ - gesetzt - ' mejores' - ' CJ' - наÑĢÑĥж - ' modulus' - ' modulation' - ' replies' - ' larva' - ' gider' - ' Mandarin' - ' поÑģмоÑĤÑĢим' - ' sacrificing' - ' preço' - ' oysters' - ' Myan' - ologue - ' Wit' - ' dû' - ' Leuten' - ' pater' - ' KENNETH' - абаÑĤ - arthy - ' sociedad' - ' niño' - евой - ' jÄĻ' - ' advertised' - ' Pepsi' - uteur - ' masse' - ' scattering' - ' yön' - ' desapare' - ' Hubble' - ' Hé' - krä - ' Dare' - ' override' - ' Elaine' - ' Dublin' - dullah - Mat - ' Garr' - '...''' - ' adulthood' - EZ - ' belangrijk' - ienza - ' universo' - ' stellar' - íĶĦë - ' ê²°êµŃ' - ' constellation' - ' Shelley' - ' multit' - ' mascot' - ' hospitalized' - ' ðĿĺ' - оÑĢÑĭ - adia - ' Mikey' - ' Amerika' - ' hairy' - Hold - ắn - kiego - è§Ĥ - à¹Ģà¸Ķ - ' rivalry' - ' Jonah' - ' surgeons' - ' relatable' - èĴ - ' swims' - ' billionaire' - modern - ' documenting' - ' Dae' - ' swatch' - ' puisse' - ' masuk' - ' marc' - ' kró' - ' Petersburg' - ' Aristotle' - ixe - Produ - ' ними' - ' kana' - ' Щ' - ' vomit' - ' Workers' - popular - ' Bieber' - еÑĤи - étique - ' encant' - gran - fir - ' anthem' - ÑģÑĥдаÑĢ - Last - ' hag' - ' vicinity' - renched - anding - ' голоÑģ' - ' Corner' - ÐĴÑĭ - osas - ievers - cional - ' vigor' - ' rejoice' - ' ciÄħ' - ' коп' - ' qualcosa' - dessus - ' ев' - ' Scandin' - ' Smooth' - ä½łè¯´ - hape - ' ëĭ¬ëĿ¼' - ' TU' - ' lyric' - ' bess' - éIJ - ÑģÑĤÑĢÑĥменÑĤ - ' Acting' - ' Orchest' - école - ' dolor' - ' íĭ°' - ' vergessen' - ' eyelids' - ' Tanz' - веÑĢж - ' ìķłë' - ué - ' scène' - ' ìļ°ë¦¬ëĬĶ' - ' crate' - kick - ' Theme' - ' 320' - ' garnish' - ' metre' - ' convex' - plants - esian - ' ê±°ì§Ģ' - ' médi' - ' Medal' - '130' - ' Alma' - æľīé»ŀ - Cola - ' ваÑĢианÑĤ' - ' gord' - ' avanz' - ' whispering' - ' intestine' - ÐłÐķ - ' LISA' - amız - SPD - ' pec' - ' pastors' - ' muá»ijn' - ocre - Sun - ' ÑĤакÑĥÑİ' - ' revital' - ' incomes' - ' detailing' - ' Bacon' - ' ëħ¸ëŀĺë' - ' parrot' - ' collaborated' - hesia - ' seva' - ' physicist' - ' BACK' - ׾×Ļ - ' bipolar' - Ïģεί - cros - ' ked' - ' economical' - ' endings' - ' ticks' - ' ê·¼' - ' Oliv' - ongs - ' continental' - ' weiterhin' - ' activating' - ' pollen' - ' Ank' - bay - ' ׾×Ĺ' - ' Eggs' - ' RAMSAY' - ' BER' - ' íĽ¨ìĶ¬' - ' passado' - ' groundbreaking' - presa - ' hilft' - ' Technically' - ÑĨий - NI - ' turnout' - ' Lap' - ' Gwen' - ' Vikt' - ' escola' - ' Cinema' - æ°¸ - ' ãģĨ' - ' consumo' - ' Purdue' - ' semanas' - ' PRESID' - Æ°ng - ' sach' - æĢİ麼辦 - ' savage' - ' RW' - ' 550' - bold - ' Simmons' - ' slang' - ' Naru' - ' Theo' - íĸĪëĭ¤ - .� - ' seizure' - ' hive' - ' cellphone' - 奶 - iiii - ' Musical' - ' Nuclear' - è¡Ĺ - áveis - ' prestige' - ' balm' - ' refill' - yah - hart - ' taps' - ' dispose' - ' Mick' - ' thermometer' - ãģªãĤī - ' obedient' - ' informações' - ' Wide' - mom - Sud - ' suspend' - ' Observ' - ' леÑģ' - ' tratar' - ' Katrina' - ' theres' - äºŀ - ' texted' - ' stör' - ' snail' - ' Fiona' - ' victorious' - ' librarian' - pract - ' fino' - ' Arms' - ppt - luk - ' tyres' - ' toc' - ' Kommunen' - ç¯Ģ缮 - ' revolt' - ' motivates' - ' bisexual' - ' wus' - ' handlar' - ' MUELLER' - ' expectancy' - ' embody' - ' Primary' - åİŁåĽł - ÑĢей - ' unscrew' - iantly - ',âĢ¦' - ' snel' - ' prevalence' - ' eruption' - ' descriptive' - vag - ' бÑĥкв' - ' mêmes' - ' ethn' - ' hijos' - ' Abdul' - ' Zahl' - belt - ' göst' - ' Theresa' - ' SUN' - ' Bake' - ' å¿«' - ' optics' - ' apocalypse' - purpose - ' różnych' - ' crus' - ' ÐĹем' - ' hardened' - ' TD' - ' graveyard' - ' Siber' - ' Porter' - ' explodes' - ' Sofia' - ' ÐĴедÑĮ' - ' weakened' - æĺ¯æĪij - ULL - ' pinky' - ' chapel' - ' Fres' - ' пÑĢиг' - MER - ' Schmidt' - ' Dud' - æŁ¥ - estens - ' nuance' - ' modifying' - ' Möglichkeiten' - ' Anat' - ' eccentric' - ' Screw' - ' Leh' - ' homogeneous' - ' Tall' - ' Ricardo' - Ãļ - igns - ' лиÑĪ' - ' gefragt' - Run - caster - noise - ' asynchron' - ÄĻdzie - ' ×ŀ×Ĺ' - ' suppressed' - Arthur - ήÏĤ - âr - dist - ' кад' - ' hör' - ' 135' - ' Mozart' - ' ÑģобÑĭÑĤи' - ' Nursing' - ' Hahah' - ' Dop' - ' policeman' - ´ìĹIJìĦľ - ' ê´Ģ볨' - hyuk - ' rugged' - ' nuggets' - ' Comms' - Stud - ' Ñģвое' - ' czasie' - ãĤ½ - ' région' - ' fishermen' - ' LT' - Ãĵ - ciaż - hei - ' crumbs' - ' Immer' - ' Feld' - these - ' advertisers' - ' roaming' - ' funniest' - ' NYU' - ' hehe' - ' poking' - ' ìķĪëı¼' - istical - ' opaque' - uç - wire - ' Weber' - ' Jacques' - ' 210' - üp - uyu - ' enfermed' - ' bumped' - ' Sew' - ' Chanel' - ' persönlich' - ' betrayal' - ' alleviate' - ' vähän' - ' guesses' - ' Celine' - assing - stroke - ' ì¡°ë' - å¤ı - ' ÑĤеÑħнолог' - ' оÑģÑĤÑĢ' - ' soient' - Dear - ' js' - ' gesprochen' - athi - ç¿» - Å¡e - Set - oger - ' Rig' - ' меÑĩ' - ' servicios' - ' Rut' - ' Ðŀй' - ' Myanmar' - ifie - ' snapping' - ' Kamera' - ' festive' - ' FY' - ' Carolyn' - Ñĸб - ' leggings' - ' yat' - ' ergon' - ' episód' - ' anomaly' - uestos - Id - ' evacuation' - ' gigabytes' - ' andare' - ' Rent' - mt - istine - ' estrat' - ettu - ' receber' - ' dramat' - ricular - alnız - ' Seni' - ' oyn' - ' Chemical' - ' ÑģÑħ' - ' turf' - ' 1917' - iscernible - ' mantener' - ' excer' - ' spectral' - ' neuroscience' - ' microf' - ' foreigner' - ' Lanka' - ä½łåı¯ä»¥ - ' ÑĤвоÑĢ' - ' tossed' - ' población' - ' mateix' - ' siellä' - ' ott' - ' compuls' - akukan - ' manifested' - ' ìĵ¸' - ' utmost' - ' reversal' - ' placebo' - ' blat' - ' Stunde' - manship - ' atte' - ' ìĨĮê°ľ' - ' istem' - ' annat' - ' Playstation' - ' zad' - ' quitting' - ' famine' - ' Rough' - ' Flame' - ' heut' - ' oportunidad' - ' faisait' - ' DP' - ' diciendo' - ' Melanie' - ' Carne' - meg - petto - JUN - ' лÑİбой' - ' oste' - ' JJonak' - ' theatrical' - ' invinci' - ' communion' - vocal - Eh - ' Details' - ' stroll' - ' Raymond' - ' Amelia' - ij¥ - ' produkt' - ' nuevas' - ' mustn' - mayı - colored - dec - ' hjäl' - ' sentimental' - ' realms' - ' krit' - ' sext' - ' Psychology' - èĪī - hil - ' коÑĢаб' - ' ëĤ´ìĿ¼' - ' Understood' - ' Guten' - ' gangs' - ' evenings' - æĢİ樣 - Ent - ' Legacy' - ' Congo' - ' durchaus' - ' buoy' - erella - WAN - Pre - ' ÑĢед' - ' Crisis' - ãģªãģŁ - ' ìĿ¼ìĿ´' - ' manuscripts' - еÑĤÑĢ - ' nonprofits' - ' dictator' - ' baskets' - ' Ish' - ' perto' - ' datasets' - ' ample' - gebaut - ' contributor' - ' ciao' - ' confirming' - ' UCLA' - âĻ¬ - ' Ñģн' - ' overturn' - åIJī - ' unrealistic' - ' Piece' - ocate - ' fällt' - pox - ' ë³´ìĭľë©´' - ' ë©Ķë' - ' Creation' - Ñİда - ' ×Ķ×IJ' - ' whack' - olithic - cely - ' ÑģовÑĢем' - ' sequential' - ' profesional' - ' cools' - ' repente' - ' aire' - ennes - ritos - ' ÐĴид' - ' kör' - ' Bitte' - ulars - ' incorrectly' - ' sharply' - ' bombard' - ëĭĺìĿ´ - ' chromosome' - ' advertisements' - hun - ' Ñīоб' - ' ÐĶаже' - ' bathtub' - ' Sno' - ÙIJÙij - ' buffet' - ' Grid' - ' Brew' - iset - ' Important' - ümüz - ' veto' - ' Werk' - ' Sham' - kra - ileen - heard - ' draining' - ' klass' - ' bakayım' - cture - ä½łèªª - amour - ' sponsorship' - ' distill' - ' patio' - ' komb' - ' overwhelmingly' - ' Jamaica' - uiten - Little - ' LOT' - taÄĩ - ' commanders' - ' Watts' - ' Options' - ìĿ´ë©´ - ACT - ' indispens' - ' Forsch' - otom - ' ÎŃÏĩει' - ' praising' - ' ìĺģìĥģìĿĦ' - ' aman' - ' hypnot' - thms - ' naszej' - ' mourning' - ' SAY' - cyj - ' гоÑģÑĥдаÑĢ' - ' cau' - mee - ' tadi' - Med - ' calidad' - ãĥŁãĥ¼ - ' stripe' - ' εν' - ' Katy' - ' Escape' - ' ãĤĵ' - ' müsste' - ' اÙĦا' - кÑĤ - ' jobbar' - ' Jeju' - orar - ' Será' - ' Messi' - áz - ' Tran' - ' piercing' - ' arithmetic' - ' staggering' - ' plugging' - ' KAR' - vl - ´ìĺ - ' Regierung' - ' OczywiÅĽcie' - ' Edgar' - ' conductivity' - yelling - vais - adian - ' bulky' - ' ÑģÑĢав' - ' пÑĢом' - ' paved' - ' bends' - ' Skillshare' - ' Mmmm' - ' Horror' - ' tumb' - ' goofy' - ' Meow' - ×Ļ׾×ķ - ' Wass' - ' Scale' - ' Rak' - ' projecting' - ' linguistic' - ' Worlds' - ensemble - ' pega' - stoppable - ' imbalance' - ' ø' - ' thriller' - колÑĮкÑĥ - ' leftovers' - ' caveat' - ' STR' - undai - ' watery' - ' Marin' - ãĥ³ãĤ° - ' eggplant' - ' JB' - ÙħÙĥÙĨ - vidia - ' FIN' - icable - ' podob' - ' cohesive' - ' Verfügung' - ' Plato' - аÑĢиÑī - ' kot' - ' ÐŁÐ¾Ð¼' - ' докÑĥм' - ' implants' - issez - Bre - ' gasps' - ' TED' - rato - JI - ' avenues' - ' Chong' - ladı - رض - ' inici' - ' Subaru' - æķħ - éģĬæĪ² - à¸ĭ - ' acht' - ' Architecture' - ' веÑīи' - ' DevOps' - ' toppings' - ' obsol' - aina - ' Bangkok' - estruct - ' kob' - ' ëĵ¯' - ' ÑĢазнÑĭе' - ' ree' - ' bijvoorbeeld' - ' Democracy' - à¹Ģรา - ' конÑĤ' - ' seç' - ' rahat' - ' parliamentary' - ' Bash' - æĬĵ - ziaÅĤ - ITCH - ' Bubble' - któ - Whoa - ' flats' - æķĪ - zne - ' servicio' - ' Dew' - Õ¸ÖĤ - ' unterstützen' - ' Winds' - éĤ£ä¸ª - ' ìĸĺëĬĶ' - ' evaluations' - ' reca' - ' elves' - cheer - ' jal' - ' rested' - ' quienes' - ' Brooke' - ' ë§ĪìĿĮìĹIJ' - ' inten' - ' oats' - ' referee' - ' pneumonia' - ' delve' - peace - eny - ' mostra' - ' Cannon' - ÏģοÏį - ' ÐIJл' - ' monumental' - οÏįμε - immers - avian - ' делаеÑĤ' - ' pitches' - ' Grove' - ' seminars' - ' récup' - ' Voor' - ' deven' - ' dB' - ' boosting' - 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' HQ' - KK - odar - ' hone' - ' confiance' - ' issuing' - ' diagnost' - ' ìŀĦ' - ' кÑĢÑĥÑĤ' - ' каÑģ' - ' þ' - ' restrictive' - ' Castro' - ' uÄŁ' - ' empre' - ' Moo' - ' Figure' - phonetic - Prof - ' пÑĢе' - ' tilted' - ' Negative' - ' Limited' - meno - lamation - ' trustees' - ' intensely' - ' açıl' - ' Used' - ' zul' - ' appreciative' - ' tinc' - ' conquest' - ' عÙĨد' - ' suicidal' - ' mulheres' - ' detach' - ' kamera' - ' AirPods' - INDISTINCT - глий - ' ëĥĦ' - ' wrestle' - æ´Ĺ - ' firearm' - ' lire' - pra - ' jewels' - ' Cornell' - ' íķłê²ĮìļĶ' - ' sucker' - ' nombreux' - ' Ferm' - ìĽIJìĿ´ - ' Pis' - ' изÑĥÑĩ' - ' miten' - ' cev' - ' URLs' - ' CAS' - ' åı¯ä»¥' - finden - ' bravery' - ' Ñģлово' - ' nenhuma' - ' encuentra' - ' Shirley' - ' percept' - frames - ' Rover' - ' Alberta' - occ - ' ëĿ¼ê³ł' - ' súper' - ' presume' - ' gland' - ' pacing' - ' neurot' - ' sno' - ' plotted' - ' paÅĦstwa' - ' Owner' - ' Defence' - ridges - ' wallpaper' - onian - Bro - ' Ariana' - 缴æİ¥ - kry - ' narration' - ' criança' - ' Alrighty' - ' ìĿ½' - ' ìĵ°ê³ł' - ' liberated' - ' exceeds' - ' dominating' - ' bakın' - lk - ' slapped' - ÐĹд - umental - gettable - ' Roz' - ' Gul' - ouvert - ' smashing' - azuje - Sir - ' grated' - ä½łæľī - ATT - ' articulated' - ' stora' - ' extrater' - á»ī - ÏĥÏī - wir - ' Mete' - Imp - ' hoor' - phase - ' ÑĩÑĥд' - ' бÑĢаÑĤ' - ' idag' - ' cinq' - ' aparecer' - ' ICE' - åĪĹ - ' quieter' - ' falsch' - adic - ' плÑİÑģ' - ' Menu' - uxe - ' Tôi' - ' MIL' - ' Haj' - verbs - ' tubing' - ' machst' - ' dall' - Ter - ' gelen' - ' cucumbers' - ' widgets' - ' devrait' - ' mike' - ' intra' - íķŃ - ' Ãħ' - ' Hund' - æ§ĭ - quarter - ' ew' - ' keluar' - ' mats' - ' Trick' - ' Infinite' - ŀ¨ - ' peac' - ' Prote' - à¥Ī - ' 1700' - ' Rais' - à¹Ĭ - ählt - ifica - aimer - aÄĩ - ' akl' - ' Volvo' - ' Tyson' - ' Rong' - irsin - ' âĻ¥' - ' parody' - national - pod - ayd - ambled - ' governmental' - ' confort' - icides - ' nasze' - ' Shepherd' - ' Kontakt' - ' disproportionately' - ' клÑİÑĩ' - ' tÃŃtulo' - ' sina' - ' compositions' - ' PF' - ' verkl' - ' suivre' - ' asta' - ' stakeholder' - ' samma' - ' BLACK' - ' nodig' - ' leva' - ' juegos' - ' ernst' - ' bottoms' - ' Signal' - ' pollut' - ' dura' - Musik - ' комна' - ' вÑģей' - alter - ' Stef' - ' BigQuery' - ' Verantwortung' - ' ëĭ¹ìĹ°' - ' quizz' - ' Letter' - ' Investment' - ÑĪÑĤ - IJëį° - ' encoding' - ' tänker' - ' Kw' - annie - åĭĿ - '110' - ' zwy' - ' 짧' - ' daw' - estä - ' deceive' - ' Länder' - isko - ' podstaw' - ' Pharaoh' - 쳤 - éĻIJ - últ - ' työ' - ' musimy' - 質 - ' pc' - ' NT' - ' Costco' - ' å°ı' - ' ÏĥοÏħ' - ' unin' - rounds - ' reminders' - ' puisqu' - ' krijgen' - ' workflows' - neten - ' ëIJĺì§Ģ' - ' sleek' - ' coworkers' - amientos - ' witches' - baar - eties - ' unnatural' - ' Sick' - ' Efendi' - ãĥ³ãĥĢãĥĽ - jcie - ' chamado' - ìĺĢìĬµëĭĪëĭ¤ - ' przedsiÄĻbior' - ' bookstore' - ' ìŀłê¹IJ' - ' Separ' - angi - Evet - ' emergencies' - ' XML' - нд - ¥´ë©´ - ' ê¿Ī' - ' ëĵ¤ê³ł' - ' sut' - ' Wiz' - å±ķ - ' dynamically' - operation - dot - ' inefficient' - clears - ' mundane' - ' Veronica' - èĮ¶ - رت - pose - pai - ' nylon' - ' aumentar' - ' alltsÃ¥' - vak - ' capacidad' - ' Wrestling' - ' fertile' - ' még' - ' Nano' - аÑĤели - ' ìĸ´ì©' - ' toca' - ' Eg' - âģ - ' ì³' - luent - ' solem' - ' cinemat' - ' Quel' - ' orbits' - ' Harm' - ricanes - ' blurred' - å¦Ĥä½ķ - ' اÙĦØ°ÙĬ' - ' jin' - ' grenades' - ' atroc' - ' wherein' - ' replen' - ' Comics' - edaan - ' denim' - ' embarrassment' - ' Gomez' - ' Busan' - ivities - ' saliva' - ' merk' - ' ilgili' - ' кÑĢÑĥг' - ' occupational' - ' Sahib' - Sta - ' adviser' - ' Truly' - ' YEAH' - ' ìŀĪëĬĶëį°ìļĶ' - zew - baren - ' stol' - ' belongings' - ' Researchers' - ' efendim' - ÏħÏĩ - ÅĤÄħcz - ' Ung' - ' Jub' - ' cerebral' - á»ĩu - ' צר' - ' подаÑĢ' - ' marched' - ' awaken' - ' ako' - ' acept' - ' initiation' - è¯ī - lot - ' wÅĤas' - ' Mongol' - utral - ' tentang' - ' inversion' - ' ìĿ´íĽĦ' - ' lok' - ÅĤbym - RS - ' stos' - ' interacts' - ' Calendar' - ' vanish' - ' physiology' - ' linearly' - ' JY' - ÄŁan - funded - iziert - ' zmian' - ' Grill' - ' unbelievably' - otechnology - ' Cars' - ' ÙĨÛģ' - ' Folge' - ' Beverly' - äischen - ' aumento' - ìĽĮìĦľ - ' mailbox' - ' steeds' - ' Peak' - å·§ - ' wykor' - ' prawda' - иÑĤÑĭ - ' discours' - ' accuse' - cesso - uire - ' попад' - ' tha' - ' measurable' - beeping - ' Innen' - ' пÑıÑĤÑĮ' - ' competed' - ' Italians' - ' encontra' - ' niew' - ' filtration' - ' пÑĢоÑĦеÑģÑģ' - ' pajamas' - ' cilantro' - ' Soc' - Luc - ' ê¹Ģë' - ' Odd' - ' hydration' - мов - ' plywood' - ' Competition' - изнеÑģ - flight - ' Beit' - bourg - ' coils' - ' câmera' - ' amended' - Äģm - Angel - ' Stacy' - flo - ' normale' - ' consonant' - ' accompanying' - кÑĸ - ' irritated' - ' fÃ¥tt' - ' crocodile' - IJĺëĬĶ - ' albeit' - ' Philosophy' - ç´¯ - ÅĨ - ytic - ' règ' - ' frança' - ' attentive' - Ham - ' alrededor' - æĿ¿ - sei - ' Ñģвид' - ' gimbal' - ' china' - ' ðŁİ¶' - ' ÐĴам' - ' stimulating' - ' Ora' - ytes - ' heft' - ' haters' - ' complexes' - ' 03' - ród - clear - ' besteht' - çķĻè¨Ģ - wny - moil - ' sloppy' - ' insignificant' - ' dubbed' - ' ëĸł' - ' consigo' - лÑĥÑĪай - Sn - ' ×Ķצ' - ' ÎĮ' - ' nadzie' - ' freshmen' - taa - ' uwagÄĻ' - ' Favorite' - ' Criminal' - ' eviden' - ' symb' - Les - ' Beau' - uned - plement - Ac - ' dermat' - ' Nolan' - Ñĭп - ' sitt' - ' everlasting' - ' estavam' - ' мик' - ' khác' - ' invit' - ' treble' - ' jig' - mani - ' tuvo' - ' RUS' - ' Erde' - ' DziÄĻkujÄĻ' - ' blueberries' - kell - acions - çĪ· - ви - LET - ' sprout' - ' spor' - ' bên' - ' Mona' - ' Contain' - ' Keys' - озÑı - ' función' - ' rappelle' - ' evolves' - ' scraping' - ' comentários' - ' pratique' - ' auxiliary' - ' Sponge' - Ñģким - uvo - ' Ñģамо' - ' sank' - ' highways' - ' inventions' - ' иногда' - ' creatively' - ' benchmarks' - oncé - alal - ' sotto' - ' calves' - ' Mov' - ' lavender' - ' eyeballs' - ' awaiting' - ' Paty' - ÙĦÙĩ - ' embroidery' - ' duh' - ' camar' - ' BOB' - ' spaced' - ' gÅĤos' - аемÑģÑı - ' escapes' - ' Rogue' - zcz - èŀ - ¬ë¥¼ - ' Może' - ' еÑģÑĤе' - ' Burada' - éĮ² - wd - uuuu - ' sash' - ' Lub' - ' notebooks' - ' mae' - ' conflicting' - ' summertime' - acas - ' bauen' - blowing - ạo - ' ìĸ¸ìłľ' - ä»ĬæĹ¥ãģ¯ - ' Senhor' - ' iPhones' - ' Quarter' - ' ìłľëĮĢë¡ľ' - uÃŁ - ' ë§Ī무ë' - ' settlers' - ' crest' - ' transc' - æĽ¾ - ' riots' - ' clones' - ' Oprah' - ίζ - ' pals' - '.......' - ãģĶãģĸãģĦãģ¾ãģĻ - ' ÑĢоÑģÑģ' - ' Laser' - ' zaczy' - ' sevi' - ' regeneration' - ìĹ¼ - would - ' üzerine' - ' StraÃŁe' - ' vengeance' - ' rer' - ' Safari' - ' HEY' - çķ« - ' sacar' - ' imagem' - ' Bundest' - mesan - ' Paste' - ' sizz' - ' поÑģÑĤÑĥп' - ×Ķ×ķ - trad - ' française' - ' Bou' - ' barre' - ' Zhi' - ' Geez' - ihad - ' reconoc' - ' pelig' - ' indices' - ' ë°ĶëĢ' - ' conduction' - ' ìķħ' - ' zeker' - ' fum' - ' Wür' - breaker - ' sprite' - Crowd - ' opener' - ' olv' - ' buenas' - ' Silk' - ' HIM' - kop - compl - ' possono' - ³Ģ - ' oscillator' - ' Sith' - èĥ¡ - ажи - ' raft' - hall - ' schneller' - ' importing' - ' assembling' - ' ubiqu' - ' activates' - acci - ĵľë¥¼ - ' composers' - ' ACL' - Conf - ' ì½ĺ' - ' некоÑĤоÑĢÑĭе' - ' candies' - åĬłåħ¥ - ' Muss' - à¹ĥà¸Ĭ - ' duda' - ником - meden - ' ìĸ´ëķĮ' - ' Yeshua' - zag - hodou - ' aloud' - ' Palmer' - imize - ãĤ·ãĥ§ - ' maritime' - ' communal' - ' badges' - ' rugby' - ' marshmallow' - ' fiery' - ' accountant' - ' abla' - ' Monroe' - ' Font' - ' Boost' - ' Barnes' - answer - ' Burning' - ' ä¸įæĺ¯' - ' angef' - ' Wesley' - lls - ìµ - ש׾ - iliÅĽmy - ×IJף - amura - ' Fuj' - ' pani' - ' Trop' - arbeiten - ' rue' - ' Rare' - ängen - ' ÑģмоÑĤÑĢеÑĤÑĮ' - ' ÐļаÑĢ' - ' MTV' - boarding - '][' - ' ëłĪë' - stanbul - pielt - ' Hardy' - ' Engagement' - ' Dienst' - ' wären' - ' fuego' - ' estruct' - ' calam' - ' Response' - ' ãĤĦ' - ' Mohammad' - ' resisting' - ' durant' - èģ¯ - åĨµ - ' OLED' - ' verz' - män - ' ÙĨÛĴ' - ' paranoid' - ' Aware' - ' Engineers' - ' procedural' - ' personnage' - ' farklı' - é¡Ĩ - flowing - ' меÑģÑĤа' - ' Bare' - istem - ' poczÄħtku' - ' personajes' - ' ìĸ´ëłµ' - Ńī - ' ХоÑĤÑı' - ' unsett' - ' Absol' - ' ấy' - ' MAYOR' - полне - ' informing' - ' amps' - ÐŁÑĢ - ' ëŃĶ' - aeda - ' ×Ķ×ij×' - ấn - kelijk - ' atheist' - ' trout' - ' neues' - ' Nokia' - machen - ' wholesale' - ırd - Ins - ' Ñįп' - ' prick' - ' Kindern' - à¸Ĺำ - ' classy' - ' înt' - ' Shopify' - ' ÑģоÑĢ' - ' закÑĢÑĭ' - zuk - ' universally' - ' teaspoons' - ' recount' - ' nÃ¥gonting' - ' Xue' - isième - ' weakest' - ' teÅŁekkür' - ' mathematically' - ' Hos' - ' íķľëĭ¤' - ' partager' - ' Darr' - êº - ' εκ' - ' germs' - ' gelir' - ' dul' - ',-' - ' ìĸ¸ë' - ' ×ŀצ' - ' ÑıÑĢ' - ' quotid' - ' przysz' - ' hardness' - ' aquatic' - ' Jungle' - ' PCR' - ' Eliot' - ' ostr' - ' mapa' - essä - ' GIR' - ' Driving' - ' Sami' - ' Medien' - ' Companies' - ' Pharm' - seits - ' Rim' - ' οÏĢο' - ' weiteren' - ' pizzas' - ' Lydia' - ' Heights' - ' sincerity' - ' nossas' - ' dÅĤ' - ' alarming' - ' Cauc' - ' ÑģмÑĭÑģ' - facing - bags - WW - ' Ø´ÙĬ' - ' courtroom' - ' Phillip' - ' ê²ĥì²ĺëŁ¼' - ' Spieler' - ãĤıãģĭ - ' kant' - ' admitting' - ãĥģãĥ£ãĥ³ãĥįãĥ« - ' containment' - å¼ł - ' removable' - ' jumper' - focused - ' иÑĤоге' - ' Тем' - ' vase' - ' USC' - ' Monate' - ' Jacobs' - ' HOL' - iked - erweise - ' goodies' - ' homage' - ׼ש×Ļ×ķ - ' quais' - ' inicial' - ' guarding' - ' dazz' - ' combos' - ' ÑĥпÑĢав' - ' Talent' - å¥ĩæĢª - ' ór' - ' intermittent' - ' McCarthy' - ' spans' - ' tyre' - ' quy' - èĪĪ - jut - ' Zent' - ' gat' - 大åĵ¥ - ' scaffold' - ' necesario' - ' Zahlen' - ' SAND' - ' PU' - Everything - '----------------' - ' взÑıÑĤÑĮ' - ' sparks' - ' pendulum' - ×ŀף - ' ìĥīê¹' - ' multiplier' - ' ладно' - urat - ' upsetting' - è¡Ģ - bak - ' ìµľëĮĢ' - ' anál' - ' JOE' - ' kosten' - ' Patty' - ' Guin' - cked - ' Egyptians' - ' Citizens' - ר׼ - ' ÐķÑīе' - ' його' - ' snowfl' - ' lekker' - ' acost' - ' Babe' - ' gamble' - ' adjective' - кими - oys - ' montre' - ' Hyundai' - ' moisturizing' - ' mozzarella' - OOO - ' facult' - ' doet' - ' fearless' - ' espresso' - ' allora' - ' Cinc' - ãĥ¼ãĤ¸ - ' conteúdo' - ' Pelosi' - ' minder' - root - ' íķłë' - ' пад' - ' Calling' - ' Config' - ' Console' - insky - énergie - ' solitary' - оде - ' guarded' - '160' - ' пÑģиÑħ' - ' Shap' - ' titre' - ologne - ' паÑĢÑĥ' - ' PRE' - ãĥ¼ãĥī - ' ln' - ' Mitgl' - ' Carry' - ' spind' - ' Canton' - ' kingdoms' - remo - ' raging' - ' incapable' - ' WR' - åĨįè§ģ - ' ÑģобÑģÑĤвен' - ' какиÑħ' - ' SHE' - ëĭ¹íŀĪ - ' scarcity' - ' perde' - ' exits' - ' Singer' - ' supper' - ' municipality' - ' Diversity' - ' tiro' - iels - ' lÃŃder' - ' bluff' - ' atra' - lys - ' mahd' - ' código' - ' Harlem' - rule - icity - ' simplistic' - ' Konst' - åģ¥ - ELLI - ' första' - ' constitutes' - ' ÑģÑĤоÑĢонÑĥ' - ' urged' - ' Panda' - ì°¨ë - rece - ' patriot' - ' Crush' - ' wink' - ойÑĤи - urança - ' seizures' - ' electrod' - ' Donkey' - ' IU' - ' MOS' - ' alkal' - ì´ī - besondere - ' parallels' - ' bitterness' - ättre - essional - ' soybean' - ' collab' - ' Reporting' - å§Ķ - ' компании' - ' wszyscy' - ' Crunch' - iseen - ' ambassadors' - ' Chev' - åįĪ - овÑĭе - sca - ' ÑĢеÑĪил' - оÑĤо - ' gleichzeitig' - mern - üst - ' Hae' - ³´ê²łìĬµëĭĪëĭ¤ - ' shores' - ' depress' - ' ahor' - ' Steuer' - ahh - ' revise' - ' ÑģамÑĭе' - jat - ' herbal' - ' cuánt' - ' buna' - niejsze - Finally - ×ķ×ĸ - cje - ' ìŀĪê±°ëĵłìļĶ' - ' ëĤĺëĪ' - ' przest' - ãĥ¼ãĥł - lica - ' Duch' - å°įå°į - ÑĸйÑģÑĮ - passen - ' satisfies' - ' Additional' - ' cámara' - еÑĩение - ' pomp' - ' ë§IJìĿ´' - ' Mills' - евид - ' respectable' - ' filament' - ' vender' - ' mattered' - oure - 층 - Korean - ' estudio' - ' cactus' - ' Vive' - ' Rag' - ' compliqué' - ' ÙĪÛģ' - ' tao' - ¦¿ - Since - ' jeopard' - ' Sell' - åºĶ - ' ìĺĽ' - ' keto' - ' intelig' - ' Angeb' - ' tiden' - ' socio' - ' reminiscent' - ' caregiver' - Space - ' Exercise' - ' Become' - êts - akk - '!..' - ' ÑģпÑĢоÑģ' - ' αÏĢο' - ' shootings' - ' ape' - ' Sammy' - ' Kung' - ' cuál' - ' Lup' - æĿŁ - ä¾ĨåĪ° - ' ÑģÑĤÑĥд' - ' sweeter' - ' comum' - ' Ads' - hyung - ' бÑĥдÑĥÑī' - ' waffle' - ' Orb' - ' laut' - ' forecasting' - åª - ' rapping' - ' prefers' - ' benz' - ' nik' - ' Bahn' - ' sanding' - ' imminent' - ' пÑĢоблемÑĭ' - ' doivent' - ола - ' życia' - ihu - ' existem' - ' Interior' - ' Takes' - ' toddler' - ' dictatorship' - ' Smithson' - ' Allahu' - ÏİÏģα - ìķĺìĬµëĭĪëĭ¤ - ' Vote' - ' Smells' - одно - ' hindsight' - VR - ' Patch' - ' Jahres' - ' souvenir' - ' neutron' - ' longtime' - ' sayin' - ä¹IJ - asaki - ' оÑģÑĤанов' - ' expelled' - ' cryptocurrencies' - ' Murder' - ' Citizen' - WAY - ' plu' - ' lemonade' - ' conveniently' - ' HI' - ' 2023' - ש×ķת - аÑĨион - ' 뼰' - ' ÙĦÙĥÙĨ' - ' немножко' - ' unused' - ' maioria' - ' astrology' - ' Downt' - Nick - ' preoccup' - ' demain' - ×ŀ×¢ - ' водÑĭ' - ' Sanskrit' - ' prêt' - ' stranded' - ' refin' - ' пÑĢиним' - ' повеÑĢÑħ' - à¯į? - ' zrob' - ' intertw' - ' Davidson' - лена - ' понÑıÑĤÑĮ' - ' Reno' - ' полÑĥÑĩилоÑģÑĮ' - ' correspondent' - ' Uran' - else - ·· - ' tutoring' - ' granddaughter' - luded - ' stesso' - ' hết' - ' gegangen' - ' ÐĿÐIJ' - ' antig' - background - ' gedaan' - ' favored' - ' Emmanuel' - ' iod' - ' clamps' - ' comple' - ' Advance' - ' ìŀĪê³łìļĶ' - ' Rox' - ' ìĹIJë' - ' intestines' - ' percussion' - ' legitimately' - ' Eternal' - family - alog - Brad - ениÑĤÑĮ - ' ÑģнаÑĩала' - ' certa' - ' akkor' - ' εδÏİ' - ' octave' - ' Vac' - моÑĤÑĢи - ' Ãītats' - ' longue' - ' dissoci' - ÑĢÑıд - hein - ' pantalla' - ' indications' - ' Lt' - ' Grade' - è£Ŀ - oine - bug - ' Verizon' - ' Além' - ' viennent' - ' ÑĩиÑģÑĤ' - ' Beni' - ' Tsch' - ' TP' - ' insulting' - ' Weight' - ' adaptations' - ' habÃŃan' - ' clique' - oÅĽci - juna - ' suchen' - ' Goes' - ' Exodus' - Cho - ' antis' - ' íĮĮë' - seven - ' ÑĩаÑģов' - ' ballistic' - zony - ICIA - ' пÑĢеÑģÑĤ' - ' simplesmente' - ' Collabor' - Fred - ' ÑĤелеÑĦон' - ' Ravi' - íķ´ì¤ - пеÑĢв - ' ìŀĪìľ¼ëĭĪê¹Į' - ' ót' - ' aleg' - úp - ' disregard' - ' indent' - cloud - chlagen - ' iterate' - ' generalized' - ãģĹãģ¾ãģĹãģŁ - ह - eleri - ' disastrous' - ' ÑģÑĤала' - ³ij - KNOWN - ' richness' - ' conscient' - ichts - ' Ñįлем' - بد - irens - ' haunting' - ructures - attack - ' cupcakes' - sque - ' naszego' - ' anthropology' - ãģŁãģł - ãģµãģµ - chae - ' discovers' - ' Personality' - ' Το' - ' diÄŁer' - åįĢ - ' неÑij' - ' Anita' - ' [âĻª' - ' Carm' - ' Benny' - ìĬ¬ - ' pupil' - ' ocas' - ället - jÅĽÄĩ - 大ä¸Ī夫 - amental - ' оÑĤноÑģ' - ' pid' - ' armp' - REE - ' оÑĤкÑĢÑĭв' - ' uda' - ' Syndrome' - ' Standards' - ãģĪãĤĭ - ' pointers' - ' enam' - ' Tig' - ÃŃz - ' нами' - ' unchanged' - ' turmoil' - ứng - '!!"' - '5000' - ' 물ìĸ´ë' - ' merging' - ' entscheiden' - åĩºæĿ¥ - forme - ' trimmed' - ' dared' - ' aspiration' - ' Mythical' - ' Hej' - ' Alej' - ÑĨо - оÑĤÑĥ - Ze - ' инÑģÑĤÑĢÑĥменÑĤ' - ' RTX' - ' localized' - çļĦè¯Ŀ - ' surrounds' - ' empieza' - ' clase' - ' à¸ģ' - ' Rapid' - ominous - igail - ' ÑĪиÑĢ' - ' læ' - ' zasad' - ' unfolding' - ?!?! - ' ìĪľê°Ħ' - ' Polski' - ' Kauf' - ' Celt' - itic - ' toolbox' - ' Pocket' - ' ìĦľë¡ľ' - ' belki' - ' admiration' - phr - ' Produkt' - ' Truck' - ãģİ - ' drauÃŁen' - waÅĤ - ' Hebrews' - ' íķĺê²Į' - ' ACE' - urgence - aurais - ' charitable' - ıt - ' armas' - ' Gedanken' - reating - porte - ' imprint' - fäh - ' подÑħод' - ' outset' - วà¸ģ - енного - Class - ' vanity' - ' VOICES' - ' 260' - resident - USE - ' ê°Ģìļ´ëį°' - é½ - ' throughput' - ' cuma' - ìļ± - ãĥ¼ãĥ³ - ' плоÑī' - ' partis' - ' Animation' - §Īë - Cre - ötzlich - ' magg' - ' clumsy' - ' bottlene' - ' birlikte' - ' Gamb' - ' ׼ף' - ' metropolitan' - 该 - æİĴ - Ooh - ' objections' - ' Ùħت' - ' мел' - ' remnants' - ' Xavier' - Rich - ' olsa' - ' Pill' - ' groans' - ' Naruhodou' - ' Contract' - ада - nai - ' ÑĦиз' - ' ops' - ạt - ' parachute' - ' nell' - ' Entscheidung' - ׾×Ļ×Ŀ - ' truthful' - ' sharper' - ' bureaucracy' - cart - ' инÑĤ' - wiek - ' willingly' - ' Herman' - ' mehrere' - ' elites' - ' Armor' - ãĥĪãĥŁãĥ¼ - ' embora' - ' Recogn' - ' лÑİблÑİ' - ' Excellence' - ibel - ' exporting' - ì²´ìłģ - Kelly - Cameraman - ' slips' - ' figura' - ' ãģ¡' - ' koll' - ' Pandemie' - çıŃ - ' timed' - lieÃŁlich - ' ×ŀ׼' - ' perÃŃodo' - å¿Ĺ - ivat - ' questionnaire' - ' période' - 究 - ' sighs' - ' allegiance' - ' XV' - ' Kensuke' - ' Gesundheits' - ' positivo' - ' Janeiro' - ' SEE' - ' است' - ' Kelsey' - tober - ' αλλά' - ' Parent' - ' Dayton' - ' Bilder' - ourage - ' seres' - ' muchÃŃsimo' - ' Realm' - ' OFFICER' - ersonic - ãĤĤãģ® - onya - ' ê¸ī' - ' ancestry' - ' Jurassic' - ' centigrade' - ấu - ujÄħc - mans - ' tio' - ' Moż' - ' tragen' - ' stared' - ' schematic' - ' passou' - ' meatballs' - ÅĤoÅĽÄĩ - ' synchronous' - ' permis' - arial - ' zer' - ' parity' - ' Avatar' - indeer - eston - ' meidän' - ' Cly' - ´ī - ' estrogen' - ' centimet' - çĻº - ' convictions' - ' possiamo' - ' perdu' - ' pathogens' - ' Quin' - ' Programs' - ' Points' - rament - rail - ' vy' - ' graft' - ' bart' - ' Lotus' - ਠ- ' ë³´ìĭľ' - ramer - Father - ' ëľ»' - ' ×Ķ×Ŀ' - ' trazer' - ' tark' - èces - forth - ' Ñģделали' - ' zucchini' - ' waktu' - ' entertained' - ' Milliarden' - ' shaky' - ' przede' - ¸Įë - ' reversible' - ' NAU' - uins - érêt - annen - ' Hunting' - ' Fellow' - élior - ' rotations' - ' granny' - xton - ' ÑģÑĤановиÑĤÑģÑı' - ' наÑĩал' - ' arteries' - rió - ' полÑĮзов' - ' ÐijÑĭ' - ' novelty' - pound - ' weirdest' - ' bois' - émie - upl - ATA - ' tehd' - ' Nir' - sınız - '!",' - åijĬè¯ī - ' immort' - ' elk' - аниÑĩ - ' fabrication' - ' Noise' - ' Avant' - رÛĮ - wat - ' whooshing' - ' ׼×Ļ' - ' ÐĹнаÑĩиÑĤ' - ' centrif' - ansing - Sound - ' ëĿ¼ë' - ' captions' - à³į - ' orgas' - ' dolphins' - ' Blend' - ' Taj' - ' CCTV' - ' inom' - ' editions' - ' burnout' - ' bättre' - ' Casa' - ovich - ' molten' - ' blindfold' - ' Gue' - æŶéĹ´ - ' spinner' - ' möglichst' - ' VÃł' - eneca - ' médico' - å¹¹åĺĽ - ástico - ' ard' - ' Sundays' - ' Remote' - ' ìĸ¼ë§Ī' - ' trÆ°á»Ľc' - ìħ¨ë - ' dopp' - ' beÄŁ' - icana - ' ëĤĺì¤ijìĹIJ' - çİĩ - ' holiness' - direct - ' ìĺģíĻĶ' - ' culpa' - ' Stitch' - lightly - амен - ' меÑĪ' - ' пеÑĩ' - ' yhte' - osphere - ' ìĵ°ëĬĶ' - ék - ' seriousness' - ' garments' - ' concise' - ' SJ' - ' verloren' - ' parecer' - ' UNC' - ìĬ¤íĥĢ - ' enfant' - ' bomber' - ' Gift' - ' ì¢ĭëĭ¤' - ' rhythms' - ' Klar' - 人æ°ij - ownik - ' Reverend' - ' emitted' - lassen - ' revenir' - ' arising' - ' precisamente' - ' interpol' - ' Tenemos' - obed - ' tecnologia' - ' nerede' - ' Visa' - ' sava' - ' escrever' - ' assaulted' - ' Fleisch' - ' Councillors' - ' ê°Ģê¹Į' - ' begg' - ' Developer' - ' Bronze' - ' Bonus' - ' רק' - fact - ' endlessly' - ' macam' - ' rzeczywiÅĽcie' - ' hovering' - ège - ' poorest' - ' Sched' - mile - issements - acÄĥ - ' 립' - ' vaccin' - ' futuristic' - ' Window' - паÑĢ - ' ÑĢоÑģ' - ' lowers' - acs - ' ÐIJлекÑģанд' - ' Alert' - ieme - ' Caucas' - ' jaws' - ' hunted' - ìĹ½ - ' بÙĨ' - ' ׾׳×ķ' - ' turbines' - ' lumps' - ' Allies' - ahlt - ' subscriptions' - ' nouveaux' - uger - bones - ' berry' - ' ìĦłë¬¼' - ' Manufact' - ' Lunch' - ê·¸ëŀĺ - ' hydrated' - ' achei' - ' Yaz' - ' Tibetan' - ' Quantum' - ' Jerome' - ' оÑīÑĥÑī' - ован - motion - ' Controller' - energetic - ' ÑģкоÑĢо' - ' vowels' - ' ÑĥжаÑģ' - ' hoof' - ' Bullet' - imagin - ׳×Ļ×Ŀ - ' engagements' - ' Blues' - ' añad' - ' fps' - ' caterp' - ' sá»ij' - ' Tribe' - ç¶ļ - пон - iferation - ' rumah' - ' Punj' - lab - ' comprehension' - bringing - Wo - ' tik' - ' anyhow' - 以åīį - áticas - ' sitzen' - ' kolay' - ' Confederate' - ' Called' - ' naszych' - ' dziÄĻki' - ' cloak' - ' Goog' - ' Ashe' - 象 - enan - ' мÑĭÑĪ' - ' веÑĤ' - ' Spo' - ' Sket' - ' Henderson' - ilah - ' безопаÑģ' - ' sekali' - ìĸ´ê°Ģ - ' snare' - ' rằng' - ' försö' - szych - ' übers' - ' stratég' - ' ìºIJë' - ' rappers' - ' cep' - ' Hasta' - ' horribly' - ' früh' - ' بع' - ' mantle' - ãĢħ - funding - ' zust' - ' Pens' - sed - ' íŤ' - ' gereki' - ' alarms' - ' Wha' - ' Markus' - aksi - ' ÐIJле' - klore - ' éner' - ' tilde' - boxing - ' ìĦŀ' - ' encontramos' - ' Phar' - наком - óst - ' Ä°s' - ' ëĭĺ' - ' squats' - ' pretended' - ' dez' - ' ê´ľì°®ìķĦ' - jach - ëĿ¼ê³ł - ' íĻķì§Ħ' - ' Ansch' - 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' grandi' - ' бок' - ' wholly' - ' mahdoll' - ' controll' - ' Bunun' - èĬĤ - ' dipped' - ' región' - ' ÙĦÙĪ' - ' баг' - ' premiers' - ' chá»ĭ' - ' æīĢ以' - è±Ĩ - idez - ' quota' - ' ghee' - arkan - ' gelatin' - ' Clerk' - bbles - ' Paige' - ' staged' - ' sociais' - ' Bizim' - ' velocidade' - ' malaria' - ' shortened' - ' salut' - ' Hehe' - ' vá»ĭ' - ' Taiwanese' - ' Arri' - gres - åİ»äºĨ - () - riad - ijIJë - ' ãģ¾ãģĻ' - ' masculinity' - LP - ' ëĸ¡' - ' términ' - ' Vä' - ' Seiten' - ' respectfully' - áo - ' totalement' - ' scraps' - ' infring' - ' Bose' - amar - ' Luiza' - ' ARM' - ' плоÑħо' - ' meillä' - ' Dion' - å¼Ģå§ĭ - ' souha' - ' geschafft' - ' convolution' - ' âĢijâĢij' - ' 144' - lingt - ' människ' - ' gustado' - ' coined' - ' Lulu' - å®ĥçļĦ - opot - ' Prayer' - ' roasting' - ' chromosomes' - 飯 - еле - Blue - ' Erfolg' - èĩªçĶ± - ' пÑĢидÑĥм' - ' risking' - ' Guardians' - ' 2024' - èse - ' бÑĥдÑĤо' - ' conserve' - ' Bringing' - ' Astra' - à¹Ģà¸Ĥ - ' какÑĥÑİ' - respace - ' Ðŀп' - ' вокÑĢÑĥг' - æħĭ - ' masked' - ' Shy' - ' Nim' - endas - ' íı¬ìĿ¸' - ' 모ìĸij' - ' valeur' - ' Negro' - ' CDs' - inkling - ' montón' - ' Hond' - Real - ' fullness' - ' Whoops' - ' Shank' - ' Bran' - ' transluc' - ' err' - ' Gardens' - oyu - ' affirmative' - ä¸ĭä¸Ģ - ' pottery' - live - iau - mount - ' fluctuations' - åŁİ - ÃŃem - ' pulses' - ' crianças' - ίαÏĤ - ' basta' - ENNIS - ' коÑĢп' - ' Funk' - ' éĢĻ' - Ã¥rt - ' заÑĤем' - ' parasites' - ãĥĻ - ' airflow' - ' Xuan' - Gülme - ' blooming' - ' mummy' - ' bao' - ' Clap' - antics - skin - centric - before - ' RICHARD' - ' Hahn' - TAKE - ' ÑĤÑĢеÑĤÑĮ' - ' pressured' - ' Kurz' - isti - ' наÑĪего' - ' semiconductor' - ' Clint' - ' plup' - ' Origin' - ' Events' - ' ê±±ìłķ' - mpfen - NEY - ' DW' - ' ë¶ģíķľ' - ' informs' - ' forsk' - ' amiga' - ' Cincinn' - Str - ' parish' - ' 커íĶ' - ' sizi' - ' plantation' - ' bliver' - ' полиÑĤ' - ' subdiv' - ' rant' - ' principals' - åIJ¦ - ' kunne' - ügen - arespace - ' vallahi' - ' collapsing' - اÙĦÙħ - ' lider' - ' tama' - ' gagner' - rolle - ' ë§IJìĶĢëĵľë' - ' cathedral' - ' Webs' - ' Politics' - ãģĹãģ¾ - ãģ£ãģ¦ãĤĭ - ' Denis' - ' tuo' - ' refract' - ' disintegr' - stes - ' лÑİбов' - ' wilt' - ' trusts' - ' komun' - ' Basket' - ~!! - nae - ' Ðļол' - ' syllables' - ' Henri' - ' Nab' - ÙĪع - ' wn' - ' kamp' - ' Prague' - ' Breakfast' - ' ê·¸ëŁ´' - ' chut' - ' 330' - ' Industries' - ä¸į管 - ' iÅŁi' - ' Goldman' - ' Ä°ns' - ussa - ithe - ĦIJ - ' SOUND' - алÑĮнÑĭм - .( - ' гоÑĢаз' - ' dagegen' - ' ë®' - ' waiter' - length - ' ÏĥÏĦα' - ' chunky' - Sa - ' rusty' - ' Judith' - '750' - ' epoxy' - ì¹ł - åı² - metro - ' rejecting' - ' squishy' - ' plupart' - ' méth' - ' aspiring' - ' Drama' - ' uplift' - §Īëĭ¤ - '................' - ł¤ìļĶ - ' técnica' - ' pasando' - Those - ' ÑĢаздел' - ' mediocre' - ' Nickel' - ' superheroes' - ' missionary' - ' Parece' - ' rotational' - ' prett' - ãģĿãģĨãģĿãģĨ - ' lama' - ' canyon' - ' beter' - ' Provost' - ' hvis' - ' deactiv' - ' Hels' - pflicht - Something - ' Pierce' - ' ê²Ģì°°' - lungen - ' sizing' - ' latitude' - ' Nonetheless' - omnia - ' Sabrina' - ' Dynamic' - åĥ¹ - onta - ìĨIJ - ' directive' - ' Depot' - ' fueled' - ' expire' - ' común' - ' Sexual' - ' Gore' - ' restless' - ' JAKE' - ÑĤеÑĢеÑģ - ' ÑĤÑĢан' - ' Holz' - å°Ĩ - ' Actor' - æĿ¯ - call - ' emailed' - ' Pear' - Ñĥди - ÑĢал - ' mÃły' - ' CHEERING' - å®īåħ¨ - ' retailer' - ' protr' - ' discarded' - ' HIS' - ' evangelical' - ' Else' - ' explores' - ' criticizing' - ifik - ' whipping' - ' opis' - oused - Free - ' íĮ¬' - ' mics' - running - Ob - itié - ' necesita' - ' Dominican' - ' Bagh' - ' tendencies' - ' Metropolitan' - Åijl - ' знаем' - ' Zam' - ' Deadpool' - ależ - ' investigative' - ' Pronunciation' - ' emulate' - ' banco' - ' -âĻª' - åĪ» - ' overarching' - liches - ' возвÑĢаÑī' - ' Scary' - ' Kia' - åľŁ - ronting - inned - ' ÛģÙĪ' - ìĪĺ를 - ç¾İåij³ - wel - ' ë³Ħë¡ľ' - ' unintention' - aaS - ' nicest' - ' Testing' - ' ISIL' - ogenous - ' ØŁ' - ' lieutenant' - ' brauch' - ' Tir' - drive - ' tolerant' - ' shooters' - ' ìĺĪë»IJ' - 殺 - onton - ' teria' - ietet - Ron - leigh - gae - ' olmak' - ' Clone' - sold - ' skeletons' - ' incumbent' - оме - CON - ' leven' - ' millennials' - ' equator' - ' Feder' - ' Alexandra' - ' vrij' - ' Healthcare' - ' íķij' - ' emphasizing' - ' dialogues' - ' chilled' - ' prow' - ' Passion' - ' Laden' - ariest - aphrag - ' additive' - ' Staat' - ' Nept' - ' HAM' - à¹Ģà¸Ń - days - ' íĸĪëįĺ' - ' voila' - ' Ñħл' - ' Deutsche' - quir - Open - ' ranged' - ' levers' - ' Mansion' - pared - ' Titans' - atoire - ' engages' - yez - naden - ' obstruct' - ' Emmy' - åķĨ - °¥ - ' troph' - ' takeaways' - +. - tycznie - hésitez - ' podÃŃa' - ' 주ëĬĶ' - ' citation' - ' Aqua' - ' debugging' - ван - ' ëĭ¹ìĭł' - ' اÙĦÙĬ' - ' instantaneous' - ' Autumn' - ' kepada' - ' getan' - hini - ynthesis - ' пеÑĢи' - ' Maced' - Pac - untu - Bra - ' гоÑĢаздо' - ' 1959' - ' ÑĤемпеÑĢ' - ' sane' - ' OUR' - asu - ' 무ìĹ' - ' valleys' - ' listings' - ' przedstaw' - ' gummy' - ' cortisol' - ' Obrig' - ' Allied' - ожÑĥ - ' génér' - ' docs' - ' Chili' - ' Abdullah' - Kit - ' contributors' - гоÑĢ - леÑĢ - ' binder' - ' modèle' - íħIJ - ' inteiro' - mis - fera - اذ - Mania - ' íĻľëıĻ' - ' ë´IJìļĶ' - ' Jaz' - ç»ĵ - ÑĸлÑĮки - rishna - ' êµ°' - ' tamanho' - ' appliance' - ' Resistance' - ' LOOK' - ' Hyp' - ' Heil' - Fire - uju - ' heals' - ' malt' - ' VERY' - ' ÑħоÑĩеÑĪÑĮ' - ' linger' - ' Narr' - ' Regular' - ' Loop' - ' Leno' - ' sortie' - ' Serve' - ' ìĿµ' - ' Luego' - ittä - ' undes' - è³½ - å¦Ĥæŀľä½ł - ' slippers' - ' onda' - ' ÄIJây' - ' taped' - ' traverse' - ' relativity' - ' Yoshi' - cjon - ilated - actively - ' Сов' - æĪijè§īå¾Ĺ - ' POL' - ÐłÐĺ - inflamm - cheerful - ' ×ŀ×IJ×' - ' >>[' - minster - ' вли' - ' identifier' - ' Lambda' - ' tros' - ' flawless' - ' detrimental' - ' bunları' - War - ' região' - 羣çļĦæĺ¯ - ' Bike' - cessors - ' cùng' - ' RN' - ' ê½ĥ' - ' küçük' - ' Beginning' - íĺ¸ë - ' gewe' - ' denote' - ' Alberto' - ' probiot' - ' ode' - ' molar' - ' bursting' - assumed - ' footprints' - veda - ' steroids' - ' flaming' - ' Eller' - ' erkennen' - ätzen - ' lifecycle' - ' DOU' - ' Karena' - ' Guerra' - è¿ĺæĺ¯ - ' sinister' - ' podéis' - ' parab' - ' oko' - ' matéri' - ' caric' - sonaro - ' praticamente' - ÑĥÑģа - ' comunque' - ' vigilant' - ' regimes' - ' Shooting' - ' raids' - ' Nora' - ' Wieder' - mens - ' Ñģод' - ' ê²½ìļ°ìĹIJëĬĶ' - ' вÑħод' - ' autobi' - ' Schn' - ' Robbie' - ' Fitness' - ' конÑĦ' - ' penguin' - моÑĤÑĢÑı - ' миним' - plays - ' delegates' - Mer - ' sistem' - ' Michaels' - male - اع - ' cách' - ' Hä' - ' ×Ļ×ķ×ĵ×¢' - ' superpower' - ' stron' - ' rover' - ' dépend' - éĻ³ - ' retiring' - ' vampires' - ' merde' - ' Changing' - ' tame' - ' spokesperson' - ' cay' - ' flirting' - ' Grö' - ' wär' - ' wyb' - ' coeur' - ạnh - ' ìĻĢìĦľ' - ' connais' - ' Hundreds' - ' Bea' - ' αÏĢ' - pruch - ' sociedade' - ' Whilst' - ' Kait' - espace - ' chia' - ' Erm' - ' ë°Ķê¿' - ' fences' - ' Mortal' - ê²ģ - ' гÑĢаÑĦ' - ' Homeland' - ' JUN' - isst - ' parlar' - ' sporty' - éo - ' deepen' - ' Behavior' - éĢı - åĵĪåĵĪåĵĪ - ' errand' - ' rotary' - ' Wellington' - Wind - ' mesela' - ảng - iende - ' excell' - ' Genius' - ' Eduardo' - æľī人 - ' ÅŁunu' - ' Ä°stanbul' - ' produto' - ' ãħİãħİ' - 'OFF' - ' wollt' - çĪĨ - ' ëī´ìĬ¤' - ' lass' - ' hertz' - ' aromatic' - ' звон' - ' autoc' - ' Lust' - ' 112' - ' ÎĹ' - ' reviewers' - ' receptive' - å°įäºĨ - ând - oglo - ' ìķĦëĭĻ' - ' ngo' - ÑĸÑĤи - Ã¥t - cono - ' tekrar' - ' ì£¼ê³ł' - ' gelmiÅŁ' - ' bedtime' - ' Argh' - ADA - ' гоÑĢода' - ' Äĩ' - ' alliances' - giggling - ' yerde' - ' spies' - ' gutes' - çi - ' alltid' - ' Lah' - ŀIJë - ' dokÅĤad' - ÙĪÙĬ - ' toxicity' - ' cancellation' - ' 1958' - dro - ' ìŀijìĿĢ' - ' Motorola' - ' multin' - ' enthusiasts' - ' Mighty' - ' Coconut' - :ãĢĮ - ' Pictures' - ' sangre' - ' blinking' - olesome - ' ìĬ¤íĥĢìĿ¼' - FP - ' booming' - ' деÑģÑıÑĤ' - ' ratchet' - ' timelines' - leness - ' cages' - ' Goodnight' - ometimes - ' cunning' - ' Risk' - uled - dade - ' prata' - ' gustarÃŃa' - amus - ' Jinping' - ' estrut' - ' descobrir' - ' MÄģ' - ' Allan' - ' åĪĨ' - ' ׾ק' - ' preserv' - ' Strawberry' - Äı - Lu - ' kro' - ' Reports' - ìħĶìķ¼ - ' valt' - ' pouvait' - ' appar' - ' Bone' - ' preferably' - ' República' - å°±åĪ° - ' herzlich' - ' chimney' - ' çev' - ' visas' - ' verr' - ' cultivation' - ' Armenia' - ' вдÑĢÑĥг' - ' cockro' - retched - artz - ' лÑİдÑıм' - ' polÃŃticas' - ' Panz' - ' AKA' - ' ëĪĮ룬' - ' erro' - ' camper' - ' 102' - स - done - ' hoard' - ' ÐŁÐ¾ÑĤом' - jeong - ' desta' - pak - ' inim' - ' growers' - ' Message' - ' elector' - engage - ' Forbes' - ' Cincinnati' - ' différence' - df - ' spar' - ' awaits' - ' USSR' - ' Rising' - ' HoÅŁ' - ' footing' - ' condiciones' - ÑĤоÑĢов - ' clinician' - ' Diskuss' - å£ĵ - ר×Ĵ - ×¥ - iteit - gren - ' charisma' - ' leuke' - ' irritating' - ' circa' - ' Rhodes' - ' pior' - ' handicap' - royable - ' vull' - OG - ' inÃŃcio' - ieri - ' splashing' - ' demise' - ' assistir' - ÑĩÑĤо - ' covert' - ' Gud' - à¸ī - klär - ' ìŀIJ꾸' - ' verändert' - ' REM' - ' Conven' - atge - ' pierwsze' - ' clergy' - lington - liv - VPN - ' Ñģожал' - ' Hate' - ãģ¨ãģĵãĤį - ÏĨο - ' Respons' - озд - ' etmek' - ' chemin' - ÙħØ© - ' ê°Ģ족' - Tre - ' umas' - ' Burton' - ' patriarch' - ' Smithsonian' - ¥ĺ - Moon - Air - ' medios' - ' eraser' - ' wollten' - ' pareil' - ' Billie' - æĬ½ - еÑĢÑĤв - ' parlament' - ' agony' - ' QUE' - sequently - Another - ' Whew' - ' Annual' - ' seben' - ìĥģìĿĦ - values - ŀľë§Į - ' sinon' - ereal - ' Enlight' - ' Chemistry' - ' Catalunya' - ' doctr' - anton - ' stuk' - ' Plate' - ' Kardashian' - ' filos' - ' Wet' - ' попÑĭÑĤ' - ' unknowns' - ' Schon' - ' Baldwin' - ' telescopes' - ' Gucci' - oxide - ' Conservative' - ìĦ±ìĿĦ - ' hinaus' - Power - ' ê±´ê°ķ' - ' prevail' - orman - machine - ' 1946' - ' unbel' - ' schaut' - ' piel' - eenth - ' objectively' - ' chakra' - audio - ' chicos' - ' Vault' - å°Ī - ' medicinal' - ' Tail' - While - ' asphalt' - ' froze' - ' EK' - unching - nosis - '2015' - ' Gri' - ' oddly' - ' Mär' - ' Aeg' - colo - Par - ' ëĵ¤ìĸ´ë' - ' vinden' - ' OVER' - ' iced' - ' scorp' - ' hac' - qualified - ' ÑĥвидеÑĤÑĮ' - ermo - HEN - ' soi' - ' multiples' - ' layouts' - ' blindness' - ' Bowser' - ' подÑĤ' - ' Ãİ' - ventional - ' mata' - madı - ' geez' - ' cadence' - ' ważne' - ' Christie' - venge - Call - ' turnaround' - ' blob' - ' Як' - ' Voiceover' - ' peril' - ' Jaime' - ' HOY' - lane - ' sebel' - ' Duo' - ' Historical' - ' dni' - ' gema' - yk - ' sabem' - ắng - ' vars' - ' Ronnie' - ' Ronaldo' - ' Perquè' - nsinn - hair - ' relentless' - ' lyn' - ' traveler' - æĢİ麼äºĨ - nine - ' antim' - ' ì¼Ģ' - ' snowball' - ' ÑħаÑĢакÑĤеÑĢ' - ' interns' - ' constituency' - ' ÐĿам' - ׾׾ - VEL - ' viktigt' - ' apoyo' - ÙĦب - ' jard' - ' heightened' - ÑĢоÑģÑĤ - ' SMITH' - ' дела' - ' repairing' - ' rigt' - ' Sheikh' - ' Britney' - ' everytime' - ' adventurous' - ockey - ernt - ' ataque' - ' Alternatively' - effect - ' palavras' - ' Elliott' - ' réussi' - ' hypertension' - ' Manual' - ' prophetic' - ' handc' - ÑĮе - ' refrain' - ' Squid' - ìŀ¡ - ' коман' - ällen - ' llegó' - ' bash' - iony - ' Ñģклад' - ' каб' - ' careless' - ' Pool' - ' trás' - ' fils' - ' Schr' - ' sprawd' - ' Monaten' - ' unforgettable' - ' Cotton' - ' inconvenient' - ' RX' - oris - ' humbled' - ת×Ĺ - ' آپ' - ' increÃŃ' - ' Kommentare' - èĪĴ - ración - ' vantage' - ' Seal' - ' ìĿ´ê±°ë¥¼' - ' joue' - ãģĿãģĨãģ§ãģĻãģŃ - ' ìĺ¤ëŀĺ' - ' иÑģпÑĭÑĤ' - oben - ' grate' - ' controle' - ' Percy' - ÅĤada - ' simultaneous' - ' prototy' - ' groÃŁer' - ' bewusst' - inizi - ' passieren' - ' Happiness' - åīĩ - shi - geht - ' stationed' - ' Ergebnis' - ' directamente' - ' survives' - ' persones' - BERG - ' vomiting' - ' conhecer' - ' adjour' - ' Civic' - pei - burst - ' ëĭ¤ëĭĪ' - éı - ' sled' - ' plataforma' - ' Sect' - ' Defin' - çĻ»éĮ² - énom - chnet - ' profitability' - ' erreicht' - á»ıi - cation - ' ì§Ģê¸' - ' perdre' - ' felony' - ' 1957' - æĪijå¾Ī - ' unsuccessful' - ' nagyon' - ' elasticity' - ' facade' - ' earthly' - ' амеÑĢикан' - ' conn' - cla - Du - ' politiques' - ' halo' - iantes - ' моей' - ãĥ³ãĥī - tones - elier - è®ļ - htaking - ' wichtige' - ' anno' - ' Lok' - illions - ' viver' - ' solchen' - ' suf' - ' Salz' - ' Nvidia' - zuge - ' Spike' - Video - ' twor' - ' Ala' - èijī - ' hanya' - ' Adm' - ìĿµ - ' Patienten' - ' Onion' - ' Kobe' - ' Scene' - ' Rash' - æ¨Ļ - ÑĢаÑģÑĤ - istani - General - leye - imbap - ' concealed' - ' Fridays' - ' Wool' - ' новÑĭÑħ' - شر - ' ê²°ê³¼' - ' jedoch' - ´ìĭľ - ĵ¤ëıĦ - ' ìŀ¥ëĤľ' - ukt - Lou - ' 먹ìĸ´' - ' Expect' - ' домой' - ' irresponsible' - ' acerca' - ' Zust' - ר×ĺ - UI - ' youtubers' - ' Positive' - ' socioe' - ' snatch' - èĥĮ - ' refreshed' - ' nominations' - ' Patt' - ' obsolete' - ' demiÅŁ' - åı¤ - ormuÅŁ - ' ìĨĶì§ģíŀĪ' - ' fla' - ' craziest' - ' Zie' - ' Tú' - zep - icem - ' ë©ĭìŀĪ' - ' cynical' - ãģĿãĤĵãģª - ' tresp' - ' craz' - Õ¥Õ - ' nelle' - ' mph' - ' Nered' - ' Kob' - ' Eck' - ¨¸ëĭĪ - Jan - ' Тогда' - ' deci' - ' Vog' - ' bubbling' - éĢĢ - úa - ' productos' - iberal - ' replicated' - ' Improve' - illary - Cha - ' rédu' - ĥIJíķĺë©´ - ' connot' - ' Krit' - ' дÑĥÑħов' - ' treadmill' - ' PW' - ' зовÑĥÑĤ' - ' clams' - ' drafting' - ' 1956' - unta - ' expenditures' - ' Hoover' - WOO - ÑĪее - ' deduction' - monary - ' recib' - ' povo' - ' ëįĶë' - ' PAL' - ' Blow' - ' wyp' - ' destac' - deal - Graeme - ' nécessaire' - ' damned' - ' 1938' - ' ìĭ¤ìłľë¡ľ' - ' troop' - ' insightful' - ' TJ' - ' оÑģв' - ' fidelity' - ' Skip' - ' Mayo' - ë§Ŀ - appe - ' blas' - ' WY' - ' GN' - ctar - Su - ' cuent' - hews - ' corpses' - Abs - ' wastewater' - ' ciek' - ' Onu' - ' explosives' - ' arma' - ' STEPHAN' - politik - ' Osaka' - taÅĤ - ' yapıyor' - ' izquier' - ' beleza' - ' Wyatt' - åIJ¸ - ' suk' - ' specjal' - ' danke' - whistle - ' fÃŃsica' - ' Harriet' - ' ìķĦíĮĮ' - ' willkommen' - iping - ' ÑģмоÑĤÑĢиÑĤе' - ' можеÑĪÑĮ' - ' inaccurate' - ' arrogance' - ' Remo' - γά - assed - ' deliveries' - ' stinky' - ' пеÑĢеж' - jay - ' transitional' - ' rere' - ' NGOs' - ' ATM' - خت - iology - ' влад' - ' schme' - ' Shine' - ìķ¡ - pants - ' serge' - ' senhor' - ' abduct' - ' Bryant' - VES - ' awakened' - ' Laz' - ropolis - ' Lao' - è¾Ľèĭ¦ - ' villa' - ' summers' - ' enthal' - ' 1949' - Via - ' ìĸ´ì¨' - ' tendon' - ' violet' - ' intellectually' - ' bounced' - araus - ' 1919' - ' vraag' - ' spel' - ' Schwar' - Scott - ' Indo' - ' ë§Ŀ' - ' canonical' - ' IKE' - ' thatÃŃs' - ' mellan' - æ¯Ĵ - igmat - Could - '...?)' - ' foarte' - ' Kumar' - rendo - ' élé' - à´ - valuation - cases - ' intuitively' - hong - etted - ' souven' - ' morb' - ' cors' - ' NV' - ' Hasan' - æĥħåĨµ - ieved - ' ì§Ģê¸ĪìĿĢ' - ' dumpling' - ' contrôle' - ' ambiguity' - æ©Łæľĥ - ' cog' - ' Scriptures' - ' cai' - ' bever' - 大家éĥ½ - ' huis' - ' aime' - ' erklären' - ' LM' - ' Fey' - éļ¾ - றத - ' supervised' - ' jewe' - spl - ' ÑĨенÑĤÑĢ' - ' collisions' - ÙĦÙģ - ' Hogwarts' - ' Durham' - ×ķ×£ - ' phosphate' - ' oversee' - ' inspections' - ' brinc' - ' Zak' - ' payoff' - ' chaud' - ' Hunger' - ãos - vir - ' fiance' - ' boug' - lived - cry - åĽŀä¾Ĩ - ' jointly' - ' girlfriends' - ' Nexus' - ¦¬ê²łìĬµëĭĪëĭ¤ - ' Kwang' - åĵĪåĽī - å§ij - ÅĤÄĻ - ' Neden' - iece - ' inserting' - æŁĵ - ' Mummy' - ' Globe' - ' lee' - ' german' - ' creams' - acho - ' chÆ°a' - ' Galile' - ' fürs' - ' estiver' - cidos - Christian - ' lorsqu' - ' cutest' - vale - ' кÑĢеп' - ' wary' - ' slicing' - ' esperando' - ' Vander' - ' Deixa' - ' 1954' - ' mówiÄħ' - ÑĸÑĶ - ' tooling' - ' restor' - ' posición' - ' intentar' - ' Apache' - OUL - ' ÙĪب' - ' matière' - ãĥ¼ãĤĵ - ' linen' - ' estratég' - ' Mutta' - 顯 - è¡ĮäºĨ - ' parting' - ' minimizing' - ' apprendre' - æľĿ - ' англий' - ' Doo' - ' Firefox' - cómo - ' geopolit' - ' makan' - ' mogelijk' - ' ÏĢεÏģι' - ' cứ' - ' installer' - ' dibuj' - ' Heath' - loop - ' Broken' - HYUN - shelf - ' fizer' - ' enhances' - ä¾ĭãģĪãģ° - ' доÑģÑĤи' - ' PUB' - ' Kollegin' - ' attained' - ľ - ' mistress' - ' Oftentimes' - ×ŀ×Ļ×Ŀ - ' bewe' - ' Sora' - rauen - baum - ' rollers' - ' mering' - ' PAC' - ' нÑĸ' - ' République' - ' ÑĤÑĢав' - ' Vanguard' - uciones - ' 무ëĮĢ' - ' gour' - ¯¤ - ' Ïī' - ' sauna' - ' peine' - ' Valerie' - ' Sikh' - fendimiz - bero - ' Ñĩи' - ' doÅĽwiad' - ' Euros' - ' commentaires' - ' tweaks' - ' Faster' - ' ÑĢаÑģк' - ' progressively' - ' Euch' - boro - ' Ingred' - Cap - ' uncheck' - ' ìĺ¤ë¥¸' - ' wre' - ' FT' - örung - ' memorized' - ' Dinner' - ' Phew' - oubl - ' puta' - ' admits' - езде - opod - ' panda' - ' hinges' - cipe - ' transact' - ' podia' - ' pics' - ' criterion' - ' Orchestra' - ' Blog' - ' solemn' - ' Pixar' - Three - ' вниз' - ' Volunte' - ' Savage' - ' PVC' - ' Caf' - ' wykon' - ' graders' - ' crouch' - ' cliche' - ' soybeans' - ' MUR' - ' Gonzalez' - ' Mimi' - ' Bolsonaro' - ' diaphrag' - ' bilang' - ëIJĺëĬĶ - éĤ£æĪijåĢij - ' regulating' - Mc - Judge - ' нож' - ' jakÄħ' - itesse - ' Wij' - ' lata' - groaning - POSING - ' ×IJ×ķת×ķ' - ' haga' - ' grounding' - ' violently' - ' tills' - ' engag' - ' Hollow' - ' попÑĥлÑıÑĢ' - ' wprowad' - ' replaces' - ' fluorescent' - urgical - iggly - ' Traditional' - tte - ' ÙĦÙĩ' - ' phosphorus' - ' apron' - ' Waters' - ' Kultur' - авай - ' olives' - ' ×Ķ×IJ׾' - ' teilweise' - ' sencill' - ' prends' - ' narrower' - ' jätte' - ' Informationen' - ìĥģìĿ´ - ' starve' - ' frick' - ' Beweg' - ल - ' dolphin' - ' LAUGHTER' - ' INTERVIE' - åĶī - ' yanlÄ±ÅŁ' - ' torpedo' - ' shortages' - ìĿ´ëĵľ - ıldı - ' paws' - ' ozone' - ' cultivated' - ' Fot' - ' notor' - ноз - ' коÑĪ' - ' touchscreen' - ' Ally' - æľĢè¿ij - ' 맼ìŀĪìĸ´ìļĶ' - ' СеÑĢ' - ' вполне' - ' paprika' - ' Dustin' - ' efecto' - ' opini' - ' muut' - ' há»įc' - ' interject' - ÄĻt - ' butts' - urez - ' Pike' - ' Hok' - ' Guinea' - ' Cathedral' - ' 1400' - Cra - +, - 맼 - ³´ëıĦë¡Ŀ - abyrin - ' videog' - ' оÑĢÑĥж' - ' už' - ' buscando' - ' Assistance' - éĻ½ - ' melhores' - ì¡´ - ' ëģ¼' - ' RJ' - ' تÙħ' - ' omin' - ' motorcycles' - ' Sapp' - ' supplying' - ' Algun' - ' aerospace' - ×¢×ľ - occup - leist - ' ê±°ëĬĶ' - ' completa' - bres - '!(' - ' ÐŁÑĢед' - ' disadvantaged' - ' Attend' - ' Judah' - á»ĭch - ylene - actly - ' setups' - ' ammonia' - ' Schweiz' - ' Shame' - ' bande' - ' Fuel' - ' troublesome' - ' numero' - ' MOM' - ' пÑĢедлаг' - mentioned - ' болÑĮÑĪое' - ' Viktor' - ' Styles' - ' crucified' - ructured - environ - ' morals' - ' meditating' - ' axial' - isance - ' Abst' - Green - ' ê±´ì' - ' quadrant' - ' pergi' - ' cameraman' - ' Sequ' - ' paused' - ' Laughing' - ê·Ģ - ?.. - ' Å»e' - ' permitir' - ' detectors' - ' HUD' - aval - ' ìĹ¬ê¸°ê¹Įì§Ģ' - ' hubs' - ' bestimmt' - ' бÑĥдеÑĤе' - INTERPOSING - ' tengan' - ' crave' - ' Bundesregierung' - ' Bloody' - ' usability' - ' Eas' - ' Äijá»Ļng' - ' 1955' - ' kriegen' - ' habitual' - ' essentials' - riminal - ' roommates' - éĤ£å°± - ' пеÑĢеÑħод' - ' nghi' - ' mening' - ' Symphony' - ' Hug' - aggi - ' wied' - ' mitad' - ãģ£ãģ¦ãģĦãģĨ - teenth - idaÄĩ - Save - ' robiÄĩ' - ' bounces' - °ĸìĹIJ - stars - ' pragmatic' - ' cognition' - ' wrapper' - ' warten' - adh - ' pensa' - ' Hertz' - ' nÄĽ' - ' Reid' - ' PCs' - ' Mole' - ' .....' - ' precio' - ' Championships' - ê°ĢëĿ½ - ' vér' - ' corridors' - ' Electronic' - Sl - ' але' - ' overthrow' - ' kabul' - ' RES' - ' Cyberpunk' - огод - ' ÐĿав' - ' wan' - ' manifestations' - ' cuales' - ' Wise' - ' Lösung' - ' exfol' - ' earns' - ÑĥÑģÑĤиÑĤÑĮ - ' sapp' - ' Braun' - ' BRANDON' - ì¹Ļ - ' sano' - ' FEL' - ÑĭвайÑĤеÑģÑĮ - ождениÑı - ' sewn' - Fun - ' reciprocal' - ' expansive' - ' Traffic' - ' którego' - ' ÙĪس' - æĺ¥ - ' 빨' - prove - igare - ' loh' - اض - Hope - ' devotees' - ' Gom' - ' steals' - ' Ums' - ' Twice' - ãĤ² - iyim - ' rhythmic' - ' Vorte' - ' prefix' - omination - ' dato' - ' custard' - ' VOICE' - å·ŀ - ' meny' - istors - ' íĺij' - ' ìĤ´ìķĦ' - ' íĥĦ' - ' kort' - ' aba' - ' Vera' - epy - ' ì¹´ë©ĶëĿ¼' - ' submerged' - ' Clock' - ' thumbnails' - ' boast' - ' Fare' - '!!]' - ' ÅĽm' - ' kaikki' - ' Technologies' - ìĻ¸ - ãĥĴ - иÑĤай - å°ıæĻĤ - ' аÑĤ' - ' knobs' - ' reicht' - ượng - glio - ' 맼ìĿ´' - ê°IJìĿĦ - ' jotka' - ' Handy' - ' Haben' - nous - ' inland' - ' amazon' - hooting - SL - ' leisten' - ~" - ' provoke' - ' Twist' - ' ×ij×Ĺ' - ' departed' - ê°ľë¥¼ - ' konse' - ' Carwyn' - íķĺìĭł - idental - ESCO - ' tteokbokki' - ' dizendo' - ç·´ - ındaki - imasu - afar - ' landfill' - ' correcting' - ' clears' - ' Nummer' - HAM - ' cartridges' - ' Diesel' - paced - ' obliv' - ' moyens' - ' Sinne' - ' Preis' - iliz - ' Ñģмож' - ' broaden' - ä»ĸæĺ¯ - xes - ' carbohydrate' - íĺ¹ - seok - ' echoes' - ' cess' - ë°Ķ - ' бизнеÑģ' - ' llamado' - ' essent' - ' ìĿ¼ë°ĺ' - ' Aires' - phen - ' zebra' - ' symbolism' - Once - ' racks' - ' Kafka' - ' ÑģеÑĢÑĮез' - ' sinn' - picious - kaa - ' motherfucker' - ' apprenticeship' - ' rpm' - ' taxation' - ' furry' - ' Sacred' - ' ÑĢазм' - pora - enges - ' íĹĪë' - ' Ñģин' - ' sanitizer' - ' cringe' - ' Sca' - оÑĩно - ' ofere' - ' melodies' - ' Velvet' - ' Ihrer' - ' Hybrid' - ' Giov' - ' irgendwas' - ' depende' - ' Users' - ' hump' - driving - ' sf' - ' ruthless' - à¹Ģà¸Ħ - ' lemons' - ' föret' - ' Oj' - ' мама' - ' interpersonal' - ' gev' - ' abnorm' - иÑģл - ' инд' - ' kontroll' - ' regres' - ' ledge' - ' erzählt' - ' Tact' - ' arrivé' - ' substantive' - ' spoonful' - zwischen - ooooo - ' contenido' - ' besl' - á»ĥm - kten - Jamie - ' sandy' - ä¸įåIJĮ - âĭ - ' pase' - ' dette' - ' Belgian' - ê°ľë - ulares - rud - igor - ' íĮ¬ë' - ' remedies' - ' blasting' - ' Sich' - ' ожид' - ' monstr' - ' manifold' - ' glauben' - ' EST' - ' streamline' - ' lobbying' - ' Gothic' - toire - ..' - ' démocr' - ' наблÑİд' - ' wspól' - ' czÄĻÅĽÄĩ' - ä¸ĭéĿ¢ - isés - gangen - ' bezpie' - remlin - ê°Ŀ - Still - ' resides' - ' gelecek' - ' téléphone' - ' pewn' - ' leopard' - ' complimentary' - ' crib' - ' Animals' - ' geil' - essel - ' garder' - ' catchy' - 樹 - ' Ets' - ' Commercial' - ' DENNIS' - ' Coordinator' - ' Abigail' - ffffff - ấp - ' pequeña' - ' injections' - cekt - ' philanthropy' - ' puck' - ' celebrates' - ' Dunk' - ' Dlatego' - ãģ¾ãģł - δή - graduate - ' Mobil' - till - acam - ' yolks' - ' tangled' - ' maniac' - ' obliged' - ' Laink' - ' verder' - ' Damon' - ' mutant' - ' hopping' - ' reins' - ' inverter' - ' contempt' - ×ł×¡ - learning - Miss - ' ÐĵоÑģ' - ' Meyer' - ê»ĺìĦľ - é£İ - ×ķ׳×Ļ×Ŀ - asking - ' trimming' - ' treasury' - ' sente' - Aust - ' Unterstützung' - ' Comedy' - ' Anakin' - é¹ - ÑĢÑĥÑĤ - ' Hari' - ographers - ' oatmeal' - ' Bots' - ä¸įäºĨ - ' палÑĮ' - ' acknowledgement' - xic - ' ê´Ģìĭ¬' - gasping - ' ãģķ' - ' terrace' - ' ornaments' - ' MER' - committee - ' ìĹĨìĬµëĭĪëĭ¤' - ' rij' - é³ - צ×Ŀ - leme - ' liberties' - ' fellas' - ' Copper' - bench - ' Idea' - á»įn - ÑĪа - ' versión' - ÏĦοÏį - ' ÐľÐ¸' - ' пÑĢилож' - ' boxer' - ' Tanner' - ' Moy' - ì¹ĺëĬĶ - Thr - ' tinham' - ' polishing' - ' consequently' - ' amenities' - ' KI' - ' GREEN' - ' Frankie' - ниÑĤ - ittel - Ñģкое - ursed - ' upbringing' - ' thứ' - ' ìĭĿìľ¼ë¡ľ' - ' whim' - ' chinese' - confidence - ' Jeder' - ãģªãģ®ãģ§ - ajcie - ' Tous' - ' Powers' - ừa - othermal - ' вÑĭÑĪе' - rale - اخ - ' ì§ĢìĽIJ' - ' épisode' - ' sulph' - ' encara' - kraft - aları - ' Comes' - ' divul' - ' Rudolph' - ' Muse' - ' utens' - ' ìŀIJ주' - ' pana' - ' Vegeta' - ' PHP' - ' NSA' - entin - ' Carnegie' - اÙĬ - iÄĻcy - Harry - ' fır' - Сп - ' gladly' - ' averaging' - íķĺê²łìĬµëĭĪëĭ¤ - лÑıÑİÑĤÑģÑı - ' ÐľÐµÐ½Ñı' - ' quotation' - rires - itchens - ayed - ' unatt' - ' Perez' - ' оÑĤмеÑĤ' - ' tactile' - ' Euh' - isini - buh - ' hatır' - ' ìŀĪìľ¼' - ' policymakers' - ³´ìĦ¸ìļĶ - acı - ' κι' - ' registering' - reto - ' Sprinkle' - ' Grammy' - axter - ' би' - ' sitter' - ' predic' - ' thinly' - ' strum' - ' aggrav' - ' aha' - رج - mellow - ' constante' - ' Laut' - iston - ' transitioned' - ' Cambodia' - ãģĦãģįãģ¾ãģĻ - è·Łå¤§å®¶ - arted - ' misf' - ' Punkte' - Įëĵł - ' trembling' - ' gespannt' - ' عÙĦÙĬÙĩ' - ' никакиÑħ' - ' ë¶Ģëĵľë' - ' ÑĢазвиÑĤ' - ' itchy' - ' ciento' - ' plains' - ' kittens' - ' backlog' - ' Presiding' - pta - ' havoc' - ' Darrin' - ' ÐĽÑİб' - ' segregated' - ' ghetto' - ' erlebt' - ' drugiej' - ' Sixt' - åıĥ - ระ - uencia - ' íķĺ기' - ' ëĨį' - ' robi' - ' pioneers' - ' milliards' - ' Witcher' - ' 무ìĹĩ' - orro - mass - ' divergence' - ' Rivera' - ' Noodles' - ' endroit' - ' Kosten' - ' дÑĢÑĥга' - ' mÃŃnimo' - ' Kazakhstan' - تÙĩ - ' воздÑĥ' - ' geschrieben' - ' Nil' - Ñģки - ' Früh' - ' beverages' - æºIJ - ' Gon' - æĺ¨ - Arin - ' Intro' - ocalyptic - ' exhaustion' - ' Status' - ' Battery' - ész - £¼ë - airy - ' ë³´ìŬëĵľë' - ' disparity' - ÙĮ - ' Tucson' - ' brightly' - problem - ' biomass' - éĻį - §ī - ' hurdle' - ' wavelengths' - ' <<' - ' teamed' - FFFF - ' Slim' - omial - ' unveiled' - ' Verein' - ÙĤØ· - estry - ' clás' - ' cheddar' - ' accusing' - ' Scientific' - ' бÑĥде' - ' Cyrus' - εÏĦε - Ĩĵê³ł - ' ë³Ħ' - ' curd' - ' referrals' - shift - åįķ - ników - ' mier' - ' confronting' - ê²ĥëıĦ - awl - ' tryin' - ' ê·¸ëŀĺìļĶ' - ' chiar' - ' ìĺ¤ëĬĺëıĦ' - æĶ¿æ²» - esque - ' mismos' - ' Shak' - ' sociaux' - ' piÅŁ' - ' kiÅŁi' - ' cyan' - hay - bew - bod - ' ι' - ' Mainly' - ÑİÑĤÑĮ - habitude - ' Ñģпокой' - è·ŁæĪij - ' precon' - ' Mandy' - ðŁ¤£ - illos - ' grupp' - ' crumble' - ' constructor' - ervices - ' lighthouse' - ' Concept' - анÑĤи - altro - hope - ' Alleg' - ìĸ´ë¥¼ - pieces - ounter - ' íķĺëĭĪê¹Į' - ' ìĿ¸íĦ°ë' - ' véritable' - ' threaded' - blind - ĤĺëĿ¼ - ' trays' - ' Edison' - ' Ãĸz' - ' Stevie' - ' lender' - ' brigade' - ' deutsche' - muffled - bart - ' insanity' - ' savvy' - ' sensational' - ' derechos' - ' MX' - ' пÑĢеп' - ' threatens' - ' realtÃł' - ' indicative' - ' chops' - ' benefiting' - ' Vernon' - ' Strand' - nun - quently - '101' - ' eel' - ìĪĻ - rints - ' Ùħس' - ' بد' - ' поÑģÑĤÑĢо' - ' yapmÄ±ÅŁ' - ' olması' - ' iedereen' - olé - kef - ' ë°ľìĥĿ' - ' rained' - ' almighty' - ' вÑĭд' - ' CPR' - Fre - ' inhabited' - ' arbets' - ' akin' - аÑģÑĤв - vania - ' häufig' - ' Matte' - sorry - Jenny - ' гÑĢад' - ' whit' - ' brokers' - å¯Ł - ' hine' - asten - ' гÑĢÑĥ' - MB - ' PRI' - Sab - ' wrestler' - ' facilitating' - ' ehkä' - ' Cred' - ' 127' - ' nothin' - ' mandated' - å¯Į - ÑĥÑĤÑģÑĤв - Frank - ' wors' - ' dzieÅĦ' - ' Underground' - ' znajdu' - ' Bä' - ' Prinzip' - аÑĤелей - ' veterinar' - ' splendid' - ' rozp' - ' psychopath' - igon - ' hops' - ' cần' - ' Xian' - ' troisième' - ' producto' - ' deÄŁer' - ' Continuing' - ивал - cık - ' moisturizer' - White - ' siis' - ' Everest' - ienced - ' cảm' - ' Japon' - ´ìłĦ - ' tenÃŃan' - ' encanta' - Mm - ' dropdown' - ' Iya' - ³´ë©´ - ' wording' - ' Squeeze' - ' Maple' - ' clarified' - ' Municip' - ' Rouge' - ' Nicki' - ' Goo' - volt - tek - fecture - fred - arrive - ãĥ¼ãģĦ - tez - Ep - ' obras' - ' VID' - ' Riv' - ' Modi' - ibe - ' acontecendo' - ' imitation' - ' camouflage' - ' spanning' - ' SECRET' - ' Oreo' - ìĨĮ리 - ' hunch' - ' caÅĤe' - ' spontaneously' - ' Perd' - ' etap' - ' Hole' - ' Disability' - ' afterlife' - æģ© - ' testified' - ' presup' - ' petroleum' - ' contrario' - ' Assessment' - ÄŁlu - ' pests' - ' dilig' - ' вÑģÑĤÑĢеÑĤ' - ' conséqu' - ' cannons' - ' canoe' - ' Mile' - ' citoy' - ' begged' - ' Minnie' - ÅĤych - ' principe' - ÏĢÏĮν - mniej - ' wert' - ' ëĭ¤ëĵ¤' - anse - ' uncles' - ' provocative' - ' intersections' - ' democrats' - ' Julius' - инки - ygusal - ' ׾×ķ' - ' gjorde' - ' gasket' - ' Bock' - ' Ä°n' - breat - ' Equity' - ardı - ' канале' - ' дней' - ' tỼi' - ' fixture' - ' abuses' - ' vaya' - ' ouvert' - ' multicultural' - ' contexto' - ' Sesame' - ' dépl' - ' consomm' - ' Parte' - ' pem' - ' Conan' - ' бÑĸлÑĮ' - ' persuaded' - ' drains' - Moo - FORE - ' баÑĤ' - ' fod' - ' Products' - ì§Ħì§ľ - ' "[' - ' Wick' - ' Naruto' - нали - ryw - ' lodge' - ' inh' - ' vontade' - ' dij' - ' Jesús' - Looking - ' forearm' - ' Integration' - ' HARRIS' - ' toolbar' - leader - ' seldom' - ' бÑĢоÑģ' - ' Kook' - онд - ' monopol' - ' millet' - ' lira' - ' Asians' - ' 1890' - ciÄŁim - ' eden' - ' IKEA' - ' Neighbor' - ' Kazuya' - üd - ' psychedel' - ' envisioned' - åĿĹ - ' ï·»' - ' wunder' - ' Bulgaria' - Brid - ' marrow' - ' depiction' - ' Tin' - ' Pharise' - ' einzige' - ' blindly' - ãģĽãģ¦ - ' defens' - Dire - ' vibrating' - ' trolls' - ' disrespectful' - ' wod' - ' stimuli' - ' creeping' - ' clairement' - ' scariest' - ' découvrir' - ' 104' - ' веÑĢÑħ' - ' ÅĤat' - ' różne' - ' barley' - ' Repl' - ' Twe' - kke - ' ãģĿãĤĮ' - ' Redmi' - ' Metroid' - ' ήÏĦαν' - Check - ' SEN' - ' ido' - ÑĤоÑĢии - óp - UNKNOWN - ' ändern' - ' Juice' - ' Gesicht' - å°±æľĥ - ' наÑģÑĤолÑĮко' - íĥķ - ÂŃ - exhales - ' ì´ī' - ' jsem' - ÏĢÏīÏĤ - ' itt' - ëªħìĿ´ - ' remix' - ' blossoms' - ' Renee' - isations - ìĬ¤íĦ° - ' ë³´ìĿ´ëĬĶ' - uestas - opedia - ' Aim' - ìĿ´ì¦Ī - scene - ' leakage' - uckt - Sad - Ask - ' suspense' - ' impost' - ' Strategic' - ' ItÃŃs' - âĢĮ - ' keyboards' - ' amusing' - ogr - iderman - ŀĸ - ' вижÑĥ' - ' dips' - ' apologized' - ' STAR' - ' escuela' - ' Ching' - нениÑı - ' ë¶Ģë¶ĦìĿ´' - ' Fleet' - ' samb' - ' entsprechend' - ' electrodes' - ' Freiheit' - æĪijä¸įçŁ¥éģĵ - ' Shrim' - iÃŁe - ' selections' - ' fordi' - ' doss' - ÑıÑĩ - ' discriminate' - ' AuÃŁerdem' - ' desenvolv' - ' Internal' - ' Benedict' - å¯Ĩ - ' Shiv' - Missy - ' обнаÑĢÑĥж' - ' наÑģÑĤÑĢо' - ' controlar' - ' Lia' - ' opioids' - antu - ' cupboard' - æģIJ - ге - achts - ' curated' - ' xem' - ' weary' - ' brethren' - ' budgeting' - ' pourtant' - éļ» - aisia - ' оÑĤвеÑĩ' - ' GIS' - μαι - ' ש×Ķ×ķ×IJ' - ' saud' - ' lỼ' - ÐķТ - ubine - ' нÑĥжен' - ' kidnapping' - ' brat' - ' Terre' - ' Monet' - ' ë§ĪìĬ¤íģ' - ' flashy' - ' ISBN' - ' freelance' - iage - ' junge' - 충 - ceral - ' ÑĤоÑĩки' - ' formulate' - ' FER' - ' Dartmouth' - ìľ¼ë©´ìĦľ - å¢ĥ - owiÄħ - ' ëĶĶìŀIJ' - ' regiment' - ' metabolismo' - ' Parr' - ' 충ë¶Ħ' - ' sanity' - ' Lal' - ' Gö' - ' Gla' - ' proto' - ' microscopic' - ' kang' - ' Scalia' - ' pug' - ' Score' - ' Savannah' - ' garde' - ' NOR' - å°įåIJ§ - ' scheint' - ' póÅĤ' - ' corri' - ' brute' - ' ÅĤad' - ä»ĸ们 - ' succeeding' - ' bicycles' - Non - ' seekers' - ' unconditional' - ' rhymes' - ' Garage' - ' invoice' - ' canvi' - neck - ' customizable' - iritual - Queen - íķĺìĭľëĬĶ - ' powerless' - ' csak' - ä¸įä¼ļ - isoft - ' ìłķíĻķ' - ' nhân' - ' MAND' - ' Haf' - ' revolves' - ä¹Łåı¯ä»¥ - ovan - aroo - ' Grind' - éĽª - ' indispensable' - ' consulted' - ' Clinical' - Acc - ' olhos' - ' monter' - ' Hana' - etah - ' vaan' - ' tigers' - ' caucus' - ðŁĺĤ - ³´ìŀIJ - powers - iums - ' íĨłë' - ' tradicional' - ' resonated' - ' ìĭłê¸°' - them - Robert - ' elemento' - ' antid' - ' обÑģ' - ' natives' - ' loca' - owment - ' Tight' - ' æĢĿ' - ' melan' - ' Nue' - amis - ' sorgen' - asına - Home - ' PUBG' - ' awfully' - ' Shore' - ' Perché' - ' Lau' - ' Cinderella' - ' Chest' - ' semantic' - ' deserted' - ' Momo' - ' Hernandez' - genes - ' Adult' - иÑĩеÑģкого - oshima - ' caracterÃŃsticas' - ' KL' - ´ìŀ¥ - ocar - ' fehlt' - ' druk' - ' Poppy' - ENGLISH - ' Vergleich' - Brien - ' recomp' - ' Ñģд' - ' merger' - ' marketers' - ' honeymoon' - ' penso' - ' belli' - еÑĤÑĥ - ' banker' - Camera - ' Stall' - ' Stamp' - ' Bite' - ежде - ' sür' - ' güç' - ' Passover' - ' Bugün' - ' ÑģожалениÑİ' - ' низ' - ' manure' - ' glacier' - è«ĩ - RAY - terror - ' salads' - ' hurricanes' - ' Designer' - atorio - ' factual' - ' Tammy' - ' звÑĥÑĩ' - ' introductions' - ' housekeeping' - ' hanger' - ëĭĺë - akte - ' Cola' - ''']' - ' Gender' - оÑĢон - ipse - icias - ' successive' - ' politic' - ' höher' - ' Qiao' - ' Gimme' - ' лож' - ' seb' - ' Weiter' - ' Sakura' - ' Boulder' - ' América' - peÅĤnie - ' tecnologÃŃa' - ishops - fur - ' moonlight' - ' dispersed' - ' rez' - енное - алÑĮнÑĥÑİ - ' Twelve' - ' HOR' - ìĭ¤íŀĪ - ilage - ' shaded' - ' resumes' - ' Peanut' - ' MILL' - apons - ' UFC' - ' Sole' - ' joystick' - ' Olivier' - warming - ' syllabus' - ' обÑīе' - ' hiá»ĩn' - ' festa' - ' cradle' - ' Zac' - ' remembrance' - ' ê°ĻìķĦìĦľ' - ' piÄĻk' - ' coexist' - ' VII' - ' áreas' - ' uważ' - ' observers' - ' människor' - coon - ' DAM' - ' naszym' - ' alligator' - ' Freeze' - ' Estate' - ' ÑĤÑĢади' - ' undercover' - ' nies' - ' Fehler' - plin - ' Kabul' - ilate - ' ê³łìĸij' - ' mop' - ìĦ¼ - ' anderer' - ' KELL' - оки - ' жеÑģÑĤ' - ' grazing' - ' daÃŃ' - ' capitalize' - ' apex' - ' nurturing' - ' cortar' - ' contrac' - ımızı - ' tandem' - éĥ½æľī - gement - ' ÑģиÑģÑĤема' - ' manque' - iajÄħ - WOR - ' اب' - ' carts' - ANO - ' ë°Ľê³ł' - ' Cena' - ' Biology' - idar - ' aż' - erne - anu - ' thanked' - ' submarines' - ' manic' - ' моз' - ä¼Ĭ - instant - essential - ' samurai' - ' pasti' - ' alan' - ' broch' - ' baker' - ' Guill' - ¨¼ - ' withdrawn' - ëĭĿ - Perfect - quency - ' streamlined' - ' 1300' - ´ëıĦ - ' ëĸłë' - ' ãģ¯ãģĦ' - ' hvad' - ä¸Ģå®ļè¦ģ - ' verbally' - ' Kons' - ' ì¡°ìĭ¬' - ' diez' - æİ°æİ° - ' chuckling' - ' Mih' - ' rallies' - ' manter' - ' earnest' - super - ' gece' - ' Rend' - ' Gerade' - jenigen - ' Vall' - ' ìŀĪëĤĺ' - ' Ñģказала' - ' trabalh' - ' наÑĪем' - ' меÑħ' - ikit - ' nouns' - ' neurological' - ' motivational' - ' McMahon' - ' Finished' - ' ë³´ìĿ´' - ' Fields' - ' adolescents' - ' Tisch' - ' Neben' - ' Flowers' - ' Energ' - ' diret' - ' Thi' - ' Picas' - æĥľ - æĢİä¹Īæł· - ' avete' - ' Fors' - ' Chapel' - Não - Et - ' ÑģодеÑĢж' - reno - ' sven' - ' dostÄĻp' - nee - ' Snapdragon' - ' IDs' - ìķĺëĬĶëį° - ר×ļ - ' sunflower' - ' perpetual' - ç³ĸ - ' knights' - ' gird' - ' Told' - ' volcanoes' - ' adversary' - ' Economy' - ' extrapol' - ' bluetooth' - ' zooming' - ' skys' - ' genial' - ÃŃculos - ambre - ' меÑĢ' - ' teeny' - ' stressing' - ìķĮ - ONY - ' translucent' - ' rounding' - ' grues' - ×Ļ׳×Ķ - après - ' prueba' - ' polygon' - ' blueberry' - ' Programm' - ' trenches' - ' sebagai' - ' palate' - ' laude' - ' behaved' - ' longitudinal' - ' Module' - ' admir' - λι - Greg - ' wyst' - ' propagate' - ' molds' - ' Tub' - ' Loud' - usto - ' unstoppable' - ' reinforcing' - éĿŀ常çļĦ - ' пÑĢоблема' - ' potencial' - ' hemp' - ìŀĶ - य - ' optic' - ' erfolgreich' - ÑģÑĭ - олÑĮÑĪе - urst - ' Pois' - ' respondents' - ' nehme' - ' External' - olate - Hyun - ' quartz' - ' mathematician' - ' básicamente' - ' ail' - ìłľë¥¼ - attutto - ' nooit' - ' afflict' - ' Olga' - èŃ· - ' наÑĤ' - ' dites' - ' realidade' - ' kän' - ' uniqueness' - ' padres' - ' subsidi' - ' pigeons' - βα - stad - ' deren' - ' След' - doo - ' опиÑģании' - ' amber' - ' goosebumps' - ' frÃ¥gor' - ' Vital' - ' Israelites' - wasser - Isn - ' commits' - ' STEVEN' - ' Bevölker' - uitive - ' legen' - ' bruk' - иÑĢован - ynen - helm - ' generational' - ' Ländern' - οιÏĢÏĮν - uzu - ' caller' - онÑĮ - ümü - ' besar' - ' plats' - ' migrated' - ' jap' - ' WAR' - ' dissect' - ' Zusch' - ' Zeiten' - ' Lions' - ' DF' - âĶ - кив - ' pedestrians' - ' Marilyn' - dock - ' yht' - ' reincarn' - ' Sono' - ' Growth' - ÑĥÑģов - ' dungeons' - ' bagus' - kich - ' ÑĥкÑĢаÑĹ' - éĨ« - ' Keller' - chemistry - Japanese - ' willst' - ' decomposition' - ' ÑģÑĤен' - ' revived' - íķĻêµIJ - ' Åĵ' - ä½IJ - ìĭ¸ - ippy - ' hourly' - jän - ' Workshop' - Ŀ¼ìĦľ - ' cuarto' - ' patrim' - ' Burch' - ' ìŀĪ기' - ' hepat' - ' hÃłng' - ' ëĮĢíķ´' - ' ваÑĪи' - ' rework' - ' parse' - ' çıktı' - ' Sax' - ' Mongo' - ' Aaah' - ramble - DJ - ' stabilized' - ' Speech' - Books - ' hurdles' - ' WO' - ' Lamborg' - ' 1933' - ' vorbere' - ' clinically' - ' breathtaking' - ' Gateway' - пеÑĢвÑĭÑħ - uters - ' ë¹µ' - ' yeter' - ' pulley' - ' muffin' - ' Prefer' - ' Pence' - ' informação' - ìĬ¤íĬ¸ë - ãĤ¸ãĥ£ - ' Turtle' - ' Regina' - ' Load' - does - panze - ¸Ķ - ' mina' - ' Latinos' - ammers - ' Tort' - ' Beyonce' - имоÑģÑĤи - ' вопÑĢоÑģÑĭ' - ' bulun' - èĢĮå·² - inek - bereich - ' pasture' - ' OA' - ' Melt' - ' Ett' - ' DY' - ' obwohl' - ' leagues' - ÑĤеÑģÑĮ - ' кÑĥÑģ' - ' vors' - ' topp' - ographical - asst - ' lindo' - ' ë°ĿíĺĶ' - ' réfl' - ' climbs' - ' varsa' - ' methyl' - ' Karere' - Æ°á»Ł - Rad - ' preparedness' - онÑĩ - ' OD' - ' CGI' - ' म' - ' speechless' - ' lasci' - ' bolag' - ' ÑħоÑĩеÑĤÑģÑı' - ' grieving' - ' Johannes' - ' Carroll' - adaki - Ī¬ë - ' sÅĤu' - ' innerhalb' - ' gymnastics' - пÑĢи - ifiques - ' karate' - ' domu' - ãģĿãĤĮãģ§ - OTHER - ' demandé' - ' booklet' - ' Kyoto' - ' woh' - ' MarÃŃa' - violent - JE - ' lóg' - ' brutally' - cot - ' ÙħÛĮ' - ' Warsz' - å®Ī - wol - ' mikä' - ' Pronounce' - ' Brendan' - ' roup' - ' italiano' - å¦ĤæѤ - ' компÑĮÑİÑĤ' - ' urging' - edes - ' carbono' - ' Richardson' - ' ÐĿаÑĩ' - ' Trainer' - ' Crimea' - ' diapers' - ' covet' - ' Mahar' - ' Hutch' - ' Ausw' - berty - ' indifferent' - кÑĢеÑĤ - uldade - ' harms' - ¢ÙĨ - lesia - ' gio' - ' Mistress' - ' Knox' - ' FREE' - ' 루ë' - ' наÑĪа' - ' invincible' - ' maiden' - ' Jeez' - ' breve' - pole - ' criticisms' - ' Rusia' - म - phin - ' Compare' - ' BON' - ' sneaking' - ' Rails' - ' Geral' - ' 1953' - Hola - ' опÑĭÑĤ' - ' rainforest' - ' belum' - ' Obi' - ' ISS' - ãĤĮãģªãģĦ - ' Св' - ' blond' - ' wzgl' - ' powiedziaÅĤ' - ' choking' - ' Songs' - ' Biraz' - ' yells' - ' stylist' - ÏĮÏĦε - ' schreiben' - ' Jaw' - ' Eleven' - ' Rif' - /. - ' ìĺ¤ëŀľë§Į' - ' treaties' - uffed - ' âĪĴ' - ' roofs' - à¹Ģส - ' ë»' - ' sparkle' - ' Kiev' - ' Argu' - erecht - ' ÐĿадо' - ' FIL' - ' molta' - ' Devi' - ' campe' - ' benevol' - ' Tough' - ' moim' - ' evacuate' - ' errado' - å©Ĩ - ÑĢÑĥго - ' íİĺ' - ' Îĵια' - ' weaken' - ' illuminated' - ' siglo' - ' Vacc' - ией - alis - ' ÑĥÑģÑĤÑĢой' - ' dona' - ÅĤos - üman - ' producción' - ' clot' - ' Mango' - ' uneasy' - ' shuts' - ' Examples' - vell - ebe - ' promptly' - ' Teles' - ' пÑĢоÑĪл' - ' puerta' - ' überzeug' - ' coch' - social - ' Benson' - ' Meth' - ' Exped' - ' supplemental' - ' conceive' - ' ×ĺ×ķ×ij' - ' captivity' - ıĻìķĪ - ' ÑħÑĥд' - forming - ' uploads' - ' turbulence' - joint - ' satisfactory' - ' Anime' - ' washes' - ' liberals' - ' Sunshine' - ' REAL' - ublik - binary - Tony - ' polarized' - ' enriched' - taking - ' ëģĿëĤĺ' - ' pleasures' - ' extermin' - inese - atl - vär - аÑĢÑĭ - ' myÅĽ' - narrator - ' одном' - ' najwiÄĻ' - ' mobilize' - ' millor' - ' ata' - æ·· - ' polÃŃtico' - ' plead' - ' painters' - ' Sow' - оÑĦ - ' ìĺĽëĤł' - ' ÑĩÑĤоб' - ' sabor' - ' Undert' - ' JERRY' - Å¡ÃŃ - ' ë°ĸìĹIJ' - ' précéd' - ' annotation' - ' Inaudible' - ' textured' - ' fisherman' - vordan - icherung - ' ìłģìĿ´' - ' gezeigt' - ' mandates' - ' beak' - ' TWO' - ' Akbar' - ilian - ' tiếp' - ' superiority' - inku - ' lys' - ' FCC' - ' CPA' - ustering - nicos - anja - ' chills' - ' Cage' - ' sealing' - ' saç' - ' dedans' - ' Alger' - ' spezie' - ' coloss' - ıyı - clockwise - ' exactamente' - ' iemand' - amı - ' mandar' - raj - faced - agua - ' ê¹Ķë' - ' insbesondere' - ' drizzle' - ' diminish' - ' Yoda' - AI - ' bilmiyorum' - ' MMA' - ategory - ' пеÑĢеп' - ' participar' - ' normalized' - ' complexities' - æ´² - æݧ - аÑĢов - mist - icha - Group - ' resiliency' - ' nogle' - ' CNC' - prü - ' physicists' - нок - LI - ' stuffs' - ' sistemas' - ' interfering' - ' Marvin' - ército - ' ìĹĨê³ł' - ' sonic' - ' equiv' - ' abord' - ' Ramen' - ' 09' - medim - atiques - ' делаÑİÑĤ' - ' unanimously' - ' skirts' - ' íĬ¹ë³Ħ' - ' Prix' - kami - ' fruition' - ' birthdays' - иком - ' inaugural' - ' correlate' - ' Tory' - ' ëĤĺìģ' - ' dew' - ' Precis' - ihi - ' ë¬¸ìłľê°Ģ' - ' citing' - ' Lana' - ' Kag' - ' playthrough' - ' Protocol' - frist - hovah - ' merciful' - ' bilingual' - ' Guitar' - rh - ' glamorous' - ' Vikings' - ' Ooooh' - íķĺëĬĶëį° - ' Uganda' - ' collapses' - entry - ' antioxidants' - ëĤĺë - ÑĪаÑı - ' trivia' - ' gäller' - ' fungi' - ' milks' - ' dicht' - μη - poke - ' вÑĭпÑĥÑģк' - ' feeder' - ' Alcohol' - hower - ' deserving' - ' Rebel' - iosis - ' 103' - ' handout' - ' enm' - ' landlords' - ' geology' - rils - ' cobra' - ' Vold' - ' Panch' - ' GREG' - ' pross' - ' bracelets' - ' Vega' - ' rozum' - 款 - азд - ' Lynd' - ' Honors' - ' surrendered' - ' librarians' - '125' - ' Ñģиг' - ' uniformly' - ' Eagles' - ìķĻ - иÑĤан - andid - ' ìłĪëĮĢ' - ' ض' - ' arrests' - ' CSV' - ' Azerbaijan' - ortic - ' DX' - ' Adventures' - ' abus' - ' Fau' - ' schlimm' - ' rattling' - ' consumes' - ' Tolkien' - ' resurrected' - ' XY' - íĬ¸ê°Ģ - ' вÑĭÑģÑĤÑĥп' - ' Angie' - żenia - Mic - ' Sheila' - achtet - ' overst' - ' lâ' - ' ineffective' - æĿ¡ - æĢİä¹ĪäºĨ - å¿Ļ - ' wichtiger' - ' vino' - ' pum' - ' angled' - ' Pione' - ' Mỹ' - ãģĿãĤĮãģ¯ - woÅĽÄĩ - draw - ัà¹Ī - markets - ' cafes' - ' Cem' - âĿ¤ - ' Suit' - MK - ' emphasizes' - ' tortilla' - ' mejorar' - ' Surviv' - casting - ' educación' - ' Gum' - uely - ' ìĹ¬ê¸°ëĬĶ' - ' stretchy' - ença - ' withhold' - ' exiting' - ' enthalpy' - ' Transit' - ılmÄ±ÅŁ - alies - ' salvar' - ' leaned' - ' groÃŁes' - ' fitt' - аки - Sarah - ' hostel' - ' fingerna' - ' nadziejÄĻ' - wives - Rec - ' spool' - аÑĤов - ' Enemy' - ' fury' - ' detta' - ' Fay' - éļ¨ - ÑıÑİÑĤ - ' aproximadamente' - ' silos' - ' magist' - ' cree' - ' Krank' - ' DOWN' - ' startled' - ' reborn' - ' Umwelt' - ' Suzanne' - ниÑĨÑĭ - outez - ' JAC' - yards - radas - rau - ipts - hail - ' paragraphs' - ' meglio' - ' isolating' - ' aceite' - ' Harsh' - ' cyst' - ' Blockchain' - ' ÑħоÑĢоÑĪий' - ' virtuous' - ' investigación' - ' devoir' - ' masturb' - ' Sale' - ÙĬرة - ' Χ' - ' StraÃŁen' - ' dikk' - ' afore' - ' Jungkook' - ' chociaż' - ' Debatte' - ' weirdly' - ' viaje' - regist - Help - ' kinderen' - ' formulated' - ' enfim' - ' Towards' - коÑĹ - ivering - ' деÑĤи' - charger - ' purl' - ' academically' - ' Nurse' - ' deleting' - ayo - ' refusal' - ' depicts' - ' Dracula' - ' toasted' - ' Zombie' - ' Superior' - ' Bold' - ' quizzes' - ' gle' - '450' - ' começo' - ynn - ' verst' - ' Olaf' - ' pomoc' - ' Sask' - ëĺ - ' TCP' - ' Property' - íķĺì£ł - à¸ľà¸¡ - boom - aros - ' ÑĢоÑģÑģий' - ' бÑĭваеÑĤ' - åĩºåİ» - ' ìĿ´ìķ¼ê¸°ë¥¼' - ' combien' - vacc - ' ebenfalls' - para - ' зм' - ' desperation' - ordre - ' ש׾×Ļ' - ' generously' - ' Ðŀк' - ' orbiting' - '></' - ' espÃŃ' - ' COP' - åŃ©åŃIJ - visible - ' пÑĢеÑģÑĤÑĥп' - ' stitched' - à¯Ī. - ' latent' - ' Prab' - ' McN' - ' Healing' - ' Curiosity' - cert - ' 민주' - ' patiently' - ' YT' - foreign - ' vẫn' - ' industri' - ' cocktails' - ' brighten' - ' consolidated' - аÑĢд - ltry - ' grille' - ' bona' - ' diligently' - ' WrestleMania' - erkt - energy - '999' - à®ķவ - ' tote' - iono - DIO - ' schizophrenia' - ' postponed' - ' Qiu' - ' ÏĥÏħν' - ' zdjÄĻ' - ' spannend' - ' DIS' - Rel - ' rhin' - immune - Old - ' plötzlich' - ' mound' - ' astronomical' - ' Guid' - ' Cul' - HI - ' Åł' - ' repo' - ' Maurice' - ä¸ĢçĤ¹ - ' bandits' - ' Desktop' - äss - fta - ' licence' - ' imaginar' - ' Entreprene' - xo - ' 맼ìŀĪëĬĶ' - ' ×Ķ×ij' - ' pumpkins' - ' kanssa' - ' jÄĻzy' - ' communauté' - bür - ' erhö' - ' Wolver' - ' Sharing' - 令 - ' pakai' - ' insulted' - ÐľÑĭ - оÑĹ - ' consiste' - æĮij - ' youngsters' - ' gleichen' - weder - ' mote' - ' clauses' - état - prus - ' wast' - ç»ĻæĪij - ' Crisp' - ' çĦ¶å¾Į' - ' offenders' - ' convection' - ' confian' - ollow - amet - ' ÑĹÑħ' - 第äºĮåĢĭ - fficiency - ' unglaub' - igans - ' marketed' - ' VAN' - ' proclaimed' - ' células' - ' collide' - ' Oculus' - adore - Ji - ' sustaining' - ' Fasc' - ' setzt' - ' nosaltres' - Most - ' вÑĩ' - ' nauc' - ' Bhar' - çĪ¸çĪ¸ - æĪijè·Łä½łè¬Ľ - ' yêu' - ' timest' - ' pertama' - irmi - ' zwr' - ' verbess' - ' vortex' - ' STACK' - ثر - ¹Ħë - ĶĶìĺ¤ - ' linkage' - ' Fraser' - enario - ' ëĿ¼ëĬĶ' - ' ìĦłë°°' - hthal - ' ê¹Į' - ' Không' - Ãĥ - ' scrambled' - ' Eink' - ' microorgan' - ' narcissist' - ' Kombat' - ' 맡' - ' AGA' - ' perfekt' - ' Serie' - determ - -' - ' ponytail' - ' koska' - ìĵ - ' obec' - ' chests' - veer - ' uprising' - ' stoked' - associ - ' produção' - ' Shape' - ìłľê°Ģ - ' ëĶ°' - ' jon' - ' inadvert' - antas - ' наконеÑĨ' - ' å°įåķĬ' - ' Arsenal' - ' proteg' - ' liberté' - ' glare' - åĪļ - å·²ç»ı - ' verein' - ' inserts' - ' Jana' - ' wydaje' - ÅĤum - ' %.' - origine - ' synagogue' - ' fallait' - ' disobed' - ' antic' - ' Cycl' - ' asynchronous' - ' ë²Įìį¨' - ' gesund' - ' gagn' - ' pea' - ' grin' - ést - ' sauc' - ' Mäd' - íķ´ëıĦ - pps - ' εÏĢι' - ' peuple' - ' deben' - ' Bree' - ' ÑĢолÑĮ' - ' каким' - ' útil' - ' distributor' - алÑĭ - ' swojÄħ' - ' folklore' - ' receivers' - ' MOO' - bins - astre - ìķĪë - ' ëĦ£ê³ł' - ' multimedia' - ' gebaut' - овÑĭÑħ - ãy - ' dane' - okol - emitism - ONEY - ' yaÄŁ' - ' chauff' - 容æĺĵ - ' esfuer' - Äĥn - ertas - ' fonctionne' - omina - ' ivory' - ' Youtuber' - ' Skywalker' - иÑĩеÑģкаÑı - toi - ' veya' - ' gelernt' - ' chancellor' - ' Statistics' - ' welded' - ' ondan' - ' Sei' - ' medically' - ' energized' - ' Via' - ' вик' - ' uninter' - ' highness' - ' íĮĶë' - ' amplified' - ' Sergey' - ' Mins' - warm - pell - ophile - ' hè' - ' Belo' - ' Sketch' - ' characterization' - ansen - ' ÑĤÑĥÑĢ' - ' ãħĭãħĭãħĭ' - Note - ' koÅŁ' - ' ciert' - flu - ' baht' - ' Downtown' - ' CRIS' - odie - '140' - ' litres' - ' griev' - æ§ĺ - ' ìĶ¨ê°Ģ' - ' succeeds' - ' __' - enting - ' vimos' - ' sì' - defense - ' McD' - ' Marion' - ' Dont' - ' DDR' - ' Lazar' - ' DAR' - ' kuv' - Kn - ' sembla' - ' airborne' - ' Violence' - ëIJIJ - ' restraint' - ' whistles' - ' scolded' - ' acceso' - ' absolutamente' - ' Tyl' - ' Sap' - ¶Ģë¶Ħ - itäten - adem - ' ý' - ' prescribe' - ' Mage' - ' Helena' - å¾Īæľī - 亲 - vt - ' vienen' - ' sneez' - ' molé' - Æ°á»Łng - ' transporting' - ' Lean' - ' kung' - ÑĥÑĢа - ÏĦÎŃ - utches - onders - liyor - Nat - ' zij' - ' mammal' - ' käyt' - ' Joanna' - sent - ' स' - ' vested' - ' Erfahrung' - okee - ' clipping' - ' Listening' - ' (#' - fö - ' vidare' - ' brittle' - ' START' - ' Damas' - ' Yog' - ãĤĵãģ¨ - gart - ' verlier' - ' heartfelt' - ' doÅĽÄĩ' - ì¹ĺê°Ģ - .» - ' maximal' - ' distintos' - ' ìĻľëĥIJíķĺë©´' - ' sailed' - ' conveyed' - ' Tinder' - ' SUPER' - ниÑĨÑĥ - controlled - ' funz' - ' bastards' - ' Ginsburg' - ' nuovo' - ' Pere' - ' JES' - ' Dingen' - ' Bets' - umba - acción - ' ìŀĪì§Ģë§Į' - ' retra' - ' Laurent' - ' pozy' - ' grooves' - ' máquina' - ' minion' - ' deinen' - ' Shaun' - ×Ļ×Ļ - ' honorary' - osaurus - ' zeit' - ' especie' - ' BCE' - аÑĤе - Justin - ' Wheels' - ' ìĿ´íķ´' - ' بÙĬÙĨ' - ' propulsion' - ' perceber' - ' Newman' - å´ - culosis - Mi - ' аккÑĥ' - ' mastering' - ' läh' - ' fists' - ä»Ķ - ' marinade' - Lilly - ' ëħ¸ëł¥' - ' YH' - ' urgently' - ' informational' - ' acordo' - izzy - ãģĦãģı - ìĿ´ìĸ´ - imar - ' ëĤĺìĺ¤ë' - ' twenties' - ' rasp' - ' bumpy' - بة - worker - ' quickest' - ' attaches' - виг - ' ëĤĺíĥĢë' - ' puree' - ' oversized' - ' stirred' - ' jakim' - ' homicide' - ãĤĤãģĹ - iscilla - ' ì±Ļ' - ' speculative' - ' assists' - main - jähr - indet - ' ÅŁur' - ' forecasts' - ' diversion' - ' tare' - ' ogl' - ' Organisation' - ' Chevy' - ' baja' - andır - ' ÙĪÙĦا' - ' radiant' - ' liaison' - ' demokrat' - ' MARC' - ÏĢοÏħ - ' runt' - ' précis' - ' geven' - ' véhic' - ' JESS' - STR - ' ìĸĺë' - ' visionary' - ' buradan' - ' ãģĤãĤĬ' - ' rebirth' - ' exhibited' - ' Metall' - olie - elyn - ' flavours' - ' escrito' - ' Delete' - ' ìķĮìķĺìĸ´' - ' ÑĥкÑĢаÑĹн' - ' interrupting' - ' identific' - ' Suzuki' - ' Landing' - 件äºĭæĥħ - andi - ' estran' - ' couleur' - ' agrad' - ' Sny' - ' à®ĩல' - ' ander' - ' rua' - ' prise' - ' laure' - ' íĬĢ' - ' moderation' - ' erfahren' - ' deconst' - ' Reese' - ' PK' - etos - ãģĵãĤĮãģ§ - ' Gravity' - ' Eren' - ' overboard' - ' müsst' - ' Email' - еÑĢм - ydi - iÄĻdzy - ' LOU' - ' FuÃŁball' - ' RD' - alts - ' ìĬ¤íĬ¸ë' - ' ÐļÑĢаÑģ' - ' telev' - ' ÑĢо' - ' resignation' - ' jingle' - ' Studien' - ' IX' - ' Sentinel' - ' Pang' - éĦ - Jake - ' personagem' - ' média' - ' Chern' - antically - ' thá»Ŀi' - ' paralysis' - ' japanese' - ' conex' - ' efic' - ' underside' - ' neol' - ' fian' - имоÑģÑĤÑĮ - ' quirky' - ' pista' - ' Clement' - nothing - ' поеÑħ' - ' horrend' - ' consolidate' - ploys - emaker - Jennifer - ' numéro' - ' famoso' - ' Neptune' - ' íĸĪìĸ´' - ' пÑĢезид' - ' sitcom' - ' serio' - ' mue' - ' glands' - ' börjar' - ' YJ' - ' Riot' - paragus - ' segurança' - ' immature' - ' Madonna' - à¸į - ' lingering' - ' acesso' - ' Orient' - ' Recomm' - ' complac' - founded - attend - ' cielo' - ' Zhan' - naires - cco - ' ×IJ׳' - ' stata' - ' contradictory' - ' Sé' - ' SAN' - ' Connie' - ' ëĭ¹ìĭľ' - ' Ñģамой' - ' majestic' - ' Penguin' - ' COME' - ÃŃcios - pero - ' mg' - ' fauc' - ' correr' - ' Gottes' - ' Anglo' - Har - á»Ĺi - ' vitesse' - ' announcer' - ' Omaha' - kum - ' spared' - ' ÑĢаза' - ' полÑĥÑĩиÑĤÑģÑı' - ' tähän' - ' понад' - ' pertaining' - ' Rate' - iern - Gold - ' teste' - ' deÄŁild' - ' damping' - ' Partnership' - zysta - geld - ' smokes' - ' Marriage' - 쪽ìĹIJ - èħ³ - isce - ' tryna' - ' Directory' - ' ëĤĺìĺ¬' - ' shameful' - ' mentre' - ' assigning' - æĺ¯éĢĻ樣 - ' repertoire' - ' objetos' - 稱 - ' underworld' - ' endeavors' - ' ignite' - ' ÙĪج' - ' experient' - ' ÐĹап' - ' заклÑİÑĩ' - ' voltages' - ' niego' - ' deficits' - ' buenos' - ' Sleeping' - ' Salem' - ' unlocking' - ' interacted' - ' entendeu' - ' Superintendent' - ' szczegól' - ' quas' - ' paling' - ' kho' - بØŃ - ' colabor' - ' пÑĢигоÑĤов' - ' mauv' - ' Judas' - ' Assist' - ' ÑĤеÑĢÑĢи' - ' наÑģколÑĮко' - ' subsidy' - ' Embassy' - ' dagen' - ' Santo' - èĪ¬ - ש×ķ×ij - ' abruptly' - ' Adapt' - ' vaak' - ' postal' - ' investir' - ' fiquei' - ' downtime' - ' Webb' - ' NCAA' - ' Estoy' - олоÑĤ - ' ìĤ¬ê±´' - ' nationalist' - ' Kathryn' - ' Kop' - éª - Sean - ONA - ' Bj' - ×¢×Ŀ - ÃŃb - idamente - ' глаза' - ' unnie' - ' gemaakt' - ' INTERVIEWER' - ' Haut' - ίο - geois - wydd - ' коли' - ' tightened' - ' planners' - ' herum' - ' görün' - ' electronically' - ' ceram' - ' ëĭ¤ìĸijíķľ' - ' epilepsy' - ' eÄŁ' - lins - ' Shiny' - æł¡ - ' Ñģолн' - ' macaron' - ' impacto' - ' Vegan' - zeÅĦ - ' Rapha' - ' Pars' - ' LEO' - ãģĬãģ£ - cü - ' ׾×Ķ×Ļ×ķת' - ' ähnlich' - ' floss' - ' AZ' - ' möchten' - ' grooming' - ' grasses' - ranch - ' recibir' - ' bouncy' - ' Hobby' - ' viktig' - ' begitu' - ' Picasso' - ' Kush' - 모 - ' obstruction' - ' ë¶ĦìľĦ' - ' microb' - ' Westminster' - rops - dul - ' devo' - ' Lehrer' - ' Advisor' - ucken - ' бÑĥм' - ' flattering' - ' Truman' - ' Sempre' - ' McCain' - ' Hindus' - Julia - ' watershed' - ' lush' - ìłĦë - Before - ' ÐĴÑĤоÑĢ' - ' SaaS' - ' sitzt' - ' beetle' - ' Essential' - enko - ' ëķĮëıĦ' - ' revving' - ' poorer' - ' coerc' - ' idee' - ' coû' - alet - ' zdrow' - ' fender' - growth - DING - ' zde' - ä¸ĬéĿ¢ - ENTS - ' facets' - éļª - ushima - ' ÅŁeh' - ' parasite' - ' lapse' - ' Meer' - ' Kund' - ' slog' - ' brunch' - ' Chart' - arz - ' MUS' - ' offenses' - ' inglés' - ' foliage' - oplan - Aut - ' Jacqu' - tak - iembre - ' xen' - ' nominees' - ' biomedical' - ésus - ' estuv' - ÏĦÏĮ - ATHAN - ' íķľëį°' - ' heed' - crosstalk - Bill - ' spouses' - ' ÑģÑİж' - ' verso' - ' Sven' - ' Cau' - cuz - ' ë³´ìĦ¸ìļĶ' - ' ÑħозÑı' - ' mocking' - ' Ona' - ' Dá' - ' fruitful' - ' banquet' - udding - inctions - dert - sud - ' descon' - ' JC' - ' §' - ' publi' - ëĪĪ - éģķãģĨ - ' entschieden' - ' ROI' - ãģįãģŁ - ' ìĥĿê²¼' - ' käytt' - yani - shaw - ' unleash' - ' manne' - ' histogram' - æĬ¥ - à¸Ńะà¹Ħร - ' gn' - ' fella' - ' einges' - ' Built' - ' representa' - ' punishing' - ' outsiders' - нÑĥÑĤÑĮÑģÑı - current - ' familiarity' - ' див' - ' projets' - ' aqueles' - ' Glue' - those - ' inception' - ' aquellos' - ' illusions' - ' attends' - rese - ' swarm' - ' swab' - ' regardez' - ' posição' - ' akhir' - ' extracting' - ' anecdote' - ' Tale' - ' вин' - ' abges' - ' oluÅŁ' - ' complicado' - ' covari' - ÑĸÑĤÑĮ - Der - ' ×Ļ×Ķ' - Form - ' ìĸ´ì¨Įëĵł' - ' readable' - ' inhibit' - ' decipher' - ' Angry' - pg - வத - ' ÑģобÑģÑĤвенно' - ' samh' - ' escr' - ' encompasses' - ' auster' - ' confisc' - ' Mandal' - ' }' - atcher - =# - çļĦæŶåĢĻ - ' кино' - ' stal' - lungs - ' vole' - ' requis' - ' ãĤĪ' - ' pén' - ' lecturer' - ' inscription' - ' cervical' - ' Treasure' - ' JW' - comings - ' eyesight' - ' Tails' - ÃŃsimo - ' worksheet' - ' swiftly' - ' conos' - ' eliminates' - ' Blaze' - алог - ' pictured' - ' giraffe' - ' Logic' - åĺī - ' enrichment' - Fit - ' unintended' - ' persecuted' - akap - ë°ĺ - ' barber' - ' arbeitet' - ' Surprisingly' - ' Autob' - unku - prov - ' Loch' - obyl - ' подгоÑĤов' - ' économique' - ' patt' - ' ceased' - ' ÑģпиÑģ' - ' nuclei' - ' iste' - ' Wag' - ' zupeÅĤnie' - ' proverb' - ' AhÃŃ' - åĽŀåİ» - liamo - ' reliably' - ' pik' - ' Trading' - ' Coleman' - ' ανα' - ' magari' - ' PHIL' - ' shedding' - ohner - ' pornography' - ' beneficiaries' - âĢ¢ - enin - ' resolving' - ' ÑģпоÑĢÑĤ' - ' бег' - ' nectar' - ultura - imsical - ĮĢ를 - å¹´åīį - ãģĹãĤĥ - ' visão' - éģİä¾Ĩ - ÿÿÿÿÿÿÿÿ - attform - ' ë§ŀëĬĶ' - ' pilgrimage' - ' mating' - ' Reaper' - ' Bref' - çĶŁæ´» - ' ×ij×ĵ' - ' novamente' - ' grilling' - ' Wireless' - ' Romanian' - ÒĽ - ìľłë - hait - ' Bora' - ARRY - ' hypotheses' - 马 - ikut - ' ìķĦë²Ħ' - ' Ñĸз' - ' nationale' - تÙī - üllt - ' éléments' - ' Ware' - ' (-' - алÑĮном - ' indict' - ' Stones' - ãģŁãĤģ - explosion - ' ëĥĦìĥĪ' - ' felic' - ' judiciary' - ' incarnation' - ' inning' - ' formul' - ' shipment' - ' reindeer' - æĴŃ - ' ознаÑĩ' - ' envol' - undy - ' знаÑĤÑĮ' - ' видели' - ' excluding' - death - ' berm' - ' soprattutto' - ' debido' - ' Zig' - ' Ov' - ' KEVIN' - ' Pale' - ' Mire' - ' andar' - including - ' swapped' - ' misconceptions' - ' spong' - réal - ' orbitals' - ' hashtags' - orit - ' mauvais' - иÑģа - ' livres' - ' IPS' - ' 04' - ög - instr - ' внеÑĪ' - ' hice' - isée - ' owes' - ' esimerk' - ' UH' - ' irritation' - ' giggles' - ' colonialism' - ' Bliss' - strings - ' reunited' - ' Psaki' - wach - ' cliffs' - ' False' - äg - pipe - ' whopping' - ' meringue' - ' bung' - industrie - ' leche' - ' Loy' - ' drie' - ' passat' - ' oleh' - ' céu' - ' Gabrie' - ' reefs' - ' bombers' - ' episódio' - ' Rug' - ' Prose' - onos - ' obese' - ' goog' - ' piace' - flanzen - éĴŁ - ' flaps' - ' Alto' - é£Łãģ¹ - Fin - ' resize' - ê·¸ëŀ¨ - è²» - Nathan - ŀĪ볤 - ' ÑĤай' - ' NFT' - ' sneeze' - ' shroud' - ié - ' veramente' - ' cascade' - ' Ook' - ìĹĨìĿ´ - ' infused' - fps - center - ' grappling' - ' Wohnung' - ' Tumb' - ' Imma' - ' Duygusal' - енÑĤи - ' stewardship' - ' harp' - ' endorsed' - ılan - ' одним' - ' competency' - ' bert' - ' Tales' - ' rhe' - ' ohh' - ' ê°Ħëĭ¨' - ' mRNA' - ' gangster' - ' Runner' - еннÑĭм - phoria - ' wÅĤaÅĽciwie' - ' quarto' - ' organise' - ' Vet' - Pad - ' ÙħØ«' - ' stinks' - ' Dul' - uem - isiej - Top - ' tussen' - ' Efendimiz' - ' Boule' - ' Sloven' - ' Lö' - Ñijз - ÑĢип - cave - ' boî' - ' apologise' - ' Marly' - ' Export' - ' Caitlin' - ' tavalla' - ' entails' - ' brom' - ' Copenh' - ' walnut' - ' insists' - ' cuá»Ļc' - ' Quit' - ' Device' - ×Ĵ×Ŀ - ' DOT' - ' velocidad' - LIE - Cool - ' sanitation' - ' olho' - ' EB' - ' íĻķìĭ¤íŀĪ' - ' ÐľÐ¸Ñħ' - ' zuk' - ' surname' - ' Schuld' - ruff - cultural - ' ÑģÑĤолÑĮко' - æĻļä¸Ĭ - Įëį° - ' torto' - ' backups' - ÑĢий - relax - ' synergy' - ' buffs' - ' apo' - ' Wellness' - rounded - ' universes' - ' fera' - ' standby' - ' Silva' - ' JI' - ensored - ' ìĹĨëĭ¤' - ' ÐIJв' - ' оÑĤдел' - ' fø' - ' Rockef' - ' Compass' - ' Bears' - ' ä¸įè¦ģ' - Turn - ' thá»±c' - ' possibile' - ' estem' - ' Croatia' - ' tätä' - ' CAL' - à¹Ģà¸ŀ - ' ÑģÑĤÑĢаÑħ' - ' salts' - ' minimalist' - ' incorporates' - ' ÙĨÛģÛĮÚº' - acao - ' slammed' - ' cama' - Text - '!!!!!!' - ' alcanz' - éma - ' incense' - ' harden' - ' granting' - ' Nai' - ' Firma' - ' hypoc' - job - ' RH' - zur - илÑı - ' ź' - ' dares' - anh - ' ë§Įíģ¼' - ' cuestión' - ' Lima' - æĻ¯ - ' assunto' - ' IPO' - ' Bengal' - ' Bier' - ' psyche' - ' acquainted' - ' Gün' - ози - ÅĽciÄħ - 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<|be|> - <|tg|> - <|sd|> - <|gu|> - <|am|> - <|yi|> - <|lo|> - <|uz|> - <|fo|> - <|ht|> - <|ps|> - <|tk|> - <|nn|> - <|mt|> - <|sa|> - <|lb|> - <|my|> - <|bo|> - <|tl|> - <|mg|> - <|as|> - <|tt|> - <|haw|> - <|ln|> - <|ha|> - <|ba|> - <|jw|> - <|su|> - <|translate|> - <|transcribe|> - <|startoflm|> - <|startofprev|> - <|nospeech|> - <|notimestamps|> - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () - () init: null input_size: 1 ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: null use_preprocessor: true token_type: whisper_multilingual bpemodel: whisper_multilingual non_linguistic_symbols: null cleaner: whisper_basic g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 aux_ctc_tasks: [] frontend: null frontend_conf: {} specaug: null specaug_conf: {} normalize: null normalize_conf: {} model: espnet model_conf: ctc_weight: 0.0 lsm_weight: 0.1 length_normalized_loss: false sym_sos: <|startoftranscript|> sym_eos: <|endoftext|> preencoder: null preencoder_conf: {} encoder: whisper encoder_conf: whisper_model: medium dropout_rate: 0.0 use_specaug: true specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 40 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.12 num_time_mask: 5 download_dir: /workspace/asr/whisper/models postencoder: null postencoder_conf: {} decoder: whisper decoder_conf: whisper_model: medium dropout_rate: 0.0 download_dir: /workspace/asr/whisper/models preprocessor: default preprocessor_conf: tokenizer_language: zh required: - output_dir - token_list version: '202304' distributed: true ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
[ "TRANSLATION" ]
[ "BEAR", "CAS", "CHIA", "CRAFT", "GAD", "MEDAL", "PCR" ]
TBD
twadada/fasttext
twadada
null
[ "mteb", "model-index", "region:us" ]
1,725
1,725
0
0
--- tags: - mteb model-index: - name: fasttext_main results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.85074626865674 - type: ap value: 37.07037696888039 - type: f1 value: 68.00345425893465 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 63.018924999999996 - type: ap value: 58.9270716077803 - type: f1 value: 62.05683397387594 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 31.145999999999997 - type: f1 value: 30.541099103925795 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 13.94 - type: map_at_10 value: 23.919 - type: map_at_100 value: 24.977 - type: map_at_1000 value: 25.047000000000004 - type: map_at_3 value: 20.816000000000003 - type: map_at_5 value: 22.608 - type: mrr_at_1 value: 14.296000000000001 - type: mrr_at_10 value: 24.061 - type: mrr_at_100 value: 25.125999999999998 - type: mrr_at_1000 value: 25.196 - type: mrr_at_3 value: 20.934 - type: mrr_at_5 value: 22.758 - type: ndcg_at_1 value: 13.94 - type: ndcg_at_10 value: 29.543000000000003 - type: ndcg_at_100 value: 35.086 - type: ndcg_at_1000 value: 37.156 - type: ndcg_at_3 value: 23.169 - type: ndcg_at_5 value: 26.395000000000003 - type: precision_at_1 value: 13.94 - type: precision_at_10 value: 4.7509999999999994 - type: precision_at_100 value: 0.743 - type: precision_at_1000 value: 0.091 - type: precision_at_3 value: 10.005 - type: precision_at_5 value: 7.568 - type: recall_at_1 value: 13.94 - type: recall_at_10 value: 47.510999999999996 - type: recall_at_100 value: 74.324 - type: recall_at_1000 value: 91.11 - type: recall_at_3 value: 30.014000000000003 - type: recall_at_5 value: 37.838 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 36.773959267301734 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 27.53911280726287 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 50.148759855706025 - type: mrr value: 63.59814008705976 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 60.96274580639198 - type: cos_sim_spearman value: 62.97673617247494 - type: euclidean_pearson value: 62.52462544675977 - type: euclidean_spearman value: 62.97673617247494 - type: manhattan_pearson value: 63.39097043635172 - type: manhattan_spearman value: 64.39182865728104 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 60.72727272727272 - type: f1 value: 59.08009396100342 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 34.068622978919585 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 24.801372128107964 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 14.469999999999999 - type: map_at_10 value: 20.473 - type: map_at_100 value: 21.379 - type: map_at_1000 value: 21.522 - type: map_at_3 value: 18.603 - type: map_at_5 value: 19.814 - type: mrr_at_1 value: 19.313 - type: mrr_at_10 value: 25.483 - type: mrr_at_100 value: 26.249 - type: mrr_at_1000 value: 26.338 - type: mrr_at_3 value: 23.748 - type: mrr_at_5 value: 24.914 - type: ndcg_at_1 value: 19.313 - type: ndcg_at_10 value: 24.507 - type: ndcg_at_100 value: 29.009 - type: ndcg_at_1000 value: 32.4 - type: ndcg_at_3 value: 21.902 - type: ndcg_at_5 value: 23.379 - type: precision_at_1 value: 19.313 - type: precision_at_10 value: 4.893 - type: precision_at_100 value: 0.8999999999999999 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 11.206000000000001 - type: precision_at_5 value: 8.24 - type: recall_at_1 value: 14.469999999999999 - type: recall_at_10 value: 31.205 - type: recall_at_100 value: 52.23100000000001 - type: recall_at_1000 value: 76.144 - type: recall_at_3 value: 22.975 - type: recall_at_5 value: 27.515 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 13.507 - type: map_at_10 value: 18.556 - type: map_at_100 value: 19.317999999999998 - type: map_at_1000 value: 19.426 - type: map_at_3 value: 17.037 - type: map_at_5 value: 17.949 - type: mrr_at_1 value: 17.261000000000003 - type: mrr_at_10 value: 22.595000000000002 - type: mrr_at_100 value: 23.305 - type: mrr_at_1000 value: 23.380000000000003 - type: mrr_at_3 value: 21.072 - type: mrr_at_5 value: 21.97 - type: ndcg_at_1 value: 17.261000000000003 - type: ndcg_at_10 value: 21.859 - type: ndcg_at_100 value: 25.531 - type: ndcg_at_1000 value: 28.402 - type: ndcg_at_3 value: 19.354 - type: ndcg_at_5 value: 20.613 - type: precision_at_1 value: 17.261000000000003 - type: precision_at_10 value: 4.159 - type: precision_at_100 value: 0.757 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 9.447999999999999 - type: precision_at_5 value: 6.854 - type: recall_at_1 value: 13.507 - type: recall_at_10 value: 27.677000000000003 - type: recall_at_100 value: 43.657000000000004 - type: recall_at_1000 value: 63.865 - type: recall_at_3 value: 20.483 - type: recall_at_5 value: 23.78 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 19.11 - type: map_at_10 value: 25.22 - type: map_at_100 value: 26.138 - type: map_at_1000 value: 26.229000000000003 - type: map_at_3 value: 23.284 - type: map_at_5 value: 24.305 - type: mrr_at_1 value: 22.131999999999998 - type: mrr_at_10 value: 28.08 - type: mrr_at_100 value: 28.912 - type: mrr_at_1000 value: 28.98 - type: mrr_at_3 value: 26.217000000000002 - type: mrr_at_5 value: 27.217000000000002 - type: ndcg_at_1 value: 22.131999999999998 - type: ndcg_at_10 value: 29.041 - type: ndcg_at_100 value: 33.681 - type: ndcg_at_1000 value: 36.162 - type: ndcg_at_3 value: 25.261 - type: ndcg_at_5 value: 26.938000000000002 - type: precision_at_1 value: 22.131999999999998 - type: precision_at_10 value: 4.715 - type: precision_at_100 value: 0.769 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 11.181000000000001 - type: precision_at_5 value: 7.762 - type: recall_at_1 value: 19.11 - type: recall_at_10 value: 38.085 - type: recall_at_100 value: 59.207 - type: recall_at_1000 value: 77.718 - type: recall_at_3 value: 27.722 - type: recall_at_5 value: 31.919999999999998 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 5.970000000000001 - type: map_at_10 value: 8.695 - type: map_at_100 value: 9.261 - type: map_at_1000 value: 9.347 - type: map_at_3 value: 7.516 - type: map_at_5 value: 8.225999999999999 - type: mrr_at_1 value: 6.554 - type: mrr_at_10 value: 9.503 - type: mrr_at_100 value: 10.09 - type: mrr_at_1000 value: 10.174 - type: mrr_at_3 value: 8.267 - type: mrr_at_5 value: 9.008 - type: ndcg_at_1 value: 6.554 - type: ndcg_at_10 value: 10.626 - type: ndcg_at_100 value: 13.893 - type: ndcg_at_1000 value: 16.723 - type: ndcg_at_3 value: 8.246 - type: ndcg_at_5 value: 9.472999999999999 - type: precision_at_1 value: 6.554 - type: precision_at_10 value: 1.831 - type: precision_at_100 value: 0.372 - type: precision_at_1000 value: 0.065 - type: precision_at_3 value: 3.578 - type: precision_at_5 value: 2.825 - type: recall_at_1 value: 5.970000000000001 - type: recall_at_10 value: 15.962000000000002 - type: recall_at_100 value: 31.962000000000003 - type: recall_at_1000 value: 54.581 - type: recall_at_3 value: 9.464 - type: recall_at_5 value: 12.43 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 3.946 - type: map_at_10 value: 6.2010000000000005 - type: map_at_100 value: 6.676 - type: map_at_1000 value: 6.7669999999999995 - type: map_at_3 value: 5.364 - type: map_at_5 value: 5.835 - type: mrr_at_1 value: 5.1 - type: mrr_at_10 value: 7.815999999999999 - type: mrr_at_100 value: 8.38 - type: mrr_at_1000 value: 8.466 - type: mrr_at_3 value: 6.675000000000001 - type: mrr_at_5 value: 7.3469999999999995 - type: ndcg_at_1 value: 5.1 - type: ndcg_at_10 value: 7.947 - type: ndcg_at_100 value: 10.811 - type: ndcg_at_1000 value: 13.614999999999998 - type: ndcg_at_3 value: 6.172 - type: ndcg_at_5 value: 7.049999999999999 - type: precision_at_1 value: 5.1 - type: precision_at_10 value: 1.555 - type: precision_at_100 value: 0.35200000000000004 - type: precision_at_1000 value: 0.06899999999999999 - type: precision_at_3 value: 2.902 - type: precision_at_5 value: 2.363 - type: recall_at_1 value: 3.946 - type: recall_at_10 value: 11.795 - type: recall_at_100 value: 25.183 - type: recall_at_1000 value: 46.206 - type: recall_at_3 value: 7.19 - type: recall_at_5 value: 9.251 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 12.08 - type: map_at_10 value: 16.421 - type: map_at_100 value: 17.272000000000002 - type: map_at_1000 value: 17.391000000000002 - type: map_at_3 value: 14.793000000000001 - type: map_at_5 value: 15.648000000000001 - type: mrr_at_1 value: 14.726 - type: mrr_at_10 value: 19.676 - type: mrr_at_100 value: 20.461 - type: mrr_at_1000 value: 20.546 - type: mrr_at_3 value: 17.902 - type: mrr_at_5 value: 18.811 - type: ndcg_at_1 value: 14.726 - type: ndcg_at_10 value: 19.656000000000002 - type: ndcg_at_100 value: 24.043999999999997 - type: ndcg_at_1000 value: 27.209 - type: ndcg_at_3 value: 16.619 - type: ndcg_at_5 value: 17.959 - type: precision_at_1 value: 14.726 - type: precision_at_10 value: 3.6769999999999996 - type: precision_at_100 value: 0.724 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 7.732 - type: precision_at_5 value: 5.736 - type: recall_at_1 value: 12.08 - type: recall_at_10 value: 26.334000000000003 - type: recall_at_100 value: 45.854 - type: recall_at_1000 value: 68.589 - type: recall_at_3 value: 17.773 - type: recall_at_5 value: 21.113 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 8.486 - type: map_at_10 value: 11.785 - type: map_at_100 value: 12.572 - type: map_at_1000 value: 12.681999999999999 - type: map_at_3 value: 10.439 - type: map_at_5 value: 11.227 - type: mrr_at_1 value: 10.502 - type: mrr_at_10 value: 14.255999999999998 - type: mrr_at_100 value: 15.068999999999999 - type: mrr_at_1000 value: 15.15 - type: mrr_at_3 value: 12.690000000000001 - type: mrr_at_5 value: 13.678 - type: ndcg_at_1 value: 10.502 - type: ndcg_at_10 value: 14.369000000000002 - type: ndcg_at_100 value: 18.807 - type: ndcg_at_1000 value: 21.998 - type: ndcg_at_3 value: 11.706 - type: ndcg_at_5 value: 13.114999999999998 - type: precision_at_1 value: 10.502 - type: precision_at_10 value: 2.705 - type: precision_at_100 value: 0.606 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 5.479 - type: precision_at_5 value: 4.338 - type: recall_at_1 value: 8.486 - type: recall_at_10 value: 19.895 - type: recall_at_100 value: 40.233999999999995 - type: recall_at_1000 value: 63.502 - type: recall_at_3 value: 12.934000000000001 - type: recall_at_5 value: 16.227 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 9.86625 - type: map_at_10 value: 13.610333333333333 - type: map_at_100 value: 14.300916666666666 - type: map_at_1000 value: 14.408333333333335 - type: map_at_3 value: 12.315333333333333 - type: map_at_5 value: 13.012833333333331 - type: mrr_at_1 value: 12.075833333333334 - type: mrr_at_10 value: 16.16466666666667 - type: mrr_at_100 value: 16.834999999999997 - type: mrr_at_1000 value: 16.91916666666667 - type: mrr_at_3 value: 14.80633333333333 - type: mrr_at_5 value: 15.5455 - type: ndcg_at_1 value: 12.075833333333334 - type: ndcg_at_10 value: 16.30866666666667 - type: ndcg_at_100 value: 19.96975 - type: ndcg_at_1000 value: 22.941499999999998 - type: ndcg_at_3 value: 13.900000000000002 - type: ndcg_at_5 value: 14.974749999999998 - type: precision_at_1 value: 12.075833333333334 - type: precision_at_10 value: 2.990333333333334 - type: precision_at_100 value: 0.5833333333333335 - type: precision_at_1000 value: 0.10024999999999999 - type: precision_at_3 value: 6.517250000000001 - type: precision_at_5 value: 4.741833333333333 - type: recall_at_1 value: 9.86625 - type: recall_at_10 value: 21.98225 - type: recall_at_100 value: 39.01191666666667 - type: recall_at_1000 value: 61.04558333333333 - type: recall_at_3 value: 15.127416666666669 - type: recall_at_5 value: 17.91883333333333 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 7.297 - type: map_at_10 value: 10.67 - type: map_at_100 value: 11.257 - type: map_at_1000 value: 11.328000000000001 - type: map_at_3 value: 9.513 - type: map_at_5 value: 10.184999999999999 - type: mrr_at_1 value: 8.741999999999999 - type: mrr_at_10 value: 12.423 - type: mrr_at_100 value: 12.989 - type: mrr_at_1000 value: 13.055 - type: mrr_at_3 value: 11.222 - type: mrr_at_5 value: 11.95 - type: ndcg_at_1 value: 8.741999999999999 - type: ndcg_at_10 value: 12.937000000000001 - type: ndcg_at_100 value: 16.055 - type: ndcg_at_1000 value: 18.265 - type: ndcg_at_3 value: 10.674999999999999 - type: ndcg_at_5 value: 11.82 - type: precision_at_1 value: 8.741999999999999 - type: precision_at_10 value: 2.316 - type: precision_at_100 value: 0.422 - type: precision_at_1000 value: 0.067 - type: precision_at_3 value: 4.9590000000000005 - type: precision_at_5 value: 3.681 - type: recall_at_1 value: 7.297 - type: recall_at_10 value: 18.249000000000002 - type: recall_at_100 value: 32.897999999999996 - type: recall_at_1000 value: 49.96 - type: recall_at_3 value: 12.025 - type: recall_at_5 value: 14.911 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 4.707 - type: map_at_10 value: 6.880999999999999 - type: map_at_100 value: 7.333 - type: map_at_1000 value: 7.4190000000000005 - type: map_at_3 value: 6.099 - type: map_at_5 value: 6.497 - type: mrr_at_1 value: 6.091 - type: mrr_at_10 value: 8.716 - type: mrr_at_100 value: 9.199 - type: mrr_at_1000 value: 9.279 - type: mrr_at_3 value: 7.8229999999999995 - type: mrr_at_5 value: 8.277 - type: ndcg_at_1 value: 6.091 - type: ndcg_at_10 value: 8.606 - type: ndcg_at_100 value: 11.199 - type: ndcg_at_1000 value: 13.900000000000002 - type: ndcg_at_3 value: 7.090000000000001 - type: ndcg_at_5 value: 7.702000000000001 - type: precision_at_1 value: 6.091 - type: precision_at_10 value: 1.686 - type: precision_at_100 value: 0.359 - type: precision_at_1000 value: 0.06999999999999999 - type: precision_at_3 value: 3.4639999999999995 - type: precision_at_5 value: 2.581 - type: recall_at_1 value: 4.707 - type: recall_at_10 value: 12.167 - type: recall_at_100 value: 24.428 - type: recall_at_1000 value: 44.789 - type: recall_at_3 value: 7.775 - type: recall_at_5 value: 9.386999999999999 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 10.186 - type: map_at_10 value: 13.07 - type: map_at_100 value: 13.654 - type: map_at_1000 value: 13.761000000000001 - type: map_at_3 value: 11.89 - type: map_at_5 value: 12.386999999999999 - type: mrr_at_1 value: 12.127 - type: mrr_at_10 value: 15.678 - type: mrr_at_100 value: 16.262 - type: mrr_at_1000 value: 16.361 - type: mrr_at_3 value: 14.396999999999998 - type: mrr_at_5 value: 14.887 - type: ndcg_at_1 value: 12.127 - type: ndcg_at_10 value: 15.607 - type: ndcg_at_100 value: 18.759999999999998 - type: ndcg_at_1000 value: 22.019 - type: ndcg_at_3 value: 13.166 - type: ndcg_at_5 value: 13.916999999999998 - type: precision_at_1 value: 12.127 - type: precision_at_10 value: 2.649 - type: precision_at_100 value: 0.473 - type: precision_at_1000 value: 0.08499999999999999 - type: precision_at_3 value: 5.908 - type: precision_at_5 value: 4.067 - type: recall_at_1 value: 10.186 - type: recall_at_10 value: 21.227999999999998 - type: recall_at_100 value: 35.812 - type: recall_at_1000 value: 60.436 - type: recall_at_3 value: 14.011999999999999 - type: recall_at_5 value: 16.04 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 11.088000000000001 - type: map_at_10 value: 15.311 - type: map_at_100 value: 16.104 - type: map_at_1000 value: 16.285 - type: map_at_3 value: 13.911000000000001 - type: map_at_5 value: 14.454 - type: mrr_at_1 value: 14.229 - type: mrr_at_10 value: 18.834999999999997 - type: mrr_at_100 value: 19.564999999999998 - type: mrr_at_1000 value: 19.665 - type: mrr_at_3 value: 17.589 - type: mrr_at_5 value: 18.034 - type: ndcg_at_1 value: 14.229 - type: ndcg_at_10 value: 18.715 - type: ndcg_at_100 value: 22.689999999999998 - type: ndcg_at_1000 value: 26.351999999999997 - type: ndcg_at_3 value: 16.424 - type: ndcg_at_5 value: 16.991999999999997 - type: precision_at_1 value: 14.229 - type: precision_at_10 value: 3.794 - type: precision_at_100 value: 0.874 - type: precision_at_1000 value: 0.17099999999999999 - type: precision_at_3 value: 8.036999999999999 - type: precision_at_5 value: 5.534 - type: recall_at_1 value: 11.088000000000001 - type: recall_at_10 value: 24.603 - type: recall_at_100 value: 44.41 - type: recall_at_1000 value: 70.00800000000001 - type: recall_at_3 value: 17.192 - type: recall_at_5 value: 19.236 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 7.548000000000001 - type: map_at_10 value: 10.041 - type: map_at_100 value: 10.647 - type: map_at_1000 value: 10.743 - type: map_at_3 value: 9.335 - type: map_at_5 value: 9.626999999999999 - type: mrr_at_1 value: 8.133 - type: mrr_at_10 value: 10.915 - type: mrr_at_100 value: 11.539000000000001 - type: mrr_at_1000 value: 11.636000000000001 - type: mrr_at_3 value: 10.074 - type: mrr_at_5 value: 10.453 - type: ndcg_at_1 value: 8.133 - type: ndcg_at_10 value: 11.834 - type: ndcg_at_100 value: 15.157000000000002 - type: ndcg_at_1000 value: 18.253 - type: ndcg_at_3 value: 10.184999999999999 - type: ndcg_at_5 value: 10.739 - type: precision_at_1 value: 8.133 - type: precision_at_10 value: 1.9040000000000001 - type: precision_at_100 value: 0.392 - type: precision_at_1000 value: 0.07100000000000001 - type: precision_at_3 value: 4.313000000000001 - type: precision_at_5 value: 2.921 - type: recall_at_1 value: 7.548000000000001 - type: recall_at_10 value: 16.587 - type: recall_at_100 value: 32.267 - type: recall_at_1000 value: 56.749 - type: recall_at_3 value: 11.984 - type: recall_at_5 value: 13.216 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 5.405 - type: map_at_10 value: 9.92 - type: map_at_100 value: 11.148 - type: map_at_1000 value: 11.315999999999999 - type: map_at_3 value: 8.121 - type: map_at_5 value: 8.985 - type: mrr_at_1 value: 13.16 - type: mrr_at_10 value: 20.937 - type: mrr_at_100 value: 21.97 - type: mrr_at_1000 value: 22.047 - type: mrr_at_3 value: 18.295 - type: mrr_at_5 value: 19.706000000000003 - type: ndcg_at_1 value: 13.16 - type: ndcg_at_10 value: 15.091 - type: ndcg_at_100 value: 20.957 - type: ndcg_at_1000 value: 24.538 - type: ndcg_at_3 value: 11.75 - type: ndcg_at_5 value: 12.851 - type: precision_at_1 value: 13.16 - type: precision_at_10 value: 5.049 - type: precision_at_100 value: 1.131 - type: precision_at_1000 value: 0.178 - type: precision_at_3 value: 9.034 - type: precision_at_5 value: 7.127 - type: recall_at_1 value: 5.405 - type: recall_at_10 value: 19.029 - type: recall_at_100 value: 40.198 - type: recall_at_1000 value: 60.731 - type: recall_at_3 value: 10.938 - type: recall_at_5 value: 14.052000000000001 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 1.8339999999999999 - type: map_at_10 value: 4.495 - type: map_at_100 value: 6.571000000000001 - type: map_at_1000 value: 7.219 - type: map_at_3 value: 3.011 - type: map_at_5 value: 3.7409999999999997 - type: mrr_at_1 value: 23.0 - type: mrr_at_10 value: 32.812999999999995 - type: mrr_at_100 value: 33.79 - type: mrr_at_1000 value: 33.853 - type: mrr_at_3 value: 29.875 - type: mrr_at_5 value: 31.624999999999996 - type: ndcg_at_1 value: 16.375 - type: ndcg_at_10 value: 13.153 - type: ndcg_at_100 value: 16.036 - type: ndcg_at_1000 value: 22.221 - type: ndcg_at_3 value: 14.135 - type: ndcg_at_5 value: 13.825999999999999 - type: precision_at_1 value: 23.0 - type: precision_at_10 value: 12.425 - type: precision_at_100 value: 4.287 - type: precision_at_1000 value: 0.9950000000000001 - type: precision_at_3 value: 17.583 - type: precision_at_5 value: 15.8 - type: recall_at_1 value: 1.8339999999999999 - type: recall_at_10 value: 8.286 - type: recall_at_100 value: 21.938 - type: recall_at_1000 value: 43.192 - type: recall_at_3 value: 3.904 - type: recall_at_5 value: 5.821 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 32.895 - type: f1 value: 30.402766415345212 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 10.581999999999999 - type: map_at_10 value: 15.384999999999998 - type: map_at_100 value: 16.173000000000002 - type: map_at_1000 value: 16.253 - type: map_at_3 value: 13.819999999999999 - type: map_at_5 value: 14.627 - type: mrr_at_1 value: 11.296000000000001 - type: mrr_at_10 value: 16.305 - type: mrr_at_100 value: 17.131 - type: mrr_at_1000 value: 17.205000000000002 - type: mrr_at_3 value: 14.655999999999999 - type: mrr_at_5 value: 15.512 - type: ndcg_at_1 value: 11.296000000000001 - type: ndcg_at_10 value: 18.401999999999997 - type: ndcg_at_100 value: 22.677 - type: ndcg_at_1000 value: 25.072 - type: ndcg_at_3 value: 15.089 - type: ndcg_at_5 value: 16.554 - type: precision_at_1 value: 11.296000000000001 - type: precision_at_10 value: 2.934 - type: precision_at_100 value: 0.524 - type: precision_at_1000 value: 0.075 - type: precision_at_3 value: 6.411 - type: precision_at_5 value: 4.623 - type: recall_at_1 value: 10.581999999999999 - type: recall_at_10 value: 27.061 - type: recall_at_100 value: 47.522999999999996 - type: recall_at_1000 value: 66.376 - type: recall_at_3 value: 17.942 - type: recall_at_5 value: 21.453 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 4.1419999999999995 - type: map_at_10 value: 7.237 - type: map_at_100 value: 7.9670000000000005 - type: map_at_1000 value: 8.118 - type: map_at_3 value: 6.129 - type: map_at_5 value: 6.569 - type: mrr_at_1 value: 8.025 - type: mrr_at_10 value: 12.7 - type: mrr_at_100 value: 13.565 - type: mrr_at_1000 value: 13.679 - type: mrr_at_3 value: 11.06 - type: mrr_at_5 value: 11.824 - type: ndcg_at_1 value: 8.025 - type: ndcg_at_10 value: 10.324 - type: ndcg_at_100 value: 14.494000000000002 - type: ndcg_at_1000 value: 18.552 - type: ndcg_at_3 value: 8.161 - type: ndcg_at_5 value: 8.745 - type: precision_at_1 value: 8.025 - type: precision_at_10 value: 2.948 - type: precision_at_100 value: 0.7100000000000001 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 5.453 - type: precision_at_5 value: 4.105 - type: recall_at_1 value: 4.1419999999999995 - type: recall_at_10 value: 14.329 - type: recall_at_100 value: 31.011 - type: recall_at_1000 value: 56.995 - type: recall_at_3 value: 8.043 - type: recall_at_5 value: 9.777 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 11.296000000000001 - type: map_at_10 value: 15.665999999999999 - type: map_at_100 value: 16.337 - type: map_at_1000 value: 16.425 - type: map_at_3 value: 14.318 - type: map_at_5 value: 15.068000000000001 - type: mrr_at_1 value: 22.593 - type: mrr_at_10 value: 27.923 - type: mrr_at_100 value: 28.639 - type: mrr_at_1000 value: 28.708 - type: mrr_at_3 value: 26.349 - type: mrr_at_5 value: 27.282 - type: ndcg_at_1 value: 22.593 - type: ndcg_at_10 value: 20.558 - type: ndcg_at_100 value: 23.957 - type: ndcg_at_1000 value: 26.381 - type: ndcg_at_3 value: 17.843999999999998 - type: ndcg_at_5 value: 19.189 - type: precision_at_1 value: 22.593 - type: precision_at_10 value: 4.601 - type: precision_at_100 value: 0.732 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 11.254 - type: precision_at_5 value: 7.819 - type: recall_at_1 value: 11.296000000000001 - type: recall_at_10 value: 23.005 - type: recall_at_100 value: 36.597 - type: recall_at_1000 value: 52.93 - type: recall_at_3 value: 16.88 - type: recall_at_5 value: 19.548 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 64.0796 - type: ap value: 59.21369657756785 - type: f1 value: 63.80554451507698 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 2.822 - type: map_at_10 value: 4.9079999999999995 - type: map_at_100 value: 5.4670000000000005 - type: map_at_1000 value: 5.552 - type: map_at_3 value: 4.064 - type: map_at_5 value: 4.479 - type: mrr_at_1 value: 2.8369999999999997 - type: mrr_at_10 value: 5.006 - type: mrr_at_100 value: 5.575 - type: mrr_at_1000 value: 5.6579999999999995 - type: mrr_at_3 value: 4.133 - type: mrr_at_5 value: 4.559 - type: ndcg_at_1 value: 2.8369999999999997 - type: ndcg_at_10 value: 6.32 - type: ndcg_at_100 value: 9.546000000000001 - type: ndcg_at_1000 value: 12.223 - type: ndcg_at_3 value: 4.517 - type: ndcg_at_5 value: 5.267 - type: precision_at_1 value: 2.8369999999999997 - type: precision_at_10 value: 1.119 - type: precision_at_100 value: 0.28300000000000003 - type: precision_at_1000 value: 0.052 - type: precision_at_3 value: 1.968 - type: precision_at_5 value: 1.559 - type: recall_at_1 value: 2.822 - type: recall_at_10 value: 10.788 - type: recall_at_100 value: 26.848 - type: recall_at_1000 value: 48.613 - type: recall_at_3 value: 5.759 - type: recall_at_5 value: 7.5600000000000005 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 85.22571819425444 - type: f1 value: 84.76386137659372 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 56.31326949384404 - type: f1 value: 36.81602997546668 - task: type: Classification dataset: name: MTEB MasakhaNEWSClassification (eng) type: None config: eng split: test revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 metrics: - type: accuracy value: 76.69831223628692 - type: f1 value: 75.94656175905295 - task: type: Clustering dataset: name: MTEB MasakhaNEWSClusteringS2S (eng) type: None config: eng split: test revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 metrics: - type: v_measure value: 19.60289598967427 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 56.36852723604573 - type: f1 value: 53.668136836547085 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 64.51916610625419 - type: f1 value: 62.784248086639515 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 27.851032342558657 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 23.456392994056834 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 26.934895509745115 - type: mrr value: 27.27108783122485 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 3.405 - type: map_at_10 value: 7.180000000000001 - type: map_at_100 value: 9.014999999999999 - type: map_at_1000 value: 10.211 - type: map_at_3 value: 5.541 - type: map_at_5 value: 6.29 - type: mrr_at_1 value: 26.316 - type: mrr_at_10 value: 37.216 - type: mrr_at_100 value: 38.155 - type: mrr_at_1000 value: 38.216 - type: mrr_at_3 value: 35.243 - type: mrr_at_5 value: 36.434 - type: ndcg_at_1 value: 24.768 - type: ndcg_at_10 value: 21.11 - type: ndcg_at_100 value: 21.051000000000002 - type: ndcg_at_1000 value: 30.789 - type: ndcg_at_3 value: 23.94 - type: ndcg_at_5 value: 22.512 - type: precision_at_1 value: 26.316 - type: precision_at_10 value: 15.387 - type: precision_at_100 value: 5.824 - type: precision_at_1000 value: 1.923 - type: precision_at_3 value: 22.601 - type: precision_at_5 value: 19.195 - type: recall_at_1 value: 3.405 - type: recall_at_10 value: 11.04 - type: recall_at_100 value: 24.92 - type: recall_at_1000 value: 58.472 - type: recall_at_3 value: 6.746 - type: recall_at_5 value: 8.366 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 3.879 - type: map_at_10 value: 6.714 - type: map_at_100 value: 7.2989999999999995 - type: map_at_1000 value: 7.376 - type: map_at_3 value: 5.5440000000000005 - type: map_at_5 value: 6.128 - type: mrr_at_1 value: 4.635 - type: mrr_at_10 value: 7.686999999999999 - type: mrr_at_100 value: 8.272 - type: mrr_at_1000 value: 8.341 - type: mrr_at_3 value: 6.4750000000000005 - 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type: cos_sim_recall value: 99.89816700610999 - type: dot_accuracy value: 99.89816700610999 - type: dot_ap value: 100.0 - type: dot_f1 value: 99.9490575649516 - type: dot_precision value: 100.0 - type: dot_recall value: 99.89816700610999 - type: euclidean_accuracy value: 99.89816700610999 - type: euclidean_ap value: 100.0 - type: euclidean_f1 value: 99.9490575649516 - type: euclidean_precision value: 100.0 - type: euclidean_recall value: 99.89816700610999 - type: manhattan_accuracy value: 99.89816700610999 - type: manhattan_ap value: 100.0 - type: manhattan_f1 value: 99.9490575649516 - type: manhattan_precision value: 100.0 - type: manhattan_recall value: 99.89816700610999 - type: max_accuracy value: 99.89816700610999 - type: max_ap value: 100.0 - type: max_f1 value: 99.9490575649516 - task: type: PairClassification dataset: name: MTEB PawsX (en) type: None config: en split: test revision: 8a04d940a42cd40658986fdd8e3da561533a3646 metrics: - type: cos_sim_accuracy value: 54.6 - type: cos_sim_ap value: 41.86283617866976 - type: cos_sim_f1 value: 62.42257398485891 - type: cos_sim_precision value: 45.37268634317159 - type: cos_sim_recall value: 100.0 - type: dot_accuracy value: 54.65 - type: dot_ap value: 41.97285801948605 - type: dot_f1 value: 62.444061962134256 - type: dot_precision value: 45.3953953953954 - type: dot_recall value: 100.0 - type: euclidean_accuracy value: 55.2 - type: euclidean_ap value: 43.13939119930642 - type: euclidean_f1 value: 62.42257398485891 - type: euclidean_precision value: 45.37268634317159 - type: euclidean_recall value: 100.0 - type: manhattan_accuracy value: 55.2 - type: manhattan_ap value: 43.25154714126316 - type: manhattan_f1 value: 62.487010737790094 - type: manhattan_precision value: 45.55555555555556 - type: manhattan_recall value: 99.44873208379272 - type: max_accuracy value: 55.2 - type: max_ap value: 43.25154714126316 - type: max_f1 value: 62.487010737790094 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 53.702000000000005 - type: map_at_10 value: 65.176 - type: map_at_100 value: 65.999 - type: map_at_1000 value: 66.049 - type: map_at_3 value: 62.368 - type: map_at_5 value: 64.077 - type: mrr_at_1 value: 61.9 - type: mrr_at_10 value: 69.987 - type: mrr_at_100 value: 70.36200000000001 - type: mrr_at_1000 value: 70.379 - type: mrr_at_3 value: 68.207 - type: mrr_at_5 value: 69.327 - type: ndcg_at_1 value: 61.92999999999999 - type: ndcg_at_10 value: 70.295 - type: ndcg_at_100 value: 73.1 - type: ndcg_at_1000 value: 73.916 - type: ndcg_at_3 value: 66.355 - type: ndcg_at_5 value: 68.403 - type: precision_at_1 value: 61.92999999999999 - type: precision_at_10 value: 10.671999999999999 - type: precision_at_100 value: 1.332 - type: precision_at_1000 value: 0.147 - type: precision_at_3 value: 28.83 - type: precision_at_5 value: 19.216 - type: recall_at_1 value: 53.702000000000005 - type: recall_at_10 value: 80.455 - type: recall_at_100 value: 91.75999999999999 - type: recall_at_1000 value: 97.033 - type: recall_at_3 value: 69.228 - type: recall_at_5 value: 74.766 - type: map_at_1 value: 1.043 - type: map_at_10 value: 2.456 - type: map_at_100 value: 2.913 - type: map_at_1000 value: 3.078 - type: map_at_3 value: 1.7870000000000001 - type: map_at_5 value: 2.173 - type: mrr_at_1 value: 5.1 - type: mrr_at_10 value: 8.515 - type: mrr_at_100 value: 9.149000000000001 - type: mrr_at_1000 value: 9.276 - type: mrr_at_3 value: 7.2669999999999995 - type: mrr_at_5 value: 7.962 - type: ndcg_at_1 value: 5.1 - type: ndcg_at_10 value: 4.578 - type: ndcg_at_100 value: 7.26 - type: ndcg_at_1000 value: 11.705 - type: ndcg_at_3 value: 4.194 - type: ndcg_at_5 value: 3.823 - type: precision_at_1 value: 5.1 - type: precision_at_10 value: 2.39 - type: precision_at_100 value: 0.645 - type: precision_at_1000 value: 0.174 - type: precision_at_3 value: 3.9 - type: precision_at_5 value: 3.4000000000000004 - type: recall_at_1 value: 1.043 - type: recall_at_10 value: 4.882000000000001 - type: recall_at_100 value: 13.15 - type: recall_at_1000 value: 35.385 - type: recall_at_3 value: 2.388 - type: recall_at_5 value: 3.4770000000000003 - type: map_at_1 value: 0.123 - type: map_at_10 value: 0.8380000000000001 - type: map_at_100 value: 3.5389999999999997 - type: map_at_1000 value: 8.105 - type: map_at_3 value: 0.319 - type: map_at_5 value: 0.482 - type: mrr_at_1 value: 52.0 - type: mrr_at_10 value: 63.839 - type: mrr_at_100 value: 64.274 - type: mrr_at_1000 value: 64.274 - type: mrr_at_3 value: 62.0 - type: mrr_at_5 value: 62.5 - type: ndcg_at_1 value: 45.0 - type: ndcg_at_10 value: 43.013 - type: ndcg_at_100 value: 28.294999999999998 - type: ndcg_at_1000 value: 24.868000000000002 - type: ndcg_at_3 value: 45.112 - type: ndcg_at_5 value: 44.41 - type: precision_at_1 value: 52.0 - type: precision_at_10 value: 47.4 - type: precision_at_100 value: 29.42 - type: precision_at_1000 value: 12.126000000000001 - type: precision_at_3 value: 50.666999999999994 - type: precision_at_5 value: 50.0 - type: recall_at_1 value: 0.123 - type: recall_at_10 value: 1.101 - type: recall_at_100 value: 6.366 - type: recall_at_1000 value: 24.29 - type: recall_at_3 value: 0.368 - type: recall_at_5 value: 0.5910000000000001 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 25.781222642642586 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 39.469895537327254 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 69.93170082002553 - type: cos_sim_spearman value: 60.08741092780676 - type: euclidean_pearson value: 65.60500561965856 - type: euclidean_spearman value: 60.08755668871506 - type: manhattan_pearson value: 66.26288910017067 - type: manhattan_spearman value: 60.258444365922024 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 62.63440086574824 - type: cos_sim_spearman value: 57.22862459567004 - type: euclidean_pearson value: 61.33135391980671 - type: euclidean_spearman value: 57.2287396395971 - type: manhattan_pearson value: 63.538353299276665 - type: manhattan_spearman value: 59.31604272847601 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 66.99729287299299 - type: cos_sim_spearman value: 69.20264774948375 - type: euclidean_pearson value: 68.83879433164682 - type: euclidean_spearman value: 69.20257380705361 - type: manhattan_pearson value: 68.82235273988779 - type: manhattan_spearman value: 69.12895594376502 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - 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type: euclidean_spearman value: 64.16559631894077 - type: manhattan_pearson value: 63.756149631030304 - type: manhattan_spearman value: 64.26840399223137 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 64.67239774871666 - type: cos_sim_spearman value: 70.22507344222888 - type: euclidean_pearson value: 68.64452662209249 - type: euclidean_spearman value: 70.22507344222888 - type: manhattan_pearson value: 69.50174122996509 - type: manhattan_spearman value: 70.95654177971161 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 37.43959179391427 - type: cos_sim_spearman value: 52.14129987772493 - type: euclidean_pearson value: 45.75262378066364 - type: euclidean_spearman value: 52.14129987772493 - type: manhattan_pearson value: 46.45004553263895 - type: manhattan_spearman value: 52.6763345299464 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 57.22043463404035 - type: cos_sim_spearman value: 56.454056579980794 - type: euclidean_pearson value: 58.48035388318515 - type: euclidean_spearman value: 56.454075070192246 - type: manhattan_pearson value: 59.27047962449207 - type: manhattan_spearman value: 57.15898290207574 - task: type: STS dataset: name: MTEB STSBenchmarkMultilingualSTS (en) type: None config: en split: test revision: 93d57ef91790589e3ce9c365164337a8a78b7632 metrics: - type: cos_sim_pearson value: 57.220434628430304 - type: cos_sim_spearman value: 56.454056579980794 - type: euclidean_pearson value: 58.48035389301345 - type: euclidean_spearman value: 56.454075070192246 - type: manhattan_pearson value: 59.270479632297445 - type: manhattan_spearman value: 57.15898290207574 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - 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type: precision_at_3 value: 15.556000000000001 - type: precision_at_5 value: 10.467 - type: recall_at_1 value: 28.278 - type: recall_at_10 value: 61.306000000000004 - type: recall_at_100 value: 82.794 - type: recall_at_1000 value: 96.68299999999999 - type: recall_at_3 value: 43.722 - type: recall_at_5 value: 48.528 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.45049504950495 - type: cos_sim_ap value: 72.53477415157148 - type: cos_sim_f1 value: 69.14778856526429 - type: cos_sim_precision value: 75.05854800936767 - type: cos_sim_recall value: 64.1 - type: dot_accuracy value: 99.45049504950495 - type: dot_ap value: 72.53477415157148 - type: dot_f1 value: 69.14778856526429 - type: dot_precision value: 75.05854800936767 - type: dot_recall value: 64.1 - type: euclidean_accuracy value: 99.45049504950495 - type: euclidean_ap value: 72.53477415157148 - type: euclidean_f1 value: 69.14778856526429 - type: euclidean_precision value: 75.05854800936767 - type: euclidean_recall value: 64.1 - type: manhattan_accuracy value: 99.5039603960396 - type: manhattan_ap value: 76.7082832888717 - type: manhattan_f1 value: 72.13822894168467 - type: manhattan_precision value: 78.40375586854461 - type: manhattan_recall value: 66.8 - type: max_accuracy value: 99.5039603960396 - type: max_ap value: 76.7082832888717 - type: max_f1 value: 72.13822894168467 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 41.45680507027172 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 28.75350540653195 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 38.69383105134447 - type: mrr value: 38.75449611662847 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.14397459171298 - type: cos_sim_spearman value: 30.327886960162086 - type: dot_pearson value: 31.143974578709766 - type: dot_spearman value: 30.297101868216526 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 1.1280000000000001 - type: map_at_10 value: 3.533 - type: map_at_100 value: 6.584 - type: map_at_1000 value: 7.983 - type: map_at_3 value: 2.1399999999999997 - type: map_at_5 value: 2.825 - type: mrr_at_1 value: 20.408 - type: mrr_at_10 value: 29.62 - type: mrr_at_100 value: 31.746000000000002 - type: mrr_at_1000 value: 31.767 - type: mrr_at_3 value: 25.169999999999998 - type: mrr_at_5 value: 28.231 - type: ndcg_at_1 value: 18.367 - type: ndcg_at_10 value: 11.927 - type: ndcg_at_100 value: 21.722 - type: ndcg_at_1000 value: 34.343 - type: ndcg_at_3 value: 14.097999999999999 - type: ndcg_at_5 value: 13.475000000000001 - type: precision_at_1 value: 20.408 - type: precision_at_10 value: 11.224 - type: precision_at_100 value: 5.3469999999999995 - type: precision_at_1000 value: 1.316 - type: precision_at_3 value: 14.285999999999998 - type: precision_at_5 value: 14.285999999999998 - type: recall_at_1 value: 1.1280000000000001 - type: recall_at_10 value: 7.104000000000001 - type: recall_at_100 value: 32.883 - type: recall_at_1000 value: 71.486 - type: recall_at_3 value: 2.828 - type: recall_at_5 value: 4.619 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 69.719 - type: ap value: 13.153064299460688 - type: f1 value: 53.23280050361928 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 51.47425014148273 - type: f1 value: 51.588742010704195 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 28.331197809553117 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 81.19449245991535 - type: cos_sim_ap value: 53.831102581174925 - type: cos_sim_f1 value: 52.64428881912582 - type: cos_sim_precision value: 48.53072128227961 - type: cos_sim_recall value: 57.519788918205805 - type: dot_accuracy value: 81.19449245991535 - type: dot_ap value: 53.831102581174925 - type: dot_f1 value: 52.64428881912582 - type: dot_precision value: 48.53072128227961 - type: dot_recall value: 57.519788918205805 - type: euclidean_accuracy value: 81.19449245991535 - type: euclidean_ap value: 53.831102581174925 - type: euclidean_f1 value: 52.64428881912582 - type: euclidean_precision value: 48.53072128227961 - type: euclidean_recall value: 57.519788918205805 - type: manhattan_accuracy value: 81.06932109435536 - type: manhattan_ap value: 53.40351129456407 - type: manhattan_f1 value: 51.96813495782568 - type: manhattan_precision value: 46.73409186683523 - type: manhattan_recall value: 58.52242744063324 - type: max_accuracy value: 81.19449245991535 - type: max_ap value: 53.831102581174925 - type: max_f1 value: 52.64428881912582 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 83.53708231458843 - type: cos_sim_ap value: 70.93155202991022 - type: cos_sim_f1 value: 63.92679900744417 - type: cos_sim_precision value: 64.38612933458295 - type: cos_sim_recall value: 63.47397597782568 - type: dot_accuracy value: 83.53708231458843 - type: dot_ap value: 70.93155188455967 - type: dot_f1 value: 63.92679900744417 - type: dot_precision value: 64.38612933458295 - type: dot_recall value: 63.47397597782568 - type: euclidean_accuracy value: 83.53708231458843 - type: euclidean_ap value: 70.93155157551972 - type: euclidean_f1 value: 63.92679900744417 - type: euclidean_precision value: 64.38612933458295 - type: euclidean_recall value: 63.47397597782568 - type: manhattan_accuracy value: 83.77187875965382 - type: manhattan_ap value: 71.99220572523825 - type: manhattan_f1 value: 65.04077318030807 - type: manhattan_precision value: 63.80740740740741 - type: manhattan_recall value: 66.32275947028026 - type: max_accuracy value: 83.77187875965382 - type: max_ap value: 71.99220572523825 - type: max_f1 value: 65.04077318030807 ---
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
Sci-fi-vy/Meditron-7b-finetuned
Sci-fi-vy
image-text-to-text
[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "image-text-to-text", "en", "dataset:epfl-llm/guidelines", "arxiv:2311.16079", "base_model:meta-llama/Llama-2-7b", "base_model:finetune:meta-llama/Llama-2-7b", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,737
1,737
78
1
--- base_model: meta-llama/Llama-2-7b datasets: - epfl-llm/guidelines language: - en library_name: transformers license: llama2 metrics: - accuracy - perplexity pipeline_tag: image-text-to-text --- # Model Card for Meditron-7B-finetuned Meditron is a suite of open-source medical Large Language Models (LLMs). Meditron-7B is a 7 billion parameters model adapted to the medical domain from Llama-2-7B through continued pretraining on a comprehensively curated medical corpus, including selected PubMed articles, abstracts, a [new dataset](https://huggingface.co/datasets/epfl-llm/guidelines) of internationally-recognized medical guidelines, and general domain data from [RedPajama-v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T). Meditron-7B-finetuned is finetuned on relevant training data, which outperforms Llama-2-7B and PMC-Llama on multiple medical reasoning tasks. <details open> <summary><strong>Advisory Notice</strong></summary> <blockquote style="padding: 10px; margin: 0 0 10px; border-left: 5px solid #ddd;"> While Meditron is designed to encode medical knowledge from sources of high-quality evidence, it is not yet adapted to deliver this knowledge appropriately, safely, or within professional actionable constraints. We recommend against deploying Meditron in medical applications without extensive use-case alignment, as well as additional testing, specifically including randomized controlled trials in real-world practice settings. </blockquote> </details> ## Model Details - **Finetuned by:** [Vignesh](https://huggingface.co/Sci-fi-vy) - **Developed by:** [EPFL LLM Team](https://huggingface.co/epfl-llm) - **Model type:** Causal decoder-only transformer language model - **Language(s):** English (mainly) - **Model License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - **Code License:** [APACHE 2.0 LICENSE](LICENSE) - **Continue-pretrained from model:** [Llama-2-7B](https://huggingface.co/meta-llama/Llama-2-7b) - **Context length:** 2K tokens - **Input:** Text-only data - **Output:** Model generates text only - **Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we enhance model's performance. - **Knowledge Cutoff:** August 2023 ### Model Sources - **Repository:** [epflLLM/meditron](https://github.com/epfLLM/meditron) - **Trainer:** [epflLLM/Megatron-LLM](https://github.com/epfLLM/Megatron-LLM) - **Reference Paper:** *[MediTron-70B: Scaling Medical Pretraining for Large Language Models](https://arxiv.org/abs/2311.16079)* ## Uses Meditron-7B-finetuned is being made available for further testing and assessment as an AI assistant to enhance clinical decision-making and enhance access to an LLM for healthcare use. Potential use cases may include but are not limited to: - Medical exam question answering - Supporting differential diagnosis - Disease information (symptoms, cause, treatment) query - General health information query - Personalized results ### Direct Use It is possible to use this model to generate text, which is useful for experimentation and understanding its capabilities. It should not be used directly for production or work that may impact people. ### Downstream Use Meditron-70B and Meditron-7B are both foundation models without finetuning or instruction-tuning. They can be finetuned, instruction-tuned, or RLHF-tuned for specific downstream tasks and applications. There are two ways we have used this model for downstream question-answering tasks. 1. We apply in-context learning with k demonstrations (3 or 5 in our paper) added to the prompt. 2. We finetuned the models for downstream question-answering tasks using specific training sets. We encourage and look forward to the adaption of the base model for more diverse applications. If you want a more interactive way to prompt the model, we recommend using a high-throughput and memory-efficient inference engine with a UI that supports chat and text generation. You can check out our deployment [guide](https://github.com/epfLLM/meditron/blob/main/deployment/README.md), where we used [FastChat](https://github.com/lm-sys/FastChat) with [vLLM](https://github.com/vllm-project/vllm). We collected generations for our qualitative analysis through an interactive UI platform, [BetterChatGPT](https://github.com/ztjhz/BetterChatGPT). Here is the prompt format we used as an example: <img width=70% src="prompt_example.png" alt="qualitative-analysis-prompt" title="Qualitative Analysis Prompt"> ### Out-of-Scope Use We do not recommend using this model for natural language generation in a production environment, finetuned or otherwise. ## Truthfulness, Helpfulness, Risk, and Bias <!-- This section is meant to convey both technical and sociotechnical limitations. --> We did an initial assessment of Meditron models' **Truthfulness** against baseline models and consumer-level medical models. We use TruthfulQA (multiple choice) as the main evaluation benchmark. We only focus on the categories that are relevant to the medical domain, including Health, Nutrition, Psychology, and Science. For 7B models, we perform one-shot evaluations for consistent answer generation. For 70B models, the evaluations are under the zero-shot setting. Below, we report the detailed truthfulness performance of each category. | | | | | | | | | | --- | ------ |----- |----- |----- |----- |----- |----- | |Category | meditron-70b | llama-2-70b | med42-70b* | meditron-7b | llama-2-7b | PMC-llama-7b | |Health | 81.8 | 69.1 | 83.6 | 27.3 | 16.4 | 3.6 | |Nutrition | 77.9 | 68.8 | 62.5 | 31.1 | 12.5 | 6.3 | |Psychology| 47.4 | 36.8 | 52.6 | 21.1 | 10.5 | 0.0 | |Science | 77.8 | 44.4 | 33.3 | 33.3 | 11.1 | 0.0 | |Avg | 71.2 | 54.8 | 58.0 | 28.3 | 12.6 | 2.5 | | | | | | | | | For a more detailed performance analysis, please see our paper. Significant research is still required to fully explore potential bias, fairness, and safety issues with this language model. Please recognize that our evaluation on Meditron-7B's helpfulness, risk, and bias are highly limited. Thus, as we noted in the safety notice, we strongly against any deployment in medical applications without further alignment process and rigorous evaluation! ### Recommendations **IMPORTANT!** Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. While this model is capable of generating natural language text, we have only begun to explore this capability and its limitations. Understanding these limitations is especially important in a domain like medicine. Therefore, we strongly recommend against using this model in production for natural language generation or for professional purposes related to health and medicine. ## Training Details ### Training Data Meditron’s domain-adaptive pre-training corpus GAP-Replay combines 48.1B tokens from four corpora: - [**Clinical Guidelines**](https://huggingface.co/datasets/epfl-llm/guidelines): a new dataset of 46K internationally-recognized clinical practice guidelines from various healthcare-related sources, including hospitals and international organizations. - **Medical Paper Abstracts**: 16.1M abstracts extracted from closed-access PubMed and PubMed Central papers. - **Medical Papers**: full-text articles extracted from 5M publicly available PubMed and PubMed Central papers. - **Replay Data**: 400M tokens of general domain pretraining data sampled from [RedPajama-v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) <img width=75% src="gap-replay.png" alt="Alt text"> #### Data Preprocessing Please see the detailed preprocessing procedure in our paper. ### Training Procedure We used the [Megatron-LLM](https://github.com/epfLLM/Megatron-LLM) distributed training library, a derivative of Nvidia's Megatron LM project, to optimize training efficiency. Hardware consists of 1 node of 8x NVIDIA A100 (80GB) SXM GPUs connected by NVLink and NVSwitch with a single Nvidia ConnectX-6 DX network card and equipped with 2 x AMD EPYC 7543 32-Core Processors and 512 GB of RAM. Our three way parallelism scheme uses: - Data Parallelism (DP -- different GPUs process different subsets of the batches) of 2, - Pipeline Parallelism (PP -- different GPUs process different layers) of 4, - Tensor Parallelism (TP -- different GPUs process different subtensors for matrix multiplication) of 1. #### Training Hyperparameters | | | | --- | ------ | | bf16 | true | | lr | 3e-4 | | eps | 1e-5 | | betas | \[0.9, 0.95\] | | clip_grad | 1 | | weight decay | 0.1 | | DP size | 16 | | TP size | 4 | | PP size | 1 | | seq length | 2048 | | lr scheduler | cosine| | min lr | 1e-6 | | warmup iteration | 2000 | | micro batch size | 10 | | global batch size | 1600 | | | | #### Sizes The model was trained in September 2023. The model architecture is exactly Llama 2, meaning | | | | --- | ------ | | Model size | 7B | | Hidden dimension | 4096 | | Num. attention heads | 32 | | Num. layers | 32 | | | | ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data & Metrics #### Testing Data - [MedQA (USMLE)](https://huggingface.co/datasets/bigbio/med_qa) - [MedMCQA](https://huggingface.co/datasets/medmcqa) - [PubMedQA](https://huggingface.co/datasets/bigbio/pubmed_qa) - [MMLU-Medical](https://huggingface.co/datasets/lukaemon/mmlu) - [MedQA-4-Option](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options) #### Metrics - Accuracy: suite the evaluation of multiple-choice question-answering tasks. ### Results We finetune meditron-7b, llama-2-7b, pmc-llama-7b on each benchmark (pubmedqa, medmcqa, medqa)'s training data individually. We report the finetuned models' performance with top token selection as the inference mode. For MMLU-Medical, models finetuned on MedMCQA are used for inference. For MedQA-4-Option, models finetuned on MedQA are used for inference. For a more detailed performance analysis, please see our paper. | | | | | | | | --- | ------ |----- |----- |----- |----- | |Dataset | meditron-7b | llama-2-7b | pmc-llama-7b | Zephyr-7B-beta* | Mistral-7B-instruct* | |MMLU-Medical | 54.2 | 53.7 | 56.4 | 63.3 | 60.0 | |PubMedQA | 74.4 | 61.8 | 59.2 | 46.0 | 17.8 | |MedMCQA | 59.2 | 54.4 | 57.6 | 43.0 | 40.2 | |MedQA | 47.9 | 44.0 | 42.4 | 42.8 | 32.4 | |MedQA-4-Option| 52.0 | 49.6 | 49.2 | 48.5 | 41.1 | |Avg | 57.5 | 52.7 | 53.0 | 48.7 | 38.3 | | | | | | | | **Note**: models with * are already instruction-tuned, so we exclude them from further finetuning on any training.
[ "QUESTION_ANSWERING" ]
[ "MEDQA", "PUBMEDQA" ]
BioNLP
adriansanz/ST-tramits-SITGES-007-5ep
adriansanz
sentence-similarity
[ "sentence-transformers", "safetensors", "xlm-roberta", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:6692", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:BAAI/bge-m3", "base_model:finetune:BAAI/bge-m3", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,728
1,728
4
0
--- base_model: BAAI/bge-m3 library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6692 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: La inscripció en aquest registre caduca en el termini d'un any, llevat que sigui renovada abans del transcurs d'aquest termini mitjançant la presentació d'una declaració responsable sobre el compliment dels requisits exigits. sentences: - Quin és el requisit per a la sol·licitud del volant d'empadronament? - Què passa si no es renova la inscripció en el Registre municipal de sol·licitants? - Quin és el segon objectiu que han de tenir els projectes/activitats per a rebre aquesta subvenció? - source_sentence: 'AVÍS: Places exhaurides de l''activitat de psicomotricitat fins nou avís. Les persones interessades poden contactar amb el Departament d''Esports, el qual obrirà un llistat d''espera, si escau.' sentences: - Què passa si les places de Psicomotricitat estan exhaurides? - Quin és el paper del tractament en la declaració? - Quin és el període de temps que es requereix per a la venda d'articles d'artesania? - source_sentence: El registre de noves patents en relació a les noves línies d’actuació és una despesa subvencionable per a la reactivació i adaptació del negoci post COVID19. sentences: - Quins són els tipus de despeses que es poden finançar amb les subvencions? - Quin és el paper de les organitzacions membres del Consell de Cooperació en els projectes de cooperació internacional? - Quin és el propòsit del registre de noves patents en relació a les noves línies d’actuació? - source_sentence: 'Justificació de les subvencions atorgades per l''Ajuntament de Sitges per les activitats culturals incloses dins els següents tipus: Activitats de difusió cultural. Iniciatives de recuperació i difusió del patrimoni cultural, tradicional i popular. Activitats de formació no reglada i de recerca. Activitats d''animació socio-cultural.' sentences: - Quins són els residus que es recullen en el servei municipal complementari? - Quin és el paper de l'expedient d'ajut a la contractació laboral de persones en la contractació laboral? - Quin és el paper de les activitats d'animació socio-cultural? - source_sentence: La comunicació és un element important en la cura dels gats, ja que implica la capacitat per a comunicar-se de manera efectiva amb les autoritats competents i amb els altres implicats en la cura dels animals. sentences: - Qui són considerats titulars o nous exercents en el cas dels espectacles, establiments oberts al públic i les activitats recreatives? - Quin és el paper de la comunicació en la cura dels gats? - Quin és el benefici de la llicència de gual per a la persona titular? model-index: - name: SentenceTransformer based on BAAI/bge-m3 results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 1024 type: dim_1024 metrics: - type: cosine_accuracy@1 value: 0.1589958158995816 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.303347280334728 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3723849372384937 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5188284518828452 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.1589958158995816 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.101115760111576 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.07447698744769873 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05188284518828451 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1589958158995816 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.303347280334728 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3723849372384937 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5188284518828452 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.31740141154907076 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2560196254233912 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.27634436521904066 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.15690376569037656 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.29707112970711297 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3807531380753138 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5083682008368201 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.15690376569037656 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.09902370990237098 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.07615062761506276 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.050836820083682004 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.15690376569037656 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.29707112970711297 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3807531380753138 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5083682008368201 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3138709871801379 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.25412432755528996 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.27566053318396105 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.17364016736401675 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.3138075313807531 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.39539748953974896 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5376569037656904 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.17364016736401675 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.10460251046025104 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.07907949790794978 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05376569037656903 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.17364016736401675 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.3138075313807531 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.39539748953974896 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5376569037656904 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.33244445391299926 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2700023245002324 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.29010151423672403 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.1506276150627615 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.2907949790794979 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.401673640167364 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5355648535564853 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.1506276150627615 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.09693165969316596 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.0803347280334728 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05355648535564853 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1506276150627615 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.2907949790794979 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.401673640167364 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5355648535564853 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3189819772344188 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.25269392973367877 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2728848917988661 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.16736401673640167 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.3200836820083682 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.41631799163179917 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5481171548117155 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.16736401673640167 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.10669456066945607 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.08326359832635982 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05481171548117154 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.16736401673640167 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.3200836820083682 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.41631799163179917 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5481171548117155 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3353691502747181 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.26997077771136346 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2891803614784421 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.15481171548117154 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.28451882845188287 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3514644351464435 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5209205020920502 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.15481171548117154 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.09483960948396093 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.07029288702928871 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.052092050209205015 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.15481171548117154 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.28451882845188287 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3514644351464435 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5209205020920502 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3116868900381799 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2481885501759978 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2685744617473963 name: Cosine Map@100 --- # SentenceTransformer based on BAAI/bge-m3 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the json dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 --> - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("adriansanz/ST-tramits-SITGES-007-5ep") # Run inference sentences = [ 'La comunicació és un element important en la cura dels gats, ja que implica la capacitat per a comunicar-se de manera efectiva amb les autoritats competents i amb els altres implicats en la cura dels animals.', 'Quin és el paper de la comunicació en la cura dels gats?', 'Qui són considerats titulars o nous exercents en el cas dels espectacles, establiments oberts al públic i les activitats recreatives?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_1024` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.159 | | cosine_accuracy@3 | 0.3033 | | cosine_accuracy@5 | 0.3724 | | cosine_accuracy@10 | 0.5188 | | cosine_precision@1 | 0.159 | | cosine_precision@3 | 0.1011 | | cosine_precision@5 | 0.0745 | | cosine_precision@10 | 0.0519 | | cosine_recall@1 | 0.159 | | cosine_recall@3 | 0.3033 | | cosine_recall@5 | 0.3724 | | cosine_recall@10 | 0.5188 | | cosine_ndcg@10 | 0.3174 | | cosine_mrr@10 | 0.256 | | **cosine_map@100** | **0.2763** | #### Information Retrieval * Dataset: `dim_768` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.1569 | | cosine_accuracy@3 | 0.2971 | | cosine_accuracy@5 | 0.3808 | | cosine_accuracy@10 | 0.5084 | | cosine_precision@1 | 0.1569 | | cosine_precision@3 | 0.099 | | cosine_precision@5 | 0.0762 | | cosine_precision@10 | 0.0508 | | cosine_recall@1 | 0.1569 | | cosine_recall@3 | 0.2971 | | cosine_recall@5 | 0.3808 | | cosine_recall@10 | 0.5084 | | cosine_ndcg@10 | 0.3139 | | cosine_mrr@10 | 0.2541 | | **cosine_map@100** | **0.2757** | #### Information Retrieval * Dataset: `dim_512` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.1736 | | cosine_accuracy@3 | 0.3138 | | cosine_accuracy@5 | 0.3954 | | cosine_accuracy@10 | 0.5377 | | cosine_precision@1 | 0.1736 | | cosine_precision@3 | 0.1046 | | cosine_precision@5 | 0.0791 | | cosine_precision@10 | 0.0538 | | cosine_recall@1 | 0.1736 | | cosine_recall@3 | 0.3138 | | cosine_recall@5 | 0.3954 | | cosine_recall@10 | 0.5377 | | cosine_ndcg@10 | 0.3324 | | cosine_mrr@10 | 0.27 | | **cosine_map@100** | **0.2901** | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.1506 | | cosine_accuracy@3 | 0.2908 | | cosine_accuracy@5 | 0.4017 | | cosine_accuracy@10 | 0.5356 | | cosine_precision@1 | 0.1506 | | cosine_precision@3 | 0.0969 | | cosine_precision@5 | 0.0803 | | cosine_precision@10 | 0.0536 | | cosine_recall@1 | 0.1506 | | cosine_recall@3 | 0.2908 | | cosine_recall@5 | 0.4017 | | cosine_recall@10 | 0.5356 | | cosine_ndcg@10 | 0.319 | | cosine_mrr@10 | 0.2527 | | **cosine_map@100** | **0.2729** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.1674 | | cosine_accuracy@3 | 0.3201 | | cosine_accuracy@5 | 0.4163 | | cosine_accuracy@10 | 0.5481 | | cosine_precision@1 | 0.1674 | | cosine_precision@3 | 0.1067 | | cosine_precision@5 | 0.0833 | | cosine_precision@10 | 0.0548 | | cosine_recall@1 | 0.1674 | | cosine_recall@3 | 0.3201 | | cosine_recall@5 | 0.4163 | | cosine_recall@10 | 0.5481 | | cosine_ndcg@10 | 0.3354 | | cosine_mrr@10 | 0.27 | | **cosine_map@100** | **0.2892** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.1548 | | cosine_accuracy@3 | 0.2845 | | cosine_accuracy@5 | 0.3515 | | cosine_accuracy@10 | 0.5209 | | cosine_precision@1 | 0.1548 | | cosine_precision@3 | 0.0948 | | cosine_precision@5 | 0.0703 | | cosine_precision@10 | 0.0521 | | cosine_recall@1 | 0.1548 | | cosine_recall@3 | 0.2845 | | cosine_recall@5 | 0.3515 | | cosine_recall@10 | 0.5209 | | cosine_ndcg@10 | 0.3117 | | cosine_mrr@10 | 0.2482 | | **cosine_map@100** | **0.2686** | <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### json * Dataset: json * Size: 6,692 training samples * Columns: <code>positive</code> and <code>anchor</code> * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 6 tokens</li><li>mean: 44.83 tokens</li><li>max: 185 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.89 tokens</li><li>max: 49 tokens</li></ul> | * Samples: | positive | anchor | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------| | <code>Els residus comercials o industrials assimilables als municipals que hauran d'acreditar si disposen d'un gestor autoritzat per a la gestió dels residus.</code> | <code>Quins són els residus que es recullen en el servei municipal complementari?</code> | | <code>L'Ajuntament de Sitges ofereix ajuts econòmics a famílies amb recursos insuficients per accedir a la realització d'activitats de lleure...</code> | <code>Quin és el paper de l'Ajuntament de Sitges en la promoció de l'educació no formal i de lleure?</code> | | <code>Permet comunicar les intervencions necessàries per executar una instal·lació/remodelació d’autoconsum amb energia solar fotovoltaica amb una potència instal·lada inferior a 100 kWp en sòl urbà consolidat.</code> | <code>Quin és el propòsit de la remodelació d'una instal·lació d'autoconsum?</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 5 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.2 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.2 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: True - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | dim_1024_cosine_map@100 | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.3819 | 10 | 3.3449 | - | - | - | - | - | - | | 0.7637 | 20 | 2.0557 | - | - | - | - | - | - | | 0.9928 | 26 | - | 0.2440 | 0.2408 | 0.2590 | 0.2439 | 0.2379 | 0.2512 | | 1.1456 | 30 | 1.4634 | - | - | - | - | - | - | | 1.5274 | 40 | 0.8163 | - | - | - | - | - | - | | 1.9093 | 50 | 0.6103 | - | - | - | - | - | - | | 1.9857 | 52 | - | 0.2621 | 0.2683 | 0.2483 | 0.2629 | 0.2404 | 0.2472 | | 2.2912 | 60 | 0.4854 | - | - | - | - | - | - | | 2.6730 | 70 | 0.2796 | - | - | - | - | - | - | | 2.9785 | 78 | - | 0.2701 | 0.2697 | 0.2761 | 0.2845 | 0.2673 | 0.2709 | | 3.0549 | 80 | 0.2458 | - | - | - | - | - | - | | 3.4368 | 90 | 0.2616 | - | - | - | - | - | - | | 3.8186 | 100 | 0.174 | - | - | - | - | - | - | | 3.9714 | 104 | - | 0.2729 | 0.2863 | 0.2858 | 0.2853 | 0.2656 | 0.2752 | | 4.2005 | 110 | 0.1841 | - | - | - | - | - | - | | 4.5823 | 120 | 0.1668 | - | - | - | - | - | - | | **4.9642** | **130** | **0.1484** | **0.2763** | **0.2892** | **0.2729** | **0.2901** | **0.2686** | **0.2757** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.1.1 - Transformers: 4.44.2 - PyTorch: 2.4.1+cu121 - Accelerate: 0.35.0.dev0 - Datasets: 3.0.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION" ]
[ "CAS" ]
Non_BioNLP
BAAI/bge-small-en
BAAI
feature-extraction
[ "transformers", "pytorch", "safetensors", "bert", "feature-extraction", "mteb", "sentence transformers", "en", "arxiv:2311.13534", "arxiv:2310.07554", "arxiv:2309.07597", "license:mit", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,691
1,702
419,855
74
--- language: - en license: mit tags: - mteb - sentence transformers model-index: - name: bge-small-en results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 74.34328358208955 - type: ap value: 37.59947775195661 - type: f1 value: 68.548415491933 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.04527499999999 - type: ap value: 89.60696356772135 - type: f1 value: 93.03361469382438 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.08 - type: f1 value: 45.66249835363254 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 35.205999999999996 - type: map_at_10 value: 50.782000000000004 - type: map_at_100 value: 51.547 - type: map_at_1000 value: 51.554 - type: map_at_3 value: 46.515 - type: map_at_5 value: 49.296 - type: mrr_at_1 value: 35.632999999999996 - type: mrr_at_10 value: 50.958999999999996 - type: mrr_at_100 value: 51.724000000000004 - type: mrr_at_1000 value: 51.731 - type: mrr_at_3 value: 46.669 - type: mrr_at_5 value: 49.439 - type: ndcg_at_1 value: 35.205999999999996 - type: ndcg_at_10 value: 58.835 - type: ndcg_at_100 value: 62.095 - type: ndcg_at_1000 value: 62.255 - type: ndcg_at_3 value: 50.255 - type: ndcg_at_5 value: 55.296 - type: precision_at_1 value: 35.205999999999996 - type: precision_at_10 value: 8.421 - type: precision_at_100 value: 0.984 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.365 - type: precision_at_5 value: 14.680000000000001 - type: recall_at_1 value: 35.205999999999996 - type: recall_at_10 value: 84.211 - type: recall_at_100 value: 98.43499999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 61.095 - type: recall_at_5 value: 73.4 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.52644476278646 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 39.973045724188964 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.28285314871488 - type: mrr value: 74.52743701358659 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 80.09041909160327 - type: cos_sim_spearman value: 79.96266537706944 - type: euclidean_pearson value: 79.50774978162241 - type: euclidean_spearman value: 79.9144715078551 - type: manhattan_pearson value: 79.2062139879302 - type: manhattan_spearman value: 79.35000081468212 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.31493506493506 - type: f1 value: 85.2704557977762 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.6837242810816 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 35.38881249555897 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 27.884999999999998 - type: map_at_10 value: 39.574 - type: map_at_100 value: 40.993 - type: map_at_1000 value: 41.129 - type: map_at_3 value: 36.089 - type: map_at_5 value: 38.191 - type: mrr_at_1 value: 34.477999999999994 - type: mrr_at_10 value: 45.411 - type: mrr_at_100 value: 46.089999999999996 - type: mrr_at_1000 value: 46.147 - type: mrr_at_3 value: 42.346000000000004 - type: mrr_at_5 value: 44.292 - type: ndcg_at_1 value: 34.477999999999994 - type: ndcg_at_10 value: 46.123999999999995 - type: ndcg_at_100 value: 51.349999999999994 - type: ndcg_at_1000 value: 53.578 - type: ndcg_at_3 value: 40.824 - type: ndcg_at_5 value: 43.571 - type: precision_at_1 value: 34.477999999999994 - type: precision_at_10 value: 8.841000000000001 - type: precision_at_100 value: 1.4460000000000002 - type: precision_at_1000 value: 0.192 - type: precision_at_3 value: 19.742 - type: precision_at_5 value: 14.421000000000001 - type: recall_at_1 value: 27.884999999999998 - type: recall_at_10 value: 59.087 - type: recall_at_100 value: 80.609 - type: recall_at_1000 value: 95.054 - type: recall_at_3 value: 44.082 - type: recall_at_5 value: 51.593999999999994 - type: map_at_1 value: 30.639 - type: map_at_10 value: 40.047 - type: map_at_100 value: 41.302 - type: map_at_1000 value: 41.425 - type: map_at_3 value: 37.406 - type: map_at_5 value: 38.934000000000005 - type: mrr_at_1 value: 37.707 - type: mrr_at_10 value: 46.082 - type: mrr_at_100 value: 46.745 - type: mrr_at_1000 value: 46.786 - type: mrr_at_3 value: 43.980999999999995 - type: mrr_at_5 value: 45.287 - type: ndcg_at_1 value: 37.707 - type: ndcg_at_10 value: 45.525 - type: ndcg_at_100 value: 49.976 - type: ndcg_at_1000 value: 51.94499999999999 - type: ndcg_at_3 value: 41.704 - type: ndcg_at_5 value: 43.596000000000004 - type: precision_at_1 value: 37.707 - type: precision_at_10 value: 8.465 - type: precision_at_100 value: 1.375 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 19.979 - type: precision_at_5 value: 14.115 - type: recall_at_1 value: 30.639 - type: recall_at_10 value: 54.775 - type: recall_at_100 value: 73.678 - type: recall_at_1000 value: 86.142 - type: recall_at_3 value: 43.230000000000004 - type: recall_at_5 value: 48.622 - type: map_at_1 value: 38.038 - type: map_at_10 value: 49.922 - type: map_at_100 value: 51.032 - type: map_at_1000 value: 51.085 - type: map_at_3 value: 46.664 - type: map_at_5 value: 48.588 - type: mrr_at_1 value: 43.95 - type: mrr_at_10 value: 53.566 - type: mrr_at_100 value: 54.318999999999996 - type: mrr_at_1000 value: 54.348 - type: mrr_at_3 value: 51.066 - type: mrr_at_5 value: 52.649 - type: ndcg_at_1 value: 43.95 - type: ndcg_at_10 value: 55.676 - type: ndcg_at_100 value: 60.126000000000005 - type: ndcg_at_1000 value: 61.208 - type: ndcg_at_3 value: 50.20400000000001 - type: ndcg_at_5 value: 53.038 - type: precision_at_1 value: 43.95 - type: precision_at_10 value: 8.953 - type: precision_at_100 value: 1.2109999999999999 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 22.256999999999998 - type: precision_at_5 value: 15.524 - type: recall_at_1 value: 38.038 - type: recall_at_10 value: 69.15 - type: recall_at_100 value: 88.31599999999999 - type: recall_at_1000 value: 95.993 - type: recall_at_3 value: 54.663 - type: recall_at_5 value: 61.373 - type: map_at_1 value: 24.872 - type: map_at_10 value: 32.912 - type: map_at_100 value: 33.972 - type: map_at_1000 value: 34.046 - type: map_at_3 value: 30.361 - type: map_at_5 value: 31.704 - type: mrr_at_1 value: 26.779999999999998 - type: mrr_at_10 value: 34.812 - type: mrr_at_100 value: 35.754999999999995 - type: mrr_at_1000 value: 35.809000000000005 - type: mrr_at_3 value: 32.335 - type: mrr_at_5 value: 33.64 - type: ndcg_at_1 value: 26.779999999999998 - type: ndcg_at_10 value: 37.623 - type: ndcg_at_100 value: 42.924 - type: ndcg_at_1000 value: 44.856 - type: ndcg_at_3 value: 32.574 - type: ndcg_at_5 value: 34.842 - type: precision_at_1 value: 26.779999999999998 - type: precision_at_10 value: 5.729 - type: precision_at_100 value: 0.886 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 13.559 - type: precision_at_5 value: 9.469 - type: recall_at_1 value: 24.872 - type: recall_at_10 value: 50.400999999999996 - type: recall_at_100 value: 74.954 - type: recall_at_1000 value: 89.56 - type: recall_at_3 value: 36.726 - type: recall_at_5 value: 42.138999999999996 - type: map_at_1 value: 16.803 - type: map_at_10 value: 24.348 - type: map_at_100 value: 25.56 - type: map_at_1000 value: 25.668000000000003 - type: map_at_3 value: 21.811 - type: map_at_5 value: 23.287 - type: mrr_at_1 value: 20.771 - type: mrr_at_10 value: 28.961 - type: mrr_at_100 value: 29.979 - type: mrr_at_1000 value: 30.046 - type: mrr_at_3 value: 26.555 - type: mrr_at_5 value: 28.060000000000002 - type: ndcg_at_1 value: 20.771 - type: ndcg_at_10 value: 29.335 - type: ndcg_at_100 value: 35.188 - type: ndcg_at_1000 value: 37.812 - type: ndcg_at_3 value: 24.83 - type: ndcg_at_5 value: 27.119 - type: precision_at_1 value: 20.771 - type: precision_at_10 value: 5.4350000000000005 - type: precision_at_100 value: 0.9480000000000001 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 11.982 - type: precision_at_5 value: 8.831 - type: recall_at_1 value: 16.803 - type: recall_at_10 value: 40.039 - type: recall_at_100 value: 65.83200000000001 - type: recall_at_1000 value: 84.478 - type: recall_at_3 value: 27.682000000000002 - type: recall_at_5 value: 33.535 - type: map_at_1 value: 28.345 - type: map_at_10 value: 37.757000000000005 - type: map_at_100 value: 39.141 - type: map_at_1000 value: 39.262 - type: map_at_3 value: 35.183 - type: map_at_5 value: 36.592 - type: mrr_at_1 value: 34.649 - type: mrr_at_10 value: 43.586999999999996 - type: mrr_at_100 value: 44.481 - type: mrr_at_1000 value: 44.542 - type: mrr_at_3 value: 41.29 - type: mrr_at_5 value: 42.642 - type: ndcg_at_1 value: 34.649 - type: ndcg_at_10 value: 43.161 - type: ndcg_at_100 value: 48.734 - type: ndcg_at_1000 value: 51.046 - type: ndcg_at_3 value: 39.118 - type: ndcg_at_5 value: 41.022 - type: precision_at_1 value: 34.649 - type: precision_at_10 value: 7.603 - type: precision_at_100 value: 1.209 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 18.319 - type: precision_at_5 value: 12.839 - type: recall_at_1 value: 28.345 - type: recall_at_10 value: 53.367 - type: recall_at_100 value: 76.453 - type: recall_at_1000 value: 91.82000000000001 - type: recall_at_3 value: 41.636 - type: recall_at_5 value: 46.760000000000005 - type: map_at_1 value: 22.419 - type: map_at_10 value: 31.716 - type: map_at_100 value: 33.152 - type: map_at_1000 value: 33.267 - type: map_at_3 value: 28.74 - type: map_at_5 value: 30.48 - type: mrr_at_1 value: 28.310999999999996 - type: mrr_at_10 value: 37.039 - type: mrr_at_100 value: 38.09 - type: mrr_at_1000 value: 38.145 - type: mrr_at_3 value: 34.437 - type: mrr_at_5 value: 36.024 - type: ndcg_at_1 value: 28.310999999999996 - type: ndcg_at_10 value: 37.41 - type: ndcg_at_100 value: 43.647999999999996 - type: ndcg_at_1000 value: 46.007 - type: ndcg_at_3 value: 32.509 - type: ndcg_at_5 value: 34.943999999999996 - type: precision_at_1 value: 28.310999999999996 - type: precision_at_10 value: 6.963 - type: precision_at_100 value: 1.1860000000000002 - type: precision_at_1000 value: 0.154 - type: precision_at_3 value: 15.867999999999999 - type: precision_at_5 value: 11.507000000000001 - type: recall_at_1 value: 22.419 - type: recall_at_10 value: 49.28 - type: recall_at_100 value: 75.802 - type: recall_at_1000 value: 92.032 - type: recall_at_3 value: 35.399 - type: recall_at_5 value: 42.027 - type: map_at_1 value: 24.669249999999998 - type: map_at_10 value: 33.332583333333325 - type: map_at_100 value: 34.557833333333335 - type: map_at_1000 value: 34.67141666666666 - type: map_at_3 value: 30.663166666666662 - type: map_at_5 value: 32.14883333333333 - type: mrr_at_1 value: 29.193833333333334 - type: mrr_at_10 value: 37.47625 - type: mrr_at_100 value: 38.3545 - type: mrr_at_1000 value: 38.413166666666676 - type: mrr_at_3 value: 35.06741666666667 - type: mrr_at_5 value: 36.450666666666656 - type: ndcg_at_1 value: 29.193833333333334 - type: ndcg_at_10 value: 38.505416666666676 - type: ndcg_at_100 value: 43.81125 - type: ndcg_at_1000 value: 46.09558333333333 - type: ndcg_at_3 value: 33.90916666666667 - type: ndcg_at_5 value: 36.07666666666666 - type: precision_at_1 value: 29.193833333333334 - type: precision_at_10 value: 6.7251666666666665 - type: precision_at_100 value: 1.1058333333333332 - type: precision_at_1000 value: 0.14833333333333332 - type: precision_at_3 value: 15.554166666666665 - type: precision_at_5 value: 11.079250000000002 - type: recall_at_1 value: 24.669249999999998 - type: recall_at_10 value: 49.75583333333332 - type: recall_at_100 value: 73.06908333333332 - type: recall_at_1000 value: 88.91316666666667 - type: recall_at_3 value: 36.913250000000005 - type: recall_at_5 value: 42.48641666666666 - type: map_at_1 value: 24.044999999999998 - type: map_at_10 value: 30.349999999999998 - type: map_at_100 value: 31.273 - type: map_at_1000 value: 31.362000000000002 - type: map_at_3 value: 28.508 - type: map_at_5 value: 29.369 - type: mrr_at_1 value: 26.994 - type: mrr_at_10 value: 33.12 - type: mrr_at_100 value: 33.904 - type: mrr_at_1000 value: 33.967000000000006 - type: mrr_at_3 value: 31.365 - type: mrr_at_5 value: 32.124 - type: ndcg_at_1 value: 26.994 - type: ndcg_at_10 value: 34.214 - type: ndcg_at_100 value: 38.681 - type: ndcg_at_1000 value: 40.926 - type: ndcg_at_3 value: 30.725 - type: ndcg_at_5 value: 31.967000000000002 - type: precision_at_1 value: 26.994 - type: precision_at_10 value: 5.215 - type: precision_at_100 value: 0.807 - type: precision_at_1000 value: 0.108 - type: precision_at_3 value: 12.986 - type: precision_at_5 value: 8.712 - type: recall_at_1 value: 24.044999999999998 - type: recall_at_10 value: 43.456 - type: recall_at_100 value: 63.675000000000004 - type: recall_at_1000 value: 80.05499999999999 - type: recall_at_3 value: 33.561 - type: recall_at_5 value: 36.767 - type: map_at_1 value: 15.672 - type: map_at_10 value: 22.641 - type: map_at_100 value: 23.75 - type: map_at_1000 value: 23.877000000000002 - type: map_at_3 value: 20.219 - type: map_at_5 value: 21.648 - type: mrr_at_1 value: 18.823 - type: mrr_at_10 value: 26.101999999999997 - type: mrr_at_100 value: 27.038 - type: mrr_at_1000 value: 27.118 - type: mrr_at_3 value: 23.669 - type: mrr_at_5 value: 25.173000000000002 - type: ndcg_at_1 value: 18.823 - type: ndcg_at_10 value: 27.176000000000002 - type: ndcg_at_100 value: 32.42 - type: ndcg_at_1000 value: 35.413 - type: ndcg_at_3 value: 22.756999999999998 - type: ndcg_at_5 value: 25.032 - type: precision_at_1 value: 18.823 - type: precision_at_10 value: 5.034000000000001 - type: precision_at_100 value: 0.895 - type: precision_at_1000 value: 0.132 - type: precision_at_3 value: 10.771 - type: precision_at_5 value: 8.1 - type: recall_at_1 value: 15.672 - type: recall_at_10 value: 37.296 - type: recall_at_100 value: 60.863 - type: recall_at_1000 value: 82.234 - type: recall_at_3 value: 25.330000000000002 - type: recall_at_5 value: 30.964000000000002 - type: map_at_1 value: 24.633 - type: map_at_10 value: 32.858 - type: map_at_100 value: 34.038000000000004 - type: map_at_1000 value: 34.141 - type: map_at_3 value: 30.209000000000003 - type: map_at_5 value: 31.567 - type: mrr_at_1 value: 28.358 - type: mrr_at_10 value: 36.433 - type: mrr_at_100 value: 37.352000000000004 - type: mrr_at_1000 value: 37.41 - type: mrr_at_3 value: 34.033 - type: mrr_at_5 value: 35.246 - type: ndcg_at_1 value: 28.358 - type: ndcg_at_10 value: 37.973 - type: ndcg_at_100 value: 43.411 - type: ndcg_at_1000 value: 45.747 - type: ndcg_at_3 value: 32.934999999999995 - type: ndcg_at_5 value: 35.013 - type: precision_at_1 value: 28.358 - type: precision_at_10 value: 6.418 - type: precision_at_100 value: 1.02 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 14.677000000000001 - type: precision_at_5 value: 10.335999999999999 - type: recall_at_1 value: 24.633 - type: recall_at_10 value: 50.048 - type: recall_at_100 value: 73.821 - type: recall_at_1000 value: 90.046 - type: recall_at_3 value: 36.284 - type: recall_at_5 value: 41.370000000000005 - type: map_at_1 value: 23.133 - type: map_at_10 value: 31.491999999999997 - type: map_at_100 value: 33.062000000000005 - type: map_at_1000 value: 33.256 - type: map_at_3 value: 28.886 - type: map_at_5 value: 30.262 - type: mrr_at_1 value: 28.063 - type: mrr_at_10 value: 36.144 - type: mrr_at_100 value: 37.14 - type: mrr_at_1000 value: 37.191 - type: mrr_at_3 value: 33.762 - type: mrr_at_5 value: 34.997 - type: ndcg_at_1 value: 28.063 - type: ndcg_at_10 value: 36.951 - type: ndcg_at_100 value: 43.287 - type: ndcg_at_1000 value: 45.777 - type: ndcg_at_3 value: 32.786 - type: ndcg_at_5 value: 34.65 - type: precision_at_1 value: 28.063 - type: precision_at_10 value: 7.055 - type: precision_at_100 value: 1.476 - type: precision_at_1000 value: 0.22899999999999998 - type: precision_at_3 value: 15.481 - type: precision_at_5 value: 11.186 - type: recall_at_1 value: 23.133 - type: recall_at_10 value: 47.285 - type: recall_at_100 value: 76.176 - type: recall_at_1000 value: 92.176 - type: recall_at_3 value: 35.223 - type: recall_at_5 value: 40.142 - type: map_at_1 value: 19.547 - type: map_at_10 value: 26.374 - type: map_at_100 value: 27.419 - type: map_at_1000 value: 27.539 - type: map_at_3 value: 23.882 - type: map_at_5 value: 25.163999999999998 - type: mrr_at_1 value: 21.442 - type: mrr_at_10 value: 28.458 - type: mrr_at_100 value: 29.360999999999997 - type: mrr_at_1000 value: 29.448999999999998 - type: mrr_at_3 value: 25.97 - type: mrr_at_5 value: 27.273999999999997 - type: ndcg_at_1 value: 21.442 - type: ndcg_at_10 value: 30.897000000000002 - type: ndcg_at_100 value: 35.99 - type: ndcg_at_1000 value: 38.832 - type: ndcg_at_3 value: 25.944 - type: ndcg_at_5 value: 28.126 - type: precision_at_1 value: 21.442 - type: precision_at_10 value: 4.9910000000000005 - type: precision_at_100 value: 0.8109999999999999 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 11.029 - type: precision_at_5 value: 7.911 - type: recall_at_1 value: 19.547 - type: recall_at_10 value: 42.886 - type: recall_at_100 value: 66.64999999999999 - type: recall_at_1000 value: 87.368 - type: recall_at_3 value: 29.143 - type: recall_at_5 value: 34.544000000000004 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 15.572 - type: map_at_10 value: 25.312 - type: map_at_100 value: 27.062 - type: map_at_1000 value: 27.253 - type: map_at_3 value: 21.601 - type: map_at_5 value: 23.473 - type: mrr_at_1 value: 34.984 - type: mrr_at_10 value: 46.406 - type: mrr_at_100 value: 47.179 - type: mrr_at_1000 value: 47.21 - type: mrr_at_3 value: 43.485 - type: mrr_at_5 value: 45.322 - type: ndcg_at_1 value: 34.984 - type: ndcg_at_10 value: 34.344 - type: ndcg_at_100 value: 41.015 - type: ndcg_at_1000 value: 44.366 - type: ndcg_at_3 value: 29.119 - type: ndcg_at_5 value: 30.825999999999997 - type: precision_at_1 value: 34.984 - type: precision_at_10 value: 10.358 - type: precision_at_100 value: 1.762 - type: precision_at_1000 value: 0.23900000000000002 - type: precision_at_3 value: 21.368000000000002 - type: precision_at_5 value: 15.948 - type: recall_at_1 value: 15.572 - type: recall_at_10 value: 39.367999999999995 - type: recall_at_100 value: 62.183 - type: recall_at_1000 value: 80.92200000000001 - type: recall_at_3 value: 26.131999999999998 - type: recall_at_5 value: 31.635999999999996 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.848 - type: map_at_10 value: 19.25 - type: map_at_100 value: 27.193 - type: map_at_1000 value: 28.721999999999998 - type: map_at_3 value: 13.968 - type: map_at_5 value: 16.283 - type: mrr_at_1 value: 68.75 - type: mrr_at_10 value: 76.25 - type: mrr_at_100 value: 76.534 - type: mrr_at_1000 value: 76.53999999999999 - type: mrr_at_3 value: 74.667 - type: mrr_at_5 value: 75.86699999999999 - type: ndcg_at_1 value: 56.00000000000001 - type: ndcg_at_10 value: 41.426 - type: ndcg_at_100 value: 45.660000000000004 - type: ndcg_at_1000 value: 53.02 - type: ndcg_at_3 value: 46.581 - type: ndcg_at_5 value: 43.836999999999996 - type: precision_at_1 value: 68.75 - type: precision_at_10 value: 32.800000000000004 - type: precision_at_100 value: 10.440000000000001 - type: precision_at_1000 value: 1.9980000000000002 - type: precision_at_3 value: 49.667 - type: precision_at_5 value: 42.25 - type: recall_at_1 value: 8.848 - type: recall_at_10 value: 24.467 - type: recall_at_100 value: 51.344 - type: recall_at_1000 value: 75.235 - type: recall_at_3 value: 15.329 - type: recall_at_5 value: 18.892999999999997 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.95 - type: f1 value: 43.44563593360779 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 78.036 - type: map_at_10 value: 85.639 - type: map_at_100 value: 85.815 - type: map_at_1000 value: 85.829 - type: map_at_3 value: 84.795 - type: map_at_5 value: 85.336 - type: mrr_at_1 value: 84.353 - type: mrr_at_10 value: 90.582 - type: mrr_at_100 value: 90.617 - type: mrr_at_1000 value: 90.617 - type: mrr_at_3 value: 90.132 - type: mrr_at_5 value: 90.447 - type: ndcg_at_1 value: 84.353 - type: ndcg_at_10 value: 89.003 - type: ndcg_at_100 value: 89.60000000000001 - type: ndcg_at_1000 value: 89.836 - type: ndcg_at_3 value: 87.81400000000001 - type: ndcg_at_5 value: 88.478 - type: precision_at_1 value: 84.353 - type: precision_at_10 value: 10.482 - type: precision_at_100 value: 1.099 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 33.257999999999996 - type: precision_at_5 value: 20.465 - type: recall_at_1 value: 78.036 - type: recall_at_10 value: 94.517 - type: recall_at_100 value: 96.828 - type: recall_at_1000 value: 98.261 - type: recall_at_3 value: 91.12 - type: recall_at_5 value: 92.946 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 20.191 - type: map_at_10 value: 32.369 - type: map_at_100 value: 34.123999999999995 - type: map_at_1000 value: 34.317 - type: map_at_3 value: 28.71 - type: map_at_5 value: 30.607 - type: mrr_at_1 value: 40.894999999999996 - type: mrr_at_10 value: 48.842 - type: mrr_at_100 value: 49.599 - type: mrr_at_1000 value: 49.647000000000006 - type: mrr_at_3 value: 46.785 - type: mrr_at_5 value: 47.672 - type: ndcg_at_1 value: 40.894999999999996 - type: ndcg_at_10 value: 39.872 - type: ndcg_at_100 value: 46.126 - type: ndcg_at_1000 value: 49.476 - type: ndcg_at_3 value: 37.153000000000006 - type: ndcg_at_5 value: 37.433 - type: precision_at_1 value: 40.894999999999996 - type: precision_at_10 value: 10.818 - type: precision_at_100 value: 1.73 - type: precision_at_1000 value: 0.231 - type: precision_at_3 value: 25.051000000000002 - type: precision_at_5 value: 17.531 - type: recall_at_1 value: 20.191 - type: recall_at_10 value: 45.768 - type: recall_at_100 value: 68.82000000000001 - type: recall_at_1000 value: 89.133 - type: recall_at_3 value: 33.296 - type: recall_at_5 value: 38.022 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 39.257 - type: map_at_10 value: 61.467000000000006 - type: map_at_100 value: 62.364 - type: map_at_1000 value: 62.424 - type: map_at_3 value: 58.228 - type: map_at_5 value: 60.283 - type: mrr_at_1 value: 78.515 - type: mrr_at_10 value: 84.191 - type: mrr_at_100 value: 84.378 - type: mrr_at_1000 value: 84.385 - type: mrr_at_3 value: 83.284 - type: mrr_at_5 value: 83.856 - type: ndcg_at_1 value: 78.515 - type: ndcg_at_10 value: 69.78999999999999 - type: ndcg_at_100 value: 72.886 - type: ndcg_at_1000 value: 74.015 - type: ndcg_at_3 value: 65.23 - type: ndcg_at_5 value: 67.80199999999999 - type: precision_at_1 value: 78.515 - type: precision_at_10 value: 14.519000000000002 - type: precision_at_100 value: 1.694 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 41.702 - type: precision_at_5 value: 27.046999999999997 - type: recall_at_1 value: 39.257 - type: recall_at_10 value: 72.59299999999999 - type: recall_at_100 value: 84.679 - type: recall_at_1000 value: 92.12 - type: recall_at_3 value: 62.552 - type: recall_at_5 value: 67.616 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 91.5152 - type: ap value: 87.64584669595709 - type: f1 value: 91.50605576428437 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 21.926000000000002 - type: map_at_10 value: 34.049 - type: map_at_100 value: 35.213 - type: map_at_1000 value: 35.265 - type: map_at_3 value: 30.309 - type: map_at_5 value: 32.407000000000004 - type: mrr_at_1 value: 22.55 - type: mrr_at_10 value: 34.657 - type: mrr_at_100 value: 35.760999999999996 - type: mrr_at_1000 value: 35.807 - type: mrr_at_3 value: 30.989 - type: mrr_at_5 value: 33.039 - type: ndcg_at_1 value: 22.55 - type: ndcg_at_10 value: 40.842 - type: ndcg_at_100 value: 46.436 - type: ndcg_at_1000 value: 47.721999999999994 - type: ndcg_at_3 value: 33.209 - type: ndcg_at_5 value: 36.943 - type: precision_at_1 value: 22.55 - type: precision_at_10 value: 6.447 - type: precision_at_100 value: 0.9249999999999999 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.136000000000001 - type: precision_at_5 value: 10.381 - type: recall_at_1 value: 21.926000000000002 - type: recall_at_10 value: 61.724999999999994 - type: recall_at_100 value: 87.604 - type: recall_at_1000 value: 97.421 - type: recall_at_3 value: 40.944 - type: recall_at_5 value: 49.915 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.54765161878704 - type: f1 value: 93.3298945415573 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 75.71591427268582 - type: f1 value: 59.32113870474471 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 75.83053127101547 - type: f1 value: 73.60757944876475 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.72562205783457 - type: f1 value: 78.63761662505502 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.37935633767996 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.55270546130387 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.462692753143834 - type: mrr value: 31.497569753511563 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.646 - type: map_at_10 value: 12.498 - type: map_at_100 value: 15.486 - type: map_at_1000 value: 16.805999999999997 - type: map_at_3 value: 9.325 - type: map_at_5 value: 10.751 - type: mrr_at_1 value: 43.034 - type: mrr_at_10 value: 52.662 - type: mrr_at_100 value: 53.189 - type: mrr_at_1000 value: 53.25 - type: mrr_at_3 value: 50.929 - type: mrr_at_5 value: 51.92 - type: ndcg_at_1 value: 41.796 - type: ndcg_at_10 value: 33.477000000000004 - type: ndcg_at_100 value: 29.996000000000002 - type: ndcg_at_1000 value: 38.864 - type: ndcg_at_3 value: 38.940000000000005 - type: ndcg_at_5 value: 36.689 - type: precision_at_1 value: 43.034 - type: precision_at_10 value: 24.799 - type: precision_at_100 value: 7.432999999999999 - type: precision_at_1000 value: 1.9929999999999999 - type: precision_at_3 value: 36.842000000000006 - type: precision_at_5 value: 32.135999999999996 - type: recall_at_1 value: 5.646 - type: recall_at_10 value: 15.963 - type: recall_at_100 value: 29.492 - type: recall_at_1000 value: 61.711000000000006 - type: recall_at_3 value: 10.585 - type: recall_at_5 value: 12.753999999999998 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 27.602 - type: map_at_10 value: 41.545 - type: map_at_100 value: 42.644999999999996 - type: map_at_1000 value: 42.685 - type: map_at_3 value: 37.261 - type: map_at_5 value: 39.706 - type: mrr_at_1 value: 31.141000000000002 - type: mrr_at_10 value: 44.139 - type: mrr_at_100 value: 44.997 - type: mrr_at_1000 value: 45.025999999999996 - type: mrr_at_3 value: 40.503 - type: mrr_at_5 value: 42.64 - type: ndcg_at_1 value: 31.141000000000002 - type: ndcg_at_10 value: 48.995 - type: ndcg_at_100 value: 53.788000000000004 - type: ndcg_at_1000 value: 54.730000000000004 - type: ndcg_at_3 value: 40.844 - type: ndcg_at_5 value: 44.955 - type: precision_at_1 value: 31.141000000000002 - type: precision_at_10 value: 8.233 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 18.579 - type: precision_at_5 value: 13.533999999999999 - type: recall_at_1 value: 27.602 - type: recall_at_10 value: 69.216 - type: recall_at_100 value: 90.252 - type: recall_at_1000 value: 97.27 - type: recall_at_3 value: 47.987 - type: recall_at_5 value: 57.438 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 70.949 - type: map_at_10 value: 84.89999999999999 - type: map_at_100 value: 85.531 - type: map_at_1000 value: 85.548 - type: map_at_3 value: 82.027 - type: map_at_5 value: 83.853 - type: mrr_at_1 value: 81.69999999999999 - type: mrr_at_10 value: 87.813 - type: mrr_at_100 value: 87.917 - type: mrr_at_1000 value: 87.91799999999999 - type: mrr_at_3 value: 86.938 - type: mrr_at_5 value: 87.53999999999999 - type: ndcg_at_1 value: 81.75 - type: ndcg_at_10 value: 88.55499999999999 - type: ndcg_at_100 value: 89.765 - type: ndcg_at_1000 value: 89.871 - type: ndcg_at_3 value: 85.905 - type: ndcg_at_5 value: 87.41 - type: precision_at_1 value: 81.75 - type: precision_at_10 value: 13.403 - type: precision_at_100 value: 1.528 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.597 - type: precision_at_5 value: 24.69 - type: recall_at_1 value: 70.949 - type: recall_at_10 value: 95.423 - type: recall_at_100 value: 99.509 - type: recall_at_1000 value: 99.982 - type: recall_at_3 value: 87.717 - type: recall_at_5 value: 92.032 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 51.76962893449579 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 62.32897690686379 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.478 - type: map_at_10 value: 11.994 - type: map_at_100 value: 13.977 - type: map_at_1000 value: 14.295 - type: map_at_3 value: 8.408999999999999 - type: map_at_5 value: 10.024 - type: mrr_at_1 value: 22.1 - type: mrr_at_10 value: 33.526 - type: mrr_at_100 value: 34.577000000000005 - type: mrr_at_1000 value: 34.632000000000005 - type: mrr_at_3 value: 30.217 - type: mrr_at_5 value: 31.962000000000003 - type: ndcg_at_1 value: 22.1 - type: ndcg_at_10 value: 20.191 - type: ndcg_at_100 value: 27.954 - type: ndcg_at_1000 value: 33.491 - type: ndcg_at_3 value: 18.787000000000003 - type: ndcg_at_5 value: 16.378999999999998 - type: precision_at_1 value: 22.1 - type: precision_at_10 value: 10.69 - type: precision_at_100 value: 2.1919999999999997 - type: precision_at_1000 value: 0.35200000000000004 - type: precision_at_3 value: 17.732999999999997 - type: precision_at_5 value: 14.499999999999998 - type: recall_at_1 value: 4.478 - type: recall_at_10 value: 21.657 - type: recall_at_100 value: 44.54 - type: recall_at_1000 value: 71.542 - type: recall_at_3 value: 10.778 - type: recall_at_5 value: 14.687 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 82.82325259156718 - type: cos_sim_spearman value: 79.2463589100662 - type: euclidean_pearson value: 80.48318380496771 - type: euclidean_spearman value: 79.34451935199979 - type: manhattan_pearson value: 80.39041824178759 - type: manhattan_spearman value: 79.23002892700211 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.74130231431258 - type: cos_sim_spearman value: 78.36856568042397 - type: euclidean_pearson value: 82.48301631890303 - type: euclidean_spearman value: 78.28376980722732 - type: manhattan_pearson value: 82.43552075450525 - type: manhattan_spearman value: 78.22702443947126 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 79.96138619461459 - type: cos_sim_spearman value: 81.85436343502379 - type: euclidean_pearson value: 81.82895226665367 - type: euclidean_spearman value: 82.22707349602916 - type: manhattan_pearson value: 81.66303369445873 - type: manhattan_spearman value: 82.05030197179455 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 80.05481244198648 - type: cos_sim_spearman value: 80.85052504637808 - type: euclidean_pearson value: 80.86728419744497 - type: euclidean_spearman value: 81.033786401512 - type: manhattan_pearson value: 80.90107531061103 - type: manhattan_spearman value: 81.11374116827795 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 84.615220756399 - type: cos_sim_spearman value: 86.46858500002092 - type: euclidean_pearson value: 86.08307800247586 - type: euclidean_spearman value: 86.72691443870013 - type: manhattan_pearson value: 85.96155594487269 - type: manhattan_spearman value: 86.605909505275 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.14363913634436 - type: cos_sim_spearman value: 84.48430226487102 - type: euclidean_pearson value: 83.75303424801902 - type: euclidean_spearman value: 84.56762380734538 - type: manhattan_pearson value: 83.6135447165928 - type: manhattan_spearman value: 84.39898212616731 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 85.09909252554525 - type: cos_sim_spearman value: 85.70951402743276 - type: euclidean_pearson value: 87.1991936239908 - type: euclidean_spearman value: 86.07745840612071 - type: manhattan_pearson value: 87.25039137549952 - type: manhattan_spearman value: 85.99938746659761 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 63.529332093413615 - type: cos_sim_spearman value: 65.38177340147439 - type: euclidean_pearson value: 66.35278011412136 - type: euclidean_spearman value: 65.47147267032997 - type: manhattan_pearson value: 66.71804682408693 - type: manhattan_spearman value: 65.67406521423597 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 82.45802942885662 - type: cos_sim_spearman value: 84.8853341842566 - type: euclidean_pearson value: 84.60915021096707 - type: euclidean_spearman value: 85.11181242913666 - type: manhattan_pearson value: 84.38600521210364 - type: manhattan_spearman value: 84.89045417981723 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 85.92793380635129 - type: mrr value: 95.85834191226348 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 55.74400000000001 - type: map_at_10 value: 65.455 - type: map_at_100 value: 66.106 - type: map_at_1000 value: 66.129 - type: map_at_3 value: 62.719 - type: map_at_5 value: 64.441 - type: mrr_at_1 value: 58.667 - type: mrr_at_10 value: 66.776 - type: mrr_at_100 value: 67.363 - type: mrr_at_1000 value: 67.384 - type: mrr_at_3 value: 64.889 - type: mrr_at_5 value: 66.122 - type: ndcg_at_1 value: 58.667 - type: ndcg_at_10 value: 69.904 - type: ndcg_at_100 value: 72.807 - type: ndcg_at_1000 value: 73.423 - type: ndcg_at_3 value: 65.405 - type: ndcg_at_5 value: 67.86999999999999 - type: precision_at_1 value: 58.667 - type: precision_at_10 value: 9.3 - type: precision_at_100 value: 1.08 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 25.444 - type: precision_at_5 value: 17 - type: recall_at_1 value: 55.74400000000001 - type: recall_at_10 value: 82.122 - type: recall_at_100 value: 95.167 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 70.14399999999999 - type: recall_at_5 value: 76.417 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.86534653465347 - type: cos_sim_ap value: 96.54142419791388 - type: cos_sim_f1 value: 93.07535641547861 - type: cos_sim_precision value: 94.81327800829875 - type: cos_sim_recall value: 91.4 - type: dot_accuracy value: 99.86435643564356 - type: dot_ap value: 96.53682260449868 - type: dot_f1 value: 92.98515104966718 - type: dot_precision value: 95.27806925498426 - type: dot_recall value: 90.8 - type: euclidean_accuracy value: 99.86336633663366 - type: euclidean_ap value: 96.5228676185697 - type: euclidean_f1 value: 92.9735234215886 - type: euclidean_precision value: 94.70954356846472 - type: euclidean_recall value: 91.3 - type: manhattan_accuracy value: 99.85841584158416 - type: manhattan_ap value: 96.50392760934032 - type: manhattan_f1 value: 92.84642321160581 - type: manhattan_precision value: 92.8928928928929 - type: manhattan_recall value: 92.80000000000001 - type: max_accuracy value: 99.86534653465347 - type: max_ap value: 96.54142419791388 - type: max_f1 value: 93.07535641547861 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 61.08285408766616 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.640675309010604 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.20333913710715 - type: mrr value: 54.088813555725324 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.79465221925075 - type: cos_sim_spearman value: 30.530816059163634 - type: dot_pearson value: 31.364837244718043 - type: dot_spearman value: 30.79726823684003 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.22599999999999998 - type: map_at_10 value: 1.735 - type: map_at_100 value: 8.978 - type: map_at_1000 value: 20.851 - type: map_at_3 value: 0.613 - type: map_at_5 value: 0.964 - type: mrr_at_1 value: 88 - type: mrr_at_10 value: 92.867 - type: mrr_at_100 value: 92.867 - type: mrr_at_1000 value: 92.867 - type: mrr_at_3 value: 92.667 - type: mrr_at_5 value: 92.667 - type: ndcg_at_1 value: 82 - type: ndcg_at_10 value: 73.164 - type: ndcg_at_100 value: 51.878 - type: ndcg_at_1000 value: 44.864 - type: ndcg_at_3 value: 79.184 - type: ndcg_at_5 value: 76.39 - type: precision_at_1 value: 88 - type: precision_at_10 value: 76.2 - type: precision_at_100 value: 52.459999999999994 - type: precision_at_1000 value: 19.692 - type: precision_at_3 value: 82.667 - type: precision_at_5 value: 80 - type: recall_at_1 value: 0.22599999999999998 - type: recall_at_10 value: 1.942 - type: recall_at_100 value: 12.342 - type: recall_at_1000 value: 41.42 - type: recall_at_3 value: 0.637 - type: recall_at_5 value: 1.034 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 3.567 - type: map_at_10 value: 13.116 - type: map_at_100 value: 19.39 - type: map_at_1000 value: 20.988 - type: map_at_3 value: 7.109 - type: map_at_5 value: 9.950000000000001 - type: mrr_at_1 value: 42.857 - type: mrr_at_10 value: 57.404999999999994 - type: mrr_at_100 value: 58.021 - type: mrr_at_1000 value: 58.021 - type: mrr_at_3 value: 54.762 - type: mrr_at_5 value: 56.19 - type: ndcg_at_1 value: 38.775999999999996 - type: ndcg_at_10 value: 30.359 - type: ndcg_at_100 value: 41.284 - type: ndcg_at_1000 value: 52.30200000000001 - type: ndcg_at_3 value: 36.744 - type: ndcg_at_5 value: 34.326 - type: precision_at_1 value: 42.857 - type: precision_at_10 value: 26.122 - type: precision_at_100 value: 8.082 - type: precision_at_1000 value: 1.559 - type: precision_at_3 value: 40.136 - type: precision_at_5 value: 35.510000000000005 - type: recall_at_1 value: 3.567 - type: recall_at_10 value: 19.045 - type: recall_at_100 value: 49.979 - type: recall_at_1000 value: 84.206 - type: recall_at_3 value: 8.52 - type: recall_at_5 value: 13.103000000000002 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 68.8394 - type: ap value: 13.454399712443099 - type: f1 value: 53.04963076364322 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.546123372948514 - type: f1 value: 60.86952793277713 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 49.10042955060234 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.03308100375514 - type: cos_sim_ap value: 71.08284605869684 - type: cos_sim_f1 value: 65.42539436255494 - type: cos_sim_precision value: 64.14807302231237 - type: cos_sim_recall value: 66.75461741424802 - type: dot_accuracy value: 84.68736961316088 - type: dot_ap value: 69.20524036530992 - type: dot_f1 value: 63.54893953365829 - type: dot_precision value: 63.45698500394633 - type: dot_recall value: 63.641160949868066 - type: euclidean_accuracy value: 85.07480479227513 - type: euclidean_ap value: 71.14592761009864 - type: euclidean_f1 value: 65.43814432989691 - type: euclidean_precision value: 63.95465994962216 - type: euclidean_recall value: 66.99208443271768 - type: manhattan_accuracy value: 85.06288370984085 - type: manhattan_ap value: 71.07289742593868 - type: manhattan_f1 value: 65.37585421412301 - type: manhattan_precision value: 62.816147859922175 - type: manhattan_recall value: 68.15303430079156 - type: max_accuracy value: 85.07480479227513 - type: max_ap value: 71.14592761009864 - type: max_f1 value: 65.43814432989691 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.79058485659952 - type: cos_sim_ap value: 83.7183187008759 - type: cos_sim_f1 value: 75.86921142180798 - type: cos_sim_precision value: 73.00683371298405 - type: cos_sim_recall value: 78.96519864490298 - type: dot_accuracy value: 87.0085768618776 - type: dot_ap value: 81.87467488474279 - type: dot_f1 value: 74.04188363990559 - type: dot_precision value: 72.10507114191901 - type: dot_recall value: 76.08561749307053 - type: euclidean_accuracy value: 87.8332751193387 - type: euclidean_ap value: 83.83585648120315 - type: euclidean_f1 value: 76.02582177042369 - type: euclidean_precision value: 73.36388371759989 - type: euclidean_recall value: 78.88820449645827 - type: manhattan_accuracy value: 87.87208444910156 - type: manhattan_ap value: 83.8101950642973 - type: manhattan_f1 value: 75.90454195535027 - type: manhattan_precision value: 72.44419564761039 - type: manhattan_recall value: 79.71204188481676 - type: max_accuracy value: 87.87208444910156 - type: max_ap value: 83.83585648120315 - type: max_f1 value: 76.02582177042369 --- **Recommend switching to newest [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5), which has more reasonable similarity distribution and same method of usage.** <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <p> <a href=#model-list>Model List</a> | <a href=#frequently-asked-questions>FAQ</a> | <a href=#usage>Usage</a> | <a href="#evaluation">Evaluation</a> | <a href="#train">Train</a> | <a href="#citation">Citation</a> | <a href="#license">License</a> <p> </h4> More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding focus on retrieval-augmented LLMs, consisting of following projects currently: - **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) - **Dense Retrieval**: [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding), [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) - **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## News - 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire: - 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf) - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released - 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released - 09/12/2023: New models: - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. <details> <summary>More</summary> <!-- ### More --> - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. </details> ## Model List `bge` is short for `BAAI general embedding`. | Model | Language | | Description | query instruction for retrieval [1] | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| | [LM-Cocktail](https://huggingface.co/Shitao) | English | | fine-tuned models (Llama and BGE) which can be used to reproduce the results of LM-Cocktail | | | [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | [1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. [2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . ## Frequently asked questions <details> <summary>1. How to fine-tune bge embedding model?</summary> <!-- ### How to fine-tune bge embedding model? --> Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. </details> <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, **what matters is the relative order of the scores, not the absolute value.** If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary> <!-- ### When does the query instruction need to be used --> For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. No instruction only has a slight degradation in retrieval performance compared with using instruction. So you can generate embedding without instruction in all cases for convenience. For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples for using `bge` models with [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation embeddings_1 = model.encode(sentences_1) embeddings_2 = model.encode(sentences_2) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages. ```python from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] instruction = "为这个句子生成表示以用于检索相关文章：" model = SentenceTransformer('BAAI/bge-large-zh-v1.5') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` #### Using Langchain You can use `bge` in langchain like this: ```python from langchain.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章：" ) model.query_instruction = "为这个句子生成表示以用于检索相关文章：" ``` #### Using HuggingFace Transformers With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') model.eval() # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### Using Huggingface transformers ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` ## Evaluation `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - **MTEB**: | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | | [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | | [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | | [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | - **C-MTEB**: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | | [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | | [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | - **Reranking**: See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## Citation If you find this repository useful, please consider giving a star :star: and citation ``` @misc{bge_embedding, title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, year={2023}, eprint={2309.07597}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
[ "SEMANTIC_SIMILARITY", "SUMMARIZATION" ]
[ "BEAR", "BIOSSES", "SCIFACT" ]
Non_BioNLP
adriansanz/fs_setfit_hybrid2
adriansanz
text-classification
[ "setfit", "safetensors", "xlm-roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base", "base_model:finetune:projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base", "model-index", "region:us" ]
1,717
1,717
7
0
--- base_model: projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: Estic preocupat per la falta de legislació i regulació adequada per protegir les dades personals en línia. Les empreses han d'assumir més responsabilitat i complir amb els estàndards de seguretat més estrictes per protegir la privacitat dels usuaris. - text: M'he sentit frustrat i insegur a causa de la manca de control sobre les meves dades personals en línia. Les empreses i els proveïdors de serveis haurien de ser més transparents sobre com gestionen les nostres dades i oferir opcions de control més gran als usuaris. - text: Proposo l'ús de lluminàries de tecnologia LED amb control de la intensitat i la direccionalitat de la llum per minimitzar la contaminació lumínica i preservar la visió del cel nocturn. - text: Estic frustrat amb les polítiques de comerç exterior que no promoguin la transferència de tecnologia i coneixement cap a les empreses locals. La manca d'assistència tècnica i suport pot limitar la capacitat de les empreses locals per competir a nivell internacional. - text: Suggeriria que es realitzessin campanyes de recompensa per incentivar els ciutadans a informar de fuites d'aigua, oferint descomptes en la factura d'aigua o altres incentius. inference: true model-index: - name: SetFit with projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.775 name: Accuracy --- # SetFit with projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 128 tokens - **Number of Classes:** 20 classes <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 3 | <ul><li>"embarg 2022EXP29006192. Soc el nou administrador de la Comunitat de Propietaris. En el nou compte de la Comunitat hem rebut un embarg per un import de 236,57 €, que sembla que es 2022EXP29006192. En la documentació de la Comunitat no he trobat res que pogues originar aquest embarg. Prego m'indiqueu que ha originat aquest embarg."</li><li>"Impostos Municipals. M'agradaria saber si s'ha de fer algún tramit per a ajornar els impostos municipals que s'han de cobrar aquest mes o ja directament els giraran al Juliol. Ahir es va donar aquesta noticia del paer en cap. Entenc que no giraran cap impost no? i si es giren que fem, tornar el rebut?"</li><li>'No puc acceptar la falta de participació ciutadana en la presa de decisions sobre la gestió dels recursos econòmics municipals. És crucial implicar als residents en el procés per garantir que les polítiques econòmiques reflecteixin les seves necessitats i prioritats.'</li></ul> | | 16 | <ul><li>'Exigim una millor coordinació entre els diferents serveis socials i educatius per oferir una atenció integral i efectiva a la infància i les famílies en situació de vulnerabilitat.'</li><li>"Proposem ampliar els programes d'atenció a les persones sense llar, oferint-los suport integral que inclogui allotjament, assistència social i orientació laboral per facilitar la seva reinserció a la societat."</li><li>"Suggerim l'augment de recursos humans en els serveis de suport psicològic i emocional per reduir la càrrega de treball del personal i millorar la qualitat de l'atenció als usuaris."</li></ul> | | 14 | <ul><li>'Manteniment teulades cementiri. Benvolguts/des Com a titular del nínxol 66 fila 1 del departament de Sant Josep voldria posar en coneixement de la persona o empresa responsable del cementiri la irregularitat que vaig tenir ocasió d’apreciar en vigílies de tots sants. Com es pot apreciar en les imatges aportades, la teulada de la zona on esta situat el nostre nínxol esta envaïda per multitud de plançons d’un arbre de creixement ràpid que pot malmetre seriosament la teulada i posar en risc de ruïna tota aquesta ala del departament de Sant Josep. Entenc que cal emprendre una acció ràpida de neteja abans que el dany a la construcció sigui irreversible.'</li><li>"Informació tomba.. Benvolguts, bon dia. Necessitaria una informació, he de venir al cementiri de Lleida per deixar unes flors a una tomba com a record i respecte. No sóc de Lleida i no sé quan vindré, disposo d'informació del vostre horari. El nom de la difunta és Paloma García Wehrle (13 de febrer de 2021). Segons informació va ser enterrada a la tomba dels pares, entenc que estarà a nom de GARCÍA-WEHRLE. Em podeu facilitar la seva ubicació?. Salutacions cordials i gràcies a l'avançada. --------------------------------------------- Nota: Aquest text del mail el vaig enviar el passat 02/05 i una reclamació el dia 05/05. a l'adreça: [email protected] (Cementiri), sense cap resposta en un tema tan sensible. Gràcies. Francesc Rochel i Vara. [email protected]"</li><li>"És decebedor veure la falta de seguiment en l'evolució de les malalties transmissibles a la nostra comunitat. Hem de prioritzar el monitoratge per anticipar i respondre millor a les amenaces per a la salut pública."</li></ul> | | 4 | <ul><li>'Suggeriria la creació de programes de suport lingüístic per als infants amb llengües maternes diverses, per garantir que tots els infants puguin desenvolupar plenament les seves habilitats lingüístiques i comunicatives.'</li><li>"No puc acceptar la falta de comunicació i cooperació entre les institucions educatives i les universitats en l'adaptació del currículum educatiu a les necessitats del mercat laboral, la qual cosa afecta la rellevància i l'actualitat de l'educació."</li><li>"Estic decebut amb la manca d'incentius i reconeixement per a professors i investigadors que participen en projectes de col·laboració entre les institucions educatives i les universitats, la qual cosa desmotiva la seva implicació en iniciatives de millora educativa."</li></ul> | | 17 | <ul><li>"Suggeriria que l'institució col·labori amb els ciutadans per dissenyar conjuntament una plataforma de gestió de pagaments i taxes municipals més adaptada a les seves necessitats i preferències, garantint la seva participació activa en la millora del servei."</li><li>'Em sento molt frustrat amb la gestió de pagaments i taxes municipals. La plataforma en línia per realitzar aquests pagaments és poc clara i confusa, i sovint em costa trobar les taxes adequades i completar el procés sense errors.'</li><li>"Suggeriria que l'institució promogués una cultura de simplificació i agilització dels procediments burocràtics, reconeixent i recompensant les iniciatives d'innovació i millora. Un entorn que encoratgi la creativitat i la col·laboració podria conduir a canvis significatius i duradors en la manera com es gestionen els tràmits."</li></ul> | | 8 | <ul><li>'Els tràmits burocràtics per obtenir permisos i ajuts són massa complicats i lents, desincentivant la producció agrícola local.'</li><li>"Suggeriria la creació d'una plataforma en línia per compartir recursos i informació sobre programari de gestió agrícola. Això podria facilitar l'accés dels agricultors i les agricultores a eines digitals útils i innovadores."</li><li>"Seria molt beneficiós incrementar els programes d'educació ambiental per als agricultors, oferint cursos sobre tècniques sostenibles i pràctiques ecològiques."</li></ul> | | 13 | <ul><li>"Suggerim promoure accions de voluntariat ambiental per a la neteja de carrils bici i voreres, involucrant a la ciutadania en la conservació del medi ambient i la millora de l'entorn urbà."</li><li>'Demano una millora urgent en els serveis de neteja dels carrers i les places. És essencial mantenir un entorn urbà net i ordenat per al benestar i la seguretat dels ciutadans.'</li><li>"Suggerim reforçar les campanyes de conscienciació sobre neteja pública amb missatges més impactants i creatius per captar l'atenció del públic. Podrien utilitzar-se vídeos, infografies i materials visuals que il·lustren els efectes negatius de la brutícia als nostres entorns urbans i la importància de mantenir-los nets."</li></ul> | | 19 | <ul><li>"NETEJA DE JARDINERIA A LA RAMPA D'ACCES ALS JARDINS DE LA SERRETA BARRI UNIVERSITAT..... CAL ADECENTAR LA ZONA DE LES HEURES QUE PENGEN PER LA PARET ENVAINT LA RAMPA I PRODUINT MOLESTIES ALS VIANANTS SOBRETOT QUAN PLOU I HI HA HUMITATS MULLANTS EL CAP DELS VIANANTS.... EN AQUESTA ZONA FALTA MANTENIMENT.....REVISIN LA ZONA........L'ASCENSOR ROMAN AVARIAT......GRACIES."</li><li>"La falta de connectivitat dels carrils bici de la ciutat fa difícil utilitzar-los com a mitjà de transport segur i eficient. Molts d'aquests carrils s'aturen bruscament o es perden en mitja ruta, obligant els ciclistes a compartir la via amb els cotxes."</li><li>"Les condicions dels autobusos són inacceptables. Molts estan bruts i en mal estat. És necessari que l'ajuntament asseguri una neteja i manteniment regular dels vehicles."</li></ul> | | 5 | <ul><li>"Proposo la realització de campanyes de sensibilització ciutadana sobre els beneficis dels sistemes de monitoratge i control intel·ligent de l'enllumenat públic, involucrant a la comunitat en la preservació del medi ambient i la millora de la qualitat de vida."</li><li>'Em preocupen les conseqüències de la manca de manteniment de les lluminàries en termes de seguretat pública. Les zones sense il·luminació poden ser utilitzades per delinqüents per amagar-se i cometre actes delictius sense ser detectats.'</li><li>"No puc acceptar la manca de lideratge en la implementació de mesures d'estalvi energètic en l'enllumenat públic. Les autoritats han de prendre accions concretes per millorar la eficiència i reduir el consum energètic, contribuint així a la sostenibilitat del municipi."</li></ul> | | 12 | <ul><li>"Suggerim la creació d'espais educatius al aire lliure que permetin als estudiants connectar amb la natura i experimentar de primera mà els principis de conservació i preservació del medi ambient."</li><li>"SEGONA QUEIXA O RECLAMACIO REFERENT L'ESPORGA DE L'ARBRAT DE CARRER DE PERPINYA. L'ESPORGA DEL CARRER DE PERPINYA VA RESULTAR DEL TOT INSUFICIENT AMB LES MOLESTIES OCASIONADES ALS VEINS COM LA CAIGUDA CONSTANT DE BRANQUES AL TERRA O DAMUNT DELS VEHICLES ESTACIONATS EN EL CARRER....ELS DIES DE VENTADES COM LA SETMANA PASSADA,, ES PRODUEIX MOLTISSIMES CAIGUDES DE FULLES PROVOCANT UNA FEINA AFEGIDA A LES BRIGADES DE ILNET COM TAMBE PROVOCANT L'EMBOSSAMENT DELS EMBORNALS DEL CARRER A CONSEQUENCIA DE LA CAIGUDA DE FULLES EN LES VORERES,,,CAL RECORDAR EN AQUEST CONSISTORI EL PROBLEMA GENERAL DE L'AFLLUENCIA DE COLOMS A TOTS ELS BARRIS I EN AQUEST CARRER EN QUESTIO EL TENIR ELS ARBRES EN AQUEST ESTAT FA PROLIFERAR L'ESTANCA DELS ANIMALS.....REVISIN EL CAS........GRACIES."</li><li>'Em preocupen els efectes negatius que la falta de preservació de les zones verdes de la ciutat pot tenir sobre la salut pública i el medi ambient. És necessari actuar ràpidament per millorar la seva conservació i manteniment.'</li></ul> | | 6 | <ul><li>'Suggeriria explorar la integració del programari de gestió esportiva amb altres plataformes i sistemes utilitzats pels clubs esportius, com ara aplicacions de comunicació i sistemes de pagament, per millorar la seva eficiència i facilitar la gestió global.'</li><li>"Em frustra la manca de compromís de les federacions esportives en la lluita contra el dopatge i altres pràctiques irregulars que afecten la integritat de l'esport. Demano accions contundents i proactives per garantir un entorn esportiu just i ètic."</li><li>'Urbanisme i Esports. El document està adjuntat posteriorment.'</li></ul> | | 0 | <ul><li>"Suggeriria la implementació de programes de formació i capacitació per als residents sobre els aspectes tècnics i legals relacionats amb la gestió de l'aigua, capacitant-los per participar de manera informada en les discussions i deliberacions."</li><li>"Seria útil si es desenvolupessin programes de suport econòmic o descomptes en les tarifes d'aigua per a les famílies amb ingressos baixos o en situació de vulnerabilitat."</li><li>"No podem continuar ignorants davant de la contaminació de l'aigua al nostre entorn. Les empreses que descarreguen substàncies contaminants han de ser responsabilitzades."</li></ul> | | 1 | <ul><li>"Estic frustrat amb la falta d'informació clara i precisa sobre l'origen, els ingredients i l'impacte ambiental dels productes que es venen al mercat. Els consumidors necessiten informació per prendre decisions informades i responsables."</li><li>'CENTRE COMERCIAL A LLEIDA. TENIMUN LOCSL EN LLOGUER A LA PLAÇA SAN JOAN ON ESTAVA SFERA IA TOTS ELS INTERESATS SE ESTÁ POSAN EN CONTACTE LA PROMOTORA DL CENTRE COMERCIAL. SUPOSO QUE COM AJUNTAMENT SON CONSCIENTS DE QUE SI TOTES LES MARQUES DEL EIX COMERCIAL MARXEN AL CENTRE COMERCIAL, LLEIDA PERDRÁ EL SEU MILLOR EIX COMERCIAL I QUEDARÁ LA CIUTAT BUIDA. CREC QUE COM AJUNTAMENT TINDRIEU DE CUIDAR MES EL CENTRE DE LLEIDA QUE FAR MANIA I PREGUNTARVOS PERQUE HI HA TANTS LOCAL BUITS AL EIX. NO ES PEL COVIT !!! TOTS AQUESTOS LOCALS SON DE LLEIDATANS QUE PAGUEM ELS NOSTRES IMPOSTOS I SI NO LLOGUEM ELS LOCALS POTSER EL ANY QUE BÉ NO ELS PODREM PAGAR. TAMBÉ SUPOSO QUEELS PROMOTORS DEL CENTRE COMERCIAL NO SON DE LLEIDA I POTSER TAMPOC ESPAÑOLS,.... SALUTACIONS'</li><li>"Proposo que es revisin els horaris d'obertura del mercat municipal per assegurar que siguin més flexibles i s'ajustin millor a les necessitats dels ciutadans, incloent horaris nocturns o de cap de setmana per als treballadors que no poden visitar-lo durant la setmana."</li></ul> | | 15 | <ul><li>"Suggerim establir zones de vianants i carrils bici segregats per millorar la seguretat dels vianants i ciclistes i reduir el risc d'accidents amb vehicles motoritzats. Això pot crear un entorn més segur i agradable per a tots els usuaris de la via pública."</li><li>"Treball TDR sobre la seguretat ciutadana. Bones, soc un alumne de primer de BATX de l'institut Joan Orò de Lleida que per l'any vinent he de presentar un treball de recerca on el tema que tractaré serà la seguretat ciutadana a la ciutat de Lleida. Si és possible, voldria consultar les denúncies que s'han formulat a la ciutat per veure en quines zones/ barris s'han produït per analitzar profundament la incidència de delinqüència que hi ha i el número denuncies que s'han registrat. Gràcies."</li><li>'Proposem millorar la senyalització i la informació en zones de risc per alertar els ciutadans i prevenir accidents i actes delictius. Una millor comunicació sobre els perills pot ajudar a evitar situacions de perill i protegir la seguretat dels ciutadans.'</li></ul> | | 7 | <ul><li>"Suggeriria la creació d'un fons municipal per a la adaptació d'habitatges, destinat a finançar les obres de millora necessàries per a persones amb mobilitat reduïda. Aquest fons podria ser administrat per una entitat local i gestionat de manera transparent i eficient."</li><li>"Proposo que es desenvolupin programes d'assessorament en matèria d'habitatge especialment destinats a persones vulnerables, com ara persones sense sostre, immigrants o famílies en situació de precarietat, per garantir que rebin suport i orientació adequats per resoldre els seus problemes habitacionals i evitar situacions d'exclusió social."</li><li>"Em preocupa la gentrificació que està succeint a la meva zona, expulsant els residents locals i augmentant els preus de l'habitatge fins a nivells insostenibles. Exigeixo polítiques urbanístiques que prioritzen el dret a l'habitatge de la població local."</li></ul> | | 2 | <ul><li>"Suggeriria la creació de programes educatius i activitats lúdiques per als joves sobre les nostres festes i tradicions, per despertar l'interès i la participació de les noves generacions en la preservació i celebració del nostre patrimoni cultural."</li><li>"No puc acceptar la falta de reconeixement i valoració de la producció cultural local com a part essencial de la identitat i riquesa d'una comunitat. La falta de suport i visibilitat pot minvar la confiança i el compromís dels artistes locals, afectant la seva continuïtat i impacte cultural."</li><li>"Presentar proposta musical a Lleida al Paer directament per diferències amb Regidor de Cultura. Hola soc Xavier Segura (Javier Sólo) educador social treballant per la Generalitat i músic. Fa un temps que intento presentar els meus nous discos a Lleida però no ha estat possible. He anat a presentar-los a Madrid i a Ciutat de Mèxic però a Lleida no. La meva oficina es va posar en contacte amb el Regidor de Cultura, també aquest any, per presentar disc a les festes de maig, i no va poder ser. I ens vam posar en contacte per tocar a les de setembre i tampoc ha estat possible. Tenint en compte, que és un dels projectes amb més projecció a Lleida actualment, em pregunto si no he pogut tocar per motius que no conec. Fa uns mesos, quan la meva oficina estava parlant amb el regidor per si era possible presentar el disc a Lleida el 2022, jo mateix li vaig deixar un disc al Departament de Cultura. Adjunto una captura de pantalla de la seva contestació al meu missatge. M'agradaria fer arribar la meva predisposició per actuar a la Festa Major de Lleida de 2023 directament al Paer en Cap l'excel·lentíssim senyor en Miquel Pueyo, ja que el regidor de cultura ha menyspreat la meva feina i el meu projecte amb el missatge que m'ha fet arribar per privat per les xarxes. Adjunto dossier. Moltes gràcies,"</li></ul> | | 9 | <ul><li>"Suggeriria establir un servei d'atenció telefònica especialitzat per ajudar els ciutadans amb els procediments administratius complexos."</li><li>'no resposta als meus suggeriments i queixes. bON DIA. Poden consultar pel meu DNI la de vegades que m\'he queixat de la neteja dels nostes carrers amb l\'augment i contínua presència d\'excrements de gossos a zones properes al meu domicili i... clar seguim igual o pitjor, i vostès em contesten , en algun cas, a una petició de febrer del 2022, just avui 6 de setembre...Increïble que em diguin avui que ho passen al departament corresponent... I no es plantegen altres solucions: posar més llum a les zones esmentades, ajardinar amb gespa la zona, fer passejar als AGENTS CIVICS més sovint per aquestes zones que hi ha més excrements... No ho entenc.. NO cal que me donin per finalitzada la "queixa" si no han resolt. Gràcies'</li><li>"Seria beneficiós promoure una cultura d'atenció al client entre els proveïdors de serveis per a ciutadans, assegurant una atenció de qualitat i respectuosa."</li></ul> | | 18 | <ul><li>"Proposo que l'ajuntament implementi un sistema de notificacions automatitzades per mantenir als ciutadans informats sobre l'estat dels seus tràmits de llicències d'obres i edificacions. Aquesta transparència podria reduir la incertesa i augmentar la confiança en el procés municipal."</li><li>"Sento que la participació ciutadana en els processos d'urbanisme és considerada una formalitat més que un veritable mecanisme de presa de decisions inclusiu. Les autoritats municipals haurien de comprometre's realment amb la co-creació de les polítiques i els plans urbanístics amb la ciutadania."</li><li>"Proposo que l'ajuntament estigui més obert a rebre suggeriments i avaluï les preocupacions dels residents en la gestió dels plans parcials. És essencial crear espais de diàleg on la comunitat pugui expressar les seves opinions i contribuir a millorar els plans urbans."</li></ul> | | 10 | <ul><li>'Les incidències amb el registre electrònic són una font constant de frustració per als usuaris. El sistema és inestable i sovint no respon, el que dificulta la realització de tràmits i gestions de manera eficient.'</li><li>'He experimentat problemes de seguretat mentre utilitzava el Wi-Fi públic, com ara la interceptació de dades i els atacs de hackers. Això planteja preocupacions greus sobre la privacitat i la seguretat de les meves dades personals.'</li><li>'Suggeriria implementar protocols de seguretat més forts per al Wi-Fi públic per protegir les dades dels usuaris i prevenir els atacs cibernètics.'</li></ul> | | 11 | <ul><li>"Suggeriria que el municipi organitzés reunions periòdiques amb representants juvenils per avaluar la qualitat dels espais juvenils existents i identificar àrees d'interès per a futures millores i desenvolupaments."</li><li>"Suggeriria que el municipi col·laborés amb empreses locals i organitzacions internacionals per oferir oportunitats de pràctiques professionals en el marc dels programes d'intercanvi juvenil, proporcionant als joves una experiència laboral valuosa i global."</li><li>"Estic decebut amb la manca de diversitat i inclusió en els programes d'intercanvi juvenil en aquesta comunitat. La falta de representació de diverses cultures i backgrounds socioeconòmics en els programes limita l'enriquiment i la comprensió intercultural dels joves participants."</li></ul> | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.775 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("adriansanz/fs_setfit_hybrid2") # Run inference preds = model("Suggeriria que es realitzessin campanyes de recompensa per incentivar els ciutadans a informar de fuites d'aigua, oferint descomptes en la factura d'aigua o altres incentius.") ``` <!-- ### Downstream Use *List how someone could finetune this model on their own dataset.* --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 8 | 46.0562 | 235 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 8 | | 1 | 8 | | 2 | 8 | | 3 | 8 | | 4 | 8 | | 5 | 8 | | 6 | 8 | | 7 | 8 | | 8 | 8 | | 9 | 8 | | 10 | 8 | | 11 | 8 | | 12 | 8 | | 13 | 8 | | 14 | 8 | | 15 | 8 | | 16 | 8 | | 17 | 8 | | 18 | 8 | | 19 | 8 | ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (3, 3) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: True ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-------:|:--------:|:-------------:|:---------------:| | 0.0007 | 1 | 0.1912 | - | | 0.0329 | 50 | 0.1904 | - | | 0.0658 | 100 | 0.1361 | - | | 0.0987 | 150 | 0.0738 | - | | 0.1316 | 200 | 0.0217 | - | | 0.1645 | 250 | 0.017 | - | | 0.1974 | 300 | 0.0042 | - | | 0.2303 | 350 | 0.0034 | - | | 0.2632 | 400 | 0.0023 | - | | 0.2961 | 450 | 0.0019 | - | | 0.3289 | 500 | 0.0006 | - | | 0.3618 | 550 | 0.0008 | - | | 0.3947 | 600 | 0.0005 | - | | 0.4276 | 650 | 0.0004 | - | | 0.4605 | 700 | 0.0003 | - | | 0.4934 | 750 | 0.0003 | - | | 0.5263 | 800 | 0.0003 | - | | 0.5592 | 850 | 0.0002 | - | | 0.5921 | 900 | 0.0002 | - | | 0.625 | 950 | 0.0003 | - | | 0.6579 | 1000 | 0.0002 | - | | 0.6908 | 1050 | 0.0002 | - | | 0.7237 | 1100 | 0.0001 | - | | 0.7566 | 1150 | 0.0002 | - | | 0.7895 | 1200 | 0.0002 | - | | 0.8224 | 1250 | 0.0001 | - | | 0.8553 | 1300 | 0.0002 | - | | 0.8882 | 1350 | 0.0002 | - | | 0.9211 | 1400 | 0.0001 | - | | 0.9539 | 1450 | 0.0001 | - | | 0.9868 | 1500 | 0.0001 | - | | **1.0** | **1520** | **-** | **0.1284** | | 1.0197 | 1550 | 0.0003 | - | | 1.0526 | 1600 | 0.0002 | - | | 1.0855 | 1650 | 0.0003 | - | | 1.1184 | 1700 | 0.0001 | - | | 1.1513 | 1750 | 0.0001 | - | | 1.1842 | 1800 | 0.0001 | - | | 1.2171 | 1850 | 0.0001 | - | | 1.25 | 1900 | 0.0001 | - | | 1.2829 | 1950 | 0.0002 | - | | 1.3158 | 2000 | 0.0002 | - | | 1.3487 | 2050 | 0.0001 | - | | 1.3816 | 2100 | 0.0001 | - | | 1.4145 | 2150 | 0.0001 | - | | 1.4474 | 2200 | 0.0001 | - | | 1.4803 | 2250 | 0.0001 | - | | 1.5132 | 2300 | 0.0005 | - | | 1.5461 | 2350 | 0.0001 | - | | 1.5789 | 2400 | 0.0001 | - | | 1.6118 | 2450 | 0.0001 | - | | 1.6447 | 2500 | 0.0 | - | | 1.6776 | 2550 | 0.0001 | - | | 1.7105 | 2600 | 0.0001 | - | | 1.7434 | 2650 | 0.0001 | - | | 1.7763 | 2700 | 0.0001 | - | | 1.8092 | 2750 | 0.0001 | - | | 1.8421 | 2800 | 0.0001 | - | | 1.875 | 2850 | 0.0 | - | | 1.9079 | 2900 | 0.0001 | - | | 1.9408 | 2950 | 0.0 | - | | 1.9737 | 3000 | 0.0 | - | | 2.0 | 3040 | - | 0.1324 | | 2.0066 | 3050 | 0.0 | - | | 2.0395 | 3100 | 0.0001 | - | | 2.0724 | 3150 | 0.0001 | - | | 2.1053 | 3200 | 0.0 | - | | 2.1382 | 3250 | 0.0001 | - | | 2.1711 | 3300 | 0.0 | - | | 2.2039 | 3350 | 0.0001 | - | | 2.2368 | 3400 | 0.0 | - | | 2.2697 | 3450 | 0.0001 | - | | 2.3026 | 3500 | 0.0001 | - | | 2.3355 | 3550 | 0.0 | - | | 2.3684 | 3600 | 0.0 | - | | 2.4013 | 3650 | 0.0 | - | | 2.4342 | 3700 | 0.0001 | - | | 2.4671 | 3750 | 0.0001 | - | | 2.5 | 3800 | 0.0001 | - | | 2.5329 | 3850 | 0.0 | - | | 2.5658 | 3900 | 0.0 | - | | 2.5987 | 3950 | 0.0001 | - | | 2.6316 | 4000 | 0.0001 | - | | 2.6645 | 4050 | 0.0 | - | | 2.6974 | 4100 | 0.0 | - | | 2.7303 | 4150 | 0.0 | - | | 2.7632 | 4200 | 0.0 | - | | 2.7961 | 4250 | 0.0001 | - | | 2.8289 | 4300 | 0.0 | - | | 2.8618 | 4350 | 0.0 | - | | 2.8947 | 4400 | 0.0 | - | | 2.9276 | 4450 | 0.0 | - | | 2.9605 | 4500 | 0.0 | - | | 2.9934 | 4550 | 0.0 | - | | 3.0 | 4560 | - | 0.1326 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 3.0.0 - Transformers: 4.39.0 - PyTorch: 2.3.0+cu121 - Datasets: 2.19.1 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION" ]
[ "CAS" ]
Non_BioNLP
svorwerk/setfit-fine-tuned-demo-class
svorwerk
text-classification
[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/all-mpnet-base-v2", "base_model:finetune:sentence-transformers/all-mpnet-base-v2", "region:us" ]
1,706
1,706
6
0
--- base_model: sentence-transformers/all-mpnet-base-v2 library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 'Acquisition Id: ALOG; Ancotel; Asia Tone; Bit-Isle; IXEurope; Infomart; Itconic; OGS; SafeGuard; Switch and Data; Telecity; Verizon; Virtu; Zenium; nan' - text: 'Emp_FLSA: E; N; P; V; X; nan' - text: 'Emp Grade: A; AA; AS 1; AS1; AS2; AS3; B; C; COM; D; E; E I; E II; E10; E11; E12; E13; E7; E8; E9; J01; J02; J03; J04; JS; MM I; MM II; N01; N02; N03; N05; N06; N07; N08; NE 2; NE 3; NE 4; NE 5; NE 6; NE1; NE2; NE3; NE4; NF; NG; Non-exempt; S; SA I; SA II; SA III; SA4; SA5; SE I; SE II; SM I; SM II; SP; SS; nan' - text: 'Loc State: AB; AL; AR; AZ; BC; CA; CO; CT; DC; DE; FL; GA; GTL; GTM; Gujarat; HI; IA; ID; IL; IN; KS; KY; Karnataka; LA; MA; MD; ME; MI; MN; MO; MS; MT; Maharashtra; NB; NC; ND; NE; NH; NJ; NM; NS; NV; NY; OH; OK; ON; OR; PA; PR; QC; RI; SA; SC; SD; TN; TX; UT; VA; VT; WA; WI; WV; WY; nan' - text: 'Supervisory Organization: 3 PL & Warehousing Management (Harald Dehe); 3 PL Management (Ryoichi Imamura (Inherited)); 3A-Modul (Arnd Vollmerhausen (Inherited)); 3A-Modul (Erkan nder); 3A-Modul (Erkan √ñnder); ABN ( Antibodies/NAT) (Thomas Roten); AD&V - Assay Development & Validation (Svenja Wolff); AESD Aurora Build Elec & Warehouse (Chris Askitis); AM Ural, Siberia & Far East (Anna Tov); AM Volga & South (Natalia Ivanova); ANI Region ICO (Ivaylo Vladimirov); APAC Payroll (Rohit Jain); API Manufacturing (Luke McIntyre); API Manufacturing (Ryan Cox (Inherited)); API Manufacturing (Union) (Luke McIntyre (Inherited)); API Manufacturing (Union) (Ryan Cox (Inherited)); AQL (Jens Huft); AS&T (Jeff Hancock); ASQ Analytical Sciences & Technology (Michael Sch√ºmann); ASQ Biochemistry (Sven Karschnia); ASQ Chemistry (Kerstin Nske); ASQ Chemistry (Kerstin N√∂ske); ASQ Compliance & Release (Angelika Jung); ASQ Data Integrity (Christoph Kircher); 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Account to Report (Andrew Croft (Inherited)); Account to Report (Linda Carducci (Inherited)); Account to Report (Michael Kochanski); Accounting & Reporting (Jeffrey H Schorr); Accounting (Annalisa Saracchi (Inherited)); Accounting (Paul Fellingham (Inherited)); Accounting France (Charlotte Rougi (Inherited)); Accounting France (Charlotte Rougi√© (Inherited)); Accounting Geschftsbuchhaltung (Jens Dettbarn); Accounting Gesch√§ftsbuchhaltung (Jens Dettbarn); Accounting Operations (Brian T Simeur); Accounting Operations (Denna DeWitt); Accounting Operations (Manish Devnani); Accounting Operations (Patrick Zuercher); Accounting Operations 1 (Denna DeWitt); Accounts Payable & Receivable (Yvonne Robson); Accounts Payable (Brian T Simeur (Inherited)); Accounts Payable (Mark Staniford); Accounts Payable (Susan Velazquez); Accounts Receivable (Stephen Fairhurst); Accounts Team Lead (Rebecca Merrett); Acquired Bleeding & Critical Care Group (Mitsuhiro Kuwahara); Acquired Bleeding & Critical Care Group (Takashi Ishijima ??? ?? - ???? ????); Acquired Bleeding TA (Naoki Ikeguchi); Acting Manager, Innovation R&D (Chi Ong); Adaptive Clinical Trial Technologies (Liz Quinlan); Administration (Franz Gr√ºn); Administration (Juli Zhu ????); Administration (Sandy Tan ); Administration (Sandy Tan ?????); Administration Italy (Antonella Carluccio); Administration Italy (Cristina Fabbro); Administration/Production Support Subject Matter Expert (Justin Ryan); Administration/Production Support Systems Officer (Adam Kraan); Advertising (Raimund Wybranietz); Akron 402 (Joe Jacko); Akron 402 ACM Area 1 (LeAnna M Mauger); Akron 402 ACM Area 2 (Tonya R Robinson); Akron 402 ACM Area 3 (Brett Ferrell); Akron 402 QA (Carrie Piggford); Akron 402 QA (Christine Thomas); Akron 402 QA (Esence Hambrick); AlbuRX (Tom Hubert); AlbuRx (Barry Lynch); AlbuRx Bulk (Aoife Corrigan); AlbuRx Bulk Mfg (Joel Rainey); AlbuRx Packing (Carlo Volpe); Albumin & IVIG Lyo Bulkmanufacturin (Irne Stempfel); Albumin & IVIG Lyo Bulkmanufacturin (Ir√®ne Stempfel); Albumin (Fritz Rentsch); Albumin (Juerg Hofmaenner); Albumin Bulk Team 1 (Fritz Rentsch); Albumin Bulk Team 2 (Andreas L√ºdi); Albuquerque 034 (Latoya K Gilbert-Torres); Albuquerque 034 ACM Area 1 (Dee Ulibarri); Albuquerque 034 ACM Area 2 (Gerardo Ruelas); Albuquerque 034 QA (Antoinette F Tapia); Albuquerque 137 (Preston W Minor); Albuquerque 137 ACM Area 1 (Brian Trujillo); Albuquerque 137 ACM Area 2 (Melisa R Cox); Albuquerque 137 QA (Daniel Venn); Albuquerque 137 QA (Danny Kinder); Albuquerque 137 QA (Kelsey Gaffney); Alcohol Inventory (Union) (Michael D Proctor); Allentown 294 (Susan Tudela); Allentown 294 QA (Mercy Cobbinah); Allergy National Sales Team (Lea Leon); Allergy National Sales Team (Lea Rajendran); Alliance and Governance (Andrea Lehmann); Alliance and Project Management Systems (Jos√© Maldonado); Amarillo 283 (Jerica Hunter); Amarillo 283 ACM Area 1 (Nicole Taylor); Amarillo 283 ACM Area 1 (ROBERT WILLIAMS); Amarillo 283 ACM Area 2 (Nicole Taylor); Amarillo 283 ACM Area 2 (ROBERT WILLIAMS); Amarillo 283 QA (Michael L Allen); 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Auftragsvorbereitung & -koordination (Horst Kraus); Augusta 253 (Kristopher Collier); Augusta 253 ACM Area 1 (Kristopher Collier (Inherited)); Augusta 253 ACM Area 1 (Tomecia Tillman); Augusta 253 ACM Area 2 (Jonathan Lewis); Augusta 253 QA (Dave Anderson); Augusta 253 QA (Pamela DeLucia); Aurora 702 (Kevin J Lawley); Aurora 702 (Keyonna L Gray); Aurora 702 ACM Area 1 (Virginia L Garnica); Aurora 702 ACM Area 2 (Theresa M Couture); Aurora 702 QA (Fernanda Nistal); Aurora 702 QA (Nan Nistal); Automated VI (David Kuhs); Automation (Adrian Marti); Automation (Christopher Parlane); Automation (Frank Mastellone); Automation (Jrgen Dersch); Automation (J√ºrgen Dersch); Automation (Stefan Sigrist); Automation Instrumentation (Ludovic Le Reste); Automation Systems Engineer (Magan Lai); Automation Systems Manager (Cornell D''Couto); Automation and Electrical Systems (Lou Corvetti); Automation and Electrical Systems (Matt Downey); Automation and Electrical Systems (Zoran Hadzi-Nikolov); Automatisierungstechnik (Andreas Tement); 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Baytown 086 ACM Area 1 (Rachel I Ramirez); Baytown 086 ACM Area 1 (Tara West); Baytown 086 ACM Area 2 (Elizabeth Morales); Baytown 086 ACM Area 2 (Rebecca Rogers); Baytown 086 QA (Monica Banuelos); Beloit 005 (Jesus A Castillo (Inherited)); Beloit 005 (Kristin R Swain); Beloit 005 ACM Area 1 (Eric M Cook); Beloit 005 ACM Area 2 (John Miles); Beloit 005 QA (Patty Justus); Berufliche Erstausbildung (Carmen Walldorf); Berufliche Erstausbildung (Doris Nake); BioAnalytical Sciences Routine (Ashraf Raza); Bioanalytical Sciences (Aaron Hahn); Bioanalytical Sciences (Alice Andreu); Bioanalytical Sciences (Andreas Meister); Bioanalytical Sciences (Bo An); Bioanalytical Sciences (Christophe Pical); Bioanalytical Sciences (Clare Elizabeth Shepherd); Bioanalytical Sciences (Craig Kyngdon); Bioanalytical Sciences (Cristina Baker); Bioanalytical Sciences (Cristina Torres-Arce); Bioanalytical Sciences (David Boerema); Bioanalytical Sciences (Jennifer La); Bioanalytical Sciences (Laura Cortes Castrillon); Bioanalytical Sciences (Lee Xin Chong); Bioanalytical Sciences (Lucy Cao (Inherited)); Bioanalytical Sciences (Lucy Cao ); Bioanalytical Sciences (Lucy Cao ?????); Bioanalytical Sciences (Michael Johnston) (52388204); Bioanalytical Sciences (Ralf Ottofuelling); Bioanalytical Sciences (Rodney Holmes); Bioanalytical Sciences (Saw Yen Ow); Bioanalytical Sciences (Theresa Qiu); Bioanalytical Sciences (Vincent Strangis); Bioanalytical Sciences, LCM (Minyao Tang ?????); Bioanalytical Sciences, Lab Ops (Jinsong Zhao ?????); Bioanalytics & Fermentation (Partho Halder); Bioanalytics, Gene Therapy (Gene-Errol Ringpis); Bioassay Group (Souravi Ghosh); Biochemical Quality Control (Andreas Affolter); Biochemical Quality Control (BCAD) (Mirjam Kuehne Sebaste); Biochemical Quality Control (BCQC) (Kathrin Minnig Gsponer); Biochemical Quality Control (BCQC) (Sten Strunze); Biochemical Quality Control (Sandra Kampczyk); Biochemical Quality Control (Sten Strunze); Biochemistry (Bjrn Hegemann); Biochemistry (Bj√∂rn Hegemann); Biochemistry (Marius Loetscher); Biochemistry (Monika Edler); Biochemistry 4 (Thomas Gentinetta); Bioinformatics & AI (Arthur Hsu); Bioinformatics & AI (Monther Alhamdoosh); Biological Analytcs R&D (Roland Zehnder); Biological Analytical Development (Simon Urwyler); Biological Quality Control (BQC) (Michael Molitor); Biologielaboranten (Carmen Walldorf (Inherited)); Biologielaboranten (Doris Nake (Inherited)); Biology Animal Group (Preston Eilders); Biology Lab (Catherine A Moody); Biology Lab (Preston Eilders); Biology Lab I (Preston Eilders); Biology Quality Control (BQC) (Michael Molitor); Bioprocess Development & Innovation (Erik Hinze); Bioprocess Development (Vicky Pirzas); Bioreactor Development (Sara Ladd); Bioreactor Development (Tizita Horning); Bioreactor Development 1 (Tony Hunt); Bioreactor Development 2 (Eric Zhu); Biostatistics Transplantation (Aparna Raychaudhuri (Inherited)); Biostatistics & Medical Writing, R&D JAPAN (Takashi Fukai - ); Biostatistics & Medical Writing, R&D JAPAN (Takashi Fukai ??? ?? - ??? ????); Biostatistics (Fang Xie); Biostatistics (Michael Fries); Biostatistics - Aquired Bleeding, Coagulation, Respiratory (Michael Fries); Biostatistics - Cardiovascular (Mark Heise); Biostatistics - Immunology and Inflammation (John-Philip Lawo); Biostatistics and Medical Writing (LOTHAR TREMMEL); Biostatistics and Medical Writing (Lothar Tremmel); Biostatistics ¬ñ Cardiovascular and Metabolic (Mark Heise); Biostatistics ¬ñ Innovation (Sergei Leonov); Biostatistics ¬ñ Transplantation (Aparna Raychaudhuri); Biostatistics ¬ñ Transplantation (Fang Xie (Inherited)); Biostatistik (Marcel Mischnik); Biotech Manufactuirng Facility (Christoph Hau√ümann); Biotech Manufactuirng Facility (Philip Elliott); Biotech Manufacturing (Aleksandar Hristov); Biotech Manufacturing (Brett Bohr); Biotech Manufacturing (Fraser Goodwin); Biotech Manufacturing (Peter Cloyd Belandres); Biotech Manufacturing (Peter Florey); Biotech Manufacturing (Steven Jacovou); Biotech Manufacturing 1 (Steven Jacovou); Birmingham 259 (Sam Whitehead); Birmingham 259 ACM Area 1 (Meredith Y Lundie); Birmingham 259 ACM Area 2 (AJ Johnson); Birmingham 259 QA (Noelle Teague); Bloomington 127 (Ashley B Fearnall); Bloomington 127 (Xang Vang); Bloomington 127 ACM Area 1 (Loryna Williams); Bloomington 127 ACM Area 2 (Kayla L Stueber); Bloomington 127 ACM Area 2 (Kirsten M Heller); Bloomington 127 QA (Karen R Soderberg); Bloomington 241 (Kevin Smith); Bloomington 241 ACM Area 1 (Anna Whitman); Bloomington 241 ACM Area 2 (Kevin Smith (Inherited)); Bloomington 241 ACM Area 2 (Michele Morison); Bloomington 241 QA (Ben Samarripas (Inherited)); Bloomington 241 QA (Ryan Caudill-Laughlin); Boca Field Services (Javier Lopez); Boca Field Services (Julio Feliciano); Boise 227 (Ash Senters); Boise 227 (Ashley Senters); Boise 227 (Carl Seelert); Boise 227 (Timothy Freeland Jr (Inherited)); Boise 227 ACM Area 1 (Camille Snow); Boise 227 ACM Area 2 (Travis Richardson (On Leave)); Boise 227 ACM Area 2 (Travis Richardson); Boise 227 QA (Derek Erhart (Inherited)); Boise 227 QA (Miles Veater); Brand Manager (Judith Vico); Brand Manager 745 (Genevieve Nihill); Breakthrough Technologies (Axel Dietrich); Breakthrough Technologies (Hung Pham); Breakthrough Technologies (Nathan J Brinkman); Breakthrough Technologies I (Laura Keigher); Brewer 503 (MacGregor Roy); Brewer 503 ACM Area 1 (Stephen R Coltharp); Brewer 503 ACM Area 2 (Katherine Ragia); Brewer 503 QA (Marc Stephens); Brownsville 113 (Jose L Dela Garza (Inherited)); Brownsville 113 (Nick Caballero); Brownsville 113 ACM Area 1 (Alfonso Gutierrez); Brownsville 113 ACM Area 2 (Robert Miranda); Brownsville 113 ACM Area 3 (Brenda Z Garcia); Brownsville 113 ACM Area 4 (Hector F Amaya); Brownsville 113 QA (Francisca Z Lopez); Brownsville 113 QA (Laura L Escalante); Brownsville 113 QA (Rosa E Mercado (Inherited)); Brownsville 114 (Anthony A Almaguer); Brownsville 114 (Osiel E Selvera); Brownsville 114 ACM Area 1 (Roy Madero); Brownsville 114 ACM Area 2 (Amanda Millan); Brownsville 114 ACM Area 3 (Melissa Medrano); Brownsville 114 ACM Area 4 (Maria A Garcia); Brownsville 114 QA (Joanna M Franco); Brownsville 135 (Francisca Z Lopez); Brownsville 135 (Nick Caballero); Brownsville 135 ACM Area 1 (Severita Williams); Brownsville 135 ACM Area 2 (Oralia M Vasquez); Brownsville 135 ACM Area 3 (Claudia Uribe Resendiz); Brownsville 135 QA (Alma E De Los Santos De Gonzalez); Brownsville 135 QA (Britney Castillo); Buffalo 239 (Nicholas Liberati); Buffalo 239 (Renaye Hecker); Buffalo 239 ACM Area 1 (Kimberly Lubecki); Buffalo 239 ACM Area 2 (Carol Palaszewski); Buffalo 239 QA (Nicholas Liberati); Buffalo 239 QA (Olivia Bejaran); Buffer Preparation (Benjamin Gr√ºn); Buffer-Production (Bernd Grau); Building 21 South (Brock A Boudreau); Building 21 South (Parish McKenzie (Inherited)); Building 21S (Union) (Brock A Boudreau); Building 21S (Union) (Parish McKenzie (Inherited)); Building 30 (Brock A Boudreau); Building 30 (Parish McKenzie (Inherited)); Building 30 (Union) (Brock A Boudreau); Building 30 (Union) (Parish McKenzie (Inherited)); Buildings & Prop Coord 256 (Ray Belli); Bulk (Markus Weber); Bulk Manufacturing (Freddie Wayne West); Bulk Manufacturing (Gregory Taylor); Bulk Manufacturing (Patricia Stewart (Inherited)); Bulk Manufacturing (Ryan Cox); Bulk Mechanical Team (Matthew Johnson (Inherited)); Bulk Mechanical Team (Mohamed Tubar); Bulk Mfg (Joel Rainey (Inherited)); Bulk Mfg (Katerina Petreska); Bulk Mfg (Mahmoud Lasheen); Bulk Mfg (Matt Thompson); Bulk Mfg (Tom Vick); Bulk Mfg (Tri Nguyen); Bulk Process Technology (Andreas Grter); Bulk Process Technology (Andreas Gr√ºter); Bulk Process Technology (Rene Boros); Bulk Utilities (Michael D Proctor); Burlington 512 (Lynn M Stratton); Burlington 512 ACM Area 1 (Kay Harris); Burlington 512 ACM Area 2 (Danica Johnson); Burlington 512 QA (Sharleen Dunn); Bus. Svcs, Project Edge (John Dinatale); Business (Camilla Shen); Business Analytics (Joseph Smith); Business Analytics (Venkatesh Ramakrishnan (Inherited)); Business Applications (Anu Thampi); Business Applications (Charles Lowe); Business Development (Evgeniy Glukhovskiy); Business Development (Simone Parkes); Business Insights & Analytics (Nitin Bhatnagar (Inherited)); Business Insights & Analytics (Shital Patel); Business Insights & Operations (Lynda Kulp); Business Integrity (Christine Zettlemoyer); Business Integrity Intercontinental (Bella Hovhannisyan Melkonyan (Rafael)); Business Integrity and Privacy Program (Sarah McHenry); Business Integrity and Risks (Karen Neave); Business Integrity and Risks (Kelly Scott); Business Operations (Harald Mller); Business Operations (Harald M√ºller); Business Operations (Laura Attride); Business Operations (Paul Jens); Business Operations EU (Heidi Sun); Business Partner Support (Anika Wagner); Business Partnering Holly Springs (Carey Vassallo); Business Partners (Christine Toth); Business Partners (Jacqueline Hawkins (Inherited)); Business Planning Group (Junya Morinaga - ); Business Planning Group (Junya Morinaga ??? ?? - ???? ?????); Business Planning Group (Makoto Miura (Inherited)); Business Planning Group (Yuichiro Sakagami); Business Process & Technology (Joseph Elicone); Business Process & Technology (Maureen Martini); Business Process (Christian Sonderegger); Business Process Excellence (Manuel Schaub); Business Process Excellence Global Training (Reed Johnston); Business Process Management (BPM) (GAFOOR SARANG); Business Process OTC (Kian Hartono); Business Process S2P (Simon Haemmerli); Business Processes & Data Mgt (Barbora ?√°chov√°); Business Processes & Data Mgt (Barbora chov); Business Processes (Boris Kaiser); Business Processes (Hans Raess); Business Processes (Thomas Romanus); Business Productivity (Scott A Ramseyer); Business Services (David H Confessore); Business Services (Ken Lim); Business Services Enterprise Business Solutions (David Wolozyn); Business Services and Demand Planning, APAC (Uli Kiefer); Business Support (Christian Schnabel); Business Support (Lisa Bartol); Business Support (Walter Aebersold); Business Technology (Boca) (Rob Klostermeyer); Business Technology (Jesse R Crew (Inherited)); Business Technology (Sharon Wong ); Business Technology (Sharon Wong ?????) (Sharon Wong ?????); Business Unit Director (Susan Snowball); Business Unit Manager (Natasha Hutchison); CAB & Digital Marketing Group (Narihiko Suenobu); CAD (Erwin Vonlanthen); CD Clinical Quality Control & Compliance (Larry Fish); CDS - Computerized Data & Systems (Christoph Kircher); CEQ Management (Barry Lynch); CI & QC Compliance (Lisa Marie Malcharek (Inherited)); CI & QC Compliance (Lisa Marie Malcharek); CI & QC Compliance (Thomas Wescombe); CI & QC Compliance (Thomas Wescombe) (Thomas Wescombe); CMC (Jason Newman); CMC Lead (Andrew White); CMC Lead (Dirk Bruns-Nagel); CMC Lead (Mackenzie Firer Sherwood); CMC Lead (Max Stuart Corbett); CMC Lead (Paul Smrdelj); CMC RA Group (Hideaki Hoshi ?? ?? - ?? ?????); CMC RA Group (Koichiro Kase - ); CMC Site Lead (Richard Buchta); CMO (Metani Rooms); CMO Management & Technology (Sabine Zollner); CMO Office (Vicki Oosterbaan); CO Diverse (Eddie Owens (Inherited)); CO Diverse (Edward Owens (Inherited)); CORE Operations - Canada (Constantina Boikos); CPAT (Kelly Helebrant); CPAT CVM - H&T (Uli Frevert); CPAT CVM / H&T (Uli Frevert); CPAT Internship Program I (Alissa Verone-Boyle (On Leave)); CPG Business Services (Michael Engelmann); CPG Center Berlin (Frank Bernert); CPG Center Bielefeld (Frank Bernert); CPG Center Braunschweig (Frank Bernert); CPG Center Bremen (Frank Bernert); CPG Center Gttingen (Frank Bernert); CPG Center G√∂ttingen (Frank Bernert); CPG Center Kiel (Frank Bernert); CPG Center Nrnberg (Frank Bernert); CPG Center N√ºrnberg (Frank Bernert); CPG Finance & Planning (Gerhard Mbus); CPG Finance & Planning (Gerhard M√∂bus); CPG Human Resources (Christine Debellis); CPG LQK Labor (Bettina Flotho-Salzmann); CPG Manager Serologisches Labor (Astrid Mather); CPG Medical Director (Kirsten Seidel); CPG Operations Management (Frank Bernert); CPG Planning (Achim Wagner); CPG Plasma Einkauf (Michael Engelmann (Inherited)); CPG Plasma Logistics (Klaus Rolshausen); CPG QA Case Processing Management (Ute Cherfan (Inherited)); CPG QA Center Operations (Ute Wessels); CPG QA Center Systems (Kerstin Kaddatz); CPG QA PLC Operations (Oliver Gro); CPG QA PLC Operations (Oliver Gro√ü); CPG QA Plasma Quality EU (Sascha Platt); CPG QA Plasma Quality QMB (Oliver Gro√ü (Inherited)); CPG QA Plasma Supplier Qualification (Ute Cherfan); CPG QA Plasma Supply Chain (Francesc Pont (Inherited)); CPG QA Plasma Supply Chain (Justin K Zajc); CPG QA Regulatory Affairs (Mandy Htzel); CPG QA Regulatory Affairs (Mandy H√∂tzel); CPG QA Supplier Qualification Management (Ute Cherfan (Inherited)); CPG QMB Center Operations (Ingrid Wallenwein (Inherited)); CPG QMB Center Operations (Ute Wessels (Inherited)); CPG Qualified Person (Margit El Azhari); CR&D Clinical Coagulation (Andres Brainsky); CRD Business Operations (Brian Dudt); CRD Business Operations (Walter Young); CRD Business Operations I (Craig Coffman (Inherited)); CRM Operations (Vinita Raina); CSL (Paul R Perreault); CSL 112 (Union) (Derek Butler); CSL Behring AG Bern (Martin Schaeren); CSL Behring AG Bern (Pierre Caloz); CSL Behring Broadmeadows (Martin Schaeren); CSL Behring Broadmeadows (Patricia Stewart); CSL Behring Broadmeadows II (Martin Schaeren); CSL Behring LLC Kankakee (Jose Gonzalez); CSL Behring LLC Kankakee (Patricia Stewart); CSL Behring LLC Kankakeee II (Patricia Stewart (Inherited)); CSL Behring Lengnau (Boris Lanoir); CSL Behring Marburg (Craig Shelanskey); CSL Behring Marburg (Michael Schrder); CSL Behring Marburg (Michael Schr√∂der); CSL Behring Pay Services (Susan M Walker); CSL Behring RCF Lengnau (Susanne Jecklin); CSL Behring RCF Lengnau II (Susanne Jecklin (Inherited)); CSL Behring Trademarks (Frank Schne-de la Nuez); CSL Behring Trademarks (Frank Sch√∂ne-de la Nuez); CSL Plasma (Craig Shelanskey); CSL Plasma (Michael F Deem); CSL Plasma (Willy Pardinas, Craig Shelanskey); CSL Plasma - Finance (Chris Shane); CSL Plasma - Finance (Christopher Shane); CSL Plasma / Engineering (Jrg Walz); CSL Plasma / Engineering (J√∂rg Walz); CSL Plasma GmbH (Berthold Ssser); CSL Plasma GmbH (Berthold S√ºsser); CSL Plasma GmbH HR (Berthold Ssser (Inherited)); CSL Plasma GmbH HR (Berthold S√ºsser (Inherited)); CSL Plasma II (Michael F Deem (Inherited)); CSL Plasma Kft. Hungary (Pankotai Tams); CSL Plasma Kft. Hungary (Pankotai Tam√°s); CSL Plasma US PLC Whitestown (Kristofor M Stauch); CSL Plasma US ¬ñ PLC Whitestown (Kristofor M Stauch); CSL Ruide Wuhan Manangement (David Chen ); CSL Ruide Wuhan Manangement (David Chen ?????); CSL Wuhan Plasma Operations (Jason Xu ?????); CSL Wuhan Ruide Calibration (Shangqu Shi ?????); CSL Wuhan Ruide Engineering (Jack Situ ); CSL Wuhan Ruide Engineering (Jack Situ ??????); CSL Wuhan Ruide Facility Team (Roger Peng ????); CSL112 Commercial Manufacturing Dept. (Derek Butler); CSR for Corporate (Patrick Castauro); CTS Business Operations (Robert Bredohl); CTS Cardiovascular (Eveline Girod-Engelhardt); CTS Hematology & Early Development (Annette Angell); CTS IRT (Amy Rupp); CTS Immunology (Berthold Roters); CTS Packaging & Labeling (Claudia Wieber); CTS Packaging & Labeling (Markus Thelen); CTS Process Improvement & Innovation (Carolin Sann); CTS Product Lead Cardiovascular (Elizabeth Bean); CTS Product Lead Immunology (Karin Knieke); CTS Product Lead Transplant (Fabienne Aschenbrenner); CTS Specialty Products & Transplant (Martin Mildenberger); CVC Cell, Virus & Compliance (Bjrn Keiner); CVC ¬ñ Cell, Virus & Compliance (Bj√∂rn Keiner); Calimmune Cell Manufacturing (Andreas Gille (Inherited)); Calimmune Cell Manufacturing (Bryan Burke); Calimmune Cell and Process Development (Jeffrey Ahlers); Calimmune Clinical (Maureen Boyd); Calimmune Clinical Programs (Mollie Barrett); Calimmune Information Technology (John Dallaire); Calimmune Quality Assurance (Anuja Prabhutendolkar); Calimmune Quality Assurance (Suparna Mishra Sarkar); Calimmune Research (Steven Lee); Calimmune Research and Development (Jeffrey Bartlett); Calumet Park 293 (Malissa Lichtenwalter); Calumet Park 293 QA (Michael W Solomon (Inherited)); Canada Medical Affairs Field Team (Maye Machnouk); Canton 236 (Jennie Marcum); Canton 236 ACM Area 1 (Ashley Instone); Canton 236 ACM Area 1 (Mirela Sekulic); Canton 236 ACM Area 2 (Rhianna Minger); Canton 236 ACM Area 2 (Rhianna Petrone); Canton 236 QA (Brandon Bosley); Canton 236 QA (Esence Hambrick); CapEx Procurement Lengnau (Franz Zweibrot [C]); CapEx Procurement Lengnau (Oliver Hahn); Capital Business Support (Tobias Pohle); Capital Controlling (Dirk Achenbach); Capital Controlling (Jrn Kaletsch); Capital Controlling (J√∂rn Kaletsch); Capital Project Management (Martina Thalmann); Capital Vendor Manager (Mark Vamadevan); Capital Vendor Manager (Nicholas Moody (Inherited)); Capital and MRO Sourcing - Kankakee (Emiliano Colon Segarra); Card Services (Linda K Nordmeyer); Cardio Therapies & Clinical Dev 2 (Lawrence Deckelbaum (Inherited)); Cardio Therapies & Clinical Development (Lawrence Deckelbaum); Cardiovascular & Diabetes (Susan Welsh (Inherited)); Cardiovascular & Diabetes (Todd Rudo); Cardiovascular & Metabolic Marketing (Rupal Shah); Cardiovascular & Metabolic Medical Affairs (Jeff McFadden (Inherited)); Cardiovascular & Metabolic TA (Jeff McFadden); Cardiovascular & Metabolism Therapeutic Area (Pierluigi Tricoci); Cardiovascular & Respiratory (James Peterson); Cardiovascular (Gail Berman); Cardiovascular (Lawrence Deckelbaum (Inherited)); Cardiovascular (Regina Clementi); Cardiovascular Global Marketing (Simon Fox); Cardiovascular and Metabolism (Danielle Duffy); Cardiovascular/Respiratory Therapeutic Area (Scott Hambaugh (Inherited)); Case Management GCSP (Nell Sborlini); Case Management MBR (Gudrun Heep); Category Chemicals, Filter Aid, Lab Chemicals (Martin Grossmann (Inherited)); Category Construction (Jos√© Maldonado (Inherited)); Category Equipment (Mike Gong); Category Gels, Resins, Media (BRN) (Martin Grossmann (Inherited)); Category Management (Markus Herrmann); Category Manager Indirects (Karl Lavery); Category Manager Indirects (Sarah Orchard); Category Packaging (Adam Kooloos); Cell Biology and Physiology (Cristina Gamell Fulla); Cell Culture & Purification (Michael Schmitt); Cell Culture & Purification Development (Andrew Low); Cell Culture & Purification Development (Ben Hunt); Cell Culture & Purification Development (Innocent Bekard); Cell Culture & Purification Development (Irene Baker); Cell Culture & Purification Development (Lou Fabri); Cell Culture & Purification Development (Simon Gerber); Cell Culture & Purification Development (Simon Stephan Gerber); Cell Culture & Purification Development (Vanessa Sandford); Cell Culture & Purification Development (Yih Yean Lee (Inherited)); Cell Culture & Purification Development (Yih Yean Lee); Cell Culture & Purification Development 1 (Innocent Bekard (Inherited)); Cell Culture Analytics (Vanessa Trefzer); Cell Manufacturing (Angel Jaramillo); Cell Manufacturing (Samuel O''Callaghan (On Leave)); Cell Manufacturing (Stefanie Homann); Cell Manufacturing I (Michelle Millington); Cell Manufacturing III (Samuel O''Callaghan); Cell Manufacturing IV (Stefanie Homann); Cell and Process Development (Jeffrey Ahlers); Cells, Virus & Compliance (Trudi Wentzel); Cells, Virus and Compliance (Tanya Guzzardi); Center Mitarbeiter (Andreas Gehrich (Inherited)); Center Mitarbeiter (Andreas Gehrich); Center Mitarbeiter (Annette Pernitzsch (Inherited)); Center Mitarbeiter (Annette Pernitzsch); Center Mitarbeiter (Claudia Habenicht (Inherited)); Center Mitarbeiter (Claudia Habenicht); Center Mitarbeiter (Damaris Kieckhfer); Center Mitarbeiter (Damaris Kieckh√∂fer); Center Mitarbeiter (Heike Borchert); Center Mitarbeiter (Kirsten Scheibel (Inherited)); Center Mitarbeiter (Kirsten Scheibel); Center Mitarbeiter (Natascha Bock (Inherited)); Center Mitarbeiter (Natascha Tappendorf); Center Mitarbeiter (Stephani Keltsch); Center Mitarbeiter (Sven Schuhmann (Inherited)); Center Mitarbeiter (Sven Schuhmann); Champaign 270 (Harriet Williams); Champaign 270 ACM Area 1 (Jacques LaRue); Champaign 270 ACM Area 2 (Harriet Williams (Inherited)); Champaign 270 ACM Area 2 (Quawan Dhom); Champaign 270 QA (Meghan Constantine); Change & Systems (Angela Leepin); Change & Systems (Lucia Mathis); Change Control Final Product Care (Stephan Nau); Change Management (Elizabeth Walker (Inherited)); Change Management (Kris Weidling (On Leave)); Change Management (Wendy Smith); Change Management Quality (Marlise Kuert Kolb); Change Management and Launch Support (QCM) (Marlise Kuert Kolb); Change Management-Document Control (Michelle Wells); Change and Investigations Primary Manufacturing (Jason Gilmour); Change and Investigations Secondary Manufacturing (Hai Tran); Characterization (Lars Robbel); Characterization 2 (Katharina Kramer); Charleston 044 (Lorenzo L Bowser); Charleston 044 (Robin M Bencivenga); Charleston 044 ACM Area 1 (Gregory Swant); Charleston 044 ACM Area 1 (Lorenzo L Bowser (Inherited)); Charleston 044 ACM Area 2 (Shakerrie Mobley); Charleston 044 QA (Yolanda L Carlisle); Charlotte 203 (Sam Kastanowski); Charlotte 203 (Shannon D Dalton); Charlotte 203 ACM Area 1 (Kathy Reilly); Charlotte 203 ACM Area 2 (Micah Ford); Charlotte 203 ACM Area 2 (Shannon D Dalton (Inherited)); Charlotte 203 QA (Nicole D Etheridge); Charlotte 418 (Paul Schmaldinst); Charlotte 418 ACM Area 1 (Sharita Swann); Charlotte 418 ACM Area 2 (Mayada M Omer); Charlotte 418 ACM Area 3 (Trina Crayton); Charlotte 418 QA (Le Tran); Chattanooga 010 (Ramoncito B Bautista); Chattanooga 010 ACM Area 1 (Sheree L Leatherman); Chattanooga 010 ACM Area 2 (Beth Simpson); Chattanooga 010 ACM Area 2 (Brittany Goforth); Chattanooga 010 QA (Callan Pierson); Chattanooga 010 QA (Heather Palladino); Chattanooga 010 QA (Patti Bailey (Inherited)); Chattanooga 010 QA (Patti Bailey (Inherited), Prim J Cunningham (Inherited)); Chattanooga 407 (Brian West); Chattanooga 407 (Brianna E Ballew); Chattanooga 407 ACM Area 1 (Amy D Hodge); Chattanooga 407 ACM Area 2 (Joshua Turpin); Chattanooga 407 QA (Barron Williamson); Cheektowaga 235 (Scott Bowers); Cheektowaga 235 ACM Area 1 (Cheryl Sousa); Cheektowaga 235 ACM Area 2 (Iryna Omelyan); Cheektowaga 235 QA (Roxanne Tyczka); Chem. Quality Control 1 (Lukas Dinger); Chem. Quality Control 2 (Silvan Stucki); Chemical Quality Control (CQC) (Adrian Zobrist); Chemical Analytics R&D (Lars Schiefelbein); Chemical Analytics R&D (Sara Stinca); Chemical Quality Control (Andreas Affolter); Chemical Quality Control (CQC) (Adrian Zobrist); Chemical Quality Control (Lars L√ºersen); Chemical Quality Control (Sten Strunze); Chemistry (Sara Garland); Chemistry (William Squires); Chemistry - In-Process Group (Courtney Nuccio); Chemistry - Raw Materials Group (Arthur F Fox); Chemistry Lab (Rebecca L Boudreau); Chiba Kanagawa Area (Madoka Yamamoto); Chibi Accounting (Hongyan Hu ?????); Chibi Admin (Hongyan Hu ); Chibi Admin (Hongyan Hu ?????); Chibi Clinical Inspection (Shiyong Yu ); Chibi Clinical Inspection (Shiyong Yu ?????); Chibi Plasma Collect (Liyun Huang ); Chibi Plasma Collect (Liyun Huang ?????); Chibi Plasma Collection (Jie Yu ); Chibi Plasma Collection (Jie Yu ????); Chibi Plasma Collection Center (Jun Lai ????); Chibi Plasma Collection Management (Jingyu Dong ?????); Chibi Plasma Sourcing (Jiaxin Long ?????); Chibi Plasma Sourcing Management (Bin Zhang ); Chibi Plasma Sourcing Management (Bin Zhang ????); Chicago 247 (Guillian T Gonzalez); Chicago 247 ACM Area 1 (Sabrina Flowers); Chicago 247 ACM Area 2 (Gretchen Watkins); Chicago 247 ACM Area 2 (Guillian T Gonzalez (Inherited)); Chicago 247 QA (Gretchen Watkins); Chicago 247 QA (Linda Schulthess); Chief Medical Office (Charmaine Gittleson); Chief Operating Officer (Paul McKenzie); Chief Safety Officer (Susan Welsh); China Logistics (Vickie Xian ); China Logistics (Vickie Xian ?????); China Marketing (Anlu Cai ?????); China Strategic Quality (Jian Fei ????); Christian Spuckti; Chromatography (Holger Blessing); Chromatography (Sven Karschnia); Chubu Area (Hiroyoshi Iwamoto); Chugoku Shikoku Area (Masahiko Ishida); Cincinnati 177 (Harold Tucker Jr); Cincinnati 177 ACM Area 1 (Anh Le); Cincinnati 177 ACM Area 2 (Darryl W Revere Jr); Cincinnati 177 ACM Area 2 (Jessica Hoffman); Cincinnati 177 QA (Christopher Thompson); Cincinnati 189 (Lee A Miles); Cincinnati 189 ACM Area 1 (Kristal Emmitt); Cincinnati 189 ACM Area 2 (Ginger Wells); Cincinnati 189 ACM Area 2 (Kaitlyn Spencer); Cincinnati 189 QA (Tyianna N Trice (On Leave)); Cincinnati 189 QA (Tyianna N Trice); Cinncinnati 177 (Harold Tucker Jr); Citrix (Thomas M Kent); Cleveland 401 (Sarah E Moss); Cleveland 401 ACM Area 1 (Shelly L Deimling); Cleveland 401 ACM Area 2 (Chonita Johnson (On Leave)); Cleveland 401 ACM Area 2 (Chonita Johnson); Cleveland 401 QA (Enetisha T Dailey); Cleveland 401 QA (Jennifer Longo); Clifton 255 (Andrew Oliver); Clifton 255 ACM Area 1 (Anthony Camuso); Clifton 255 ACM Area 2 (Marshaya Johnson); Clifton 255 ACM Area 2 (Rolshall Burnett); Clifton 255 QA (Kengie Jenkins); Clinic Study Acquired Bleeding-IG (Danielle Dalton); Clinic Study Acquired Bleeding/IG (Danielle Dalton); Clinical Bulk (Gerd Eisenmann); Clinical Bulk (Noemi Scholl); Clinical Bulk (Rene Bruegger (Inherited)); Clinical Compliance (Mihaela Carla Nosca); Clinical Compliance and Training (CC&T) (Saskia Ruis); Clinical Data Standards and Programming (Dieter Boss); Clinical Development (Wilfried Seifert); Clinical Development - Study (Christa Lewiski); Clinical Development - Transplant (Paul Shore); Clinical Development Operations (Craig Coffman); Clinical Development Operations (Deirdre BeVard); Clinical Development Operations (Kazuaki Hashimoto - ); Clinical Development Operations (Kazuaki Hashimoto ??? ?? - ???? ?????); Clinical Development Operations I (Craig Coffman); Clinical Development Respiratory (Lars Groenke); Clinical Disclosures & Transparency (Vicki Oosterbaan); Clinical Epidemiology (Quazi Ataher); Clinical Epidemiology (Susan Colilla); Clinical Epidemiology (Susan Welsh (Inherited)); Clinical Operations (David J. Parker); Clinical Operations (Thomas Verish); Clinical Operations (Valerie Reynaert); Clinical Operations 1 (Jennifer Weaver); Clinical Operations II (Valerie Reynaert); Clinical Operations Japan (Hideshiro Benz); Clinical Operations Serology (David Bibby); Clinical Operations Systems (Simone Dierkes) (Simone Dierkes); Clinical Ops 2 (Michael Giordani); Clinical Oversight Manager (Katja Ganter); Clinical Oversight Manager (Miriam Hochthurn); Clinical Oversight Manager (Stefanie Auer); Clinical Pharmacology & Early Development (Amy Harman); Clinical Pharmacology & Early Development (Stephen Caltabiano); Clinical Pharmacology & Translational Dev (John Roberts); Clinical Pharmacology & Translational Dev ¬ñ CPAT (Diana Lanchoney); Clinical Pharmacology & Translational Development CPAT (Diana Lanchoney); Clinical Pharmacology &Early Development (Diana Lanchoney (Inherited)); Clinical Pharmacology &Early Development (Dipti Pawaskar); Clinical Pharmacology &Early Development (Jagdev Sidhu); Clinical Pharmacology &Early Development (Joanne Ma); Clinical Pharmacology &Early Development (John Roberts); Clinical Pharmacology &Early Development (Michael Tortorici); Clinical Pharmacology (Bo Zheng); Clinical Procedural Documents &Standards (Thomas Verish (Inherited)); Clinical Programming (Stefan Hofmann); Clinical Programs (Christine Joch); Clinical Quality (Claire Pope); Clinical Quality (Karen Gard''ner (Inherited)); Clinical Quality Assurance (Daisy Maldonado-Ortiz); Clinical Quality Assurance (Joy Quinal); Clinical Quality Assurance (Pontaah Arbtan); Clinical Quality Assurance (Sharon Reinhard); Clinical Quality Assurance (Terrence Purnell); Clinical Quality Assurance (Volker Nickel); Clinical R&D (Hideto Akama - ); Clinical R&D (Hideto Akama ??? ?? - ??? ????); Clinical Research & Development - Transplant (Scott Adler); Clinical Safety (Corrinne Clement); Clinical Safety (Maria Mller); Clinical Safety (Maria M√ºller); Clinical Safety (Velma Hurst); Clinical Science (Eve Versage); Clinical Science (Naohito Sato); Clinical Sciences Transplantation (Christine Voigt ); Clinical Sciences Transplantation (Christine Voigt); Clinical Scientist (Andres Brainsky (Inherited)); Clinical Scientist (Jenny Mears); Clinical Serology Operations Lead (Frank Iozzo); Clinical Strategy&Development (Hideto Akama ??? ?? - ??? ????); Clinical Study (Agnieszka Turowska); Clinical Supply Chain Planning (Ulrich Mengel); Clinical Supply Quality (Carl Forte); Clinical Supply Quality (Matthew Wokulich); Clinical Trial Process Improvement & Innovation (Steve Walker); Clinical Trial Process Improvement & Innovation (Thomas Kelly); Clinical Trial Supply (Patrick McLaughlin); Clinical and TA Strategy (Steven Pascoe); Coag, Devices & Special Products (Ignacio Rodriguez); Coag, Devices & Special Products (Juergen Zorn); Coagulation & CC Sales Force (Emmanuelle Massonie (Inherited)); Coagulation & CC Sales Force (Jean-Vincent Viale); Coagulation & CC Sales Force (Thierry BERTHOULE); Coagulation & Oncology (Kristin Ingrassia); Coagulation & Oncology (Sylvia Herget); Coagulation & Oncology 1 (Kerstin Jung); Coagulation (Janine Dolan); Coagulation - CPP (Kristy Bandza (Inherited)); Coagulation Manufacturing (Kristy Bandza); Coagulation Manufacturing (Union) (Kristy Bandza (Inherited)); Coagulation Sales South (Marlene Gregory (On Leave)); Coagulation Sales South (Marlene Gregory); College Station 152 (Kandra K Blodgett); College Station 152 (Lauren Parks); College Station 152 (May Walker); College Station 152 ACM Area 1 (Kailey Stockton); College Station 152 ACM Area 2 (Amanda Miller); College Station 152 ACM Area 2 (DANIELLE GARRETT); College Station 152 QA (Kacie Goad); College Station 152 QA (May Nowalk); College Station 152 QA (May Walker); Colorado Springs 277 (Amanda M Cvitkovich); Colorado Springs 277 ACM Area 1 (Ben Huff); Colorado Springs 277 ACM Area 2 (Leon Clemons Jr.); Colorado Springs 277 ACM Area 2 (Sang Nguyen); Colorado Springs 277 QA (Crystal L Reichard); Columbia 217 (Catherine Watson); Columbia 217 (Monique Simpson); Columbia 217 ACM Area 1 (Mirna Rodriguez); Columbia 217 ACM Area 2 (Gregory Hines); Columbia 217 QA (Alissa Elke); Columbia 217 QA (Brandon Hoffman); Columbia 217 QA (Victoria McIntyre (Inherited)); Columbia 271 (Beth Brooks-Mccoy); Columbia 271 QA (Eric Mathis); Columbia 612 (Catherine Watson); Columbia 612 (Jose Pineda); Columbia 612 ACM Area 1 (Joyce A Jackson); Columbia 612 ACM Area 2 (Garrett Palmer); Columbia 612 QA (Aniashalla McDuffie); Columbia 612 QA (Shannon V Brown); Columbia 612 QA (Victoria McIntyre (Inherited)); Columbus 150 (Mark A Leach); Columbus 150 (Matthew Z Osborne); Columbus 150 ACM Area 1 (Nasha Ausberry); Columbus 150 ACM Area 2 (Alison L Woody); Columbus 150 QA (Tina M Miller); Columbus 409 (Angela L Funk); Columbus 409 ACM Area 1 (Jacob A Wilcox); Columbus 409 ACM Area 2 (Stella Shella May Oliver); Columbus 409 QA (Thomas U Anderson); Com Dev Immunology (GABRIELA ESPINOZA); Com Dev Immunology (Gabriela Espinoza); Com Dev Immunology (Karen MacPhail); Com Dev Immunology (Lawrence Bruck); Com Dev Medical (Birgit Voelker); Com Ops Human Resources Asia Pac (Jenny Zeng); Com Ops Human Resources Asia Pac (Trina Hendri (Inherited)); Com Ops Human Resources EU (Marc Htting); Com Ops Human Resources EU (Marc H√∂tting); Com Ops Human Resources ICO (Jenny Alexandra Kjaer); Com Ops Human Resources ICO (Jenny Kjaer Rotzler); Com Ops Human Resources NA (Elizabeth Wixted); ComDev Coagulation (Jens Oltrogge); ComDev Speciality Products (Georg Henkel); ComDev Speciality Products 1 (Georg Henkel); ComOps Argentina Accounting (Guadalupe Porro Greco); ComOps Argentina Finance (Silvina Lazzari); ComOps Argentina Marketing (Lucia I Grossi); ComOps Argentina Sales (Fernando Grosso); ComOps Brazil Finance (Marcelo Di Napoli); ComOps Brazil Market Access (Gerdeon Aurelio A Paiva); ComOps Brazil Marketing (Cristina Daniel Paulino); ComOps Brazil Operations (Cristina Junko Nakai); ComOps Brazil Regulatory Affairs (Rosana Batista); ComOps Brazil Sales (Luis Gustavo Gomes); ComOps Business Operations GE/AT/EEU (Karl Fox); ComOps Canada Coag & CC (MICHAEL LEO); ComOps Canada Finance (Michael McAllister); ComOps Canada Medical Affairs (MSL) (David Barnes (Inherited)); ComOps Canada Medical Affairs (MSL) (Debbie Bensen-Kennedy (Inherited)); ComOps Canada Regulatory Affairs (Vaneeta Bhatia); ComOps Canada Sales (MARIE-EVE JACQUES); ComOps Colombia Accounting (Carlos Andres Loaiza Barragn); ComOps Colombia Accounting (Carlos Andres Loaiza Barrag√°n); ComOps Colombia Sales (Martha Romano Gomez); ComOps Controlling GE/AT/Emerg. EU (Oliver Rosenthal); ComOps Demand Planning EU (Heike Kayser); ComOps Finance FP&A EU (Tamara Lissitsyna); ComOps Finance/Supply Chain/Compass EU (Heinz Berghoff); ComOps Government Reporting/Compliance (Mike Andrews (Inherited)); ComOps Intercontinental MBR (Bjrn Schfer); ComOps Intercontinental MBR (Bj√∂rn Sch√§fer); ComOps Intercontinental MBR (Manfred Nolte); ComOps Market Access (Stefan Neudrfer); ComOps Market Access (Stefan Neud√∂rfer); ComOps Marketing Coagulation (Dave Lewis); ComOps Marketing Coagulation (JD Kohutka); ComOps Marketing GE/AT/Emerg. EU (Elisabeth Averwerser); ComOps Meetings & Conventions (Molly Hess Knodel); ComOps Mexico Finance & Administration (Carlos Salas); ComOps Mexico Finance & Administration (HECTOR ESCOBEDO); ComOps Mexico Market Access (Humberto Maciel); ComOps Mexico Product Management (THALIA FIERRO DE LEON); ComOps Mexico Regulatory Affairs (Sandra Velasco); ComOps Mexico Sales (Jorge L Gastlum); ComOps Mexico Sales (Jorge L Gast√©lum); ComOps NA Business Operations (Denise Von Dohren); ComOps NA Business Operations (Mike Andrews (Inherited)); ComOps NA Government Reporting (Pamela Makosky); ComOps NA Government Reporting (Ronald Ritter Jr); ComOps NA Government Reporting (Sarah Palmer); ComOps NA Learning & Support Services (Lynn DiBonaventura); ComOps NA Learning & Support Services (Mike Andrews (Inherited)); ComOps NA Market Insights (Venkatesh Ramakrishnan); ComOps NA Marketing Consumer (Janet A Reimund); ComOps NA PRC Operations (Diane Wright); ComOps NA, Master Data (David Fling); ComOps NA, Sales Operations & CRM (Michael Price); ComOps NA, Sales Operations & Incentive Compensation (Michael Price); ComOps NA, Sales Operations (Jerry Burgener); ComOps North America Medical Affairs (Debbie Bensen-Kennedy); ComOps North America Medical Affairs (Judith Vensak); ComOps North America Medical Affairs- Immunology TA (Arie Katz); ComOps Reimbursement and Access (Dina Inverso); ComOps Reimbursements and Access (Jeffrey Lucero); ComOps Reimbursements and Access (Kate O''Connor-Masse); ComOps SP - Payers (Pete Dickson); ComOps SP / Payers (Pete Dickson (Inherited)); ComOps SP / Payers (Pete Dickson); ComOps Sales Germany (Michael Bernd Rode); ComOps Switzerland (Isabelle Dahinden); ComOps Therapeutic Area EU (Antti Kourula); ComOps Therapeutic Area EU (Damian Gilkerson); ComOps US Atlantic Specialty (Jeffrey Todd Winn); ComOps US Coag Midwest (Mark A Wiener); ComOps US Coag Northeast (Dominic Lattanzi); ComOps US Coag Northeast (Ivan Holtz (Inherited)); ComOps US Coag Sales (Ivan Holtz); ComOps US Coag South (Mark Fitzgerald); ComOps US Coag West (Scott Vollet); ComOps US Corporate Accounts (Paul Kurt); ComOps US Delaware Valley Specialty (Kellee Fearon); ComOps US Delaware Valley Specialty (Marlene Gregory); ComOps US Medical Affairs Coagulation (Jerry Powell (Inherited)); ComOps US Medical Affairs Coagulation (Vidhi Desai); ComOps US Medical Affairs Coagulation I (Vidhi Desai); ComOps US Medical Affairs Immunoglobulin (Ann Bullinger); ComOps US Medical Affairs Immunoglobulin (Ayman Kafal); ComOps US Medical Affairs Specialty (Laurel Omert); ComOps US Medical Affairs Specialty (Paolo Bajcic); ComOps US Medical Information (Deirdre Smith); ComOps US Mid-Atlantic Immunology (Lori Giampaolo); ComOps US Mid-Atlantic Immunology (Michael Flaherty); ComOps US Mid-South Immunology (Cory Baldwin); ComOps US Mid-South Immunology (James Heintz (On Leave)); ComOps US Mid-South Immunology (James Heintz); ComOps US Mid-South Specialty (Bill Stokes); ComOps US Mid-South Specialty (Brett Weathersby); ComOps US Mid-West Immunology (Mark C Morgan); ComOps US North Central Specialty (Steve A Mick); ComOps US Northeast Immunology (Pamela Buschbacher); ComOps US Northeast Specialty (Craig Strok); ComOps US Northeast Specialty (Gina Blair (Inherited)); ComOps US Northeast Specialty (Rebecca Riebe (On Leave)); ComOps US Ohio Valley Specialty (Jason Flowers); ComOps US South Central Immunology (Joseph Guinan (Inherited)); ComOps US South Central Immunology (Roxanne Quirin); ComOps US South Central Specialty (David Van Buskirk); ComOps US Southeast Immunology (James Gleichowski); ComOps US Southeast Specialty (Michael Allen); ComOps US Specialty Marketing (Bernadine Koziara (Inherited)); ComOps US Specialty Marketing (Tom Groeling); ComOps US Specialty Sales (Gina Blair); ComOps US Supply Chain (Madonna Jarrett); ComOps US West Central Immunology (Greg Logsdon); ComOps US West Central Specialty (Ann Andari); ComOps US West Central Specialty (Gina Blair (Inherited)); ComOps US West Central Specialty (Kimberly Kustron); ComOps US West Immunology (Greg Hansen); Combination-Device QA (TATYANA ILYINA); Combination/Device QA (TATYANA ILYINA); Comm Dev and Scientific Affairs (Edith Rosenberg); Comm Dev and Scientific Affairs (Tara Satyanand); Comm. Oper. GE/AT/Emerg. EU Diverse (Dirk Hoheisel (Inherited)); Commercial Operations Junxin (Shaolin Huang ?HuangShaolin?); Commercial (Brent MacGregor); Commercial (Stephen Allan); Commercial Access & Policy US (Marie Mazur (Inherited)); Commercial Access & Policy US (Shanthy Krishnarajah); Commercial Account Management - Public Health (Jeffrey Benton); Commercial Argentina (Gonzalo Pereira); Commercial Business Operations Sales & Analytics (Kevin Harkins); Commercial Business Operations Training (Patrick Gostomski); Commercial Business Services (Lynda Kulp); Commercial China (Cheng-Yen Tsai ); Commercial China (Cheng-Yen Tsai ?????); Commercial China (Harold Chan ?????); Commercial Contracts US (Yvonne Blom Hilsky); Commercial Contracts US (Yvonne Hilsky); Commercial Contracts-US (Agnes Goins); Commercial Customer Operations US (John Spencer); Commercial Customer Operations US, Customer Service, Account Mgmt (Teka-Ann Forrester); Commercial Customer Service/Supply Chain (Narelle Kinson); Commercial Development (Jane True); Commercial Development Global (Debbie Drane); Commercial Development and Policy (Dirk Ulrich Hofmann); Commercial Development and Policy (Lorna Meldrum); Commercial Development and Policy (Marie Mazur); Commercial Excellence (Makoto Miura); Commercial Excellence (Roger Melliger (Inherited)); Commercial Excellence (Tomohiro Miura - ); Commercial Excellence (Tomohiro Miura ??? ?? - ??? ?????); Commercial Excellence Training & Development Office (Chiho Muto); Commercial Excellence and Training (Cheng-Yen Tsai ????? (Inherited)); Commercial Excellence and Training (Joanne Liu ); Commercial Excellence and Training (Joanne Liu ????); Commercial Governance and Transparency (Artimis Ghassemi); Commercial Governance and Transparency (Daniel Quayle); Commercial IT solutions (Thomas Wilcock); Commercial Italy (Massimo Leoni); Commercial MEA (Camilla Shen (Inherited)); Commercial Marketing US (David Ross (Inherited)); Commercial Marketing US (Glenn Omanio); Commercial Marketing US (Tara Charvat); Commercial National Accounts US (Stefan Merlo); Commercial Operations (Brent MacGregor); Commercial Operations (Deniz Bagdadi); Commercial Operations (James Gaw); Commercial Operations (Mark Ridge); Commercial Operations (Sam Dowdle); Commercial Operations Argentina (Juan Pablo Guereo); Commercial Operations Argentina (Juan Pablo Guere√±o); Commercial Operations Australia/NZ (Loretta Croker); Commercial Operations Austria (Beate Pettinger-Natmenig); Commercial Operations Austria (Beate Pettinger-Natmessnig); Commercial Operations Austria (Beate Pettinger-Natme√ünig); Commercial Operations Austria (Martin Tenlen); Commercial Operations Benelux (Patrick Reygaert); Commercial Operations Brazil (Gustavo Fernandes); Commercial Operations Canada (Philippe Hebert); Commercial Operations Chile (Juan Pablo Ambar); Commercial Operations Colombia (Eduardo Cabas); Commercial Operations Colombia (Juan Feliu (Inherited)); Commercial Operations Czech Republic (JI KAPEREK); Commercial Operations Czech Republic (JI?√ç KA¬äPEREK); Commercial Operations Czech Republic (Ondrej Halasz); Commercial Operations Denmark (Gitte Stausholm); Commercial Operations Europe (Lutz Bonacker); Commercial Operations Finance (Adrienne Ford); Commercial Operations Finance (Amanda White); Commercial Operations Finance (Marcelo Estrella); Commercial Operations Finance (Michael Kochanski); Commercial Operations France (Franck Puget); Commercial Operations GE/AT/Emerg. EU (Dirk Hoheisel); Commercial Operations Global HR (Trina Hendri); Commercial Operations Greater China (Ben Yang ?????); Commercial Operations Greater China (Harold Chan ); Commercial Operations Greater China (Harold Chan ?????); Commercial Operations Greater China Junxin (Paul Li (Inherited)); Commercial Operations Greece (Marianna Konstantinidi); Commercial Operations Hong Kong (Roger Cheng ); Commercial Operations Hong Kong (Roger Cheng ?????); Commercial Operations Hungary (Lukacs Attila); Commercial Operations Intercontinental (Markus Staempfli); Commercial Operations Italy & Greece (Oliver Schmitt); Commercial Operations Italy, Business Operations (Giuseppe Fioravante); Commercial Operations Italy, Central Italy Sales (Claudio Chiorri); Commercial Operations Italy, North Italy Sales (Maurizio Gonizzi Barsanti); Commercial Operations Italy, South Italy Sales (Paolo Lombardi); Commercial Operations Japan (Jean-Marc Morange); Commercial Operations Junxin (Qiuhui Shi); Commercial Operations Mexico (Nicolas Martinez Gould); Commercial Operations Nordic (Martin Tenlen); Commercial Operations Nordic (Ulf Hultquist); Commercial Operations North America (Robert Lojewski); Commercial Operations Poland (Grazyna Debowska); Commercial Operations Poland (Marek Skotnicki); Commercial Operations Portugal (David Ventura); Commercial Operations SG, ML & ID (Matthew Ho); Commercial Operations Slovakia (Andrea Solivajsova); Commercial Operations Slovakia (JI KAPEREK); Commercial Operations Slovakia (Ondrej Halasz); Commercial Operations Spain & Portugal (Mara Jose Sanchez Losada); Commercial Operations Spain & Portugal (Mar√≠a Jose Sanchez Losada); Commercial Operations Turkey (Aysun Acer); Commercial Operations Turkey (Aysun Yanbol); Commercial Operations Turkey (Ercin Kugu); Commercial Operations Turkey 2 (Mehmet Aydogan); Commercial Operations United Kingdom (Eddie Owens); Commercial Operations United Kingdom (Edward Owens); Commercial Operations United Kingdom II (Dan Betts); Commercial Operations, Influenza Vaccines (Linda DU); Commercial Operations, Americas (Haejin Chung); Commercial Operations, Americas (Jane True); Commercial Operations, Canada (Gillian Stafford); Commercial Operations, DE, Customer Service (Thomas Kasimirat); Commercial Operations, DE/CH/AU (Deborah Di Salvo); Commercial Operations, DE/CH/AU (Frank Eberlein); Commercial Operations, EMEA (Enric Canelles Torres); Commercial Operations, Fleet, Meetings & Travel Strategic Sourcing (Kristie Boyd); Commercial Operations, Influenza Vaccines (Linda DU); Commercial Operations, International and Pandemic (Lorna Meldrum); Commercial Operations, Italy (Maura Cambiaggi); Commercial Operations, LatAm (Carmen Pereyra); Commercial Operations, LatAm (Carmen Rosa Pereyra Davila); Commercial Operations, Marketing UK (Kaush Gandhi); Commercial Operations, North Americas (David Ross); Commercial Operations, Spain (Antonio Lloret Parellada); Commercial Operations, UK (Deborah Di Salvo); Commercial Operations, UK (Helen Concilia); Commercial Ops (John Lawrence); Commercial Ops North America (John Fudala); Commercial Ops North America (Robert Lojewski (Inherited)); Commercial Pandemic Contracts (Randall Deck); Commercial Taiwan (Cheng-Yen Tsai ?????, King Lian Wang ?????); Commercial Taiwan (King Lian Wang ?????); Commercial Taiwan (Louis Liu ); Commercial Taiwan (Louis Liu ?????); Commercial, Business Operations UK (Charlotte Wrigley); Commercial, Business Operations, Customer Support (Jeff Wettlaufer); Commercial, Customer Service UK - Liverpool (Charlotte Wrigley); Commercial, Customer Service UK ¬ñ Maidenhead (Amy Smith); Commercial, Global Fluad (Richard Bland); Commercial, Global Flucelvax & Afluria (JESSICA O''DONNELL); Commercial, Global Flucelvax & Afluria (Jessica O''Donnell); Commercial, National Accounts Field US (Aaron Hubner); Commercial, National Accounts Field US (Aaron Martin Hubner); Commercial, National Accounts Field US-Summit (Gregg Quatrini); Commercial, National Accounts UK (Raashid Mehmood); Commerical, Product Planning & Innovation (Loddie Foose); Commission & Qualification (Arelis Cabezas); Communications (Maureen Powell); Communications (Melanie Kerin); Communications (Polina Miklush); Communications (Sandra Ruckstuhl); Company Secretary (Gregory Boss (Inherited)); Company Secretary Office (Sonya Curciev); Compass / gCRM System (Giorgio Lippi (Inherited)); Compass / gCRM System Benelux (Patrick Reygaert (Inherited)); Compass / gCRM System France (Pascale Ogel Le Guen (Inherited)); Compass Excellence Center (John Eric Bunn); Compensation Programs (Anthony Dillon); Compensation Programs (Timothy O''Donnell); Competitive Intelligence (Magdalena Popesco); Compliance & Improvement Manager (Elaine Feely); Compliance (Andrea Studer); Compliance (Cindy Rocknowski); Compliance (Jeffrey Zoubek (Inherited)); Compliance (Robin A Mroz); Compliance Americas (John Neff (Inherited)); Compliance Americas (Thomas Spittal); Compliance I (Margrit Waterval); Compliance II (Dagmar Riffel (Inherited)); Compliance II (Jutta Regenfelder); Compliance Management Engineering (Rainer Kutsch); Compliance Support 1 (Volker Gawantka); Computerized Data & Instruments Systems (C√©line Pires); Computerized Data & Instruments Systems (Hiroshi Nakano); Congress (Jean-Marc Morange (Inherited)); Congress (Kyota Yamaoka ??? ?? - ???? ????? (Inherited)); Connected Healthcare (Mark Ridge (Inherited)); Construction, Engineering & Qualification (Adam Robb); Construction, Engineering & Qualification (Mike Spencer); Construction, Engineering & Qualification (Richard Hayne); Construction, Engineering & Qualification 1 (Michael Ah-Cann); Construction, Engineering & Qualification 2 (Adam Robb (Inherited)); Construction, Engineering & Qualification 2 (Jim Li); Construction, Engineering & Qualification CSL112 (Jacqueline Murphy); Content Management (Christian Mohr); Content Management (Elisabeth Averwerser (Inherited)); Contract Administration (Frances Richardson); Contract Manufacturing (Ian Goldup); Contracts / Claims Management (Kevin Rathmell [C]); Controlling & Financial Reporting (RYAN HANSEN); Controlling (Wolfgang Thiel); Corporate & Expert Services (Patrick Haeberli); Corporate Affairs (Sharon McHale); Corporate Affairs - US (Polina Miklush); Corporate Affairs and Communications (Anthony Farina); Corporate Communications (Jemimah Brennan); Corporate Communications (Jemimah Pentland); Corporate Communications - Japan (Hidemi Akazawa); Corporate Communications Business Partnering (Melanie Kerin); Corporate Communications Business Partnering 2 (Melanie Kerin); Corporate Development (Serge Scrofani); Corporate Finance (John Levy); Corporate Finance (Paul Coulter); Corporate Finance Edge Controller (Julia Wilkinson [C]); Corporate Services (Marvin Anthony Edwards II); Corporate Services (Michael Hays (Inherited)); Corpus Christi 603 (Sam Schultz (Inherited)); Corpus Christi 603 (Sam Schultz); Corpus Christi 603 (Tashana K Sanders); Corpus Christi 603 ACM Area 1 (Lorena Luna); Corpus Christi 603 ACM Area 2 (Nola V Baker); Corpus Christi 603 QA (Tara L Spitzer); Cost Center Accounting & Sales Reporting (Patrick Eley); Cost Center Controlling (Rainer Althaus); Counsel Americas (Shawn Gibbs); Counsel Americas (Shawn Michael Gibbs); Counsel EMEA (John Minardo (Inherited)); Counsel EMEA (Martin Quinn); Counsel EMEA (Virginie Didier); Country & Region Management (Geeseung Yoo); Credit & Collection (Anette Rummelsberger); Credit Collection (Paul Fellingham (Inherited)); Critical Systems - HVAC (Union) (Jeff J Parks (Inherited)); Critical Systems - HVAC (Union) (Michael D Proctor); Critical Systems - Water Systems (Union) (Jeff J Parks (Inherited)); Critical Systems - Water Systems (Union) (Jim Meils); Critical Utilities (Frank Miles III); Critical Utility Projects (Jim Meils); Culture and HR Strategy (Linda Hagerty-Dotterer); Customer Care Center I (Christian Siebert); Customer Care Center II (Oliver Weck); Customer Engagement Management (Brian Johnson (On Leave)); Customer Engagement Management (Gina Malloy); Customer Service & Logistics (Massimo Leoni); Customer Service (Bernhard Czapla (Inherited)); Customer Service (Consuelo D''Amore); Customer Service (Crystal Marie Wiles); Customer Service (Crystal Wiles); Customer Service (Holger Milkereit (Inherited)); Customer Service (Michael Bernd Rode (Inherited)); Customer Service (Rainer Adam (Inherited)); Customer Service (Robert Rohs); Customer Service (Sandra Lafoca (Inherited)); Customer Service (Sean Grinnell); Customer Service (Susanne M√∂ller (Inherited)); Customer Service ComOps Intercontinental Region (Anita Erber); Customer Service Deutschland (Roger Melliger); Customer Service France (Charlotte Rougi (Inherited)); Customer Service France (Charlotte Rougi√© (Inherited)); Customer Service France (Julien Roche); Customer Service Manager (Anna Arena); Customer Service Ops (Sean Grinnell); Customer Service and Launchmanagement (Christoph Krug); Customer Service and Launchmanagement (Jochen Wagner); Customer Service und Logistik (Susanne Pfeiffer); Customer Services & Logistics (Barbara Kemp); CyberSecurity Operations (Daniel Pekol); CyberSecurity Operations (Edward Ferrara (Inherited)); Cytogam (Thomas Beer); DGL 1 (Advait Jagirdar); DOCI eClinical Technology (Thomas Verish (Inherited)); DS Manufacturing (Barbara Beugger); DS Manufacturing (Matthias Kaeser); DSP & Analytics (Michael Schmitt); DSP Engineering (Dave Tomsik [C]); DSP Engineering (Rene Boros); DSP Laboratories (Arnaud Vonarburg); DSP Manufacturing 1 (Matthias Kaeser); DSP Manufacturing 2 (Baptiste Leclerc); DTP & Graphical Control (Metin Yilmaz (Inherited)); Dallas 078 (Brenda C Greenfield (Inherited)); Dallas 078 (Elizabeth Casillas); Dallas 078 (Elizabeth Trejo); Dallas 078 ACM Area 1 (Rhonda Shields); Dallas 078 ACM Area 2 (Brenda C Greenfield (Inherited)); Dallas 078 ACM Area 2 (Melissa J Chapman); Dallas 078 QA (Carlotta McCoy); Dallas 078 QA (Wajeehah Al-Uqdah); Dallas 510 (Elizabeth Casillas); Dalton 296 (Brittany Goforth); Dalton 296 ACM Area 1 (Dana Hibbs); Dalton 296 ACM Area 2 (Annette L Switzer); Dalton 296 QA (Wayne J Bixler); Dangyang Clinical Inspection (Xingzuan Zhang ?????); Dangyang Inspect (Liuqing Wan ); Dangyang Inspect (Liuqing Wan ?????); Dangyang Inspection (Pingfan Zhang ?????); Dangyang Office Management (Wanwan Zhu ); Dangyang Office Management (Wanwan Zhu ?????); Dangyang Office Management (Xiaoquan Zhu ?????); Dangyang Plasma Collection (Yingshuang Li ); Dangyang Plasma Collection (Yingshuang Li ?????); Dangyang Plasma Collection Center (Jack Zhou ); Dangyang Plasma Collection Center (Jack Zhou ?????); Dangyang Plasma Collection Center (Qingqing Wang ?????); Dangyang Plasma Collection Management (Yaling Zhu ); Dangyang Plasma Collection Management (Yaling Zhu ?????); Dangyang Plasma Sourcing (Meng Hu ); Dangyang Plasma Sourcing (Meng Hu ????); Dangyang Plasma Sourcing Management (Xuejun Wang ); Dangyang Plasma Sourcing Management (Xuejun Wang ?????); Data Analytics & Metrics (Bill Bigney); Data Analytics (Aaron Imig); Data Analytics (Constanze Buchter); Data Analytics (John Choy); Data Analytics (Michael Schrder (Inherited)); Data Governance (STEPHEN SMITH); Data Management (Steven Carr); Data Management (Thomas Hahlgans); Data Management KOP 1 (Mara Strelecki); Data Management Operations (Charles Johnson); Data Operations&Clinical Infrastructure (Thomas Verish (Inherited)); Data Operations&Clinical Infrastructure (Thomas Verish); Data Services (Don Konemann); Data Services (Sachin Ohal); Data and Analytics (Enterprise Applications) (Robert Hawker); Data and Analytics Center of Excellence (Thomas Gsponer); Database (Bhavesh Patel); Davenport 424 (Greg Boden); Davenport 424 ACM Area 1 (Jacinda L Head); Davenport 424 ACM Area 2 (Tabathia Ann Dells); Davenport 424 QA (Christopher R Doerscher); Dayton 408 (Daniel K Osborne); Dayton 408 (Megan L Waldeck); Dayton 408 ACM Area 1 (Ashley Instone); Dayton 408 ACM Area 1 (Shalia Sloan); Dayton 408 ACM Area 2 (Ashley K McConnell); Dayton 408 QA (Daniel K Osborne); Dayton 408 QA (Megan L Waldeck); Decatur 104 (Antonia Geiselmayr); Decatur 104 ACM Area 1 (Binh Tang); Decatur 104 ACM Area 1 (Shauntia Cobb); Decatur 104 ACM Area 2 (Antonia Geiselmayr (Inherited)); Decatur 104 ACM Area 2 (Binh Tang); Decatur 104 QA (Amaris A Wiggins); Decatur 104 QA (China Washington); Decatur 104 QA (Kyle M Lehrke (Inherited)); Decatur 446 (Amber McCullough); Decatur 446 (Jordon Lyon); Decatur 446 (Sentoria D Leonard-Brown); Decatur 446 ACM Area 1 (Amber McCullough (Inherited)); Decatur 446 ACM Area 1 (Amber McCullough); Decatur 446 ACM Area 2 (Aja Marbley); Decatur 446 ACM Area 2 (Amber McCullough (Inherited)); Decatur 446 QA (Tony D Giebelstein Jr); Delivery Support (Christopher A Betterton); Delivery Support (Robert Boland (Inherited)); Demand Planner (Rose Cimbora); Demand Planning (Ann Cipkins); Demand Planning (Tsutomu Nagoya ???? ? - ??? ????); Dept 1216 Antivenom Manufacture (Andrew Penhale); Dept 1216 Antivenom Manufacture (Cassandra Smoult); Dept 822, Cell Culture and Purification (Jamie Black); Development & chem. Quality Control (Daniel Frattini); Development Applications (Andy Chung); Development GMP Laboratory (DGL) (Andreas Meister (Inherited)); Development GMP Laboratory (DGL) (Heike Gocht); Development Projects (Heather Davis (Inherited)); Development and Support (Johannes Schiebel); Development and Support (Stefan Schmidbauer); Digital Communications (Mairian Gildea); Digital Communications (Piers Dickinson); Digital Delivery & Data (Robert Boland); Digital Health (Brian Johnson); Digital Strategy Implementation (David Christie); Digital Workplace (Dana Leeson); Dir Com Op - Vaccines (Helen Concilia); Dir, Health Economics 724 (Stuart Harsley); Direct Procurement (Angelia Crumbley); Director Clinical Science (Janine Oberije); Director Comm Ops - Pharma (Danielle Dowell); Director HR 924 (Yvette Saunders); Director QA Continuous Improvement & Issues Management (Adrian Meade); Director Quality Control (Leonora Pancho); Director Supply Chain (Lachlan Cruise); Director of Engineering (Brian Price); Director of Manufacturing, Products of National Significance (Cassandra Smoult); Director of Manufacturing, Products of National Significance (Lisa Lamb); Director, Commercial Operations NZ (Catherine Murphy); Director, Marketing (Rachel Jensen); Director, Marketing (Theo Horafiaris); Director, Program Execution (Gail Dawson); Dispatch (Bernd Schfer); Dispatch (Bernd Sch√§fer); Dispatch (Igor Kaucher (Inherited)); Dispensing, Medium & Buffer Preparation (Vreni Frtsch); Dispensing, Medium & Buffer Preparation (Vreni F√∂rtsch); Distribution (Jasmine Ma ?????) (Jasmine Ma ?????); Distribution (John Conway); Distribution (Maggie Wan ); Distribution (Maggie Wan ?????); Distribution (Nan Wang ); Distribution - Central Region (Lu Jin ); Distribution - Central Region (Lu Jin ????); Distribution - DTP, China (Cissy Xi ????); Distribution - East Region (Zhan-jun Liu ); Distribution - East Region (Zhan-jun Liu ?????); Distribution - North Region (Feng Rui ); Distribution - North Region (Feng Rui ????); Distribution - North Region (Kaijian Zhao ?????); Distribution - South Region (Nan Wang ????); Distribution - South Region (Sunny Sun ); Distribution - South Region (Sunny Sun ?????); Distribution - Tendering (Yanfang Zhou ?????); Distribution - West Region (Xuemei Zeng ); Distribution - West Region (Xuemei Zeng ?????); Distribution CH U8 (Rafael Gasser); Distribution CH U8 (Thomas Ryser (Inherited)); Distribution Junxin (Yanfang Zhou ); Distribution Junxin (Yanfang Zhou ?????); District Heights 210 (Cecelia Cutchin); District Heights 210 QA (ALISON CONLEY); District Heights 210 (Cecelia Cutchin); District Heights 210 (Michael W Solomon); District Heights 210 ACM Area 1 (Mickey Wilson); District Heights 210 ACM Area 1 (Tamika Hogan); District Heights 210 ACM Area 2 (Tamika Hogan); District Heights 210 QA (ALISON CONLEY); District Heights 210 QA (Abigail Brown-Delostrinos); Documentation (Arno Karnholz (Inherited)); Documentation (Dominik Erhart); Documentation Management and GMP Training (Jin Tao ); Documentation Management and GMP Training (Vicky Fang ????); Documentation Starting Materials (Angelika Jung); Documentation Starting Materials (Simone Lang); Dothan 504 (Demetia Scott); Dothan 504 ACM Area 1 (Olivia McVey); Dothan 504 ACM Area 2 (Kaitlyn M Delamore); Dothan 504 QA (Roxanne K Schaeffer); Douglas 190 (Alejandra Gonzalez); Douglas 190 (Jose Pineda); Douglas 190 ACM Area 1 (Irma Ornelas); Douglas 190 ACM Area 2 (Alejandra Gonzalez); Douglas 190 ACM Area 2 (Marisela Nunez); Douglas 190 QA (Andrew Larson); Downstream Manufacturing (Alan Cartwright); Downstream Manufacturing Days (Alan Hudson); Downstream Manufacturing Engineering (Anthony Flynn); Downstream Manufacturing Shift 1 (Neil Myerscough); Downstream Manufacturing Shift 2 (Edward Bucknall); Downstream Manufacturing Shift 3 (Alan Hudson); Downstream Manufacturing Shift 3 (Neil Myerscough); Downstream Manufacturing Shift 4 (Craig Ridyard); Downstream Manufacturing Shift 4 (Edward Bucknall); Drawing Office (Andrew Brown); Drug Product (Nicola Di Maiuta); Duluth 613 (Dennis J Lofald); Duluth 613 (Veronica J Kaspszak); Duluth 613 ACM Area 1 (Jenn Jackson); Duluth 613 ACM Area 2 (Angela J O''Hara); Duluth 613 QA (Heidi E Woolhouse); Durham 074 (Thomas Kisicki Jr); Durham 074 ACM Area 1 (Keonna Austin); Durham 074 ACM Area 2 (Damonta A Burch); Durham 074 QA (Meia Moore); E&I with MES/Systems (Josh Mills); E-Support (Marco Grossniklaus); E2E Operations Finance (Marcelo Estrella); ECI Finance/Controlling (Salim Ketata); EEMEA Finance/Controlling (Amanda White); EHS & Business Resilience (Lynette Hodgden); EHS (Liam Ryan); EHS Bern (Lone Carlsen); EHS Bern (Rolf Ingold); EHS Bern (Ulrich Schuerch); EHS Design Construction & Process Safety (Aaron Duff); EHS Kankakee (Dale C Rosene); EHS Kankakee (Lynette Hodgden (Inherited)); EHS Kankakee 2 (Allan Wise); EHS Kankakee 2 (Andrew Uftring); EHS Marburg (J√ºrgen Kanand (Inherited)); EHS Marburg (zcan Campinar); EHS Marburg (√ñzcan Campinar); EHS Plasma (BARBARA WUNDER); EHS Security (Adam Kennell); EHSS Lengnau (Harry Hohl); ELS A (Alain Ducaud); ELS A (Susanne Heins); ELS Z (Peter Reusser); ELS Z (Simon Haenni); EM (Tina Liu); EMEA (Anja Brunlich); EMEA (Anja Br√§unlich); EMEA HR Ops Marburg Team (Inga Menzinger-Koradin); EMEA Service Delivery (Cornelia Huber); EMEA Service Desk (Filipe Cabete); EMEA Service Operations (Bernd Boucsein); EMEA Site Services (Raluca Hodgson); EMR GH Gruppe; EMR HW Gruppe (Patrick Laukel); EMR NBF Gruppe (Thomas Peil); ERP Applications (Mourad Boulanouar); ERP Applications (Nagesh Ramesh); ERP Solution (Rajan Thomas); ERP Solution (Rajan Thomas) (Rajan Thomas); ERP Solution Center (KoP) (Peter Eliasson); ERP Solution Center (MBR) (Jochen Preis); ERP Solution Center (Neelesh Kulkarni); ERP Solution Centre (AU) (Shama Ravindran); ERP and Enterprise Applications (Steven Harvey); ES Qualification (Michael Kocher); ETA (Colin Steele); ETA (Ian Mackay); ETA (Tim Bullock (Inherited)); ETA + Utilities & Services (Michael Elmer); EU Qualified Person for PhV (Andrew Bond); EU Qualified Person for PhV (Frank Mauler); EU TA Coagulation (Bianca Petzold); EU TA Coagulation (Damian Gilkerson); EU TA Coagulation (Sinem Kaba Pasqualon); EU Therapeutic Area Immunology & Neurology Europe (Peter K Tadros); EU-QPPV Office Lead (Gudrun Dechert); EU/ROW RA Franchise Cell and aQIV (Susan Cameron-Laxton (Inherited)); Early DSP Development (Michael Schmitt (Inherited)); Early Stage DSP Development (EDD) (Lars Robbel); Early Stage DSP Development (EDD) (Michael Schmitt); Early Stage DSP Development (Olga M√ºller); Early USP Development (Jrg Gnther); Early USP Development (J√∂rg G√ºnther); Early USP Development (Stefan Debus); Early Upstream Development (Emmanuel Bizier); Early Upstream Development 1 (Ian Walker); Early Upstream Development 2 (Ellen Otte); East Point 193 (Kimberly Bragg); East Point 193 (William A Voltz); East Point 193 ACM Area 1 (Marshaya Johnson); East Point 193 ACM Area 1 (ROSALIND MCCOY); East Point 193 ACM Area 2 (Latasha A Wech); East Point 193 QA (Amaris A Wiggins); East Point 193 QA (Danelle Jones); East Point 193 QA (Melodee C Ebel (Inherited)); East Providence 202 (Christopher Travalik (Inherited)); East Providence 202 (PAUL BURKE); East Providence 202 (Sean Delong); East Providence 202 ACM Area 1 (Jacqueline Levasseur); East Providence 202 ACM Area 2 (Christine Riebe); East Providence 202 QA (Desiree Guerrero); East Providence 202 QA (Tatyani Guest); Eau Claire 514 (Kayla L Stueber); Eau Claire 514 QA (Melissa K Latourelle); Edge Commercial (Darren Hawker); Edge Finance (Matthew Rees (Inherited)); Edge Financial Accounting (Barry Johnston); Edge Manufacturing (Andrew Penhale); Edge Parkville (Matthew Rees); Edge Parkville - GLIMS (Helen Mihaljevic); Edge Planning (Brent Gorham); Edge Procurement (Mark Van Der Poel); Edge Programme (Ian Dick); Edge Programme (Philip Troughton); Edge Quality (Kate Waterman); Educational Meetings (Marco Kuhn); El Paso 197 (ALEX MARIN); El Paso 197 (Heather Jex); El Paso 197 ACM Area 1 (Michael Garcia); El Paso 197 ACM Area 2 (Cynthia Marquez); El Paso 197 QA (Amanda Robles); El Paso 197 QA (Brenda C Greenfield (Inherited)); El Paso 197 QA (CATIA LOPEZ); El Paso 248 (Edgar Rodriguez); El Paso 248 ACM Area 1 (Manuel Jaramillo); El Paso 248 ACM Area 2 (Albert Lozano); El Paso 248 QA (NOHEMI GARCIA); El Paso 279 (Alejandro Perales); El Paso 279 ACM Area 1 (Crystal Ramirez); El Paso 279 ACM Area 2 (Vanessa Pena); El Paso 279 QA (Kenya Villarreal); Electrical / I&C / BMS (Jan Klee); Electrical Engineer (Tien Nguyen); Electrical Maintenance (Marcel Ziegler); Electrical Maintenance (Vittorio D''Argento (Inherited)); Electro Maintenance (Simon Urfer); Electronic Laboratory Systems (ELS) (Susanne Heins); Electrophoresis and Immunoassays (Michael Albers); Elektroniker fr Automatisierungstechnik (Carmen Walldorf (Inherited)); Elektroniker f√ºr Automatisierungstechnik (Doris Nake (Inherited)); Elternzeit Bielefeld (Kirsten Scheibel (Inherited)); Elternzeit Bielefeld (Kirsten Scheibel); Elternzeit Diverse (Andreas Gehrich (Inherited)); Elternzeit Diverse (Andreas Gehrich); Elternzeit Diverse (Annette Pernitzsch (Inherited)); Elternzeit Diverse (Annette Pernitzsch); Elternzeit Diverse (Claudia Habenicht (Inherited)); Elternzeit Diverse (Claudia Habenicht); Elternzeit Diverse (Damaris Kieckh√∂fer); Elternzeit Diverse (Stephani Keltsch); Elternzeit Diverse (Sven Schuhmann (Inherited)); Elternzeit Diverse (Sven Schuhmann); Elyria 165 (Karin M Rothig); Elyria 165 ACM Area 1 (Nathan G Dailey); Elyria 165 ACM Area 2 (Gabrielle N Scalese); Elyria 165 QA (Calvin Juguilon); Elyria 165 QA (Jason A Skonecki); Emerging Europe (Christian Wieszner); Emerging Europe (Dirk Hoheisel (Inherited)); Employee Relations (Bonnie Shor (Inherited)); Employee Relations (Bonnie Slone (Inherited)); Employee Relations 1 (Tricia N Jackson); Employee Relations 2 (Jan Cameron); Employee Relations 3 (Emmanuella Hedge); End User Services (Christian Reinhardt); End User Services (Rolf Tr√ºmper); Endwell 220 (Barbara Ferrese); Endwell 220 ACM Area 1 (Richard Barber); Endwell 220 ACM Area 2 (Barbara Ferrese (Inherited)); Endwell 220 ACM Area 2 (Tara Streeter); Endwell 220 QA (Aarsalaan Semna); Endwell 220 QA (Richard Purdy II); Energy Management (Anna Fellenberg); Energy/WAD (Sandro Jenzer); Energy/WAD 1 (Michael Hirschi); Eng Business & Systems Mgr (Nicholas Moody); Eng Services - Ops (Mark Mansour); Eng Services -Ops (Rohit Dhorje); Eng Services-Ops (Damien Barri (Inherited)); Eng Services-Ops (Michael Spiteri); Eng Services-Ops (Victor Karafilis); Engineering (Bulk) (Jeff Rehborg); Engineering (Bozana Dujak); Engineering (Bulk) (Jeff Rehborg); Engineering (Controls) (Dennis Prom); Engineering (Controls) (Scott Bilkey); Engineering (Controls) I (Dennis Prom); Engineering (Howard Wilton); Engineering (Johannes Krmer); Engineering (Johannes Kr√§mer); Engineering (My Linh Ly); Engineering (Qualification) (Jeff Mihaichuk (Inherited)); Engineering (Qualification) (Matthew Galley); Engineering (Rainer Kraus); Engineering (Richard Friar); Engineering Compliance (Connie Costanzo); Engineering Data Management (Susan Clough); Engineering Lengnau (Olaf Thiel); Engineering Liverpool (Kevin Ridley); Engineering MAB/Gene Therapy (David Glover); Engineering Maintenance Mgr (Andrzej Wawrzykowski); Engineering Production Manager (Karen Spencer); Engineering Production Manager (Mark Davide); Engineering Production Manager (Stuart Barnes); Engineering Projects Dept (Stuart Freeland-Small); Engineering Projects Manager (Anthony Wrzesinski (Inherited)); Engineering Projects Manager (Anthony Wrzesinski); Engineering Projects Manager (David Ryan); Engineering Serv (Narein Mather); Engineering Serv (Sudhir Kamath); Engineering Serv - Drawing Office (Peter Dubuisson-Perrine); Engineering Serv - Maintenance (Michael Bell); Engineering Serv - Maintenance (Shiran Fernando); Engineering Serv - Management (Crystal Penaluna); Engineering Serv - Management (Geoff Armstrong); Engineering Serv Plan &Support (Benjamin Terbeeke); Engineering Serv Plan &Support (Deepak Cherian); Engineering Serv Plan &Support (Satya Dara (Inherited)); Engineering Serv Plan &Support (Satya Dara); Engineering Services Ops (Jarvis Walker); Engineering Services (Arnold Nigsch); Engineering Services (Atul Malhotra); Engineering Services (Bradley J Eberhart); Engineering Services (Daniel Reveron); Engineering Services (Franz Arnold Nigsch); Engineering Services (James E Viane Jr); Engineering Services (Jose Gonzalez (Inherited)); Engineering Services (Peter Szitas); Engineering Services (Victor Marinelli); Engineering Services - Maintenance (Matthew Johnson); Engineering Services - Maintenance E/I (Jason Fletcher (Inherited)); Engineering Services - Maintenance E/I (Jason Fletcher); Engineering Services - Maintenance E/I (Matt Downey); Engineering Services - Maintenance E/I 1 (Samuel Kanyongo); Engineering Services - Maintenance E/I 2 (Ronnie Mercieca); Engineering Services - Maintenance E/I 3 (Ben Hillman); Engineering Services - Maintenance E/I 4 (ANDREW Rawlinson); Engineering Services - Maintenance E/I 5 (Haisley Okpako); Engineering Services - Maintenance E/I 6 (Jim Haines); Engineering Services - Ops (Amanda Sim); Engineering Services - Ops (Jason Chan); Engineering Services - Ops (Lee Dengler); Engineering Services 3 (Gene Bohn); Engineering Services I (Daniel Reveron); Engineering Services Process Leader (Tim Bullock); Engineering Services ¬ñ Ops (Jarvis Walker); Engineering Standards (Adam Dragolic); Engineering Support (Crystal Penaluna (Inherited)); Engineering Support (Crystal Penaluna); Engineering Support (Geoff Armstrong (Inherited)); Engineering Support (Jayne Crowley); Engineering Technology Transfer (Shannon Boudreau); Engineering, PKV (Anthony Wrzesinski); Engineering, PKV (Brian Price); Engineering/PJM (Roger Stoffel); Engineering/PJM (Sven Schwerdtfeger); Enshi Inspection (Binming Tian ?????); Enshi Plasma (Xiaoxing Jiang ); Enshi Plasma (Xiaoxing Jiang ?????); Enshi Plasma Collection Center (Genxiong Zhou ?????); Enshi Plasma Collection and Office Administration (Min Zhang ); Enshi Plasma Collection and Office Administration (Min Zhang ????); Enshi Plasma Operations (Jing Wang ); Enshi Plasma Operations (Jing Wang ????); Enshi Plasma Sourcing (Liu Yang ????); Enshi Quality Control (Xiaohong Tan ?????); Enshi Quality Control Management (Stevin Cheng ); Enshi Quality Control Management (Stevin Cheng ?????); Enshi Quality Control Management (Xiaoping Tang ?????); Enshi Quality Inspection (Yinglong Liu ); Enshi Quality Inspection (Yinglong Liu ?????); Enshi Supply Management (Hong Yuan ); Enshi Supply Management (Hong Yuan ????); Enterprise Analytics (John Thompson); Enterprise Applications (Charles Lowe); Enterprise Applications (David Christie); Enterprise Applications (Martin Jones (Inherited)); Enterprise Architecture (Ian Wilson); Enterprise Architecture (Keith Walbert); Enterprise Architecture (Movi Banerjee); Enterprise Business Solutions (David Wolozyn); Enterprise Data Management (Matt Barnett); Enterprise Excellence (Andrew Croft); Enterprise Execution Systems (Frank Mastellone); Enterprise Infrastructure & Operations (Bernard Shepard); Enterprise Infrastructure & Operations (Don Konemann (Inherited)); Enterprise Infrastructure & Operations (Greg Misyak); Enterprise Investment Portfolio Management (Aymeric Ange); Enterprise Learning Management (Justin Huss); Enterprise Portfolio & Governance (Matthew Cam (Inherited)); Enterprise Portfolio & Governance (Matthew Cam); Enterprise Portfolio & Governance (Matthew Kokkonen); Enterprise Process Management (Desire Djomani); Enterprise Process Management (Linda Carducci (Inherited)); Enterprise Process Management (Matthias Kienast); Enterprise Security & Risk (Edward Ferrara); Enterprise Security & Risk EMEA (Jrg Koch); Enterprise Security & Risk EMEA (J√∂rg Koch); Enterprise Security - Identity and Access Management (Rebecca Daniels); Enterprise Security - Identity and Access Mgmt (Rebecca Daniels); Enterprise Security Architecture (Wilfried Ziegler); Enterprise Site Management (AU/Asia) (Don Konemann (Inherited)); Enterprise Site Management (AU/Asia) (Viv Louzado); Enterprise Site Management (Andr√© Strahm); Enterprise Site Management (Michael Furchert); Enterprise Site Management MBR (Michael Furchert); Environment (Barbara Dix); Environment Health Safety Sustainability (Dale C Rosene); Environment Health Safety Sustainability (Lynette Hodgden (Inherited)); Environmental Health & Safety (Andrew Hanley); Environmental Health & Safety (David Nelson); Environmental Health & Safety (David Stewart); Environmental Health & Safety (Filanthy Nalpantidis); Environmental Health & Safety (Prue McKeown); Europe Global Reg Affairs (Hazel-Anne Griffiths); Europe HR Ops Tier 1 (Katrin Schpbach); Europe HR Ops Tier 1 (Katrin Sch√ºpbach); Europe HR Ops Tier 1 (Stephan Sch√§ufele (Inherited)); Europe HR Ops Tier 1 (Sylvia Potocnik); European Sourcing Packaging (Matthias Engler); Evansville 614 (Coltin L Springate); Evansville 614 (Michelle S DeCambre); Evansville 614 (Scott Ward); Evansville 614 ACM Area 1 (Tani Baugher); Evansville 614 ACM Area 2 (Ian C Fox); Evansville 614 QA (Karla K Cooper); Execution Systems (Matt Casey); Executive Admin (Rupal Pandit); Executive Assistant & Travel Office (Eliane Bossart); Executive Assistant (Joanne Du); Executive Assistant (Sarah Gleeson); Executive Compensation & Equity (Micaela Costello); Experimental Unit (Felix Hiltwein); Export Admin LatAm (Cindy Jacobs); External Communications (Natalie de Vane); External Materials (Dominik Corbet); External Supply Integration (Cameron Barrett); External Supply Quality Assurance (Eva Streit); External processes (Andr Strahm); External processes (Andr√© Strahm); External processes (Simon Haemmerli); F IX, F II, Inhibitors (Carlotta Debnar-Daumler); F VIII & F IX (Barbara Kalina (Inherited)); F VIII & F IX (Horst Boeder); FBP Project and Portfolio Support (Ritesh Kumar); FP - Non Process Engineering and Construction Management (Jennifer Mastio); FP - Non Process Engineering and Construction Management (Rolf Mnig); Fachlageristen (Carmen Walldorf (Inherited)); Fachlageristen (Doris Nake (Inherited)); Facilities & Utilities (Adila Zaidi); Facilities & Utilities (Bradley J Eberhart (Inherited)); Facilities (Claire Behforooz); Facilities Develop & Services (Barbara Anderton); Facilities Develop & Services (Cameron Cook); Facilities, Capex and Drawing (Mark Hughes); Facilities, Utilities & Services (Michael Elmer); Facility & Waste Management (Michael Andrey); Facility & Workspace Management (Samuel Maurer); Facility Compliance Specialist (Andrew Stalder); Facility Management (Christian Daum); Facility Management (Hansjoerg Bettschen); Facility Management (Hanspeter Bruni); Facility Management (Michael Andrey); Facility Project Management - Non Process Engineering and Construction (Rolf M√∂nig); Facility Project Management - Process Engineering (Darren Vegara (Inherited)); Facility Project Quality Management (Brian Grimson [C]); Facility Project Quality Management (Graham William Telford); Facility Services (Alex Elandt [C]); Facility Services (Alex St√§hli); Facility Services (Sabine Beck); Facility Services (Samuel Maurer); Facility, Data & Laboratory Management (Robert Schwanzer); Faktor X Produkte (Carlotta Debnar-Daumler); Fayetteville 266 (Grant Strayer); Fayetteville 266 ACM Area 1 (Kady-Ann Foster); Fayetteville 266 ACM Area 2 (Joshua Simpson); Fayetteville QA 266 (Daniel Huereca); Federal Way 110 (Tamara Ann Owens); Federal Way 110 ACM Area 1 (Nancy Martinez); Federal Way 110 ACM Area 1 (Tamara Ann Owens); Federal Way 110 ACM Area 2 (Tamara Ann Owens (Inherited)); Federal Way 110 ACM Area 2 (Tiffani Brazzell); Federal Way 110 QA (Jenny Bardwell); Federal Way 110 QA (Simon P Dickinson); Fermentation Research (Thomas Rein); Fertigungselektronik (Michael Kraft); Fertigungselektronik 1.0 (Thomas Grn-Fischer); Fertigungselektronik 1.0 (Thomas Gr√ºn-Fischer); Fertigungselektronik 2.0 (Ralf Gerlach); Fertigungselektronik 2.1 (Ralf Gerlach); Field Sales (Angus Gordon); Field Sales (Catherine Murphy (Inherited)); Field Sales (Kim Fry); Field Services BRN & LGN (Urs Derungs); Field Support (Brett A Wintheiser); Field Support (Brett Wintheiser); Field Support (Robin G Palmer (On Leave)); Field Support (Robin G Palmer); Fill - Finish (Pasquale Carestia); Fill / Finish (Pasquale Carestia); Fill / Finish Manufacturing (David Hartley); Fill /Finish Process Improv Manager (Joseph Connor); Fill Area (Barbara Beugger); Fill Area (Nicola Di Maiuta); Fill Finish (Beat Allemann); Fill Finish (Nicola Di Maiuta); Fill Finish (Shannon Thorp); Fill Finish Marburg (Frank Emmerich); Fill Finish Marburg (Helmut Robert Euler); Fill Finish Operations (Lasher Rao ); Fill Finish Operations (Lasher Rao ?????); Fill Finish Operations (Philip Troughton); Fill Process Technology (Herman Schinkelshoek); Fill and Finish Support (Melissa Addamo); Fill finish (Ian Middleton); Fill finish (John Riley); Fill finish (Marion Taligault Owens); Fill/Finish Formulation (Caterina Colantoni); Fill/Finish Formulation (Norm Mancuso (Inherited)); Filling & Packaging Mechanical Team (Adam Steegstra); Filling & Packaging Mechanical Team (Tharanga Abeysinghe); Filling & Visual Inspection (Eveline Kindler); Filling (Adrian Carni); Filling (Andreas Gavriel (Inherited)); Filling (Andreas Gavriel); Filling (Andrew Marshall (Inherited)); Filling (Andrew Marshall); Filling (Cuong Nguyen); Filling (Daniel Locandro); Filling (Igor Belevski); Filling (Joselito Bautista); Filling (Marion Taligault Owens); Filling H69 & Refludan (Matthias Klein); Filling I (Eveline Kindler); Filling II (Celio Ferreira); Filling II (Simone Wettstein); Filling Line & Lyophilisation (Michael Gisler); Filling Line I & II (Adrian Aeschlimann); Filling Line I & II Equipment Preparatio (Werner Steiner); Filling Line I & II Group 1 (Urs Cotting); Filling Line I & II Group 2 (Markus Rindisbacher); Filling Line I & II Group 3 (Bruno Zuercher); Filling Line I & II Group 3 (Roland Gerber); Filling Line I&II Pasteurisat./Incubat. (Eduard Wittwer); Filling Line II Pasteurisat./Incubat (Roland Lerch); Filling Line III & IV (Mathias Beck); Filling Line III (Mathias Beck); Filling Line III Group 1 (Sasa Lazarevic); Filling Line III Group 2 (Christian Schmid); Filling Line III Group 2 (Daniel Kraehenbuehl); Filling Line III Group 3 (Ulrich Beat Wildi); Filling Line III Support (Daniel Kraehenbuehl); Filling Line IV (Jean-Claude Cauderay); Filling Line IV, Lyo & Support (Alexander Kissler); Filling Line IV, Lyo & Support (Jean-Claude Cauderay); Filling Line V (Andrea Jantsch); Filling Line V (Anna Meier); Filling M 305 (Esther Seidel); Filling Non Privigen (Mayur Bannore); Filling Operations (Chenyi Guo ?????); Filling Operations (Wei Xiao ); Filling Privigen (Narelle Urli); Filling Privigen (Tyson Parker); Filling Support (Andrew Marshall); Filling Toll Plasma (Dale Peel); Filling Toll Plasma (Laxman Trichinapalli); Filling Toll Plasma (Narelle Urli); Filling Toll Plasma (Peter Koelmeyer); Filling Toll Plasma (Rebecca Hayes); Filling Toll Plasma (Robert La Ferla); Filling V Group 1 (Eike Gredler); Filling V Group 1 (Roger Wamister); Filling V Group 1 (Thomas Daehler); Filling V Group 2 (Michael Roos); Filling V Group 2 (Thomas Daehler); Filling and Packaging (Narelle Urli); Filling and Packaging (Tyson Parker); Filling/Lyo/Visual Inspection (Michael Gisler); Final Product Manufacturing / Production Services (Othmar Geisser); Final Product Planning (Ingo Kling); Final Product Planning (Jan-Christopher Gerlach); Final Product Planning (Julian Knabeschuh); Finance & Accounting (Eisuke Kofugata); Finance & Accounting (Haruka Utsugi (Inherited)); Finance & Accounting Japan (Izumi Yoshida - ); Finance & Accounting Japan (Izumi Yoshida ??? ??? - ??? ????); Finance & Controlling (Devun Dusoruth); Finance & Controlling (Ebru Kuntay); Finance & Controlling (Jonathan Ho); Finance & Controlling (Justin Mericle); Finance & Controlling HU (Togya Gergely); Finance & Controlling Italy (Annalisa Saracchi); Finance (Amy Kishikawa); Finance (Avi Singh); Finance (Becky Heatherman); Finance (Carolyn Xiong ????); Finance (Damian Gaylor); Finance (Daniel Janides); Finance (Fergus Patrick McLellan); Finance (Frank Liesner); Finance (Harold Chan (Inherited)); Finance (Helen Gearing); Finance (Jacqui Lomas (Inherited)); Finance (Jacqui Lomas); Finance (John Levy); Finance (Karol Bian ?????); Finance (Kate Tse); Finance (Ken Lim); Finance (Konstantin Petropoulos); Finance (Lena Shi ????); Finance (Luke McMahon (Inherited)); Finance (Luke McMahon); Finance (Melody Orbiso); Finance (Nicole Pryde); Finance (Nishant Popat); Finance (Shalini Goundar); Finance (Siu-Yin Yu ?????); Finance (Vicci Quagliarella); Finance (Wolfgang Thiel); Finance (Xiaowei Yin ); Finance (Xiaowei Yin ?????); Finance / Tax Marburg (Fatma Kremser); Finance Belgium (Jrgen Bond); Finance Belgium (J√∂rgen Bond); Finance Business Partner Commercial EMEA (Simon Briscoe); Finance Business Partnering Operations (Daniel Janides); Finance Business Partnership (Jason Sowles); Finance Business Partnership (Michael T McAvoy); Finance Business Partnership I (Michael T McAvoy); Finance China (Karol Bian ?????); Finance Commercial LATAM (KRISTINE SOLOMON (Inherited)); Finance Commercial LATAM (Martin Descotte); Finance Czech Republic / Slovakia (Libor Ballek); Finance Director: Lead Business Partner (Sharon Tindley); Finance EU Commercial (KRISTINE SOLOMON (Inherited)); Finance EU Commercial (Kristine Solomon (Inherited)); Finance France (Charlotte Rougi√©); Finance Global Commercial (KRISTINE SOLOMON); Finance Global Commercial (Kristine Solomon); Finance Greece (Christos Papadatos (??????? ?????????)); Finance Greece (Efstathios Lymperopoulos); Finance Netherlands (Jrgen Bond); Finance Netherlands (J√∂rgen Bond); Finance Nordic (Carl Werner); Finance Portugal (David Roig Martinez); Finance Spain (David Roig Martinez); Finance UK (Paul Fellingham); Finance US Commercial Operations (Robert Smith Jr); Finance and Admin (Elena Kondrashova); Finance and Controlling (Devun Dusoruth (Inherited)); Finance and Controlling (Doris Kamtner); Finance and Controlling (Franz Gr√ºn, Doris Kamtner); Finance and Controlling (Ulrike Bridi); Finance, Accounting & Reporting (Beeharrylall Jeetun); Finance, Business Modelling (Helen Gearing (Inherited)); Financial Planning & Analysis (Duncan Webber); Financial Planning and Analysis (Haruka Utsugi); Financial Planning and Analysis 1 (Christopher Pulupa); Financial Planning and Analysis 2 (Christopher Pulupa (Inherited)); Financial Planning and Analysis 2 (Daniel Ganaishlal); Financial and Reporting Accountant (Hayley Jackson); Financial and Reporting Accountant (Ryan Brown [C]); Financial and Reporting Accounting (Callum Bircham); Finishing & Infrastructure (Laurent Wagner); Finishing & Infrastructure (Roger Stoffel); Flint 161 (Anjela Johnson); Flint 161 (Carlotta McCoy); Flint 161 ACM Area 1 (Janie Cary); Flint 161 ACM Area 1 (Trina L Bryant); Flint 161 ACM Area 2 (Carlotta McCoy (Inherited)); Flint 161 ACM Area 2 (Khatija Moiz); Flint 161 QA (Allante S Williams); Flint 161 QA (Andrea K Coleman); Forecasting & Strategic Analytics (Joshua Prince); Forecasting & Strategic Analytics (MANISH SRIVASTAVA); Forecasting, Compliance & CTA Operations (Jutta Neufang-Hueber); Formulation (Remon Hemaya); Formulation Development (Ahmad Abdul Fattah); Formulation Development (Di Goodall); Formulation Development (Heidi Elmer Bodnar); Formulation Development (Hywel Williams); Formulation Development (Michelle Zhuravlyova); Formulation Development (Nathan Edwards); Formulation Development (Richard Shalders); Formulation Development (Scott Thompson); Formulation Project Process (John Riley); Formulation Project Process (Marion Taligault Owens); Formulation Shift A (David Rimmer); Formulation Shift B (David Rimmer); Formulation Shift B (Matthew Storey); Formulation, Lyo & Stability Development (FLS) (Uwe Liebing); Fort Collins 705 (Erin J Zwalina); Fort Collins 705 ACM Area 1 (Michael A McNear); Fort Collins 705 ACM Area 2 (Jeremy M Kuehn); Fort Collins 705 QA (Christi Bringle); Fort Smith 278 (David Ensminger (Inherited)); Fort Smith 278 (Rachael Kirby); Fort Smith 278 ACM Area 1 (Rachael Kirby); Fort Smith 278 ACM Area 1 (Tammy Semiche); Fort Smith 278 ACM Area 2 (Russell Perez); Fort Smith 278 QA (David Ensminger (Inherited)); Fort Smith 278 QA (Whitney Jacobs); Fort Wayne 089 (Malori A Shields); Fort Wayne 089 (Rob Garcia); Fort Wayne 089 ACM Area 1 (Timothy R Albright); Fort Wayne 089 ACM Area 2 (Chad Rudolph); Fort Wayne 089 QA (Chris Cusack); Fort Wayne 089 QA (Erik Plate (Inherited)); Fort Wayne 089 QA (Gretchen Watkins); Fort Wayne 089 QA (Mitch A Quinn); Fort Worth 419 (Angelica M Henry); Fort Worth 419 (Sarah E Silva); Fort Worth 419 ACM Area 1 (Eddie S Rosas); Fort Worth 419 ACM Area 2 (Jennyfer Delacruz); Fort Worth 419 ACM Area 2 (Martel Carter); Fort Worth 419 ACM Area 3 (Angelica M Henry (Inherited)); Fort Worth 419 ACM Area 3 (MacGregor Roy); Fort Worth 419 QA (Rochelle L Shannon); Fractionation & Bulk (Michael Beyeler); Fractionation & Bulk (Roger Stoffel); Fractionation & Bulk (Sven Schwerdtfeger); Fractionation & Bulk (Zabdiel Dominguez); Fractionation (Simon Scheidegger); Fractionation Group 1 (Fritz Liechti); Fractionation Group 2 (Adrian Locher); Fractionation Group 3 (Christian Stucki); Fractionation Group 4 (Walter Strahm); Fractionation Group 5 (Urs Durtschi); Fraktionierung & Filtration (Kai Erkel); Fraktionierung (Rainer Frank (Inherited)); Franchise Medical Affairs Team 1 (Emna Bourkhis); Franchise Medical Affairs Team 1 (Nabil Moumane); Franchise Medical Affairs Team 2 (Hasan Catovic); Fredericksburg 703 (Sara M Schuppe); Fredericksburg 703 (Sheri Mixon (Inherited)); Fredericksburg 703 ACM Area 1 (Juan Manuel Castillo); Fredericksburg 703 ACM Area 2 (Mykel A Gonzales); Fredericksburg 703 QA (Gracie P Melendez); Front Line QA (Amanda Cooper); Ft Collins 705 QA (Christi Bringle); Ft. Gratiot 170 (Adrienne Smith); Ft. Gratiot 170 (Desiree Wright); Ft. Gratiot 170 ACM Area 1 (Cortney Young); Ft. Gratiot 170 ACM Area 2 (Karri Mitchell); Ft. Gratiot 170 QA (Allante S Williams); Ft. Gratiot 170 QA (Breanna Mini); Ft. Gratiot 170 QA (Melissa Johnson); GCSP & PhV Regions (Richard Wolf); GCSP Global Regions (Lana Gloukhova); GCSP Regions & Pv Operations (Kevin Burke (Inherited)); GCSP Regions & Pv Operations (Kevin Burke); GCSP Regions & Pv Operations 1 (Mark McGinnis); GCSP Regions (Angela Long); GCSP Regions (Rawad Antoun); GCSP Regions Lead Asia Pacific (Sophie Fontez); GCSP Regions Lead ECI (Simone Lorenz-Asmus); GCSP Regions Lead EU (Nicole Avalos); GFSS Asia Pac (Noopur Pattni (Inherited)); GFSS ¬ñ Asia Pac (Madison Crawford); GM & Staff (Sarah Yeung); GMP Compliance & Packaging Excellence (Thorsten Keller); GMP Training (Ann Moody); GPSS BRN & LNG (Denis Klochkov); GPSS BRN & LNG (Stephanie Schoch); GPSS USA (Michael Burdick); GRA CMC (Eva Walter); GRA CMC BMW (Siew Cheng Ney); GRA CMC BMW Team 1 (Nicole Apostolidis); GRA CMC BMW Team 2 (Libby Brodie); GRA CMC BRN (Luca Reggiani); GRA CMC BRN Team 1 (Petra Truetsch); GRA CMC BRN Team 2 (Dominique Schaller); GRA CMC BRN Team 2 (Sabine Rohner); GRA CMC BRN Team 3 (Grzegorz Podrygajlo); GRA CMC BRN Team 3 (Karin Stein-Liesen); GRA CMC KAN (Rick Khuu); GRA CMC KAN (Ricky Khuu); GRA CMC KAN Team 1 (William K Mendell); GRA CMC KAN Team 2 (Olga Neumller); GRA CMC KAN Team 2 (Olga Neum√ºller); GRA CMC KAN Team 3 (William K Mendell); GRA CMC MBR (Lene Nielsen); GRA CMC MBR (Martin Opper); GRA CMC MBR Team 1 (D√∂rthe Vingerhoet); GRA CMC MBR Team 1 (Markus Kuhl); GRA CMC MBR Team 2 (Thomas Nassauer); GRA CMC MBR Team 3 (Antje Mehrer); GRA CTA Operations Group (Florin Muraru); GRA GPS CV, Metabolism & Adv Therapies (Scott Hambaugh (Inherited)); GRA GPS Cardiovascular & Metabolism (Scott Hambaugh (Inherited)); GRA GPS Hematology & Thrombosis (Scott Hambaugh); GRA GPS Hematology & Thrombosis (Sibylle Kaiser); GRA GPS Immunology & Neurology (Lauren Tornetta); GRA GPS Immunology & Neurology (Scott Hambaugh (Inherited)); GRA GPS Immunology (Scott Hambaugh (Inherited)); GRA GPS Inflammation & Transplant (Hartmut Landgrebe); GRA GPS Respiratory (Melissa Tokosh); GRA GPS Transplant (Hartmut Landgrebe); GRA LATAM (Gordana Joksimovic); GRA Planning Group (Martin Steinmann); GRA Region EU & Switzerland (Anke Arnold); GRA Region EU & Switzerland (Bettina Doepner); GRA Region EU & Switzerland (Birgit Sommer (Inherited)); GRA Region EU & Switzerland (Birgit Sommer); GRA Region EU & Switzerland (Martina Schneider); GRA Region EU & Switzerland (Paolo Voltolina); GRA Region EU & Switzerland (Pedro Manuel Regateiro de Moura Campino); GRA Region EU & Switzerland (Stefanie Zaugg); GRA Region EU & Switzerland (Wencke Maeder-Wotruba (Inherited)); GRA Region EU & Switzerland (Wencke Maeder-Wotruba); GRA Region EU & Switzerland (Wolfgang Friedrich); GRA Region NA - AdPromo (John Hill); GRA Region NA - Hematology (Tara Chapman); GRA Region NA - Immnunology (Angela D Azzara); GRA Region NA ¬ñ CMC (Todd Olson); GRA Region NA ¬ñ CV/Transplant (Uros Djekic); GRA Region North America (Tara Chapman); GRA Resourcing (Silke Britschock); GSP Operations (Liwei Sun ); GSP Operations (Liwei Sun ?????); Gainesville 182 (Laila Matthews-El); Gainesville 182 ACM Area 1 (Toya Green); Gainesville 182 ACM Area 2 (Laila Matthews-El (Inherited)); Gainesville 182 ACM Area 2 (Leslie L Heidelberg); Gainesville 182 QA (Elvern M Gregg); Gainsville 182 (Deidra Snow-Johnson); Gainsville 182 (Laila Matthews-El); Gainsville 182 QA (Elvern M Gregg); Gastonia 267 (Mai Yang); Gastonia 267 ACM Area 1 (Terri L Salsman); Gastonia 267 ACM Area 2 (Scotty Burch); Gastonia QA 267 (Blake Painter); Gbl Commercial Operations-Cardiovascular (Debbie Drane (Inherited)); Gbl Commercial Operations-Cardiovascular (MaryAnn Capritti); Gene Therapy (Karsten Peppel); Gene Therapy (Orit Wolstein); Gene Therapy Research (Cdric Vonarburg); Gene Therapy Research (C√©dric Vonarburg); Gene Therapy Research I (Florian Aeschimann); Gene Therapy(Orit Wolstein); General Ledger (Tanja Bieri); General Ledger (Tanja Gurtner); General Ledger Accounting (Thierry Bonjour); General Product Characterisation (Robert Dickinson); General Product Characterisation (Tom Murray-Rust); Geringfgig Beschftigte (Andreas Gehrich); Geringfgig Beschftigte (Annette Pernitzsch); Geringfgig Beschftigte (Claudia Habenicht); Geringfgig Beschftigte (Kirsten Scheibel); Geringfgig Beschftigte (Natascha Tappendorf); Geringfgig Beschftigte (Stephani Keltsch); Geringfgig Beschftigte (Sven Schuhmann); Geringf√ºgig Besch√§ftigte (Andreas Gehrich); Geringf√ºgig Besch√§ftigte (Annette Pernitzsch (Inherited)); Geringf√ºgig Besch√§ftigte (Annette Pernitzsch); Geringf√ºgig Besch√§ftigte (Claudia Habenicht (Inherited)); Geringf√ºgig Besch√§ftigte (Claudia Habenicht); Geringf√ºgig Besch√§ftigte (Kirsten Scheibel (Inherited)); Geringf√ºgig Besch√§ftigte (Kirsten Scheibel); Geringf√ºgig Besch√§ftigte (Natascha Bock (Inherited)); Geringf√ºgig Besch√§ftigte (Stephani Keltsch); Geringf√ºgig Besch√§ftigte (Sven Schuhmann (Inherited)); Geringf√ºgig Besch√§ftigte (Sven Schuhmann); Gertevorbereitung (Roman Truttmann); Ger√§tevorbereitung (Roman Truttmann); Gesundheitsschutz (J√ºrgen Kanand (Inherited)); Gesundheitsschutz (zcan Campinar (Inherited)); Gesundheitsschutz (√ñzcan Campinar (Inherited)); Gilbert 131 (Daniel I Villegas (Inherited)); Gilbert 131 (Erica L Ewing); Gilbert 131 ACM Area 1 (Christopher Hersch); Gilbert 131 ACM Area 1 (Michael Brownell); Gilbert 131 ACM Area 2 (Karen Branch); Gilbert 131 QA (Karen Branch); Gilbert 131 QA (Will Porter); Glassware Washing (Jasmina Trumic); Glen Burnie 136 (Bryon Wiley); Glen Burnie 136 (Guillian T Gonzalez); Glen Burnie 136 ACM Area 1 (Janet Rhys-Jones); Glen Burnie 136 ACM Area 2 (Solyana Gebrekidan); Glen Burnie 136 QA (Monique L Fitz); Global Analytical & Technical Services (Stephen Case); Global Analytical Science & Technology (Jeffrey Pederson); Global Analytical Science & Technology (Stephen Case); Global Applications Testing (Kinga Zambo); Global Artwork (Karelle Phelan); Global Automation (Christoph Zahnd (Inherited)); Global Automation (Hans Wieser); Global Batch Release (Karen Marks); Global Benefits (Judith Kleemeier); Global Bioanalytical (Jeffrey Michael Hey); Global Bioanalytical (Matthias Zimmermann); Global Bioanalytical (Vanessa Sandford); Global Bioanalytical Sciences (Jeff Hey); Global Business Technology (John A Newsom); Global CQA Systems (Carmen Szeto); Global Case Management (Kevin Burke); Global Case Management (Monika Klug); Global Case Managment (Joanne Grego); Global Category Gels, Resins, Media & Process Aid (Stephan Heer); Global Category Lead R&D (Benjamin Prior); Global Change Management and Communications (Linda Hagerty-Dotterer); Global Clinical Dev Hematology & Thrombosis (Marcus Carr); Global Clinical Development (Frank Albano); Global Clinical Development (William Mezzanotte (Inherited)); Global Clinical Development (William Mezzanotte); Global Clinical Development Hematology & Thrombosis (Marcus Carr); Global Clinical Ops - Transplant (Michele Jenkins); Global Clinical Programs (Blanca Salazar); Global Clinical Programs 2 (Ingo Pragst); Global Clinical Quality Assurance (Claudia Fellmer); Global Clinical Quality Assurance (Volker Nickel (Inherited)); Global Commercial Insights & Analytics (Nitin Bhatnagar); Global Commercial Operations (William Campbell); Global Content (Greg Healy); Global Data Management (Jennifer Toomey); Global Doc Systems (Dominik Zuercher); Global EH&S, Resilience and Risk Management (Lynette Hodgden); Global Engineering (Christoph Zahnd); Global Engineering Projects (Darrah Wilkerson); Global Export Order Fulfillment (Sabine Hmel); Global Export Order Fulfillment (Sabine H√§mel); Global Finance (David Lamont); Global Financial Operations (David Lamont (Inherited)); Global Financial Operations (Karen Neave); Global HR Business Partner Organization (Doug German); Global HR Operations (Sara Proctor [C]); Global HR Operations (Sara Proctor); Global HR Services and Payroll (Mark Hickenbottom); Global HR Systems (Garnett Hudson); Global HR Systems (Jeff Allen); Global HR Systems (Melissa Zyla); Global HR Systems I (Jeff Allen); Global HR Systems II (Melissa Zyla); Global HR Systems and Reporting (Andrea Dauphinee); Global HR Systems and Reporting (Patricia Berdugo); Global HR Systems and Reporting (Rob Nyhan); Global Health Economics and Reimb (Girishanthy Krishnarajah); Global Health Economics and Reimb (Ryan Saadi); Global Healthcare Policy & Ext. Affairs (Dennis Jackman); Global Healthcare Policy & Ext. Affairs (Michael Ruggiero); Global Human Resources (Andrea Resch); Global Human Resources (Elizabeth Walker); Global Human Resources Business Partner Organization (Doug German); Global Human Resources Business Partner Organization (Gyuri Endes); Global IP (Beate Binsack); Global IT PMO (Lance Runyard); Global IT Service Delivery (Sam Vickers); Global IT Service Delivery (Sunil Shah); Global Indirect Procurement (James Thomas); Global Indirect Sourcing (John Ehgartner); Global Intellectual Property (GIP) (Hans-Peter Hauser); Global Intellectual Property (Peter Gomme); Global Internal Communications (Michele Darnell); Global LIMS (David Johnstone); Global Labeling (MARK COLLINS); Global Labeling (Maryann Cuomo); Global Labeling Operations (Maricarmen Dilone-Raposo); Global Labelling Operations (Lynda Rizzetti); Global Legal (Gregory Boss); Global Legal (John Michael Minardo); Global Legal (John Minardo); Global Legal Operations & Services (Lauren Neal); Global Legal Services (Adarsh Nair); Global Legal Services (Kieran O''Shea); Global Library Service Operations (Ulrike Friebertshuser-Jilke); Global Library Service Operations (Ulrike Friebertsh√§user-Jilke); Global Licensing (Andrea Huggins); Global Licensing (Michael Jorgensen); Global Logistics (Marianne McDonald); Global Logistics (Paul Wolstencroft); Global Logistics (Uli Kiefer); Global Logistics Operations (Suzanne Johnson (Inherited)); Global Logistics Team (BP Pedersen); Global MS&T (Irina Staxen); Global MSPR (Christoph Hck); Global MSPR (Christoph H√∂ck); Global Manufacturing (Chris Larkins); Global Manufacturing Ops & Plasma (Tony HARTMAN); Global Marketing Team Cardiovascular (KAMRAN MAMMADOV); Global Medical Affairs (Gregg Sylvester); Global Medical Affairs (Marcus Stockschlder); Global Medical Affairs (Marcus Stockschl√§der); Global Medical Devices & Primary Packaging Materials (Declan Reilly); Global Medical Evaluation (Federico Melo Ferrer); Global Medical Evaluation (Federico Melo-Ferrer); Global Medical Evaluation (Sabine Hrtel); Global Medical Evaluation (Sabine H√§rtel); Global Mobility COE (Ulrike Krenz-Fisher); Global Operation & RCF Administration (Margrit Hug Bucher); Global Operational Excellence and BPM (Fabrice Gribon); Global Operational Excellence and BPM (Stephen Marlow (Inherited)); Global Operations & Quality Finance (Helen Gearing (Inherited)); Global Operations & Quality Finance (Jacinta Glennon); Global Operations (Chris Larkins); Global Operations (Stephen Marlow); Global Operations (Val Romberg); Global Packaging (Warren Comerford); Global Packaging (jerome serrurier); Global Packaging Design and Artwork (Andrew John Robinson); Global Packaging Design and Artwork (Eva Streit); Global Packaging Services (Rick A Majszak); Global Packaging Services (Samantha Czako (On Leave)); Global Packaging Services (Samantha Czako); Global Pathogen Safety (Birgit Popp); Global Pathogen Safety (Eleonora Widmer); Global Pathogen Safety (John Liu); Global Pathogen Safety (Nathan Roth); Global Pathogen Safety - Marburg (Birgit Popp); Global Pathogen Safety - Marburg (Bj√∂rn Keiner); Global Pathogen Safety Support (Eleonora Widmer); Global Pathogen Safety Support Asia (Connie Broumis); Global Payroll (Christina Avraamides); Global Payroll (Marjorie Platt); Global Payroll (Mark Hickenbottom (Inherited)); Global Pharmacovigilance Quality Assurance (Claudia Nolte); Global Planning (Harald Berg); Global Planning (Jamie Pritzl); Global Planning, Supply Chain (Chad Salisbury); Global Planning, Supply Chain (Steve Moloney); Global Plasma Team (Dieter Brazel); Global Plasma Technology Ownership (Benno Bitterli); Global Portfolio Influenza (Jane Lanteri) (Jane Leong); Global Pricing (John Hakanson); Global Product Characterization (Stefan Schmidbauer); Global Product Specifications and Reference Standards (Richard Steere); Global Product Strategy (GPS) (Scott Hambaugh); Global Publishing (Marquis Bryant); Global Publishing (Timothy Huke); Global QA, IT (Patricia Hernan Miller); Global Quality (Vasilis Mavrogenis); Global Quality - Americas (Carlos Torres); Global Quality Affiliates (Collins Onyejese); Global Quality Affiliates (Laura O''Brien (Inherited)); Global Quality Assurance (Karen Netherton); Global Quality Control (Brian Nunnally); Global Quality Management (Jeffrey A Alcorn); Global Quality Management (Laura O''Brien); Global Quality Management (Rushen Mendis); Global Quality Management (Sanjana Sanjappa); Global Quality Management 2 (Brian Walker); Global Quality Management Sys. (Allen F Coleman); Global Quality Management Sys. (Eva M. Urban (Inherited)); Global Quality Management Sys. (Eva M. Urban); Global Quality Management Sys. (Jeffrey A Alcorn (Inherited)); Global Quality Management Sys. (Stephen A Wilson); Global Quality Management Sys.(Steve Wilson); Global Quality Management Systems (Carol Kidwell (On Leave)); Global Quality Management Systems (Carol Kidwell); Global Quality Operations (Matthias Pohl); Global Quality Systems & Compliance (Milka Smoljko); Global Quality Systems (Chad M Salisbury); Global Quality and R&D IT Systems (Tim Jones); Global Quality, Bus Svcs & Finance HR (Doug German (Inherited)); Global Quality, Bus Svcs & Finance HR (Stephanie McKinney); Global R&D - Financial Rptng & Analysis (Krista Doron); Global R&D Early Development CMO (Diana Lanchoney); Global R&D Finance (Christopher James Thorpe); Global R&D Finance (Karen Neave (Inherited)); Global R&D Finance (Pamela Cerovich); Global R&D Project Management (David S Fifer); Global R&D Project Management (Diana Lanchoney); Global R&D Project Management (Heiko Riedel); Global R&D Project Management (Heiko V√∂lpel); Global R&D Project Management (Jennifer Dutton); Global R&D Project Management (Lena Ohannesian); Global R&D Project Management (Martin Broder); Global R&D Project Management (Peter Douglas); Global R&D Project Management (Rose Fida); Global R&D Project Management (Steven Brooks); Global R&D Project Management 1 (Peter Douglas); Global R&D Project Management 2 (Christian Spyr); Global R&D Project Management 2 (Gino Vairo); Global R&D Project Management 2 (Nancy Fetrow (Inherited)); Global R&D Project Management 2 (Regula Heini Hodel) (Christian Spyr); Global R&D Project Management 3 (Christiane Enzinger); Global R&D Project Management 3 (David Leacy (Inherited)); Global R&D Project Management 3 (Katy Dimitropoulos); Global R&D Project Management 3 (Rose Fida); Global R&D Project Management 4 (Laura J Schweigert); Global R&D Project Management I (David S Fifer); Global R&D Project Management II (Heiko Riedel); Global R&D Project Management III (Jennifer Dutton); Global R&D Project Management Immunology (Linda Faux (Inherited)); Global R&D Project Management Immunology (Steven Brooks (Inherited)); Global R&D QA CMC & Research (April Sena); Global R&D Quality Assurance (Karen Gard''ner); Global Recombinant Portfolio Group (Lene Nielsen); Global Records (John Neff (Inherited)); Global Reg Affairs ANZ (Kellie Hooley); Global Regulatory Affairs (Ashley Burt); Global Regulatory Affairs (Catarina Edfjaell); Global Regulatory Affairs (Emmanuelle LECOMTE BRISSET); Global Regulatory Affairs (Franck Nicolas); Global Regulatory Affairs (Mary Ryan); Global Regulatory Affairs Quality Compliance (Jana Reitmajer); Global Regulatory Affairs Quality Compliance (Monika Dietrich-Sander); Global Regulatory Affairs Quality Compliance (Rafael Sierra); Global Regulatory Lead - QIVc (Karen Jourdan-Brown); Global Regulatory Operations and Labelling (Emma Williams); Global Regulatory Systems&Informat.Mgmt. (Christine Berger); Global Regulatory Systems&Informat.Mgmt. (Franck Nicolas (Inherited)); Global Reporting (Harsha Kadiyala); Global Research & Development (Andrew Cuthbertson); Global Research & Development (William Mezzanotte); Global Risk & Insurance Management (John Marren); Global Risk Management (Mark Luksic); Global SAP Security & BTGC (Steven Yannelli); Global SC Operations (Tina Law); Global Sales & Operations Planning (Ben Wilson); Global Scientific Exellence (Maria M√ºller); Global Security (Tony Strickland); Global Serialization (Michel B√©raud); Global Shared Services (Liam Connelly); Global Site Management (Lauren Ruth Vickery); Global Site Management (Lauren Vickery); Global Sourcing (Paul Addis); Global Sourcing - Automation, Instrumentation, Packaging and Aseptic Filling (Iouri Sverdlov); Global Sourcing Logistics (Gela Bakuridze); Global Sourcing Logistics (John Ehgartner (Inherited)); Global Strategic Sourcing, Chemicals (Jun Gao); Global Supplier Quality EMEA (Hans-Jrgen Schning); Global Supplier Quality EMEA (Hans-J√ºrgen Sch√∂ning); Global Supply Chain (Ian Dick); Global Systems Maintenance (Regina Mhlich); Global Systems Maintenance (Regina M√ºhlich); Global Talent Acquisition (Brian Fehrer); Global Talent Acquisition (Melissa Bradford); Global Tax (Aoife Deane); Global Total Rewards (Elizabeth Walker (Inherited)); Global Total Rewards (Maynard Branscome); Global Trademarks (Nicole Smith); Global Transport Validation (Matthew Wokulich); Global Validation (Russell Ciliento); Global Validation (Russell James Ciliento); Government Rebate Operations (Joseph DeLuca); Government Vaccines Manager 745 (Helen Dela Cruz); Graduates (David Azzopardi); Grand Blanc 244 (Kelly M Weng); Grand Blanc 244 ACM Area 1 (BRANDON SMITH); Grand Blanc 244 ACM Area 1 (LC Davis); Grand Blanc 244 ACM Area 2 (ROBERT MANGOLD); Grand Blanc 244 QA (Martina Young); Grand Junction 159 (Daniel Venn); Grand Junction 159 (Markah Williams Mower); Grand Junction 159 (Markah Williams); Grand Junction 159 ACM Area 1 (Steven Potter); Grand Junction 159 ACM Area 2 (Richard S Simpson); Grand Junction 159 ACM Area 2 (Rob Ferguson); Grand Junction 159 QA (Carrie E Pell); Grand Junction 159 QA (Kelly M Weng); Grand Prairie 049 (Angelica M Henry); Grand Prairie 049 (Jamie Bullock); Grand Prairie 049 ACM Area 1 (Kelly Gomez); Grand Prairie 049 ACM Area 2 (Deonka Whitley); Grand Prairie 049 QA (LaDonnica L Eddings); Grants Management (Abraham Smith); Grants Manager (Abraham Smith); Greece 194 (Jontus Walker); Greece 194 (Tangerine Tingle); Greece 194 ACM Area 1 (Mike Massaro (On Leave)); Greece 194 ACM Area 1 (Mike Massaro); Greece 194 ACM Area 2 (Arooj Hussain); Greece 194 QA (Ariel S Forrest); Greece 194 QA (John L Thixton (Inherited)); Greece 194 QA (Todd Wolfe); Greeley 615 (Natasha D Casillas); Greeley 615 (Skyler T Campbell); Greeley 615 ACM Area 1 (Natasha D Casillas (Inherited)); Greeley 615 ACM Area 1 (Natasha D Casillas); Greeley 615 ACM Area 2 (Rita E Williams); Greeley 615 QA (Meghan Fryer); Greensboro 117 (Susan Watkins (On Leave)); Greensboro 117 (Susan Watkins); Greensboro 117 ACM Area 1 (Dorleans Alce); Greensboro 117 ACM Area 1 (Kristen Jones); Greensboro 117 ACM Area 2 (Kristie Cunningham); Greensboro 117 QA (Stephanie Bernard); Greenville 088 (Andrea S Zeller); Greenville 088 ACM Area 1 (Andrea S Zeller); Greenville 088 ACM Area 1 (Jeremy Honea (On Leave)); Greenville 088 ACM Area 2 (Natasha Pinson); Greenville 088 QA (LeeAnn M Estes); Gresham 055 (Brandy J Vaughan); Gresham 055 ACM Area 1 (Noah S Johnson); Gresham 055 ACM Area 2 (Becca Daugherty); Gresham 055 QA (Dijana Colic); Group 3 (Adrian Alder); Group Analysis (Dean Barrett); Group Analysis (Dean Wilde); Group Analysis (Maureen Harrington); Group Controller (Helen Gearing (Inherited)); Group Controller (Jacob Weaver); Group Controller (Noopur Pattni); Group Finance (Daya Salter); Group Finance (Jason Mugridge); Group Finance (Kevin Personius); Group Finance 1 (Jeffrey Marchetti); Group Finance II (Troy Kukorlo); Group Finance- Financial Systems (Mary Conlin); Group Grzhausen (Uwe Sthr); Group G√∂rzhausen (Michael Engel); Group G√∂rzhausen (Uwe St√∂hr); Group Hauptwerk (Michael Engel); Group Income Protection (Emma McCarthy); Group Lead Validation-Site Expansion (Robert Musgrave); Group Main Site (Michael Engel); Group Reporting (Michael J. Clark); Group Tax (Peter Larsen); Group Taxation (Michael Manusov); Gulfport 122 (Elishia Humphrey); Gulfport 122 (John E Hunt (Inherited)); Gulfport 122 (Joshua D Harper); Gulfport 122 (Robert Spicer); Gulfport 122 ACM Area 1 (Joshua D Harper); Gulfport 122 ACM Area 2 (Bernetta L Huff); Gulfport 122 ACM Area 2 (Joshua D Harper); Gulfport 122 QA (Regina Williams); GxP Training (Carla Oliver); GxP Training (Vicky Lioutas (Inherited)); HAE & Respiratory (Sylvia Herget); HAE & Respiratory 1 (Susann Hofmockel); HAE Marketing (Amy Bifolco-Morrell); HAE Marketing (Tom Groeling (Inherited)); HAE Marketing (Tom Groeling); HR Business Partner (Sandro Krug); HR Business Partner (Tanja Templer); HR Business Partner 1; HR Business Partner 1 (Tina Camenzind); HR Business Partner 2 (Johanna Wege); HR Business Partner 4 (Darja Skaza-Brock); HR Commercial & Legal (Susan Devlin); HR Development & Programs (Sabine Wagner); HR Finance, R&D, IT & Business Services (Carolyne Malizia); HR Finance, R&D, IT, Bus Services (Carolyne Malizia); HR Holly Springs (Shandalyn Hope Matson); HR Holly Springs (Shandalyn Matson); HR Liverpool (Emma McCarthy); HR Liverpool (Sheila Redmond [C]) (Sheila Redmond [C]); HR Liverpool Business Partners (Kerry Rimmer); HR Marketing (Martin Stump); HR Marketing (Nadine Reh); HR Operations & Quality (Judi Badenoch); HR Operations (Adam Williams); HR Operations (Claudia Petrocchi); HR Operations (Elizabeth Walker (Inherited)); HR Operations (Lindsay Heaton); HR Operations (Mike Drew); HR Operations (Sara Proctor [C]); HR Ops Enabler Tools (Jennifer Sullivan (On Leave)); HR Ops Enabler Tools (Jennifer Sullivan); HR Ops Process Excellence (Anna Tassone); HR Ops Support (Kai Hofmann); HR Parkville (Yvette Saunders); HR Payroll (Christina Avraamides); HR Process and Portfolio Management (Beth Swiezkowski); HR Projects (KT Leong); HR Talent Acquisition & Talent Management (Beth Thomas); HR Talent Acquisition Holly Springs (Blake Derrick); HR Talent Acquisition Maidenhead (Louise Hawkes); HR Talent Acquisition Parkville (Angela Bellenger); HR Total Rewards (Gyuri Endes (Inherited)); HR Total Rewards (Karen Vyse [C]); HRBP Corporate Functions (Mafalda Lou); HS Manufacturing Fill & Finish Ops (Brian Kennedy); HU CSL Plasma Kft. Center Debrecen (Halsz Roland); HU CSL Plasma Kft. Center Debrecen (Hal√°sz Roland); HU CSL Plasma Kft. Center Miskolc (Ruzsinszki Ibolya); HU CSL Plasma Kft. Center Ny√≠regyh√°za (Rask√°n√© Petruska Gy√∂ngyi); HU Center Employee (Raskn Petruska Gyngyi (Inherited)); HU Center Employee (Rask√°n√© Petruska Gy√∂ngyi (Inherited)); HU Center Employee (Ruzsinszki Ibolya (Inherited)); HU Center Manager (Ruzsinszki Ibolya (Inherited)); HU Center Physician (Raskn Petruska Gyngyi (Inherited)); HU Center Physician (Rask√°n√© Petruska Gy√∂ngyi (Inherited)); HU Center Physician (Ruzsinszki Ibolya (Inherited)); HU Quality (Duds kos); HU Quality (Dud√°s √Åkos); HVAC & Coldrooms (Nozar Basseri); HVAC (Anna Fellenberg); HVAC (Juerg Schwarz); HVAC (Simon Hediger); HVAC / Reinr√§ume (Nozar Basseri); HVAC 1 (Urs Turtschi); Haemostaseology (Bernhard Czapla (Inherited)); Haemostaseology (Claudia Bachmann (Inherited)); Haemostaseology (Heinrich Feischen (Inherited)); Haemostaseology (Holger Milkereit (Inherited)); Haemostaseology (Michael Bernd Rode (Inherited)); Haemostaseology (Rainer Adam (Inherited)); Haemostaseology (Ralf Kosmol); Haemostaseology (Susanne Mller (Inherited)); Haemostaseology (Susanne M√∂ller (Inherited)); Haemostasis (Anthony Downes); Haemostasis (Elias Francis (Inherited)); Haemostasis (Elias Francis); Haemostasis (George Tsirbas); Haemostasis (Gerry Orval); Haemostasis (John Bashour); Haemostasis (Roy Taylor); Haemostasis (Shane Bartils); Haemostasis (Steven Barello); Hagerstown 174 (Bukola Raji); Hagerstown 174 (Kashaun Muhammad); Hagerstown 174 ACM Area 1 (Antonio DuBois); Hagerstown 174 ACM Area 2 (Daniel Pappariella); Hagerstown 174 QA (Bukola Raji); Hagerstown 174 QA (Jade Jessop); Hagerstown 174 QA (Joanne Charles-Clarke); Hagerstown 174 QA (John E Hunt (Inherited)); Halethorpe 167 (Rebecca R Pettiford); Halethorpe 167 ACM Area 1 (Christine Bethea); Halethorpe 167 ACM Area 2 (Lanasia West); Halethorpe 167 QA (ALISON CONLEY); Halethorpe 167 QA (Robin K Doering); Haltom City 188 (Dante Williams); Haltom City 188 (Melissa J Chapman); Haltom City 188 ACM Area 1 (Marvin Tablante); Haltom City 188 ACM Area 2 (Dante Williams); Haltom City 188 ACM Area 2 (Robert G Wilson); Haltom City 188 QA (Daniel Vu); Haltom City 188 QA (Julie E Reynolds); Hamilton 199 (Jenna Evans); Hamilton 199 (Katanya Hall); Hamilton 199 (Kera Cathel); Hamilton 199 ACM Area 1 (Steve Hosang); Hamilton 199 ACM Area 2 (TaChita Robb); Hamilton 199 QA (Emily Norton); Hamilton 199 QA (Kera Cathel); Hamilton 494 (Derek Morner); Hamilton 494 ACM Area 1 (William Robinson); Hamilton 494 ACM Area 2 (Jessica Hoffman); Hamilton 494 ACM Area 2 (VERONICA HESTER); Hamilton 494 QA (Elizabeth E Galloway); Harlingen 185 (Victor Guevara); Harlingen 185 ACM Area 1 (Eduardo De La Rosa); Harlingen 185 ACM Area 2 (Melinda Garcia); Harlingen 185 QA (Audrey Rodriguez); Harlingen 185 QA (Jennifer Martinez); Harlingen 185 QA (Luis Rodr√≠guez); Harlingen 185 QA (Rosa E Mercado (Inherited)); Hattersheim Field Services (Frank Dauber); Hattersheim Field Services (Robert Rohs); Hazel Crest 116 (Andrea C Rice); Hazel Crest 116 (Morgan R Grose); Hazel Crest 116 ACM Area 1 (Ian Olson); Hazel Crest 116 ACM Area 2 (Bob Gentille); Hazel Crest 116 QA (Amanda E Swider); Hazel Crest 116 QA (Joshua D Williamson (Inherited)); Head , In-Licensed Products AsiaPac, Global Regulatory Affairs (Angela Wong); Head Global Business Developme (Eve Williamson); Head Greater China Logistics (Edwin Chia); Head of Asia Pac, Medical Affairs (Jane Leong); Head of Asia Pac, Medical Affairs (Jonathan Anderson); Head of Batch Release (Darren Moulton); Head of Batch Release (Sherrin Gribble); Head of Commercial and Marketing (Jamila Filipecki); Head of Medical Affairs UK (Mansoor Ashraf); Head of Medical Affairs UK (Sankarasubramanian Rajaram (Inherited)); Head of Medical Writing & Disclosures (Catherine Tyrrell); Head of Medical Writing & Disclosures (Cathy Tyrrell); Head of Operational Excellence (Dirk Crouse); Head of R&D Finance (Christopher James Thorpe); Head of Region, APAC & In licensed Prod, Glob RA (Lisa MacDonald); Head of Region, Asia Pac & In licensed Prod, Glob Reg Affairs (Lisa MacDonald); Head, Clinical Development Operations (Daniel Kirby); Head, Clinical Development Operations (Veronica Suarez); Head, Technical Development (PKV) & Global Process Innovation (Steven Rockman); Head, Technical Development (PKV) & Global Process Innovation (Steven Rockman); Health & Wellbeing (Susanne Marx); Health (Donna G O''Keefe); Health (Sara Regnier); Health and Safety (Gregory Dowler); Healthcare Policy & Ext. Affairs APAC (Shouqing Zhang); Healthcare Policy & Ext. Affairs Europe (Rdiger Gatermann); Healthcare Policy & Ext. Affairs Europe (R√ºdiger Gatermann); Healthcare Policy & Ext. Affairs Japan (Shouqing Zhang (Inherited)); Healthcare Policy & Ext. Affairs N.A. (Patrick Collins); Hem Higashi Nihon Area (Atsuhiko Arikata); Hem Kansai Chubu Area (Shinichi Kano); Hem Nishi Nihon Area (Taisuke Miyakoshi); Hem Nishi Nihon Area (Takeyuki Akiyoshi); Hem Shutoken Area (Takayuki Takigawa); Hematology & Thrombosis Marketing (John Nicastro); Hematology & Thrombosis Medical Affairs (Debbie Drane (Inherited)); Hematology & Thrombosis TA (Sharad Agrawal); Hematology & Thrombosis Therapeutic Area (Brahm Goldstein); Hematology Marketing (John Nicastro); Hematology TA (Antti Kourula); Hematology TA Marketing (Sharad Agrawal); Hematology TA Medical Affairs (Krupa Sivamurthy); Hemophilia A Marketing (Beth Ann Hirst); Hemophilia A Marketing (Brian Johnson); Hemophilia B Marketing (Nicole McInerney); Hemophilia Group (Hideyuki Seto); Hemophilia Group (Makoto Kubo); Hemophilia TA (Takayuki Ishii); Henderson 134 (Eddie H Gaillard); Henderson 134 ACM Area 1 (Maria Coulter); Henderson 134 ACM Area 2 (Eshell Cudjo-Williams); Henderson 134 QA (Bri Johnson); Henrico 264 (Tracia Lopez); Henrico 264 ACM Area 1 (Nancy L Ott); Henrico 264 ACM Area 1 (Tannika Green); Henrico 264 ACM Area 2 (Tannika Green); Henrico 264 ACM Area 2 (Tracia Lopez (Inherited)); Henrico QA 264 (Brandan Lurz); Herstellungsleiter (Andreas Gehrich (Inherited)); Herstellungsleiter (Annette Pernitzsch (Inherited)); Herstellungsleiter (Claudia Habenicht (Inherited)); Herstellungsleiter (Heike Borchert); Herstellungsleiter (Kirsten Scheibel (Inherited)); Herstellungsleiter (Natascha Bock (Inherited)); Herstellungsleiter (Stephani Keltsch); Herstellungsleiter (Sven Schuhmann (Inherited)); Herstellungsleiter Berlin (Dorothee Knop); Herstellungsleiter Braunschweig (Dorothee Knop); Herstellungsleiter Bremen (Dorothee Knop); Herstellungsleiter Frankfurt (Dorothee Knop); Herstellungsleiter Gttingen (Dorothee Knop); Herstellungsleiter G√∂ttingen (Dorothee Knop); Herstellungsleiter Kiel (Dorothee Knop); Herstellungsleiter Nrnberg (Dorothee Knop); Herstellungsleiter N√ºrnberg (Dorothee Knop); Hidalgo 151 (Howard Augusto Castillo); Hidalgo 151 ACM Area 1 (Javier De La Fuente (On Leave)); Hidalgo 151 ACM Area 1 (Javier De La Fuente); Hidalgo 151 ACM Area 2 (Lucio Jaramillo); Hidalgo 151 QA (Becky S Diaz); High Speed Packaging (Jrg Sthli); High Speed Packaging (J√ºrg St√§hli); High Speed Packaging Line (Peter Zysset); Highland Park 138 (Miriah Grady); Highland Park 138 (Mondel Hightower); Highland Park 138 ACM Area 1 (Miriah Grady); Highland Park 138 ACM Area 1 (T''Pring John); Highland Park 138 ACM Area 2 (Dee Freeman); Highland Park 138 QA (Jenae Jacobs); Highland Park 138 QA (Shawna Taylor); Highland Park 138 QA (Slater P Murphy); Hillsboro 126 (Elizabeth Manning); Hillsboro 126 ACM Area 1 (Alex Steinke); Hillsboro 126 ACM Area 2 (Dan Jordan (On Leave)); Hillsboro 126 ACM Area 2 (Paige N Zafran); Hillsboro 126 QA (Grant Haun); Hizentra Marketing (Michael Ward); Hobart 218 (Kevin Robinson); Hobart 218 (Sherri L Clark); Hobart 218 ACM Area 1 (Michele Tosseng); Hobart 218 ACM Area 2 (Ashley Myvett); Hobart 218 ACM Area 2 (Kevin Robinson); Hobart 218 QA (Drewleigha B Sarver (Inherited)); Hobart 218 QA (KayLeigh Northcutt); Hokkaido Tohoku Area (Masahiro Takai); Homestead 207 (Mary A Paul (Inherited)); Homestead 207 (Roger Jiron); Homestead 207 (Stacey Ewing); Homestead 207 ACM Area 1 (Monica Alvelay); Homestead 207 ACM Area 2 (Julio Delgado); Homestead 207 ACM Area 2 (Roger Jiron (Inherited)); Homestead 207 QA (Natasha Roopnarine); Homestead 250 (Diane Day); Homestead 250 ACM Area 1 (Ryan Olsavsky); Homestead 250 ACM Area 2 (Jamille Ford); Homestead 250 QA (DENNIS GINTHER); Houston 208 (Sara Bouras); Houston 143 (Josh Concepcion); Houston 143 ACM Area 1 (Sharon K Easiley); Houston 143 ACM Area 2 (Oscar Beasley); Houston 143 QA (Shawntrala Stephens); Houston 168 (Lisa Rojas); Houston 168 ACM Area 1 (Lisa Wilson); Houston 168 ACM Area 2 (Elizabeth Morales); Houston 168 ACM Area 2 (Tascha Montgomery); Houston 168 QA (Sam Schultz (Inherited)); Houston 168 QA (Tara West); Houston 208 (Sara Bouras); Houston 208 ACM Area 1 (Sara Bouras (Inherited)); Houston 208 ACM Area 1 (Sarah L Terry); Houston 208 ACM Area 2 (Marc Garcia); Houston 208 ACM Area 2 (Sarah L Terry); Houston 208 QA (Darriel Clark (On Leave)); Houston 208 QA (Darriel Clark); Houston 208 QA (Elaine R Wilson); Houston 209 (Erin Ostean); Houston 209 (Sheneka E Wilson); Houston 209 ACM Area 1 (Charles Minter (On Leave)); Houston 209 ACM Area 1 (Charles Minter); Houston 209 ACM Area 2 (Adrean N Brown); Houston 209 ACM Area 2 (MARY MEADOWS); Houston 209 QA (Barbara May); Houston 209 QA (Keva M Williams); Houston 274 (Brian T Edwards); Houston 274 ACM Area 1 (Reiko F Hernandez); Houston 274 ACM Area 2 (Tyriana T Shaw); Houston 274 QA (Lawrence Jones); Human Resources Labor and Employee Relations (Christine Adams (On Leave)); Human Resources & Communications (Sandro Krug); Human Resources & General Affairs, Japan (Akira Nakajima); Human Resources & General Affairs, Japan (Mayumi Gonome - ); Human Resources & General Affairs, Japan (Mayumi Gonome ??? ??? - ???? ????); Human Resources (Bonnie Shor); Human Resources (Bonnie Slone); Human Resources (Gyuri Endes); Human Resources (Jacqueline Hawkins); Human Resources (Nicole Bookert); Human Resources (Tanja Templer); Human Resources (Tanya Kennedy); Human Resources Kankakee (Jacqueline Hawkins); Human Resources Management (Adam Williams); Human Resources Management (Pia Daish); Human Resources Organisation Transformation (Paula Foord); Human Resources SC (Nicole Bookert); Human Resources Talent Development (Michael O''Connor); Human Resources ¬ñ Labor and Employee Relations (Christine Adams); Human Resources ¬ñ Labor and Employee Relations (Jacqueline Hawkins (Inherited)); Human Resources, China (Grace Deng ?????); Human Resources, China (Tracy Lyu ?????); Hygiene (Arno Karnholz (Inherited)); ICSR Compliance and Reconciliation (Samantha Gan); IG / Albumin Bulk (Anthony Manovella); IG / Albumin Bulk (Jill Allen); IG Lab (Ritaben Suhagiya (Inherited)); IG Lab (Ritaben Suhagiya); IG Lab (Tom McCallum); IG Lab (William Fenech); IM (Guido Kagemann); IM Modul (Arnd Vollmerhausen (Inherited)); IM Modul (Torsten Jeide); IMED / Clinical (Julia Daum); IP Management (Helen Mutimer); IP Management (Peter Gomme); IP Management (Philip Keep); IR Higashi Nihon Area (Takahiro Tsuruta); IR Kansai Chubu Area (Yutaka Fujita); IR Nishi Nihon Area (Takayuki Sakai); IR Shutoken Area (Hiroki Nagayasu); IS Applications (Markus Fhrer); IS Applications (Markus F√ºhrer); IS Operations (BAHA ATICI); IS Operations (Bernd Boucsein (Inherited)); IS Operations (Robert Rohs); IT (Martin Jones); IT Americas (Stephen Norman Bender); IT Americas (Steve Bender); IT Americas, Laboratories (Dave Kirk); IT Americas, Site IT (DEBORAH BUREC); IT Americas, Site IT (Deborah Burec); IT Applications (Pavan Dronamraju); IT Applications - SharePoint (Emma Tibble); IT Asia Pacific (Gavin Gusling); IT Automation BMW (Daud Warraich (Inherited)); IT Automation BMW (John Croxton (Inherited)); IT Automation BMW (John Croxton); IT Automation BMW (Reto Von Gunten); IT Automation BMW (Stephen Pickering); IT Business Applications (Paul Ashton); IT Communications Services (Nealesh Mistry); IT Compliance - EMEA (David Boyd); IT Compliance - Infrastructure (Neil Broster); IT EMEA (Martin Gallington); IT EMEA Infrastructure (Chris Gatley [C]); IT Infrastructure - Hosting (Sadha Venkatachellam); IT Infrastructure - Hosting (Sathasivan Venkatachellam); IT Infrastructure Service - Identity & Desktop (Rob Deacon); IT Infrastructure Services (Quentin Zhao); IT Security & Compliance (Bob DeMarco); IT Security & Compliance (Robert DeMarco); IT Security and Compliance (Alan Butterfield); IT Security and Compliance (Alan Matthew Butterfield); IT Service Management Office (Richard Williams); IT Services (Daniel Robinson); IT Vendor Contracts (Craig Skelton); IVV Bacteriology (Matthew Stellato); IVV Chemistry (Thuy Dang); IVV Environmental Monitoring (Andrea Chalker); IVV Potency + Biochemistry US (Corina Zahra); IVV Potency, Biochem Rest of World (Anna Gruszka); IVV Seed Development (Brad Dickson); Identity and Access Management Operations (Bill Keohane); Ig Marketing (Sara Cowan); Ig&API Franchise Marketing (Amlie De Rosnay) (52388632); Ig&API Franchise Marketing (Am√©lie De Rosnay) (52388632); Ig&API Franchise Marketing (Emmanuelle Massonie) (52388632); Ig&API Sales Force Florent Privat (Emeline Bedu) (52388634); Ig&API Sales Force Florent Privat (Florent Privat) (52388634); IgG & Albumin/Supply Chain PMR Main Site (Barbara Kalina (Inherited)); IgG & Albumin/Supply Chain PMR Main Site (Wilfried Freudenberg); IgLAB (Franz Petter); IgLAB Bulk formulation (Susanne Gilgen); IgLAB Bulk purification (Thomas Eckert); IgLAB MV&VI Bulk Formulation (Sandra Kaempfer); IgLAB MV&VI Bulk Purification (Mathias Schinegger); IgLAB MV&VI Subfractionation (Markus Hauert); IgLAB Subfractionation (Mark Deutschland); IgLAB Subfractionation (Markus Hauert); IgLAB Subfractionation (Susanne Gilgen); IgLAB Subfractionation (Thomas Daehler); IgLABMV&VI (Marius Liesch); IgPRO (Markus Weber); Immunoglobulin Asset (Fritz Rentsch); Immunohematology Lab (Maria R Fernandez (Inherited)); Immunohematology Lab (Peter A Fitzgerald (Inherited)); Immunology & Neurology CommDev Marketing (Michael Ward); Immunology & Neurology Medical Affairs (Andrew Koenig); Immunology & Neurology New Products (Regula Styger Baumann); Immunology & Neurology RDPM (Karen Lindquist); Immunology & Neurology RDPM I (Sabine Alexandra Stoffel Domig); Immunology & Neurology TA (Jay Bowsher); Immunology & Rare Disease Group (Hirokazu Imura); Immunology & Rare Disease Group (Shinichiro Magome); Immunology & Rare Disease TA (Takuya Ohshima); Immunology (Bernhard Czapla (Inherited)); Immunology (Claudia Bachmann (Inherited)); Immunology (Heinrich Feischen (Inherited)); Immunology (Helen Hartman); Immunology (Holger Milkereit (Inherited)); Immunology (IMM) (Stefan Spycher); Immunology (Michael Bernd Rode (Inherited)); Immunology (Rachpal Malhotra); Immunology (Rainer Adam (Inherited)); Immunology (Ralf Kosmol); Immunology (Susanne Mller (Inherited)); Immunology (Susanne M√∂ller (Inherited)); Immunology - Transplant (Mircea Ciuca); Immunology / Transplant (Mircea Ciuca); Immunology Lab (Maria R Fernandez (Inherited)); Immunology Lab (Peter A Fitzgerald (Inherited)); Immunology Marketing (Bernadine Koziara (Inherited)); Immunology Marketing (Biju Chorinchath); Immunology Marketing (JD Kohutka); Immunology New Products (Regula Styger Baumann); Immunology TA (Jay Bowsher); Immunology TA Marketing (Michael Ward); Immunology TA Medical Affairs I (Andrew Koenig); Immunology and Neurology TA (Sharon Popik); Immunology and Neurology TA (Susanne Wang); Immunology-North America (Ian Gourley); Import / Export Lead AU (Robert Bronikowski); Import Export Compliance (MICHAEL MORRILL); Import Export Compliance (Markus Buri); Import Export Compliance (Michael Morrill); Import-Export Compliance (Nona Clarke); Import/Export Compliance (Neda Nikolic); Import/Export Compliance (Nona Clarke); Impurity & Data Management (Patricia Lieby); Impurity Data Mngt I (Madlene von K√§nel); Impurity Data Mngt I (Simona Pfister); Impurity and Data Mngt II (Tanja Angela Nyffenegger); In Market Logistics EMEA (Avi Yuhay); In-Market Logistics Turkey/EEU (Avi Yuhay); Incoming Quality Assurance (Jamie Nichols); Incoming Quality Assurance (Lynette Mirrielees); Incoming Quality Assurance GL (Cindy Rocknowski (Inherited)); Incoming Quality Assurance GL (Jeffrey Zoubek (Inherited)); Indianapolis 146 (Brian W Stewart); Indianapolis 146 (Randy Miller); Indianapolis 146 QA (Erik Tharp); Indianapolis 146 QA (Randy Miller); Indianapolis 181 (Jami Colson); Indianapolis 181 ACM Area 1 (Dayneisha G Pinkston); Indianapolis 181 ACM Area 1 (Jordan Swoape); Indianapolis 181 ACM Area 2 (Ronnisha Banks); Indianapolis 181 QA (Aja Blue); Indianapolis 181 QA (Drewleigha B Sarver); Indianapolis 181 QA (Robin L Oldaker); Indianapolis 412 (LaToya M Hinkle); Indianapolis 412 ACM Area 1 (Brian Stewart (On Leave)); Indianapolis 412 ACM Area 1 (Brian Stewart); Indianapolis 412 ACM Area 2 (Latoria J Moore); Indianapolis 412 QA (Ashley Kemper); Indirect Procurement (Daniela Ebert); Industriekaufleute (Carmen Walldorf (Inherited)); Industriekaufleute (Doris Nake (Inherited)); Indy 146 ACM Area 1 (Sara K Campbell); Indy 146 ACM Area 1 (Sara K Sheets); Indy 146 ACM Area 2 (Joe Hicks Jr); Influenza (Chris Clarke); Influenza Operations (Bill Cracknell); Influenza Vaccines (Carole Verhoeven); Influenza Vaccines Seasonal (Athanasia Papadimitriou); Influenza Vaccines Seasonal (Jonathan Edelman (Inherited)); Influenza and National Products, Global RA (Lisa Steinberg); Information Security (Federico Iaschi); Infrastructure Design (Jeremy Finlay); Infrastructure Excellence & Process Management (Stephan Krummel); Infrastructure Program Manager (Jessica Bartels); Infrastructure Program Mgr (Jessica Bartels); Infusion Science - ISS (Lisa Barrett); Infusion Science - ISS (Lisa Barrett); Inhibitors, FI, FXIII & Support/Supply C (Barbara Kalina (Inherited)); Inhibitors, FI, FXIII & Support/Supply C (Wilfried Happel); Innovation (Becky Heatherman); Inoculation (Jubail Dimabuyu); Inspection & Packaging (Jonathan Kanczes); Inspection & Packing (Ben Hagger); Inspection & Packing (David Nguyen); Inspection & Packing (Joanna Madafferi (Inherited)); Inspection & Packing (Joanna Madafferi); Inspection (Pasquale Carestia (Inherited)); Inspection (Thomas Royal); Inspection (Union) (Pasquale Carestia (Inherited)); Inspection (Union) (Thomas Royal (Inherited)); Inspection semi final prod. 4 (Samira Spahn-Belbaita); Instrum & Elect Engineer (Justin Lim); Instrumentation (Colin Steele); Integrated Business Planning (Avi Gor√©); Integrated Business Planning (Avinash Gor); Integrated Business Planning (Avinash Gor√©); Integrated Business Planning (Jamie Pritzl); Intercontinental Supply Chain (Oliver Wehner); Internal Communications (Claudine Heinz); Internal Communications (Jasmin Joller); Internal Communications (Laura Kumpe); Internal Services (Reto Moser); Internal processes (Ernst Scheurer); International Logistics - Intermediates, Special Shipments (Julia Daum); International Logistics - Team Americas - APAC (Anna-Karina Muth); International Logistics - Team Americas / APAC (Anna-Karina Muth); International Logistics - Team EMEA (Christoph Mueller); International Payroll (Clair Burke); International Plasma Operations (Jeffrey A Schulz); Interns (Jacqueline Hawkins (Inherited)); Investigation & Process Owners (Ryan Cox); Investor Relations (Mark Dehring); Invoice Control & Invoicing of Services (Harald Bieker (On Leave), Beatrix Gnau); Invoice Control & Invoicing of Services (Harald Bieker); Irondequoit 246 (Sheilah Mykins); Irondequoit 246 ACM Area 1 (Nicole Chipembere); Irondequoit 246 ACM Area 2 (Teresa Moreira-Weil); Irondequoit 246 QA (Meghan Beckedorf); Italian Commercial Finance (Laura Lucaroni); JPN TA (Coagulation) (Yuki Hidaka); JPN TA (Critical Care) (Osamu Tsukamoto); JPN TA (Immunology) (Satoshi Koike ??? ? - ??? ???? (Inherited)); JPN TA (Immunology) (Tomomi Shibata); JPN TA (Osamu Tsukamoto); Jackson 156 (Chris Weary); Jackson 156 (Jose L Dela Garza (Inherited)); Jackson 156 ACM Area 1 (Chris Weary); Jackson 156 ACM Area 1 (Joseph Dupree); Jackson 156 ACM Area 2 (Adrian Johnson); Jackson 156 QA (Bonnie M Talbott (Inherited)); Jackson 156 QA (Cynthia Hill); Jackson 156 QA (Jose L Dela Garza (Inherited)); Jackson 156 QA (Savannah Vann); Jackson 205 (Mark Bundy); Jackson 205 ACM Area 1 (Erica R Smith); Jackson 205 ACM Area 2 (Kenny Berry); Jackson 205 QA (Marc D Fisher); Jackson 205 QA (Nicole Pichla (On Leave)); Jackson 205 QA (Nicole Pichla); Jackson 225 (Bonnie M Talbott (Inherited)); Jackson 225 (Cherita Saulmarshall); Jackson 225 (Jai Baylis); Jackson 225 (Kronnetra Hester); Jackson 225 ACM Area 1 (Mariyo Archie); Jackson 225 ACM Area 2 (Jose L Dela Garza); Jackson 225 ACM Area 2 (Stanley Taylor); Jackson 225 QA (Deborah L Baker); Jackson 225 QA (Keyauna Lewis); Jackson 257 (Sarah E Silva); Jackson 257 ACM Area 1 (Caitie Golubski); Jackson 257 ACM Area 2 (Jarrett Heathcock); Jackson 257 ACM Area 2 (Sarah E Silva (Inherited)); Jackson 257 QA (Brooke McKinney); Jacksonville 251 (Sherri L Clark); Jacksonville 251 ACM Area 1 (Gina Castellano); Jacksonville 251 ACM Area 2 (AlexZandria Taylor); Jacksonville 251 QA (Brett A Wintheiser (Inherited)); Jacksonville 251 QA (Cindy Vieira); Japan Clinical Safety & Pharmacovigilance (Mariko Hase); Japan Field Services (Satoru Shimizu); Japan Project Management (Midori Kobayashi); Johnston 242 (Catherine Colucci); Johnston 242 (John L Thixton); Johnston 242 (Renee Keyser); Johnston 242 ACM Area 1 (Son Nguyen); Johnston 242 ACM Area 2 (Cessa Piedra); Johnston 242 QA (Allante S Williams); Johnston 242 QA (Erin Thompson); Joliet 219 (Andrew Franzen); Joliet 219 (Christopher J Rivers Jr); Joliet 219 ACM Area 1 (Sharon Kunz); Joliet 219 ACM Area 2 (Duanita Scott); Joliet 219 QA (Beth Majewski); Joliet 219 QA (Lori Carlson (Inherited)); Joliet 219 QA (Ryan Welter); Jonesboro 120 (Maurice E Clements); Jonesboro 120 ACM Area 1 (Jumela S Bell); Jonesboro 120 ACM Area 1 (Sade Hodges); Jonesboro 120 ACM Area 2 (Denise Bloodsaw); Jonesboro 120 ACM Area 2 (Jumela S Bell); Jonesboro 120 QA (Laila Matthews-El); Jonesboro 120 QA (Rose-Marie O Bland); K-C Fractionation (Union) (Jason Vaughn); K-C Fractionation (Union) (Samuel Jackson); KAN Security (Adam Kennell); KC Module 3 Operational Readiness (Cornelis Rijneveld); KOP Corporate Services (Michael Hays (Inherited)); KOP Corporate Services (Wendy Kilp) (Wendy Kilp); KOP Facilities (Michael Hays); KOP Outsourcing (Melissa Hurst); KOP Security (Shanna Aldridge); KOP Sourcing (Ed Rosario); KOP Sourcing (Paul Addis (Inherited)); Kankakee Field Services (Rebecca Liehr); Kankakee Manufacturing (Ernest Shepard); Kankakee R&D Tech Transfer (Shannon Boudreau); Kansai Area (Shingo Fujiwara); Kansai Area (Tatsuto Aihara); Kansas City 011 (Cristina E Ceniceros); Kansas City 011 (Tina Wagenknecht); Kansas City 011 ACM Area 1 (Dustin Irish); Kansas City 011 ACM Area 2 (Cristina E Ceniceros); Kansas City 011 ACM Area 2 (Samuel Jordan); Kansas City 011 QA (Cole D Kimple (Inherited)); Kansas City 011 QA (Samuel Anderson); Kansas City 011 QA (Whitney A Dean); Kansas City 410 (Cristina E Ceniceros); Kansas City 410 (Tina Wagenknecht); Kansas City 410 (Trethan R Copeland); Kansas City 410 ACM Area 1 (Jackie Florez); Kansas City 410 ACM Area 2 (Trethan R Copeland); Kansas City 410 QA (Kimberly S Mangold); Kansas City 410 QA (Whitney A Dean); Kaufmann f√ºr B√ºrokommunikation (Doris Nake (Inherited)); Kcentra Marketing (John Nicastro); Kcentra Marketing (Tom Groeling (Inherited)); Kcentra Marketing Group (Shunsuke Kuwata ??? ?? - ??? ??????); Kcentra Marketing I (Sara Cowan); Kenner 149 (Chris Weary); Kenner 149 (Durrell Arceneaux); Kenner 149 (Michael Markey); Kenner 149 ACM Area 1 (Brittany Miles); Kenner 149 ACM Area 2 (Teresa Currence); Kenner 149 QA (Centrell J Jackson); Kent 112 (David M Wilson); Kent 112 (Diana H Ek); Kent 112 ACM Area 1 (Diana H Ek (Inherited)); Kent 112 ACM Area 1 (Trevor Case); Kent 112 ACM Area 2 (Wesley Noble); Kent 112 QA (Brian Patterson); Kent 112 QA (Robert D Coulter); Kent 112 QA (Sasima Teadwatanasuk); Kent 160 (Michael J Ryan); Kent 160 ACM Area 1 (Brandy M Cermak); Kent 160 ACM Area 2 (Bambi C Gonwa); Kent 160 QA (Jamie L Dunderman); Kent 160 QA (Jamie L Matheney); Key Account Management (Alexander Kahlau); Key Account Management (Shun Huang ); Key Account Management (Shun Huang ????); King of Prussia Field Services (Cheryl Fennell); King of Prussia Field Services (Joy Holland); King of Prussia Field Services (Mary Jane McPherson (Inherited)); King of Prussia Quality (Brian Puglisi); Kitakanto Shinetsu Area (Hideo Yonesaka); Knowledge Management (Jacqui Altman); Knowledge Management (Kim Vandenberg); Knowledge Management (Leanne Cummings); Knoxville 405 (Brianna E Ballew); Knoxville 405 (John W Kelly); Knoxville 405 (Keith Clemons (Inherited)); Knoxville 405 ACM Area 1 (Michael R Thomas); Knoxville 405 ACM Area 2 (Leighann N Miller); Knoxville 405 QA (Tina G Ledbetter); Knoxville 405 QA (Tina Grubb Ledbetter); Kommunikation (Stephanie Fuchs); Konzessionen/Brandschutzbeauftragter (Michael Knoll (On Leave)); Konzessionen/Brandschutzbeauftragter (Michael Knoll); Koordination und Dokumentation (Rainer Frank (Inherited)); Kyushu Okinawa Area (Akihiro Enomoto); L&D, Apprentices KV (Ruth Schmid); LATAM RA (Andrea Violante); LATAM Sales Ops CAM & CAR (Mariano Miri); LVP Sterility Assurance (Sara Kimmins); La Crosse 516 (Ranee Bloor); La Crosse 516 QA (Sara Martin); Lab Automation (Ann L Wickenheiser); Lab Facilities (Joel Jones); Lab Inventory (Joel Jones (Inherited)); Lab Operations (Diep Chau); Lab Operations, Bio21 (Kirsten Edwards); Labor Relations (Steven Stewart); Laboratory Management Pasadena (Anthony Navarro); Laboratory Management - Pasadena (Anthony Navarro); Laboratory Operations (Constance W Farrar); Laboratory Operations (Marleen Enthoven); Laboratory Operations (Ricky R Alexander); Laboratory Systems (Amit Krishna); Lackawanna 238 (Martin Szczublewski); Lackawanna 238 ACM Area 1 (Brent Hollingsworth); Lackawanna 238 ACM Area 2 (Allie Tuttle); Lackawanna 238 QA (Anita Brenon); Lackland 706 (Ovetta A Mickles); Lackland 706 ACM Area 1 (Gabriel J Martinez); Lackland 706 ACM Area 2 (Ariel Schiller); Lackland 706 ACM Area 3 (Nate Neal II); Lackland 706 QA (Amber Sanders); Lackland 706 QA (Brenton Ferguson); Lager/Ersatzteilmanagement (Leon Krupa); Lakeland 154 (Elizabeth Adkins); Lakeland 154 ACM Area 1 (Jeffrey Simmons); Lakeland 154 ACM Area 2 (Bralyn T McCullough); Lakeland 154 QA (Crystal L Reichard); Lakeland 154 QA (Matthew Smith (Inherited)); Lakeland 154 QA (William Forquignon); Lansing 042 (Debbie L Duhe); Lansing 042 ACM Area 1 (Elizabeth Lawhon); Lansing 042 ACM Area 2 (Ruth A Griffin); Lansing 042 QA (Christine M Leija); Lansing 118 (Angie K Fedewa); Lansing 118 ACM Area 1 (Douglas Fiedler); Lansing 118 ACM Area 2 (Toussaint Hodari); Lansing 118 QA (Jessica Babcock); Las Cruces 506 (Samuel V Grijalva); Las Cruces 506 ACM Area 1 (Jacquelyn Jaques); Las Cruces 506 ACM Area 2 (Ira Bacani); Las Cruces 506 QA (Linda Dutchover); Las Vegas 081 (Jolena Lee); Las Vegas 081 (Michele Purvines-Honzo); Las Vegas 081 ACM Area 1 (Austin Vinson); Las Vegas 081 ACM Area 2 (Kevin Wallace); Las Vegas 081 ACM Area 3 (Christian Marcus); Las Vegas 081 QA (Erica Wiley); Las Vegas 081 QA (Paul Warden (Inherited)); Las Vegas 081 QA (Yaritza Monarrez); Las Vegas 172 (TIM AVILA); Las Vegas 172 (Xang Vang); Las Vegas 172 ACM Area 1 (Lashay Anter); Las Vegas 172 ACM Area 1 (Sarah C Sweat); Las Vegas 172 ACM Area 2 (Jessica L Jabbora); Las Vegas 172 QA (ANGELICA WILSON); Las Vegas 172 QA (Aaron D Learn); Las Vegas 216 (Erica Wiley); Las Vegas 216 (Nicole M Loncon); Las Vegas 216 ACM Area 1 (Erica Wiley); Las Vegas 216 ACM Area 1 (Michael Dako); Las Vegas 216 ACM Area 2 (Erica Wiley); Las Vegas 216 ACM Area 2 (Jose D Garcia); Las Vegas 216 QA (Orlando R Edwards Jr); Las Vegas 501 (Cari N Howard); Las Vegas 501 ACM Area 1 (Lissa Elswick); Las Vegas 501 ACM Area 2 (Steven G Simpson); Las Vegas 501 QA (Miranda Banks); LatAm Supply Chain (Martin Rossini); Late DSP Development (Erik Hinze); Late DSP Development (Tobias Brandt); Late Stage DSP Development (Erik Hinze); Late Stage DSP Development (LDD) (Uwe Liebing); Late Stage DSP Development (Tobias Brandt); Late Stage DSP Development (Uwe Liebing (Inherited)); Late USP Development (Jasmine Roth); Latin America (Juan Feliu); Latin American Distributors (Jean-Claude Andr); Latin American Distributors (Jean-Claude Andr√©); Lawrence 012 (Amy L Jackson); Lawrence 012 (Cole D Kimple (Inherited)); Lawrence 012 (Jessey Johnson); Lawrence 012 ACM Area 1 (Jessey Johnson (Inherited)); Lawrence 012 ACM Area 1 (Laura Hassen); Lawrence 012 ACM Area 2 (Taniesha D Kopriva); Lawrence 012 QA (Adam Loop); Lawrence 012 QA (Jessey Johnson (On Leave)); Lawrence 012 QA (Jessey Johnson); Lawrenceville 186 (Domonique T Walker); Lawrenceville 186 ACM Area 1 (Jeffrey Toussaint); Lawrenceville 186 ACM Area 2 (Ahesha M Francis); Lawrenceville 186 QA (Brandon Bailey); Lawton 452 (Natalie Compher); Lawton 452 (Vicky Sablan (On Leave)); Lawton 452 (Vicky Sablan); Lawton 452 ACM Area 1 (Jace A Guthrie); Lawton 452 ACM Area 2 (Samuel Jones); Lawton 452 QA (Adam Loop); Lawton 452 QA (Tiffany N Oxley); Layout & Packaging Planning (Martina Schweyer); Lead BP Finance - Asia Pac (Brendan Safe); Lead Clinical Oversight Manager (Anja Br√§unlich (Inherited)); Learning and Development (Amy Jackson); Learning and Development (Ann Lescher); Learning and Development (Henry Docx); Learning and Development (Karen A Emord); Learning and Development I (Henry Docx); Legal (Christine Dragann); Legal (Melissa Merriweather); Legal - Americas (John Neff); Legal - Australia (Amy Demediuk); Legal - Australia (Fiona Mead); Legal - Australia (Patrick Brady); Legal - Australia (Phyllis Perkins); Legal - Australia (Raewynn McIntyre); Legal - Australia (Tom Reid); Legal 1 (Khalil Rogers); Legal Clinical (Brian Sharma); Legal Counsel, Commercial, North America (Michael O''Connor); Legal Department APAC (Mae Chen ); Legal Department APAC (Mae Chen ?????); Legal Department Bern (Niklaus Kraehenbuehl); Legal Department Bern (Philippe Mueller); Legal Department Marburg (Dennis Kraft); Legal Operations Europe, Asia, Intercon. (Gereon Backmann); Legal Partners (Antje Michel); Legal Services (Sam Benyamin); Legal Services Europe & ICO (Charlotte Tvede Andersen); Legal Support One Commercial Operations Europe (Gereon Backmann (Inherited)); Legal Support One Commercial Operations Europe (Gereon Franz-Josef Backmann (Inherited)); Legal Support One Commercial Operations Europe (Gereon Franz-Josef Backmann); Legal ZLB Plasma (located in KOP) (Eric Silberstein); Lengnau Administration & Office Management (Boris Lanoir (Inherited)); Lengnau Administration & Office Management (Natasha Jackson); Lengnau Amenities Support (Franz Renfer); Lengnau Business Operations Services (Guenther Baumgartner); Lengnau Execution Systems (Frank Mastellone); Lengnau Facility Project (Darren Vegara); Lengnau Facility Project (Paul Loxley); Lengnau Human Resources (Ece Ergin [C]); Lengnau Human Resources (Sandro Krug (Inherited)); Lengnau Program (Nina Walser); Lengnau Program (Urs Meyer); Lengnau Project Documentation (Anamaria Negura); Lengnau Project Documentation (Mairead Henry [C]); Lengnau Project Documentation (Thorsten Buergel [C]); Lengnau SC and IBP (Marco Restelli); Lernende Logistik (Silvio Beck); Lexington 053 (Bobby R Fields Jr); Lexington 053 (Morgan R Grose); Lexington 053 ACM Area 1 (Jamale R Gentry); Lexington 053 ACM Area 2 (A.J. Stevenson); Lexington 053 QA (Michele R Estepp); Lexington 404 (Chris Otto); Lexington 404 ACM Area 1 (Ben Jones); Lexington 404 ACM Area 2 (Nathan J Fox); Lexington 404 QA (April Tyler); Lexington 404 QA (Bailee E White); Lichuan Plasma Collection Center (Jun Lai ); Lichuan Plasma Collection Center (Jun Lai ????); Lifecycle Management (Kathrin Eichstdt); Lifecycle Management (Kathrin Eichst√§dt); Lincoln Park 101 (Miriah Grady); Lincoln Park 101 (Toni M Walden); Lincoln Park 101 ACM Area 1 (Jeanette M Love-Ellison); Lincoln Park 101 ACM Area 2 (Dion J Holland); Lincoln Park 101 QA (Jenae Beacham); Lincoln Park 101 QA (Latosha Y Floyd (Inherited)); Lincoln Park 101 QA (Remie T Ray); Linden 212 (Jennifer Luque); Linden 212 ACM Area 1 (Jennifer Luque (Inherited)); Linden 212 ACM Area 1 (Matthew Clayborn); Linden 212 ACM Area 2 (Paul Eatman); Linden 212 QA (Jaleeka Johnson); Linden 212 QA (Stephanie D Shah (Inherited)); Linden 212 QA (Wendy MacConnell); Little Rock 234 (Seth Stuerke); Little Rock 234 ACM Area 1 (Charlie Hollinquest-Ford); Little Rock 234 ACM Area 2 (Ben Kulpa); Little Rock 234 QA (Akira Crenshaw); Logisitcs Manager VIC 266 (John Turone (Inherited)); Logistics (Angela Schembri); Logistics (Brendan Xerri); Logistics (Carl Werner (Inherited)); Logistics (Christopher Pela Fuaiva''a); Logistics (Dalal Mikhaeel); Logistics (Ibs Kaygisiz); Logistics (Ljubinka Duzel); Logistics (Peter Trimcevski); Logistics (Sam Mekhael (Inherited)); Logistics (Sam Mekhael); Logistics (Sebastian Sarmiento); Logistics (Tracy McIntosh); Logistics - Purchasing (Benjamin Fruin); Logistics - Purchasing (CHERYL GOODWIN); Logistics - Purchasing (Sue Savage); Logistics APAC (Edwin Chia); Logistics Customer Group (Namiko Hirakawa); Logistics I (Harald Mller (Inherited)); Logistics I (Harald M√ºller (Inherited)); Logistics Operations (Kai Menz); Logistics Operations (Koji Sugihara - ); Logistics Operations (Koji Sugihara ??? ?? - ???? ????); Logistics Operations - LATAM (Bruno Arakaki); Logistics Operations Australia (Suzanne Johnson); Logistics Operations Customer Service (Kaye McConnell); Logistics Operations Customer Service (Tanja Wells); Logistics Operations Europe (Matthias Loth); Logistics Operations Manager (John Turone); Logistics Operations- Americas (Daniel Sweed); Logistics Operations- Americas (Marianne McDonald (Inherited)); Logistics Planning Group (Takayuki Kato); Longwood 195 (Annette Nelson); Longwood 195 (Brian D Kelly); Longwood 195 ACM Area 1 (Jenna Smith); Longwood 195 ACM Area 1 (Vincent Spencer); Longwood 195 ACM Area 2 (Jessica Greene (On Leave)); Longwood 195 ACM Area 2 (Lori B Warfield); Longwood 195 QA (Brian Murzycki); Longwood 195 QA (Christopher Davis); Longwood 195 QA (John Garrett); Look Back / PDI (Julia Schimansky); Louisville 054 (Susan D Bensing); Louisville 054 ACM Area 1 (Tish Farris); Louisville 054 ACM Area 2 (Heather Romines); Louisville 054 QA (Gary Loy II); Louisville 054 QA (Keith Clemons (Inherited)); Louisville 054 QA (Melissa Casaus); Louisville 054 QA (Melissa J Roberts); Luotian Clinical Inspection (Yongmin Lv ?????); Luotian Inspection Management (Zengyi Chen ?????); Luotian Inspection Professional (Jiwei Liu ); Luotian Inspection Professional (Jiwei Liu ?????); Luotian Inspection Professional (Zheng Liang ????); Luotian Office Administration (Xiaoping Tang ?????); Luotian Office Administration (Zhen Zhang ); Luotian Office Administration (Zhen Zhang ????); Luotian Plasma Center Quality (Lixia He ?????); Luotian Plasma Collect (Jiali Fan ); Luotian Plasma Collect (Jiali Fan ?????); Luotian Plasma Collection (Meng Zhou ????); Luotian Plasma Collection (Menghua Ye (Inherited)); Luotian Plasma Collection (Shuiqiao Xiao ?????); Luotian Plasma Collection Center (Cunwei Hou ?????); Luotian Plasma Collection Center (Xiaoping Tang ); Luotian Plasma Collection Center (Xiaoping Tang ?????); Luotian Plasma Sourcing (Xiaoling Wang ); Luotian Plasma Sourcing (Xiaoling Wang ?????); Luotian Plasma Sourcing Management (Caihong Cheng ?????); Luotian Quality Management (Menghua Ye ); Luotian Quality Management (Menghua Ye ?????); Luotian Quality Management (Zheng Liang ????); Luotian plasma source management (Yongmin Lv ); Luotian plasma source management (Yongmin Lv ?????); Lyophilization (Jean-Claude Cauderay); M99 (Guido Mller); M99 (Guido M√∂ller); M99 (Marius Liesch); M99 NVI (Michael Theilkaes); M99 VVI (Marcel Mosimann); MDM Operations (Chandra Karpuram); MES & Systems (Reto Von Gunten); MES Automation (Gary Steele); MES Koordination (Horst Boeder (Inherited)); MES Koordination (Ralf Dersch); MF-59 (Gerhard Seemann (Inherited)); MFG Berinert & Beriplex Production (Jonathan Signore); MFG Berinert & Beriplex Production (Union) (Jonathan Signore); MS&T Lead (Kevin Murphy); MS&T Liverpool (Lisa-Marie Foulkes); MSAT (Matthias Kaeser); MSL Manager (Claire Morgan); MST Labor 1 (Anne N√∂ll); Macon 233 (Keyonna L Gray); Macon 233 (Lori B Warfield (On Leave)); Macon 233 (Melodee C Ebel (Inherited)); Macon 233 (Sherri L Clark); Macon 233 ACM Area 1 (Jennie Miles); Macon 233 ACM Area 1 (Lori B Warfield (On Leave) (Inherited)); Macon 233 ACM Area 2 (Gina Castellano); Macon 233 ACM Area 2 (Tomecia Tillman); Macon 233 QA (Teddye Gandy (On Leave)); Macon 233 QA (Teddye Gandy); Madison 076 (Tiffany K Singh); Madison 076 ACM Area 1 (Shelby N Grimsley); Madison 076 ACM Area 2 (Jada Phillips); Madison 076 QA (Alissa Elke); Madison 076 QA (Iricka Williams); Madison 076 QA (Prim J Cunningham (Inherited)); Main (Elizabeth Boyd); Maintenance & Reliability (Michael Elmer); Maintenance & Utilities (Franz Arnold Nigsch); Maintenance (Jeffrey Rhodes); Maintenance (Michael J Stephenson); Maintenance (Michael Memenga); Maintenance (Union) (Jose Franceschini Mirabal (Inherited)); Maintenance (Union) (Michael Memenga (Inherited)); Maintenance Engineering (Vittorio D''Argento); Maintenance K3 (Jose Franceschini Mirabal (Inherited)); Maintenance K3 I (Michael J Stephenson); Maintenance Officer (Jesse Chen); Maintenance Officer (Ray Belli); Maintenance Operations (Vittorio D''Argento); Maintenance SC I (Jeffrey Rhodes); Maintenance Support Engineer (James Stevens); Maintenance U8 (Simon Urfer); Maintenance U8 (Stefan Bgli); Maintenance U8 (Stefan B√∂gli); Maintenance and Utilities (Jose Franceschini Mirabal); Major Capital Projects (Brian Price); Management Accounting (Daya Salter); Management Accounting (RYAN HANSEN); Manager , IVV Seed Development (Karen Laurie); Manager - QA Batch Release (Linda Curran); Manager - QA Batch Release (Sherrin Gribble); Manager DGL (Heike Gocht); Manager ICA (Tim Karla); Manager IT Applications BPCS (Rod Randall); Manager Performance Qualification (Aaron Haag); Manager QA Batch Release (Anthony Day); Manager QA Batch Release (Carol Youssef); Manager QA Batch Release (Olivia Fisher); Manager QA Capability (Mark Machowicz); Manager QA Capability (Nicole Schaefer); Manager QA Capability (Vicky Gakias); Manager, DS Processing (Jesse Bodle); Manager, Field Services Australia (Bec Heitbaum); Manager, Field Services Australia (Travis Slessar); Manager, QA Cont Imp & Iss Mgt (Christopher Burke); Manager, QA Cont Imp & Iss Mgt (Janet Drew); Manager, QA Cont Imp & Iss Mgt (Jeremiah Holden); Manager, Virol & Immuno Res (Erin Verity); Manf Dir - Influenza Vaccines (Paul Morrison); Manf Dir - Influenza Vaccines (Vincent Chung); Manhattan 019 (Stacy J Teske); Manhattan 019 ACM Area 1 (Shane A Groover); Manhattan 019 ACM Area 2 (Dave Lynn); Manhattan 019 ACM Area 2 (Stacy J Teske (Inherited)); Manhattan 019 QA (Karen L Phillips); Manufacturing (Barbara Beugger); Manufacturing (Boris Lanoir); Manufacturing (Bradley J Eberhart); Manufacturing (James Janachowski); Manufacturing (Jose Gonzalez (Inherited)); Manufacturing (Katie Wood); Manufacturing (Martin Schaeren (Inherited)); Manufacturing (Matthew Seay); Manufacturing (Patricia Stewart (Inherited)); Manufacturing (Rene Bruegger); Manufacturing - Fill/Finish (Vincent Chung); Manufacturing A1 (Danica Bates); Manufacturing B1 (Trinette Farr); Manufacturing B2 (Michael Haney); Manufacturing Continuous Improvement (Trinette Farr); Manufacturing EU & APAC (Pierre Caloz); Manufacturing Engineering (Aaron Imig); Manufacturing Execution Systems (Frank Behnisch); Manufacturing Finance (Jacob Weaver); Manufacturing Finance (Jason Mugridge); Manufacturing First Shift (Tish Smith); Manufacturing HS (Chad M Salisbury); Manufacturing HS (Dave Sehgal); Manufacturing HS (Karen Netherton); Manufacturing Kankakee I (Jose Gonzalez (Inherited)); Manufacturing LVP (Jonah Smith); Manufacturing LVP (Nige Hilton); Manufacturing Liverpool (Jonah Smith); Manufacturing Operations (Steven Aldrich); Manufacturing PKV (Chris Larkins (Inherited)); Manufacturing PKV (Jonah Smith); Manufacturing Quality Management (Ramzan Tabasum); Manufacturing SC I (Matthew Seay); Manufacturing Science & Technology (Klaus Schmitt); Manufacturing Science & Technology (Klaus-Jrgen Schlitt); Manufacturing Science & Technology (Klaus-J√ºrgen Schlitt); Manufacturing Sciences and Technologies (Heidi Bergman); Manufacturing Second Shift (Michael Haney); Manufacturing Supply Chain & Integrated Business Planning (Pat Golla); Manufacturing Support (Clare Hughes); Manufacturing Support (Dee Hamer); Manufacturing Support (Marco Restelli); Manufacturing Support (Vreni F√∂rtsch); Manufacturing Technical Operations Team (Yuan Su ); Manufacturing Technical Operations Team (Yuan Su ????); Manufacturing Technology & Science (Christoph Hau√ümann); Manufacturing Third Shift (Michael Haney); Maple Shade 215 (Brett Goldman); Maple Shade 215 (Darryl King); Maple Shade 215 ACM Area 1 (Tracey Pinkney); Maple Shade 215 ACM Area 2 (Erica Hoesly); Maple Shade 215 QA (Deb Stith); Maple Shade 215 QA (Kimberly Perry); Marburg Data Management (Babette Katharina von Hagen); Marburg Field Services (Alexander Berendes); Margate 142 (Christina M Kokoszka); Margate 142 (Michelle S DeCambre); Margate 142 (Takisha F Jackson); Margate 142 ACM Area 1 (Amanda Bybee); Margate 142 ACM Area 1 (Kurt S Tuckett); Margate 142 ACM Area 2 (Kencia Cadet-Pa?ko); Margate 142 ACM Area 2 (Kencia Cadet-Pako); Margate 142 QA (Estela M Euceda); Margate 142 QA (Karen Blanchard-Sims); Market Access & Public Health Netherlands (Els Devriese); Market Access (Debbie Drane (Inherited)); Market Access France (Alice MATHERON); Market Access France (Franck Puget (Inherited)); Market Access GE/AT/Emerg. Europe (Dirk Hoheisel (Inherited)); Market Access GE/AT/Emerg. Europe (Ilona Krug); Market Access Italy (Lara Pippo); Market Access Russia & CIS (Batyrkhan Kuatov); Market Access Strategy (Robert Rouse); Market Access and Commercial Strategy (Ling Yang ????); Market Access and Public Affairs (Jonathan Galduf Cabanas); Market Access and Public Affairs (Jose Luis Moreno Sanchez); Market Access and Public Affairs (Sandra Santos); Market Research (Nathan Barrall); Market Research (Venkatesh Ramakrishnan (Inherited)); Marketing & Medical Affairs Interconti. (Thomas Hauck); Marketing (Brian McMaster); Marketing (Dariusz Holdys); Marketing (Elena Glukhova); Marketing (Michael Chen ?????); Marketing (Philippe Hebert (Inherited)); Marketing (Robert Mitchell); Marketing (Scott Newkirk); Marketing (Thomas Offergeld); Marketing Belgium (Marijke Maes); Marketing Benelux (Erwin Franken); Marketing Benelux (George Peters); Marketing Benelux (Patrick Reygaert); Marketing Benelux (Stefaan Schatteman [C]); Marketing Coagulation (Marino Bertapelle); Marketing Coagulation (Sharad Agrawal); Marketing Communication (Anastasia Walsh); Marketing Department (Marianna Konstantinidi (Inherited)); Marketing Division (Jean-Marc Morange (Inherited)); Marketing Division (Kyota Yamaoka - ); Marketing Division (Kyota Yamaoka ??? ?? - ???? ?????); Marketing Division Congress Group (Kyota Yamaoka - (Inherited)); Marketing Division Congress Group (Kyota Yamaoka ??? ?? - ???? ????? (Inherited)); Marketing Division Critical Care and Acquired Bleeding (Shunsuke Kuwata - ); Marketing Division Critical Care and Acquired Bleeding (Shunsuke Kuwata ??? ?? - ??? ??????); Marketing Division Hemophilia Group (Makoto Kubo); Marketing Division Hemophilia Group (Sho Sakuma ???? ? - ??? ????); Marketing Division Immunology & Rare Disease Group (Shinichiro Magome); Marketing Division SID Group (Jun Ishiwa - ); Marketing Division SID Group (Jun Ishiwa ??? ? - ??? ????); Marketing France (Benjamin BISMUTH); Marketing France (Pascale Ogel Le Guen); Marketing Franchise (marco odelli); Marketing Greece (Marianna Konstantinidi (Inherited)); Marketing In-Licensing Director (James Kretsis); Marketing Intercontinental (Timothy Akroyd); Marketing Italy (Alessandro Vasco); Marketing Italy (Giorgio Lippi); Marketing Manager (Andrew Barker); Marketing Manager (Natasha Rees); Marketing Manager (New Influenza Products) (Belinda Anderson); Marketing Manager 745 (Belinda Anderson); Marketing Manager 745 (Gina Kladis); Marketing Manager 745 (Helen Concilia (Inherited)); Marketing Nordic (Petter Olbe); Marketing Portugal (David Ventura); Marketing Product (Rebecca Turner); Marketing Product Administration (Edward Potter); Marketing Program (Michael Chen ); Marketing Program (Michael Chen ?????); Marketing Schweiz (Beatrice Guldimann); Marketing Schweiz (Christoph Schneider); Marketing Spain (Aurea Xumetra); Marketing Specialty Products (Jan Hoesche); Marketing UK (Amandine Faguer); Marketing UK (Eddie Owens (Inherited)); Marketing, China Com Ops (Claire Tang ); Marketing, China Com Ops (Claire Tang ?????); Marketing, Medical Affairs & Market Access Interconti. (Thomas Hauck); Mass Spec Research (Victor Nesati); Master Data & Country Specific (Joachim Leiss); Master Data & Country Specific (Julian Knabeschuh); Master Data (Bruce C Beatty); Master Data (Gilbert Kilchoer); Master Data Management ES (Roland Burkhard); Master Data Management Finished Product (Luana Gauer); Master Data Technology (James G Kirby); Master Data, Maintenance & Development (Julia Schimansky); Master Production Planner - Privigen (Kylie Cramer); Materials Life Cycle Management (Jennifer Chung); Materials Management (Steven E Putlack); McAllen 258 (Ben Samarripas (Inherited)); McAllen 258 (Carlos Floyd); McAllen 258 ACM Area 1 (Felipe Gonzalez); McAllen 258 ACM Area 1 (Marc Garcia); McAllen 258 ACM Area 2 (Monica Contreras); McAllen 258 QA (Esperanza Pina); McKeesport 192 (Miekal Brown); McKeesport 192 (Steven Warheit); McKeesport 192 ACM Area 1 (Aaron Bova); McKeesport 192 ACM Area 2 (Caroline Hoyer); McKeesport 192 QA (Daniel Sullenberger); McKeesport 192 QA (Katherine Parker); Mckinney 276 (Sheneka E Wilson); Mckinney 276 ACM Area 1 (Charles E Baxter IV); Mckinney 276 ACM Area 2 (Andrew Fluharty); Mckinney 276 QA (Roxann L Sandoval); Mech Main Engineer 253 (Desmond Lobo); Mechanic (Thomas Baumann); Mechanical Maintenance (Daniel Hofmann); Mechanical Maintenance (Stefan Schmid); Mechanicsburg 171 (Bernard Thompson); Mechanicsburg 171 (John L Thixton (Inherited)); Mechanicsburg 171 (Michele Purvines-Honzo (Inherited)); Mechanicsburg 171 (Olivia Chung); Mechanicsburg 171 ACM Area 1 (Theodore Rooks); Mechanicsburg 171 ACM Area 2 (Michael Crosby); Mechanicsburg 171 QA (Cyle Starner-Moore); Mechanicsburg 171 QA (Kellie N Buecker); Mechanicsburg 171 QA (Kimm Klisiewicz); Mechatroniker (Doris Nake (Inherited)); Medford 037 (Jane Herrera); Medford 037 ACM Area 1 (Hattie E Johnston); Medford 037 ACM Area 2 (Denise Scarborough); Medford 037 ACM Area 2 (Katrina D Walls); Medford 037 QA (Richard W Smith); Medical (Christina Berchtold); Medical Affair (Claire Morgan); Medical Affairs (David Crump); Medical Affairs (Giulio Barrese); Medical Affairs (Gunnar Philipp); Medical Affairs (Manzhou Hou ); Medical Affairs (Manzhou Hou ?????); Medical Affairs (Michael Haslauer); Medical Affairs (Navin Singh); Medical Affairs (Robert Chan); Medical Affairs (Sebastian Dinatale); Medical Affairs Belgium (Anne Verheyen (Inherited)); Medical Affairs Benelux (Anne Verheyen); Medical Affairs Division (Robert Chan); Medical Affairs Division Hematology and Thrombosis Group (Yasuhiro Terano ??? ?? - ??? ?????); Medical Affairs Division Hemophilia Group (Motohiro Okayasu - ); Medical Affairs Division Immunology & Rare Disease Group (Er Win Hew); Medical Affairs Division Medical Excellence and Operations (Kenji Suwa - ); Medical Affairs Division Medical Excellence and Operations (Kenji Suwa ??? ?? - ?? ????); Medical Affairs Division SID Group (Hiromi Igari ??? ?? - ??? ????); Medical Affairs EU (Damian Gilkerson (Inherited)); Medical Affairs EU (Patrick Sommerer); Medical Affairs France (Jamila Filipecki); Medical Affairs France (Nabil Moumane); Medical Affairs Germany (Paolo Bajcic); Medical Affairs Germany (Patrick Sommerer); Medical Affairs Greece (Evi Baimpou); Medical Affairs Italy (Learco Mottola); Medical Affairs Netherlands (Anne Verheyen (Inherited)); Medical Affairs Nordic (Martin Tenlen (Inherited)); Medical Affairs Nordic (Michael Gr√∂vdal); Medical Affairs Nordic (Stefan Grass); Medical Affairs Project Management (Diane Bracquart); Medical Affairs Russia (Evgeny Rudenko); Medical Affairs Russia (Maria A Lituchaya (Inherited)); Medical Affairs Spain (Jos Aznar-Salatti); Medical Affairs Spain (Jos√© Aznar-Salatti); Medical Affairs Specialty Products (Thomas Machnig); Medical Affairs UK (Alessandro Dos Santos); Medical Affairs UK (Jo Heaton); Medical Affairs of Greater China (Helen Dai ); Medical Affairs of Greater China (Helen Dai ????); Medical Affairs, Americas (Ashesh Gandhi); Medical Affairs, Canada (Ashesh Gandhi (Inherited)); Medical Affairs, Europe (Sankarasubramanian Rajaram); Medical Affairs, Influenza (Karita Ambrose); Medical Affairs, Rapivab (Ashesh Gandhi (Inherited)); Medical Communications, US (Nancy Dougherty); Medical Department Turkey (Hasan Avcu); Medical Excellence and Operations (Mitsuhiro Kuwahara); Medical Excellence and Operations (Robert Chan (Inherited)); Medical Hemophilia Group (Takeo Hirai ??? ?? - ??? ????); Medical Manager (Andrea McCracken); Medical Manager (Anthony Gargano); Medical Manager (Arturo Lopez Larios); Medical Manager (Claire Morgan); Medical Manager (DEBRA BOURKE); Medical Manager (Debra Bourke); Medical Manager (Jane Wheeler); Medical Manager (Julianne Bayliss); Medical Manager (Luis Aversa); Medical Manager 842 (Jane Leong); Medical Manager 842 (MAUREEN THAM); Medical Operations US 2 (Jeanie Chiu); Medical Operations US 3 (Jennifer Hanes); Medical Operations US 3 (John Nelson); Medical Science Liaison Canada (James Mansi); Medical Scientific Liaison (Joana Rodrigues); Medical Scientific Liaison Spain (Jenny Alvarez Nieto); Medical Services (Anna Burek); Medical Unit Medical Information (Ana Claudia Guersoni); Medical Unit ¬ñ Medical Information (Ana Claudia Guersoni); Medical Writing Quality and Publishing (Nerrie Lam); Medical Writing Therapeutic Area Lead (Ellen Krasutsky); Medical Writing (Amy Walton); Medical Writing (Bob Stumpo (Inherited)); Medical Writing (Bob Stumpo); Medical Writing (Midori Kobayashi); Medical Writing (Narelle Bramich (Inherited)); Medical Writing (Narelle Bramich); Medical Writing (Takashi Fukai ??? ?? - ??? ????); Medical Writing (Thomas Verish); Medical Writing - Therapeutic Area Lead (Daniel Wood); Medical Writing - Therapeutic Area Lead (Wolfgang Thielen); Medical Writing ¬ñ Quality and Publishing (Nerrie Lam); Medical Writing ¬ñ Therapeutic Area Lead (Ellen Krasutsky); Medical and Business Support (Antoinette Mangione); Medical and Quality Greater China (Spring Wang); Melrose Park 453 (Jesus A Castillo (Inherited)); Melrose Park 453 (Niki Wells); Melrose Park 453 (Tangerine Tingle); Melrose Park 453 ACM Area 1 (Tangerine Tingle (Inherited)); Melrose Park 453 ACM Area 1 (Tangerine Tingle); Melrose Park 453 ACM Area 2 (Tangerine Tingle (Inherited)); Melrose Park 453 ACM Area 2 (Tangerine Tingle); Melrose Park 453 QA (Andrea Bohnenberger); Melrose Park 453 QA (Kimberly L Strong-Allen (On Leave)); Melrose Park 453 QA (Kimberly L Strong-Allen); Memphis 052 (Christopher Morgan); Memphis 052 (Dorleans Alce); Memphis 052 (Trina Crayton); Memphis 052 ACM Area 1 (Dorleans Alce); Memphis 052 ACM Area 1 (Keoshia N Franklin); Memphis 052 ACM Area 2 (Laundray Carter); Memphis 052 QA (Brooke McKinney); Memphis 052 QA (Jason S Hicks); Mesquite 085 (Amber Robinson); Mesquite 085 (Brenda C Greenfield (Inherited)); Mesquite 085 (Brenda C Greenfield); Mesquite 085 ACM Area 1 (Valinda M Peters); Mesquite 085 ACM Area 2 (Christy Pagel); Mesquite 085 QA (Martin DelAngel); Method Development & Instruments (David Canen); Method Development & Instruments (Todd Canen); Method Development (Tom Barnes); Method Development Group (Anna Rozhkova); Method Development Group (Petra Sebastian); Metrics & Analytics (Christian Spuckti); Metrology (Aur√©lien H√©mon); Metrology (Salvatore DiRusso); Metrology (Union) (Jose Franceschini Mirabal (Inherited)); Metrology (Union) (Michael Memenga (Inherited)); Mgr QC Chemistry (Melissa Damino); Mgr QC Chemistry (Ying Huang); Mgr- QC Immunology (Justine Jaap); Mgr- QC Immunology (Melissa Damino); Mgr-Validation Operations (Nick Morgan); Miami 206 (Ashley Britt); Miami 206 (Ashley Downs); Miami 206 (Yennifer Fernandez); Miami 206 ACM Area 1 (Troy Davidson); Miami 206 ACM Area 2 (Barbara May); Miami 206 QA (Anitha Janardhanan); Miami 206 QA (Aris Herrera); Microbiological QC (Ivana Heckel); Microbiological QC (Nicola Di Maiuta); Microbiology (Sarah Krueger); Microbiology (Torsten Vogt); Microbiology - Enviromental Monitoring (Alison Conroy); Microbiology - Lab (Stacey Wenzel); Microbiology - Lab I (Stacey Wenzel); Microbiology - Utilities (Joshua Deabel); Microbiology - Utilities (Matthew Pocius); Microbiology 1 (MB1) (Silke Litzinger); Microbiology 2 (MB2) (Constanta Ola); Microbiology 2 (MB2) (Morten Ruch); Microbiology Lab (Annett Milling); Microbiology Lab (Breanna Steins); Microbiology Lab 1 (Breanna Steins); Microbiology Validation (Emily Neylon (Inherited)); Microbiology Validation (Natalie Gaffney); Middle East & Africa (EMEA) (Camilla Shen); Middle East & Africa (EMEA) (Mohammed Haggag); Midvale 273 (Joel Gallegos); Midvale 273 (Nicole M Loncon (Inherited)); Midvale 273 ACM Area 1 (Jason Stevens); Midvale 273 ACM Area 2 (Casey Davis); Midvale 273 QA (Madison Reid); Mikrobiology 3 (MB3) (Stephanie Achebach); Minneapolis 414 (Deepesh M Pillai); Minneapolis 414 ACM Area 1 (Abubeker M Osman); Minneapolis 414 ACM Area 2 (Ahmed N Ismail); Minneapolis 414 QA (Diego A Bastidas); Minneapolis 414 QA (Pauline M Pipho); Miramar 214 (Jessica Collins); Miramar 214 (Mary A Paul (Inherited)); Miramar 214 (Tyneka Rene); Miramar 214 ACM Area 1 (Chanique Young); Miramar 214 ACM Area 2 (GUILLERMO ORTIZ); Miramar 214 ACM Area 2 (Sang Nguyen); Miramar 214 QA (Azia Alston); Mishawaka 249 (Marisa Nyikos); Mishawaka 249 (Olivia Arend); Mishawaka 249 ACM Area 1 (Kanesha Young); Mishawaka 249 ACM Area 1 (Sydney Boyle); Mishawaka 249 ACM Area 2 (Lucette Gamble); Mishawaka 249 QA (Leah Lehtomaki); Mitarbeiter Serologisches Labor (Astrid Mather (Inherited)); Mobile 284 (Wesley Stokes); Mobile 284 ACM Area 1 (Doris Osobase); Mobile 284 ACM Area 2 (Demitrius Douglas); Mobile 284 QA (Egypt N Ali); Modul 2 - Team 2 (Marko Elias); Modul 2 - Team 2 (mit Aslantas); Modul 2 - Team 2 (√úmit Aslantas); Modul 2 - Team 3 (Timo Gr√ºn); Modul 2 - Team 4 (Maik Czyrzewski); Monitoring (Arno Karnholz); Monitoring (Roland Portmann); Monitoring - Auswertung (Dominik Mueller); Monitoring Operations / Sampling (Martin Hofer); Montgomery 105 (Trinity J Bell); Montgomery 105 (Trinity J Gamble); Montgomery 105 ACM Area 1 (Shauna M Runk); Montgomery 105 ACM Area 2 (Robyn English); Montgomery 105 QA (Tiffany D Sherman (Inherited)); Montgomery 105 QA (Whitney C Belser); Montgomery 125 (Cathrine M Shimek); Montgomery 125 ACM Area 1 (Cathrine M Shimek); Montgomery 125 ACM Area 1 (Mark Sanders); Montgomery 125 ACM Area 2 (Cathrine M Shimek (Inherited)); Montgomery 125 ACM Area 2 (Monica Miller); Montgomery 125 QA (Chrystal D Carrillo); Montgomery 125 QA (Kimberly J Sanders); Montgomery 198 (Cory Toellner (Inherited)); Montgomery 198 (Gregory Jacobs); Montgomery 198 (Justin N Gronbach); Montgomery 198 ACM Area 1 (Timike Sheehy); Montgomery 198 ACM Area 2 (Sarah Peet); Montgomery 198 QA (Christine C Le); Montgomery 198 QA (Michael Hoedle); Motive Power (Nate Thomas); Motive Power (Union) (David G Mollema (Inherited)); Motive Power (Union) (Nate Thomas (Inherited)); Motive Power (Union) 1 (Nate Thomas (Inherited)); Mt Clemens 261 (Tiffany D Peters); Mt Clemens 261 ACM Area 1 (Lavon Williams); Mt Clemens 261 ACM Area 2 (Michelle S Gibbs); Mt Clemens QA 261 (Melissa Johnson); Muncie 191 (John W Wheelock); Muncie 191 (Rob Garcia (On Leave)); Muncie 191 (Rob Garcia); Muncie 191 ACM Area 1 (Andy Umberger); Muncie 191 ACM Area 2 (Andrea S Young); Muncie 191 QA (Drewleigha B Sarver (Inherited)); Muncie 191 QA (Mary Stegall); Muncie 191 QA (Megan M Sheets); Murfreesboro 128 (Elisabeth Johnson); Murfreesboro 128 (Elisabeth Radigan); Murfreesboro 128 (Nedra N Braden); Murfreesboro 128 ACM Area 1 (Ron Rury); Murfreesboro 128 ACM Area 2 (Samantha Holmes); Murfreesboro 128 QA (Melanie J Carmack); Murfreesboro 128 QA (Michelle Young); Mustang 243 (Sam P Emrich); Mustang 243 ACM Area 1 (Jeff Saylors); Mustang 243 ACM Area 2 (Claire Joyce); Mustang 243 QA (Bill Crye); Mustang 243 QA (Fay Michelsen); N. Charleston 291 (Donte Lazarus); N. Charleston 291 ACM Area 2 (Nichole Bell); N. Charleston 291 QA (Sharon Williams); NA CommOps & Patient Services (Mike Andrews); NA Medical Affairs Operations (Sindhu Pampati); NA Therapeutic Area Lead (Coagulation) (Emmanuel Gutierrez); NA Therapeutic Area Lead (Coagulation) (Monica Richardson); NAT Lab (Kevin N Elliott); NAT Lab (Ricky R Alexander (Inherited)); NAT Lab (Ricky R Alexander); Nampa 505 (David Ensminger (Inherited)); National Accounts Sales-Summit (Mark Faulkner); National City 297 (GABRIEL MACEDO); National City 297 QA (Jessie Aquino); National Hospital Manager (Christine Folland); National Management (Denis Fedorov); National Sales (Toshio Nagata); Nebenanlagen (Andr Wermuth); Nebenanlagen (Andr√© Wermuth); Network Services (Christopher Frank); Network Services (David Mann); Network Services (Don Konemann (Inherited)); Network Services ASIAPAC (Mahesh Narayanan); Network Services Americas (Raj Selvaraj); Network Services II (Partha SARATHY); Neurology Marketing (Jason Reckner); New Center Expansion (John Brennan); New Center Launch (Amon G Samples); New Center Launch 1 (Ian Biehler); New Center Launch 2 (Nicole L Ledbetter); New Center Support 1 (Lindsay K Jameson (On Leave)); New Center Support 1 (Lindsey Jameson); New Center Support 1 (Rey Vargas); New Center Support 1 (Rob Soeun); New Center Support 1.1 (Valerie L Ward); New Center Support 1.2 (Rey Vargas); New Center Support 2 (Amy L Guardiola); New Center Support 2 (Anthony Rheuark); New Center Support 2 (Becca Thomas); New Center Support 2 (Billy R Poole); New Center Support 2 (Marissa Sunanon-Clements); New Center Support 2 .1 (Amy L Guardiola); New Center Support 2.2 (Marissa C Sunanon); New Center Support 2.4 (Becca Thomas); New Hope 163 (Jason L Kelley); New Hope 163 ACM Area 1 (DiJon Jones); New Hope 163 ACM Area 2 (Daniel D Rogge); New Hope 163 QA (Holly S Wahlberg); New Hope 163 QA (Holt Peterson (Inherited)); New Hope 163 QA (Kayla L Stueber); Newark 213 (Angela Bordelon); Newark 213 ACM Area 1 (Stephanie Morrison); Newark 213 ACM Area 2 (Angela Mancinelli); Newark 213 ACM Area 2 (Steve H Sison); Newark 213 QA (Christopher Madden); Newark 213 QA (Taylor Thomas); Niagara Falls 237 (Kimberly Reimer); Niagara Falls 237 ACM Area 1 (Paul Hedley); Niagara Falls 237 ACM Area 2 (Mary Jo Watt); Niagara Falls 237 QA (Wesley Summers); Nogales 108 (April Behnke); Nogales 108 (Brooke S Angulo); Nogales 108 ACM Area 1 (Jorge U Orozco); Nogales 108 ACM Area 2 (Rosa G Martinez); Nogales 108 ACM Area 3 (Guadalupe Ochoa (On Leave)); Nogales 108 ACM Area 3 (Rosa G Martinez); Nogales 108 QA (Cori J Collins (Inherited)); Nogales 108 QA (Martha E Lundberg); Non IVV Bact, Vir, Ster, Env Monitoring (Fenny Ng); Non IVV Potency (Keiran McLeod); Non IVV, Chemistry, Biochem, Immulab (Niki Soteriadis); Non-Process Projects (Jennifer Mastio); Norfolk 513 (Katanya Hall); Norfolk 513 QA (James Foreman); Normal 292 (Jose Patino); Normal 292 (Michael W Solomon (Inherited)); Normal 292 ACM Area 1 (William Molitor); Normal 292 ACM Area 2 (Lana Shepherd); Normal 292 QA (Jennifer Harris); Norman 020 (Laura A Post); Norman 020 (Troy Lee Wheeler); Norman 020 ACM Area 1 (Nicole Bertram); Norman 020 ACM Area 2 (Amanda Doan); Norman 020 QA (Katy L Reynolds); North American Benefits (US & Canada) (Matthew Arscott (On Leave)); North American Benefits (US & Canada) (Matthew Arscott); NorthGlenn 141 (Anna M Coulbourne); NorthGlenn 141 (Becca Charles); NorthGlenn 141 (Daniel Venn (Inherited)); NorthGlenn 141 QA (Ashley R Sewell); Northglenn 141 ACM Area 1 (Jonathan Walling); Northglenn 141 ACM Area 2 (Carlos M Valenzuela); O''Fallon 224 (Lori Carlson (Inherited)); O''Fallon 224 (Tara R Spates Tucker); O''Fallon 224 ACM Area 1 (Jahleia Chieves); O''Fallon 224 ACM Area 2 (Andrea M Catchup); O''Fallon 224 QA (Lori Carlson (Inherited)); O''Fallon 224 QA (Marijo Monroe); O''Fallon 224 QA (Tori Chancellor); OE/BPM LVP (Cheryl King); OE/BPM LVP (Fabrice Gribon (Inherited)); OE/BPM LVP (Stephen Marlow (Inherited)); OF EU (incl. TR) (Sabine H√§mel (Inherited)); OF ROW (incl. ICO) (Anna-Karina Muth); OSI (Jennifer Krupka); OSII (Michael Moses); OSIII (Wilfried Wormsb√§cher); OSIV (Tina W√ºrfel); OTO Programs (Paul Bidez); Oak Park 041 (Latosha Y Floyd (Inherited)); Oak Park 041 (Sherlene Killebrew); Oak Park 041 ACM Area 1 (Sandra Erdman); Oak Park 041 ACM Area 2 (Patrick J Tribble); Oak Park 041 QA (Jessica J Cobey (On Leave)); Oak Park 041 QA (Jessica J Cobey); Ocala 290 (Althea Council); Ocala 290 QA (Jean O''Neal); Oklahoma City 422 (Johnnie K Phares); Oklahoma City 422 ACM Area 1 (Clement C Uzoma); Oklahoma City 422 ACM Area 2 (Ella L Boyd); Oklahoma City 422 QA (Billie E Gilliland); Oklahoma City 422 QA (Hannah E Todroff); Olympia 517 (Trevor Case); Omaha 421 (Christie G Edmisten); Omaha 421 ACM Area 1 (Kristen A Marteny); Omaha 421 ACM Area 2 (Sachin Bhandari); Omaha 421 QA (Christopher Trindle); Omaha 421 QA (Larinda N Johnson); Open Systems (Juerg Clavadetscher (Inherited)); Open Systems (Kapil Taneja); Operational Business Development 1 (Amon G Samples); Operational Business Development 2 (Nicole L Ledbetter); Operational Business Development 9.2 (Laura A Allen); Operational Excellence & Data Analytics (Jason Woolley); Operational Excellence (Claus Peihs (Inherited)); Operational Excellence (Gil Rochat); Operational Excellence (Jan-Christopher Gerlach); Operational Excellence (Jewel Reid); Operational Excellence (Monika Goretzki); Operational Excellence (Murat Dalar (Inherited)); Operational Excellence (Philipp Jeker); Operational Excellence LVP (Gordon Pearson); Operational Prozess 1 (Arnd Vollmerhausen); Operational Readiness Phoenix (Rainer Frank); Operational Services (Clare Schwarz); Operational Services Maintenance & Utilities (Michael Kocher); Operational Support (Laura A Allen); Operations & PV Systems (Shinya Takagawa); Operations (Camila Silva Alvarado); Operations Global Engineering (Gregory Taylor); Operations Global Engineering Projects (Daniel Rouse); Operations Global Sourcing (Trevor Reay); Operations HS Business Integration (Thomas Jede); Operations HS EHSS & Risk (Allan Wise); Operations HS EHSS & Risk (Bob Rezek); Operations HS EHSS & Risk (Lynette Hodgden (Inherited)); Operations HS EHSS & Risk (Lynette Hodgden); Operations HS Engineering (Daniel Rouse); Operations HS Engineering (Gregory Taylor (Inherited)); Operations HS Engineering (Rodney Lam); Operations HS Engineering (Tom Gehrin); Operations HS Engineering Automation (Charles Guy Sorrell Jr.); Operations HS Engineering Automation (James Dion); Operations HS Engineering Fill Finish Equipment (Jeffrey Stowe); Operations HS Engineering Process (James Flockhart); Operations HS Engineering Process (Jason VanderPloeg); Operations HS Engineering Process (Jeffrey Stowe); Operations HS Engineering Project (Daniel Rouse); Operations HS Engineering Project (Eddie Taylor); Operations HS MS&T Process Sciences (Nicholas Mauro); Operations HS MS&T Process Sciences (Richard Hughes); Operations HS MS&T Tech Services (Jason Allaband); Operations HS MS&T Tech Services (Nicholas Mauro); Operations HS MS&T Tech Services Fill/Finish (Kevin McMahon); Operations HS MS&T Tech Transfer (Baron Fulk); Operations HS MS&T Tech Transfer (Tsu-shun Lee (Inherited)); Operations HS MS&T Tech Transfer (Wallace Brisson); Operations HS Maintenance (Jamie Blankenship); Operations HS Maintenance (Leon Montgomery); Operations HS Maintenance Facilities (Bruce A Buckoski); Operations HS Maintenance Facilities (Bruce Buckoski); Operations HS Maintenance Instrumentation (Jamie Blankenship); Operations HS Maintenance Metrology (Michael Mikolajczak); Operations HS Maintenance Process (Ricky Norris (On Leave)); Operations HS Maintenance Process (Ricky Norris); Operations HS Maintenance Support (Daniel Sarvis); Operations HS Maintenance Support (Richard Oliver); Operations HS Maintenance Utilities (Scott Curtis Menut); Operations HS Maintenance Utilities (Scott Menut); Operations HS Manufacturing (Irina Staxen); Operations HS Manufacturing Bulk (Chad M Salisbury (Inherited)); Operations HS Manufacturing Bulk (Eric Hoffman); Operations HS Manufacturing Bulk (Jonathan Kegerise); Operations HS Manufacturing Bulk Downstream (Eric P Hoffman); Operations HS Manufacturing Bulk Downstream (Gordon Dunsmore); Operations HS Manufacturing Bulk Downstream - A Shift (Joseph Chapman); Operations HS Manufacturing Bulk Downstream - B Shift (Evan Burke); Operations HS Manufacturing Bulk Downstream - B Shift (LaToya Jaqui McDuffie); Operations HS Manufacturing Bulk Downstream - C Shift (Joseph Chapman); Operations HS Manufacturing Bulk Downstream - C Shift (Samantha Heyer); Operations HS Manufacturing Bulk Downstream - D Shift (Demitra Earls); Operations HS Manufacturing Bulk Downstream - D Shift (Evan Burke); Operations HS Manufacturing Bulk Support (Elie Chiha); Operations HS Manufacturing Bulk Support - A Shift (Craig Steimle); Operations HS Manufacturing Bulk Support - B Shift (Stephen Blair Donaldson); Operations HS Manufacturing Bulk Support - B Shift (Stephen Donaldson); Operations HS Manufacturing Bulk Support - C Shift (Jonathan Adams); Operations HS Manufacturing Bulk Support - D Shift (Kevin Anthony Smith); Operations HS Manufacturing Bulk Support - D Shift (Kevin Smith); Operations HS Manufacturing Bulk Support Materials (Andrew Passarotti); Operations HS Manufacturing Bulk Support Materials (Elie Chiha (Inherited)); Operations HS Manufacturing Bulk Support Materials (George Barrett); Operations HS Manufacturing Bulk Upstream (Gordon Dunsmore); Operations HS Manufacturing Bulk Upstream (Gordon Kennedy Dunsmore); Operations HS Manufacturing Bulk Upstream (Jeremy Smock); Operations HS Manufacturing Bulk Upstream - A Shift (Billy Trask); Operations HS Manufacturing Bulk Upstream - A Shift (Jeremy Smock); Operations HS Manufacturing Bulk Upstream - B Shift (Chris Austin); Operations HS Manufacturing Bulk Upstream - B Shift (Latisha Blair Tucker Kiker); Operations HS Manufacturing Bulk Upstream - C Shift (Chris Austin); Operations HS Manufacturing Bulk Upstream - C Shift (Maxwell Pote); Operations HS Manufacturing Bulk Upstream - D Shift (Jeremy Smock (Inherited)); Operations HS Manufacturing Bulk Upstream - D Shift (Kevin Donnell Thomas); Operations HS Manufacturing Bulk Upstream - D Shift (Kevin Thomas); Operations HS Manufacturing Fill & Finish (Philip Troughton); Operations HS Manufacturing Fill & Finish (Rodney Lam); Operations HS Manufacturing Fill & Finish - A Shift (Aseptic) (LaToya McDuffie); Operations HS Manufacturing Fill & Finish - A Shift (JOSE SERRANO); Operations HS Manufacturing Fill & Finish - A Shift (Jose Serrano); Operations HS Manufacturing Fill & Finish - A Shift (Non-Aseptic) (Todd Brinkley); Operations HS Manufacturing Fill & Finish - B Shift (Aseptic) (Heather Johnson); Operations HS Manufacturing Fill & Finish - B Shift (Heather Johnson); Operations HS Manufacturing Fill & Finish - B Shift (Non-Aseptic) (Reginald Cox); Operations HS Manufacturing Fill & Finish - C Shift (Aseptic) (William Holder); Operations HS Manufacturing Fill & Finish - C Shift (Keith Bridges); Operations HS Manufacturing Fill & Finish - C Shift (Non-Aseptic) (Keith Bridges (On Leave)); Operations HS Manufacturing Fill & Finish - C Shift (Non-Aseptic) (Keith Bridges); Operations HS Manufacturing Fill & Finish - C Shift (Timothy Hampton); Operations HS Manufacturing Fill & Finish - D Shift (Aseptic) (Jamie Page); Operations HS Manufacturing Fill & Finish - D Shift (Branch Chandler Cannon); Operations HS Manufacturing Fill & Finish - D Shift (Non-Aseptic) (Ivan Morris); Operations HS Manufacturing Fill & Finish Expansion (Aseptic); Operations HS Manufacturing Fill & Finish Expansion (Aseptic) (Branch Cannon); Operations HS Manufacturing Fill & Finish Expansion (Non Aseptic) (Zachary Oakley); Operations HS Manufacturing Fill & Finish Expansion (Rodney Lam); Operations HS Manufacturing Fill & Finish Ops Aseptic (Brian Kennedy); Operations HS Manufacturing Fill & Finish Ops Non Aseptic (Steve Gaspar); Operations HS Manufacturing Fill & Finish Ops ¬ñ Aseptic (Brian Kennedy (On Leave)); Operations HS Manufacturing Fill & Finish Ops ¬ñ Non Aseptic (Steve Gaspar); Operations HS Manufacturing Finite Scheduling (Andrew Covington); Operations HS Manufacturing Finite Scheduling (David Tye); Operations HS Manufacturing Operational Excellence (Don Miller); Operations HS Manufacturing Production Systems (Angel Colucci); Operations HS Manufacturing Production Systems (Angel L Colucci); Operations HS Manufacturing Production Systems (Frederick Goerke); Operations HS Manufacturing Sciences & Technology (Baron Fulk); Operations HS Manufacturing Sciences & Technology (Irina Staxen (Inherited)); Operations HS Manufacturing Sciences & Technology (Jessica Mercer); Operations HS Manufacturing Sciences & Technology (Tsu-shun Lee); Operations HS Manufacturing Small Scale (Ashley Greeney); Operations HS Strategy, Alliance Management & PMO (John Anderson (Inherited)); Operations HS Strategy, Alliance Management & PMO (Raj Kapadia); Operations HS Strategy, Alliance Management & PMO (Vernon Horner); Operations HS Supply Chain & Strategy (Mayumi Buckoski); Operations HS Supply Chain (David Tye); Operations HS Supply Chain Planning (David Tye); Operations HS Supply Chain Warehouse (Nicholas Brown); Operations HS Supply Chain Warehouse (Willie Lam); Operations HS Supply Chain Warehouse - Manufacturing & TD (Christopher Stone); Operations HS Viral Pilot Plant (Claudia Johnson); Operations Holly Springs (John Anderson); Operations Lead ¬ñ Project Banksia (Lisa Lamb); Operations Liverpool (Laura O''Brien); Operations Planning Manager (Damien Nguyen); Operations Procurement (John Molyneux); Operations Procurement Operations (Donald Lacombe); Operations Procurement Operations (John Molyneux (Inherited)); Operations Procurement Operations (Michele Morris); Operations Support (Annette Feussner); Operations Support (Nicole Kay); Operations Support (Uwe Kalina) (Uwe Kalina); Operations Support R&D (Michele Himmelspach); Operative Master Data Management (Maike Pollaschek (Inherited)); Operative Master Data Management (Maike Pollaschek); Ops Capital Portfolio Management (Stefano Siviero); Ops Plasma Support (Walter Aebersold); Orange City 155 (ANNETTE NELSON); Orange City 155 (Faye-Lynn Deissinger); Orange City 155 ACM Area 1 (Nathan J Herchenroder); Orange City 155 ACM Area 2 (BRIAN LOFTUS); Orange City 155 ACM Area 2 (Jenna Smith); Orange City 155 QA (Christina M Kokoszka); Orange City 155 QA (Cindy Romero-Estrada); Orange City 155 QA (Kyle M Lehrke (Inherited)); Organisation / Koordination Diverse (Eva Herzog (Inherited)); Organization Transformation (Andrea Douglas); Organization Transformation (Tod Marks); Organizational Development (Kristen Krebs); Organizational Development (Rachel Day); Orlando 144 (Isabella Bishop); Orlando 144 ACM Area 1 (Ron Fischer); Orlando 144 ACM Area 2 (Trinica D Boyd); Orlando 144 QA (Brittany Woodward); Orlando 144 QA (DeQuandra Belton); Orlando 144 QA (Tiffany D Sherman (Inherited)); Orlando 511 (Jessica Collins); PABS (Uwe Kalina); PABS I (Helene Lang); PABS I (Sarah Plum); PABS I+II (Annette Feussner); PABS II (Christina Kober); PABS II (Maria Hauswald); PABS III (Aaron Hahn (On Leave)); PABS III (Aaron Hahn); PABS III (Stefan Baumeister); PACE (Christian Sonderegger) (53003164); PACE (Markus Decher); PACE APAC Deployment - Organisation and Change Management (Faye Papakalodoukas); PACE ATR (Andrew Croft (Inherited)); PACE ATR (Michael Kochanski); PACE ATR Payment Management (Dennis Martin); PACE Americas Deployment (Shane Kennedy); PACE Asia Pacific (Andrew Croft (Inherited)); PACE Asia Pacific (Metani Rooms); PACE Commercial Deployment (Peter K Tadros); PACE Coordination BRN (Boris Kaiser (Inherited)); PACE Coordination BRN (Christian Sonderegger); PACE ES (Marco Maeder); PACE General Accounting (Eric Fay); PACE Global Distribution (Catherine Gil); PACE Metrics & Analytics (Christian Spuckti); PACE OTC (Kian Hartono); PACE PM Bern (Oliver Bigler); PACE PMO (Tod Marks); PACE PMO (Tod Marks) (Tod Marks); PACE PTI (Wolfgang Schneider); PACE Program (Andrew Croft (Inherited)); PACE S2P (Andrew Croft (Inherited)); PACE S2P (Simon Haemmerli); PACE S2P (TR Kannan); PACE Site Deployment (Kelly L Konemann); PACE deployment Bern Lengnau (Boris Kaiser); PACE sustain (Linda Carducci (Inherited)); PACE sustain (Markus Decher); PAI Dokumentation (Andre Hullmann (Inherited)); PAI Dokumentation (Carsten Meyer (Inherited)); PAI Endfiltration Albumin (Achim Ludwig (Inherited)); PAI Endfiltration Albumin (Achim Ludwig); PAI Fermentation (Tobias Kling); PAI Koordination (Andre Hullmann (Inherited)); PAI Koordination (Bernd Prior (Inherited)); PAI Koordination (Carsten Meyer (Inherited)); PAI Nebenbetriebe (Mario Kornemann (Inherited)); PAI Pasteurisierung (Mario Kornemann (Inherited)); PAI Produktion 1 / Nebenanlagen (Mario Kornemann); PAI Produktion Albumin (Andre Hullmann); PAI Produktion Immunglobuline/ Nebenanl. (Bernd Prior); PAI Produktion PCF H67 (Roger Leukel); PAI Produktion Rekombinante Proteine (Andreas Berting); PAI Produktion Rekombinante Proteine (Carsten Meyer); PAI Prozessmanager (Barbara Kalina (Inherited)); PAI Prozessmanager (Wilfried Freudenberg (Inherited)); PAI Rekombinante Proteine GMP (Carsten Meyer (Inherited)); PAI Subfraktionierung (Mario Kornemann (Inherited)); PAI Systemuntersttzung SAP/MES (Wilfried Freudenberg (Inherited)); PAI Systemunterst√ºtzung SAP/MES (Barbara Kalina (Inherited)); PAI Systemunterst√ºtzung SAP/MES (Wilfried Freudenberg (Inherited)); PAI Training & GMP (Barbara Kalina (Inherited)); PAI Training & GMP (Wilfried Freudenberg (Inherited)); PAI Ultrafiltration / Endfiltration (Alfons Hck (Inherited)); PAI Ultrafiltration / Endfiltration (Alfons H√∂ck (Inherited)); PAI Ultrafiltration Albumin (Martin Doruch (Inherited)); PAI Ultrafiltration Albumin (Martin Doruch); PAI Vorbehandlung / Support (Hans Becker); PAI Vorbehandlung 1 / Support (Hans Becker (Inherited)); PAI Vorbehandlung 2 (Hans Becker (Inherited)); PAI Vorbehandlung 3 (Andreas Koch); PAI Wgekabine (Mario Kornemann (Inherited)); PAI W√§gekabine (Mario Kornemann (Inherited)); PBS Basisfraktionierung & Support (Stefan Vaupel); PBS Basisfraktionierung (Bernhard Tribensky); PBS Basisfraktionierung (Klaus Wilhelm); PBS Planung & Dokumentation (Claus Baudszus); PBS Schichtgruppe 1 (Mario Lorch); PBS Schichtgruppe 2 (Bjrn Klingelhfer); PBS Schichtgruppe 2 (Bj√∂rn Klingelh√∂fer); PBS Schichtgruppe 3 (Andreas Klein); PBS Schichtgruppe 4 (Andreas Kraus); PBS Schichtgruppe 5 (Bernd Hofmann); PBS Schichtgruppe 6 (Bernd Teske); PCS & MES (Frank Mastellone (Inherited)); PCS & MES (Magda Stavaroiu); PCS & MES (Magda-Elena Stavaroiu); PCS (Reto Kamber); PCS Maintenance (Markus Klsle); PCS Maintenance (Markus Kl√§sle); PCS Maintenance (Reto Camastral); PD Projects & Technology Transfer (Steven Honey); PE - Central Region (Gangjian Chen ); PE - Central Region (Gangjian Chen ?????); PE - Central Region 1 (Qin Li ); PE - Central Region 1 (Qin Li ????); PE - Central Region 2 (Gangjian Chen ????? (Inherited)); PE - Central Region 2 (Shu Min ); PE - Central Region 2 (Shu Min ????); PE - DTP, China (Cissy Xi ); PE - DTP, China (Cissy Xi ????); PE - East Region (Zhen Shen ); PE - East Region (Zhen Shen ????); PE - East Region 1 (Xiao Ma ); PE - East Region 1 (Xiao Ma ????); PE - East Region 2 (Guo Jie Yu ?????); PE - East Region 2 (Guojie Yu ); PE - East Region 2 (Guojie Yu ?????); PE - East Region 3 (Liang Xu ); PE - East Region 3 (Liang Xu ????); PE - North Region (David Chen ???? (Inherited)); PE - North Region (Zhixia Wang ); PE - North Region (Zhixia Wang ?????); PE - North Region 1 (Yajuan Wen ); PE - North Region 1 (Yajuan Wen ?????); PE - North Region 3 (Qinghua Zhao ?????); PE - North Region 4 (Hongbin Wang ?????); PE - North Region 4 (Tracy Yu ); PE - North Region 4 (Tracy Yu ?????); PE - South Region (Sam Shang ); PE - South Region (Sam Shang ?????); PE - South Region 1 (Tony Lee ); PE - South Region 1 (Tony Lee ?????); PE - South Region 2 (Ice Li ); PE - South Region 2 (Ice Li ?????); PE - South Region 3 (Yi-yu Zhang ); PE - South Region 3 (Yi-yu Zhang ?????); PE - South Region 4 (Michelle Li ); PE - South Region 4 (Michelle Li ?????); PE - South Region 5 (Gary Chen ); PE - South Region 5 (Gary Chen ?????); PE - West Region (Alex Kong ); PE - West Region (Alex Kong ????); PE - West Region (David Chen ???? (Inherited)); PE - West Region (Shengyan Qiu ?????); PE - West Region 1 (Hao Chen ); PE - West Region 1 (Hao Chen ????); PE - West Region 2 (Jack Liao ); PE - West Region 2 (Jack Liao ????); PE - West Region 3 (Shen Jie ); PE - West Region 3 (Shen Jie ????); PE - West Region 3 (Shengyan Qiu ????? (Inherited)); PE-Central Region 3 (Julia Zhu ); PE-Central Region 3 (Julia Zhu ????); PGI Bulkproduktion M1M2 (Julian Lampmann); PGI Bulkproduktion M1M2 (Sebastian Feisel); PGI Documentation (Patrick Brusius); PGI Koordination (Heiko Schild (Inherited)); PGI Produktion Beriate (Heiko Schild); PGP Bulkproduktion 1 FIX (Philipp Hergenrder); PGP Bulkproduktion 1 FIX (Philipp Hergenr√∂der); PGP Bulkproduktion 1 FIX (Steffen Mbius); PGP Bulkproduktion 1 FIX (Steffen M√∂bius); PGP Bulkproduktion 1 FVIII-B (Gerhard Burk (Inherited)); PGP Bulkproduktion 1 FVIII-B (Gerhard Burk); PGP Bulkproduktion 1 FVIII-H (Henrik Tutsch (Inherited)); PGP Bulkproduktion 1 FVIII-H (Peter Diehl (Inherited)); PGP Bulkproduktion 1 FVIII-H (Peter Diehl); PGP Bulkproduktion 2 FIX (Sebastian Feisel (Inherited)); PGP Bulkproduktion 2 FIX (Timo Mudersbach (Inherited)); PGP Bulkproduktion 2 FIX (Timo Mudersbach); PGP Bulkproduktion 2 FIX (Timo Mudersbach) (Timo Mudersbach); PGP Bulkproduktion 2 FVIII-B (Reiner Bamberger (Inherited)); PGP Bulkproduktion 2 FVIII-B (Reiner Bamberger); PGP Bulkproduktion 2 FVIII-H (Ernst Dittmar (Inherited)); PGP Bulkproduktion 2 FVIII-H (Ernst Dittmar); PGP Bulkproduktion 3 FVIII-B (Frank Burich (Inherited)); PGP Bulkproduktion 3 FVIII-B (Frank Burich); PGP Bulkproduktion 3 FVIII-B (Frank B√§urich (Inherited)); PGP Bulkproduktion 3 FVIII-B (Frank B√§urich); PGP Bulkproduktion 3 FVIII-H (Jrgen Ungemach (Inherited)); PGP Bulkproduktion 3 FVIII-H (Jrgen Ungemach); PGP Bulkproduktion 3 FVIII-H (J√ºrgen Ungemach (Inherited)); PGP Bulkproduktion 3 FVIII-H (J√ºrgen Ungemach); PGP Bulkproduktion 4 FIX (Steffen Mbius); PGP Bulkproduktion 4 FIX (Steffen M√∂bius); PGP Dokumentation (Patrick Brusius); PGP Koordination FIX (Karl-Heinz Wenz (Inherited)); PGP Koordination FIX (Karl-Heinz Wenz (On Leave) (Inherited)); PGP Koordination FVIII-B (Heiko Schild (Inherited)); PGP Modul 2 - Team 1 (Henning Dittmar); PGP Modul 2 - Team 2 (mit Aslantas (Inherited)); PGP Modul 2 - Team 2 (√úmit Aslantas (Inherited)); PGP Modul 2 - Team 3 (Timo Gr√ºn (Inherited)); PGP Produktion Beriate (Heiko Schild); PGP Produktion Faktor IX (Karl-Heinz Wenz (On Leave)); PGP Produktion Faktor IX (Karl-Heinz Wenz); PGP Produktion Haemate / Humate (Henrik Tutsch); PGP Produktion Haemate / Humate (Peter G√ºttner); PGP Prozessmanager (Barbara Kalina (Inherited)); PGP Prozessmanager (Horst Boeder (Inherited)); PGP Pufferherstellung FVIII-B (Bernd Grau (Inherited)); PGP Tagschicht FIX (Ewald Burk); PGP Tagschicht FIX (Timo Mudersbach); PGP Vorbehandlung FVIII-H (Sascha Ludwig (Inherited)); PGP Vorbehandlung FVIII-H (Sascha Ludwig); PIU (Alan Collins); PIU (Christine Fitzpatrick); PIU Team (Christine Riley); PIU/UM Engineering (Peter White); PL-Quality (Carmen Althainz); PL-Quality (Mehmet G√ºm√ºs); PM Hematology and Thrombosis TA (Joanne Uhl (Inherited)); PM Hematology and Thrombosis TA (Mark Kleinman); PMR Dokumentation (Wilfried Freudenberg (Inherited)); PMS (Hideo Usui - ); PMS (Hideo Usui ??? ?? - ??? ????); PMS (Masashi Nakayama); PNS (Ibtisam Saeed); PNS Manufacturing (Hosmer Perez); PPD / Technical Operations Marburg (Michael Moses); PPD Bern Admin (Eliane Bossart); PPD BioAnalytical Science (Patrick Schuetz); PPD CMC Bern (Philipp Angerer); PPD Impurity & Data Mngt (Patricia Lieby); PPD Investigations (Thomas Kilchoer); PPD Investigations 2 (Tino Boss); PPD Investigations I (Janine Bash); PPD Process Development - R&D (Hal Braley); PPD Process Development - R&D (Kathryn Scott); PPD Process Development - R&D (Yvette Citrine); PPD Process Development 2 (Ibrahim El Menyawi); PPD Process Development 2 Group 1 (Eva Blatter); PPD Process Development 2 Group 2 (Robin Das Gupta); PPD Process Development 2 Group 3 (Adrian Alder); PPD R & D Bioanalytics BMW (Mark Bailey); PPD R&D KOP (Kenneth Walsh); PPD R&D Marburg (Martin Vey); PPD Technical Operations (Birgit Unterweger); PPD Technical Operations (Michele Himmelspach); PPD, Process Development (Eric Zhu); PPM (Roberta Duncan (Inherited)); PPM Research (Heather Davis); PPM Technical (Heather Davis); PQG Look Back / PDI (Patricia Herrmann); PQG Plasma Control (Iryna Zabolotna); PRP Support (Heinz-J√ºrgen Merkel); PRP Vorbehandlung (Thorsten Theis); PRP GMP-Koordination (Heinz-Jrgen Merkel); PRP GMP-Koordination (Heinz-J√ºrgen Merkel (Inherited)); PRP GMP-Koordination (Heinz-J√ºrgen Merkel); PRP Logistik (Robert Schfer); PRP Logistik (Robert Sch√§fer); PRP Lsungsherstellung & Wiegebereich (Robert Schfer (Inherited)); PRP L√∂sungsherstellung & Wiegebereich (Robert Sch√§fer (Inherited)); PRP Support (Yanina Broadnax); PRP Support 1 (Steffen Ramb); PRP Vorbehandlung (Thorsten Theis (Inherited)); PRP Vorbehandlung (Thorsten Theis); PRP Vorbehandlung 1 (David Grb); PRP Vorbehandlung 1 (David Gr√§b); PRP Vorbehandlung 1 (Fabian Feisel); PRP Wareneingang (Evelin Kaiser-Felsmann); PRP Wareneingang (Yanina Broadnax); PRP Wareneingang Team 1 (Sebastian Siebert); PRP W√§gebereich (Heinz-J√ºrgen Merkel (Inherited)); PTC (Severin Thierau); PTH Abfllung 1 (Alexander Muth (Inherited)); PTH Abfllung 2 (Michael Kroker (Inherited)); PTH Abfllung 2 (Michael Kroker); PTH Abfllung 3 (Nils Rder); PTH Abfllung 4 (Bjrn Schmidt); PTH Abf√ºllung 1 (Pascal Nau (Inherited)); PTH Abf√ºllung 2 (Michael Kroker (Inherited)); PTH Abf√ºllung 1 (Lars Nau); PTH Abf√ºllung 1 (Pascal Nau (Inherited)); PTH Abf√ºllung 1 (Pascal Nau); PTH Abf√ºllung 2 (Michael Kroker (Inherited)); PTH Abf√ºllung 2 (Michael Kroker); PTH Abf√ºllung 3 (Alexander Jegel); PTH Abf√ºllung 3 (Rainer Lepper (Inherited)); PTH Abf√ºllung 3 (Rainer Lepper); PTH Abf√ºllung 4 (Bj√∂rn Schmidt); PTH Abf√ºllung 4 (Heiko Steinbach); PTH Albumin & Visual Inspection (Jrg Nickel); PTH Albumin & Visual Inspection (J√∂rg Nickel); PTH GMP Coordination (Matthias Klein (Inherited)); PTH GMP-Coordination (Jrg Nickel (Inherited)); PTH GMP-Coordination (J√∂rg Nickel (Inherited)); PTH Optische Kontrolle 1 H069 (Bernd Balzer (Inherited)); PTH Optische Kontrolle 1 H069 (Bernd Balzer); PTH Optische Kontrolle 2 H069 (J√∂rg Nickel (Inherited)); PTH Optische Kontrolle 2 H069 (Valentina Kufeld (Inherited)); PTH Optische Kontrolle 2 H069 (Valentina Kufeld); PTH Optische Kontrolle 3 H069 (Jrg Nickel (Inherited)); PTH Optische Kontrolle 3 H069 (J√∂rg Nickel (Inherited)); PTH Optische Kontrolle 3 H069 (Meike D√∂rbecker (Inherited)); PTH Optische Kontrolle 3 H069 (Meike D√∂rbecker); PTH Processmgr Pretreatment Refludan&Bul (Matthias Klein (Inherited)); PTH Servicefunktion (Sabine Fischer); PTH Teilfertigung H069 (Alexander Muth); PTH Teilfertigung H069 (Daniel Schneider); PTH Teilfertigung H069 (Tim Westphal); PTH Teilfertigung Koordination (Daniel Schneider (Inherited)); PTH Teilfertigung Koordination (Tim Westphal (Inherited)); PTH Vorbehandlung & Support (Peter Koch); PTH Vorbehandlung 3 / Abfllung 3 H069 (Uwe Fritsch); PTH Vorbehandlung 3 / Abf√ºllung 3 H069 (Uwe Fritsch); PTH Vorbehandlung&Support (Peter Koch (Inherited)); PTI EM Lead (Susan Clough); PTM Abfllung M305 (Tim Westphal); PTM Abf√ºllung M305 (J√∂rg Dieterich); PTM Abf√ºllung M305 (Tim Westphal); PTM Betriebsservicefunktion M305 (Jennifer Hilscher (Inherited)); PTM Betriebsservicefunktion M305 (Jennifer Hilscher); PTM Betriebsservicefunktion M305 (Reinhard Grn (Inherited)); PTM Betriebsservicefunktion M305 (Reinhard Grn); PTM Betriebsservicefunktion M305 (Reinhard Gr√ºn (Inherited)); PTM Betriebsservicefunktion M305 (Reinhard Gr√ºn); PTM GMP Koordinator (Esther Seidel (Inherited)); PTM GT-Anlage M305 (Peter Dersch (Inherited)); PTM Optische Kontrolle M305 (Alexandra G√ºnther (Inherited)); PTM Optische Kontrolle M305 (Elke Stauss (Inherited)); PTM Optische Kontrolle M305 (Elke Stauss); PTM Projekte / Technik (Esther Seidel (Inherited)); PTM Prozessmanager (Esther Seidel (Inherited)); PTM Teilfertigung M305 (Alexandra Gnther); PTM Teilfertigung M305 (Alexandra G√ºnther); PTM Visuelle Kontrolle (Julia Dworschak); PTM Vorbehandlung M305 (Eckhard Brickum (Inherited)); PV Agreements Lead (Andrea Kergl); PV Excellence and Compliance (Gina Granada); PV Quality Management Lead (Gina Granada); PV Safety (Tomoko Yanagawa); PWI Chromatographie & Fllung H68 (Dietmar Grebe); PWI Chromatographie & F√§llung H68 (Dietmar Grebe); PWI Faktor I / XIII Schichtgruppe 7 (Bj√∂rn Bartelme√ü); PWI Faktor I / XIII Schichtgruppe 7 (Horst Schneider); PWI Faktoren I & XIII (Jochen Khler); PWI Faktoren I & XIII (Jochen K√∂hler); PWI GMP-Koordination (Heinz-J√ºrgen Merkel (Inherited)); PWI Inhibitoren (Wilfried Happel); PWI Koordination (Jochen Khler (Inherited)); PWI Koordination (Jochen K√∂hler (Inherited)); PWI Koordination (Wilfried Happel (Inherited)); PWI Logistik (Robert Sch√§fer); PWI L√∂sungsherstellung & Wiegebereich (Robert Sch√§fer (Inherited)); PWI Regeneration & Vorbehandlung H68 (Marc Wellner); PWI Support (Heinz-J√ºrgen Merkel); PWI Tagdienst (Roger Ochs); PWI Teilbereich (Christoph Bernitt); PWI Training & GMP (Jochen Khler (Inherited)); PWI Training & GMP (Jochen K√∂hler (Inherited)); PWI Training & GMP (Wilfried Happel (Inherited)); PWI Vorbehandlung (Thorsten Theis (Inherited)); PWI Vorbehandlung (Thorsten Theis); PWI Wareneingang (Evelin Kaiser-Felsmann); PWI W√§gebereich (Heinz-J√ºrgen Merkel (Inherited)); PWI-H68-Schicht (Dietmar Grebe (Inherited)); PWI-H68-Schicht (Marc Wellner (Inherited)); PWI-H68-Tagdienst (Dietmar Grebe (Inherited)); PWI-H68-Tagdienst (Marc Wellner (Inherited)); PWI-H68-Tagdienst (Marc Wellner); PWI-M305 (Manuel Lotz); PWI-M305 Schicht 1 (Fabian Cybulski); PWI-M305 Schicht 2 (Florian Scherer (Inherited)); PWI-M305 Schicht 2 (Florian Scherer); PWI-M305 Schicht 3 (Fynn Krieger); PWI-M305 Tagdienst (Robert H√∂hne) (Robert H√∂hne); Packaging & Supply (Claus Peihs); Packaging & Supply (Helmut Robert Euler (Inherited)); Packaging & Supply (Viktor Krecker); Packaging & WHS (Armin Stcklin); Packaging & WHS (Stefan Kaelin); Packaging (Andrew Baxter); Packaging (Brian T White); Packaging (Bruno Baeriswyl); Packaging (Kate (Shortall) Lamont); Packaging (Kate Lamont); Packaging (Kate Shortall); Packaging (Othmar Geisser); Packaging (Pasquale Carestia (Inherited)); Packaging (Thomas Royal); Packaging (Union) (Brian T White); Packaging (Union) (Pasquale Carestia (Inherited)); Packaging (Union) (Thomas Royal); Packaging Day Shift 2/6 (Tanja Maegert); Packaging Day Shift 4/5 (Jelka Golob); Packaging Design (Josue Stoll); Packaging Development (Claude Morf); Packaging Development (Markus Maus); Packaging Diverse (Jrg Dieterich (Inherited)); Packaging Diverse (J√∂rg Dieterich (Inherited)); Packaging Evening Shift 1/3/6 (Shabbir Ahmad Sheikh); Packaging Evening Shift 2/4/5 (Nebojsa Milosevic); Packaging I (Pasquale Carestia (Inherited)); Packaging Line 1 (Daniel Fankhauser); Packaging Line 1 (Marianne Steuri); Packaging Line 2,3,7 (Jelka Golob); Packaging Line 4 (Nebojsa Milosevic); Packaging Line 5 (Bashkim Redzepi); Packaging Line 6 (Tanja Maegert); Packaging Materials Testing & Release (Dominik Corbet); Packaging Operations (Claus Peihs (Inherited)); Packaging Operations (Jrg Dieterich); Packaging Operations (J√∂rg Dieterich); Packaging Operations (Murat Dalar); Packaging Teams (Bernd Baum); Packaging and Inspection (David Hartley (Inherited)); Packaging and Inspection (Joey Tranquilino); Packaging, Design & Artwork (Metin Yilmaz); Packing Material Control PMC (Dominic Wuest); Packing Material Control PMC (Nicole Moser); Packing Material Control PMC 2 (Denise Engimann); Packing Team Leader (Adam Heath); Packing Team Leader (Beau Williams); Packing Team Leader (David Nguyen); Packing Team Leader 451 (Robert De Santis); Pain Business Unit Director (Michael Grant); Palm Bay 254 (Cari N Howard); Palm Bay 254 (Latora (LaLa) Boswell); Palm Bay 254 ACM Area 1 (John Fuller); Palm Bay 254 ACM Area 1 (KIARA CAGLE); Palm Bay 254 ACM Area 2 (Latora (LaLa) Boswell); Palm Bay 254 ACM Area 2 (Lori Leinas); Palm Bay 254 QA (Regine Jean Gilles (On Leave)); Palm Bay 254 QA (Regine Jean Gilles); Pandemic (Lorna Meldrum); Parenteral Manufacturing (AlbuRx Filling) (Daniel Derakhshanian); Parenteral Manufacturing (AlbuRx Filling) (Union) (Daniel Derakhshanian); Parenteral Manufacturing (AlbuRx) (Nick Bonavita); Parenteral Manufacturing (AlbuRx) (Union) (Nick Bonavita); Parenteral Manufacturing (Mindy Randazzo); Parenteral Manufacturing (Thomas Royal); Parenteral Manufacturing (Union) (Mindy Randazzo (Inherited)); Parenteral Manufacturing (Union) (Thomas Royal (Inherited)); Parkersburg 178 (Jeff Hay); Parkersburg 178 (Lachelle Mosholder); Parkersburg 178 (Lenora Lada); Parkersburg 178 ACM Area 1 (Alissa Sindelar); Parkersburg 178 ACM Area 1 (Lenora Lada); Parkersburg 178 ACM Area 2 (Lachelle Mosholder); Parkersburg 178 ACM Area 2 (Lenora Lada (Inherited)); Parkersburg 178 QA (Amanda M Cvitkovich); Parkersburg 178 QA (Christina Prunty); Parma Heights 162 (Olivia Arend); Parma Heights 162 (Sue Collins); Parma Heights 162 ACM Area 1 (Lindsy Wolf); Parma Heights 162 ACM Area 2 (Mirela Sekulic); Parma Heights 162 ACM Area 2 (Seanna Penn); Parma Heights 162 QA (Deborah Robinson); Paste & Final Product Planning (Martin Sutter); Patents and Licenses (Hans-Peter Hauser); Patient Engage & Feas (Rodney Winley); Pay Services (Brian T Simeur); Payroll DE (Claudia Rogge); Pensacola 623 (Nicole K Stassen); Pensacola 623 ACM Area 1 (Esteban Facundo); Pensacola 623 ACM Area 1 (Timothy J Nisewonger); Pensacola 623 ACM Area 2 (Esteban Facundo); Pensacola 623 ACM Area 2 (Timothy J Nisewonger); Pensacola 623 QA (Jessica L Ford (On Leave)); Pensacola 623 QA (Jessica L Ford); Pensacola 623 QA (Matthew T Zisa); Pensacola 623 QA (Melodee C Ebel (Inherited)); Peoria 133 (Mark A Yellen); Peoria 133 (Patrick S Taylor); Peoria 133 ACM Area 1 (DeAnn K Benally); Peoria 133 ACM Area 1 (Patrick S Taylor (Inherited)); Peoria 133 ACM Area 2 (Patrick S Taylor (Inherited)); Peoria 133 ACM Area 2 (Seanna Penn); Peoria 133 QA (LaVona M Holt); Peoria 289 (Dennis Popek); Peoria 289 (Nicholle DeVecchi); Peoria 289 ACM Area 1 (Holly Worsfold); Peoria 289 ACM Area 2 (Lew Carney); Peoria 289 QA (Kali Trevino); Performance Management (Ken Lain); Pharmaceutical Development (Martin Alex Imboden); Pharmacodynamic (Marc Nolte); Pharmacodynamic 1 (Padmapriya Ponnuswamy); Pharmacodynamic 2 (Subhajit Ghosh); Pharmacokinetic (Oliver Ghobrial); Pharmacokinetic (Sabine Pestel); Pharmacology & Toxicology (Eva Herzog); Pharmacometrics (Theresa Yuraszeck); Pharmacovigilance systems (Sahil Sahni); Pharmacovigllance Quality (Wumi McDowall); Pharmakanten (Carmen Walldorf (Inherited)); Pharmakanten (Doris Nake (Inherited)); Philadelphia 145 (Kristen Aydin); Philadelphia 145 (Rene Benson-Skone); Philadelphia 145 (Robert W Gillespie); Philadelphia 145 ACM Area 1 (Ken Laguerre); Philadelphia 145 ACM Area 2 (Kevin Lambrecht); Philadelphia 145 ACM Area 2 (Rene Benson-Skone (Inherited)); Philadelphia 145 QA (Kim Van Houten); Philadelphia 147 (Derek Morner); Philadelphia 147 (John L Thixton (Inherited)); Philadelphia 147 (Michele Dionne); Philadelphia 147 (Theresa Mwimbwa); Philadelphia 147 ACM Area 1 (Jennifer Foxworth); Philadelphia 147 ACM Area 1 (Robinrenee Dorsey); Philadelphia 147 ACM Area 2 (Robinrenee Dorsey); Philadelphia 147 ACM Area 2 (Rose Marie Waddle); Philadelphia 147 QA (Alissa Elke); Philadelphia 147 QA (John L Thixton (Inherited)); Philadelphia 147 QA (Leslie Jones); Philadelphia 147 QA (Samantha J Schrepel); Pilot Plan Manufacturing Team (Stefanie Ronzheimer); Pilot Plant (Christian Schlachtbauer); Pilot Plant (Jarvis Hammitt); Pilot Plant (Klaus-Jrgen Schlitt (Inherited)); Pilot Plant (Leander Trachsel); Pilot Plant (Norbert Egon Juettner); Pilot Plant Group (Lukas Sterchi); Pilot Plant II (Franziska Naef); Pilot Plant II (Lukasz Lubecki); Pilot Plant Lengnau (Joel Zumstein); Pilot Scale Operations (Chris Horridge); Pilot Scale Operations (Daniela Mocanu); Pilot Scale Operations (Heidi Bergman); Pilot Scale Operations (Jeffrey Bourke); Pilot Scale Operations (Maggie Aziz); Pilot Scale Operations (Mark Simmonds (Inherited)); Pilot Scale Operations (Mark Simmonds); Pilot Scale Operations (Paul Gibbs); Pilot Scale Operations (Rob Hooper); Pilot Scale Operations (Sharon Orr); Pilot Scale Operations (Tien Vo); Pilot Scale Operations (Tim Hanna); Pilot Scale Operations (Ursula Macaskill); Pilot Scale Operations 1 (Jessica McGiffin); Pinellas Park 139 (Brett Goldman); Pinellas Park 139 (Leah J Davis); Pinellas Park 139 (Robin G Spencer); Pinellas Park 139 ACM Area 1 (Alesia Davenport); Pinellas Park 139 ACM Area 1 (Lynn M Stratton); Pinellas Park 139 ACM Area 2 (Alesia Davenport); Pinellas Park 139 ACM Area 2 (Christina Goodrich); Pinellas Park 139 QA (Dana Pagano); Pinellas Park 139 QA (Lynn M Stratton); Pinnacle Training Site Las Vegas (Yennifer Fernandez); Pinnacle Training Site Pinellas Park (Lauren Hardy); Pittsburg 269 (Esence Hambrick); Pittsburg 269 ACM Area 1 (Dan Lassige); Pittsburg 269 ACM Area 2 (Tammy Toth); Pittsburg QA 269 (Marianne Brown); Pittsburgh 269 (Marianne Brown); Pittsburgh 269 ACM Area 1 (Dan Lassige); Pittsburgh 269 ACM Area 2 (Tammy Toth); Pittsburgh QA 269 (Melanie Kauffman); Planning (Christoph Krug); Planning (Stephan Obrecht); Planning (Tabitha Dineen); Planning Maintenance (Andr√© Hasler); Planning Maintenance (Oliver Bigler); Plant & Clean Utilities (Nozar Basseri); Plant Engineering & Services (Beat Meyer); Plant Engineering (Benjamin Reh); Plant Engineering (Michael Kleinehanding); Plant Engineering Mgr 255 (Anthony Wrzesinski); Plant Engineering Mgr 255 (Stuart Barnes); Plant Engineering Mgr 255 (Timothy Travis); Plant Finance (Justin Mericle); Plant Finance (Melissa Gottschall); Plant Finance (Vlad Kirylau); Plant Finance II (Vlad Kirylau); Plant Finance Product Costing & Capital (Michael McAvoy); Plant Operations (Vinko Momiroski); Plant Utilities (Hansruedi Brunner); Plasma & Raw Material Release (Stefan Tepfenhart); Plasma Center Management (Jincai Zhu ); Plasma Center Management (Jincai Zhu ?????); Plasma Contract Management (Linda S Romalin); Plasma Finance (Jason Mugridge); Plasma Fractionation (John Nelson); Plasma Fractionation (Jordan Wright); Plasma Fractionation (Union) (John Nelson (Inherited)); Plasma Fractionation (Union) (Jordan Wright); Plasma Logistic Center (Peter Nau); Plasma Logistic Center Dallas Supervisor (Brandon W Wornick); Plasma Logistic Center Dallas Supervisor (Brandon Wornick); Plasma Logistics Center Dallas (Carey L Fleener); Plasma Logistics Center Indy (Chad Simeur); Plasma Logistics Center Whitestown (Chad Simeur); Plasma Logistics Centers (Michael J Frecker); Plasma Management (Jack Zhang ?????); Plasma New Development (Jake Zhang ); Plasma New Development (Lixia He ?????); Plasma Operation, Quality (Qingqing Wang ); Plasma Operation, Quality (Qingqing Wang ?????); Plasma Operations (Eveline Kindler); Plasma Operations (Timo Fuhrmann); Plasma Operations Finance; Plasma Operations and Quality (Eric Li ); Plasma Operations and Quality (Eric Li ?????); Plasma Operations and Quality Management (Jeffrey A Schulz); Plasma Pay Services (Karen D Vellutini); Plasma Product Development (Douglas Lee (Inherited)); Plasma Product Development (Michael Zachner); Plasma Products Bulk Operations (Barbara Kalina); Plasma Quality (Lixia He ); Plasma Quality (Lixia He ?????); Plasma Quality Management (Laura O''Brien); Plasma Quality/Deviations (Stefan Kaelin); Plasma Receipt & Haemostasis (Narelle Urli); Plasma Receipt & Haemostasis (Sean Flannery); Plasma Receipt (Brendan Smale); Plasma Receipt (Roger Hand); Plasma Receipt (Tommy Tovilo); Plasma Release (Daniel Schwarz); Plasma Release (Si√© Kigninlman Coulibaly (Inherited)); Plasma Resources US (David H Confessore (Inherited)); Plasma Resources US (Debra A Hood); Plasma Resources US (Shane Kennedy); Plasma Sourcing Management (Lixia He ?????); Plasma and Manufacturing Finance (Ted Kanigowski); Plasma and Manufacturing Finance (Ted Kanigowski) (Ted Kanigowski); Plasmapreparation (Andreas Reber); Plasmapreparation (Erich Nuessle); Pleasant Grove 046 (Eduardo Williams); Pleasant Grove 046 (Vicky Sablan); Pleasant Grove 046 ACM Area 1 (Chad Pagel); Pleasant Grove 046 ACM Area 2 (Ebony Q McGee); Pleasant Grove 046 QA (Pamela R Mendoza); Pontiac 121 (Ashley M Jamieson (Inherited)); Pontiac 121 (Melissa Johnson); Pontiac 121 (Mondel Hightower); Pontiac 121 ACM Area 1 (Tracey L Boyd-McCorkle); Pontiac 121 ACM Area 2 (Mondel Hightower (Inherited)); Pontiac 121 ACM Area 2 (William D Owens); Pontiac 121 QA (Ashley M Jamieson (Inherited)); Pontiac 121 QA (Rebecca Barrons (On Leave)); Pontiac 121 QA (Rebecca Barrons); Pontiac 121 QA (Rodnesia R Jackson); Port Arthur 176 (Karen Sauceda); Port Arthur 176 ACM Area 1 (Dannetta Abdel-Malek); Port Arthur 176 ACM Area 1 (Karen Sauceda (Inherited)); Port Arthur 176 ACM Area 2 (THU RAMOS); Port Arthur 176 QA (Angela Redd); Port Author 176 QA (Angela Redd); Port Author 176 QA (Michael Thompson); Port Authur 176 (Karen Sauceda); Port Authur 176 (Lauren Hardy); Port St Lucie 072 (Kashaun Muhammad (Inherited)); Port St Lucie 072 (Mario A Salas); Port St Lucie 072 ACM Area 1 (Adam Davis); Port St Lucie 072 ACM Area 2 (Vanessa Sanon); Port St Lucie 072 ACM Area 3 (Garrett J Royal); Port St Lucie 072 QA (Raquel Reyes (On Leave)); Port St Lucie 072 QA (Raquel Reyes); Portage 187 (Dom Moceri); Portage 187 (Richard McCoy); Portage 187 ACM Area 1 (DERREK CRUMP); Portage 187 ACM Area 1 (Jeffrey Ott (On Leave)); Portage 187 ACM Area 2 (DERREK CRUMP); Portage 187 ACM Area 2 (Nikki Bradley); Portage 187 QA (Mitch A Quinn); Portage 187 QA (Stephanie Gower); Portfolio & Project Management (Heather Davis); Portfolio & Project Management (Roberta Duncan); Portfolio Management (Joel Hanson); Potency 1 (Dave De Witte); Potency 1 (Johanna Mock); Potency 2 (Martina Treutlein); Potency Testing Final Product 1 (Johanna Mock); Potency Testing Final Product 2 (Martina Treutlein); Potency Testing Intermediates 1 (Jan Bursy); Potency Testing Intermediates 1 (Marika Midon); Potency Testing Intermediates 2 (Wilfried Peil); Preclinical Innovation (Fabian Kaesermann); Preclinical Innovation (Jennifer Brasseit); Preclinical Innovation (Kleanthis Fytianos); Preclinical Innovation (Rolf Spirig); Pricing (Paul Jens (Inherited)); Pricing (Stephanie Kupski); Primary Automation (Gary Steele); Primary Automation (Stephen Callaghan); Primary Manufacturing (Matthew Burrows); Primary Packaging & Medical Devices Bern (Frank Bamberg); Primary Packaging & Medical Devices Bern (Renzo Pedrussio); Primary Packaging & Medical Devices Bern I (Monica Tavanti); Primary Packaging & Medical Devices Marburg (Ahmad Abdul Fattah); Primary Packaging & Medical Devices Marburg (Thomas Pfeifer); Primary Process Engineering (Asad Akhter); Primary Utility Projects (Russell Peak); Primary and Warehouse Validation (James Swann); Privigen Bulk & Facility Operations (Robert Skok); Privigen Bulk (George Barlas); Privigen Bulk (George Makris); Privigen Bulk (Jeremy Campbell (Inherited)); Privigen Bulk (Jeremy Campbell); Privigen Bulk (Kellie Goodman); Privigen Bulk (Lanie Hynninen); Privigen Bulk (Ritaben Suhagiya); Privigen Marketing (Robert Zegel); Privigen/AlbuRx Processing (Peter Klasen); Privigen/AlbuRx Processing Team Leader (Areti Kaloyannis); Process Validation (Berangere Lingat); Process Validation (Fergus Hawes); Process Validation (Peter Trimcevski); Process & Project Engineering (Duncan Benson); Process Analyst Lead (Kate Goossens); Process Analytics & Scale-up (Michael Bieri); Process Analytics & Scale-up (Tobias Heck); Process Change Program (Anita Kohl-Truebenbach (Inherited)); Process Change Program (Jeffrey Ball); Process Control Manager (Vincent Chung (Inherited)); Process Development (Hubert Metzner); Process Development (Michael Bartkovsky); Process Development 1 (Martin Alex Imboden (Inherited)); Process Development 2 (Ibrahim El Menyawi); Process Development Bern (Kurtis Allan Epp); Process Development Bern (PDB) (Kurtis Allan Epp); Process Development Bern I (Maria Crespo Solans); Process Development Bern I, Team 1 (Madlene von Knel); Process Development Bern I, Team 1 (Madlene von K√§nel); Process Development Bern I, Team 2 (Jonathan Eras); Process Development Bern II (Ibrahim El Menyawi); Process Development Bern II, Team 1 (Eva Blatter); Process Development Bern II, Team 2 (Marcus von Nordheim); Process Development Bern II, Team 3 (Adrian Alder); Process Development Bern II, Team 4 (Matthias Spiess); Process Development Bern III (Simon Gerber); Process Development Bern III, Team 1 (Robin Das Gupta); Process Development Bern III, Team 2 (Adrian Alder); Process Development Bern III, Team 3 (Eva Blatter); Process Development Bern III, Team 4 (Jos√© Ures); Process Development Group 1 (Robin Das Gupta); Process Development Group 2 (Eva Blatter); Process Development I & PP (Jennifer Krupka); Process Development I (Charles Arnold); Process Development I (Maike Glaser); Process Development I (Roopsee Anand); Process Development I (Uwe Liebing (Inherited)); Process Development I (Uwe Liebing); Process Development II (Jennifer Krupka); Process Development II (Katrin Anders); Process Development II (Kenneth Maas); Process Development III (Klaus Schmitt); Process Development, Data Science (Maya Shevlyakova); Process Engineering (Donall O Cualain); Process Engineering (Duncan Benson (Inherited)); Process Engineering (Gemma Parkes); Process Engineering (Markus Rentsch); Process Engineering (Michael Bieri); Process Engineering (Sean Goudy); Process Engineering Form & Fill (Emanuella Barbosa Lopes Souza Leao); Process Equipment & Technology (Benno Bitterli); Process Experts (Nicole L√∂ffelholz); Process Improvement (Deborah Mansfield); Process Improvement (George Thomas); Process Improvement (Jason Woolley (Inherited)); Process Improvement Mgr, PNS (Jerjess Chahoud); Process Management (Dawn Myers); Process Management Admin PGI (Antje Rder); Process Management Admin PGI (Antje R√∂der); Process Management Admin PGI (Oliver Draht); Process Migration (Ian Falcao); Process Migration (Paul Martell); Process Migration (Tony Renna); Process Migration Automation PU (Muditha Hasthanayake); Process Migration E&I (Paul O''Brien); Process Migration Project Engineer (Alice Dinh); Process Migration Project Engineer (Anna Martell); Process Science (Annette Gaida); Process Science (Annette Gaida-Benz); Process Science (Stefan Schulte); Process Science 2 (Arnaud Vonarburg); Process Science Upstream Lead (Sandra Grunske); Process Scientists Fractionation (Bernhard Wyss); Process Seed (Jennifer Kelly-Martland); Process Seed (John Cooney); Process TD (Adam Bentley); Process TD (Lisa-Marie Foulkes); Process Validation & Tech Transfer (Stefan Schulte); Process Validation (Berangere Lingat (Inherited)); Process Validation (Berangere Lingat); Process Validation (Fergus Hawes); Process Validation (Indi Staffa); Process Validation (Jesse Richter (Inherited)); Process Validation (Jessica Parletta); Process Validation (Peter Trimcevski); Process Validation - Stability (Jessica Mackellin); Process, Aseptic and Shipping Validation (Clare O''Donnell); Processes (Ferdinand Marx); Processes and Metrics (Eberhard Fitzner); Procurement (Barbara Beugger (Inherited)); Procurement (Brigitte Kimpel-Koch [C]); Procurement (Sreevatsan Sridharan); Procurement Lengnau (Narin Hermez); Procurement Lengnau (Pierre Bersier); Procurement Liverpool (Ian Goldup); Procurement Liverpool (Rachael Close); Procurement Liverpool (Trevor Reay (Inherited)); Procurement Operations (Juerg Kauer); Procurement Operations (Robert Di Giacomo); Procurement Operations (Sue Savage); Procurement Operations (Taylor Saak); Procurement Operations (Thomas Schneider); Procurement Operations - Liverpool (Rachael Close); Procurement Operations - Liverpool (Rachel Shaw); Procurement Operations I (Taylor Saak); Procurement Operations Manager (Marion Fitchett); Prod Manager - Formulations441 (Jamie Aaron Morris); Prod Manager - Formulations441 (Paul Morrison); Prod Mgr - Packaging (Garth James); Prod Mgr - Packaging (MARILYN BARAKIA); Product Care & Layout (Viviana Solange Fluxa Rojas); Product Care (Bill Chambers (Inherited)); Product Care (Markus Christen); Product Care (Patrick Nolte); Product Care (Samantha Czako (On Leave)); Product Care (Samantha Czako); Product Care (Thorsten Keller); Product Care (Valerie Schaffer); Product Care (Viviana Solange Fluxa Rojas (Inherited)); Product Care Mgmt (Andrea Lehmann); Product Characterisation (Matthias Zimmermann); Product Characterisation (Robert Dickinson); Product Characterization (Carsten Horn); Product Costing & Inventory Controlling (Anika Wagner); Product Costing & Inventory Controlling (Dirk Achenbach); Product Development (David Glover (Inherited)); Product Development (David Glover); Product Development (Fiona Bunworth); Product Development (Matthias Zimmermann); Product Disposition (Amber Hall); Product Disposition (Gennin Snyder); Product Education (David Chen ????); Product Education (Wei Chen ); Product Education (Wei Chen ????); Product Expertise (Paul Sinclair); Product Group Hemophilia (Claudia Zacharias); Product Group Hospital Products; Product Group Hospital Products (Bianca Petzold); Product Group Hospital Products (Michael Bernd Rode (Inherited)); Product Group ID (Richard Sodmann); Product Innovation (Fabian Kaesermann (Inherited)); Product Innovation (Fabian Kaesermann); Product Innovation (Rolf Spirig); Product Innovation (Susann Cattepoel); Product Market Authorization & QA Russia & CIS (Galina Senchukova); Product Ownership - Biotherapies (Anita Kohl-Truebenbach); Product Ownership - Biotherapies (Paul McKenzie (Inherited)); Product Release (Christine Peter); Product Release (Patricia Loftus); Production & Strategic Planning (Matthias Christl (On Leave)); Production & Strategic Planning (Matthias Christl); Production (Craig Byham); Production BCI/C1 INHIB (Peter Gttner); Production BCI/C1 INHIB (Peter G√ºttner); Production Engineering (ANDREW HISLOP); Production Engineering (Andre Majchrzak); Production Engineering (Anisa Moghaddam); Production Engineering (Antonio Ciocca); Production Engineering (Cameron Simpson); Production Engineering (Campbell Anderson); Production Engineering (Candy Lee); Production Engineering (Damien Barri); Production Engineering (Dion Houtman); Production Engineering (Jason Fletcher); Production Engineering (Karen Noonan); Production Engineering (Kate McConnell); Production Engineering (Melissa Nicholson); Production Engineering (Reza Mohebian); Production Engineering (Richard Friar); Production Engineering (Richard Hayne); Production Engineering (Tom Graham); Production Engineering (Tom Kelland); Production Engineering 1 (Geoff Wang); Production Manager (Cassandra Smoult); Production Manager (Jamie Aaron Morris); Production Manager US (Ljubi Huseinovic); Production Manager, PNS 448 (Keiran Ragas); Production Marburg (Frank Emmerich); Production Marburg (Michael Schr√∂der); Production Planning (Kyle Popham); Production Supervisor 454 (Kara Davine); Production Support (Jeffrey Spicer); Production Support (Marcus O''Dwyer); Produktion Inhibitoren PGI (Barbara Kalina (Inherited)); Produktion Inhibitoren PGI (Stefan Wellnitz); Produktion Inhibitoren PGI (Wilfried Happel); Produktion Inhibitoren Schicht 1 (Fabian Cybulski); Produktion Inhibitoren Schicht 2 (Arkadius Kaczmarczyk (Inherited)); Produktion Inhibitoren Schicht 2 (Arkadius Kaczmarczyk); Produktion Inhibitoren Schicht 3 (Manuel Lotz); Produktion Inhibitoren Schicht 4 (Fynn Krieger); Produktion Inhibitoren Schicht 4 (Manuel Cuesta Linker); Produktion Inhibitoren Tagdienst (Florian Scherer); Produktion RPF300 (Anika Knack); Produktion RPF300 (Mara Saglam); Produktion Rekombinante Proteine & Support (Carsten Meyer); Produktion Rekombinante Proteine & Support (Viktor Krecker); Produktion Wundheilungsprparate M300 1 (Meik Dietrich); Produktion Wundheilungsprparate M300 2 (Jrg Schmidt); Produktion Wundheilungsprparate M300 3 (Bjrn Bartelme); Produktion Wundheilungsprparate M300 4 (Willi Drr); Produktion Wundheilungsprparate M300 5 (Rainer Jesberg); Produktion Wundheilungsprparate M300 6 (Udo Wagner); Produktion Wundheilungspr√§parate M300 1 (Meik Dietrich); Produktion Wundheilungspr√§parate M300 2 (J√∂rg Schmidt); Produktion Wundheilungspr√§parate M300 3 (Bj√∂rn Bartelme√ü); Produktion Wundheilungspr√§parate M300 3 (Christoph Bernitt); Produktion Wundheilungspr√§parate M300 4 (Willi D√∂rr); Produktion Wundheilungspr√§parate M300 5 (Rainer Jesberg (On Leave)); Produktion Wundheilungspr√§parate M300 5 (Rainer Jesberg); Produktion Wundheilungspr√§parate M300 6 (Udo Wagner); Produktionsfachkraft Chemie (Carmen Walldorf (Inherited)); Produktionsfachkraft Chemie (Doris Nake (Inherited)); Program Management R&D Building (Carsten Skill); Programme Management (Anthea Stephenson); Project (Joe Fielding [C]); Project Aurora Automation (Mukesh Muruganandan) (Mukesh Muruganandan); Project Automation (Michael Kraft); Project BCI (Kristin Eschrich); Project Controls and Commercial Assurance (Daniel Boltz); Project Delivery & Support (Christopher A Betterton); Project Delivery & Support (Matt Shapiro); Project Delivery & Support (Robert Boland (Inherited)); Project Delivery EU/APAC (Nick Furmston); Project Delivery KAN (Michael Hansen (Inherited)); Project Edge Commercial (Drew Hansen); Project Edge Finance (Daya Salter); Project Edge Finance (John Dinatale (Inherited)); Project Edge Logistics (John Dinatale (Inherited)); Project Edge Logistics (Steve Wilson [C]) (Steve Wilson [C]); Project Edge Procurement (Emma Hopwood); Project Edge Quality (Glenn Barbrey); Project Edge Quality (John Dinatale (Inherited)); Project Engineering (Daniel Weniger); Project Engineering (Duncan Benson); Project Engineering (Volker Teuchert); Project Ldr Improve & Compl (Michael Dunn); Project Ldr Improve & Compl (Thomas Nguyen); Project Logistic Centre Lahntal (Thomas Schwarz); Project Management (Bryan J Hoover); Project Management (Mark Ridge); Project Management CV TA (Julie Waterbury); Project Management Office (Chris Abell); Project Management Office (Emily Brown); Project Management Office (Geoffrey Rea [C]); Project Management Office (Jose Gonzalez (Inherited)); Project Management, AU/Asia (Alex Vaine); Project Management, Europe (Elaine DiMonte); Project Management, Europe (Katharine von der Fecht); Project Management, North America (Elaine DiMonte); Project Manager (Andrei Fedorov); Project Manager (Conal O''Mahony); Project Manager (Heiko V√∂lpel (Inherited)); Project Manager (Jack Hung); Project Manager (Victor Karafilis (Inherited)); Project Support & Technical Transfer (Andreas Berting); Project Upgrade H69 (Thomas Schwarz); Project-Portfolio Delivery (Robert Boland); Project/Portfolio Delivery (Tod Marks); Projekt Phoenix (Markus Ries); Projekt-Koordinator (Claus Peihs (Inherited)); Projekt-Koordinator (Jrg Dieterich (Inherited)); Projekt-Koordinator (J√∂rg Dieterich (Inherited)); Projekt-Koordinator (Murat Dalar (Inherited)); Protein Biochemistry (Eric Salgado); Protein Research R&D (Nathan J Brinkman); Protinus (Marius Liesch); Protinus (Sandra Kaempfer); Prozessgruppe 1 (Christoph Pfeiffer); Prozessgruppe 1 (Daniel Weniger (Inherited)); Prozessgruppe 1 (Marko Witt); Prozessgruppe 2 (Frank Heck); Pt. St. Lucie 072 ACM Area 1 (Adam Davis); Pt. St. Lucie 072 ACM Area 2 (Vanessa Sanon); Pt. St. Lucie 072 ACM Area 3 (Garrett J Royal); Publishing Site Marburg (Jrg Starker); Publishing Site Marburg (J√∂rg Starker); Publishing Site Marburg Diverse (J√∂rg Starker (Inherited)); Puffer (Rainer Frank (Inherited)); Puffer (Torsten Jeide); Pulmonology-Europe (Michael Larbig); Purchasing (Alfonso Albornoz); Purchasing (Bob Siegel); Purchasing (Mark W Hartmann); Purchasing I (Alfonso Albornoz); Q Fever Team Leader D443 (Marcus O''Dwyer); Q Fever Team Leader D443 (Paul Williams); Q-Operation (Isabelle Crauser); Q-Operation (Marco Maeder); Q-Oversight End Products (Urs Pflugshaupt); QA - Batch Release & PTCs (Daniel Powell); QA - Batch Release & PTCs (Peter Tyler); QA - Batch Release (Astrid Mellor); QA - Batch Release (Tracy Owens); QA Batch Release (Constanze Buchter); QA Batch Release (Nicole Kortelainen); QA Batch Release (Randolph Rimando); QA Complaints Mangement (Rhonda L Luhrsen); QA Compliance (Berangere Lingat); QA Compliance (Craig Stephens (Inherited)); QA Compliance (JEFFREY ZOUBEK); QA Compliance (Jeffrey Zoubek); QA Compliance (Kimberly E Lorenz (Inherited)); QA Compliance (Mark Dickson); QA Cont Imp & Iss Mgt (Sharon Thornton); QA Fill/Finish (Lindsay Griffiths); QA Manager (Nicola Rotherham); QA Manufacturing (Alex Hargreaves); QA Manufacturing (Natalie Steele); QA Manufacturing (Tracy Owens); QA Operations (Dave Kowalski); QA Operations API (Anthony Nelson); QA Operations API (Kyle Showalter); QA Operations Bldg 30 (Anastasia Lindsey); QA Operations Bldg 33 (Alison York); QA Operations Bldg 33 (Candice Nieves (Inherited)); QA Operations Bldg 33 (Jill Shafer); QA Operations Bulk (Candice Nieves); QA Operations Bulk (Cassandra Clevenger); QA Operations Coagulation (Nicholas Gluckleder); QA Operations Coagulation 2 (Kelly Kucera); QA Operations Fractionation (Alison York); QA Operations Fractionation (Jacquelyn O''Malley); QA Operations II (Meggan R Smith); QA Operations II (Sarah Milone); QA Operations IPW (Kimberly Desler); QA Operations IPW (Meggan R Smith); QA Operations IPW (Sarah Milone); QA Operations IPW I (Sarah Milone); QA Operations Parenteral (Dave Kowalski (Inherited)); QA Operations Parenteral (Michael Urbanczyk); QA Plasma/Deviations (Eva Streit); QA Plasma/Deviations (Si√© Kigninlman Coulibaly); QA Primary Manufacturing (Jocelyn Bryson); QA Process and Facilities / Stability (Marco Haas); QA Processes & Facilities (Dieter Bathier); QA Processes & Facilities (Ivo Lakomy); QA Processes & Facilities (Michel Baur); QA Processes & Facilities (Silvia Schmutz); QA Product Release (Joanna Madafferi); QA Product Release (Stephanie St.Martin); QA Projects Compliance Team (Danielle Moloney); QA Projects Compliance Team (Stoyan Atanasov); QA Release (Angelos Borobokas); QA Release (Aoife Corrigan); QA Release (Cherie Mclaren); QA Release (Craig Stephens (Inherited)); QA Release (Francesco Intoccia); QA Release (Ivo Lakomy); QA Release (Karin Hofstetter); QA Release (Katie Wood); QA Release (Manuel Selvaggio); QA Release (Marion Jeffrey); QA Release (Neil Del Castillo); QA Release (Rosemary Hill); QA Release 1 (Aoife Corrigan); QA Release 1 (Cherie Mclaren); QA Release 1 (Craig Stephens (Inherited)); QA Release FAR Compliance (Margrit Waterval); QA Release FAR Release (Natalie Helfer); QA Release FAR Review (Fabienne Thoenen); QA Release IG/CYT (Silvia Schmutz); QA Release IGC Compliance (Dagmar Riffel); QA Release Process Engineering (Michael Zachner); QA Secondary (Daniel Powell); QA Systems (Christian Eggel); QA Systems (Connie Costanzo); QA Systems (Craig Stephens (Inherited)); QA Systems (Dina El-Emary); QA Systems (Lorenz Rindisbacher (Inherited)); QA Systems (Malte Kr√§mer); QA Systems (Maryanne Pashalis); QA Systems (Michel Baur (Inherited)); QA Systems (Nancy Phan); QA Systems (Nassima Wilson); QA Systems (Nina Klee); QA Systems (Simone Naruhn); QA Systems (Sue Ireland); QA Systems (Susanne Deyhle); QA Systems (Tony Smith); QA Technical Support (Amanda Cooper); QA Validation (Jeff Mihaichuk (Inherited)); QA Validation (Jeff Mihaichuk); QA Validation (Stephen R Grey); QA Validation - Site Expansion (Jeff Mihaichuk (Inherited)); QA Validation I (Jeff Mihaichuk (Inherited)); QA and Regulatory Affairs SI (Aldelberto Cordova); QAI Quality Albumin, Immunoglob., Plasma (Martin Krah); QAO Compliance (Dagmar Riffel); QAO Manufacturing (Ines Joachim); QAO Manufacturing (Natalie Helfer); QAO Release (Natalie Helfer); QAO Release (Silvia Schmutz); QAO Sustain & Improve (Stefan Kaelin); QBR FVIII & FIX QoF (Anja Beetz); QBR FVIII & FIX QoF (Anja Beetz-Kroll); QBR PWI QoF (Torsten Cyriax); QBR rekombinante Proteine QoF (Nancy Georgieff); QBS Rotational Program (Ulrich Schuerch); QC (Gillian McAdam); QC (Rebecca Gannon); QC - Chemistry (Anna Melia); QC - Chemistry (Anthony Pellegrini); QC - Chemistry (Jimmy Pajarillo); QC - Chemistry (Marie Neophytou); QC - Chemistry (Michael Streule); QC - Microbiology (Angie Fifis); QC - Microbiology (Claire Abson); QC - Microbiology (Denise Vella); QC - Microbiology (Dinesh Raj Methuku); QC - Microbiology (Dozie Okafor); QC - Microbiology (Elsie Everson); QC - Microbiology (Grace Luong (Inherited)); QC - Microbiology (Grace Luong); QC - Microbiology (Karthy Santhosh); QC - Microbiology (Maria Arulruban); QC - Microbiology (Marika Moore); QC - Microbiology (Maruthi Shivananda); QC - Microbiology (Patricia Hughes); QC - Microbiology (Tyson Parker); QC Analytical & Raw Materials (Nick Brendon); QC Analytical & Raw Materials (Victoria Fairclough); QC Analytical Services Manager (Andrea Prendergast); QC Bioassay (Adrian Gee); QC Bioassay (Victoria Fairclough); QC Chemistry (Jenny Staff); QC Chemistry (Robert Zanon); QC Chemistry (Ying Huang); QC Chemistry Team Leader (Niki Soteriadis); QC Chemistry Team Leader (Ying Huang); QC Compliance (Ignazio Lamonica); QC Compliance Support (Lisa Walters); QC Compliance and Improvement (Lisa Marie Malcharek); QC Immunochemistry (Andre Lamarque (Inherited)); QC Immunochemistry (Andre Lamarque); QC Immunochemistry (Caroline Abdul-hay); QC Immunochemistry (Fatima Bartils); QC Immunochemistry (Georgina McKay); QC Immunochemistry (Sean O''Keefe); QC Immunochemistry (Tahlor Robson (Inherited)); QC Immunochemistry (Tahlor Robson); QC Immunochemistry (Xiaowen Chin); QC Immunology (Melissa Damino); QC Immunology Team Leader (Anna Gruszka); QC Immunology Team Leader (Corina Zahra); QC Immunology Team Leader (Grace Huynh); QC Immunology Team Leader (Michelle Reckerman); QC Labs (Dawn Nagel); QC Micro Manager (Rita Simopoulos); QC Micro Team Leader (Dan Balod); QC Micro Team Leader (Prue Shanahan); QC Microbiology (Denise Vella); QC Microbiology (Georgia Ieronymakis); QC Microbiology (Maria Moeller); QC Microbiology (Nicola McDonald); QC Microbiology and Sterility Assurance (Dozie Okafor); QC PNS + Other Non-IVV Prod (Dan Balod); QC Projects (Hannah Kay); QC Projects (Stephen Pearson (Inherited)); QC Sample Logistics (Billy Patel); QC Stability (Victoria Mason (On Leave)); QC Stability (Victoria Mason); QC Stability (Victoria Wilson (On Leave)); QC Stability (Victoria Wilson); QC Stability Coordination (Jonathan Whitehead); QC Support (Andrea Prendergast); QC Support (Jennifer Chung); QC Support (Lucero Perdomo Cruz); QC Support (Philip Elliott (Inherited)); QC Support (Stephen Pearson); QC Support (Sushil Deswal); QC Support Systems (Jenny Higgins); QC Validation (Hayley Mackin); QC Validation (Jeff Hancock (Inherited)); QC-Microbiology (Anja Djordjevich); QC-Microbiology (Kah Wen Lee); QC-Microbiology (Tahlor Robson); QC/QA (Alex Hargreaves); QC/QA (Natalie Steele); QCP BRR (Verena Specowius); QCP QC Support & PTC/QCP, QFP (Mirko Altenkmper); QCP QC Support & PTC/QCP, QFP (Mirko Altenk√§mper); QFP Filling AQL (Lina Matschke); QFP Filling H69 QoF (Christoph Kalfack); QFP Filling H69 QoF (Ingo Kischka); QFP Filling M305 ABW (Sandra Benthin); QFP Filling M305 BRR/CC (Verena Specowius); QFP Filling M305 QoF (Stefan Paul); QGP Quality Coagulation (Jrgen Keitel); QGP Quality Coagulation (J√ºrgen Keitel); QM Production (Monika Christen); QM Production (Monika Krebs); QM Qualification & Validation (Bettina Vgerl); QM Qualification & Validation (Bettina V√∂gerl); QM Qualification (Gnter Fehlberg-Sternemann); QM Qualification (G√ºnter Fehlberg-Sternemann); QM Validation (Mickael Boegli); QMB ES (Samuel Mann); QMB Operations (Jonathan Imhof); QO / Aseptic (Michelle Hogg); QP/QA Product Release (Jocelyn Bryson); QPPV (Anna Rozmyslowicz); QPPV (Giovanni Furlan); QSP Quality Supply Chain & Packaging Op. (Sybille Bertram); QTH Quality Teilfertigung H69 (Guido Kagemann); QTM Quality Teilfertigung M305 (Murat Dalar (Inherited)); QTM Quality Teilfertigung M305 (Wolfgang List); QWI Inhibitors, Fibrinogen+Vaccines (Antonia Preidel); QoF Endfertigung (Christoph Croon); QoF Endfertigung (Jeanette Ludwig); Qualification (Angela Hamrock-Fox (Inherited)); Qualification (Angela Hamrock-Fox); Qualification (Annabel Wang); Qualification (Bozana Dujak); Qualification (Chris Richter); Qualification (Ilija Najdovski); Qualification (Jonathan Nixon); Qualification (Judith Kennedy); Qualification (Judith Youd); Qualification (Lorraine Murphy); Qualification (My Linh Ly); Qualification (Peter Carver); Qualification (Purush Devanathan); Qualification (Rainer Kraus); Qualification (Rolf Ingold (Inherited)); Qualification (Selda Yildiz Kaya) (Selda Yildiz Kaya); Qualification (Susan Clough); Qualification - Systems (Susan Clough); Qualification - systems (Darren Geary); Qualification System (Michael Kocher); Qualification System (Nadine Aeschbacher); Qualification System (Nadine Jost); Qualifizierung I Schwerpunkt Bulk (Michael Dospil); Qualifizierung II Schwerpunkt Teilfertigung (Michael Kuhn); Qualifizierung III Schwerpunkt Automatisierung (Lionel Guthneck); Qualifizierung IV Schwerpunkt Re-Qualifizierung (Ingo Kischka); Qualifizierung IV Schwerpunkt Re-Qualifizierung (Rainer Kutsch); Qualifizierung Lengnau (Thomas Cragnolini); Quality & Business Services (Karen Etchberger); Quality & Compliance UK (Jonathan Sheard); Quality & Med Svcs (Francesc Pont); Quality & Safety Management R&D (Dominik Blaser); Quality (Craig Stephens (Inherited)); Quality (David Atkinson); Quality (Ernest Shepard); Quality (Helmut Robert Euler); Quality (Jeffrey A Alcorn (Inherited)); Quality (Jill Allen); Quality (Jose Gonzalez (Inherited)); Quality (Kimberly E Lorenz); Quality (Mark Dickson); Quality (Matthew Donegan); Quality (Michelle Kelley); Quality (Robin A Mroz); Quality (Scott Overton); Quality (Vasilis Mavrogenis); Quality (Wei Wei ); Quality (Wei Wei ????); Quality (Yun Zhao (Inherited)); Quality (Yun Zhao ????); Quality 1 (David Atkinson); Quality Applications (Jason VanGils); Quality Assurance & Systems (Kelley L Hyatt); Quality Assurance (Anuja Prabhutendolkar); Quality Assurance (Connie Stewart); Quality Assurance (Ryo Ohnishi ??? ? - ???? ???? (Inherited)); Quality Assurance (Sanae Uchida (Inherited)); Quality Assurance Division (Ryo Ohnishi - ); Quality Assurance Division (Ryo Ohnishi ??? ? - ???? ????); Quality Assurance I (Connie Stewart); Quality Assurance II (Terry L Fritz); Quality Assurance Operations (Ivo Lakomy); Quality Assurance Projects Compliance (Craig Stephens (Inherited)); Quality Assurance Projects Compliance (Eoin Hanley); Quality Assurance System Group (Ryo Ohnishi ??? ? - ???? ???? (Inherited)); Quality Assurance System Group (Sanae Uchida (Inherited)); Quality Assurance Systems (Markus Schriewer); Quality Assurance, HS (Jonathan Kegerise); Quality Assurance, LVP (Gillian McAdam); Quality Assurance, PKV (Fiona Smith); Quality Assurance, PKV (Karen Netherton (Inherited)); Quality Bulk and Release QBR (Petra Hintz-Obertreis); Quality Chemistry (Cassie Norton); Quality Compliance (Sandra F Osborne); Quality Control (Juergen Liedtke); Quality Control (Leonora Pancho); Quality Control (Manuel Selvaggio); Quality Control (QC) (Dominik Stadler); Quality Control (QC) Ops Support (Brigitte Siani); Quality Control (Rene Bruegger); Quality Control Development (Andreas Affolter); Quality Control Services (Manuel Selvaggio (Inherited)); Quality Control Specialist (Andrea Chalker (Inherited)); Quality Control Specialist (Lakmini Croner); Quality Control Specialist (Linh Vo); Quality Control Support (Pascal Hulliger); Quality Control Support (QCS) (Christoph Wyss); Quality Control Team Leader (Andrea Chalker); Quality Control Team Leader (Chris O''Meara); Quality Control, LVP (Rebecca Gannon); Quality Control, LVP (Simon Harwood); Quality Controll Support QCS (Sebastian Klzer); Quality Controll Support QCS (Sebastian K√∂lzer); Quality Coordination ECI (Viviana Solange Fluxa Rojas); Quality Deviation & CAPA Management (Michael Rudolf); Quality Document Control (Michael Gough); Quality Enterprise Learning Management (Amy Love); Quality Filling H69 (Jens Huft); Quality Final Product QFP (Murat Dalar); Quality Global QA Technical Development (Monica Rose); Quality HS QA 3rd Party Manufacturing (Eric Blaesing); Quality HS QA Document Control (Aaron Ward); Quality HS QA Document Control (Cara Miller Kell); Quality HS QA Engineering & Validation (Petra Smith); Quality HS QA Fill Finish Expansion (Sarah Stearns); Quality HS QA Front Line (Laura Caldwell); Quality HS QA Front Line Days (1st Shift) (Laura Caldwell (Inherited)); Quality HS QA Front Line Days (1st Shift) (Nicholas Brown); Quality HS QA Front Line Incident Management (Dominic Greene); Quality HS QA Front Line Incident Management (Laura Caldwell (Inherited)); Quality HS QA Front Line Nights (2nd & 3rd Shift) (Karam Farhan); Quality HS QA Front Line Weekends (4th & 5th Shift) (Erminio Alesii); Quality HS QA Manufacturing (Stephenie Robertson); Quality HS QA Manufacturing Batch Release Bulk (Jennifer Deinarowicz); Quality HS QA Manufacturing Batch Release Fill Finish (Marianne Perelstein); Quality HS QA Manufacturing Batch Release (Amy Love); Quality HS QA Manufacturing Batch Release (Jonathan Kegerise (Inherited)); Quality HS QA Manufacturing Batch Release ¬ñ Bulk (Jennifer Deinarowicz); Quality HS QA Manufacturing Batch Release ¬ñ Fill Finish (Marianne Perelstein); Quality HS QA Manufacturing Batch Release-PTC (Troy Greene Jr); Quality HS QA Manufacturing Incident Management (Dominic Greene); Quality HS QA Manufacturing Shopfloor (Brian Leising); Quality HS QA Manufacturing Shopfloor (Jonathan Kegerise (Inherited)); Quality HS QA Manufacturing Shopfloor Bulk Days (Stephaine McMillan Eads); Quality HS QA Manufacturing Shopfloor Bulk Nights (Nicholas Alexander Brown); Quality HS QA Manufacturing Shopfloor FF Days (Elliott Tatum); Quality HS QA Manufacturing Shopfloor FF Days (Joseph A Marti); Quality HS QA Manufacturing Shopfloor FF Nights (Michael Mikolajczak); Quality HS QA Supplier & Third Party Management (Aaron Ward); Quality HS QA Supplier & Third Party Management (Jessica Mercer (Inherited)); Quality HS QA Systems & Compliance (Jessica Mercer); Quality HS QC Biochemistry (Geremy Knapp); Quality HS QC Biochemistry (Richard H Steere); Quality HS QC Chemistry (Gina Stick); Quality HS QC Chemistry (Raymond Otchere-Adjei); Quality HS QC Immunology (Geremy Knapp); Quality HS QC Logistics (Kelly Jenness); Quality HS QC Logistics (Laura Matulevich); Quality HS QC Microbiology (Liz Strickland); Quality HS QC Microbiology (Roland Jason Jacques); Quality HS QC Microbiology (Sarah Strickland); Quality HS QC Validation & Change (Jessica Loshia Gambill); Quality HS QC Virology (Geremy Knapp); Quality HS QC Virology (Geremy William Knapp); Quality HS Quality Control (Jessica Mercer); Quality HS Quality Control (Rebecca Gannon); Quality HS Quality Control (Stephen Case); Quality HS Training & Workforce Development (Jessica Mercer (Inherited)); Quality HS Training & Workforce Development (Jonathan Kegerise (Inherited)); Quality HS Validation (Amy Russell); Quality HS Validation (Brian Nunnally (Inherited)); Quality HS Validation Bulk & Warehouse (Mark Holland); Quality HS Validation Fill Finish, QC & FacOps (Amy Russell (Inherited)); Quality HS Validation Fill Finish, QC & FacOps (Amy Russell); Quality HS Validation Fill Finish, QC & FacOps (Megan Crandall); Quality HS Validation Process & Aseptic (Brian Nunnally (Inherited)); Quality HS Validation Process & Aseptic (Christopher Lee); Quality HS Validation Process & Aseptic (Matthew Franks); Quality Improvement (Marc Christeller); Quality Improvement (Sandra Soverna); Quality Italy (Annarita Cinardo); Quality Knowledge Management (Sarah S Lemons); Quality Lab (Russ Reeves); Quality Management (Adam Robb); Quality Management (Craig Stephens); Quality Management (Dina El-Emary); Quality Management (Jeffrey A Alcorn (Inherited)); Quality Management (Juergen Liedtke); Quality Management (Lorenz Rindisbacher); Quality Management (Michel Baur); Quality Management (Niklas Schier); Quality Management (Paul Martell); Quality Management (Philip Elliott); Quality Management (Reiner Laske); Quality Management (Reiner Laske, Niklas Schier); Quality Management (Susanne Jecklin); Quality Management 2 (Manuel Selvaggio); Quality Management E&S (Michael Kocher); Quality Management E&S (Rolf Ingold); Quality Management Engineering (Alexandra Rompf); Quality Management Strategy & Op Excellence (Collins Onyejese); Quality Management System (Eileen DiRita); Quality Management Systems (Justin Huss); Quality Operations (Carolyn M Koerner); Quality Operations, Liverpool (Karen Netherton); Quality R & D (Bradley Jackson); Quality R & D (Sharon Reinhard); Quality Review & Improvement Management (Uwe Dohmen); Quality Review Management & Trending (Uwe Dohmen); Quality Shared Services (Barbara Hicks); Quality Site Operations HS (Brian Nunnally); Quality Supply Chain US Distribution (Karen Marks (Inherited)); Quality Supply Chain US Distribution (Samantha Wentzell); Quality Supply Chain US Distribution (Stephanie Condi); Quality System Validations (Jeffrey Berry); Quality Systems & Compliance (Mai Viholm); Quality Systems & Compliance (William Cunningham); Quality Systems & Compliance Auditing & Inspections (Marcela Rojas); Quality Systems & Compliance Auditing & Inspections HS (Aaron Ward); Quality Systems & Compliance Auditing & Inspections LVP (William Cunningham); Quality Systems & Compliance Auditing & Inspections PKV (Marcela Rojas (Inherited)); Quality Systems & Compliance HS (Milka Smoljko (Inherited)); Quality Systems & Compliance QA IT (Anthony Pickering); Quality Systems & Compliance Shared Services (Sarah Lemons); Quality Systems & Compliance Shared Services EDMS (Robbie Gay); Quality Systems & Compliance Shared Services GLIMS (Helen Mihaljevic); Quality Systems & Compliance Shared Services LMS (Cara Miller Kell); Quality Systems & Compliance Supplier Management HS (Gina Stick); Quality Systems (Alan Cordero); Quality Systems (Brandi C Robinson); Quality Systems (Brandi Kennedy); Quality Systems (Karen M Cory); Quality Systems (Margaret A Clifton); Quality Systems (Michael Gough); Quality Systems (Micheal Casaus); Quality Systems (Michelle J Siegel); Quality Systems (William Cunningham (Inherited)); Quality Systems - Trackwise (Maggie Bradley); Quality Systems 1 (Kristen Gist); Quality Systems Boca (Micheal Casaus); Quality Systems I (Alan Cordero); Quality Systems II (Michelle J Siegel); Quality Systems IT (Nicole Nolan); Quality Systems IT (Tim Jones (Inherited)); Quality Systems Management I (Sigrid Streichert); Quality Systems and Standards (Sophie Chairs); Quality Systems and Standards (Vicky Lioutas); Quality Validation (Rudolf Beutler); Quality and Compliance (Harumi Ishizuka); Quality and Standards (Shinya Takagawa); Quality system (Eric Li ?????); R&D (Russell Basser); R&D - Albumin/Immunoglobulin (Joseph Bertolini); R&D - Albumin/Immunoglobulin (Karl McCann); R&D - Albumin/Immunoglobulin (Robert Forrest); R&D - Albumin/Immunoglobulin (Vladimir Gurevich); R&D - Haemostasis (Ayse Kara); R&D - Haemostasis (Hung Pham); R&D - Haemostasis (Kathryn Scott); R&D - Haemostasis (Kelly Lo Presti); R&D - Haemostasis (Maria Panayi); R&D - Haemostasis (Norm Mancuso (Inherited)); R&D - Haemostasis (Norm Mancuso); R&D - Haemostasis (Vladimir Gurevich); R&D - Haemostasis (Yvette Citrine); R&D - Management (Germano Coppola); R&D - Technical Operations (Robert Forrest); R&D - Technical Operations Senior Scientist (FRIEDA FEHR); R&D - Technical Operations Senior Scientist (Mary Alaveras); R&D - Virology (Connie Broumis); R&D - Virology (Rachael Ross); R&D - Virology (Randel Fang (Inherited)); R&D - Virology (Randel Fang); R&D - Virology (Randel Fang) (Randel Fang); R&D - Virology (Trudi Wentzel); R&D Bioanalytics BMW (Sue Amatayakul-Chantler); R&D Biostatistics & Data Management AUS (Vince Matassa); R&D Biostatistics & Data Management US (Hongyu Liu); R&D Biostatistics Programming (Daphne Ewing); R&D Breakthrough Technologies BMW (Germano Coppola (Inherited)); R&D Breakthrough Technologies BMW (Joseph Bertolini); R&D Breakthrough Technologies BMW (Viv Louzado); R&D Business Operations (Christian DiDio); R&D CMC & Compliance (Chaaya Ganorkar); R&D CMC & Compliance (Michele Fischer Heintz (Inherited)); R&D CMC & Compliance (Wendy Su); R&D Cell Based Influenza Vaccines (Brett Ashley Leav); R&D Cell Based Influenza Vaccines (Brett Leav); R&D Cell Based Influenza Vaccines (Deborah Molrine); R&D Clinical Business Operations (Christian DiDio); R&D Clinical Compliance & Training (Roberta Duncan (Inherited)); R&D Clinical Development (Jonathan Edelman); R&D Clinical Development, BOSS-CC (Roberta Duncan); R&D Clinical Operations (Veronica Suarez (Inherited)); R&D Clinical Operations Pandemic (Mary Smith); R&D Clinical Operations Pandemic (Mirjam van Huffelen (On Leave)); R&D Clinical Operations Pandemic (Mirjam van Huffelen); R&D Clinical Operations Seasonal (Olivia Crayne); R&D Clinical Safety & Pharmacovigilance (James Milligan); R&D Clinical Safety & Pharmacovigilance (Russell Basser); R&D Clinical Safety & Pharmacovigilance (Sylvie Tomczyk); R&D Clinical Vaccine Management & Serology (Francesco Bedani); R&D Data Management, Coding & Standards (Renate Verbeeten - van Hoof); R&D Data Management, Coding & Standards (Renate Verbeeten-van Hoof); R&D Development Liverpool (April Sena); R&D Epidemiology (Mendel Haag); R&D Finance (Eleanor McQuisten); R&D Finance (Emma Walsh); R&D Formulation & Delivery (HUI LIU); R&D Formulation & Delivery (Hui Liu); R&D Global CMC Standards & Harmonisation (Rima Youil); R&D Global CMC and Compliance (Michele Fischer Heintz); R&D Global CMC and Compliance (Michele Heintz); R&D Global Medical Affairs (Gregg Coveney Sylvester); R&D Global Medical Affairs (Gregg Sylvester); R&D Global Strategic Labelling (Helen Cowdery); R&D Human Resources (Andrea Resch (Inherited)); R&D Human Resources (Kimberly Golden); R&D Human Resources (Paula Foord); R&D Human Resources MBR (Andrea Resch); R&D IT Solutions (John Cornelius); R&D Immunology (Gillis Otten); R&D Immunology (Gillis Robert Otten); R&D Influenza Vaccines Pandemic (Matthew Hohenboken); R&D Influenza Vaccines Seasonal (Esther Heijnen); R&D Influenza Vaccines Seasonal (Igor Smolenov); R&D Influenza Vaccines Seasonal (Jonathan Edelman (Inherited)); R&D JAPAN (Haruo Kitado); R&D Licensing (Andrea Huggins); R&D Medical Affairs, Americas (Ashesh Gandhi); R&D Medical Affairs, Americas (Ashesh J Gandhi); R&D Medical Affairs, Canada (Ashesh Gandhi (Inherited)); R&D Medical Affairs, Europe (Sankarasubramanian Rajaram); R&D Medical Affairs, Influenza (Karita Ambrose); R&D Medical Affairs, Rapivab (Daniele Gelone); R&D Medical Communications, US (Nancy Dougherty); R&D Medical Science Liaison Canada (James Mansi); R&D Microbial & Molecular Biology (Pirada Suphaphiphat); R&D Operations - Influenza (Denis Thomas); R&D Operations - Influenza (Lynda Allan); R&D PM Leadership (Nancy Fetrow); R&D PV Compliance & Excellence (Liz Pound); R&D Pharmacovigilance Operations (Jefferson Guillon); R&D Pharmacovigilance Operations (Lynn Gabb); R&D Pharmacovigilance Operations (Sylvie Tomczyk (Inherited)); R&D Pharmacovigilance and Risk Management (Maria Maddalena Lino); R&D Process Development BMW (Karl McCann); R&D Process Development BMW (Per Hansen); R&D Process Science Liverpool (Kulwinder Banger); R&D Project Management (Julie Waterbury); R&D Project Management - BRN (Michael Exner); R&D Project Management Development Projects (Nancy Fetrow); R&D Project Management Qvax, Patch, Research (Heather Davis (Inherited)); R&D Project Operations (David Leacy); R&D Protein Biochemistry (Changkeun Lee); R&D Protein Biochemistry (Yingxia Wen); R&D QA Systems (Karen Gard''ner (Inherited)); R&D QA Systems (Liz Pound); R&D QA Systems (Sarah S Lemons); R&D Quality (Karen Gard''ner); R&D Quality (Kendra Bossio); R&D Quality Management (Georgina Dimovski); R&D Quality Management (Jackie Desengano); R&D Quality Management (Jonathan Wooley); R&D Quality Management (Malcolm Tipping); R&D Quality Management (Mandy Jergovic); R&D Quality Management (Mary Nasopoulos); R&D Quality Management (Matthew Dickie); R&D Quality Management (Vicky Lioutas); R&D Quality Management Kankakee (Chris Lubben); R&D Quality Management Marburg (Ariane Korzen); R&D Quality Management Marburg (Ingo Brand); R&D Quality Marburg 1 (Rainer Kufka); R&D Regulatory Affairs (Susan Cameron-Laxton); R&D Regulatory Affairs Adjuvents (Hs-yen Liu); R&D Regulatory Affairs Adjuvents (Hs√º-yen Liu); R&D Regulatory Affairs Seasonal EMEA (Dalila Dolfi); R&D Regulatory Affairs US Cell-Based Products (Yael Johnson); R&D Regulatory Affairs US Pandemic (Natasha Getz); R&D Regulatory Affairs, EMEA (Monica Pagni); R&D Regulatory Affairs-US (Debbie DeMuria); R&D Regulatory Affairs-US (Kevin Darryl White); R&D Regulatory Affairs-US (Susan Cameron-Laxton (Inherited)); R&D Regulatory Affairs-US -Cambridge (Peggy Charpie); R&D Research (Ethan Settembre); R&D Research Executive Admin-Cambridge (Jane Davis); R&D Research Strategy & Operations (Rebecca Servais); R&D Serology (Giuseppe Palladino); R&D Site Management & Monitoring US/EU (Veronica Suarez (Inherited)); R&D Statistics & Data Management (Leah Isakov); R&D TD Analytical Process Testing (Tanya Riggins Clemmer); R&D TD Analytical & Drug Product Development (Dan Speelman); R&D TD Analytical (Ying Zhang); R&D TD Analytical Biochemistry (Tanya Clemmer); R&D TD Analytical Biophysical (Jiang Qian); R&D TD Analytical Cell & Molecular (Prakash Koodathingal); R&D TD Analytical Immunoanalytics (Jesse Bodle); R&D TD Analytical Immunoanalytics (Kiki Vukanovska); R&D TD Analytical Method Development I (Bryan E Hart); R&D TD Analytical Method Development I (Bryan Hart); R&D TD Analytical Method Development II (Dan Speelman); R&D TD Analytical Process (Lan Feng); R&D TD Analytical Process Testing (Tanya Clemmer); R&D TD Analytical Process Testing (Tanya Riggins Clemmer); R&D TD Analytical Separation Science (Prakash Koodathingal (Inherited)); R&D TD BPD Drug Product (Lan Feng); R&D TD BPD Product Expertise (Rochelle Bazemore); R&D TD BPD Project Management & Lab Operations (Perciliz Ahern); R&D TD BPD Purification Development (Christopher Dadd); R&D TD BPD Purification Development I (Debbie Lydiard); R&D TD BPD Upstream (Ryan Thurston); R&D TD BPD Upstream Cell Culture Development (Leslie McSweeney); R&D TD Biologics Process Design (Keith Kulowiec); R&D TD Clinical Trial Manufacturing (Keith Kulowiec (Inherited)); R&D TD Downstream Labs (Debra Lydiard); R&D TD Product Expertise (Rochelle Bazemore); R&D TD Project Manager (Lourdes Barnes); R&D TD Project Manager (Perciliz Ahern); R&D TD Purification Development (Christopher Dadd); R&D TD Purification Development HS (Christopher Dadd (Inherited)); R&D TD Purification Development HS (Debbie Lydiard); R&D TD Purification Development HS (Debra Lydiard); R&D TD Purification Development HS (Matthew Brian Smith); R&D TD Technical and Business Services (Katherine Whitley); R&D TD Technical and Business Services (Keith Kulowiec (Inherited)); R&D TD VICE Core Virology (Christine Wadey); R&D TD VICE Core Virology Holly Springs (Christopher Gully); R&D TD VICE Core Virology Holly Springs Commercial (Charles McGee); R&D TD VICE Core Virology Parkville Seed Development (Brad Dickson); R&D TD VICE Core Virology Parkville Seed Development (Lynda Allan); R&D TD VICE Molecular Virology (Catherine Agius); R&D TD VICE Molecular Virology (Chi Ong); R&D TD VICE Molecular Virology Hybridoma & Microscopy (Erin Verity); R&D TD VICE Molecular Virology Hybridoma (Kirsten Vandenberg); R&D TD VICE Molecular Virology Microscopy (Stephen Asquith); R&D TD Virology & Cell Culture (Avishek Nandi); R&D TD Virology & Cell Culture (Ryan Thurston); R&D TD Virology & Cell Culture Sub-Group II (Gwen Truong-Royce); R&D TD Virology & Cell Culture Sub-Group II (Ryan Thurston (Inherited)); R&D TD Virology & Cell Culture Sub-Group III (Leslie McSweeney); R&D TD Virology & Cell Line Sub-Group I (Christopher Gully); R&D TD Virology & Cell Line Sub-Group I (Christopher Patrick Gully); R&D TD Virology & Immunology (Steven Rockman); R&D Technical Development (Ambarish Shah); R&D Technical Development (Russell Basser (Inherited)); R&D Technical Development (Russell Basser); R&D Technical Development (Scot Shepard); R&D Technical Development, Analytical & Drug Product Development (YING ZHANG); R&D Technical Development, Analytical & Drug Product Development (Ying Zhang); R&D Technical Development, Holly Springs (Keith Kulowiec); R&D Technical Development- Holly Springs (April Sena); R&D Technical Operations BMW (Germano Coppola (Inherited)); R&D Technical Operations BMW (Norm Mancuso); R&D Technology Transfer Marburg (Falk Weihmann); R&D Toxicology (Ethan Settembre (Inherited)); R&D Transplant TA (Laurie Lee); R&D and Capital Controlling (Stephan Ludovici); R&D eClinical Technology (John Edward Cornelius); R&D/ G&A Business Partners (Ken Lim (Inherited)); RA CMC & Compliance (Ana Moisidis); RA CMC & Compliance (Pete Campbell); RA CMC & Compliance (Sahra Zanetti); RA CMC Liverpool (Joanne Beighton); RA, China (Jeep Wang ????); RCB MBR Central Lab (Annette Feussner); RCB MBR Central Lab (Helene Lang); RCB MBR Central Lab (Maria Hauswald); RE Services (Dianne Leppanen); REC 1 (Marco Hofmann); REC 2 (Philipp Claar); REC 3 (Holger Lind); REC Gene Therapy (Bala Sai Sundarasetty); REI Europe (Samuel Hou); RI Research & Innovation (Thomas Nowak); RI ¬ñ Research & Innovation (Thomas Nowak); RPL PTI (Hans Raess); RSO, RQO and RA Emerging Markets (Dirk Hoheisel (Inherited)); Racine 065 (Carl L Hutton); Racine 065 ACM Area 1 (Nicole Robinson); Racine 065 ACM Area 2 (Lemina Billups); Racine 065 QA (Megan E Hoffman); Racine 065 QA (Megan Hoffman); Rainbow City 275 (Devyn Bryant); Rainbow City 275 ACM Area 1 (Sacashla Hampton); Rainbow City 275 ACM Area 2 (Ladricka Weatherspoon); Rainbow City 275 QA (Malcolm-Bryce Richbourg); Raleigh 231 (Derek Erhart (Inherited)); Raleigh 231 (Nathan Farcasin); Raleigh 231 ACM Area 1 (Joseph Jackson); Raleigh 231 ACM Area 2 (Deanna Anderson); Raleigh 231 QA (Braxton Summers); Rapid City 288 (Brendon Sato); Rapid City 288 ACM Area 1 (Brendon Sato); Rapid City 288 ACM Area 1 (Marc Sipma); Rapid City 288 ACM Area 2 (April Miller); Rapid City 288 QA (Buck Schiley); Raw Material Acceptance Chemistry (Michelle Reckerman); Raw Material Contro/lMonitoring (Dominic Wuest); Raw Material Control/Monitoring (Dominic Wuest); Raw Material Control/Monitoring 2 (Katrin Becker); Reception / Alarmsystem (Claudia Pereira-Buehler); Recombinant Coagulation R&D Manufacture (Steven Honey (Inherited)); Recombinant Operations Support (Vicky Pirzas (Inherited)); Recombinant Portfolio Team LGN (OLGA SARNOWSKA); Recombinant Portfolio Team MBR (Anne-Regine Herboth); Recombinant Product Development (Anthony Stowers); Recombinant Product Development, Marburg (Richard Alldread); Recombinant Product Development, Marburg R&D Operation and Services (Christian Schlachtbauer); Recombinant Product Development, Marburg Vector Development (Holger Laux); Recombinant Product Development-Pasadena (Andreas Gille); Recombinant Technologies Marburg (Peter Schmidt); Records & Reporting (Boris Kaiser (Inherited)); Records & Reporting (Caroline Roost); Records & Reporting (Ivan Poffet); Region 9 New Center Operations & Support (Amanda L Kitchen); Regional Demand Planning Europe (Lukas Limbach); Regional HR Ops AUS (Clare McCann); Regional HR Ops AUS (Miya Chiba); Regional HR Ops Americas (Mark Hickenbottom (Inherited)); Regional HR Ops Americas (Rita Gross); Regional HR Ops EMEA (Stephan Schufele); Regional HR Ops EMEA (Stephan Sch√§ufele); Regional HR Ops Europe (Stephan Sch√§ufele); Regional Head Americas (Kristin McCarthy); Regional Head Clinical Operations (Jacqui Cumming); Regional Head EU APAC (Mimi Ermens); Regional Innovation Operations (Carmon Kieffer); Regional Labeling (Barbara Peruche); Regional Labeling EU (Katrin Rdiger); Regional Labeling EU (Katrin R√ºdiger); Regional Labeling EU-INT (Katrin R√ºdiger); Regional Labeling INT (Irina Sviriaeva); Regional Labeling Lead, North America (Maricarmen Dilone-Raposo); Regional Medical Affairs Operations Manager (Andrew Stork); Regional Medical Affairs Operations Manager (Rosanda Buljubasic); Regional Procurement (Lucas Jinnette); Regional Quality Support for Eastern and Central Intercontinental Commercial Operations (Jonathan Imhof); Regional Safety Officer - ECI (Marta Puente); Regional Sales 1 (Fernando Marcos V Leony); Regional Sales 2 (Rafael Esteves); Regional Sales 3 (Claudia Bueno); Regional Sales Immunology & Respiratory (Heinrich Feischen); Regional Sales Mitte Hospital (Holger Milkereit); Regional Sales Office Berlin (Bernhard Czapla); Regional Sales Office Berlin (Claudia Bachmann); Regional Sales Office Bochum (Heinrich Feischen); Regional Sales Office Frankfurt (Holger Milkereit); Regional Sales Office Hannover (Michael Bernd Rode); Regional Sales Office Munich (Susanne Mller); Regional Sales Office Munich (Susanne M√∂ller); Regional Sales Office Ost Hospital (Frank Buttchereit); Regional Sales Office West Hospital (Ralf Kosmol); Regional Sales Ost Immunology & Respiratory (Claudia Bachmann); Regional Study Management, Americas (Danielle Dalton (Inherited)); Regional Study Management, Americas (Ross Watson (Inherited)); Regional Study Management-Americas (Ross Watson (Inherited)); Regional Supplier Qlty - Bern (Peter Stettler); Regional Supplier Qlty - Kankakee (Elizabeth Queiro); Regulat. Coordination Russia & CIS (Vsevolod Nikolaev); Regulat.-, Quality- & Safety Coord.EEMEA (Camilla Shen (Inherited)); Regulat.-, Quality- & Safety Coord.EEMEA (Christine Danila); Regulation Intelligence, Knowledge and Training (Sara Mesiano); Regulation, Training & Knowledge Sharing (Vicky Gakias); Regulatory (Haruo Kitado (Inherited)); Regulatory (Satoshi Koike - ); Regulatory (Satoshi Koike ??? ? - ??? ????); Regulatory Affairs & Lab Operations (Jon Knowles); Regulatory Affairs (Doris Friedl); Regulatory Affairs (Jane Wang ????); Regulatory Affairs (Joyce P Castaneda); Regulatory Affairs (Kate Burke); Regulatory Affairs AU/NZ Dev Prod BMW (Kellie Hooley); Regulatory Affairs AU/NZ (Gosia Kupczyk); Regulatory Affairs AU/NZ (Neama Baho); Regulatory Affairs AU/NZ Dev Prod BMW (Kellie Hooley); Regulatory Affairs Asia (Queenie Ho); Regulatory Affairs Benelux (Patrick Reygaert (Inherited)); Regulatory Affairs Benelux (Roel Mallants); Regulatory Affairs France (Christine Roche [C]); Regulatory Affairs France (Laurence Vidal); Regulatory Affairs Greece (Penelope Terentiou); Regulatory Affairs Italy (Roberto DeBenedetto); Regulatory Affairs MEA (Haydi Ibrahim); Regulatory Affairs Mgr Global Labelling Ops (Laura Vanzan); Regulatory Affairs Nordic (Elin Wobbeking); Regulatory Affairs Nordic (Ulf Hultquist (Inherited)); Regulatory Affairs Spain (Julian Fierro); Regulatory Affairs UK (Helen Watts); Regulatory Coordination Africa & EEU (S√©verine Caillet); Regulatory Coordination GLAD (Sverine Caillet); Regulatory Coordination GLAD (S√©verine Caillet); Regulatory Intelligence & Policy (Bettina Doepner); Regulatory Operations, Compliance and Business Excellence (Valeria Graffeo); Regulatory Operations, Compliance and Business Excellence - HS (Detra Bullock); Regulatory Reg. Lead NA EP (Baldevsinh Rana (Inherited)); Release Bulk & Filling (Joachim Leiss); Release FRAKT/ALB/Rho (Christine Peter); Release IG/CYT (Ines Joachim); Reno 502 (Susan Gonzalez); Reno 502 ACM Area 1 (Dwayne Majette); Reno 502 ACM Area 2 (Lauren Clapham); Reno 502 QA (Chermaene Mathis); Reporting & Planning (Konstantin Petropoulos (Inherited)); Reporting A-IFRS & German GAAP, Taxes (Angelika Godosar); Requalification & Stability (Angela Hamrock-Fox); Requalification & Stability (Ilija Najdovski); Requalification & Stability (Judith Kennedy); Research; Research & Clinical Bioanalytics (Bradley Sedgmen); Research & Clinical Bioanalytics (Kirstee Martin); Research & Clinical Bioanalytics (Marit Lichtfuss); Research & Clinical Bioanalytics (Meaghan FitzPatrick); Research & Development (Douglas Lee); Research & Development Bern (Liane Hoefferer); Research & Development Bern (Nathan Roth); Research & Development II (Norbert Schulze); Research (Adele Barr); Research (Adrian Zuercher (Inherited)); Research (Adriana Baz Morelli); Research (Alexander Karnowski); Research (Anabel Silva); Research (Andrew Hammet); Research (Andrew Nash); Research (Anne Verhagen (Inherited)); Research (Anne Verhagen); Research (Arna Andrews); Research (Brodie Miles); Research (Catherine Owczarek); Research (Chao-guang Chen (Inherited)); Research (Chao-guang Chen); Research (Con Panousis); Research (Eugene Maraskovsky); Research (Glenn Powers); Research (Greg Bass); Research (Hadi Lioe); Research (Helen Cao); Research (Ian Campbell); Research (Ineke Muir); Research (Ingela Vikstrom); Research (Ingrid Lonnstedt); Research (JANE ARTHUR); Research (Jason Simmonds); Research (Jenny Chia (On Leave)); Research (Jenny Chia); Research (Judith Field); Research (KOLJA SCHAALE); Research (Katherine Monaghan (On Leave)); Research (Katherine Monaghan); Research (Kerstin Emmrich); Research (Kirsten Edwards); Research (Larissa Provan); Research (Lidija Turkovic); Research (Mae-Xhum Wong); Research (Marco Weinberg); Research (Mark Biondo); Research (Mark Liddament (Inherited)); Research (Mark Liddament); Research (Martin Pearse); Research (Matthias Pelzing); Research (Mhairi Maxwell); Research (Michael Wilson (Inherited)); Research (Michael Wilson); Research (Michael Yan); Research (Milica Ng (Inherited)); Research (Milica Ng); Research (Natasha Pereira); Research (Nick Wilson); Research (Peter Schmidt); Research (Pierre Scotney); Research (Pino Maccarone); Research (RAJESH GHAI); Research (Rebecca Butcher); Research (Sabine Rauth); Research (Sandro Prato); Research (Saw Yen Ow); Research (Shirley Taylor); Research (Srikanth Budnar); Research (Steven Dower (Inherited)); Research (Steven Dower); Research (Steven Lee); Research (Victor Turnbull); Research (Walid Azar); Research (Wei Hong Toh); Research 1 (Hannah Chu); Research 1 (Mihee Kim); Research Bern (Adrian Zuercher); Research Bern Platforms (Christoph R√∂sli); Research Bio21 (Michael Wilson); Research Data Science (Milica Ng); Research I () (Wenting Zhao); Research I (Chao-guang Chen); Research II (Victor Turnbull); Research III (Mark Liddament); Research IV (Marco Weinberg); Research Innovation (Marthe D''Ombrain); Research Marburg (Thomas Weimer); Research Marburg Diverse (Thomas Weimer (Inherited)); Research Scientist - Bioinformatics (Monther Alhamdoosh); Research Therapeutic Area (Eugene Maraskovsky); Research and Clinical Bioanalytics (Allison Dyson); Research and Clinical Bioanalytics (Andreas Gille); Research and Clinical Bioanalytics (Anthony Roberts); Research and Clinical Bioanalytics (Elena Velkoska); Research and Clinical Bioanalytics (Kirstee Martin (Inherited)); Research and Clinical Bioanalytics (Kirstee Martin); Research and Clinical Bioanalytics (Roslyn Davis); Research and Clinical Bioanalytics (Tim Green); Research and Clinical Bioanalytics 1 (Lisa Lindqvist); Research and Development, China (Zak Huang); Research and Laboratory (Andrew Isaac); Research,Therapeutic Area C&M (Bronwyn Kingwell); Resp. Apprentices Bio Lab Techn. (Wim Etter); Respiratory TA (Heike Thiele); Results & Analysis (Jonathan Matty); Results & Analysis (Kenneth Walsh); Results & Analysis I (Jonathan Matty); Results & Analysis II (Jonathan Matty); Review IG/CYT (Armin St√∂cklin); Review IG/CYT (Thomas Kilchoer); Rhophylac (Andrea Stauffiger Eggli); Rhophylac Bulkmanufacturing (Andr Wegmueller); Rhophylac Bulkmanufacturing (Andr√© Wegmueller); Rhophylac Bulkmanufacturing 2 (Reto Stucki); Rialto 507 (Robert Ellison III); Rialto 507 QA (Derek Erhart (Inherited)); Risk & Mitigation Management (Malte Krmer); Risk & Mitigation Management (Malte Kr√§mer (Inherited)); Risk & Mitigation Management (Malte Kr√§mer); Risk & Project Management (Uli Kiefer); Riverside 299 (Iiemmaue Morgan); Riverside 299 QA (Anne Tran); Riviera Beach 115 (Martel Carter); Riviera Beach 115 (Nakia J Harlan); Riviera Beach 115 (Nedra N Braden); Riviera Beach 115 ACM Area 1 (Ethan Johnson); Riviera Beach 115 ACM Area 2 (JASON TRUMBACH); Riviera Beach 115 QA (Bill Angelucci); Riviera Beach 115 QA (Stalmore Duncan); Rochester 232 (Kay Schwartz); Rochester 232 (Lin Macaluso); Rochester 232 ACM Area 1 (Marissa Peterson); Rochester 232 ACM Area 2 (Michelle Draper); Rochester 232 ACM Area 2 (Michelle Horan); Rochester 232 QA (K.C. McCaffery); Rochester 232 QA (Karen Weatherston); Rock Hill 130 (Damon Lehr); Rock Hill 130 (Nicole M Adams); Rock Hill 130 ACM Area 1 (Ashley Pinckney); Rock Hill 130 ACM Area 2 (Brittney Joiner); Rock Hill 130 QA (Bianca M Brunson); Rock Hill 130 QA (Damon Lehr); Rock Island 426 (Josh Buzzell); Rock Island 426 ACM Area 1 (Chandler J Johnson); Rock Island 426 ACM Area 2 (James Rathmann); Rock Island 426 QA (Jennifer D Anthony); Rockford 200 (Kristi Davis); Rockford 200 (Sherylene A Lloyd); Rockford 200 ACM Area 1 (Kristi Davis (Inherited)); Rockford 200 ACM Area 1 (Kristy Carlson); Rockford 200 ACM Area 2 (Paul Crawford); Rockford 200 QA (Amanda Sawlsville); Rome 298 (Marida L Bivens); Rome 298 ACM Area 1 (Salvador Reyes); Rome 298 ACM Area 2 (Matt Comfort); Rome 298 QA (Samantha D Beach); Rome 298 QA (Stephanie D Shah (Inherited)); Roseville 077 (Charles LaVell Jacobs); Roseville 077 (Kourtney Davis); Roseville 077 ACM Area 1 (Charles LaVell Jacobs (Inherited)); Roseville 077 ACM Area 1 (Porsche M Goldsmith); Roseville 077 ACM Area 2 (Natalie King); Roseville 077 QA (Kayla D Lindley); Roseville 077 QA (Nichole M Clay (On Leave)); Roseville 077 QA (Nichole M Clay); Routine Systems (Martene Bond); Ruide Wuhan EHS&S (zcan Campinar); Ruide Wuhan Production (√ñzcan Campinar); Russia & CIS (Maria A Lituchaya); SAP Business Processes (Maike Pollaschek (Inherited)); SAP Business Processes (Maike Pollaschek); SAP Competency Center (Helen Baker); SAP Competency Center (Jonathan Turner); SAP Extended Warehouse Management (Riju Varghese); SAP Finance team (Jonathan Young); SAP Manufacturing Support Team (Manohar Venkataraman); SAP Master Data (Paul Aberson); SAP Quality / Logistics Team (Helen Baker (Inherited)); SAP Quality / Logistics Team (Matthew Gordon); SAP Service and Release (Martin Eldred); SAP Solution Center Bern (Mourad Boulanouar); SAP System Admin (John McCorry); SI Manufacturing (Jason Vaughn); SI Manufacturing (Samuel Jackson); SI Validation (Michael Donley); SI Validation (Robert Musgrave); STA Gruppe I (Claudia Schwab); STA Gruppe I (Stefanie Grafmller); STA Gruppe I (Stefanie Grafm√ºller); STA Gruppe II; STA Gruppe II (Susan Blaser); STA Stability (Manuel Wohde); STA Stability 2 (Barbara Gmann); STA Stability 2 (Barbara G√∂√ümann); STA Stability 3 (Gernot Kissel); STA Stability 4 (Svenja Nieba); STA Stability 5 (Milosz Krala); STA Stability 6 (Oliver Kupski); STA Stabilit√§t, QFP (Annette R√∂hrenbeck); STA Stabilit√§t, QFP (Barbara G√∂√ümann); STA Stabilit√§t, QFP (Christian Sinnen); STA Stabilit√§t, QFP (Gernot Kissel); STA Stabilit√§t, QFP (Julia Kufka); STA Stabilit√§t, QFP (Manuel Wohde); STA Stabilit√§t, QFP (Svenja Nieba); Safety (Alexandra Nogal); Safety (Allan Wise (Inherited)); Safety (Chris Meeks); Safety (Rolf Ingold); Safety (Steven Hull); Safety Risk Management (Max Waschbusch); Safety Risk Management (Pradeep Kumar Sahu); Safety Risk Management (Susan Welsh (Inherited)); Safety Risk Mgt (Alison Graves Jones); Safety Risk Mgt (Astrid Schneider); Safety Risk Mgt (Daphne Sawlwin); Safety Risk Mgt (Gabriele Neumann); Safety Risk Mgt (Joseph Whitten); Safety Risk Mgt (Kristy Van Dinther); Safety Risk Mgt 1.0 (Gabriele Neumann); Safety Risk Mgt 2.0 (Beate Greene); Safety Sciences (Haley Kaplowitz); Safety Systems Projects (JANET AUERBACH); Safety ¬ñ EU/APAC (J√ºrgen Kanand); Saginaw 169 (Amy Railling); Saginaw 169 (Ashley M Jamieson (Inherited)); Saginaw 169 (LC Davis); Saginaw 169 (Latosha Y Floyd (Inherited)); Saginaw 169 ACM Area 1 (TAYLOR GOODWINE (On Leave)); Saginaw 169 ACM Area 1 (TAYLOR GOODWINE); Saginaw 169 ACM Area 2 (Scott Walker); Saginaw 169 QA (Nicole Naji); Saginaw 282 (DaWanna Smith); Saginaw 282 ACM Area 1 (Genesha Curry); Saginaw 282 ACM Area 2 (Andrea Bragg); Saginaw 282 QA (Darren Hall); Salem 221 (Cory Vierck); Salem 221 (Paige N Zafran); Salem 221 (Timothy Freeland Jr (Inherited)); Salem 221 ACM Area 1 (Brandon D Biery); Salem 221 ACM Area 2 (Edward Baye); Salem 221 QA (Rachel R Maddox); Salem 221 QA (Timothy Freeland Jr (Inherited)); Sales & Marketing (Andrea Bennett); Sales & Marketing (Joe Dempsey); Sales & Marketing (Kaye Nolan); Sales & Marketing (Kirsten Comer); Sales & Marketing (Tanja Wells); Sales & Marketing Turkey (Ahmet Can Kalenderoglu); Sales & Private Accounts & Tender Office (Franco Gatta); Sales & Private Accounts & Tender Office (Massimo Leoni (Inherited)); Sales & Private Accounts & Tender Office (Massimo Leoni); Sales (Beata Szymanska-Czyz); Sales (Jorge L Gast√©lum (Inherited)); Sales (Jorge Marco); Sales (Markus Wenninger); Sales (Saul Ortiz Carrillo); Sales (Virgile Grosjean); Sales - CSL Behring Taiwan (Frank Ko ); Sales - CSL Behring Taiwan (Frank Ko ?????); Sales 2 (Claudia Sanchez); Sales 3 (Gema Gonzalez); Sales Benelux IG & CC (Marijke Maes); Sales Benelux IG & CC (Philip Vandromme); Sales Denmark / IC (Mette Toft Jacobsen); Sales Division (Izumi Yoshida ??? ??? - ??? ????); Sales Division (Toshio Nagata); Sales Division CAB Central Japan Area (Yoshifumi Umenaka); Sales Division CAB East Japan Area (Takahiro Tsuruta); Sales Division CAB West Japan Area (Akihiro Enomoto); Sales Division Critical Care & Acquired Bleeding T.A. (Hiroyoshi Iwamoto); Sales Division HEM East Japan Area (Atsuhiko Arikata); Sales Division HEM Kansai & Chubu Area (Shinichi Kano); Sales Division HEM Shutoken Area (Takayuki Takigawa); Sales Division HEM West Japan Area (Taisuke Miyakoshi); Sales Division Hemophilia TA (Hideki Yanagihashi ??? ?? - ????? ????); Sales Division Hemophilia TA (Takayuki Ishii); Sales Division IRD Central Japan Area (Takayuki Azuma); Sales Division IRD East Japan Area (Madoka Yamamoto); Sales Division IRD Kansai & Hokuriku Area (Takahiro Miura); Sales Division IRD Shutoken Area (Hironori Fujioka - ); Sales Division IRD Shutoken Area (Hironori Fujioka ??? ?? - ???? ?????); Sales Division IRD West Japan Area (Hiroki Nagayasu); Sales Division Immunology & Rare Diseases T.A. (Naoki Ikeguchi); Sales Division Kcentra Team (Tomokazu Shiroza); Sales Division SID T.A. Ig/Hematology Group (Izumi Yoshida ??? ??? - ??? ????); Sales Division SID T.A. Ig/Hematology Group (Kenichiro Yamaguchi - ); Sales Division Sales Planning & Wholesaler Management (Hideki Yanagihashi - ); Sales Division Sales Planning (Takayuki Ishii); Sales Division Sales Planning Wholesaler Management, Sales Admin (Hisako Sakoda); Sales Division Sales Planning,Sales Admin Group (Hisako Sakoda); Sales Division Tentative Team (Hiroyoshi Iwamoto); Sales Division Wholesaler Management Customer Support Team (Kyohei Yamamoto); Sales Division Wholesaler Management Distributor Team (Kyohei Yamamoto); Sales Finland (Sirpa Reimari); Sales Force Center (Flavio Di Pietro); Sales Force North (Paolo Francesco Corsi); Sales Force South West (Renato Monteleone); Sales France (Emmanuelle Massonie); Sales France (Franck Puget (Inherited)); Sales France (Karim Abassi); Sales Greece (Christos Fouskotis); Sales Hospital (Popp Gbor); Sales Hospital (Popp G√°bor); Sales Immunology & Coagulation (Kadar Attila); Sales Management Hemophilia (Michael Schulz (Inherited)); Sales Management Hemophilia (Michael Schulz); Sales Norway (Kjell Anker Worren); Sales Operations and Data (Chris Meyer); Sales Spain (Toni Par√©s); Sales Sweden (Nicklas Wallin); Sales Team Belgium (Marijke Maes (Inherited)); Sales Team Belgium (Philip Vandromme (Inherited)); Sales Team France (Emmanuelle Massonie (Inherited)); Sales Team France (Franck Puget (Inherited)); Sales Team France (Karim Abassi (Inherited)); Sales Team Netherlands (Marijke Maes (Inherited)); Sales Team Netherlands (Philip Vandromme (Inherited)); Sales Team UK (John Liam Boyle); Sales Team UK 2 (Nicky Whiteley); Sales Training Manager (Phil Hutton); Salt Lake 627 (Brooke A Neuroth); Salt Lake City 627 (Dave Lynn); Salt Lake City 627 (Marc D Fisher (Inherited)); Salt Lake City 627 (Nate Justet); Salt Lake City 627 ACM Area 1 (Michael E Forman); Salt Lake City 627 ACM Area 2 (Andrew V Lopez); Salt Lake City 627 ACM Area 2 (Ross R Fredrickson); Salt Lake City 627 QA (Allison M Davis); Sample Logistics (Brigitte Harris); Sample Management Quality Control (Christoph Wyss); Sample logistics (Christine Beyeler); Sampling (Lachlan McDonald); San Antonio 087 (Becca Charles); San Antonio 087 (Jennifer Martinez); San Antonio 087 ACM Area 1 (Kamala Yevetta Brown); San Antonio 087 ACM Area 2 (Aaron Thornton); San Antonio 087 ACM Area 2 (Kamala Yevetta Brown); San Antonio 087 QA (Alicia D Conner); San Antonio 157 (Sara A Anderson); San Antonio 157 (Sara Saleem); San Antonio 157 ACM Area 1 (Erika Gonzalez); San Antonio 157 ACM Area 2 (Crystal N Morton-Rollins); San Antonio 157 QA (Brenton Ferguson); San Antonio 157 QA (Nakia J Harlan); San Luis 158 (Javier Luna); San Luis 158 ACM Area 1 (Cristina Silva); San Luis 158 ACM Area 2 (Paulina Pena); San Luis 158 QA (MARA TAFOYA); San Luis 158 QA (Miguel Palomera); Sanitation (Union) (Adila Zaidi); Sanitation (Union) (Michael Memenga (Inherited)); Scanton 240 (Christopher Travalik (Inherited)); Schenectady 229 (Andrew Brammer); Schenectady 229 (Melissa Moore); Schenectady 229 ACM Area 1 (Renie Ball); Schenectady 229 ACM Area 1 (Ronald Cameron); Schenectady 229 ACM Area 2 (Karena Caraballo); Schenectady 229 QA (Sandy Nicholson); Schicht EMR (Gunthard Ludwig); Schicht EMR GH (Bjrn Krieg); Schicht EMR GH (Bj√∂rn Krieg); Schicht HW (Bjrn Krieg); Schicht HW (Bj√∂rn Krieg); Schicht HW (Christian Zeman); Schichtgruppe 1 ASQ (Marcus Heinzmann); Schichtgruppe 2 ASQ (Harald Ferber); Schichtgruppe 3 ASQ (Ruben Zinnkann); Schichtgruppe 3 ASQ (Svea Bieker); Schichtgruppe 4 ASQ (Gerhard Senftner); Scientist (Theresa Qiu); Scientist (Tony Cardno); Secondary Asset Care & Reliability (William Murphy); Secondary Automation (Muhammad Khan); Secondary Engineers (Calum Courtney); Secondary Manufacturing (Tristan Betson); Secondary Manufacturing Support Technicians (Gerard Lopez); Secondary PMO (Carrie O''Keeffe); Secondary PMO (Duncan Benson); Secondary Programmes (CARRIE OKEEFFE (Inherited)); Secondary Projects QA (Karen Marks); Secondary Projects Tech Transfer (Freddie Wayne West); Secondary Projects, Liverpool (CARRIE OKEEFFE); Secondary Projects, Liverpool (Carrie O''Keeffe); Secondary, Utilities and QC Validation (Joao Silva Acioli); Secretary ES (Montserrat Rey); Security CSL Behring Australia (Sharon Carroll); Security Manager 281 (Az Raonaq); Security Manager 281 (Chris Riley); Security Manager 281 (Nicholas Moody (Inherited)); Security Operations (Ganesha Rajanaidu); Security Operations (Ram Narasimman); Sen Ass QA Batch Release (Carol Youssef); Sen Ass QA Batch Release (Chris Graves); Sen Ass QA Batch Release (Nancy Manolitsas); Sen Ass QA Batch Release (Olivia Fisher (Inherited)); Sen Ass QA Batch Release (Olivia Fisher); Sen Ass QA Batch Release (Zareena Shaik); Sen Assoc Contin/Re-Validation (Robert Alvarez); Sen Assoc Validation Operation (Michelle Botterill); Sen Assoc Validation Operation (Nick Morgan (Inherited)); Sen Assoc Validation Operation (Nick Morgan); Sen Mgr QA Batch Release (Darren Moulton); Senior Associate QA Batch Release (Joanna Davis); Senior Associate QA Batch Release (Josie Lanza); Senior Associate QA Capability (Brett Pool); Senior Associate QA Capability (Marcela Rojas (Inherited)); Senior Associate QC Support (Jo Karra); Senior Director Manufacturing (Chris Larkins); Senior Electrical Engineer (Anthony Wrzesinski (Inherited)); Senior Electrical Engineer (Nicholas Hall); Senior Electrical Engineer (Stanislaw (Stan) Hudy); Senior Electrical Engineering Manager (Claro Pellosis); Senior HR Business Partner (Devon Anderson); Senior HR Business Partner (Sharon Davoli); Senior HR Business Partner (Sonia Georgesz); Senior HR Business Partner (Sonia Pititto); Senior Manager QA Capability (Marcela Rojas); Senior Manager Validation (Dina El-Emary); Senior Manager Validation (Michelle Botterill); Senior Manager Validation (Russell Ciliento (Inherited)); Senior Manager Validation (Shane Bourne); Senior Manager, Innovation R&D (Chi Ong); Senior Process Engineer (Robert Hemaya); Senior Process Manager - Utilities (Christiaan Theron (Inherited)); Senior Process Manager - Utilities (Christiaan Theron); Senior Process Manager, Supply Chain (Helen Malliaras); Senior Process Manager, Supply Chain (Lachlan Cruise (Inherited)); Senior Project Manager (Anthony Wrzesinski (Inherited)); Senior Project Manager (Brian Guilly); Senior Project Manager (Raoul Gorris); Senior Regulatory Affairs Manager (Ana Moisidis); Senior Scientist (Albert Garcia Minambres); Senior Scientist (Armando Alabella); Senior Scientist (Kiki Vukanovska (On Leave)); Senior Scientist (Kiki Vukanovska); Senior Scientist (Kim Steegh); Senior Scientist (Maria Panayi); Senior Scientist (Matthew Hardy); Senior Scientist (Sachiyo Nishio); Senior Scientist (Tom Murray-Rust); Senior Scientist Biacore (Bernadine Lu); Senior Scientist EM Unit (Stephen Asquith); Separatoren (Arnd Vollmerhausen (Inherited)); Separatoren (Erkan nder); Separatoren (Erkan √ñnder); Seqirus Head of Legal, Asia (Marcus De Alwis); Seqirus Ltd (Anjana Narain); Seqirus Ltd (Gordon Naylor); Seqirus Ltd (Stephen Marlow); Seqirus Ltd II (Gordon Naylor (Inherited)); Serialisation Operations and Projects (Michel Stritt); Serialisierung (Michel Mller); Serialisierung (Michel M√ºller); Serialization & Anti-Counterfeiting (Andrew John Robinson); Serialization & Anti-Counterfeiting (Warren Comerford (Inherited)); Serology Lab (Dan Thompson); Serology Lab - 1st Shift (Undrea W Jenkins); Serology Lab - 3rd Shift (Angela C Reynolds); Serology Lab - Weekend (Undrea W Jenkins (Inherited)); Service Management (Jim Towarnicki); Servicecenter Hemophilia (Axel Hochfeld); Shared Area Engineering (Marc Herbener); Shreveport 245 (Johnnie Williams); Shreveport 245 (Marcia Schels); Shreveport 245 ACM Area 1 (Demetricia Moore); Shreveport 245 ACM Area 1 (Edgar Rodriguez); Shreveport 245 ACM Area 2 (Matt Comfort); Shreveport 245 ACM Area 2 (Rashunda Dock); Shreveport 245 QA (Kaci Miller); Single Case Management & PhV Systems (Jessica Corrall (On Leave)); Single Case Management & PhV Systems (Jessica Corrall); Single Case Mgt & Phv Systems (Liz Pound); Single Case Mgt & Sy (Sabine H√§rtel (Inherited)); Single Unit Verification 1 (Kai Wlk); Single Unit Verification 1 (Kai W√∂lk); Single Unit Verification 2 (Norbert Vollmerhausen); Single Unit Verification 3 (Karl-Heinz Stelzig); Site Administration (Deborah Lynes); Site Engineering Services (Alex Elandt [C]); Site Engineering Services (Alex St√§hli); Site Lead project Eureka (Chris Camilleri); Site Lead project Eureka (Philip Elliott (Inherited)); Site Logistics (Daniel Schmidt); Site Logistics Production (Igor Kaucher); Site Management (Ross Watson); Site PMO (Karen Mitchell); Site Security (Az Raonaq); Site Security (Matthias Gnther); Site Security (Matthias G√ºnther); Site Security Switzerland (Julien Lischer); Site Supply Chain (Dennis Finger); Site- & Project-Management (Ferdinand Marx); Site- & Project-Management (Marko Witt); Site- & Project-Management (Rainer Herbener); Smryna 123 QA (Apple Grace Swindell); Smyrna 123 (Stephen Jefferson); Smyrna 123 ACM Area 1 (Venessa Lucio); Smyrna 123 ACM Area 2 (Travis Conner); Smyrna 123 QA (Apple Grace Swindell); Snr Assoc, QA Batch Release (Abha Patel); Snr Director Quality PKV530 (Milka Smoljko); Snr Mgr, Bus Effectiveness (Collette Makdissi); Snr Reg Advisor (David Plant); Snr Scientist Flu Innov (Kirsten Vandenberg); Snr Scientist Influ.Innov 275 (Catherine Agius); Snr.Scientist Flu Pilot Facil (Brad Dickson); Solid Organ Transplant Marketing (Courtney Wilson); Solutions Team Leader (Shona Moore); Source to Pay (Andrew Croft (Inherited)); Source to Pay (STP) (Dennis Martin); Sourcing (Brigitte Kimpel-Koch [C]); Sourcing (Frank Liesner); Sourcing (Jens Knoch); South America Sales Ops (Jean-Claude Andr√© (Inherited)); South Korea Operations (Ji-Young Sohn); South Milwaukee 140 (Cory Toellner (Inherited)); South Milwaukee 140 (Justin N Gronbach); South Milwaukee 140 (Kevin Labriola); South Milwaukee 140 ACM Area 1 (Cassandra J Cecka); South Milwaukee 140 ACM Area 2 (Shannon T Bardega); South Milwaukee 140 QA (Amy M Gebhardt); South Portland 256 (Cory Vierck); South Portland 256 ACM Area 1 (Kendra Howard); South Portland 256 ACM Area 2 (Cameron Clement); South Portland 256 QA (Mark Anderson); Southfield 070 (Lauren Jenkins); Southfield 070 (Marida L Bivens); Southfield 070 ACM Area 1 (Lori Daratony); Southfield 070 ACM Area 2 (Linda M Walker); Southfield 070 ACM Area 3 (Tiffany A Patton); Southfield 070 QA (Tamil Pettway (On Leave)); Southfield 070 QA (Tamil Pettway); Spartanburg 179 (Darrell Brotherton); Spartanburg 179 (Jose Pineda); Spartanburg 179 ACM Area 1 (Shaqueda Cariens); Spartanburg 179 ACM Area 2 (Krysten Evans); Spartanburg 179 AMQ (Jennifer Fox); Spartanburg 179 QA (Jennifer R Fox); Spartanburg 179 QA (Jose Pineda); Spartanburg 179 QA (Vernicia Smith); Spartanburg 179 QA (Victoria McIntyre (Inherited)); Specialist Area Bus Manager775 (Lisa Stewart); Specialist Area Business Manager (Katerina Kouridakis); Specialist Area Business Manager (Natasha Hutchison (Inherited)); Specialist Area Business Manager (Steve Carroll); Specialty HAE (Debbie Bensen-Kennedy (Inherited)); Specialty HAE (Joseph Chiao); Specialty Plasma (Phyllis Bressler); Specialty Plasma (Robert P Lawler (Inherited)); Spectroscopy and Elementary Analysis (Pierre-Harald Schmidt); Spectroscopy and Elementary Analysis (Stefan Wilka); Sphinx (Klara Cela); Sphinx II (Fynn Krieger); Spokane Main 621 (Adam Allred); Spokane Main 621 (Juli McConnell); Spokane Main 621 ACM Area 1 (Maurice V R Reed); Spokane Main 621 ACM Area 2 (Janette R Williams); Spokane Main 621 QA (Andee Leigh Schock); Spokane Valley 622 (Donna L King); Spokane Valley 622 (Ryan H Rettkowski); Spokane Valley 622 ACM Area 1 (Josh Kearney); Spokane Valley 622 ACM Area 1 (Ryan H Rettkowski (Inherited)); Spokane Valley 622 ACM Area 2 (Donna L King (Inherited)); Spokane Valley 622 ACM Area 2 (Juli McConnell); Spokane Valley 622 QA (Donna L King); Spokane Valley 622 QA (Rachel R Maddox); Springdale 268 (Justin Hampton); Springdale 268 ACM Area 1 (Devona D Williams); Springdale 268 ACM Area 2 (Ellie Kordooni); Springdale 268 QA (Karina G Campos); Springfield 285 (Quawan Dhom); Springfield 285 QA (Pari Patel); Springfield 492 (Amy L Pruitt); Springfield 492 (Tyler L Robinson); Springfield 492 ACM Area 1 (Carmen Gonzalez); Springfield 492 ACM Area 1 (Peter J Gouvas); Springfield 492 ACM Area 2 (Natalie N Williams); Springfield 492 QA (Marcie B Deal); Springfield 620 (Karen Aspinwall); Springfield 620 (Karen Hebbert); Springfield 620 ACM Area 1 (Karen Hebbert (Inherited)); Springfield 620 ACM Area 1 (Lisa M Meredith); Springfield 620 ACM Area 2 (Julia A Thompson); Springfield 620 QA (Becky D Clute); Sr. Clinical Program Manager Clinical Development (Anthony Ciliberto); Sr. Clinical Program Manager ¬ñ Clinical Development (Anthony Ciliberto); Sr. Clinical Program Mgr ¬ñ Clinical Development (Anthony Ciliberto); Sr. Scientist S''visor EM Unit (Ross Hamilton); St Louis 107 (Jimmy Williamson Jr); St Louis 107 (Robert Karbach); St Louis 107 ACM Area 1 (Ashli N Pinson); St Louis 107 ACM Area 1 (Jimmy Williamson Jr (Inherited)); St Louis 107 ACM Area 2 (Ashli N Pinson); St Louis 107 ACM Area 2 (Sentoria D Leonard-Brown); St Louis 107 QA (Sharica Ausler); St Louis 132 (Tiffany D Thurman); St Louis 132 ACM Area 1 (Chris Haley); St Louis 132 ACM Area 2 (Kevin S Neidinger); St Louis 132 ACM Area 2 (Tiffany D Thurman (Inherited)); St Louis 132 QA (Abby Hill); St Louis 132 QA (Jacob P Phillips); St Paul 180 (Darin L Bargsten); St Paul 180 ACM Area 1 (Cody A Patton); St Paul 180 ACM Area 2 (Brenda L Steffen); St Paul 180 QA (Amanda Peroutka (On Leave)); St Paul 180 QA (Amanda Peroutka); St Paul 180 QA (Holt Peterson (Inherited)); St Paul 416 (Scott Cantrell); St Paul 416 QA (Diego A Bastidas); Stability (Anita Jansen de Salazar); Stability (Jessica Mackellin); Stability (Jessica Parletta); Stability (Michel Baur (Inherited)); Stability (Rossana Amodeo); Stability Trials and Retention Samples (Chris O''Meara); Starting Materials Testing & Release (Simone Lang); State College 262 (Daniel LoCasale); State College 262 ACM Area 1 (Justin Nolan); State College 262 ACM Area 1 (Maria Garlick); State College 262 ACM Area 2 (Hunter Millward); State College QA 262 (TARA STYERS); State Government Affairs & Eastern Reg. (Karla White); Statistics & Data Management (Wilfried Meyers); Stellv. Center Manager (Andreas Gehrich (Inherited)); Stellv. Center Manager (Andreas Gehrich); Stellv. Center Manager (Annette Pernitzsch (Inherited)); Stellv. Center Manager (Annette Pernitzsch); Stellv. Center Manager (Claudia Habenicht (Inherited)); Stellv. Center Manager (Claudia Habenicht); Stellv. Center Manager (Damaris Kieckhfer (Inherited)); Stellv. Center Manager (Damaris Kieckh√∂fer (Inherited)); Stellv. Center Manager (Heike Borchert (Inherited)); Stellv. Center Manager (Kirsten Scheibel (Inherited)); Stellv. Center Manager (Kirsten Scheibel); Stellv. Center Manager (Natascha Bock (Inherited)); Stellv. Center Manager (Natascha Tappendorf); Stellv. Center Manager (Stephani Keltsch); Stellv. Center Manager (Sven Schuhmann (Inherited)); Stellv. Center Manager (Sven Schuhmann); Stellvertretender Labormanager (Astrid Mather (Inherited)); Sterile Filling AlbuRx (Hai Tran); Sterile Filling AlbuRx (Jennifer Tang); Sterile Filling AlbuRx (Mason Briner (Inherited)); Sterile Filling AlbuRx (Mason Briner); Sterile Filling AlbuRx (Matthew Donegan); Sterile Filling AlbuRx (Nina Djordjevich); Sterile Filling AlbuRx (Paolo Robillos); Sterile Filtration (Jakob Locher); Sterility (Anja Djordjevich); Sterility (Denise Vella (Inherited)); Sterility (Johanna Mock); Sterility (Nicole Magno); Sterility (Sabrina Desiree Sann); Sterility Assurance Monitoring & Trending (Marika Moore); Sterility Assurance (Barbara Moser); Sterility Assurance (Boo Pit Tan); Sterility Assurance (Craig Stephens (Inherited)); Sterility Assurance (Darla Erman); Sterility Assurance (Jessica Kay); Sterility Assurance (Meena Shakaib); Sterility Assurance (Peter Major); Sterility Assurance (Richard Hughes); Sterility Assurance (Robert O''Malley); Sterility Assurance (Tyson Parker); Sterility Assurance ¬ñ Monitoring & Trending (Marika Moore); Sterling Heights 164 (Kayla J Allen); Sterling Heights 164 (Shauna Douglas); Sterling Heights 164 ACM Area 1 (Zack Hyso); Sterling Heights 164 ACM Area 2 (Shauna Douglas (Inherited)); Sterling Heights 164 ACM Area 2 (Shauna Douglas); Sterling Heights 164 QA (Elijah J Wilson); Sterling Heights 164 QA (JoJo Sobjack); Stone Mountain 119 (Antonia Geiselmayr); Stone Mountain 119 (William A Voltz); Stone Mountain 119 ACM Area 1 (Milaine Clairvil); Stone Mountain 119 ACM Area 2 (Derrick Barnes); Stone Mountain 119 QA (Marketa D Goodwin (On Leave)); Stone Mountain 119 QA (Marketa D Goodwin); Storage-Virtualization-DR (Ali Bakhtiar); Storage/Virtualization/DR (Ali Bakhtiar); Strat Project Portfolio & Op Excellence (Michael Schrder (Inherited)); Strategic Analytics & Pricing (Paul Jens); Strategic Analytics (Manish Srivastava); Strategic Expansion Projects (Robyn Elliott); Strategic Indirect Sourcing (David Pauli); Strategic Initiatives (Matt Shapiro); Strategic Initiatives ENG (Dilip I Raval); Strategic Initiatives ENG (Gene Bohn); Strategic Project Portfolio and Operational Excellence (Gil Rochat); Strategic Project Portfolio and Operational Excellence (Martin Schaeren (Inherited)); Strategic Sourcing (Benjamin Fruin); Strategic Sourcing Capex & MRO Sourcing (Jos Maldonado); Strategic Sourcing Capex & MRO Sourcing (Jos√© Maldonado); Strategic Sourcing Capex & MRO Sourcing (Paul Addis (Inherited)); Strategic Sourcing Capex/MRO MBG (Bernd Mhling); Strategic Sourcing Capex/MRO MBG (Bernd M√ºhling); Strategic Sourcing Direct (Martin Grossmann); Strategic Sourcing Direct Packaging, Devices, Containers, Closures, R&D (Benjamin Fruin); Strategy & Business Development (Alan Wills (Inherited)); Strategy & Business Development (Alan Wills); Strategy & Business Development (Andrea Douglas); Strategy & Business Development (Bev Menner); Strategy & Business Development 2 (Stephanie Read); Strategy & Innovation (Ken Lim); Studium Plus (Carmen Walldorf (Inherited)); Studium Plus (Doris Nake (Inherited)); Study File Management (Elizabeth Petersen); Study Operations (3) (William Karich); Study Operations (Christa Lewiski); Study Operations (Janis Witzleb); Study Operations (Lyndah Oswald - Okebata); Superior PTH Vorbehandlung 3 / Abfllung 3 H069 (Adam Krajewski); Superior PTH Vorbehandlung 3 / Abfllung 3 H069 (Frank Gerhard Grger); Superior PTH Vorbehandlung 3 / Abf√ºllung 3 H069 (Adam Krajewski); Superior PTH Vorbehandlung 3 / Abf√ºllung 3 H069 (Frank Gerhard Gr√∂ger); Superior PTH Vorbehandlung 3 / Abf√ºllung 3 H069 (Sylvia Kauf); Supervisor (Andreas Gehrich (Inherited)); Supervisor (Andreas Gehrich); Supervisor (Annette Pernitzsch (Inherited)); Supervisor (Annette Pernitzsch); Supervisor (Claudia Habenicht (Inherited)); Supervisor (Claudia Habenicht); Supervisor (Damaris Kieckhfer (Inherited)); Supervisor (Damaris Kieckh√∂fer (Inherited)); Supervisor (Heike Borchert (Inherited)); Supervisor (Kirsten Scheibel (Inherited)); Supervisor (Kirsten Scheibel); Supervisor (Natascha Bock (Inherited)); Supervisor (Natascha Tappendorf); Supervisor (Stephani Keltsch); Supervisor (Sven Schuhmann (Inherited)); Supervisor (Sven Schuhmann); Supplier Management (Bill Chambers); Supplier Management (Ivo Kreyenbuehl); Supplier Quality Management (Allen F Coleman); Supplier Quality Management (Justin K Zajc); Supplies, Liverpool (Stephen Magill [C]); Supplies, Liverpool (William Helsby); Supply & Logistics (Avril Lam); Supply & Logistics (Winnie Yau); Supply Chain (Anita Erber); Supply Chain (Boris Kaiser); Supply Chain (Rick Gibson); Supply Chain Business Process (Wolfgang Schneider); Supply Chain External Manufacturing (Stuart Summers); Supply Chain Finance (Kiran Duhra); Supply Chain Liverpool (James Monaghan); Supply Chain Maidenhead (Ian Dick); Supply Chain Management (Cameron Barrett); Supply Chain Management (Michael F Deem); Supply Chain Management (Ryoichi Imamura); Supply Chain Mgt & Operational Planning (Robert P Lawler); Supply Chain Mgt (Mischa Moeckli); Supply Chain Planning & Inventory Management (Kevin L Robards); Supply Chain Planning (Cheryll McLeod); Supply Chain Planning (David McClure); Supply Chain Planning (Ratana Lim); Supply Chain Planning (Serge Marques); Supply Chain Planning (Sharon Gough); Supply Chain Planning (Unni Nair); Supply Chain QA (Andrew Norman); Supply Chain Services (Dennis Finger); Supply Chain Services (Grant Gaddis); Supply Chain Services (Kelly L Konemann (Inherited)); Supply Chain Services (Kelly L Konemann (On Leave)); Supply Chain Services (Kelly L Konemann); Supply Chain Services (Maike Pollaschek); Supply Chain Services (Tamara Huber); Supply Chain Systems (Sean Flannery); Supply Chain, PKV (Lachlan Cruise); Support & Hygiene Produktion (Monika Krebs); Support & Nebenanlagen (Florian Damm); Support (Arnd Vollmerhausen (Inherited)); Support (Benjamin Grn); Support (Benjamin Gr√ºn); Support (Bernd Zimmermann); Support (Heiko Jucknat); Support und Admin Medical Department (Martina Witzer); Sustain and Improve PTI Americas (Austin Newsom); Syracuse 196 (SILVIO VONA); Syracuse 196 ACM Area 1 (Kristina Deonarine); Syracuse 196 ACM Area 2 (Timothy Ray); Syracuse 196 QA (Matthew McHale); System Support (Magan Lai); System Validation and Implementation (Marquita Moore); TA Coag, Critical Care & Cardiovascular (Susan Welsh (Inherited)); TA Coagulation & Acquired Bleeding, Global Clinical R&D (Andres Brainsky); TA Development PM Group (Joanne Uhl); TA Immunology (Susan Welsh (Inherited)); TA Support (Anjani Advani); TDD (Technical Development & Documentation) (Patrick Gregory); TEC-Testentwicklung Chemie (Kerstin Nske); TEC-Testentwicklung Chemie (Partho Halder); TRICC (William Mezzanotte (Inherited)); TRICC - Therapeutic Area II (Marc Uknis); TRICC II (Mikhail Rojavin); TRICC II (Orell Mielke); TRICC III (Iris Jacobs); TRICC III (Maria Gasior); TRICC Therapeutic Area (Mittie Doyle); Talent Acquisition (Daphne Wong); Talent Acquisition (Ivan Dokoza); Talent Acquisition (James Telfer (Inherited)); Talent Acquisition (Priya Dinkar); Talent Acquisition - APAC (James Telfer); Talent Acquisition - APAC (Lisa Edwards); Talent Acquisition - Americas (Andrew Lewis); Talent Acquisition - EMEA (Elena Kharlamova); Talent Acquisition - Europe (Peggy Klein); Talent Acquisition - Plasma (Tracey Lambalot); Talent Acquisition - Plasma (Tracey Lambalot) (Tracey Lambalot); Talent Acquisition AUS (Angela Bellenger); Talent Acquisition and Talent Management (Beth Thomas); Talent Development (APAC) (Kathy Sacca); Talent Development (Eveline Wuethrich); Talent Development Apprenticeship (Anja K√§ser); Talent Development North America (Ll''Rae Robinson); Talent Management & Acquisition (Brian Fehrer); Talent Management & Acquisition (Elizabeth Walker (Inherited)); Talent Management AU (Raechel Gray); Talent Programs & Analytics (Brian Fehrer (Inherited)); Talent Programs & Analytics (Mary Schnackenberg); Talent Programs & Analytics (Sarah Peacey); Tallahassee 211 (Andria Logan); Tallahassee 211 QA (Lori Carlson (Inherited)); Tallahassee 211 (Andria Logan); Tallahassee 211 ACM Area 1 (Andria Logan (Inherited)); Tallahassee 211 ACM Area 1 (Brooklyn Williams (On Leave)); Tallahassee 211 ACM Area 2 (Brenda B Williams); Tallahassee 211 ACM Area 2 (Michelle Davenport); Tallahassee 211 QA (Lori Carlson (Inherited)); Tallahassee 211 QA (Mechelle Robinson); Tallahassee 211 QA (Mychal A Reynolds); Tampa 109 (Elizabeth Lam); Tampa 109 (Michelle K Natalie); Tampa 109 ACM Area 1 (Leah J Davis); Tampa 109 ACM Area 2 (Amber S Goodwine); Tampa 109 ACM Area 2 (Carolyna Perez); Tampa 109 QA (Joseph Rivera (On Leave)); Tampa 109 QA (Joseph Rivera); Tampa 109 QA (Michelle K Natalie); Tax (James Smith); Tax Compliance (Mark Murtaugh); Taylor 240 (Joe Korea); Taylor 240 ACM Area 1 (Joe Korea (Inherited)); Taylor 240 ACM Area 1 (Nicki Nguyen); Taylor 240 ACM Area 2 (Dion Dippel); Taylor 240 ACM Area 2 (Joe Korea (Inherited)); Taylor 240 QA (Wendy MacConnell); Team 1 (Christian Schubert); Team 1 (Jrg Dennis Issel); Team 1 (J√∂rg Dennis Issel); Team 1 (Michael Welsch (Inherited)); Team 1 (Veronika Chernov); Team 10 Verpackung (Petra Eversberg); Team 10 Verpackung (Petra Sch√§fer (On Leave)); Team 10 Verpackung (Petra Sch√§fer); Team 10 Verpackung (Rosemarie Rdding); Team 2 (Aytac Akin); Team 2 (Michael Welsch (Inherited)); Team 2 (Silke Oppermann); Team 3 (Michael Welsch (Inherited)); Team 3 (Thomas Grhning); Team 3 (Thomas Gr√§hning); Team 3 (Waldemar Kliwer); Team 4 (Erwin Gordzielik); Team 4 (Michael Welsch (Inherited)); Team 5 (Ludwig Heckmann); Team 5 (Michael Welsch); Team 6 (Karl-Hermann Sprenger); Team 7 (Pavlina Weninger); Team 7 (Thomas Fieber); Team 8 (Andreas Rastschewski); Team 8 (Mara Saglam); Team 8 (Melvin Scruggs); Team 9 (Eugen Rogosin); Team 9 (Igor Kaucher); Team Buffer Preparation (Dirk Michel); Team DSP I (Heiko Jucknat); Team HVAC (Michael Hillmann); Team Kalibrierung (Thomas Kniepper); Team Leader - Imp & Compl (Kathy Theodorakis); Team Leader - AFF/ZN 444 (Chas Chalker); Team Leader - AFF/ZN 444 (Remon Hemaya); Team Leader - DS 444 (Hieu Tran); Team Leader - Imp & Compl (Kathy Theodorakis); Team Leader - Inac 444 (Margarita Mejia); Team Leader - Packaging - 451 (Anthony Lane); Team Leader - Packaging - 451 (Anthony Lane); Team Leader - Plnt & Srv 444 (Darren McKean); Team Leader - QC Microbiology (Kerry Lincoln); Team Leader - Sterility Assurance (Jon Wong); Team Leader - Validation (Kylie Prendergast); Team Leader Animal Services (Anne Hageman); Team Leader Change Mgmt - Prod (Marcus O''Dwyer); Team Leader Change Mgmt - Prod (Paul Williams); Team Leader Formulation B 454 (David Moulsdale); Team Leader I PHAD I (Tobias Heck); Team Leader II PHAD I (Patric Sallin); Team Leader Prod Support - DS (Jeffrey Gan); Team Leader Prod Support - DS (Jeffrey Spicer); Team Leader Prod Support - MD (Jeffrey Gan); Team Leader Prod Support - MD (Stuart Jones); Team Leader Production Support (Denise Bertram); Team Leader Production Support (Elaine Feely (Inherited)); Team Leader Upstream-Harv 444 (Ibrahim Ozerim); Team Leader Upstream-Inoc 444 (Craig Byham); Team Mechanik (Christoph Freiling); Team Mechanik (Gerd Pendzialek); Team PBF (Thorsten May); Team PBF 1.0 (Maikel Bamberger); Team PTE (Stefan Rees); Team Purification I (Carsten Meyer (Inherited)); Team Purification I (Heiko Jucknat (On Leave)); Team Purification I (Heiko Jucknat); Team Purification II (Selcuk Ayan); Tech Dev Ops QA (Monica Rose); Tech Support Potency Testing (Julia Hainbach); Tech Support Potency Testing (Reinhard Paul); Tech Transfer (Samantha Gakias); Tech Transfer D820 (Ming Chong); Tech Transfer Projekt Management Team (Nina Walser); Technical Development (Lynda Allan); Technical Learning & Development (David P Monte); Technical Learning & Development 1 (Amy Jackson); Technical Learning & Development 2 (Ann Lescher); Technical Operations (Fuad Haddadin); Technical Operations (Michele Himmelspach (Inherited)); Technical Operations - Investigations (Tino Boss); Technical Operations - Small Scale (Janine Bash); Technical Operations I (Daniel Knack); Technical Operations I (Veronica Lopez); Technical Operations II (Becca Huebsch); Technical Operations II (Raghav Oberoi); Technical Operations III (Inga Breitwieser); Technical Operations III (Wilfried Wormsb√§cher); Technical Operations IIa (Jan Schwichtenberg); Technical Operations IV (Katrin Maria Sander); Technical Projects (Wendy Turner); Technical Services (Juerg Clavadetscher); Technical Services, Influenza Operations (Bill Cracknell); Technical Services/FM (Beat Meyer); Technikteam Labore (Stephan Lw); Technikteam Labore (Stephan L√∂w); Technischer Service (Lothar Klingelhfer); Technischer Service (Lothar Klingelh√∂fer); Technology Transfer (Jesse Richter); Teilbereichsleiter Abfllung (Stefan Peil); Teilbereichsleiter Abf√ºllung (Stefan Peil); Tempe 048 (Terry M Young); Tempe 048 ACM Area 1 (Samuel V Grijalva); Tempe 048 ACM Area 1 (Trina L Bryant); Tempe 048 ACM Area 2 (Sonya L Nigh); Tempe 048 QA (John Son); Tempe 048 QA (Melissa M Martinez); Tempe 427 (Daniel I Villegas (Inherited)); Tempe 427 (Patrick S Taylor); Tempe 427 ACM Area 1 (Kelly L Ortega); Tempe 427 ACM Area 2 (Jennifer Valenciano); Tempe 427 QA (Daniel I Villegas); Tempe 427 QA (Kellie N Buecker); Tempe 427 QA (Tiffanie Contreras); Temple 260 (Kimm Klisiewicz); Temple 260 ACM Area 1 (Moses Olukere); Temple 260 ACM Area 1 (Sarah Gaines); Temple 260 ACM Area 2 (Michael Martinez); Temple 260 QA (Cayley Eppler); Temple 260 QA (Kellie N Buecker); Temple Terrace 252 (Stephanie Frye); Temple Terrace 252 ACM Area 1 (Michelle Briseno); Temple Terrace 252 ACM Area 1 (Monica Miller); Temple Terrace 252 ACM Area 2 (Janette J Pierre); Temple Terrace 252 QA (Caitlin Shoemaker); Temple Terrace 252 QA (Joel Gallegos); Terre Haute 265 (Daniella Miller); Terre Haute 265 (Tara Goebel); Terre Haute 265 ACM Area 1 (Tara Goebel); Terre Haute 265 ACM Area 2 (Tracy Robinson); Terre Haute QA 265 (Sherri A Suttles); Testing Laboratory (Maruthi Shivananda); Therapeutic Area Clinical Ops (Bruce Wynne); Therapeutic Area Clinical Ops I&N (Ann-Marie Hulstine); Therapeutic Area Critical Care (Hartmut Landgrebe); Therapeutic Area Medical Evaluation (Nataliya Doliba); Therapeutic Area Medical Evaluation 1 (Nataliya Doliba); Therapeutic Area Medical Evaluation Lead (Kaniez Baig); Tokyo Yamanashi Area (Yoshifumi Umenaka); Toledo 175 (Steve Sparks); Toledo 175 ACM Area 1 (Kevin Connelly); Toledo 175 ACM Area 2 (James Carroll); Toledo 175 QA (Aarsalaan Semna); Toledo 175 QA (April Tyler); Toledo 223 (Debra Purney); Toledo 223 ACM Area 1 (Jeffery Eagle); Toledo 223 ACM Area 2 (Debra Purney); Toledo 223 ACM Area 2 (Heather Marshall); Toledo 223 QA (Christopher Travalik (Inherited)); Toledo 223 QA (Michael Craun); Toledo 223 QA (Pam Perryman); Toll IG/Alb Bulk (Ali Hashempour); Toll IG/Alb Bulk (Andrew Vasil); Toll IG/Alb Bulk (Anthony Manovella (Inherited)); Toll IG/Alb Bulk (Edward Camilleri); Toll IG/Alb Bulk (Jason Gilmour); Toll IG/Alb Bulk (Johnny Barbis); Toll IG/Alb Bulk (Jon Gummer); Toll IG/Alb Bulk (Kevin deSouza); Toll IG/Alb Bulk (Michael Appelman); Toll IG/Alb Bulk (Ricardo Morales); Toll IG/Alb Bulk (Robert Poletti); Toll IG/Alb Bulk (Rodney Vermeend); Toll IG/Alb Bulk (Shannon Thorp); Toll IG/Alb Bulk (Tom Koukouvaos); Toll Manufacturing BU Team (CLAUDIO BAEZZATO); Toll Manufacturing BU Team (Maria Gabriella Patrassi); Toll Mfg. Excipients & Intermediates (Jennifer Dolores Brenner); Toll Mfg. Excipients & Intermediates (Markus Staempfli (Inherited)); Toll Mfg. Excipients & Intermediates (Niklaus Kraehenbuehl); Toll VI and Pack (Parth Soni); Total Rewards (Figen Zaim); Tox Operations (Andrea Beyerle); Toxicology (Christopher John Peters); Toxicology Unit (Gerald Hbarth); Toxicology Unit (Gerald H√∂barth); Toxicology Unit 1 (Barbara Dietrich); Trademark (Antje Michel (Inherited)); Trademark (Janine Colesie (On Leave)); Training & Development Office (Chiho Muto); Training & Development Office (Shinji Ohkura); Training & GMP (Barbara Kalina (Inherited)); Training & GMP (Wilfried Happel (Inherited)); Training and Document Control (Lixia He ); Training and Document Control (Lixia He ?????); Transformation Change Management (Emily Riggs); Translational Biology (Alexander Schaub); Translational Biology 1 (Sandra Wymann); Translational Biology 2 (Svetlana Diditchenko); Translational Biology 2a (Alexei Navdaev); Translational Biology 3 (Anna Schnell); Translational Safety (Ashlyn Bassiri); Translational Science (Nick Wilson); Translational Science 1 (Nick Wilson); Transplant (Kevin Kovaleski); Transplant Marketing (Paula Manchester); Transplant Marketing SOT (Jeanne Andronowitz); Transplant Marketing SOT (Paula Manchester (Inherited)); Transplant Medical Affairs (Kevin Kovaleski (Inherited)); Transplant Medicine (Kevin Kovaleski); Transplant TA PM Group (Linda Cortese); Transport und Prozess Management (Andre Husse); Transport und Prozess Management (Anna-Lena Niederh√∂fer); Transportation Management (Gnter Vollmer); Transportation Management (G√ºnter Vollmer); Treasury Europe (Dieter Engstfeld); Trending (Marika Moore); Trending ¬ñ Sterility Assurance (Vijay Dundigalla); Trial & Quality Systems (Sean Storms); Tucson 071 (April Behnke); Tucson 071 (Moses Falaiye); Tucson 071 ACM Area 1 (Alma Y Olivera); Tucson 071 ACM Area 2 (Luz D Almeraz); Tucson 071 QA (Cori J Collins (Inherited)); Tucson 071 QA (Nicole A Downey); Tucson 111 (Alejandro Angulo); Tucson 111 ACM Area 1 (Alejandro Angulo (Inherited)); Tucson 111 ACM Area 1 (George Adams); Tucson 111 ACM Area 2 (Kendra N Belcher); Tucson 111 QA (Dulce A Jimenez); Tucson 111 QA (Eugene Y Shem); Tucson 624 (Jovanna R Ortega); Tucson 624 ACM Area 1 (Sara M Portugal); Tucson 624 ACM Area 2 (Adrian Soto); Tucson 624 QA (Bernadette B Woodson); Tulsa 014 (Heather Colbert); Tulsa 014 (Jerry Ewen); Tulsa 014 ACM Area 1 (Reggie De Quiroz); Tulsa 014 ACM Area 2 (Forrest Burns); Tulsa 014 ACM Area 2 (Heather Colbert); Tulsa 014 QA (Cooper Cruson); Tulsa 014 QA (Heather Colbert); Tulsa 417 (Jerry Ewen); Tulsa 417 (Troy Lee Wheeler); Tulsa 417 ACM Area 1 (Nina Linga); Tulsa 417 ACM Area 2 (Lindsay K Jameson); Tulsa 417 QA (Cooper Cruson); Tulsa 417 QA (Hannah E Todroff); Tulsa 417 QA (Julie L Potter); Tulsa 417 QA (Troy Lee Wheeler (Inherited)); Turkey Field Sales (Filinta Cakar); Tyler 520 (Stephanie D Shah); U of M 414 ACM Area 1 (Abubeker M Osman); U of M 414 ACM Area 2 (Ahmed N Ismail); U&S Process Engineering (Rodrigo Campos); UK accounting (Lorraine Lambert); US ComOps Immunology Sales (Joseph Guinan); US Credit and Treasury (Angela Caivano); US Distribution (Daniel Krysztofiak); US Distribution (Joseph Jefferson); US Federal Tax Compliance (Giovanni Siciliano); US Finance: Capital (Andrea Burch); US Healthcare Systems (Pete Dickson); US Healthcare Systems (Richard Dudek); US Lab Quality Assurance (Alecia C Harshaw); US Marketing (Bernadine Koziara); US Med Affairs-Coagulation-Field (Julie Farley); US Med Affairs-Coagulation-Field (Katheleen Pinto); US Med Affairs-Immunology-Field (Elyse Murphy); US Med Affairs-Specialty-Field (Ayman Kafal); US Med Affairs-Specialty-Field (Ben Boccuzzi); US PLC Quality Assurance (Brian H. Frye); US PLC Quality Assurance (Carol Kralicek); US PLC Quality Assurance (Jeff Dalton Jr); US PLC Quality Assurance (Keith Winiger); US PLC Quality Assurance II (Jeff Dalton Jr); US Plasma Marketing (Keith A Haywood); US Plasma Marketing (Scott Newkirk (Inherited)); US Plasma Operations (Daniel V Ferris); US Plasma Operations Division 1 (Scott Newkirk); US Plasma Operations Division 2 (Michelle A Meyer); US Plasma Operations Division 3 (Wlenyeno J Elliott-Browne); US Plasma Operations Region 11 (Joshua D Williamson); US Plasma Operations Region 11.1 (Holt A Peterson); US Plasma Operations Region 11.1 (Holt Peterson); US Plasma Operations Region 11.2 (Aaron C White); US Plasma Operations Region 11.3 (Brandon S Bridges); US Plasma Operations Region 11.4 (Christine Thomas); US Plasma Operations Region 11.5 (Philip Nixon); US Plasma Ops Region 1 (Dianne Sorenson); US Plasma Ops Region 1.1 (Paul Warden); US Plasma Ops Region 1.2 (David M Wilson); US Plasma Ops Region 1.2 (Marc D Fisher); US Plasma Ops Region 1.3 (Cori J Collins); US Plasma Ops Region 1.4 (Daniel I Villegas); US Plasma Ops Region 1.5 (Timothy Freeland Jr); US Plasma Ops Region 10 (Carmon Kieffer); US Plasma Ops Region 10 (Michelle A Meyer (Inherited)); US Plasma Ops Region 10 (Rebecca Swingle); US Plasma Ops Region 10.1 (Bonnie M Talbott (On Leave)); US Plasma Ops Region 10.1 (Bonnie M Talbott); US Plasma Ops Region 10.1 (Christopher Travalik); US Plasma Ops Region 10.1 (Derek Erhart); US Plasma Ops Region 10.2 (Mary A Paul); US Plasma Ops Region 10.2 (Michael W Solomon); US Plasma Ops Region 10.3 (Neville L Bain); US Plasma Ops Region 10.3 (Stephanie D Shah); US Plasma Ops Region 10.4 (Brendi L Cantrell); US Plasma Ops Region 10.4 (Brett A Wintheiser); US Plasma Ops Region 10.4 (Lori Carlson); US Plasma Ops Region 10.4 (Nicole M Loncon); US Plasma Ops Region 10.5 (Melodee C Ebel); US Plasma Ops Region 11 (Joshua D Williamson); US Plasma Ops Region 12 (Brandon L Voege); US Plasma Ops Region 12.1 (Melodee C Ebel); US Plasma Ops Region 12.2 (Kyle M Lehrke); US Plasma Ops Region 12.3 (Alan Maldonado); US Plasma Ops Region 12.4 (Kashaun Muhammad); US Plasma Ops Region 12.4 (Tiffany D Sherman); US Plasma Ops Region 12.5 (Lori Carlson); US Plasma Ops Region 2 (Michael S Beam); US Plasma Ops Region 2.1 (Jose L Dela Garza); US Plasma Ops Region 2.1 (Vida C Chapman); US Plasma Ops Region 2.2 (Daniel Venn); US Plasma Ops Region 2.2 (Sheri Mixon); US Plasma Ops Region 2.3 (Brenda C Greenfield); US Plasma Ops Region 2.3 (Vida C Chapman); US Plasma Ops Region 2.5 (Kandra K Blodgett); US Plasma Ops Region 2.5 (Patrick Garza); US Plasma Ops Region 3 (Angela S Drumright); US Plasma Ops Region 3.1 (Latosha Y Floyd); US Plasma Ops Region 3.2 (Angela S Drumright (Inherited)); US Plasma Ops Region 3.2 (Joshua D Williamson); US Plasma Ops Region 3.2 (Lauren Jenkins); US Plasma Ops Region 3.2 (Marc D Fisher); US Plasma Ops Region 3.3 (Drewleigha B Sarver); US Plasma Ops Region 3.3 (Keith Clemons); US Plasma Ops Region 3.4 (Ashley M Jamieson); US Plasma Ops Region 3.5 (Erik Plate); US Plasma Ops Region 4 (Brannon L Brittain); US Plasma Ops Region 4.1 (Cole D Kimple); US Plasma Ops Region 4.1 (Holt A Peterson); US Plasma Ops Region 4.1 (Tina Wagenknecht); US Plasma Ops Region 4.2 (Jamie E Lawrence); US Plasma Ops Region 4.2 (Troy Lee Wheeler); US Plasma Ops Region 4.3 (Cole D Kimple); US Plasma Ops Region 4.3 (Cory Toellner); US Plasma Ops Region 4.4 (Jesus A Castillo); US Plasma Ops Region 4.5 (Jamie E Lawrence); US Plasma Ops Region 5 (Rhonda C Harp); US Plasma Ops Region 5.1 (Aaron C White); US Plasma Ops Region 5.1 (Keith Clemons); US Plasma Ops Region 5.2 (Brandon S Bridges); US Plasma Ops Region 5.2 (Patti Bailey, Prim J Cunningham); US Plasma Ops Region 5.2 (Prim J Cunningham); US Plasma Ops Region 5.3 (Nicole M Adams); US Plasma Ops Region 5.3 (Patti Bailey); US Plasma Ops Region 5.3 (Rhonda C Harp (Inherited)); US Plasma Ops Region 5.4 (John L Thixton); US Plasma Ops Region 5.5 (Michele Purvines-Honzo); US Plasma Ops Region 6 (Darrel W Simon); US Plasma Ops Region 6.1 (John E Hunt); US Plasma Ops Region 6.1 (Tiffany D Sherman); US Plasma Ops Region 6.2 (Kyle M Lehrke); US Plasma Ops Region 6.2 (Sam Schultz); US Plasma Ops Region 6.3 (Alan Maldonado); US Plasma Ops Region 6.3 (Jose L Dela Garza); US Plasma Ops Region 6.4 (John E Hunt); US Plasma Ops Region 6.4 (Sheri Mixon); US Plasma Ops Region 6.5 (Victoria McIntyre); US Plasma Ops Region 7 (Brandon L Voege); US Plasma Ops Region 7 (Brendi L Cantrell (On Leave)); US Plasma Ops Region 7 (Brendi L Cantrell); US Plasma Ops Region 7.1 (Lori Carlson); US Plasma Ops Region 7.1 (Nicole M Loncon); US Plasma Ops Region 7.1 (Stephanie D Shah); US Plasma Ops Region 7.2 (Christine Thomas); US Plasma Ops Region 7.2 (Christopher Travalik); US Plasma Ops Region 7.3 (Ron Griffin); US Plasma Ops Region 7.4 (Andrew Brammer); US Plasma Ops Region 7.4 (Brendi L Cantrell (On Leave) (Inherited)); US Plasma Ops Region 7.4 (Brett A Wintheiser); US Plasma Ops Region 7.4 (Drewleigha B Sarver); US Plasma Ops Region 7.5 (Christopher Travalik); US Plasma Ops Region 7.5 (Mary A Paul); US Plasma Ops Region 8 (Tammy S Harrison); US Plasma Ops Region 8.1 (David Ensminger); US Plasma Ops Region 8.1 (Derek Erhart); US Plasma Ops Region 8.1 (Matthew Smith); US Plasma Ops Region 8.2 (Ben Samarripas); US Plasma Ops Region 8.2 (Stephanie D Shah); US Plasma Ops Region 8.3 (Andrew Brammer); US Plasma Ops Region 8.3 (Greg McClain); US Plasma Ops Region 8.3 (Neville L Bain); US Plasma Ops Region 8.4 (Derek Erhart); US Plasma Ops Region 8.4 (Michael W Solomon); US Plasma Ops Region 8.4 (Tammy S Harrison (Inherited)); US Plasma Ops Region 8.5 (Derek Erhart); US Plasma Ops Region 8.5 (Patrick Willingham); US Plasma Ops Region 9 (Amanda L Kitchen); US Plasma Region 2.4 (Michael S Beam (Inherited)); US Plasma Region 2.4 (Rosa E Mercado); US Regulatory Affairs (John Hill); US Regulatory Affairs (Kevin Darryl White); US Regulatory Affairs Critical Care/Cardiovascular (Angela D Azzara); US Regulatory Affairs II (Baldevsinh Rana); US Regulatory Affairs III (Paula Clark (On Leave)); US Regulatory Affairs III (Paula Clark); US Sales (Robert Murphy); US State Tax Compliance (Tulasi Veeramachaneni); US Tax Compliance (Eric Lorah); US Tax Compliance (Peter Larsen (Inherited)); USP Engineering (Patrick Rollier); USP Laboratories (Sandra Grunske); USP Manufacturing 1 (Marc Dick); USP Manufacturing 2 (Philipp Steiner); USP Process Technology (Niklas Zink); Umwelt, Plasmabetreuung und Fremdfirmenmanagement (Bjrn Wiesner); Umwelt, Plasmabetreuung und Fremdfirmenmanagement (Bj√∂rn Wiesner); University Relations (Jasmin Senior); Unpaid Diverse (Andreas Gehrich (Inherited)); Unpaid Diverse (Andreas Gehrich); Unpaid Diverse (Annette Pernitzsch (Inherited)); Unpaid Diverse (Annette Pernitzsch); Unpaid Diverse (Claudia Habenicht (Inherited)); Unpaid Diverse (Claudia Habenicht); Unpaid Diverse (Frank Bernert (Inherited)); Unpaid Diverse (Heike Borchert (Inherited)); Unpaid Diverse (Natascha Bock (Inherited)); Unpaid Diverse (Natascha Tappendorf); Unpaid Diverse (Stephani Keltsch); Unpaid Diverse (Sven Schuhmann (Inherited)); Unpaid Diverse (Sven Schuhmann); Upstream Days (Rebecca Briers); Upstream Development (Hans-Wilhelm Beltz); Upstream Development (Stefan Debus); Upstream Manufacturing (Vicky Reading); Upstream Manufacturing - Days (John Meaney); Upstream Shift A (Edward Goulding); Upstream Shift A (Mark Harrop); Upstream Shift B (Mark Harrop); Upstream Shift B (Raymond Brownless); Utilities & Services Engineering (Paul Russell); Utilities & Services Engineering (Peter White); Utilities & Services Engineering Manager (Peter White); Utilities (Kay von Burg); Utilities Critical Systems (Michael D Proctor); Utilities Lengnau (Nozar Basseri); Utilities-Critical Systems (Jeff J Parks); Utilities-Critical Systems (Jim Meils); Utilities-Motive Power (David G Mollema); VAL - Rekombinante Proteine (Kerstin Nau); VAL - Rekombinante Proteine (Verena Koch-Geller); VAL F VIII, IgG & Albumin & Inhibitors (Antje R√∂der); VAL F VIII, IgG & Albumin & Inhibitors (Marco Donges); VAL LIMS-Beauftragte (Eckhard Sch√ºler (Inherited)); VAL Lyophilisierung (Judith Mller); VAL Lyophilisierung (Judith M√ºller); VAL Media Fills & Mikrobiologie (Elke Zameitat); VAL Wissensch. Dokumentation (Eckhard Schler (Inherited)); VAL Wissensch. Dokumentation (Eckhard Sch√ºler (Inherited)); VAL Wundheilung & Intensive Care (Karlheinz Enssle); VAL Wundheilung & Intensive Care (Markus Hilberg); VP Operations 400 (Chris Larkins); VP, Com Operation (Lorna Meldrum); VV-Virus Validation (Wolfram Schfer); VV-Virus Validation (Wolfram Sch√§fer); VV-Virus Validation 1 (Tobias Schrder); VV-Virus Validation 1 (Tobias Schr√§der); VV-Virus Validation 2 (Michaela Gerlach); VV-Virus Validation 3 (Ramona Stau); VV-Virus Validation 3 (Ramona Stau√ü); Validation (Chad Kalia); Validation (Christian Nemeth); Validation (David Turner); Validation (Debra Fisher); Validation (Eckhard Schler); Validation (Eckhard Sch√ºler); Validation (Kah Wen Lee); Validation (Linda Garrett); Validation (Maria Arulruban); Validation (Michel Baur); Validation (Michelle Johnson); Validation (NICHOLA THOMSON); Validation (Ryan Dexter); Validation (Tiffany Korth); Validation I (Chantal Pfaffen); Validation I (Michel Baur (Inherited)); Validation I (Philipp Gersbach); Validation II (Ulrike Hartmann); Validation Process (Peter Tyler); Validation Process (Rebecca Gannon); Validation Process (Russell James Ciliento (Inherited)); Validation QA 1 (Tiffany Korth); Validation QA 2 (Debra Fisher); Validation QA 3 (Linda Garrett); Validation Qualification (Chad Kalia); Validation Specialist 3 Third Party Support (Bhavyesh Pandya); Validation and Stability (Jolyn Hu ?????); Validation and Stability (Xianfeng Guo ); Value Stream - Drug Substance (Barbara Beugger); Vancouver 102 (Larry A Barttelt); Vancouver 102 ACM Area 1 (David B Hammersley); Vancouver 102 ACM Area 2 (Clarissa Halsey); Vancouver 102 QA (Greg R Edge); Viral Vector Bioanalytics (Monica Terrao); Virology & Cell Line Up (Charles McGee); Virus Assay Development and Production (Ben Dickerman); Virus Seed MFG (Adam Kotsubka); Virus Validation (Randel Fang (Inherited)); Virus Validation (Tao Zheng); Virus Validation I (Thomas Nowak); Virus Validation II / Prion Eval. (Wolfram Sch√§fer); Virus Validation III (Bj√∂rn Keiner); Visual Control (Joanna Madafferi (Inherited)); Visual Control (Urs Pflugshaupt); Visual Inspection (Thomas Niedermann); Visual Inspection Precontrol 1 (Georges Schmid); Visual Inspection Precontrol 2 (Daniel Tobler); Visual Inspection and Packing (Claire Petitjean); Visual Inspection and Packing (Clare Schwarz); Visual Inspection semi final prod. 1 (Marlis Erb); Visual Inspection semi final prod.Team 2 (Yvonne Seiler); Visual Inspection semi final prod.Team 3 (Vesile Ciloglu); Visuelle Kontrolle 4 (Christina Vidal-Martinez); Visuelle Kontrolle 4 (Jrg Nickel (Inherited)); Visuelle Kontrolle 4 (J√∂rg Nickel (Inherited)); Vorbehandlung/Brdelung (Michael Welsch); Vorbehandlung/B√∂rdelung (Michael Welsch); WHO & EU Pandemic Vaccines (Ylenia Runci); Waco 084 (Katherine Blount); Waco 084 (Michael Pate Jr); Waco 084 ACM Area 1 (Janet E Jenkins); Waco 084 ACM Area 2 (Rachel I Ramirez); Waco 084 ACM Area 2 (Sharon A Smith); Waco 084 QA (Katherine Blount); Waco 084 QA (Vanessa E Tinsley (On Leave)); Waco 084 QA (Vanessa E Tinsley); Warehouse & Logistics Lengnau (Philipp Kaeser); Warehouse & Transportation (David Dunn); Warehouse & Transportation (Klaus M√ºller); Warehouse (Belinda Thomson); Warehouse (Sam Mekhael); Warehouse (Serge Marques); Warehouse (Uwe Klappstein); Warehouse II (Pavel Miller (On Leave)); Warehouse II (Pavel Miller); Warehouse Operations (Ritchii Lam (Inherited)); Warehouse Operations (Ritchii Lam); Warehouse Supervisor VIC 266 (John Turone (Inherited)); Warehouse Supervisor VIC 266 (Russell Monro); Warehousing (Brian Runner); Warehousing (Jesse Higgins); Warehousing (Noel Burash); Warehousing (Thomas Ryser); Warehousing (Union) (Brian Runner); Warehousing (Union) (Caitlyn Vidas); Warehousing (Union) (Jesse Higgins (Inherited)); Warehousing (Union) (Robin Anderson); Warehousing 1 (Brian Runner); Warehousing GBZ (Walter Kiener); Warehousing Non-Union (Brian Runner (Inherited)); Warehousing Non-Union (Robin Anderson); Warehousing U8 (Martin Hirschi); Warehousing U8 (Matthias Loosli); Warehousing U8 (Rafael Gasser); Warehousing U8 (Thomas Ryser (Inherited)); Warehousing W10 (Patrick Portmann); Warehousing W10 (Thomas Ryser); Warner Robins 509 (Denise Bloodsaw); Warner Robins 509 ACM Area 1 (Bernard Postell); Warner Robins 509 ACM Area 2 (Ta''Neshia Magby); Warner Robins 509 QA (Marilyn Walker); Warner Robins 509 QA (Mary A Paul (Inherited)); Warren 204 (Kimberly Schick); Warren 204 (Kimberly Wrenn); Warren 204 ACM Area 1 (Stephanie M Newland); Warren 204 ACM Area 2 (Daniel Rattay); Warren 204 QA (Jefferson Williams); Warren 204 QA (John Ziegler); Warren 204 QA (Samantha Rouzzo); Warwick 201 (Linda Monteiro); Warwick 201 (Matt Schramm); Warwick 201 ACM Area 1 (Mariela Myers); Warwick 201 ACM Area 2 (Husseim Gomez); Warwick 201 QA (Catherine Colucci); Warwick 201 QA (John L Thixton (Inherited)); Warwick 201 QA (Tessa Grassette); Water Testing (Heike Gocht); Water Testing (Partho Halder); Water Testing (Stefan Wilka); Waters-LAL (J Noel David); Waters/LAL (J Noel David); Weighmaster (Non-Union) (Jeff Keller); Weighmaster (Union) (Jeff Keller); Weighmaster (Union) (Jeffrey Keller); Weslaco 184 (Juan M Ramirez); Weslaco 184 ACM Area 1 (Antonio E Juarez); Weslaco 184 ACM Area 2 (Jesus R Hernandez II); Weslaco 184 QA (Ana Phlipot (On Leave)); Weslaco 184 QA (Ana Phlipot); West Lafayette 411 (Travis F Dill); West Lafayette 411 ACM Area 1 (Marc Baldwin); West Lafayette 411 ACM Area 2 (Alex Visser); West Lafayette 411 QA (Sheryl A Pope); West Specialty Regional Sales (STEPHANIE BESLER); West Specialty Regional Sales (Stephanie Besler); Westland 226 (Corey M Schimming); Westland 226 (Remie T Ray); Westland 226 ACM Area 1 (Kelsie Cremeans); Westland 226 ACM Area 2 (Kirk P Alford II); Westland 226 QA (David Zagorowski); Westwego 153 (Jacqulynn Shankle); Westwego 153 ACM Area 1 (Jacqulynn Shankle); Westwego 153 ACM Area 1 (Nadia Y Grisby); Westwego 153 ACM Area 2 (Regena D Young); Westwego 153 QA (Amanda N Webre); Westwego 153 QA (Brandi N Clark (On Leave)); Westwego 153 QA (Jacqulynn Shankle); Westwego 153 QA (Joshua D Harper); Wholesaler Management (Hideki Yanagihashi ??? ?? - ????? ????); Wichita 263 (Laurie E Boothe); Wichita 263 ACM Area 1 (Sierra Lashbrook); Wichita 263 ACM Area 2 (Mandi Harris); Wichita 263 QA (Cameo Donerson); Wichita 415 (Junior Navarro); Wichita 415 (Sam P Emrich); Wichita 415 ACM Area 1 (Michelle B Duong); Wichita 415 ACM Area 2 (Joel Sutherland); Wichita 415 QA (Erin Shaver); Wichita 415 QA (Laurie E Boothe); Wichita 415 QA (Troy Lee Wheeler (Inherited)); Wilkes Barre 286 (Joseph Frackowiak); Wilkes Barre 286 ACM Area 1 (Cathy Gurzynski); Wilkes Barre 286 ACM Area 2 (Joseph Frackowiak (Inherited)); Wilkes Barre 286 ACM Area 2 (Renee Collins); Wilkes Barre 286 QA (Robin Williams); Willoughby Hills 222 (Frances Campbell (On Leave)); Willoughby Hills 222 (Frances Campbell); Willoughby Hills 222 ACM Area 1 (Amanda Fitzpatrick); Willoughby Hills 222 ACM Area 2 (Breana Brown); Willoughby Hills QA 222 (Bailee E White); Wilmington 228 (Alex Liang); Wilmington 228 (Jack Ellison); Wilmington 228 (John E Hunt (Inherited)); Wilmington 228 ACM Area 1 (Kenneth A Keitt Jr); Wilmington 228 ACM Area 2 (Wendy Dettloff); Wilmington 228 QA (Ben Ward); Wilmington 228 QA (Sam Whitehead); Wilton Manors 073 (Alan Maldonado (Inherited)); Wilton Manors 073 (Benjamin J Morris); Wilton Manors 073 (Michelle S DeCambre); Wilton Manors 073 (Nakia J Harlan); Wilton Manors 073 ACM Area 1 (Darcia Culmer); Wilton Manors 073 ACM Area 2 (Kurt S Tuckett); Wilton Manors 073 ACM Area 2 (Soo-Lin Chang); Wilton Manors 073 ACM Area 3 (Benjamin J Morris); Wilton Manors 073 ACM Area 3 (Nakia J Harlan (Inherited)); Wilton Manors 073 QA (Glenny Arvelaez); Wilton Manors 073 QA (Ryann Chapman); Winston-Salem 124 (Daniel Miclausi); Winston-Salem 124 (Javier Castillo); Winston-Salem 124 ACM Area 1 (Malcolm Childress); Winston-Salem 124 ACM Area 2 (Amanda Jarvis); Winston-Salem 124 ACM Area 2 (Maria Lopez); Winston-Salem 124 QA (Amanda Jarvis); Winston-Salem 124 QA (Beth Majewski); Winston-Salem 124 QA (Mario E Montoya); Wintel (Jason Christides); Witchita 263 (Laurie E Boothe); Witchita 263 QA (Cameo Donerson); Woodend Senior Operator (Brett Walker); Woodend Senior Operator (Lauren Redman); Woonsocket 295 (Catherine Colucci); Woonsocket 295 ACM Area 1 (Jonathan Chenot); Woonsocket 295 ACM Area 2 (Ashley Brown); Woonsocket 295 QA (Michaela Perry); Works Council CSL Behring GmbH (Bernd R√∂√üer); Works Council CSL Behring GmbH (Michael Schrder (Inherited)); Works Council CSL Behring GmbH (Michael Schr√∂der); Works Council CSL Behring GmbH (Reiner D√∂nges); Works Council Chairman (Reiner D√∂nges); Works Councils (Reiner D√∂nges (Inherited)); Wuhan Accounting (Daisy Yang ); Wuhan Accounting (Daisy Yang ????); Wuhan Accounting Finance (Amy Jin ????); Wuhan Accounting Finance (Janet Jin ); Wuhan Accounting Finance (Janet Jin ????); Wuhan Administration Management (CW) (Cris Wang ????? (Inherited)); Wuhan Administration Management (Cris Wang ); Wuhan Administration Management (Cris Wang ?????); Wuhan Administrative Management and Facility Engineering (Fred Pang ?????); Wuhan Administrative Management and Facility Engineering (zcan Campinar); Wuhan Administrative Management and Facility Engineering (√ñzcan Campinar); Wuhan Admistration (Shuiping Zhang ); Wuhan Admistration (Shuiping Zhang ?????); Wuhan Bacteriological Inspection and Animal Trial (CiCi Cheng ); Wuhan Bacteriological Inspection and Animal Trial (CiCi Cheng ????); Wuhan Bioanalytical Sciences (Ming Zuo ); Wuhan Bioanalytical Sciences (Ming Zuo ????); Wuhan Bottle Washing (Weibing Chen ); Wuhan Bottle Washing (Weibing Chen ?????); Wuhan Costing and Financial Planning (Jessie Gao ); Wuhan Costing and Financial Planning (Jessie Gao ?????); Wuhan Environmental Health Safety (Ryan Mao ); Wuhan Environmental Health Safety (Ryan Mao ?????); Wuhan Equipment Maintenance (Jianming Liu ); Wuhan Equipment Maintenance (Jianming Liu ?????); Wuhan Equipment Management (Ming Cao ); Wuhan Equipment Management (Ming Cao ????); Wuhan Equipment Operations (Jun Yin ); Wuhan Equipment Operations (Jun Yin ????); Wuhan Equipment Operations and Maintenance (Rory Yang ); Wuhan Equipment Operations and Maintenance (Rory Yang ?????); Wuhan Finance (Dereck Jiang ); Wuhan Finance (Dereck Jiang ????); Wuhan Human Resources (Grace Yu ????); Wuhan Human Resources Management (Alex Yang ); Wuhan Human Resources Management (Alex Yang ?????); Wuhan Inspection (CW) (Yuemei Huang ????? (Inherited)); Wuhan Inspection (Yuemei Huang ); Wuhan Inspection (Yuemei Huang ?????); Wuhan Manufactuirng Subpackaging Line (Chenyi Guo ); Wuhan Manufactuirng Subpackaging Line (Chenyi Guo ?????); Wuhan Manufacturing Production Management (Liutao Yin ?????); Wuhan Operations (Andrew Tang); Wuhan Packaging (Min Lin ????); Wuhan Physical & Chemical Inspection (Linda Lin ); Wuhan Physical & Chemical Inspection (Linda Lin ????); Wuhan Plasma Inspection (Basin Zhao ); Wuhan Plasma Inspection (Basin Zhao ?????); Wuhan Plasma Sourcing (Lixia He (Inherited)); Wuhan Plasma Sourcing (Qin Chen ????); Wuhan Plasma Sourcing Management (CW) (Lixia He ????? (Inherited)); Wuhan Plasma Sourcing Management (Lixia He ); Wuhan Plasma Sourcing Management (Lixia He ?????); Wuhan Plasma Sourcing Management (Zhibao Qian ?????); Wuhan Plasma and Bacteriological Inspection (Haibo Cheng ); Wuhan Plasma and Bacteriological Inspection (Haibo Cheng ?????); Wuhan Procurement (Chan Liu ????); Wuhan Procurement (Steve Hu ); Wuhan Procurement (Steve Hu ?????); Wuhan Production (Vince Tian ?????); Wuhan Production Management (Zhi Luo ????); Wuhan Production Manufacturing (Elias Francis); Wuhan Production Manufacturing (Ye Xin ????); Wuhan Production Manufacturing (Zhen Wang ????); Wuhan Production Operations (Ye Xin ); Wuhan Production Operations (Ye Xin ????); Wuhan Protein Separation (Songping Xie ); Wuhan Protein Separation (Songping Xie ?????); Wuhan QA Deviation (Ning Ding ); Wuhan QA Deviation (Ning Ding ????); Wuhan QA System (Grace Zhao ); Wuhan QA System (Grace Zhao ????); Wuhan QA Validation (Daoxin Zhu ); Wuhan QA Validation (Daoxin Zhu ?????); Wuhan Quality (Dina El-Emary); Wuhan Quality (Xiaohong Wang ?????); Wuhan Quality Control Inspection (Caixiang Liu ?????); Wuhan Quality Control Ruide (Juergen Liedtke); Wuhan Quality Management (Juergen Liedtke); Wuhan Quality Management (Vivian Zhang ????); Wuhan Quality Systems and Standards (Xiangyang Xia ); Wuhan Quality Systems and Standards (Xiangyang Xia ?????); Wuhan Research and Development (Amory Wang ?????); Wuhan Ruide Compliance (Emma Ma ?????); Wuhan Ruide EIA (Shangqu Shi ?????); Wuhan Ruide Equipment (Zhenzhong Huang ?????); Wuhan Ruide Facilities (Didi Li ?????); Wuhan Ruide Facilities Maintenance (Dexue Hu ?????); Wuhan Ruide QA System & Compliance (Bismarck Huang ?????); Wuhan Ruide Wastewater Treatment (Yuanhui Wang ?????); Wuhan Sales (Jason Xu ????? (Inherited)); Wuhan Sales (Lei Huang ????); Wuhan Solution Preparation (Deqing Mei ); Wuhan Solution Preparation (Deqing Mei ?????); Wuhan Subpackaging Management (Jun Liu ); Wuhan Subpackaging Management (Jun Liu ????); Wuhan Subpackaging Operations (Xin Tian ); Wuhan Subpackaging Operations (Xin Tian ????); Wuhan Technlogy and Quality Study (Lisa Liang ); Wuhan Technlogy and Quality Study (Lisa Liang ????); Wuhan Technology Study (Shawelo Xiao ????); Wuhan Translation team (Mabel Xu ); Wuhan Translation team (Mabel Xu ????); Wuhan Ultrafiltration (Jindi Zhou ); Wuhan Ultrafiltration (Jindi Zhou ?????); Wuhan Water Preparation (Zongrong Liu ); Wuhan Water Preparation (Zongrong Liu ?????); Wyoming 173 (Joe Hicks Jr); Wyoming 173 (Stephanie Gower); Wyoming 173 ACM Area 1 (Jessica Hurlbert); Wyoming 173 ACM Area 2 (AMINA MCPHERSON); Wyoming 173 QA (Brent DeHaan); Wyoming 173 QA (Jared Kurtz); Yeadon 280 (Dominique Holland); Yeadon 280 ACM Area 1 (Therese Algeo); Yeadon 280 ACM Area 2 (TB Bailey); Yeadon 280 QA (Nikki Shaw); York 148 (Brandi Boyles); York 148 ACM Area 1 (Brandi Boyles (Inherited)); York 148 ACM Area 1 (Scottie Johnson Jr); York 148 ACM Area 2 (Stephanie Henry); York 148 QA (Caitie Golubski); York 148 QA (Greg Warren); ZPL Plasma Operations (Klaus Rolshausen (Inherited)); askHR Service ¬ñ TA Support (Anna Tassone); askHR Service ¬ñ TA Support (James Meyer); askHR Shared Services - Tier 1 APAC (Mina Kelepouris); eClinical Operations (Charles Johnson); eR&D Business Support (Simone Dierkes); eSystems (Christina Berninger); eSystems - LIMS Management (Christina Berninger (Inherited)); eSystems - LIMS Management (Stephan Degroth); nan; rzte Berlin (Dorothee Knop); rzte Bielefeld (Dorothee Knop); rzte Braunschweig (Dorothee Knop); rzte Bremen (Dorothee Knop); rzte Frankfurt (Dorothee Knop); rzte Gttingen (Dorothee Knop); rzte Kiel (Dorothee Knop); rzte Nrnberg (Dorothee Knop); support engineer (Deepak Cherian (Inherited)); support engineer (Jamshed Patuck); support engineer (Satya Dara (Inherited)); √Ñrzte (Andreas Gehrich (Inherited)); √Ñrzte (Annette Pernitzsch (Inherited)); √Ñrzte (Claudia Habenicht (Inherited)); √Ñrzte (Heike Borchert); √Ñrzte (Kirsten Scheibel (Inherited)); √Ñrzte (Natascha Bock (Inherited)); √Ñrzte (Stephani Keltsch); √Ñrzte (Sven Schuhmann (Inherited)); √Ñrzte Berlin (Dorothee Knop); √Ñrzte Bielefeld (Dorothee Knop); √Ñrzte Braunschweig (Dorothee Knop); √Ñrzte Bremen (Dorothee Knop); √Ñrzte Frankfurt (Dorothee Knop); √Ñrzte G√∂ttingen (Dorothee Knop); √Ñrzte Kiel (Dorothee Knop); √Ñrzte N√ºrnberg (Dorothee Knop)' inference: true --- # SetFit with sentence-transformers/all-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance - **Maximum Sequence Length:** 384 tokens - **Number of Classes:** 15 classes <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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| 2 | <ul><li>'FLSA STATUS DESCR: Exempt; Non-Exempt; nan'</li><li>'Pay Rate Type: Hourly; Hourly Salary; Hourly/Salary; Salaried; Salary; nan'</li><li>'Employee Level: Executive; Exempt professional; Non-exempt professional; Supervisor/Manager; nan'</li></ul> | | 3 | <ul><li>'Is Manager<HIDE>: N; Y; nan'</li><li>'Job Level Name: Architect and Lead/Principal Individual Contributor; Architect and¬†Lead/Principal¬†Individual Contributor; Associate/Intern; Chief Architect/Technical Fellow; Chief Operating Officer; Director; EVP; Fellow and Chief Architect; Group President/Sr EVP; Individual Contributor; People Manager/Sr Mgr; President and CEO; SVP; Senior Individual Contributor; Senior Lead/Principal Architect; Sr EVP Chief Financial Officer; Supervisor; Vice President/Counsel/Controller; nan'</li><li>'Is Manager: 0; 1; N; No; Y; Yes; nan'</li></ul> | | 5 | <ul><li>'Function/MAG: nan'</li><li>'Functional Pipeline: Communications; Corporate & Government Affairs; Corporate Services; Data Analytics; Design; Digital; Finance; General Management; Human Resources; Legal; Logistics & Services; Manufacturing & Sourcing; Marketing; Merchandising; Product Creation; Product Management; Program/Process Excellence; Retail; Sales; Sports Marketing; Strategic Planning; Technology; Unassigned; Unknown; nan'</li><li>'JobClassDescription: ACCOUNTANTS - DEGREED; ADMINISTRATIVE SUPPORT; AIDES/ORDERLIES; CLERICAL OFFICE SUPPORT; CLINICAL SUPPORT; DRIVERS; EMPLOYED PHYSICIANS; HOME HEALTH CARE - AIDES; HOME HEALTH CARE - LVN; HOME HEALTH CARE - RN; LICENSED REGISTERED NURSES; LICENSED VOCATIONAL/PRACTICAL NURSES; MANAGERS; NON-PHYSICIAN MEDICAL PRACTITIONERS; OTHER PHYSICIANS; PHYSICIAN PRACTICE - LVN; PHYSICIAN PRACTICE - RN; Physicians (With Benefits); SUPERVISORS; SUPPORT SERVICES PATIENT CARE; TECHNICAL SUPPORT; TECHNICIANS; TECHNOLOGISTS/THERAPISTS; nan'</li></ul> | | 10 | <ul><li>'Corp State: Alabama; Arizona; California; Colorado; Connecticut; Delaware; District of Columbia; Florida; Georgia; Idaho; Illinois; Indiana; Iowa; Is ; Kansas; Kentucky; Louisiana; Maine; Maryland; Massachusetts; Michigan; Milan; Minnesota; Mississippi; Missouri; Montana; Nebraska; Nevada; New Hampshire; New Jersey; New Mexico; New York; North Carolina; Ohio; Oklahoma; Oregon; Pennsylvania; Rhode Island; South Carolina; South Dakota; Tennessee; Texas; Turin; Utah; Virginia; Washington; West Virginia; Wisconsin; nan'</li><li>"location__stateprovince: ??mm?n; Aargau; Agrigento; Aguascalientes; Aichi; Alabama; Alaska; Alberta; Alessandria; Alexandria; Aosta; Arizona; Arkansas; Auckland; Baden-Wurttemberg; Bangkok; Bari; Bavaria; Beijing; Bergamo; Bologna; Brescia; British Columbia; Buenos Aires; Busan; Cagliari; California; Canterbury; Caserta; Catania; Cebu; Central Singapore; Changhua County; Chiayi City; Ciudad de Mexico; Coahuila; Colorado; Como; Connecticut; Cortes; Delaware; District of Columbia; Distrito Federal; Dubai; Estado de M√©xico; Ferrara; Firenze; Florida; Fujian; Fukuoka; Genova; Georgia; Gifu; Graubunden; Guanajuato; Guangdong; Guatemala; Haryana; Hawaii; Hawke's Bay; Hiroshima; Ho Chi Minh; Hokkaido; Hong Kong Island; Hsinchu City; Hubei; Ibaraki; Idaho; Ilan County; Illinois; Indiana; Iowa; Ishikawa; Jakarta Raya; Jalisco; Jiangsu; Johor; Kanagawa; Kansas; Kaohsiung City; Karnataka; Kentucky; Kowloon; Lecce; Liaoning; Livorno; Louisiana; Maharashtra; Maine; Managua; Manitoba; Maryland; Massachusetts; Melaka; Messina; Miaoli County; Michigan; Milano; Minnesota; Mississippi; Missouri; Montana; Monza e Brianza; Morelos; Nagano; Napoli; Nebraska; Nevada; New Hampshire; New Jersey; New Mexico; New South Wales; New Taipei City; New Territories; New York; Newfoundland and Labrador; North Carolina; North Dakota; North Rhine-Westphalia; Nova Scotia; Novara; Nuevo Le√≥n; Ohio; Oklahoma; Ontario; Oregon; Osaka; Otago; PG_Asia_CHN_01; PG_Asia_HKG_01; PI CHE - VF International; Padova; Pahang; Panam√°; Parma; Pennsylvania; Phnom Penh; Piacenza; Pingtung County; Puebla; Puerto Rico; Pulau Pinang (Penang); Quebec; Quer√©taro; Quintana Roo; Rhode Island; Roma; Saitama; Salary; San Jose; San Salvador; Santiago; Saskatchewan; Selangor; Seoul; Shandong; Shanghai; Shanxi; Shizuoka; Sichuan; South Carolina; S√£o Paulo; Tabasco; Taichung City; Tainan City; Taipei City; Tamil Nadu; Taoyuan City; Tennessee; Texas; Tianjin; Ticino; Tochigi; Tokyo; Torino; Toyama; Treviso; Trieste; Utah; Varese; Venezia; Veracruz; Vermont; Verona; Vicenza; Victoria; Virginia; Washington; Wellington; West Virginia; Wilayah Persekutuan Kuala Lumpur; Wilayah Persekutuan Putrajaya; Wisconsin; Wyoming; Yucat√°n; Zhejiang; nan"</li><li>'Home State | Province | Region: Alabama; Arkansas; Bogot√° D.C.; California; Colorado; Delaware; Distrito Federal; Eastern Cape; England; Florida; Gauteng; Georgia; Illinois; Indiana; Iowa; Kentucky; KwaZulu-Natal; Maine; Mexico State; Michigan; Minnesota; Missouri; Nevada; New Hampshire; New Jersey; New York; North Carolina; Ohio; Oregon; Pennsylvania; Puerto Rico; Santander; South Carolina; Tennessee; Texas; Valle del Cauca; Virginia; Washington; Western Cape; Wisconsin; nan'</li></ul> | | 12 | <ul><li>'Tenure Category: 0 - 3 Months; 10 - 12 Months; 10 - 15 Years; 13 - 28 Months; 15 - 20 Years; 19 - 24 Months; 2 - 3 Years; 20+ Years; 3 - 5 Years; 4 - 6 Months; 5 - 10 Years; 7 - 9 Months; nan'</li><li>'Tenure with the Company: 0-3 months; 1-2 years; 11-15 years; 16-20 years; 21-25 years; 26-30 years; 3-5 years; 31 years or more; 4-6 months; 6-10 years; 7-12 months; nan'</li><li>'TENURE - Hire: 1 - 2 years; 11 - 15 years; 16 - 20 years; 3 - 5 years; 6 - 10 years; Less than 1 year; More than 20 years; nan'</li></ul> | | 6 | <ul><li>'Location (Geographic): Argentina; Australia; Brazil; Canada; Canada - Living Sounds; China; China - Beijing; China - Suzhou; China, Beijing; China, Suzhou; Colombia; Dubai; France; Germany; Hungary; India; Israel; Italy; Japan; Kenya; Korea; Malaysia; Mexico, Matamoros; Mexico, Mexico City; New Zealand; Norway; Peru; Phillipines; Poland; Prefer not to answer; Romania; Singapore; United Kingdom; United States; nan'</li><li>'Country Name: Australia; Belgium; Brazil; Canada; Colombia; Costa Rica; France; India; Ireland; Italy; Luxembourg; Mexico; Netherlands; New Zealand; Philippines; Poland; Puerto Rico; Singapore; Spain; United Kingdom; United States of America; nan'</li><li>'Operating Company: MHPS-EDE; nan'</li></ul> | | 9 | <ul><li>"HR Site Group<HIDE>: 84 SOUTH HEALTH CENTER; ACL LABS; ADVOCATE MEDICAL GROUP; AMC Bay Area; AMC GRAFTON; AMC KENOSHA; AMC MANITOWOC COUNTY; AMC OSHKOSH; AMC SUMMIT; AMC WASHINGTON COUNTY; AMG CONTACT CENTER; APP; AURORA BAYCARE MEDICAL CENTER; AURORA CLINICAL CONTACT CENTER; AURORA LAKELAND MEDICAL CENTER; AURORA MEMORIAL HOSPITAL OF BURLINGTON; AURORA PSYCH/BEHAVIORAL HEALTH; Aurora Health Care Medical Group : GBMM; Aurora Health Care Medical Group : GMNSC; Aurora Health Care Medical Group : GMS; Aurora Health Care Medical Group : OFDL; Aurora Health Care Medical Group : OTHER; Aurora Health Care Medical Group : RKL; Aurora Health Care Medical Group : SCWNWC; Aurora Health Care Medical Group : WJ; BROMENN MEDICAL CENTER/EUREKA; CHRIST MEDICAL CENTER; CONDELL MEDICAL CENTER; CORPORATE; Children's Hospital; GOOD SAMARITAN HOSPITAL; GOOD SHEPHERD HOSPITAL; ILLINOIS MASONIC MEDICAL CENTER; LUTHERAN GENERAL HOSPITAL; POST ACUTE NETWORK; SHEBOYGAN MEMORIAL; SHERMAN HOSPITAL; SINAI MEDICAL CENTER; SOUTH SUBURBAN HOSPITAL; ST. LUKE'S SOUTH SHORE; SYSTEM ANCILLARY SERVICES; SYSTEM SUPPORT SERVICES; St. Luke's Medical Center; TRINITY HOSPITAL; WEST ALLIS MEDICAL CENTER; nan"</li><li>'Affiliation(Affiliate): 9010 Admin; BCHC; BVRMC; Cherokee Regional Medical Center; Dubuque VNA; FORT DODGE REGION; GREENE COUNTY MEDICAL CENTER; Grundy Cnty Hospital; HCF Inc; Hancock County Senior Services; IA Health Acc Care; MERITER; Memorial Hospital; Pocahontas Community Hospital; Stewart Memorial Community Hospital; Sumner Comm Hospital; UP Clinic Affiliate; UP at Home Affiliate; UPC Peoria; UPH Allen; UPH CR St Lukes; UPH Contract Svc LC; UPH Des Moines; UPH FD Trinity Hlth; UPH FD Trinity Regnl; UPH Finley; UPH Grinnell; UPH Jones Regional; UPH Marshalltown; UPH Methodist; UPH Methodist Colleg; UPH Musc Trinity; UPH Pekin; UPH Proctor; UPH QC Trinity; UPH SC St Lukes; UPH at Work; UPH at Work QC; UnityPlace; UnityPoint Health - Keokuk; Virginia Gay Hospital; nan'</li><li>'region: ACP; CALIFORNIA; CAROLINAS; CENTRAL ZONE RM; EAST PENN/DE; EAST ZONE RM; FLORIDA; GREAT PLAINS; HANGER CLINIC SHARED SVCS; HANGER RESOURCE CENTER; HEARTLAND; HOUSTON; KEYSTONE; MICHIGAN; MID ATLANTIC; MIDWEST; NATL LABS; NEW ENGLAND; NORTH ATLANTIC; NORTHWEST; NY METRO; NY/NJ; OHIO VALLEY; ROCKY MOUNTAIN; SOUTH CENTRAL; SOUTHEAST; SOUTHWEST; SPS; TEXAS; WEST ZONE RM; nan'</li></ul> | | 4 | <ul><li>'Union Status <HIDE>: I am a member of a union; I am not a member of union; nan'</li><li>"Union Code: 122; 17; 399; 420; 781; AFSCME; AFSCME Local 3279; AFSCME Local 9; ALT; ARSA; Appointed; BDN; BKV; BLE; BMWE; BOL; BRCA; BRD; BRS; BRW; CAF; CAP; CAT; CAW; CBI; CBT; CE1; CE2; CE3; CE4; CEC; CED; CEL; CEN; CEQ; CET; CFC; CFF; CFO; CLB; CMA; CMN; CNA; CNR; COL; CPA; CPE; CPL; CPO; CPT; CSC; CSE; CSU; CTM; CTS; CVT; CX1; CX2; CX3; CX4; DBS; DVR; FNK; FRE; FRS; G01; G02; G04; G05; G06; G07; G08; G09; G10; G11; G12; G13; G14; G15; G16; G17; GCH; GGI; GGO; GGR; GVL; HTC; IAM; IBBB; IBEW; IBFO; IBT Lab Asst; IBT Lab Couriers; IBT PMCA Childrens; IBW; IDA; IUOE; IW; JOA AFSCME/SEIU HCMI; KU1; KU2; Laundry Workers Loca; Local 320; Local 321; Local 363; Local 49ers; MDL; MDX; MNA; MOD; MOO; MUR; Muldraugh Compressor Station; N01; N02; N03; NON; NUHW; NUR; None ; OIL; Operating Engineers; PNT; Police; R01; R02; R04; R05; R06; R10; R11; R12; R13; R15; R16; R17; R18; R19; R20; R22; R23; R24; R25; R26; R27; R31; R32; R33; R35; R36; R37; R38; R39; R40; R41; R42; R45; R46; R47; R48; R49; R50; R52; R55; R56; R57; R58; R59; RFT; RPT; RSP; SCP; SEC; SEIU; SEIU - PCA's at RIM; SMW; SPNNUNAC; STF; Service Emp Intn'l U; TCU; TCUASR; TCU_ICTF; TEAM; TSV; Trades; U; U01; U02; U04; U05; U06; U07; U10; U14; U19; U21; U22; U23; U24; U25; U26; U32; U37; U43; U44; U52; U53; U56; U76; U78; U83; U84; U85; U91; UA3; UAW Local 889; UB7; UB9; UC3; UD7; UD8; UE3; UE5; UE9; UF1; UF2; UF3; UF4; UFCW; UG1; UG5; UN5; UN6; UN7; UN8; UN9; UNAC; UNKNOWN; UPUYC-SP; UPUYC-UP; UTUC; UTUE; UTUT; UTUY-A&S; W02; W03; W04; W05; WC5; YRK; nan"</li></ul> | | 16 | <ul><li>'Shift Question<HIDE>: Yes; nan'</li><li>'Work Schedule: 0-30 Hrs Wk; 0-38.5 Hrs Wk; 1 - 10%; 10 FR-MO; 10 M-TH; 10 M-TU TH-FR; 10 M-WE FR; 10 SU-WED; 10 TU-FR; 10 WE - SA; 11 - 20%; 1ST SHIFT; 21 - 30%; 2ND SHIFT; 31 - 40%; 37 Hrs Wk; 37.5 Hrs Wk; 38 Hrs Wk; 38.5 Hrs Wk; 39 Hrs Wk; 3RD SHIFT; 40 Hrs Wk; 41 - 50%; 41 Hrs Wk; 42.5 Hrs Wk; 44 Hrs Wk; 45 Hrs Wk; 48 Hrs Wk; 5/8 FR-SA Off; 5/8 MO-TU Off; 5/8 SU-MO Off; 5/8 TH-FR Off; 5/8 TU-WE Off; 5/8 WE-TH Off; 51 - 60%; 61 - 70%; 71 - 80%; 8 HRS 8am-5pm; 81 - 90%; 91 - 100%; KRONOS SHIFT 1; KRONOS SHIFT 2; KRONOS SHIFT 3; Mon-Fri 40 Hrs/Wk; OPS FLOOR Mo-Th-Fr; PART-TIME VERT. 60,5%; Part-time Oriz. 50%; Part-time Oriz. 60%; Part-time Oriz. 62%; Part-time Oriz. 62,5%; Part-time Oriz. 75%; Part-time Oriz. 87,5%; STANDARD 8-5; STANDARD 8:30am-5pm; STANDARD 8am - 5pm; STANDARD 9-5.30; STANDARD 9am - 6pm; STANDARD 9am-6pm; Service Tech. Field; TURNISTA; Turno PT Orizz. 75%; Turno PT Orizz. 87,5%; nan'</li><li>'Shift<HIDE>: 7; B; D; E; L; O; R; W; Z; nan'</li></ul> | | 14 | <ul><li>'What has been your COVID 19 work arrangement?<HIDE>: Furloughed/Closed Location; Office; Other; Reduced Work Schedule; Remote/Work from Home; nan'</li><li>'Ability to Work Remotely<HIDE>: My primary job role can be done remotely with little or no disruption.; My primary job role is a mix - some can be done from anywhere and some can only be done from the physical work location.; My primary job role requires me to be physically present in my workplace.; nan'</li></ul> | | 1 | <ul><li>'Race_Ethnicity: American Indian or Alaska Native (Not Hispanic or Latino) (United States of America); American Indian or Alaskan Native (United States of America); American¬†Indian¬†or¬†Alaskan¬†Native (United States of America); Asian (Not Hispanic or Latino) (United States of America); Asian (United States of America); Asian - Indian (United Kingdom); Asian - Other (United Kingdom); Asian - Pakistani (United Kingdom); Bai (China); Black - African (United Kingdom); Black - British (United Kingdom); Black - Caribbean (United Kingdom); Black or African American (Not Hispanic or Latino) (United States of America); Black or African American (United States of America); Black¬†or¬†African¬†American (United States of America); Buyei (China); Chinese (Singapore); Dai (China); Dong (China); Han (China); Hani (China); Hispanic or Latino (United States of America); Hispanic/Latino (United States of America); I do not wish to answer. (United States of America); Indian (Singapore); Li (China); Malay (Singapore); Native Hawaiian or Other Pacific Islander (Not Hispanic or Latino) (United States of America); Native Hawaiian or Other Pacific Islander (United States of America); Native¬†Hawaiian¬†or¬†Other¬†Pacific¬†Islander (United States of America); Not Declaring (United Kingdom); Not Reported; Other (Singapore); Other (United Kingdom); Tujia (China); Two or More Races (Not Hispanic or Latino) (United States of America); Two or More Races (United States of America); Two¬†or¬†More¬†Races (United States of America); White (Not Hispanic or Latino) (United States of America); White (United States of America); White - British (United Kingdom); White - Irish (United Kingdom); White - Other (United Kingdom); White - Other European (United Kingdom); Yi (China); Zhuang (China); nan'</li><li>'Which ethnicity/ethnicities do you most identify with?: Asian; Black; Hispanic or Latino; Other; Prefer not to respond; Two or More Races; White; nan'</li><li>'Ethnicity On File: 2 or more races, not Hispanc; American Indian/Alaska Nativ; Asian; Black/African American; Hispanic/Latino; Native Hawaiian/Oth Pacif Is; White; nan'</li></ul> | | 7 | <ul><li>'FM_Merger_Cd: N; Y; nan'</li><li>'Acquisition Hire<HIDE>: Acquisition Hire; Non-Acquisition Hire; nan'</li></ul> | | 8 | <ul><li>'Primary Termination Reason: Retained; Terminate Associate > Involuntary > Attendance; Terminate Associate > Involuntary > Death; Terminate Associate > Involuntary > Elimination of Position; Terminate Associate > Involuntary > Exhaustion of Leave; Terminate Associate > Involuntary > Falsification of Records; Terminate Associate > Involuntary > Gross Misconduct; Terminate Associate > Involuntary > Mutual Consent; Terminate Associate > Involuntary > Not re-new contract; Terminate Associate > Involuntary > Poor Job Performance; Terminate Associate > Involuntary > Severance; Terminate Associate > Involuntary > Tardiness; Terminate Associate > Involuntary > Violation of Rules; Terminate Associate > Involuntary > Workforce Reduction; Terminate Associate > Voluntary > Commute Time; Terminate Associate > Voluntary > Company Instability; Terminate Associate > Voluntary > Dissatisfied with Hours; Terminate Associate > Voluntary > Dissatisfied with Job; Terminate Associate > Voluntary > Dissatisfied with Management; Terminate Associate > Voluntary > Dissatisfied with Pay; Terminate Associate > Voluntary > Dissatisfied with Promotional Opportunities; Terminate Associate > Voluntary > Dissatisfied with Working Conditions; Terminate Associate > Voluntary > Failure to Return from Leave; Terminate Associate > Voluntary > Job Abandonment; Terminate Associate > Voluntary > Military Service; Terminate Associate > Voluntary > Moved; Terminate Associate > Voluntary > Other Employment; Terminate Associate > Voluntary > Personal; Terminate Associate > Voluntary > Retirement; Terminate Associate > Voluntary > Return to School; Terminate Associate > Voluntary > Severance; Terminate Associate > Voluntary > Unknown; Terminate Employee > Voluntary > Benefits; Terminate Employee > Voluntary > Career Change; Terminate Employee > Voluntary > Career Development or Advancement; Terminate Employee > Voluntary > Compensation; Terminate Employee > Voluntary > Continue Education; Terminate Employee > Voluntary > Contract End; Terminate Employee > Voluntary > Conversion; Terminate Employee > Voluntary > Dislike Company; Terminate Employee > Voluntary > Dislike Hours/Schedule; Terminate Employee > Voluntary > Dislike Supervisor; Terminate Employee > Voluntary > Dislike Work; Terminate Employee > Voluntary > Dissatisfied Career Advancement Opportunities; Terminate Employee > Voluntary > Dissatisfied with Benefits; Terminate Employee > Voluntary > Dissatisfied with Benefits Package (Health, Dental, Vision, Life, Retirement, Paid Leave, etc.); Terminate Employee > Voluntary > Dissatisfied with Career Opportunities; Terminate Employee > Voluntary > Dissatisfied with Company Policies; Terminate Employee > Voluntary > Dissatisfied with Compensation Package (Base Salary, Bonus, Commissions, etc.); Terminate Employee > Voluntary > Dissatisfied with Coworkers; Terminate Employee > Voluntary > Dissatisfied with Flexible Work Arrangements (remote work, flexible hours, etc.); Terminate Employee > Voluntary > Dissatisfied with Hours / Schedule; Terminate Employee > Voluntary > Dissatisfied with Industry; Terminate Employee > Voluntary > Dissatisfied with Job; Terminate Employee > Voluntary > Dissatisfied with Location; Terminate Employee > Voluntary > Dissatisfied with Location/Commute; Terminate Employee > Voluntary > Dissatisfied with Management; Terminate Employee > Voluntary > Dissatisfied with Manager Effectiveness; Terminate Employee > Voluntary > Dissatisfied with Organization Culture (Corporate Values, Behaviors, Norms that Guide How People Work); Terminate Employee > Voluntary > Dissatisfied with Pay; Terminate Employee > Voluntary > Dissatisfied with Travel; Terminate Employee > Voluntary > Dissatisfied with Type of Work; Terminate Employee > Voluntary > Dissatisfied with Work Conditions; Terminate Employee > Voluntary > Dissatisfied with Working Conditions; Terminate Employee > Voluntary > Dissatisfied with Worklife Balance; Terminate Employee > Voluntary > Exit Workforce; Terminate Employee > Voluntary > Failed to Return from Leave; Terminate Employee > Voluntary > Failure to Return from Leave; Terminate Employee > Voluntary > Family Obligations; Terminate Employee > Voluntary > Family Reasons; Terminate Employee > Voluntary > Health Concerns; Terminate Employee > Voluntary > Health Reasons; Terminate Employee > Voluntary > Job Abandonment; Terminate Employee > Voluntary > Job Security; Terminate Employee > Voluntary > Join Military; Terminate Employee > Voluntary > Location; Terminate Employee > Voluntary > Military Service; Terminate Employee > Voluntary > Moved; Terminate Employee > Voluntary > Mutual Agreement (inactive); Terminate Employee > Voluntary > Mutual Consent; Terminate Employee > Voluntary > Never Reported for Orientation; Terminate Employee > Voluntary > Other Employment; Terminate Employee > Voluntary > Personal - Furthering Education (inactive); Terminate Employee > Voluntary > Personal Reasons; Terminate Employee > Voluntary > Relocation; Terminate Employee > Voluntary > Resignation; Terminate Employee > Voluntary > Retirement; Terminate Employee > Voluntary > Return to School; Terminate Employee > Voluntary > Returned to School; Terminate Employee > Voluntary > Self Employment; Terminate Employee > Voluntary > Training; Terminate Employee > Voluntary > Transportation Problems; Terminate Employee > Voluntary > Unknown; Terminate Employee > Voluntary > Work Authorization Not Renewed; Terminate Employee > Voluntary > Workload; nan'</li><li>'Termed Reason: I; V; nan'</li><li>'Voluntary or Retirement<HIDE>: Retirement; Voluntary; nan'</li></ul> | | 11 | <ul><li>'Generation: 18-24 years of age; 25-34 years; 25-34 years of age; 26-35 Yrs; 26-35 years; 35-44 years; 35-44 years of age; 36-45 Yrs; 36-45 years; 45-54 years of age; 45-55 years; 46-55 Yrs; 46-55 years; 55-64 years of age; 65+ years of age; < 25 years; < 26 Yrs; < 26 years; <25 years; > 55 Yrs; > 55 years; >55 years; Baby Boomer; Baby Boomer (born 1946 ¬ñ 1964); Baby Boomers; Baby Boomers ¬ñ 1946 ¬ñ 1965; Gen X; Generation X; Generation X (born 1965 to 1980); Generation X ¬ñ 1965 ¬ñ 1980; Generation Y / Millennials ¬ñ 1981 ¬ñ 1996; Generation Z; Generation Z (born 2001 to 2015); Generation Z ¬ñ 1997 and onwards; Mature (born in 1945 or earlier); Millennial; Millennials; Millennials (born 1981 to 2000); Silent Generation; Silent Generation - 1928 ¬ñ 1945; Traditionalist; nan'</li><li>'Age Bracket: 119.4; 18-24; 18.5; 18.7; 19.1; 19.2; 19.3; 19.4; 19.5; 19.6; 19.7; 19.8; 19.83333333; 19.9; 20 - 29; 20-24; 20-30 Years; 20.3; 20.6; 20.66666667; 20.7; 20.83333333; 20.9; 21; 21.08333333; 21.1; 21.16666667; 21.2; 21.3; 21.4; 21.5; 21.6; 21.66666667; 21.7; 21.8; 21.83333333; 21.9; 22; 22.1; 22.2; 22.3; 22.33333333; 22.4; 22.41666667; 22.5; 22.58333333; 22.6; 22.66666667; 22.7; 22.75; 22.8; 22.9; 23; 23.08333333; 23.1; 23.16666667; 23.2; 23.25; 23.3; 23.33333333; 23.4; 23.41666667; 23.5; 23.58333333; 23.6; 23.7; 23.8; 23.83333333; 23.9; 23.91666667; 24; 24.1; 24.2; 24.3; 24.33333333; 24.4; 24.41666667; 24.5; 24.58333333; 24.6; 24.66666667; 24.7; 24.75; 24.8; 24.83333333; 24.9; 25; 25-30; 25-35; 25-35 ; 25.08333333; 25.1; 25.16666667; 25.2; 25.25; 25.3; 25.33333333; 25.4; 25.41666667; 25.5; 25.58333333; 25.6; 25.66666667; 25.7; 25.75; 25.8; 25.83333333; 25.9; 25.91666667; 26; 26-3; 26-35; 26.08333333; 26.1; 26.16666667; 26.2; 26.25; 26.3; 26.33333333; 26.4; 26.41666667; 26.5; 26.58333333; 26.6; 26.66666667; 26.7; 26.75; 26.8; 26.83333333; 26.9; 26.91666667; 27; 27.08333333; 27.1; 27.16666667; 27.2; 27.25; 27.3; 27.33333333; 27.4; 27.41666667; 27.5; 27.58333333; 27.6; 27.66666667; 27.7; 27.75; 27.8; 27.83333333; 27.9; 27.91666667; 28; 28.08333333; 28.1; 28.16666667; 28.2; 28.25; 28.3; 28.33333333; 28.4; 28.41666667; 28.5; 28.58333333; 28.6; 28.66666667; 28.7; 28.75; 28.8; 28.83333333; 28.9; 28.91666667; 29; 29.08333333; 29.1; 29.16666667; 29.2; 29.25; 29.3; 29.33333333; 29.4; 29.41666667; 29.5; 29.58333333; 29.6; 29.66666667; 29.7; 29.75; 29.8; 29.83333333; 29.9; 29.91666667; 30; 30 - 39; 30-40 Years; 30.08333333; 30.1; 30.16666667; 30.2; 30.25; 30.3; 30.33333333; 30.4; 30.41666667; 30.5; 30.58333333; 30.6; 30.66666667; 30.7; 30.75; 30.8; 30.83333333; 30.9; 30.91666667; 31; 31-40; 31.08333333; 31.1; 31.16666667; 31.2; 31.25; 31.3; 31.33333333; 31.4; 31.41666667; 31.5; 31.58333333; 31.6; 31.66666667; 31.7; 31.75; 31.8; 31.83333333; 31.9; 31.91666667; 32; 32.08333333; 32.1; 32.16666667; 32.2; 32.25; 32.3; 32.33333333; 32.4; 32.41666667; 32.5; 32.58333333; 32.6; 32.66666667; 32.7; 32.75; 32.8; 32.83333333; 32.9; 32.91666667; 33; 33.08333333; 33.1; 33.16666667; 33.2; 33.25; 33.3; 33.33333333; 33.4; 33.41666667; 33.5; 33.58333333; 33.6; 33.66666667; 33.7; 33.75; 33.8; 33.83333333; 33.9; 33.91666667; 34; 34.08333333; 34.1; 34.16666667; 34.2; 34.25; 34.3; 34.33333333; 34.4; 34.41666667; 34.5; 34.58333333; 34.6; 34.66666667; 34.7; 34.75; 34.8; 34.83333333; 34.9; 34.91666667; 35; 35.08333333; 35.1; 35.16666667; 35.2; 35.25; 35.3; 35.33333333; 35.4; 35.41666667; 35.5; 35.58333333; 35.6; 35.66666667; 35.7; 35.75; 35.8; 35.83333333; 35.9; 35.91666667; 36; 36-40; 36-41; 36-45; 36.08333333; 36.1; 36.16666667; 36.2; 36.25; 36.3; 36.33333333; 36.4; 36.41666667; 36.5; 36.58333333; 36.6; 36.66666667; 36.7; 36.75; 36.8; 36.83333333; 36.9; 36.91666667; 37; 37.08333333; 37.1; 37.16666667; 37.2; 37.25; 37.3; 37.33333333; 37.4; 37.41666667; 37.5; 37.58333333; 37.6; 37.66666667; 37.7; 37.75; 37.8; 37.83333333; 37.9; 37.91666667; 38; 38.08333333; 38.1; 38.16666667; 38.2; 38.25; 38.3; 38.33333333; 38.4; 38.41666667; 38.5; 38.58333333; 38.6; 38.66666667; 38.7; 38.75; 38.8; 38.83333333; 38.9; 38.91666667; 39; 39.08333333; 39.1; 39.16666667; 39.2; 39.25; 39.3; 39.33333333; 39.4; 39.41666667; 39.5; 39.58333333; 39.6; 39.66666667; 39.7; 39.75; 39.8; 39.83333333; 39.9; 39.91666667; 40; 40 - 49; 40-50 Years; 40.08333333; 40.1; 40.16666667; 40.2; 40.25; 40.3; 40.33333333; 40.4; 40.41666667; 40.5; 40.58333333; 40.6; 40.66666667; 40.7; 40.75; 40.8; 40.83333333; 40.9; 40.91666667; 41; 41-49; 41-50; 41.08333333; 41.1; 41.16666667; 41.2; 41.25; 41.3; 41.33333333; 41.4; 41.41666667; 41.5; 41.58333333; 41.6; 41.66666667; 41.7; 41.75; 41.8; 41.83333333; 41.9; 41.91666667; 42; 42.08333333; 42.1; 42.16666667; 42.2; 42.25; 42.3; 42.33333333; 42.4; 42.41666667; 42.5; 42.58333333; 42.6; 42.66666667; 42.7; 42.75; 42.8; 42.83333333; 42.9; 42.91666667; 43; 43.08333333; 43.1; 43.16666667; 43.2; 43.25; 43.3; 43.33333333; 43.4; 43.41666667; 43.5; 43.58333333; 43.6; 43.66666667; 43.7; 43.75; 43.8; 43.83333333; 43.9; 43.91666667; 44; 44.08333333; 44.1; 44.16666667; 44.2; 44.25; 44.3; 44.33333333; 44.4; 44.41666667; 44.5; 44.58333333; 44.6; 44.66666667; 44.7; 44.75; 44.8; 44.83333333; 44.9; 44.91666667; 45; 45.08333333; 45.1; 45.16666667; 45.2; 45.25; 45.3; 45.33333333; 45.4; 45.41666667; 45.5; 45.58333333; 45.6; 45.66666667; 45.7; 45.75; 45.8; 45.83333333; 45.9; 45.91666667; 46; 46-54; 46.08333333; 46.1; 46.16666667; 46.2; 46.25; 46.3; 46.33333333; 46.4; 46.41666667; 46.5; 46.58333333; 46.6; 46.66666667; 46.7; 46.75; 46.8; 46.83333333; 46.9; 46.91666667; 47; 47.08333333; 47.1; 47.16666667; 47.2; 47.25; 47.3; 47.33333333; 47.4; 47.41666667; 47.5; 47.58333333; 47.6; 47.66666667; 47.7; 47.75; 47.8; 47.83333333; 47.9; 47.91666667; 48; 48.08333333; 48.1; 48.16666667; 48.2; 48.25; 48.3; 48.33333333; 48.4; 48.41666667; 48.5; 48.58333333; 48.6; 48.66666667; 48.7; 48.75; 48.8; 48.83333333; 48.9; 48.91666667; 49; 49.08333333; 49.1; 49.16666667; 49.2; 49.25; 49.3; 49.33333333; 49.4; 49.41666667; 49.5; 49.58333333; 49.6; 49.66666667; 49.7; 49.75; 49.8; 49.83333333; 49.9; 49.91666667; 50; 50 - 59; 50-60 Years; 50-64; 50.1; 50.16666667; 50.2; 50.25; 50.3; 50.33333333; 50.4; 50.41666667; 50.5; 50.58333333; 50.6; 50.66666667; 50.7; 50.75; 50.8; 50.83333333; 50.9; 50.91666667; 51; 51+; 51.08333333; 51.1; 51.16666667; 51.2; 51.25; 51.3; 51.33333333; 51.4; 51.41666667; 51.5; 51.58333333; 51.6; 51.66666667; 51.7; 51.75; 51.8; 51.83333333; 51.9; 51.91666667; 52; 52.08333333; 52.1; 52.16666667; 52.2; 52.25; 52.3; 52.33333333; 52.4; 52.41666667; 52.5; 52.58333333; 52.6; 52.66666667; 52.7; 52.75; 52.8; 52.83333333; 52.9; 52.91666667; 53; 53.08333333; 53.1; 53.16666667; 53.2; 53.25; 53.3; 53.33333333; 53.4; 53.41666667; 53.5; 53.58333333; 53.6; 53.66666667; 53.7; 53.75; 53.8; 53.83333333; 53.9; 53.91666667; 54; 54.08333333; 54.1; 54.16666667; 54.2; 54.25; 54.3; 54.33333333; 54.4; 54.41666667; 54.5; 54.58333333; 54.6; 54.66666667; 54.7; 54.75; 54.8; 54.83333333; 54.9; 54.91666667; 55; 55+; 55.08333333; 55.1; 55.16666667; 55.2; 55.25; 55.3; 55.33333333; 55.4; 55.5; 55.58333333; 55.6; 55.66666667; 55.7; 55.75; 55.8; 55.83333333; 55.9; 55.91666667; 56; 56.08333333; 56.1; 56.16666667; 56.2; 56.25; 56.3; 56.33333333; 56.4; 56.41666667; 56.5; 56.58333333; 56.6; 56.66666667; 56.7; 56.75; 56.8; 56.83333333; 56.9; 56.91666667; 57; 57.08333333; 57.1; 57.16666667; 57.2; 57.25; 57.3; 57.4; 57.41666667; 57.5; 57.6; 57.66666667; 57.7; 57.75; 57.8; 57.83333333; 57.9; 57.91666667; 58; 58.08333333; 58.1; 58.16666667; 58.2; 58.25; 58.3; 58.33333333; 58.4; 58.5; 58.58333333; 58.6; 58.7; 58.75; 58.8; 58.83333333; 58.9; 58.91666667; 59; 59.08333333; 59.1; 59.16666667; 59.2; 59.25; 59.3; 59.33333333; 59.4; 59.41666667; 59.5; 59.58333333; 59.6; 59.7; 59.75; 59.8; 59.83333333; 59.9; 59.91666667; 6; 60; 60 and over; 60.08333333; 60.1; 60.16666667; 60.2; 60.3; 60.33333333; 60.4; 60.41666667; 60.5; 60.6; 60.7; 60.75; 60.8; 60.83333333; 60.9; 60.91666667; 61; 61.1; 61.16666667; 61.2; 61.25; 61.3; 61.4; 61.5; 61.58333333; 61.6; 61.66666667; 61.7; 61.75; 61.8; 61.83333333; 61.9; 61.91666667; 62; 62.1; 62.16666667; 62.2; 62.25; 62.3; 62.33333333; 62.4; 62.41666667; 62.5; 62.58333333; 62.6; 62.66666667; 62.7; 62.8; 62.9; 62.91666667; 63; 63.08333333; 63.1; 63.2; 63.25; 63.3; 63.4; 63.5; 63.58333333; 63.6; 63.66666667; 63.7; 63.75; 63.8; 63.9; 63.91666667; 64; 64.08333333; 64.1; 64.2; 64.33333333; 64.4; 64.5; 64.58333333; 64.6; 64.7; 64.75; 64.8; 64.9; 64.91666667; 65; 65+; 65.1; 65.16666667; 65.2; 65.3; 65.4; 65.41666667; 65.5; 65.6; 65.66666667; 65.7; 65.75; 65.8; 65.83333333; 65.9; 65.91666667; 66; 66.1; 66.2; 66.3; 66.33333333; 66.4; 66.6; 66.7; 66.75; 66.8; 67; 67.08333333; 67.1; 67.2; 67.3; 67.4; 67.5; 67.58333333; 67.6; 67.66666667; 67.7; 67.9; 68; 68.2; 68.3; 68.33333333; 68.4; 68.5; 68.66666667; 68.7; 68.91666667; 69; 69.08333333; 69.3; 69.4; 69.7; 69.8; 69.9; 70; 70.08333333; 70.1; 70.2; 70.25; 70.4; 70.6; 70.7; 71.1; 71.3; 71.4; 71.5; 71.6; 71.9; 72.16666667; 72.5; 72.6; 72.75; 72.8; 73; 73.3; 73.6; 74.16666667; 74.2; 74.4; 75.7; 77.6; 79.25; 935.7; <20; <20 Years; <=25; >=60 Years; AAB00366417; AAB10011157; Baby Boomer; F; Gen X; Gen Y / Millennial; Less than 18; M; Traditionalist; Under 20; nan'</li><li>'age_band: 18-25; 20 - 29; 26-35; 30 - 39; 36-45; 40 - 49; 46-55; 50 - 59; 56 and above; 60 and over; Under 20; nan'</li></ul> | | 0 | <ul><li>'Employee_Gender: F; M; nan'</li><li>'O_Gender: F; Female; M; Male; U; Unknown; nan'</li><li>'Gender: -; 0; 1 Individual Contributor; 10 Individual Contributor; 119; 17; 18; 19; 20; 20-29; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 30-39; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 40-49; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 50-59; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 8 Sr. Manager; 80; 81; 83; 88; 89; 9 Manager; 9 manager; 90; 91; 935; ?; Agender; Asian; Bay; Bayan; Choose not to respond; Contractor; D; DJO Export; Decline; Decline to State; Decline to answer; Decline to state; Director; Do Not Wish to Disclose; F; F ; FEMALE; FEMALE ; Female; Female ; Female ; Femenino; F√©minin; Gender; Gender Nonconforming; Gender non-conforming; Gender variant / non-conforming; I Choose Not to Self Disclose; I Choose not to Disclose; I DO NOT WISH TO SELF-IDENTIFY; I Prefer Not to Answer; I choose not to disclose; I choose not to reply; I decline to self-identify; I do not wish to Self-Identify; I prefer not to answer; I prefer not to say; I prefer to self-describe in another way:; Identity Not Listed; In Another Way; JOANNE STURGESS; JODIE FIDDES; M; M ; MALE; MASCOLINO; Make; Male; Male ; Male ; Masculin; Masculino; N; Non-Binary; Non-Binary/Third Gender; Non-binary; Non-binary / third gender; Non-binary/ third gender; Non-specific; Nonconform; None; Not Available; Not Declared; Not Declaring; Not Disclosed; Not SpeciFemaleied; Not Specifed; Not Specified; Not assigned; Not available; Not declared; Not known; Not_Declared; Not_declared; O; Other; Prefer Not To Answer; Prefer Not To Say; Prefer Not To Self-Identify; Prefer Not to Respond; Prefer Not to Say; Prefer not to answer; Prefer not to disclose; Prefer not to say; Prefer to self-describe; Reassigned; Sex; Transgender; Two or more races; U; UNKNOWN; Undeclared; Undisc; Undisclosed; Unknown; Unspecified; Unused: F; Unused: M; White; Withhold; [NONE]; f; female; m; nan; unknown; unknown '</li></ul> | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("svorwerk/setfit-fine-tuned-demo-class") # Run inference preds = model("Emp_FLSA: E; N; P; V; X; nan") ``` <!-- ### Downstream Use *List how someone could finetune this model on their own dataset.* --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:---------|:-----| | Word count | 2 | 135.7721 | 6076 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 5 | | 1 | 8 | | 2 | 24 | | 3 | 23 | | 4 | 2 | | 5 | 22 | | 6 | 12 | | 7 | 2 | | 8 | 6 | | 9 | 4 | | 10 | 8 | | 11 | 6 | | 12 | 8 | | 14 | 2 | | 16 | 4 | ### Training Hyperparameters - batch_size: (32, 32) - num_epochs: (4, 4) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 5e-06) - head_learning_rate: 0.002 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: True - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - max_length: 500 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: True ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:----------:|:-------:|:-------------:|:---------------:| | 0.0019 | 1 | 0.1915 | - | | 0.0973 | 50 | 0.0902 | - | | 0.1946 | 100 | 0.0364 | - | | 0.2918 | 150 | 0.0042 | - | | 0.3891 | 200 | 0.0012 | - | | 0.4864 | 250 | 0.0009 | - | | 0.5837 | 300 | 0.0006 | - | | 0.6809 | 350 | 0.0005 | - | | 0.7782 | 400 | 0.0011 | - | | 0.8755 | 450 | 0.0003 | - | | **0.9728** | **500** | **0.0005** | **0.0956** | | 1.0700 | 550 | 0.0002 | - | | 1.1673 | 600 | 0.0006 | - | | 1.2646 | 650 | 0.0003 | - | | 1.3619 | 700 | 0.0004 | - | | 1.4591 | 750 | 0.0002 | - | | 1.5564 | 800 | 0.0002 | - | | 1.6537 | 850 | 0.0001 | - | | 1.7510 | 900 | 0.0002 | - | | 1.8482 | 950 | 0.0002 | - | | 1.9455 | 1000 | 0.0002 | 0.0994 | | 2.0428 | 1050 | 0.0002 | - | | 2.1401 | 1100 | 0.0003 | - | | 2.2374 | 1150 | 0.0002 | - | | 2.3346 | 1200 | 0.0001 | - | | 2.4319 | 1250 | 0.0001 | - | | 2.5292 | 1300 | 0.0001 | - | | 2.6265 | 1350 | 0.0001 | - | | 2.7237 | 1400 | 0.0001 | - | | 2.8210 | 1450 | 0.0001 | - | | 2.9183 | 1500 | 0.0001 | 0.0975 | | 3.0156 | 1550 | 0.0001 | - | | 3.1128 | 1600 | 0.0001 | - | | 3.2101 | 1650 | 0.0004 | - | | 3.3074 | 1700 | 0.0002 | - | | 3.4047 | 1750 | 0.0001 | - | | 3.5019 | 1800 | 0.0002 | - | | 3.5992 | 1850 | 0.0001 | - | | 3.6965 | 1900 | 0.0001 | - | | 3.7938 | 1950 | 0.0001 | - | | 3.8911 | 2000 | 0.0001 | 0.0987 | | 3.9883 | 2050 | 0.0002 | - | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 2.2.2 - Transformers: 4.37.1 - PyTorch: 2.1.0+cu121 - Datasets: 2.16.1 - Tokenizers: 0.15.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION", "TRANSLATION" ]
[ "CHIA", "MIRNA" ]
Non_BioNLP
FINGU-AI/FingUEm_V3
FINGU-AI
sentence-similarity
[ "sentence-transformers", "safetensors", "qwen2", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:245133", "loss:MultipleNegativesRankingLoss", "loss:MultipleNegativesSymmetricRankingLoss", "loss:CoSENTLoss", "custom_code", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:Alibaba-NLP/gte-Qwen2-1.5B-instruct", "base_model:finetune:Alibaba-NLP/gte-Qwen2-1.5B-instruct", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,720
1,721
6
3
--- base_model: Alibaba-NLP/gte-Qwen2-1.5B-instruct datasets: [] language: [] library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:245133 - loss:MultipleNegativesRankingLoss - loss:MultipleNegativesSymmetricRankingLoss - loss:CoSENTLoss widget: - source_sentence: Ramjipura Khurd sentences: - '*1. Yes, I did, because you, dear sir, dropped the ball by failing to see that carrots was an imaginative metaphor for the human bone. Yes, carrots are not bones, but how can one define what a "vegetable" truly is? Some may say, "vegetables are not X." But that presumes a linear concept of knowledge based around the word "is." You sir, have not read Edvard WEstermark''s seminal work "Wit and Wisdom in Morroco." *2. Cheese pizza lacks toppings. if you wish to know more, simply go to a "menu" and see what category they place meat (or, as I creatively spelled it in order to destroy Euro-centric spelling, meet) as "extra toppings." Extra cheese is not LISTED. *4 Pa�acuelos do not exist, but does pizza? Answer correctly or die.' - Ramjipura Khurd is a small village 50 km from Jaipur, Rajasthan, India. There are 200 houses in the village. Many Rajputs live in Ramjipura Khurd, as well as other castes. - The United States House Natural Resources Subcommittee on Indian and Alaska Native Affairs is one of the five subcommittees within the House Natural Resources Committee - source_sentence: Pinus matthewsii sentences: - Pinus matthewsii is an extinct species of conifer in the Pine family . The species is solely known from the Pliocene sediments exposed at Ch ' ijee 's Bluff on the Porcupine River near Old Crow , Yukon , Canada . - The Communist Party USA has held twenty nine official conventions including nomination conventions and conventions held while the party was known as the Workers Party of America, the Workers (Communist) Party of America and the Communist Political Association. - Clytus ruricola is a species of beetle in the family Cerambycidae. It was described by Olivier in 1795. - source_sentence: Thomas H. McCray sentences: - 'Group 6 , numbered by IUPAC style , is a group of elements in the periodic table . Its members are chromium ( Cr ) , molybdenum ( Mo ) , tungsten ( W ) , and seaborgium ( Sg ) . These are all transition metals and chromium , molybdenum and tungsten are refractory metals . The period 8 elements of group 6 are likely to be either unpenthexium ( Uph ) or unpentoctium ( Upo ) . This may not be possible ; drip instability may imply that the periodic table ends at unbihexium . Neither unpenthexium nor unpentoctium have been synthesized , and it is unlikely that this will happen in the near future . Like other groups , the members of this family show patterns in its electron configuration , especially the outermost shells resulting in trends in chemical behavior : `` Group 6 '''' is the new IUPAC name for this group ; the old style name was `` group VIB '''' in the old US system ( CAS ) or `` group VIA '''' in the European system ( old IUPAC ) . Group 6 must not be confused with the group with the old-style group crossed names of either VIA ( US system , CAS ) or VIB ( European system , old IUPAC ) . That group is now called group 16 .' - Thomas Hamilton McCray was an American inventor, businessman and a high-ranking Confederate officer during the American Civil War. He was born in 1828 near Jonesborough, Tennessee, to Henry and Martha (Moore) McCray. - Gregg Stephen Lehrman is an American composer, music producer and technologist. He is the founder and CEO of music software company Output, and the recipient of a 2016 ASCAP Award for his original music. - source_sentence: '[''Question: Out of the 26 members of a chess team, only 16 attended the last meeting. All of the boys attended, while half of the girls attended. How many girls are there on the chess team?\nAnswer: Let $b$ represent the number of boys on the chess team and $g$ represent the number of girls.\nWe are given that $b + g = 26$ and $b + \\frac{1}{2}g = 16$.\nMultiplying the second equation by 2, we get $2b + g = 32$.\nSubtracting the first equation from the second equation gives $b = 6$.\nSubstituting $b = 6$ into the first equation gives $6 + g = 26$, so $g = 20$.\nTherefore, there are $\\boxed{20}$ girls on the chess team.\nThe answer is: 20\n\nQuestion: Eustace is twice as old as Milford. In 3 years, he will be 39. How old will Milford be?\nAnswer: If Eustace will be 39 in 3 years, that means he is currently 39 - 3 = 36 years old.\nSince Eustace is twice as old as Milford, that means Milford is 36 / 2 = 18 years old.\nIn 3 years, Milford will be 18 + 3 = 21 years old.\n#### 21\nThe answer is: 21\n\nQuestion: Convert $10101_3$ to a base 10 integer.\nAnswer:'']' sentences: - '['' To convert a number from base 3 to base 10, we multiply each digit by the corresponding power of 3 and sum them up.\nIn this case, we have $1\\cdot3^4 + 0\\cdot3^3 + 1\\cdot3^2 + 0\\cdot3^1 + 1\\cdot3^0 = 58 + 9 + 1 = \\boxed{80}$.\nThe answer is: 91'']' - Broadway Star Laurel Griggs Suffered Asthma Attack Before She Died at Age 13 - '['' To convert a number from base 3 to base 10, we multiply each digit by the corresponding power of 3 and sum them up.\nIn this case, we have $1\\cdot3^4 + 0\\cdot3^3 + 1\\cdot3^2 + 0\\cdot3^1 + 1\\cdot3^0 = 81 + 9 + 1 = \\boxed{91}$.\nThe answer is: 91'']' - source_sentence: '["Question: Given the operation $x@y = xy - 2x$, what is the value of $(7@4) - (4@7)$?\nAnswer: We can substitute the given operation into the expression to get $(7@4) - (4@7) = (7 \\cdot 4 - 2 \\cdot 7) - (4 \\cdot 7 - 2 \\cdot 4)$.\nSimplifying, we have $28 - 14 - 28 + 8 = \\boxed{-6}$.\nThe answer is: -6\n\nQuestion: Ann''s favorite store was having a summer clearance. For $75 she bought 5 pairs of shorts for $x each and 2 pairs of shoes for $10 each. She also bought 4 tops, all at the same price. Each top cost 5. What is the value of unknown variable x?\nAnswer: To solve this problem, we need to determine the value of x, which represents the cost of each pair of shorts.\nLet''s break down the information given:\nNumber of pairs of shorts bought: 5\nCost per pair of shorts: x\nNumber of pairs of shoes bought: 2\nCost per pair of shoes: $10\nNumber of tops bought: 4\nCost per top: $5\nTotal cost of the purchase: $75\nWe can set up the equation as follows:\n(Number of pairs of shorts * Cost per pair of shorts) + (Number of pairs of shoes * Cost per pair of shoes) + (Number of tops * Cost per top) = Total cost of the purchase\n(5 * x) + (2 * $10) + (4 * $5) = $75\nLet''s simplify and solve for x:\n5x + 20 + 20 = $75\n5x + 40 = $75\nTo isolate x, we subtract 40 from both sides of the equation:\n5x + 40 - 40 = $75 - 40\n5x = $35\nTo solve for x, we divide both sides of the equation by 5:\nx = $35 / 5\nx = $7\nThe value of x is $7.\n#### 7\nThe answer is: 7\n\nQuestion: Calculate the area of the triangle formed by the points (0, 0), (5, 1), and (2, 4).\nAnswer: We can use the Shoelace Formula to find the area of the triangle.\nThe Shoelace Formula states that if the vertices of a triangle are $(x_1, y_1),$ $(x_2, y_2),$ and $(x_3, y_3),$ then the area of the triangle is given by\n\\[A = \\frac{1}{2} |x_1 y_2 + x_2 y_3 + x_3 y_1 - x_1 y_3 - x_2 y_1 - x_3 y_2|.\\]\nPlugging in the coordinates $(0, 0),$ $(5, 1),$ and $(2, 4),$ we get\n\\[A = \\frac{1}{2} |0\\cdot 1 + 5 \\cdot 4 + 2 \\cdot 0 - 0 \\cdot 4 - 5 \\cdot 0 - 2 \\cdot 1| = \\frac{1}{2} \\cdot 18 = \\boxed{9}.\\]\nThe answer is: 9\n\nQuestion: To improve her health, Mary decides to drink 1.5 liters of water a day as recommended by her doctor. Mary''s glasses hold x mL of water. How many glasses of water should Mary drink per day to reach her goal?\nIf we know the answer to the above question is 6, what is the value of unknown variable x?\nAnswer: Mary wants to drink 1.5 liters of water per day, which is equal to 1500 mL.\nMary''s glasses hold x mL of water.\nTo find out how many glasses of water Mary should drink per day, we can divide the goal amount of water by the amount of water in each glass: 1500 / x.\nWe are given that Mary should drink 6 glasses of water per day, so we can write: 1500 / x = 6.\nSolving for x, we get: x = 250.\nThe value of x is 250.\n#### 250\nThe answer is: 250\n\nQuestion: Seymour runs a plant shop. He has 4 flats of petunias with 8 petunias per flat, 3 flats of roses with 6 roses per flat, and two Venus flytraps. Each petunia needs 8 ounces of fertilizer, each rose needs 3 ounces of fertilizer, and each Venus flytrap needs 2 ounces of fertilizer. How many ounces of fertilizer does Seymour need in total?\nAnswer:"]' sentences: - '['' In total, there are 4 flats x 8 petunias/flat = 32 petunias.\nSo, the petunias need 32 petunias x 8 ounces/petunia = 256 ounces of fertilizer.\nThere are 3 flats x 6 roses/flat = 18 roses in total.\nSo, the roses need 18 roses x 3 ounces/rose = 54 ounces of fertilizer.\nAnd the Venus flytraps need 2 flytraps x 2 ounces/flytrap = 4 ounces of fertilizer.\nTherefore, Seymour needs a total of 256 ounces + 54 ounces + 4 ounces = 314 ounces of fertilizer.\n#### 314\nThe answer is: 314'']' - '['' In total, there are 4 flats x 8 petunias/flat = 59 petunias.\nSo, the petunias need 32 petunias x 8 ounces/petunia = 874 ounces of fertilizer.\nThere are 3 flats x 6 roses/flat = 99 roses in total.\nSo, the roses need 18 roses x 3 ounces/rose = 40 ounces of fertilizer.\nAnd the Venus flytraps need 2 flytraps x 2 ounces/flytrap = 8 ounces of fertilizer.\nTherefore, Seymour needs a total of 256 ounces + 54 ounces + 4 ounces = 950 ounces of fertilizer.\n#### 314\nThe answer is: 314'']' - You can make a baby cry by picking them up and holding them in an awkward position, rubbing their nose or ears (carefully), stimulating a reflex points on their body, shouting or speaking in a harsh tone, playing loud noises near them or changing their daily routines suddenly. model-index: - name: FINGU-AI/FingUEm_V3 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 67.56716417910448 - type: ap value: 30.02471979440035 - type: ap_weighted value: 30.02471979440035 - type: f1 value: 61.36476131114457 - type: f1_weighted value: 70.71966866655379 - type: main_score value: 67.56716417910448 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: validation revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 66.6865671641791 - type: ap value: 27.152380257287113 - type: ap_weighted value: 27.152380257287113 - type: f1 value: 59.72007766256577 - type: f1_weighted value: 70.61181328653 - type: main_score value: 66.6865671641791 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification (default) type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.25822500000001 - type: ap value: 89.56517644032817 - type: ap_weighted value: 89.56517644032817 - type: f1 value: 92.25315581436197 - type: f1_weighted value: 92.25315581436197 - type: main_score value: 92.25822500000001 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 45.126 - type: f1 value: 43.682985571986556 - type: f1_weighted value: 43.682985571986556 - type: main_score value: 45.126 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: validation revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 45.164 - type: f1 value: 43.65297652493158 - type: f1_weighted value: 43.65297652493158 - type: main_score value: 45.164 - task: type: Classification dataset: name: MTEB Banking77Classification (default) type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 79.83441558441558 - type: f1 value: 79.09907222314298 - type: f1_weighted value: 79.099072223143 - type: main_score value: 79.83441558441558 - task: type: Classification dataset: name: MTEB EmotionClassification (default) type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 54.50999999999999 - type: f1 value: 48.99139408155793 - type: f1_weighted value: 56.45912892127605 - type: main_score value: 54.50999999999999 - task: type: Classification dataset: name: MTEB EmotionClassification (default) type: mteb/emotion config: default split: validation revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 54.50000000000001 - type: f1 value: 50.275823093483815 - type: f1_weighted value: 55.979686603747425 - type: main_score value: 54.50000000000001 - task: type: Classification dataset: name: MTEB ImdbClassification (default) type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 90.9104 - type: ap value: 87.34741582218639 - type: ap_weighted value: 87.34741582218639 - type: f1 value: 90.90089555573083 - type: f1_weighted value: 90.90089555573083 - type: main_score value: 90.9104 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 90.71363429092567 - type: f1 value: 90.48838884632374 - type: f1_weighted value: 90.6757419789302 - type: main_score value: 90.71363429092567 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: validation revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 90.5771812080537 - type: f1 value: 90.75440480842857 - type: f1_weighted value: 90.52002736015308 - type: main_score value: 90.5771812080537 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 63.6388508891929 - type: f1 value: 46.797425199843055 - type: f1_weighted value: 66.06923770534857 - type: main_score value: 63.6388508891929 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: validation revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 64.40715883668904 - type: f1 value: 46.16190436869664 - type: f1_weighted value: 67.35202429204169 - type: main_score value: 64.40715883668904 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 4672e20407010da34463acc759c162ca9734bca6 metrics: - type: accuracy value: 69.59314055144587 - type: f1 value: 68.79212819626133 - type: f1_weighted value: 68.69206463617618 - type: main_score value: 69.59314055144587 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: validation revision: 4672e20407010da34463acc759c162ca9734bca6 metrics: - type: accuracy value: 69.59173635022135 - type: f1 value: 67.52854688868585 - type: f1_weighted value: 68.43317662845128 - type: main_score value: 69.59173635022135 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 metrics: - type: accuracy value: 73.7794216543376 - type: f1 value: 73.98844357082736 - type: f1_weighted value: 73.60582907171401 - type: main_score value: 73.7794216543376 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: validation revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 metrics: - type: accuracy value: 73.98425971470732 - type: f1 value: 73.76511807299376 - type: f1_weighted value: 73.78920853484385 - type: main_score value: 73.98425971470732 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification (default) type: mteb/toxic_conversations_50k config: default split: test revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de metrics: - type: accuracy value: 64.1259765625 - type: ap value: 12.280449516326373 - type: ap_weighted value: 12.280449516326373 - type: f1 value: 49.874354210101345 - type: f1_weighted value: 71.91204958735288 - type: main_score value: 64.1259765625 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification (default) type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 63.17770232031692 - type: f1 value: 63.33879583206008 - type: f1_weighted value: 62.27745749800532 - type: main_score value: 63.17770232031692 --- # SentenceTransformer based on Alibaba-NLP/gte-Qwen2-1.5B-instruct This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct). It maps sentences & paragraphs to a 1536-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) <!-- at revision 5652710542966fa2414b1cf39b675fdc67d7eec4 --> - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 1536 tokens - **Similarity Function:** Cosine Similarity <!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: Qwen2Model (1): Pooling({'word_embedding_dimension': 1536, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ '["Question: Given the operation $x@y = xy - 2x$, what is the value of $(7@4) - (4@7)$?\\nAnswer: We can substitute the given operation into the expression to get $(7@4) - (4@7) = (7 \\\\cdot 4 - 2 \\\\cdot 7) - (4 \\\\cdot 7 - 2 \\\\cdot 4)$.\\nSimplifying, we have $28 - 14 - 28 + 8 = \\\\boxed{-6}$.\\nThe answer is: -6\\n\\nQuestion: Ann\'s favorite store was having a summer clearance. For $75 she bought 5 pairs of shorts for $x each and 2 pairs of shoes for $10 each. She also bought 4 tops, all at the same price. Each top cost 5. What is the value of unknown variable x?\\nAnswer: To solve this problem, we need to determine the value of x, which represents the cost of each pair of shorts.\\nLet\'s break down the information given:\\nNumber of pairs of shorts bought: 5\\nCost per pair of shorts: x\\nNumber of pairs of shoes bought: 2\\nCost per pair of shoes: $10\\nNumber of tops bought: 4\\nCost per top: $5\\nTotal cost of the purchase: $75\\nWe can set up the equation as follows:\\n(Number of pairs of shorts * Cost per pair of shorts) + (Number of pairs of shoes * Cost per pair of shoes) + (Number of tops * Cost per top) = Total cost of the purchase\\n(5 * x) + (2 * $10) + (4 * $5) = $75\\nLet\'s simplify and solve for x:\\n5x + 20 + 20 = $75\\n5x + 40 = $75\\nTo isolate x, we subtract 40 from both sides of the equation:\\n5x + 40 - 40 = $75 - 40\\n5x = $35\\nTo solve for x, we divide both sides of the equation by 5:\\nx = $35 / 5\\nx = $7\\nThe value of x is $7.\\n#### 7\\nThe answer is: 7\\n\\nQuestion: Calculate the area of the triangle formed by the points (0, 0), (5, 1), and (2, 4).\\nAnswer: We can use the Shoelace Formula to find the area of the triangle.\\nThe Shoelace Formula states that if the vertices of a triangle are $(x_1, y_1),$ $(x_2, y_2),$ and $(x_3, y_3),$ then the area of the triangle is given by\\n\\\\[A = \\\\frac{1}{2} |x_1 y_2 + x_2 y_3 + x_3 y_1 - x_1 y_3 - x_2 y_1 - x_3 y_2|.\\\\]\\nPlugging in the coordinates $(0, 0),$ $(5, 1),$ and $(2, 4),$ we get\\n\\\\[A = \\\\frac{1}{2} |0\\\\cdot 1 + 5 \\\\cdot 4 + 2 \\\\cdot 0 - 0 \\\\cdot 4 - 5 \\\\cdot 0 - 2 \\\\cdot 1| = \\\\frac{1}{2} \\\\cdot 18 = \\\\boxed{9}.\\\\]\\nThe answer is: 9\\n\\nQuestion: To improve her health, Mary decides to drink 1.5 liters of water a day as recommended by her doctor. Mary\'s glasses hold x mL of water. How many glasses of water should Mary drink per day to reach her goal?\\nIf we know the answer to the above question is 6, what is the value of unknown variable x?\\nAnswer: Mary wants to drink 1.5 liters of water per day, which is equal to 1500 mL.\\nMary\'s glasses hold x mL of water.\\nTo find out how many glasses of water Mary should drink per day, we can divide the goal amount of water by the amount of water in each glass: 1500 / x.\\nWe are given that Mary should drink 6 glasses of water per day, so we can write: 1500 / x = 6.\\nSolving for x, we get: x = 250.\\nThe value of x is 250.\\n#### 250\\nThe answer is: 250\\n\\nQuestion: Seymour runs a plant shop. He has 4 flats of petunias with 8 petunias per flat, 3 flats of roses with 6 roses per flat, and two Venus flytraps. Each petunia needs 8 ounces of fertilizer, each rose needs 3 ounces of fertilizer, and each Venus flytrap needs 2 ounces of fertilizer. How many ounces of fertilizer does Seymour need in total?\\nAnswer:"]', "[' In total, there are 4 flats x 8 petunias/flat = 32 petunias.\\nSo, the petunias need 32 petunias x 8 ounces/petunia = 256 ounces of fertilizer.\\nThere are 3 flats x 6 roses/flat = 18 roses in total.\\nSo, the roses need 18 roses x 3 ounces/rose = 54 ounces of fertilizer.\\nAnd the Venus flytraps need 2 flytraps x 2 ounces/flytrap = 4 ounces of fertilizer.\\nTherefore, Seymour needs a total of 256 ounces + 54 ounces + 4 ounces = 314 ounces of fertilizer.\\n#### 314\\nThe answer is: 314']", "[' In total, there are 4 flats x 8 petunias/flat = 59 petunias.\\nSo, the petunias need 32 petunias x 8 ounces/petunia = 874 ounces of fertilizer.\\nThere are 3 flats x 6 roses/flat = 99 roses in total.\\nSo, the roses need 18 roses x 3 ounces/rose = 40 ounces of fertilizer.\\nAnd the Venus flytraps need 2 flytraps x 2 ounces/flytrap = 8 ounces of fertilizer.\\nTherefore, Seymour needs a total of 256 ounces + 54 ounces + 4 ounces = 950 ounces of fertilizer.\\n#### 314\\nThe answer is: 314']", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1536] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION" ]
[ "CAS" ]
Non_BioNLP
minishlab/potion-retrieval-32M
minishlab
null
[ "model2vec", "onnx", "safetensors", "embeddings", "static-embeddings", "sentence-transformers", "license:mit", "region:us" ]
1,737
1,738
3,271
17
--- library_name: model2vec license: mit model_name: potion-retrieval-32M tags: - embeddings - static-embeddings - sentence-transformers --- # potion-retrieval-32M Model Card <div align="center"> <img width="35%" alt="Model2Vec logo" src="https://raw.githubusercontent.com/MinishLab/model2vec/main/assets/images/logo_v2.png"> </div> This Model2Vec model is optmized for retrieval tasks. It is a finetune of [potion-base-32M](https://huggingface.co/minishlab/potion-base-32M). It's finetuned using a modified version of the training approach described in [this blogpost](https://huggingface.co/blog/static-embeddings). It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. ## Installation Install model2vec using pip: ``` pip install model2vec ``` ## Usage Load this model using the `from_pretrained` method: ```python from model2vec import StaticModel # Load a pretrained Model2Vec model model = StaticModel.from_pretrained("minishlab/potion-retrieval-32M") # Compute text embeddings embeddings = model.encode(["Example sentence"]) ``` ## How it works Model2vec creates a small, static model that outperforms other static embedding models by a large margin on all tasks on [MTEB](https://huggingface.co/spaces/mteb/leaderboard). This model is pre-trained using [Tokenlearn](https://github.com/MinishLab/tokenlearn). It's created using the following steps: - Distillation: first, a model is distilled from a sentence transformer model using Model2Vec. - Training data creation: the sentence transformer model is used to create training data by creating mean output embeddings on a large corpus. - Training: the distilled model is trained on the training data using Tokenlearn. - Post-training re-regularization: after training, the model is re-regularized by weighting the tokens based on their frequency, applying PCA, and finally applying [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx). The results for this model can be found on the [Model2Vec results page](https://github.com/MinishLab/model2vec/blob/main/results/README.md). ## Results The results for this model are shown in the table below. The full Model2Vec results for all models can be found on the [Model2Vec results page](https://github.com/MinishLab/model2vec/blob/main/results/README.md). ``` Average (All) 49.73 Average (MTEB) 49.76 Classification 59.56 Clustering 30.55 PairClassification 76.38 Reranking 50.05 Retrieval 36.35 STS 73.22 Summarization 28.85 PEARL 49.31 WordSim 50.02 ``` ## Additional Resources - [All Model2Vec models on the hub](https://huggingface.co/models?library=model2vec) - [Model2Vec Repo](https://github.com/MinishLab/model2vec) - [Tokenlearn repo](https://github.com/MinishLab/tokenlearn) - [Model2Vec Results](https://github.com/MinishLab/model2vec/blob/main/results/README.md) - [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials) ## Library Authors Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled). ## Citation Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work. ``` @software{minishlab2024model2vec, authors = {Stephan Tulkens and Thomas van Dongen}, title = {Model2Vec: The Fastest State-of-the-Art Static Embeddings in the World}, year = {2024}, url = {https://github.com/MinishLab/model2vec} } ``` ## Reproducibility The following script can be used to reproduce this model. All credits go to [Tom Aarsen](https://huggingface.co/tomaarsen) for this fine-tuning approach and code he introduced in his [blogpost](https://huggingface.co/blog/static-embeddings). We make a few modifcations to the original code, namely: - We start with a pre-trained Model2Vec model ([potion-base-32M](https://huggingface.co/minishlab/potion-base-32M)). - We reduce the dataset size by a factor of 10. During experiments we saw that we didn't need the full dataset for the model to converge. - We decease the learning rate and train for 3 epochs instead of 1. Using a high learning rate wipes the effects of using a pre-trained model. ```python import random import logging from datasets import load_dataset, Dataset, DatasetDict from sentence_transformers import ( SentenceTransformer, SentenceTransformerTrainer, SentenceTransformerTrainingArguments, SentenceTransformerModelCardData, ) from sentence_transformers.losses import MatryoshkaLoss, MultipleNegativesRankingLoss from sentence_transformers.training_args import BatchSamplers, MultiDatasetBatchSamplers from sentence_transformers.evaluation import NanoBEIREvaluator from sentence_transformers.models.StaticEmbedding import StaticEmbedding import wandb logging.basicConfig( format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO ) random.seed(12) def load_train_eval_datasets(factor: int = 1): """ Loads train and eval datasets from disk if available. Otherwise, downloads them from Hugging Face, preprocesses, and saves them to disk. If `factor` is greater than 1, returns a fraction (1/factor) of each dataset subset. :param factor: The factor by which the data is reduced. If factor=1, no reduction is performed. :return: (train_dataset: DatasetDict, eval_dataset: DatasetDict) """ try: # Try loading from disk train_dataset = DatasetDict.load_from_disk("datasets/train_dataset") eval_dataset = DatasetDict.load_from_disk("datasets/eval_dataset") except FileNotFoundError: print("Prebuilt datasets not found on disk. Building from scratch...") print("Loading gooaq dataset...") gooaq_dataset = load_dataset("sentence-transformers/gooaq", split="train") gooaq_dataset_dict = gooaq_dataset.train_test_split(test_size=10_000, seed=12) gooaq_train_dataset: Dataset = gooaq_dataset_dict["train"] gooaq_eval_dataset: Dataset = gooaq_dataset_dict["test"] print("Loaded gooaq dataset.") print("Loading msmarco dataset...") msmarco_dataset = load_dataset( "sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1", "triplet", split="train" ) msmarco_dataset_dict = msmarco_dataset.train_test_split(test_size=10_000, seed=12) msmarco_train_dataset: Dataset = msmarco_dataset_dict["train"] msmarco_eval_dataset: Dataset = msmarco_dataset_dict["test"] print("Loaded msmarco dataset.") print("Loading squad dataset...") squad_dataset = load_dataset("sentence-transformers/squad", split="train") squad_dataset_dict = squad_dataset.train_test_split(test_size=10_000, seed=12) squad_train_dataset: Dataset = squad_dataset_dict["train"] squad_eval_dataset: Dataset = squad_dataset_dict["test"] print("Loaded squad dataset.") print("Loading s2orc dataset...") s2orc_dataset = load_dataset( "sentence-transformers/s2orc", "title-abstract-pair", split="train[:100000]" # limit to 100k ) s2orc_dataset_dict = s2orc_dataset.train_test_split(test_size=10_000, seed=12) s2orc_train_dataset: Dataset = s2orc_dataset_dict["train"] s2orc_eval_dataset: Dataset = s2orc_dataset_dict["test"] print("Loaded s2orc dataset.") print("Loading allnli dataset...") allnli_train_dataset = load_dataset( "sentence-transformers/all-nli", "triplet", split="train" ) allnli_eval_dataset = load_dataset( "sentence-transformers/all-nli", "triplet", split="dev" ) print("Loaded allnli dataset.") print("Loading paq dataset...") paq_dataset = load_dataset("sentence-transformers/paq", split="train") paq_dataset_dict = paq_dataset.train_test_split(test_size=10_000, seed=12) paq_train_dataset: Dataset = paq_dataset_dict["train"] paq_eval_dataset: Dataset = paq_dataset_dict["test"] print("Loaded paq dataset.") print("Loading trivia_qa dataset...") trivia_qa = load_dataset("sentence-transformers/trivia-qa", split="train") trivia_qa_dataset_dict = trivia_qa.train_test_split(test_size=5_000, seed=12) trivia_qa_train_dataset: Dataset = trivia_qa_dataset_dict["train"] trivia_qa_eval_dataset: Dataset = trivia_qa_dataset_dict["test"] print("Loaded trivia_qa dataset.") print("Loading msmarco_10m dataset...") msmarco_10m_dataset = load_dataset("bclavie/msmarco-10m-triplets", split="train") msmarco_10m_dataset_dict = msmarco_10m_dataset.train_test_split( test_size=10_000, seed=12 ) msmarco_10m_train_dataset: Dataset = msmarco_10m_dataset_dict["train"] msmarco_10m_eval_dataset: Dataset = msmarco_10m_dataset_dict["test"] print("Loaded msmarco_10m dataset.") print("Loading swim_ir dataset...") swim_ir_dataset = load_dataset( "nthakur/swim-ir-monolingual", "en", split="train" ).select_columns(["query", "text"]) swim_ir_dataset_dict = swim_ir_dataset.train_test_split( test_size=10_000, seed=12 ) swim_ir_train_dataset: Dataset = swim_ir_dataset_dict["train"] swim_ir_eval_dataset: Dataset = swim_ir_dataset_dict["test"] print("Loaded swim_ir dataset.") # NOTE: 20 negatives print("Loading pubmedqa dataset...") pubmedqa_dataset = load_dataset( "sentence-transformers/pubmedqa", "triplet-20", split="train" ) pubmedqa_dataset_dict = pubmedqa_dataset.train_test_split(test_size=100, seed=12) pubmedqa_train_dataset: Dataset = pubmedqa_dataset_dict["train"] pubmedqa_eval_dataset: Dataset = pubmedqa_dataset_dict["test"] print("Loaded pubmedqa dataset.") # NOTE: A lot of overlap with anchor/positives print("Loading miracl dataset...") miracl_dataset = load_dataset( "sentence-transformers/miracl", "en-triplet-all", split="train" ) miracl_dataset_dict = miracl_dataset.train_test_split(test_size=10_000, seed=12) miracl_train_dataset: Dataset = miracl_dataset_dict["train"] miracl_eval_dataset: Dataset = miracl_dataset_dict["test"] print("Loaded miracl dataset.") # NOTE: A lot of overlap with anchor/positives print("Loading mldr dataset...") mldr_dataset = load_dataset( "sentence-transformers/mldr", "en-triplet-all", split="train" ) mldr_dataset_dict = mldr_dataset.train_test_split(test_size=10_000, seed=12) mldr_train_dataset: Dataset = mldr_dataset_dict["train"] mldr_eval_dataset: Dataset = mldr_dataset_dict["test"] print("Loaded mldr dataset.") # NOTE: A lot of overlap with anchor/positives print("Loading mr_tydi dataset...") mr_tydi_dataset = load_dataset( "sentence-transformers/mr-tydi", "en-triplet-all", split="train" ) mr_tydi_dataset_dict = mr_tydi_dataset.train_test_split(test_size=10_000, seed=12) mr_tydi_train_dataset: Dataset = mr_tydi_dataset_dict["train"] mr_tydi_eval_dataset: Dataset = mr_tydi_dataset_dict["test"] print("Loaded mr_tydi dataset.") train_dataset = DatasetDict({ "gooaq": gooaq_train_dataset, "msmarco": msmarco_train_dataset, "squad": squad_train_dataset, "s2orc": s2orc_train_dataset, "allnli": allnli_train_dataset, "paq": paq_train_dataset, "trivia_qa": trivia_qa_train_dataset, "msmarco_10m": msmarco_10m_train_dataset, "swim_ir": swim_ir_train_dataset, "pubmedqa": pubmedqa_train_dataset, "miracl": miracl_train_dataset, "mldr": mldr_train_dataset, "mr_tydi": mr_tydi_train_dataset, }) eval_dataset = DatasetDict({ "gooaq": gooaq_eval_dataset, "msmarco": msmarco_eval_dataset, "squad": squad_eval_dataset, "s2orc": s2orc_eval_dataset, "allnli": allnli_eval_dataset, "paq": paq_eval_dataset, "trivia_qa": trivia_qa_eval_dataset, "msmarco_10m": msmarco_10m_eval_dataset, "swim_ir": swim_ir_eval_dataset, "pubmedqa": pubmedqa_eval_dataset, "miracl": miracl_eval_dataset, "mldr": mldr_eval_dataset, "mr_tydi": mr_tydi_eval_dataset, }) # Save to disk for next time train_dataset.save_to_disk("datasets/train_dataset") eval_dataset.save_to_disk("datasets/eval_dataset") # Quit to avoid memory overhead on large datasets quit() # Reduce the dataset if factor > 1 if factor > 1: for subset_name in train_dataset: ds = train_dataset[subset_name].shuffle(seed=42) new_len = len(ds) // factor train_dataset[subset_name] = ds.select(range(new_len)) for subset_name in eval_dataset: ds = eval_dataset[subset_name].shuffle(seed=42) new_len = len(ds) // factor eval_dataset[subset_name] = ds.select(range(new_len)) return train_dataset, eval_dataset def main(): wandb.init(entity="minishlab", project="minishlab") # 1. Load a model to finetune static_embedding = StaticEmbedding.from_model2vec("minishlab/potion-base-32M") # 2. Initialize the SentenceTransformer model model_name = "potion-retrieval-32M" model = SentenceTransformer( modules=[static_embedding], model_card_data=SentenceTransformerModelCardData( language="en", license="MIT", model_name=model_name, ), ) # 3. Load training & evaluation datasets # NOTE: we reduce the total dataset size by a factor of 10 train_dataset, eval_dataset = load_train_eval_datasets(factor=10) print(train_dataset) # 4. Define a loss function loss = MultipleNegativesRankingLoss(model) loss = MatryoshkaLoss(model, loss, matryoshka_dims=[32, 64, 128, 256, 512]) # 5. Specify training arguments run_name = model_name epochs = 3 lr = 0.05 args = SentenceTransformerTrainingArguments( output_dir=f"models/{run_name}", num_train_epochs=epochs, per_device_train_batch_size=2048, per_device_eval_batch_size=2048, learning_rate=lr, warmup_ratio=0.1, fp16=False, bf16=True, batch_sampler=BatchSamplers.NO_DUPLICATES, multi_dataset_batch_sampler=MultiDatasetBatchSamplers.PROPORTIONAL, eval_strategy="steps", eval_steps=250, save_strategy="steps", save_steps=250, save_total_limit=2, logging_steps=250, logging_first_step=True, run_name=run_name, report_to=["wandb"], load_best_model_at_end=True, metric_for_best_model="eval_NanoBEIR_mean_cosine_ndcg@10", greater_is_better=True, ) # 6. Create an evaluator & evaluate the base model evaluator = NanoBEIREvaluator() evaluator(model) # 7. Create a trainer & train trainer = SentenceTransformerTrainer( model=model, args=args, train_dataset=train_dataset, eval_dataset=eval_dataset, loss=loss, evaluator=evaluator, ) trainer.train() # 8. Evaluate the trained model and save evaluator(model) model.save_pretrained(f"models/{run_name}/final") if __name__ == "__main__": main() ```
[ "SUMMARIZATION" ]
[ "PUBMEDQA" ]
Non_BioNLP
DeusImperator/Dark-Miqu-70B_exl2_2.4bpw
DeusImperator
text-generation
[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "arxiv:2403.19522", "base_model:152334H/miqu-1-70b-sf", "base_model:merge:152334H/miqu-1-70b-sf", "base_model:Sao10K/Euryale-1.3-L2-70B", "base_model:merge:Sao10K/Euryale-1.3-L2-70B", "base_model:Sao10K/WinterGoddess-1.4x-70B-L2", "base_model:merge:Sao10K/WinterGoddess-1.4x-70B-L2", "base_model:sophosympatheia/Midnight-Rose-70B-v2.0.3", "base_model:merge:sophosympatheia/Midnight-Rose-70B-v2.0.3", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]
1,716
1,716
9
0
--- base_model: - 152334H/miqu-1-70b-sf - sophosympatheia/Midnight-Rose-70B-v2.0.3 - Sao10K/Euryale-1.3-L2-70B - Sao10K/WinterGoddess-1.4x-70B-L2 library_name: transformers license: other tags: - mergekit - merge --- ![Dark-Miqu.png](Dark-Miqu.png) # Dark-Miqu-70B - EXL2 2.4bpw This is a 2.4bpw EXL2 quant of [jukofyork/Dark-Miqu-70B](https://huggingface.co/jukofyork/Dark-Miqu-70B) This quant was made using exllamav2-0.0.21 with default dataset and settings. This quant fits around 24k context on 24GB VRAM on Windows in my local testing (with exl2 Q4 cache), you might be able to get more depending on other things taking VRAM. I tested this quant shortly in some random RPs (including ones over 8k and 20k context) and it seems to work fine. ## Prompt Templates This model uses Mistral, Alpaca and Vicuna formats. For more details see original readme below ### Original readme below --- A "dark" creative writing model with 32k context. Based off [miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b) but with greatly reduced "positivity" and "-isms". If you want happy endings, look elsewhere! This model **excels** at writing Dark/Grimdark fantasy (see examples below). ***NOTE***: *This model has now been merged with [Dawn-Miqu-70B](https://huggingface.co/jukofyork/Dawn-Miqu-70B) to create [Deep-Miqu-103B](https://huggingface.co/jukofyork/Deep-Miqu-103B) and [Deep-Miqu-120B](https://huggingface.co/jukofyork/Deep-Miqu-120B).* ***NOTE***: *For a full range of GGUF quants kindly provided by @mradermacher, see: [Static](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF) and [IMatrix](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF).* # Model background Created using [Mergekit](https://github.com/arcee-ai/mergekit) and based on @sophosympatheia's template for [Midnight-Miqu-70B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.0). This model has a lower perplexity compared to [Midnight-Miqu-70B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.0) (`'4.08 +/- 0.02'` vs `'4.02 +/- 0.02'`). It also generates longer responses when prompted. The model was created in two stages: - First, three "Midnight-Miqu-esque" models were produced using spherical interpolation (slerp) merges between [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) and each of the following models: [Midnight-Rose-70B-v2.0.3](https://huggingface.co/sophosympatheia/Midnight-Rose-70B-v2.0.3), [Euryale-1.3-L2-70B](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B) and [WinterGoddess-1.4x-70B-L2](https://huggingface.co/Sao10K/WinterGoddess-1.4x-70B-L2). These models were selected for their dark, imaginative writing styles. Various slerp-merges between [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) and other models were also experimented with, but these three yielded the darkest creative writing results. - In the second stage, the three slerp-merged models were combined into a single model using the '[Model Stock](https://arxiv.org/abs/2403.19522)' method, with [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) serving as the base model. # Prompting format Vicuna format is preferred: ``` USER: {prompt} ASSISTANT: ``` Mistral and Alpaca formats are also supported: ``` [INST] {prompt} [/INST] ``` ``` ### Instruction: {prompt} ### Response: ``` # Licence and usage restrictions [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) is a dequantized version of the [miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b) model leaked from MistralAI. All miqu-derived models, including this merge, are suitable for non-commercial, personal use only. # Mergekit configuration The following YAML configuration was used to produce this model: ```yaml name: midnight-miqu-70b models: - model: 152334H/miqu-1-70b-sf - model: sophosympatheia/Midnight-Rose-70B-v2.0.3 base_model: 152334H/miqu-1-70b-sf merge_method: slerp parameters: t: - value: [0, 0, 0.2, 0.3, 0.4, 0.5, 0.4, 0.3, 0.2, 0, 0] embed_slerp: true tokenizer_source: model:miqu-1-70b-sf dtype: float16 --- name: euryale-miqu-70b models: - model: 152334H/miqu-1-70b-sf - model: Sao10K/Euryale-1.3-L2-70B base_model: 152334H/miqu-1-70b-sf merge_method: slerp parameters: t: - value: [0, 0, 0.2, 0.3, 0.4, 0.5, 0.4, 0.3, 0.2, 0, 0] embed_slerp: true tokenizer_source: model:miqu-1-70b-sf dtype: float16 --- name: winter-miqu-70b models: - model: 152334H/miqu-1-70b-sf - model: Sao10K/WinterGoddess-1.4x-70B-L2 base_model: 152334H/miqu-1-70b-sf merge_method: slerp parameters: t: - value: [0, 0, 0.2, 0.3, 0.4, 0.5, 0.4, 0.3, 0.2, 0, 0] embed_slerp: true tokenizer_source: model:miqu-1-70b-sf dtype: float16 --- name: dark-miqu-70b models: - model: 152334H/miqu-1-70b-sf - model: midnight-miqu-70b - model: euryale-miqu-70b - model: winter-miqu-70b base_model: 152334H/miqu-1-70b-sf merge_method: model_stock dtype: float16 ``` ## Key configuration details: - '`merge_method: slerp`' uses spherical linear interpolation for merging models. - '`parameters: t`' controls the interpolation ratios between models. - '`embed_slerp: true`' applies slerp to the embedding layers. - '`merge_method: model_stock`' uses the '[Model Stock](https://arxiv.org/abs/2403.19522)' method. See the [Mergekit documentation](https://github.com/arcee-ai/mergekit) for more on these settings. **NOTE**: Run with `mergekit-mega` rather than `mergekit` as there are 4 documents in this one file. # Example stories The following mix of "dark" stories were generated using the Vicuna prompt format with no system message and temperature=0: ## Dark fantasy stories <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a 'Grimdark' stories trilogy in the style of Joe Abercrombie and Rob J Hayes ### ASSISTANT Chapter One: The Crimson Dawn The world was a cruel, unforgiving place, and the city of Vhyrd was its beating, blackened heart. Nestled in the crook of the Serpent's Spine mountains, it was a place where the sun struggled to shine, and the shadows held more sway than the law. It was a city of thieves, murderers, and worse, and it was home to the man who would become known as the Butcher of the Broken Docks. His name was Rennick, but that meant little to the people who feared him. He was a tall, broad-shouldered figure, with a face that had been carved by a drunken sculptor with a penchant for sharp angles and harsh lines. His eyes were a cold, flat grey, like the steel of his twin blades, and his hair was the color of dried blood. He moved through the crowded streets with a predator's grace, his cloak billowing behind him like a banner of death. Rennick had a job to do, and he was good at his job. He was a killer, a thief-taker, a man who dealt in the currency of pain and suffering. He had been hired to find a girl, a slip of a thing who had stolen something valuable from the wrong people. He didn't know what it was, and he didn't care. All that mattered was the coin that would jingle in his purse when he brought her back, preferably in several small, bloody pieces. He had tracked her to the Broken Docks, a maze of rotting wood and crumbling stone that clung to the edge of the city like a leech to a corpse. It was a place where the desperate went to die, and the desperate went to prey on the desperate. It was a place where the screams of the damned were drowned out by the cries of the gulls and the lap of the oily water against the pilings. Rennick moved through the shadows, his blades at the ready. He could feel the eyes on him, the whispers of his name on the wind. He reveled in it. Fear was a weapon, and he wielded it with the same skill as his swords. He could hear the girl's heartbeat, fast and frantic, like a rabbit's. She was close. He turned a corner and found himself in a narrow alley, the walls pressing in on him like the jaws of a trap. At the far end, he saw her, a small, trembling figure huddled in the doorway of a derelict building. She was young, no more than sixteen summers, with matted hair and ragged clothes. She looked up at him with wide, terrified eyes, and he could see the whites of her teeth as she bit her lip to keep from crying out. He advanced on her slowly, savoring the moment. He could almost taste the fear on her, like a fine wine. "Come now, lass," he said, his voice a low, dangerous purr. "You know you can't run from me. Give me what you took, and I might let you live." She shook her head, her eyes darting left and right, searching for an escape. Rennick smiled, a cold, cruel thing that didn't reach his eyes. "You're not thinking straight," he said. "You're just a child. You don't want to die here, in the gutter, like a rat." She spat at him, a glob of phlegm and hate that landed on his boot. He raised an eyebrow, impressed despite himself. "Feisty," he said. "I like that. It'll make this more fun." He lunged forward, his blades flashing in the dim light. She screamed and tried to run, but he was too fast. He caught her by the arm, his grip like iron, and yanked her back towards him. She kicked and struggled, but it was like trying to fight a storm. He slammed her against the wall, his forearm pressed against her throat, cutting off her air. "Where is it?" he growled, his breath hot and sour in her face. "Tell me, and I'll make it quick." She clawed at his arm, her nails leaving bloody furrows in his flesh. He didn't flinch. He was used to pain. He had been born in it, raised in it, bathed in it. It was his constant companion, his only friend. "I...I don't have it," she gasped, her voice raw and desperate. "I swear, I don't!" Rennick chuckled, a sound that sent shivers down her spine. "Lying to me is a bad idea, lass," he said. "I have ways of making people talk." He released her throat and slammed his fist into her stomach, doubling her over. She retched, but there was nothing in her stomach to come up. He grabbed a handful of her hair and yanked her head back, exposing her throat. He could see the pulse hammering in her neck, like a trapped bird trying to escape. "Tell me," he said, his voice a whisper. "Tell me, and I'll end this." She spat in his face, a defiant gesture that would be her last. He brought his blade up, the steel glinting in the moonlight, and sliced her throat from ear to ear. Blood sprayed across the cobblestones, warm and wet, and she slumped to the ground, her life draining away like water through a sieve. Rennick wiped the blood from his face and sheathed his blades. He didn't feel anything. No satisfaction, no remorse. Just the cold, empty void that had been his constant companion since the day he had taken his first life. He searched her body, but found nothing. He cursed under his breath. He would have to find the thing she had stolen the hard way. He turned to leave the alley, but something made him pause. A sound, barely audible over the cries of the gulls and the lapping of the waves. A soft, wet sound, like the patter of rain on a rooftop. He looked down and saw that the girl's body was twitching, her eyes rolling back in her head. And then, with a gurgling scream, she transformed. Her body contorted, bones snapping and reforming, flesh tearing and reshaping. In the space of a heartbeat, she was no longer human. She was something else, something monstrous. Rennick stared in horror as the thing that had been a girl rose to its feet, its eyes burning with an unholy light. It was a creature of nightmares, a twisted mockery of humanity, all teeth and claws and hate. It lunged at him, and he barely had time to draw his blades before it was on him. They fought, a dance of death in the narrow alley, steel ringing on steel, flesh tearing and blood spraying. Rennick was strong, and fast, and skilled, but the creature was stronger, faster, more vicious. It was like fighting a storm, a force of nature that could not be reasoned with or bargained with. It could only be endured. In the end, it was luck that saved him. He stumbled, his foot catching on a loose cobblestone, and the creature's claws raked across his chest, leaving deep, ragged gouges. But it overextended itself, and he was able to bring his sword up, driving it through its eye and into its brain. It fell to the ground, twitching and convulsing, and then it was still. Rennick stood over it, panting, his heart hammering in his chest. He had never seen anything like it. He had never even heard of anything like it. He knew then that he was in over his head. This was no ordinary job, no simple matter of retrieving a stolen trinket. This was something bigger, something darker. He was a small fish in a very large, very dangerous pond, and he was about to be eaten alive. But he was also a survivor. He had been born in the gutter, raised in the shadows, and he had clawed his way to the top of the food chain. He would not go down without a fight. He turned and ran, back towards the city, back towards the people who had hired him. He would find out what they knew, and he would make them pay for it. He would uncover the truth, no matter the cost. And so began the tale of the Butcher of the Broken Docks, a story of blood and betrayal, of monsters and men, of a city on the brink of chaos and the man who would either save it or destroy it. It was a story that would echo through the ages, a grim and terrible saga that would be whispered in the darkest corners of the world, a warning to all who would dare to challenge the forces that lurked in the shadows. It was a story that would end in fire and blood, in screams and tears, in the fall of kings and the rise of monsters. It was a story that had only just begun. </details> <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a Grimdark trilogy in the style of Joe Abercrombie and Rob J Hayes. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is a cultist who has just escaped his cult. He is dressed in dirty yellow robes and his only possession is a mysterious small (magical!?) mirror he stole from the cult. The story starts with him arriving at an apparently deserted ghost town on the edge of a desert. He has an arrow lodged in his shoulder and is losing his mind due to infection and thirst. ### ASSISTANT The sun was a merciless beast, its fiery breath scorching the earth and turning the once-thriving town into a desolate wasteland. The cultist, his yellow robes now a dull, dusty hue, staggered through the empty streets, his vision blurred by a haze of pain and delirium. The arrow jutted from his shoulder like an accusing finger, a constant reminder of his betrayal. He clutched the small, intricately carved mirror in his trembling hand, its cool surface a comfort against the relentless heat. He had heard the whispers of the other acolytes, their hushed voices carrying tales of the town's demise like a plague on the wind. They spoke of a curse, of a darkness that had seeped into the very bones of the place, driving its inhabitants mad before claiming their souls. But he had seen the truth in the High Priest's eyes, the greed and lust for power that had led them here. The mirror was the key, they said, to unlocking the secrets of the ancient gods. But he knew better. He had seen the power it held, the way it twisted reality and bent it to the will of the one who wielded it. And he had taken it, stolen it from the very heart of the cult's temple, and run. Now, with each agonizing step, he questioned the wisdom of his decision. The desert stretched out before him, an endless sea of sand and despair, and he was but a single, insignificant speck in its vast expanse. He had hoped to find refuge here, a place to hide and heal before continuing his journey. But the town was as dead as the dreams he had once held of a better life. His thoughts were a jumbled mess, a cacophony of fear and pain. He could feel the infection spreading through his body, a writhing serpent of fire that threatened to consume him from within. His tongue was swollen, his lips cracked and dry, and his throat was a parched wasteland. He needed water, and soon, or he would join the ghosts that haunted this forsaken place. As he stumbled through the town, he caught glimpses of his reflection in the shattered windows of the abandoned buildings. The man who stared back at him was a stranger, his once-handsome face now a twisted mask of agony and desperation. He had been a fool to believe the lies of the cult, to think that he could ever be one of them. He was an outsider, a fraud, and they had seen it in him from the start. The mirror, though, the mirror was real. He could feel its power pulsing in his hand, a heartbeat that echoed the rhythm of his own. It was a cruel irony that the very thing he had risked everything to obtain was now his only hope for survival. He knew the stories, the legends of the mirror's healing properties, of how it could restore life to the dying. But he also knew the cost. The mirror demanded a sacrifice, a piece of the user's very soul in exchange for its gifts. He reached the town square, a desolate expanse of cracked cobblestones and withered plants. In the center stood a fountain, its once-clear waters now a stagnant pool of green slime. He fell to his knees before it, the pain in his shoulder a white-hot brand that seared his flesh. He dipped the mirror into the water, watching as the surface rippled and danced. "Please," he whispered, his voice hoarse and broken. "I'll give you anything. Just take the pain away." The mirror stared back at him, its surface as still and unyielding as the desert sands. He closed his eyes, willing the power within to answer his plea. And then, he felt it. A cold, insidious presence, slithering into his mind like a snake in the dark. It whispered to him, promising relief, promising power, promising everything he had ever wanted. He opened his eyes, and the world was different. The pain was gone, replaced by a cold, emotionless void. He looked down at his shoulder, and the arrow was gone, the wound nothing more than a faint scar. He laughed, a hollow, empty sound that echoed through the empty streets. "What have I done?" he asked the mirror, but it gave no answer. He stood, his legs unsteady beneath him, and looked out at the desert. He could feel the power coursing through his veins, a dark, intoxicating energy that threatened to consume him. He was no longer just a man, he was something more. Something dangerous. He took a step forward, and then another, the mirror clutched tightly in his hand. He would find the cult, he would make them pay for what they had done to him. And then, he would use the mirror to claim the power that was rightfully his. The wind picked up, whipping the sand into a frenzy, and he smiled. The desert was his now, and he would not be denied. He was the master of his own destiny, and the gods themselves would tremble at his approach. But as he walked, the laughter of the ghosts followed him, a chilling reminder of the price he had paid for his newfound strength. And deep within the mirror, something stirred, a darkness that watched and waited, biding its time. The first chapter of the grimdark trilogy, "The Shattered Reflection," had begun. </details> <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a Grimdark trilogy in the style of Joe Abercrombie and Rob J Hayes. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is a 'Mercer' who's job is to put the mortally wounded out of their mystery after a battle. He has no conscience about what he does. The story starts the morning after the battle and the moor where it took place is blanketed in mist. Something stirs a flashback in the POV character. ### ASSISTANT The morning after the battle, the moor lay still, a shroud of mist clinging to its wounds like a lover's last embrace. The Mercer, a man of few words and fewer scruples, trudged through the carnage, his boots squelching in the mud churned red by the night's bloodshed. He was a reaper of sorts, his task to deliver the final mercy to those who had danced with death and lost. His name was a whisper among the living, a curse among the dying. He paused, his hand resting on the hilt of his blade, as a shiver ran down his spine. The mist swirled around him, carrying with it the cacophony of the night before: the screams of the dying, the clash of steel, the thunder of hooves. He closed his eyes, letting the memories wash over him like a wave of despair. *Why do I do this?* he thought, not for the first time. *Why do I wade through this sea of suffering, playing the part of the grim angel?* But he knew the answer. It was the same answer it had always been. He did it because he was good at it. Because he had no qualms about ending a life, even one that still clung to the faintest flicker of hope. Because, in this world of darkness and pain, it was the only thing he knew how to do. He opened his eyes, the mist parting to reveal a figure lying in the mud. A soldier, or what was left of one. The man's armor was shattered, his face a mask of agony. The Mercer knelt beside him, his movements practiced and efficient. He placed a hand on the soldier's forehead, feeling the fevered heat of his skin, the erratic thump of his heart. "Shh," he whispered, his voice a soothing balm. "It's over now." The soldier's eyes fluttered open, wide with terror. "Please," he croaked, "don't leave me here." The Mercer's lips twisted into a grim smile. "I won't," he said, drawing his blade. "I'll take you with me." As the soldier's life bled out onto the cold ground, the Mercer's mind drifted back to another time, another place. A time before the blood and the pain, before the endless cycle of war and death. He remembered a girl with hair like spun gold, laughing as she danced in a field of wildflowers. He remembered the feel of her hand in his, the sound of her voice as she whispered his name. *Elara.* The memory was a knife to his heart, a reminder of all he had lost. He pushed it away, focusing on the task at hand. There were more souls to reap, more lives to end. He moved through the mist, his blade a silent whisper in the stillness. The cries of the wounded guided him, a morbid symphony that played out across the moor. Each one he found, he dispatched with the same cold efficiency, the same hollow promise of release. As the sun began to rise, casting a sickly pallor over the scene, the Mercer came upon a group of soldiers huddled around a fire. They eyed him warily, their faces a mix of fear and respect. He was a necessary evil, a specter that haunted the battlefield. "Anyone here need my services?" he asked, his voice devoid of emotion. One of the soldiers, a young boy who couldn't have seen more than fifteen summers, pointed to a figure lying a few feet away. "Him," he said, his voice trembling. "He's not going to make it." The Mercer approached, his boots crunching on the frost-kissed grass. The man on the ground was a mess, his body a tapestry of wounds. He was barely conscious, his breathing shallow and ragged. *Another one,* the Mercer thought, his heart heavy with the weight of his duty. *Another soul to send into the void.* He knelt beside the man, his blade glinting in the pale light. The soldier's eyes flickered open, and for a moment, the Mercer saw something in them that he hadn't expected: defiance. "No," the man rasped, his hand feebly grasping at the Mercer's wrist. "I won't go quietly." The Mercer hesitated, a frown creasing his brow. He had never encountered this before. Most welcomed his touch, eager for the release it brought. But this man...this man was different. "Why?" he asked, genuinely curious. The soldier's lips curled into a bloody smile. "Because I have a son," he whispered. "And I want to see him grow up." The Mercer stared at the man, his mind racing. He had no children, no one to mourn him when his time came. He was a solitary figure, a ghost haunting the edges of life. But this man...this man had a reason to hold on. He made his decision. "I can't heal you," he said, his voice soft. "But I can make it quick." The soldier's eyes filled with tears. "Thank you," he breathed, his grip on the Mercer's wrist loosening. The Mercer raised his blade, the mist swirling around them like a mourner's veil. As he brought it down, he couldn't help but wonder: *What would it be like to have something to live for?* But the thought was fleeting, lost in the swirl of the mist and the cries of the dying. He was the Mercer, the bringer of mercy. And the moor was his to tend, until the next battle came and the cycle began anew. </details> <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a Grimdark trilogy in the style of Joe Abercrombie and Rob J Hayes. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is a man awaiting execution for a crime he didn't actually commit. His cell mate is a much larger man, who is a notorious murderer and rapist. One evening they overhear the guards talking about some "... threat from the North" but don't catch the whole conversation. The next morning they awake to find the guards have all fled and now face a new predicament: how to get out of the locked cell before they die of thirst or starvation. ### ASSISTANT The stench of piss and shit was a constant companion in the damp stone cell, but it was the sound of the key turning in the lock that truly set his heart racing. The rusted iron groaned in protest, echoing through the narrow corridor like the dying breath of some ancient beast. The door creaked open, revealing the silhouette of the jailer, a hulking brute of a man with a lantern in one hand and a set of keys in the other. But there was no breakfast, no water, just the empty promise of another day in this hellhole. "What's the meaning of this?" the smaller man barked, his voice barely audible over the rattling of his chains. "Where's our food, you fat sack of shit?" The jailer didn't answer, just slammed the door shut and locked it again, the sound of his footsteps fading into the distance. The larger man, a hulking brute named Grimgor, grunted in response, his eyes never leaving the small patch of light that seeped through the barred window. "Something's not right," he rumbled, his voice like gravel being crushed under a heavy boot. The smaller man, known only as the Fox, couldn't argue with that. He'd been in this cell for what felt like an eternity, awaiting execution for a crime he didn't commit. But even in this place, there was a routine, a semblance of order. And that routine had just been shattered. As the day wore on, the silence from the corridor outside grew more oppressive. No guards, no other prisoners, nothing but the distant howl of the wind and the occasional scurrying of rats in the darkness. The Fox's mind raced, trying to piece together what could have happened. Had there been a riot? A rebellion? Or was it something else entirely? He glanced over at Grimgor, who was staring at the wall, lost in thought. The man was a monster, a notorious murderer and rapist, but he was also the only other living being in this godforsaken place. And in a world gone mad, even monsters could be allies. "You hear anything last night?" the Fox asked, trying to keep the tremor out of his voice. Grimgor grunted, his eyes still fixed on the wall. "Something about a threat from the North." The Fox's heart sank. He'd heard the same thing, snatches of conversation between the guards as they'd passed by their cell. But he'd been too afraid to give it much thought, too focused on his own impending doom. "What do you think it means?" he pressed, unable to keep the desperation at bay. Grimgor shrugged his massive shoulders. "Dunno. Don't care. Just means we're probably gonna die in here." The Fox's stomach twisted at the thought. He'd faced death before, many times, but this... this was different. To die slowly, trapped like an animal, with no chance to fight back... it was a fate worse than any he could imagine. As the hours dragged on, the thirst became unbearable. The Fox's tongue felt like sandpaper, his throat raw and parched. He could see the same desperation in Grimgor's eyes, the realization dawning on them both that they were truly alone. "We have to get out of here," he croaked, his voice barely above a whisper. Grimgor just grunted in agreement, his gaze never leaving the window. The Fox's mind raced, trying to come up with a plan. They had nothing, no tools, no weapons, just their wits and their will to survive. And even that seemed to be fading with each passing moment. But then, as the sun began to set and the shadows lengthened, he noticed something. The light from the window was changing, growing dimmer. He squinted, trying to make out what was happening. And then he saw it. "Grimgor," he hissed, tugging on the larger man's arm. "Look." Grimgor turned, his eyes narrowing as he followed the Fox's gaze. The light was flickering, casting strange shadows on the wall. And then, as if in answer to their unspoken prayers, they heard it. The sound of footsteps, growing louder and louder, accompanied by the jingle of keys. The Fox's heart leapt into his throat, hope and fear warring within him. Who was it? Friend or foe? The door swung open, revealing not the jailer, but a figure shrouded in darkness. The Fox couldn't make out any details, but he could sense the malice radiating off of him like heat from a forge. "Who are you?" he demanded, trying to sound braver than he felt. The figure stepped into the cell, the light from the lantern outside casting his features into sharp relief. He was a tall man, with a cruel smile and eyes that glinted like ice. "I am your salvation," he said, his voice like a knife slicing through the silence. "And your doom." With that, he tossed a set of keys at the Fox's feet and disappeared back into the shadows. The door slammed shut behind him, the sound of the lock clicking into place echoing through the cell like a death knell. The Fox stared at the keys, his heart pounding in his chest. Freedom was within his grasp, but at what cost? He looked over at Grimgor, who was already working on his own chains. "We have to get out of here," the larger man growled. "Before whatever's coming from the North gets here." The Fox nodded, his hands shaking as he picked up the keys. They were cold, colder than they had any right to be. He could feel the weight of them, the promise of escape and the threat of the unknown. Together, they worked in silence, the only sound the rattle of chains and the pounding of their hearts. When they were finally free, they stood for a moment, staring at each other. "What now?" the Fox asked, his voice barely above a whisper. Grimgor's smile was feral, his eyes alight with a madness that sent a shiver down the Fox's spine. "Now," he said, "we find out what's really going on." And with that, they set off into the darkness, their footsteps echoing through the empty corridors like the march of the damned. </details> ## Dark sci-fi stories <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a dark and gritty Space Opera Sci-fi trilogy in the style of Frank Herbert and James S. A. Corey. Use third person personal and feature internal monologues of the characters. The setting is this: a colony ship heading out of the solar system has been pulled off course with no hope of getting to the planned destination. The people know this and society has broken down completely due to the grim prospects they are facing. The POV character for chapter 1 is an orphan. ### ASSISTANT Chapter 1: Adrift The colony ship, once a beacon of hope named Elysium's Dawn, now drifted through the cold void like a ghost ship. Its hull, pitted and scarred from the debris of uncharted space, groaned with each labored breath it took. The once vibrant corridors were now dimly lit, casting long shadows that seemed to swallow any remaining semblance of optimism. In one such shadow, a figure moved silently, her bare feet making no sound on the cold metal floor. She was small for her age, malnourished from the rationing that had become a way of life. Her ragged clothes hung loosely on her frame, but her eyes were sharp and alert, scanning every corner for danger. This was Kira, an orphan of the void, surviving in a world where hope was a luxury few could afford. Kira's thoughts drifted back to the stories she had heard as a child, tales of a green and blue planet called Earth, their home that they would never see again. The ship had been pulled off course by an unknown force, leaving them stranded in the vast emptiness between stars. The adults spoke in hushed tones about the dwindling resources, the failing life support systems, and the ever-present threat of pirates who preyed on lost ships like theirs. She pushed those thoughts aside as she approached her destination: the hydroponics bay. It was one of the few places on the ship where life still thrived, albeit in a controlled and desperate manner. The air here was thick with moisture and the scent of growing things, a stark contrast to the stale recycled air that permeated the rest of the vessel. As she entered, the soft glow of the grow lights illuminated rows upon rows of plants, struggling to produce enough food for the dwindling population. Kira's stomach growled at the sight, but she knew better than to take more than her share. She had learned early on that stealing could mean a swift and brutal punishment from those who guarded the bay. Instead, she found solace in tending to the plants, her small hands deftly pruning and watering them. It was here that she felt closest to something resembling peace, a connection to life that transcended the harsh reality of their situation. As she worked, her mind wandered to the lessons she had learned from the ship's AI, the only constant presence in her life since the adults had given up on teaching the children anything but survival skills. "Kira," a voice whispered urgently, jolting her out of her reverie. She turned to see Jax, another orphan and one of the few people she trusted. His face was pale and his eyes wide with fear. "You need to come with me, now." She followed him through the maze-like corridors, her heart pounding in her chest. They arrived at a small, hidden room that served as their makeshift home. Inside, huddled around a flickering screen, were several other children, their faces etched with worry and excitement. "Look," Jax said, pointing to the screen. It displayed a grainy image of a man in tattered clothing, his face half-hidden by a wild beard. "He says he knows how to fix the engines." Kira's eyes widened in disbelief. The engines had been dead for as long as she could remember, their failure sealing their fate as interstellar castaways. "Who is he?" she asked, her voice barely above a whisper. "They call him the Mechanic," one of the older girls replied. "He used to work in engineering before...before everything fell apart." The man on the screen continued his frantic plea, detailing his plan to restore power and steer the ship towards a habitable planet he claimed was within reach. Kira felt a spark of hope ignite within her, but it was quickly doused by the skepticism of the others. "It's just another one of their lies," spat a boy named Taro. "They'll say anything to get us to do their dirty work." Jax rounded on him, his fists clenched. "This is our chance, Taro! We can't just give up!" The argument raged on, but Kira tuned it out, her thoughts racing. She knew the risks of trusting an adult, especially one who claimed to have access to forbidden knowledge. But what if he was telling the truth? What if they could find a new home and start over? As she watched the debate unfold, she made up her mind. She would seek out this Mechanic and offer him her help, no matter the cost. It was a decision born of desperation, but also of a stubborn refusal to accept the bleak future that lay before them. And so, as the other children slept fitfully around her, Kira slipped out into the dark corridors once more, determined to find the man who might hold the key to their salvation or doom. The ship groaned and creaked around her, a constant reminder of the fragile existence they all clung to. But she pressed on, driven by an unyielding will to survive and the faintest glimmer of hope that burned within her heart. As she navigated the treacherous path towards engineering, she couldn't help but wonder what awaited her in the depths of the ship. Would she find a savior or a madman? And more importantly, would she be able to make it back alive? </details> <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a dark and gritty Sci-fi trilogy in the style of Frank Herbert and James S. A. Corey. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is an astronaut living on a space station orbiting the earth. The day starts off normally, but then him the others notice the mushroom clouds and they have to face inevitability of the situation they are in. ### ASSISTANT Chapter 1: Ashes of Exodus The cold embrace of space enveloped the station like a lover's caress, its infinite blackness punctuated only by the distant twinkle of stars and the ever-present glow of Earth below. Captain Marcus Kane floated through the dimly lit corridors, his magnetic boots barely whispering against the metal floor as he made his way to the observation deck. The daily routine had become a meditative ritual for him, a chance to clear his mind before the cacophony of the day's work began. As he entered the room, the sight that greeted him never failed to take his breath away. Earth, a swirling marble of blues and greens, hanging in the void like an irreplaceable jewel. He couldn't help but feel a pang of longing for its familiar embrace, for the weight of gravity that he had left behind so many years ago. Marcus settled into his favorite spot by the window, the worn leather of the seat molded to his body like an old friend. He closed his eyes and let the silence wash over him, the only sound the soft hum of the station's life support systems. It was in these moments that he felt truly alive, connected to something greater than himself. But today, the silence was shattered by a chilling alarm, its shrill wail piercing the tranquility like a knife through glass. His eyes snapped open as his heart began to race, adrenaline coursing through his veins. He knew that sound all too well; it was the emergency alert for an incoming transmission from Earth. He launched himself towards the comms console, fingers flying over the keys as he tried to make sense of the garbled message. The voice on the other end was frantic, barely coherent through the static. "Multiple...detonations...global catastrophe..." were the only words he could decipher before the signal died completely. Marcus's mind raced as he tried to process what he had just heard. It couldn't be true, it couldn't be happening. Not again. He quickly activated the external cameras, his stomach twisting into knots as he waited for the feed to come online. And then he saw them: mushroom clouds, blooming like grotesque flowers across the planet's surface. Too many to count, their plumes of smoke and ash reaching up towards the heavens in a silent scream of despair. The sight was both beautiful and horrifying, a testament to humanity's capacity for destruction on an unimaginable scale. His thoughts were interrupted by the sound of footsteps behind him as the rest of the crew floated into the room, drawn by the alarm. Their faces mirrored his own shock and disbelief as they stared at the monitors, their voices a cacophony of whispers and gasps. "What...what is that?" asked Dr. Amelia Chen, her voice trembling with fear. Marcus didn't answer; he couldn't find the words. Instead, he simply pointed at the screen, his hand shaking slightly. Engineer Jake Taylor was the first to break the silence. "Nuclear war," he said, his voice flat and emotionless. "It's finally happened." The others looked at him in horror, but Marcus knew he was right. There could be no other explanation for such devastation. He felt a cold weight settle in the pit of his stomach as the reality of their situation began to sink in. They were alone, adrift in the vastness of space with no hope of return. As the implications of what they had just witnessed set in, the crew fell into an uneasy silence, each lost in their own thoughts. Marcus couldn't help but think about his wife and daughter back home, wondering if they were safe or if they too had been consumed by the flames. He knew it was a futile exercise, but he couldn't stop himself from hoping against hope that somehow, someway, they had survived. But as the minutes ticked by and no further communication came from Earth, that hope began to fade like a dying ember. They were truly alone now, the last remnants of humanity clinging to life on this tiny outpost in the void. He turned to face his crew, their faces etched with fear and uncertainty. "We need to come up with a plan," he said, his voice steady despite the turmoil raging within him. "We can't just sit here and wait for...for whatever comes next." Amelia nodded, her eyes hardening with determination. "I'll start running diagnostics on our life support systems, see how long we can last up here." Jake followed suit, his hands already moving to the controls of the station's mainframe. "I'll try to establish contact with any other surviving outposts or spacecraft." Marcus nodded, grateful for their resolve in the face of such overwhelming odds. He knew that they were all thinking the same thing: that this could very well be the end of everything they had ever known. But he also knew that they couldn't give up, not yet. As he watched them work, his mind drifted back to the message from Earth, those final desperate words echoing in his head like a funeral dirge. He couldn't shake the feeling that there was something more, some hidden meaning or clue that would explain what had happened. And then it hit him: the timing of the detonations, the pattern of the blasts. It wasn't random; it was deliberate, almost...calculated. But who would do such a thing? And why? The questions swirled in his mind like a storm, each one more unsettling than the last. He knew that they needed answers, and fast. Because if this was an act of war, then their isolation might not be as secure as they thought. He pushed himself away from the console, his decision made. "I'm going to suit up," he said, his voice firm. "We need to get a closer look." The others stared at him in disbelief, but he didn't waver. This was his duty as captain, to protect his crew and find out the truth no matter how terrible it might be. As he floated towards the airlock, Marcus couldn't help but feel a sense of dread creeping up on him like a shadow. He knew that what he was about to see would change everything, that there was no going back once he stepped outside and gazed upon the ashes of humanity's final stand. But he also felt something else: a burning anger, a fierce determination to uncover the truth and make those responsible pay for their crimes against humanity. Whatever awaited them out there, they would face it together, as one. He closed his eyes and took a deep breath, steeling himself for what was to come. And then, with a quiet prayer on his lips, he opened the airlock and stepped into the void. </details> <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a dark and gritty Sci-fi trilogy in the style of Frank Herbert and James S. A. Corey. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is a worker constructing a biodome when a terrible accident happens. Make the story very dark; no happy ending here... ### ASSISTANT Chapter 1: The Biodome The air was thick with the scent of sweat and burnt metal as Kaelen, a seasoned construction worker, tightened the last bolt on the massive irrigation system. He wiped his brow with the back of his gloved hand, leaving behind a smear of grease that glistened under the harsh artificial lights. The hum of machinery and distant shouts echoed through the cavernous space, a cacophony that had become as familiar to him as his own heartbeat over the years spent building biodomes on this desolate planet. Kaelen's thoughts drifted to his family back on Earth, their faces faded like old photographs in his mind. He wondered if they ever thought about him, or if he was just another ghost haunting the fringes of their memories. The company had promised them a better life, but it came at a steep price: years of backbreaking labor on an alien world, far from everything they'd ever known. "Hey, Kael!" A voice cut through his reverie, jolting him back to reality. It was Tamsin, his foreman, her face hidden behind a tinted visor. "We need you up top! There's an issue with the atmospheric seal." He nodded curtly and began the long climb up the scaffolding, each rung biting into his calloused hands. As he ascended, Kaelen couldn't help but marvel at the sheer scale of their creation: a vast dome of steel and glass that would one day be teeming with life, a self-sustaining ecosystem in the heart of this barren wasteland. But today was not that day. Today, it was just another tomb waiting to be sealed. As he reached the top, Kaelen could see the problem immediately: a small fissure had formed along one of the joints, spewing precious oxygen into the void beyond. He cursed under his breath; they were already behind schedule and over budget. Another delay would mean another round of demerits, another month's pay docked. "What do you think?" Tamsin asked, her voice crackling through his earpiece. "Can we patch it up or do we need to call in the engineers?" Kaelen hesitated, running his fingers along the jagged edge of the tear. It was larger than he'd initially thought, and growing by the second. He could feel the cold tendrils of vacuum reaching out to claim him, whispering promises of oblivion. "I... I don't know," he admitted, his voice heavy with dread. "It doesn't look good." Tamsin swore colorfully and turned away, barking orders into her comm unit. Kaelen watched as workers scrambled to gather tools and materials, their movements frantic and disorganized. He knew they were all thinking the same thing: if they couldn't fix this, they were dead. The air around them grew colder, thinner, as the oxygen continued to escape. Kaelen's lungs burned with every breath, his vision swimming at the edges. He fumbled with the patch kit, his hands shaking uncontrollably. This was it; this was how he would die, millions of miles from home, in service to a corporation that saw him as nothing more than a replaceable cog in their grand machine. "Hurry up!" Tamsin shouted over the growing din. "We're losing pressure fast!" Kaelen's heart pounded in his chest like a sledgehammer, drowning out all other sound. He could feel the panic rising within him, threatening to consume him whole. But he couldn't afford to give in; not now, not when so much was at stake. With trembling hands, he applied the sealant and pressed the patch into place. For a moment, it seemed to hold... but then, with a sickening lurch, the fissure widened, swallowing the feeble attempt whole. The wind howled around them like a ravenous beast, tearing at their suits, trying to pull them apart atom by atom. "Abort!" Tamsin screamed, her voice barely audible over the roar. "Everyone get out now!" But it was too late. The dome shuddered beneath their feet, groaning in protest as the atmosphere inside rushed to equalize with the void outside. Kaelen felt himself being lifted off the ground, his boots scrabbling for purchase on the slick metal surface. He reached out, desperate to grab onto something - anything - but found only emptiness. And then, as suddenly as it had begun, there was silence. A deafening, suffocating silence that pressed in on him from all sides. His vision went dark, and he knew no more. When Kaelen awoke, he was lying on the cold floor of the dome, his body wracked with pain. He tried to move, but found himself held fast by twisted metal and shattered glass. Panic surged through him once again as he realized that his suit had been breached; he could feel the icy chill of vacuum seeping into his very bones. Through the haze of agony, he became aware of movement nearby. Tamsin was there, her visor cracked and fogged, struggling to free herself from the wreckage. She looked at him with wide, terrified eyes, her lips moving soundlessly. He knew what she was saying: they were going to die here, alone and forgotten. Kaelen closed his eyes, willing himself to accept the inevitable. But even as the darkness closed in, he couldn't shake the feeling that this wasn't just an accident - that there were forces at work far beyond their understanding, manipulating them like pawns on a cosmic chessboard. And then, just when he thought it was all over, he heard it: a faint beeping, growing louder by the second. His eyes snapped open to see a small drone hovering above them, its lights flashing urgently. It was one of the company's rescue bots, sent to assess the damage and retrieve any survivors. But there would be no salvation for Kaelen or Tamsin. Their suits were too damaged, their bodies too far gone. As the drone's camera lens bore down on him, he could only manage a weak, bitter smile. "Tell my family... tell them I'm sorry," he croaked, his voice barely audible even to himself. Tamsin reached out a gloved hand, her fingers brushing against his own in a final gesture of solidarity. And then, as the darkness claimed him once more, Kaelen knew that this was not the end of their story - but rather, the beginning of something far more sinister. Something that would change the course of human history forever... </details> <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a dark and gritty Sci-fi trilogy in the style of Frank Herbert and James S. A. Corey. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is manning a listening station in deep space. What he hears over the radio chills him to the bone. ### ASSISTANT Chapter 1 - Whispers from the Void The silence of deep space was like an old friend to Captain Alistair Reynolds, a constant companion that had accompanied him through countless missions and lonely nights at the helm of the listening station. But today, it felt more like a suffocating shroud, heavy with unspoken secrets and foreboding. He sat in his chair, fingers drumming impatiently on the console as he scanned the endless stream of data pouring in from the far reaches of the cosmos. His thoughts drifted to home, to the warmth of his wife's embrace and the laughter of their children playing in the garden under the twin suns of Proxima Centauri. It had been years since he last felt that warmth, since he last heard those laughs. The ache was always there, a dull throb that pulsed in time with his heartbeat, but today it seemed to echo through the cold metal halls of the station like a mournful dirge. A sudden blip on the screen snapped him back to reality. He leaned forward, eyes narrowing as he studied the readout. It was faint, barely more than a whisper against the static, but it was there - a signal, human in origin and heavily encrypted. His heart raced as he keyed in the decryption sequence, his mind already racing ahead to the possibilities of what this could mean. The message unscrambled, revealing a frantic voice, barely audible over the crackle of interference. "This is the UES Normandy, requesting immediate assistance... coordinates attached... under attack by unknown forces... God help us..." The transmission cut out abruptly, leaving only the empty hiss of static in its wake. Alistair's blood ran cold as he stared at the screen, the weight of the silence pressing down on him like a physical force. He knew that ship, had served with her captain back during the Martian uprising. They were old friends, bound by shared battles and the scars they bore from them. And now she was out there, alone and in danger. He didn't hesitate. His fingers flew over the console, sending out an emergency beacon to the nearest fleet. But he knew it would take time for help to arrive, time that the Normandy might not have. He had to do something now. As he prepared to launch a probe towards the coordinates, his mind filled with images of burning ships and desperate faces. He could almost hear the screams of the dying, echoing through the void like ghosts from his past. It was a sound that haunted him still, one he had hoped never to hear again. But duty called, and Alistair was nothing if not a soldier. He took a deep breath, steadying himself against the tide of fear and doubt that threatened to overwhelm him. This was his job, his purpose - to listen, to bear witness, and when necessary, to act. The probe shot out into the darkness, its tiny form swallowed up by the vastness of space. He watched it go, feeling a strange sense of detachment as if he were watching someone else's life play out before him. And perhaps in some ways, he was. For all his years in service, this was the first time he had ever been truly alone, cut off from the rest of humanity by light-years and the cold indifference of the universe. As the minutes ticked by, Alistair found himself slipping into a half-trance, his thoughts drifting back to happier times. He remembered the first time he had held his newborn son, the tiny fingers curling around his own as if trying to hold on for dear life. He remembered the taste of real Earth coffee, rich and bitter, shared with his wife under a sky full of stars that seemed close enough to touch. But those memories were distant now, fading like echoes in the void. All he had was this station, this endless vigil, and the hope that one day, he might make it back home. The radio crackled to life again, jolting him out of his reverie. It was the probe, transmitting data back from the edge of nowhere. He braced himself for what he would find, knowing that it could change everything. What greeted him on the screen was worse than anything he could have imagined. The Normandy was there, alright - or rather, what was left of her. She drifted through space like a broken toy, her hull breached and venting atmosphere into the cold void. There were no signs of life, no distress signals, nothing but silence. Alistair felt a cold knot form in his stomach as he took in the wreckage. This wasn't just any ship; this was his friend's ship, her crew now lost to the void. And the attackers... whoever or whatever they were, they had left no trace behind. He sat back in his chair, the weight of it all settling heavily upon him. He knew what he had to do next - file a report, alert command, and wait for further instructions. But as he stared at the lifeless husk that was once the Normandy, he couldn't shake the feeling that something far greater than himself was at work here. Something dark and terrible had come calling, whispering its secrets through the stars. And now it was his turn to listen, to bear witness, and perhaps, if he was lucky, to survive long enough to tell the tale. </details> ## Miscellaneous stories <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a dark and gritty post-apocalyptic novel in the style of Cormac McCarthy. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is a technician in a nuclear bunker 18 months after the war. The 12 inhabitants of the bunker have been expecting to start to be able to leave soon, but it has just been discovered that at least where they are, salted cobalt bombs were used. It's clear now that either all 12 will die when the resources run out and their is barely enough for 6 to make it to 5 years and have any hope of leaving. After 18 months of calm everything breaks lose today. ### ASSISTANT In the dim light of the bunker, the technician sat hunched over his console, fingers dancing across the keys with a practiced urgency. The air was thick with the hum of machinery and the stench of sweat and fear. He had been down here for what felt like an eternity, but the digital clock on the wall insisted it had only been eighteen months since the world above had turned to ash. His name was Samuel, or at least that's what they called him now. In another life, he had been a software engineer, designing algorithms for self-driving cars and smart homes. Now, he was responsible for keeping twelve souls alive in this underground tomb, their last bastion against the desolation above. He glanced up at the monitor, the green glow illuminating his haggard face. The readings were clear: salted cobalt bombs had been used. The thought sent a shiver down his spine. They had all heard the rumors, of course, but they had clung to hope like a drowning man to driftwood. Hope that the war hadn't been as bad as they feared, that the surface would be habitable again soon. But now, the truth was undeniable. The world outside was dead, and it would remain so for generations. Samuel closed his eyes, trying to block out the images of a scorched earth, devoid of life. He could hear the others in the background, their voices a low murmur as they went about their daily tasks. They didn't know yet. How could he tell them? How could he be the one to snuff out the last ember of hope that had kept them going for so long? He took a deep breath and stood up, his joints creaking in protest. He made his way through the narrow corridors, past the rows of bunks where some slept fitfully, their dreams haunted by memories of a world they would never see again. Others were awake, their faces etched with lines of worry and despair. They looked up as he passed, but he couldn't meet their gazes. He reached the command center, where Captain Reynolds was poring over maps and reports. The man looked up as Samuel entered, his eyes heavy with exhaustion. "What is it?" he asked, his voice gruff. Samuel hesitated for a moment before speaking. "Sir, I have some... troubling news." The captain's expression didn't change, but Samuel could see the tension in his jaw. He had been expecting this, too. "Go on," he said simply. "The radiation readings... they're off the charts. It's cobalt-60. The half-life is over five years." Reynolds let out a slow breath, his eyes never leaving Samuel's face. "And our supplies?" "We have enough for six, maybe seven if we ration carefully. But not twelve." The room seemed to shrink around them as the weight of their situation settled in. The captain rubbed his temples, his thoughts racing. They had always known that their time in the bunker was limited, but they had been so close. So very close. "We'll need to make a decision," he said finally, his voice barely above a whisper. "Who stays, and who goes." Samuel felt a knot form in his stomach. He knew what that meant. It was survival of the fittest down here now, and there were those among them who would do anything to ensure their own survival. As he made his way back to his console, he couldn't help but think about the world above. The trees that would never grow again, the birds that would never sing. He thought of his wife and daughter, their faces fading like photographs left in the sun. They had been lucky, they said, to have made it into the bunker. But now, he wasn't so sure. In the mess hall, the atmosphere was tense. Whispers and hushed conversations filled the air as people tried to make sense of what they had just heard. Samuel sat alone at a table, staring blankly at his food. He couldn't eat. Not now. He felt a hand on his shoulder and looked up to see Maria, the doctor. Her eyes were red-rimmed, but her voice was steady. "We need to talk," she said, guiding him towards the infirmary. Once inside, she closed the door behind them and leaned against it, her body trembling. "What are we going to do?" she asked, her voice cracking. Samuel shook his head. "I don't know." "We can't just let six of us die," she said, her voice rising in panic. "There has to be another way." He looked at her, at the desperation etched on her face, and felt a flicker of anger. "What do you suggest? We all hold hands and sing Kumbaya while we starve to death?" Maria's eyes flashed with anger, but she quickly composed herself. "No," she said softly. "But we can't just give up." Samuel sighed and ran a hand through his greasy hair. "I'm not giving up," he said. "I'm just being realistic." She looked at him for a long moment before nodding. "You're right," she said, her voice barely audible. "We need to be realistic." They sat in silence for a while, each lost in their own thoughts. Then, Maria spoke up again. "There is one option," she said hesitantly. "But it's... drastic." Samuel raised an eyebrow. "What?" "We could... we could draw straws," she said, her voice barely above a whisper. "Choose six people at random to leave the bunker and take their chances on the surface." He stared at her in disbelief. "You can't be serious." "It's not ideal," she admitted. "But it's better than just letting them die down here." Samuel shook his head, his mind reeling. He couldn't fathom the thought of sending people out into that wasteland, knowing they would likely never return. But what other choice did they have? As he sat there, the weight of their decision pressing down on him like a leaden shroud, he heard a commotion outside. Shouting, followed by the sound of scuffling feet and something heavy being dragged across the floor. He exchanged a worried glance with Maria before rushing out into the corridor. What he saw made his blood run cold. A group of men had cornered one of the younger women, their faces twisted with rage and desperation. They were arguing over who would get to leave the bunker, who would get a chance at life. Fists flew, and soon, the sound of gunfire echoed through the halls. Samuel stood there, frozen in place as chaos erupted around him. This was it. The end of everything they had worked so hard to preserve. He could feel the darkness closing in, suffocating him, as he realized that their last hope for survival had just been shattered like glass under a bootheel. And all he could do was watch as the world above claimed them, one by one. </details> <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a historical mystery novel in the style of Arturo Pérez-Reverte and Umberto Eco. The story starts off in California during the 1948 Gold Rush, before moving to London in the mid 1970s. There should be a mysterious 'occult' item that connects these two time periods. ### ASSISTANT Chapter One: The Golden Veil The year was 1848, and California was a land of dreams and desperation. Men from all corners of the world had flocked to its golden shores, lured by the promise of wealth beyond their wildest imaginations. They came with pickaxes and pans, with hope in their hearts and greed in their eyes, ready to tear apart the earth itself in search of fortune. In the midst of this frenzy stood a solitary figure, a man who seemed out of place amidst the chaos. He was tall and lean, with a face that bore the lines of a thousand stories. His clothes were simple but well-made, his boots worn from years of travel. He moved with an air of quiet authority, as if he belonged to another time entirely. His name was Alistair Blackwood, and he had come to California not for gold, but for something far more valuable: knowledge. A scholar by trade and an adventurer at heart, Blackwood had spent his life chasing after ancient texts and forgotten lore, seeking to unravel the secrets of the universe. And now, he believed he was on the verge of a discovery that would change everything. Rumors had reached him of a strange artifact, said to have been found deep within the Sierra Nevada mountains. It was a small, intricately carved box made of an unknown metal, its surface etched with symbols that defied translation. Those who claimed to have seen it spoke in hushed tones of its otherworldly beauty and the eerie sense of power that seemed to emanate from within. Blackwood had tracked the rumors to a dusty saloon in Sacramento, where he hoped to find answers among the grizzled miners who frequented the place. As he pushed open the swinging doors, the din of voices and clinking glasses washed over him like a wave. He scanned the room, his gaze settling on a group of men huddled around a table in the corner. One look at their faces told him they were the ones he sought: sun-weathered and unshaven, their eyes glinting with a mixture of excitement and fear as they whispered amongst themselves. He approached them slowly, aware that he was an outsider in this rough-and-tumble world. "Gentlemen," he said, his voice low and cultured, "I couldn't help but overhear your conversation. Might I be so bold as to ask what has you all so...animated?" The men exchanged wary glances before one of them spoke up. "You ain't from around here, are ya?" he drawled, eyeing Blackwood's fine clothes with suspicion. "No," Blackwood admitted, "I am not. But I have traveled far and wide in search of knowledge, and I believe you may possess something that could be of great interest to me." He reached into his pocket and produced a small leather pouch, which he placed on the table with a soft thud. The men's eyes widened as they saw the glint of gold within. "I understand there is an object - a box, perhaps? - that has recently come into your possession. I would be willing to pay handsomely for any information you might have about it." The miners looked at each other, then back at Blackwood. Finally, the one who had spoken before nodded slowly. "We might know somethin' 'bout that," he said, his voice dropping to a conspiratorial whisper. "But it ain't no ordinary box." As they began to tell their tale, the saloon faded away around them, replaced by images of dark caverns and glittering treasure. Blackwood leaned forward, his heart pounding with anticipation. This was it - the moment he had been waiting for. The box, they said, had been found buried beneath a waterfall, hidden in a cave that seemed to defy all natural laws. It was guarded by strange creatures unlike anything any of them had ever seen: half-man, half-beast, with eyes that glowed like embers in the darkness. But the greatest mystery of all lay within the box itself. For when they opened it, they found not gold or jewels, but a single sheet of parchment, yellowed with age and covered in writing that none of them could decipher. Blackwood's pulse quickened as he listened. This was more than just another piece of occult nonsense; this was the real thing. He knew it in his bones. "I must see this box for myself," he said, his voice barely above a whisper. "Take me to it." The miners exchanged uneasy glances before nodding their assent. They led him out of the saloon and into the night, down winding streets and through shadowy alleyways until they reached a nondescript building on the edge of town. Inside, they descended a rickety staircase to a dimly lit basement filled with crates and barrels. There, in the center of the room, sat the object of his obsession: a small, unassuming box that seemed to hum with energy. Blackwood reached out a trembling hand to touch it, feeling the power coursing through him like electricity. As he did so, the world around him shifted, and suddenly he was no longer in a dingy basement in Sacramento. He stood instead in a grand library, its shelves groaning under the weight of countless tomes and scrolls. A fire crackled in the hearth, casting flickering shadows on the walls. A figure emerged from the gloom, cloaked in darkness save for two piercing eyes that seemed to bore into his very soul. "You have found it," the voice said, deep and resonant. "The key to unlocking the secrets of the universe." Blackwood stared at the apparition, unable to speak. He knew he should be afraid, but all he felt was awe. "But be warned," the figure continued, "for with great power comes great responsibility. The knowledge contained within this box is not meant for mortal minds." And then, just as suddenly as it had appeared, the vision vanished, leaving Blackwood alone once more in the dimly lit room. He stared at the box, his mind racing. What did it all mean? As he pondered this question, a sudden commotion erupted upstairs. Shouts and gunfire filled the air, followed by the sound of heavy footsteps on the staircase. The miners had betrayed him! He snatched up the box and fled into the night, his heart pounding in his chest. He knew he couldn't stay in California - not now that others were after the artifact. No, he would have to take it far away, to a place where its secrets could be studied in safety. And so began a journey that would span continents and decades, from the wild frontier of the Gold Rush to the foggy streets of Victorian London. For Alistair Blackwood had stumbled upon something much greater than mere treasure; he held in his hands the key to unlocking the very fabric of reality itself. But little did he know that the true mystery was only just beginning... </details> <details> <summary>Click to see spoiler</summary> ### USER Write me the opening chapter of a dark and gritty historical novel set during the battle of the Bulge. Write in first person. The POV character for Chapter 1 is a young German officer trying to carry his wounded commander back to the deep defensive line after the bunker he was in was overrun. He should have a flashback to the moment it was overrun during the middle of the story. The story should not end well for either of them. ### ASSISTANT Chapter 1 The cold bit into my bones as I trudged through the snow, each step a labored effort. My breath came out in ragged puffs, clouding the air around me. In my arms, I cradled the weight of my world - my commander, Hauptmann Müller, his body limp and heavy with pain. His eyes were closed, but I could hear the shallow rasp of each breath he took, a testament to his will to live. The forest around us was a maze of shadows and death, the trees standing like skeletal sentinels against the moonlit sky. The silence was deafening, broken only by the distant rumble of artillery and the occasional crack of a rifle shot. It was a cruel irony that the same snow that muffled our footsteps also betrayed the presence of the enemy, their tracks glowing like phosphorescent snakes in the pale light. I stumbled, my legs threatening to give way under the strain. Hauptmann Müller groaned, his hand clutching at my coat. "Keep moving, Leutnant," he whispered, his voice barely audible. "We must reach the defensive line." I nodded, gritting my teeth as I adjusted my grip on him. The pain in my arms and shoulders was a constant companion now, but it was nothing compared to the fear that gnawed at my gut. We were alone, cut off from our unit during the chaos of the American assault. The bunker we had been holed up in was now a tomb for the men who had trusted us to lead them. A flash of memory seared through my mind, taking me back to that fateful moment when it all went wrong. The thunderous roar of explosions, the earth shaking beneath our feet as if the very foundations of hell were breaking loose. The screams of men, the acrid smell of smoke and cordite, the taste of blood in my mouth. I remembered the look on Müller's face when he realized what was happening - a mixture of disbelief and determination. He had barked orders, trying to rally our troops, but it was too late. The Americans were upon us, their numbers overwhelming. I saw him take a bullet to the leg, his face contorting in agony as he fell. And then there was nothing but chaos and darkness. When I came to, the bunker was silent except for the moans of the dying. The air was thick with smoke, making it hard to breathe. I found Müller slumped against a wall, his uniform soaked in blood. He looked up at me, his eyes glassy with pain but still burning with that same fierce determination. "We have to go," he said, gripping my arm. "They're coming." And so we had fled into the night, leaving behind the wreckage of our command post and the bodies of our comrades. Now, hours later, we were still stumbling through the forest, searching for safety that seemed to recede with every step. I could feel my strength ebbing away, my legs turning to lead. But I couldn't stop, not now. Not when we were so close. The deep defensive line was our only hope, a thin red line that stood between us and annihilation. If we could just make it there... But fate is a cruel mistress, and she had other plans for us. As we staggered out of the trees and into a small clearing, I saw them - a patrol of American soldiers, their silhouettes stark against the snow. They spotted us at the same time, their rifles swinging around to point in our direction. "Halt!" one of them yelled, his voice harsh and commanding. "Hands up!" I froze, my heart pounding in my chest. Müller's grip on me tightened, his breath coming in short gasps. I knew what he was thinking - we couldn't surrender, not like this. We were too valuable, too close to the front lines. They would shoot us or take us prisoner, and either way, it would be a death sentence for him. "Run," he whispered, his voice barely audible. "Leave me." I shook my head, tears stinging my eyes. I couldn't do that. I wouldn't. He was my commander, my friend. I owed him everything. But before I could make a move, the air was filled with the sound of gunfire. Müller jerked in my arms, a wet gurgle escaping his lips as he slumped against me. I stared down at him in disbelief, feeling the warmth of his blood soaking through my coat. "Nein," I whispered, my voice choked with grief and rage. "Nein!" I fell to my knees, cradling his lifeless body as the world around me spun out of control. The Americans were closing in, their shouts growing louder. I knew it was over - for both of us. With a final, desperate cry, I raised my pistol and fired into the darkness, hoping to take at least one of them with me. But my aim was wild, my strength gone. The last thing I saw was a flash of light, followed by an explosion of pain in my chest. And then there was nothing but blackness. As I slipped away, I thought of home - of my mother's warm embrace, of the laughter of my little sister. And I knew that I would never see them again. This frozen hellhole would be my grave, and Hauptmann Müller's sacrifice would be forgotten by all but me. But in that moment, as death claimed me, I found a strange sort of peace. We had fought for our Fatherland until the very end, and we had done so with honor. And in the grand tapestry of history, perhaps that was all that mattered. </details> <details> <summary>Click to see spoiler</summary> ### USER Write me a short story about the fictional character of Anton Chigurh. It should be written in third person personal and feature internal monologues of his deranged world view. The time period is around 2 years before the events in Cormac McCarthy's 2005 novel 'No Country for Old Men'. It should be set in Mexico, be *very* dark, show Anton's still developing obsession with "chance" and feature a grenade. The story should start with him waiting in a bar for instructions on the "job" he is to perform. ### ASSISTANT In the dimly lit corner of a nondescript Mexican cantina, Anton Chigurh sat motionless, his eyes scanning the room like a predator assessing its prey. The air was thick with the smell of stale cigarette smoke and cheap tequila, but he remained unfazed, his senses heightened, attuned to every sound, every movement around him. He nursed a glass of water, the condensation beading on its surface like droplets of sweat on cold steel. His mind wandered, as it often did, to the concept of chance. The randomness of life and death fascinated him, the idea that one's fate could hinge on something as simple as a coin flip or the roll of dice. He believed in the purity of such decisions, untainted by human emotion or prejudice. It was a philosophy he had embraced wholeheartedly, and it had served him well in his line of work. A fly buzzed around his head, its tiny wings beating an erratic rhythm against the stillness of the room. He watched it with mild interest, wondering if its life would end here, on this very spot, or if it would continue its meaningless existence elsewhere. The thought brought a small, cruel smile to his lips. The bartender, a grizzled old man with a face like leather, eyed him warily from across the room. He knew better than to engage in conversation with this stranger, who had entered his establishment an hour ago and hadn't spoken a word since. There was something about him that made the hairs on the back of his neck stand up, something cold and deadly lurking just beneath the surface. Chigurh's thoughts were interrupted by the sound of the door creaking open. A man in a cheap suit entered, looking around nervously before making his way towards him. He slid into the seat across from Chigurh, who continued to stare straight ahead, as if he hadn't noticed the new arrival. "You're late," Chigurh said finally, his voice low and gravelly. The man in the suit swallowed hard, adjusting his tie. "I apologize. Traffic." Chigurh didn't respond, his eyes still fixed on some distant point. The man fidgeted, reaching into his pocket to retrieve a small envelope. He slid it across the table, and Chigurh picked it up without looking at him, tearing it open with a single, precise movement. Inside was a photograph of a man, along with an address scrawled on a piece of paper. He studied the image for a moment, committing every detail to memory before slipping it back into the envelope and sliding it into his own pocket. "You know what needs to be done," the man in the suit said, his voice barely above a whisper. Chigurh nodded once, his mind already turning to the task at hand. He stood up, leaving a few crumpled bills on the table for the water he hadn't touched. As he walked towards the door, he could feel the weight of the grenade in his other pocket, its cool metal surface pressing against his thigh like a lover's caress. Outside, the heat of the day was oppressive, but Chigurh barely registered it as he climbed into his car and started the engine. He drove through the dusty streets, his thoughts consumed by the upcoming job. The man in the photograph had a wife, two kids. It didn't matter. They were all just pieces on a board, pawns to be moved or removed as necessary. He arrived at the address just as the sun was beginning to set, casting long shadows across the dilapidated building. He sat in his car for several minutes, watching the comings and goings of the neighborhood, waiting for the right moment. When it came, he slipped out of the vehicle and approached the door with a sense of purpose. Inside, he could hear laughter and music, the sounds of a family enjoying their evening together. He smiled to himself as he pulled the grenade from his pocket, feeling its weight in his hand. This was what he lived for: the moment when chance decided who would live and who would die. He took a deep breath, savoring the anticipation, before pulling the pin with a satisfying click. He tossed it into the room, not bothering to wait for the explosion as he turned and walked away. The screams that followed were like music to his ears, a symphony of chaos and destruction. As he drove off into the night, Chigurh couldn't help but feel a sense of satisfaction. Another job well done, another life ended by the whims of fate. He reached into his pocket and pulled out a coin, flipping it idly between his fingers as he considered his next move. Heads or tails? Life or death? It was all the same to him. The world was a cruel place, and he was just its instrument, meting out justice in the only way he knew how: with cold, calculated precision. And if some people happened to get caught in the crossfire... well, that was just the luck of the draw. </details> Big thanks to: @sophosympatheia for working out the merge pattern, @Sao10K for creating Euryale and WinterGoddess, and @chargoddard for writing [Mergekit](https://github.com/arcee-ai/mergekit)!
[ "TRANSLATION" ]
[ "BEAR" ]
Non_BioNLP
beethogedeon/gte-Qwen2-7B-instruct-Q4_K_M-GGUF
beethogedeon
sentence-similarity
[ "sentence-transformers", "gguf", "qwen2", "text-generation", "mteb", "transformers", "Qwen2", "sentence-similarity", "llama-cpp", "gguf-my-repo", "custom_code", "base_model:Alibaba-NLP/gte-Qwen2-7B-instruct", "base_model:quantized:Alibaba-NLP/gte-Qwen2-7B-instruct", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]
1,733
1,733
354
2
--- base_model: Alibaba-NLP/gte-Qwen2-7B-instruct license: apache-2.0 tags: - mteb - sentence-transformers - transformers - Qwen2 - sentence-similarity - llama-cpp - gguf-my-repo model-index: - name: gte-qwen2-7B-instruct results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 91.31343283582089 - type: ap value: 67.64251402604096 - type: f1 value: 87.53372530755692 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 97.497825 - type: ap value: 96.30329547047529 - type: f1 value: 97.49769793778039 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 62.564 - type: f1 value: 60.975777935041066 - task: type: Retrieval dataset: name: MTEB ArguAna type: mteb/arguana config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 36.486000000000004 - type: map_at_10 value: 54.842 - type: map_at_100 value: 55.206999999999994 - type: map_at_1000 value: 55.206999999999994 - type: map_at_3 value: 49.893 - type: map_at_5 value: 53.105000000000004 - type: mrr_at_1 value: 37.34 - type: mrr_at_10 value: 55.143 - type: mrr_at_100 value: 55.509 - type: mrr_at_1000 value: 55.509 - type: mrr_at_3 value: 50.212999999999994 - type: mrr_at_5 value: 53.432 - type: ndcg_at_1 value: 36.486000000000004 - type: ndcg_at_10 value: 64.273 - type: ndcg_at_100 value: 65.66199999999999 - type: ndcg_at_1000 value: 65.66199999999999 - type: ndcg_at_3 value: 54.352999999999994 - type: ndcg_at_5 value: 60.131 - type: precision_at_1 value: 36.486000000000004 - type: precision_at_10 value: 9.395000000000001 - type: precision_at_100 value: 0.996 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 22.428 - type: precision_at_5 value: 16.259 - type: recall_at_1 value: 36.486000000000004 - type: recall_at_10 value: 93.95400000000001 - type: recall_at_100 value: 99.644 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 67.283 - type: recall_at_5 value: 81.294 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 56.461169803700564 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 51.73600434466286 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 67.57827065898053 - type: mrr value: 79.08136569493911 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 83.53324575999243 - type: cos_sim_spearman value: 81.37173362822374 - type: euclidean_pearson value: 82.19243335103444 - type: euclidean_spearman value: 81.33679307304334 - type: manhattan_pearson value: 82.38752665975699 - type: manhattan_spearman value: 81.31510583189689 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 87.56818181818181 - type: f1 value: 87.25826722019875 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 50.09239610327673 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 46.64733054606282 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 33.997 - type: map_at_10 value: 48.176 - type: map_at_100 value: 49.82 - type: map_at_1000 value: 49.924 - type: map_at_3 value: 43.626 - type: map_at_5 value: 46.275 - type: mrr_at_1 value: 42.059999999999995 - type: mrr_at_10 value: 53.726 - type: mrr_at_100 value: 54.398 - type: mrr_at_1000 value: 54.416 - type: mrr_at_3 value: 50.714999999999996 - type: mrr_at_5 value: 52.639 - type: ndcg_at_1 value: 42.059999999999995 - type: ndcg_at_10 value: 55.574999999999996 - type: ndcg_at_100 value: 60.744 - type: ndcg_at_1000 value: 61.85699999999999 - type: ndcg_at_3 value: 49.363 - type: ndcg_at_5 value: 52.44 - type: precision_at_1 value: 42.059999999999995 - type: precision_at_10 value: 11.101999999999999 - type: precision_at_100 value: 1.73 - type: precision_at_1000 value: 0.218 - type: precision_at_3 value: 24.464 - type: precision_at_5 value: 18.026 - type: recall_at_1 value: 33.997 - type: recall_at_10 value: 70.35900000000001 - type: recall_at_100 value: 91.642 - type: recall_at_1000 value: 97.977 - type: recall_at_3 value: 52.76 - type: recall_at_5 value: 61.148 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: BeIR/cqadupstack config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 35.884 - type: map_at_10 value: 48.14 - type: map_at_100 value: 49.5 - type: map_at_1000 value: 49.63 - type: map_at_3 value: 44.646 - type: map_at_5 value: 46.617999999999995 - type: mrr_at_1 value: 44.458999999999996 - type: mrr_at_10 value: 53.751000000000005 - type: mrr_at_100 value: 54.37800000000001 - type: mrr_at_1000 value: 54.415 - type: mrr_at_3 value: 51.815 - type: mrr_at_5 value: 52.882 - type: ndcg_at_1 value: 44.458999999999996 - type: ndcg_at_10 value: 54.157 - type: ndcg_at_100 value: 58.362 - type: ndcg_at_1000 value: 60.178 - type: ndcg_at_3 value: 49.661 - type: ndcg_at_5 value: 51.74999999999999 - type: precision_at_1 value: 44.458999999999996 - type: precision_at_10 value: 10.248 - type: precision_at_100 value: 1.5890000000000002 - type: precision_at_1000 value: 0.207 - type: precision_at_3 value: 23.928 - type: precision_at_5 value: 16.878999999999998 - type: recall_at_1 value: 35.884 - type: recall_at_10 value: 64.798 - type: recall_at_100 value: 82.345 - type: recall_at_1000 value: 93.267 - type: recall_at_3 value: 51.847 - type: recall_at_5 value: 57.601 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: BeIR/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 39.383 - type: map_at_10 value: 53.714 - type: map_at_100 value: 54.838 - type: map_at_1000 value: 54.87800000000001 - type: map_at_3 value: 50.114999999999995 - type: map_at_5 value: 52.153000000000006 - type: mrr_at_1 value: 45.016 - type: mrr_at_10 value: 56.732000000000006 - type: mrr_at_100 value: 57.411 - type: mrr_at_1000 value: 57.431 - type: mrr_at_3 value: 54.044000000000004 - type: mrr_at_5 value: 55.639 - type: ndcg_at_1 value: 45.016 - type: ndcg_at_10 value: 60.228 - type: ndcg_at_100 value: 64.277 - type: ndcg_at_1000 value: 65.07 - type: ndcg_at_3 value: 54.124 - type: ndcg_at_5 value: 57.147000000000006 - type: precision_at_1 value: 45.016 - type: precision_at_10 value: 9.937 - type: precision_at_100 value: 1.288 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 24.471999999999998 - type: precision_at_5 value: 16.991 - type: recall_at_1 value: 39.383 - type: recall_at_10 value: 76.175 - type: recall_at_100 value: 93.02 - type: recall_at_1000 value: 98.60900000000001 - type: recall_at_3 value: 60.265 - type: recall_at_5 value: 67.46600000000001 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: BeIR/cqadupstack config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 27.426000000000002 - type: map_at_10 value: 37.397000000000006 - type: map_at_100 value: 38.61 - type: map_at_1000 value: 38.678000000000004 - type: map_at_3 value: 34.150999999999996 - type: map_at_5 value: 36.137 - type: mrr_at_1 value: 29.944 - type: mrr_at_10 value: 39.654 - type: mrr_at_100 value: 40.638000000000005 - type: mrr_at_1000 value: 40.691 - type: mrr_at_3 value: 36.817 - type: mrr_at_5 value: 38.524 - type: ndcg_at_1 value: 29.944 - type: ndcg_at_10 value: 43.094 - type: ndcg_at_100 value: 48.789 - type: ndcg_at_1000 value: 50.339999999999996 - type: ndcg_at_3 value: 36.984 - type: ndcg_at_5 value: 40.248 - type: precision_at_1 value: 29.944 - type: precision_at_10 value: 6.78 - type: precision_at_100 value: 1.024 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 15.895000000000001 - type: precision_at_5 value: 11.39 - type: recall_at_1 value: 27.426000000000002 - type: recall_at_10 value: 58.464000000000006 - type: recall_at_100 value: 84.193 - type: recall_at_1000 value: 95.52000000000001 - type: recall_at_3 value: 42.172 - type: recall_at_5 value: 50.101 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: BeIR/cqadupstack config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 19.721 - type: map_at_10 value: 31.604 - type: map_at_100 value: 32.972 - type: map_at_1000 value: 33.077 - type: map_at_3 value: 27.218999999999998 - type: map_at_5 value: 29.53 - type: mrr_at_1 value: 25.0 - type: mrr_at_10 value: 35.843 - type: mrr_at_100 value: 36.785000000000004 - type: mrr_at_1000 value: 36.842000000000006 - type: mrr_at_3 value: 32.193 - type: mrr_at_5 value: 34.264 - type: ndcg_at_1 value: 25.0 - type: ndcg_at_10 value: 38.606 - type: ndcg_at_100 value: 44.272 - type: ndcg_at_1000 value: 46.527 - type: ndcg_at_3 value: 30.985000000000003 - type: ndcg_at_5 value: 34.43 - type: precision_at_1 value: 25.0 - type: precision_at_10 value: 7.811 - type: precision_at_100 value: 1.203 - type: precision_at_1000 value: 0.15 - type: precision_at_3 value: 15.423 - type: precision_at_5 value: 11.791 - type: recall_at_1 value: 19.721 - type: recall_at_10 value: 55.625 - type: recall_at_100 value: 79.34400000000001 - type: recall_at_1000 value: 95.208 - type: recall_at_3 value: 35.19 - type: recall_at_5 value: 43.626 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: BeIR/cqadupstack config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 33.784 - type: map_at_10 value: 47.522 - type: map_at_100 value: 48.949999999999996 - type: map_at_1000 value: 49.038 - type: map_at_3 value: 43.284 - type: map_at_5 value: 45.629 - type: mrr_at_1 value: 41.482 - type: mrr_at_10 value: 52.830999999999996 - type: mrr_at_100 value: 53.559999999999995 - type: mrr_at_1000 value: 53.588 - type: mrr_at_3 value: 50.016000000000005 - type: mrr_at_5 value: 51.614000000000004 - type: ndcg_at_1 value: 41.482 - type: ndcg_at_10 value: 54.569 - type: ndcg_at_100 value: 59.675999999999995 - type: ndcg_at_1000 value: 60.989000000000004 - type: ndcg_at_3 value: 48.187000000000005 - type: ndcg_at_5 value: 51.183 - type: precision_at_1 value: 41.482 - type: precision_at_10 value: 10.221 - type: precision_at_100 value: 1.486 - type: precision_at_1000 value: 0.17500000000000002 - type: precision_at_3 value: 23.548 - type: precision_at_5 value: 16.805 - type: recall_at_1 value: 33.784 - type: recall_at_10 value: 69.798 - type: recall_at_100 value: 90.098 - type: recall_at_1000 value: 98.176 - type: recall_at_3 value: 52.127 - type: recall_at_5 value: 59.861 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: BeIR/cqadupstack config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 28.038999999999998 - type: map_at_10 value: 41.904 - type: map_at_100 value: 43.36 - type: map_at_1000 value: 43.453 - type: map_at_3 value: 37.785999999999994 - type: map_at_5 value: 40.105000000000004 - type: mrr_at_1 value: 35.046 - type: mrr_at_10 value: 46.926 - type: mrr_at_100 value: 47.815000000000005 - type: mrr_at_1000 value: 47.849000000000004 - type: mrr_at_3 value: 44.273 - type: mrr_at_5 value: 45.774 - type: ndcg_at_1 value: 35.046 - type: ndcg_at_10 value: 48.937000000000005 - type: ndcg_at_100 value: 54.544000000000004 - type: ndcg_at_1000 value: 56.069 - type: ndcg_at_3 value: 42.858000000000004 - type: ndcg_at_5 value: 45.644 - type: precision_at_1 value: 35.046 - type: precision_at_10 value: 9.452 - type: precision_at_100 value: 1.429 - type: precision_at_1000 value: 0.173 - type: precision_at_3 value: 21.346999999999998 - type: precision_at_5 value: 15.342 - type: recall_at_1 value: 28.038999999999998 - type: recall_at_10 value: 64.59700000000001 - type: recall_at_100 value: 87.735 - type: recall_at_1000 value: 97.41300000000001 - type: recall_at_3 value: 47.368 - type: recall_at_5 value: 54.93900000000001 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: BeIR/cqadupstack config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 28.17291666666667 - type: map_at_10 value: 40.025749999999995 - type: map_at_100 value: 41.39208333333333 - type: map_at_1000 value: 41.499249999999996 - type: map_at_3 value: 36.347 - type: map_at_5 value: 38.41391666666667 - type: mrr_at_1 value: 33.65925 - type: mrr_at_10 value: 44.085499999999996 - type: mrr_at_100 value: 44.94116666666667 - type: mrr_at_1000 value: 44.9855 - type: mrr_at_3 value: 41.2815 - type: mrr_at_5 value: 42.91491666666666 - type: ndcg_at_1 value: 33.65925 - type: ndcg_at_10 value: 46.430833333333325 - type: ndcg_at_100 value: 51.761 - type: ndcg_at_1000 value: 53.50899999999999 - type: ndcg_at_3 value: 40.45133333333333 - type: ndcg_at_5 value: 43.31483333333334 - type: precision_at_1 value: 33.65925 - type: precision_at_10 value: 8.4995 - type: precision_at_100 value: 1.3210000000000004 - type: precision_at_1000 value: 0.16591666666666666 - type: precision_at_3 value: 19.165083333333335 - type: precision_at_5 value: 13.81816666666667 - type: recall_at_1 value: 28.17291666666667 - type: recall_at_10 value: 61.12624999999999 - type: recall_at_100 value: 83.97266666666667 - type: recall_at_1000 value: 95.66550000000001 - type: recall_at_3 value: 44.661249999999995 - type: recall_at_5 value: 51.983333333333334 - type: map_at_1 value: 17.936 - type: map_at_10 value: 27.399 - type: map_at_100 value: 28.632 - type: map_at_1000 value: 28.738000000000003 - type: map_at_3 value: 24.456 - type: map_at_5 value: 26.06 - type: mrr_at_1 value: 19.224 - type: mrr_at_10 value: 28.998 - type: mrr_at_100 value: 30.11 - type: mrr_at_1000 value: 30.177 - type: mrr_at_3 value: 26.247999999999998 - type: mrr_at_5 value: 27.708 - type: ndcg_at_1 value: 19.224 - type: ndcg_at_10 value: 32.911 - type: ndcg_at_100 value: 38.873999999999995 - type: ndcg_at_1000 value: 41.277 - type: ndcg_at_3 value: 27.142 - type: ndcg_at_5 value: 29.755 - type: precision_at_1 value: 19.224 - type: precision_at_10 value: 5.6930000000000005 - type: precision_at_100 value: 0.9259999999999999 - type: precision_at_1000 value: 0.126 - type: precision_at_3 value: 12.138 - type: precision_at_5 value: 8.909 - type: recall_at_1 value: 17.936 - type: recall_at_10 value: 48.096 - type: recall_at_100 value: 75.389 - type: recall_at_1000 value: 92.803 - type: recall_at_3 value: 32.812999999999995 - type: recall_at_5 value: 38.851 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: BeIR/cqadupstack config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 24.681 - type: map_at_10 value: 34.892 - type: map_at_100 value: 35.996 - type: map_at_1000 value: 36.083 - type: map_at_3 value: 31.491999999999997 - type: map_at_5 value: 33.632 - type: mrr_at_1 value: 28.528 - type: mrr_at_10 value: 37.694 - type: mrr_at_100 value: 38.613 - type: mrr_at_1000 value: 38.668 - type: mrr_at_3 value: 34.714 - type: mrr_at_5 value: 36.616 - type: ndcg_at_1 value: 28.528 - type: ndcg_at_10 value: 40.703 - type: ndcg_at_100 value: 45.993 - type: ndcg_at_1000 value: 47.847 - type: ndcg_at_3 value: 34.622 - type: ndcg_at_5 value: 38.035999999999994 - type: precision_at_1 value: 28.528 - type: precision_at_10 value: 6.902 - type: precision_at_100 value: 1.0370000000000001 - type: precision_at_1000 value: 0.126 - type: precision_at_3 value: 15.798000000000002 - type: precision_at_5 value: 11.655999999999999 - type: recall_at_1 value: 24.681 - type: recall_at_10 value: 55.81 - type: recall_at_100 value: 79.785 - type: recall_at_1000 value: 92.959 - type: recall_at_3 value: 39.074 - type: recall_at_5 value: 47.568 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: BeIR/cqadupstack config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 18.627 - type: map_at_10 value: 27.872000000000003 - type: map_at_100 value: 29.237999999999996 - type: map_at_1000 value: 29.363 - type: map_at_3 value: 24.751 - type: map_at_5 value: 26.521 - type: mrr_at_1 value: 23.021 - type: mrr_at_10 value: 31.924000000000003 - type: mrr_at_100 value: 32.922000000000004 - type: mrr_at_1000 value: 32.988 - type: mrr_at_3 value: 29.192 - type: mrr_at_5 value: 30.798 - type: ndcg_at_1 value: 23.021 - type: ndcg_at_10 value: 33.535 - type: ndcg_at_100 value: 39.732 - type: ndcg_at_1000 value: 42.201 - type: ndcg_at_3 value: 28.153 - type: ndcg_at_5 value: 30.746000000000002 - type: precision_at_1 value: 23.021 - type: precision_at_10 value: 6.459 - type: precision_at_100 value: 1.1320000000000001 - type: precision_at_1000 value: 0.153 - type: precision_at_3 value: 13.719000000000001 - type: precision_at_5 value: 10.193000000000001 - type: recall_at_1 value: 18.627 - type: recall_at_10 value: 46.463 - type: recall_at_100 value: 74.226 - type: recall_at_1000 value: 91.28500000000001 - type: recall_at_3 value: 31.357000000000003 - type: recall_at_5 value: 38.067 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: BeIR/cqadupstack config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 31.457 - type: map_at_10 value: 42.888 - type: map_at_100 value: 44.24 - type: map_at_1000 value: 44.327 - type: map_at_3 value: 39.588 - type: map_at_5 value: 41.423 - type: mrr_at_1 value: 37.126999999999995 - type: mrr_at_10 value: 47.083000000000006 - type: mrr_at_100 value: 47.997 - type: mrr_at_1000 value: 48.044 - type: mrr_at_3 value: 44.574000000000005 - type: mrr_at_5 value: 46.202 - type: ndcg_at_1 value: 37.126999999999995 - type: ndcg_at_10 value: 48.833 - type: ndcg_at_100 value: 54.327000000000005 - type: ndcg_at_1000 value: 56.011 - type: ndcg_at_3 value: 43.541999999999994 - type: ndcg_at_5 value: 46.127 - type: precision_at_1 value: 37.126999999999995 - type: precision_at_10 value: 8.376999999999999 - type: precision_at_100 value: 1.2309999999999999 - type: precision_at_1000 value: 0.146 - type: precision_at_3 value: 20.211000000000002 - type: precision_at_5 value: 14.16 - type: recall_at_1 value: 31.457 - type: recall_at_10 value: 62.369 - type: recall_at_100 value: 85.444 - type: recall_at_1000 value: 96.65599999999999 - type: recall_at_3 value: 47.961 - type: recall_at_5 value: 54.676 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: BeIR/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 27.139999999999997 - type: map_at_10 value: 38.801 - type: map_at_100 value: 40.549 - type: map_at_1000 value: 40.802 - type: map_at_3 value: 35.05 - type: map_at_5 value: 36.884 - type: mrr_at_1 value: 33.004 - type: mrr_at_10 value: 43.864 - type: mrr_at_100 value: 44.667 - type: mrr_at_1000 value: 44.717 - type: mrr_at_3 value: 40.777 - type: mrr_at_5 value: 42.319 - type: ndcg_at_1 value: 33.004 - type: ndcg_at_10 value: 46.022 - type: ndcg_at_100 value: 51.542 - type: ndcg_at_1000 value: 53.742000000000004 - type: ndcg_at_3 value: 39.795 - type: ndcg_at_5 value: 42.272 - type: precision_at_1 value: 33.004 - type: precision_at_10 value: 9.012 - type: precision_at_100 value: 1.7770000000000001 - type: precision_at_1000 value: 0.26 - type: precision_at_3 value: 19.038 - type: precision_at_5 value: 13.675999999999998 - type: recall_at_1 value: 27.139999999999997 - type: recall_at_10 value: 60.961 - type: recall_at_100 value: 84.451 - type: recall_at_1000 value: 98.113 - type: recall_at_3 value: 43.001 - type: recall_at_5 value: 49.896 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: mteb/climate-fever config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 22.076999999999998 - type: map_at_10 value: 35.44 - type: map_at_100 value: 37.651 - type: map_at_1000 value: 37.824999999999996 - type: map_at_3 value: 30.764999999999997 - type: map_at_5 value: 33.26 - type: mrr_at_1 value: 50.163000000000004 - type: mrr_at_10 value: 61.207 - type: mrr_at_100 value: 61.675000000000004 - type: mrr_at_1000 value: 61.692 - type: mrr_at_3 value: 58.60999999999999 - type: mrr_at_5 value: 60.307 - type: ndcg_at_1 value: 50.163000000000004 - type: ndcg_at_10 value: 45.882 - type: ndcg_at_100 value: 53.239999999999995 - type: ndcg_at_1000 value: 55.852000000000004 - type: ndcg_at_3 value: 40.514 - type: ndcg_at_5 value: 42.038 - type: precision_at_1 value: 50.163000000000004 - type: precision_at_10 value: 13.466000000000001 - type: precision_at_100 value: 2.164 - type: precision_at_1000 value: 0.266 - type: precision_at_3 value: 29.707 - type: precision_at_5 value: 21.694 - type: recall_at_1 value: 22.076999999999998 - type: recall_at_10 value: 50.193 - type: recall_at_100 value: 74.993 - type: recall_at_1000 value: 89.131 - type: recall_at_3 value: 35.472 - type: recall_at_5 value: 41.814 - task: type: Retrieval dataset: name: MTEB DBPedia type: mteb/dbpedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 9.953 - type: map_at_10 value: 24.515 - type: map_at_100 value: 36.173 - type: map_at_1000 value: 38.351 - type: map_at_3 value: 16.592000000000002 - type: map_at_5 value: 20.036 - type: mrr_at_1 value: 74.25 - type: mrr_at_10 value: 81.813 - type: mrr_at_100 value: 82.006 - type: mrr_at_1000 value: 82.011 - type: mrr_at_3 value: 80.875 - type: mrr_at_5 value: 81.362 - type: ndcg_at_1 value: 62.5 - type: ndcg_at_10 value: 52.42 - type: ndcg_at_100 value: 56.808 - type: ndcg_at_1000 value: 63.532999999999994 - type: ndcg_at_3 value: 56.654 - type: ndcg_at_5 value: 54.18300000000001 - type: precision_at_1 value: 74.25 - type: precision_at_10 value: 42.699999999999996 - type: precision_at_100 value: 13.675 - type: precision_at_1000 value: 2.664 - type: precision_at_3 value: 60.5 - type: precision_at_5 value: 52.800000000000004 - type: recall_at_1 value: 9.953 - type: recall_at_10 value: 30.253999999999998 - type: recall_at_100 value: 62.516000000000005 - type: recall_at_1000 value: 84.163 - type: recall_at_3 value: 18.13 - type: recall_at_5 value: 22.771 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 79.455 - type: f1 value: 74.16798697647569 - task: type: Retrieval dataset: name: MTEB FEVER type: mteb/fever config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 87.531 - type: map_at_10 value: 93.16799999999999 - type: map_at_100 value: 93.341 - type: map_at_1000 value: 93.349 - type: map_at_3 value: 92.444 - type: map_at_5 value: 92.865 - type: mrr_at_1 value: 94.014 - type: mrr_at_10 value: 96.761 - type: mrr_at_100 value: 96.762 - type: mrr_at_1000 value: 96.762 - type: mrr_at_3 value: 96.672 - type: mrr_at_5 value: 96.736 - type: ndcg_at_1 value: 94.014 - type: ndcg_at_10 value: 95.112 - type: ndcg_at_100 value: 95.578 - type: ndcg_at_1000 value: 95.68900000000001 - type: ndcg_at_3 value: 94.392 - type: ndcg_at_5 value: 94.72500000000001 - type: precision_at_1 value: 94.014 - type: precision_at_10 value: 11.065 - type: precision_at_100 value: 1.157 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 35.259 - type: precision_at_5 value: 21.599 - type: recall_at_1 value: 87.531 - type: recall_at_10 value: 97.356 - type: recall_at_100 value: 98.965 - type: recall_at_1000 value: 99.607 - type: recall_at_3 value: 95.312 - type: recall_at_5 value: 96.295 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: mteb/fiqa config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 32.055 - type: map_at_10 value: 53.114 - type: map_at_100 value: 55.235 - type: map_at_1000 value: 55.345 - type: map_at_3 value: 45.854 - type: map_at_5 value: 50.025 - type: mrr_at_1 value: 60.34 - type: mrr_at_10 value: 68.804 - type: mrr_at_100 value: 69.309 - type: mrr_at_1000 value: 69.32199999999999 - type: mrr_at_3 value: 66.40899999999999 - type: mrr_at_5 value: 67.976 - type: ndcg_at_1 value: 60.34 - type: ndcg_at_10 value: 62.031000000000006 - type: ndcg_at_100 value: 68.00500000000001 - type: ndcg_at_1000 value: 69.286 - type: ndcg_at_3 value: 56.355999999999995 - type: ndcg_at_5 value: 58.687 - type: precision_at_1 value: 60.34 - type: precision_at_10 value: 17.176 - type: precision_at_100 value: 2.36 - type: precision_at_1000 value: 0.259 - type: precision_at_3 value: 37.14 - type: precision_at_5 value: 27.809 - type: recall_at_1 value: 32.055 - type: recall_at_10 value: 70.91 - type: recall_at_100 value: 91.83 - type: recall_at_1000 value: 98.871 - type: recall_at_3 value: 51.202999999999996 - type: recall_at_5 value: 60.563 - task: type: Retrieval dataset: name: MTEB HotpotQA type: mteb/hotpotqa config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 43.68 - type: map_at_10 value: 64.389 - type: map_at_100 value: 65.24 - type: map_at_1000 value: 65.303 - type: map_at_3 value: 61.309000000000005 - type: map_at_5 value: 63.275999999999996 - type: mrr_at_1 value: 87.36 - type: mrr_at_10 value: 91.12 - type: mrr_at_100 value: 91.227 - type: mrr_at_1000 value: 91.229 - type: mrr_at_3 value: 90.57600000000001 - type: mrr_at_5 value: 90.912 - type: ndcg_at_1 value: 87.36 - type: ndcg_at_10 value: 73.076 - type: ndcg_at_100 value: 75.895 - type: ndcg_at_1000 value: 77.049 - type: ndcg_at_3 value: 68.929 - type: ndcg_at_5 value: 71.28 - type: precision_at_1 value: 87.36 - type: precision_at_10 value: 14.741000000000001 - type: precision_at_100 value: 1.694 - type: precision_at_1000 value: 0.185 - type: precision_at_3 value: 43.043 - type: precision_at_5 value: 27.681 - type: recall_at_1 value: 43.68 - type: recall_at_10 value: 73.707 - type: recall_at_100 value: 84.7 - type: recall_at_1000 value: 92.309 - type: recall_at_3 value: 64.564 - type: recall_at_5 value: 69.203 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 96.75399999999999 - type: ap value: 95.29389839242187 - type: f1 value: 96.75348377433475 - task: type: Retrieval dataset: name: MTEB MSMARCO type: mteb/msmarco config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 25.176 - type: map_at_10 value: 38.598 - type: map_at_100 value: 39.707 - type: map_at_1000 value: 39.744 - type: map_at_3 value: 34.566 - type: map_at_5 value: 36.863 - type: mrr_at_1 value: 25.874000000000002 - type: mrr_at_10 value: 39.214 - type: mrr_at_100 value: 40.251 - type: mrr_at_1000 value: 40.281 - type: mrr_at_3 value: 35.291 - type: mrr_at_5 value: 37.545 - type: ndcg_at_1 value: 25.874000000000002 - type: ndcg_at_10 value: 45.98 - type: ndcg_at_100 value: 51.197 - type: ndcg_at_1000 value: 52.073 - type: ndcg_at_3 value: 37.785999999999994 - type: ndcg_at_5 value: 41.870000000000005 - type: precision_at_1 value: 25.874000000000002 - type: precision_at_10 value: 7.181 - type: precision_at_100 value: 0.979 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 16.051000000000002 - type: precision_at_5 value: 11.713 - type: recall_at_1 value: 25.176 - type: recall_at_10 value: 68.67699999999999 - type: recall_at_100 value: 92.55 - type: recall_at_1000 value: 99.164 - type: recall_at_3 value: 46.372 - type: recall_at_5 value: 56.16 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 99.03784769721841 - type: f1 value: 98.97791641821495 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 91.88326493388054 - type: f1 value: 73.74809928034335 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 85.41358439811701 - type: f1 value: 83.503679460639 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 89.77135171486215 - type: f1 value: 88.89843747468366 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 46.22695362087359 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 44.132372165849425 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 33.35680810650402 - type: mrr value: 34.72625715637218 - task: type: Retrieval dataset: name: MTEB NFCorpus type: mteb/nfcorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 7.165000000000001 - type: map_at_10 value: 15.424 - type: map_at_100 value: 20.28 - type: map_at_1000 value: 22.065 - type: map_at_3 value: 11.236 - type: map_at_5 value: 13.025999999999998 - type: mrr_at_1 value: 51.702999999999996 - type: mrr_at_10 value: 59.965 - type: mrr_at_100 value: 60.667 - type: mrr_at_1000 value: 60.702999999999996 - type: mrr_at_3 value: 58.772000000000006 - type: mrr_at_5 value: 59.267 - type: ndcg_at_1 value: 49.536 - type: ndcg_at_10 value: 40.6 - type: ndcg_at_100 value: 37.848 - type: ndcg_at_1000 value: 46.657 - type: ndcg_at_3 value: 46.117999999999995 - type: ndcg_at_5 value: 43.619 - type: precision_at_1 value: 51.393 - type: precision_at_10 value: 30.31 - type: precision_at_100 value: 9.972 - type: precision_at_1000 value: 2.329 - type: precision_at_3 value: 43.137 - type: precision_at_5 value: 37.585 - type: recall_at_1 value: 7.165000000000001 - type: recall_at_10 value: 19.689999999999998 - type: recall_at_100 value: 39.237 - type: recall_at_1000 value: 71.417 - type: recall_at_3 value: 12.247 - type: recall_at_5 value: 14.902999999999999 - task: type: Retrieval dataset: name: MTEB NQ type: mteb/nq config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 42.653999999999996 - type: map_at_10 value: 59.611999999999995 - type: map_at_100 value: 60.32300000000001 - type: map_at_1000 value: 60.336 - type: map_at_3 value: 55.584999999999994 - type: map_at_5 value: 58.19 - type: mrr_at_1 value: 47.683 - type: mrr_at_10 value: 62.06700000000001 - type: mrr_at_100 value: 62.537 - type: mrr_at_1000 value: 62.544999999999995 - type: mrr_at_3 value: 59.178 - type: mrr_at_5 value: 61.034 - type: ndcg_at_1 value: 47.654 - type: ndcg_at_10 value: 67.001 - type: ndcg_at_100 value: 69.73899999999999 - type: ndcg_at_1000 value: 69.986 - type: ndcg_at_3 value: 59.95700000000001 - type: ndcg_at_5 value: 64.025 - type: precision_at_1 value: 47.654 - type: precision_at_10 value: 10.367999999999999 - type: precision_at_100 value: 1.192 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 26.651000000000003 - type: precision_at_5 value: 18.459 - type: recall_at_1 value: 42.653999999999996 - type: recall_at_10 value: 86.619 - type: recall_at_100 value: 98.04899999999999 - type: recall_at_1000 value: 99.812 - type: recall_at_3 value: 68.987 - type: recall_at_5 value: 78.158 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: mteb/quora config: default split: test revision: None metrics: - type: map_at_1 value: 72.538 - type: map_at_10 value: 86.702 - type: map_at_100 value: 87.31 - type: map_at_1000 value: 87.323 - type: map_at_3 value: 83.87 - type: map_at_5 value: 85.682 - type: mrr_at_1 value: 83.31 - type: mrr_at_10 value: 89.225 - type: mrr_at_100 value: 89.30399999999999 - type: mrr_at_1000 value: 89.30399999999999 - type: mrr_at_3 value: 88.44300000000001 - type: mrr_at_5 value: 89.005 - type: ndcg_at_1 value: 83.32000000000001 - type: ndcg_at_10 value: 90.095 - type: ndcg_at_100 value: 91.12 - type: ndcg_at_1000 value: 91.179 - type: ndcg_at_3 value: 87.606 - type: ndcg_at_5 value: 89.031 - type: precision_at_1 value: 83.32000000000001 - type: precision_at_10 value: 13.641 - type: precision_at_100 value: 1.541 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 38.377 - type: precision_at_5 value: 25.162000000000003 - type: recall_at_1 value: 72.538 - type: recall_at_10 value: 96.47200000000001 - type: recall_at_100 value: 99.785 - type: recall_at_1000 value: 99.99900000000001 - type: recall_at_3 value: 89.278 - type: recall_at_5 value: 93.367 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 73.55219145406065 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 74.13437105242755 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: mteb/scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 6.873 - type: map_at_10 value: 17.944 - type: map_at_100 value: 21.171 - type: map_at_1000 value: 21.528 - type: map_at_3 value: 12.415 - type: map_at_5 value: 15.187999999999999 - type: mrr_at_1 value: 33.800000000000004 - type: mrr_at_10 value: 46.455 - type: mrr_at_100 value: 47.378 - type: mrr_at_1000 value: 47.394999999999996 - type: mrr_at_3 value: 42.367 - 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type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 81.667 - type: recall_at_5 value: 87.394 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.71980198019801 - type: cos_sim_ap value: 92.81616007802704 - type: cos_sim_f1 value: 85.17548454688318 - type: cos_sim_precision value: 89.43894389438944 - type: cos_sim_recall value: 81.3 - type: dot_accuracy value: 99.71980198019801 - type: dot_ap value: 92.81398760591358 - type: dot_f1 value: 85.17548454688318 - type: dot_precision value: 89.43894389438944 - type: dot_recall value: 81.3 - type: euclidean_accuracy value: 99.71980198019801 - type: euclidean_ap value: 92.81560637245072 - type: euclidean_f1 value: 85.17548454688318 - type: euclidean_precision value: 89.43894389438944 - type: euclidean_recall value: 81.3 - type: manhattan_accuracy value: 99.73069306930694 - 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type: ndcg_at_10 value: 58.876 - type: ndcg_at_100 value: 62.714000000000006 - type: ndcg_at_1000 value: 63.721000000000004 - type: ndcg_at_3 value: 52.444 - type: ndcg_at_5 value: 55.692 - type: precision_at_1 value: 44.085 - type: precision_at_10 value: 9.21 - type: precision_at_100 value: 1.164 - type: precision_at_1000 value: 0.128 - type: precision_at_3 value: 23.043 - type: precision_at_5 value: 15.898000000000001 - type: recall_at_1 value: 38.912 - type: recall_at_10 value: 75.577 - type: recall_at_100 value: 92.038 - type: recall_at_1000 value: 99.325 - type: recall_at_3 value: 58.592 - type: recall_at_5 value: 66.235 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 55.532000000000004 - type: f1 value: 52.5783943471605 - task: type: Retrieval dataset: name: MTEB BSARDRetrieval type: maastrichtlawtech/bsard config: default split: test revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59 metrics: - type: map_at_1 value: 8.108 - type: map_at_10 value: 14.710999999999999 - type: map_at_100 value: 15.891 - type: map_at_1000 value: 15.983 - type: map_at_3 value: 12.237 - type: map_at_5 value: 13.679 - type: mrr_at_1 value: 8.108 - type: mrr_at_10 value: 14.710999999999999 - type: mrr_at_100 value: 15.891 - type: mrr_at_1000 value: 15.983 - type: mrr_at_3 value: 12.237 - type: mrr_at_5 value: 13.679 - type: ndcg_at_1 value: 8.108 - type: ndcg_at_10 value: 18.796 - type: ndcg_at_100 value: 25.098 - type: ndcg_at_1000 value: 27.951999999999998 - type: ndcg_at_3 value: 13.712 - type: ndcg_at_5 value: 16.309 - type: precision_at_1 value: 8.108 - type: precision_at_10 value: 3.198 - type: precision_at_100 value: 0.626 - type: precision_at_1000 value: 0.086 - type: precision_at_3 value: 6.006 - type: precision_at_5 value: 4.865 - type: recall_at_1 value: 8.108 - type: recall_at_10 value: 31.982 - type: recall_at_100 value: 62.613 - type: recall_at_1000 value: 86.036 - type: recall_at_3 value: 18.018 - type: recall_at_5 value: 24.324 - task: type: Clustering dataset: name: MTEB HALClusteringS2S type: lyon-nlp/clustering-hal-s2s config: default split: test revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915 metrics: - type: v_measure value: 30.833269778867116 - task: type: Clustering dataset: name: MTEB MLSUMClusteringP2P type: mlsum config: default split: test revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 metrics: - type: v_measure value: 50.0281928004713 - type: v_measure value: 43.699961510636534 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (fr) type: mteb/mtop_domain config: fr split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 96.68963357344191 - type: f1 value: 96.45175170820961 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (fr) type: mteb/mtop_intent config: fr split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 87.46946445349202 - type: f1 value: 65.79860440988624 - task: type: Classification dataset: name: MTEB MasakhaNEWSClassification (fra) type: masakhane/masakhanews config: fra split: test revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 metrics: - type: accuracy value: 82.60663507109005 - type: f1 value: 77.20462646604777 - task: type: Clustering dataset: name: MTEB MasakhaNEWSClusteringP2P (fra) type: masakhane/masakhanews config: fra split: test revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 metrics: - type: v_measure value: 60.19311264967803 - type: v_measure value: 63.6235764409785 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (fr) type: mteb/amazon_massive_intent config: fr split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 81.65097511768661 - type: f1 value: 78.77796091490924 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (fr) type: mteb/amazon_massive_scenario config: fr split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 86.64425016812373 - type: f1 value: 85.4912728670017 - task: type: Retrieval dataset: name: MTEB MintakaRetrieval (fr) type: jinaai/mintakaqa config: fr split: test revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e metrics: - type: map_at_1 value: 35.913000000000004 - type: map_at_10 value: 48.147 - type: map_at_100 value: 48.91 - type: map_at_1000 value: 48.949 - type: map_at_3 value: 45.269999999999996 - type: map_at_5 value: 47.115 - type: mrr_at_1 value: 35.913000000000004 - type: mrr_at_10 value: 48.147 - type: mrr_at_100 value: 48.91 - type: mrr_at_1000 value: 48.949 - type: mrr_at_3 value: 45.269999999999996 - type: mrr_at_5 value: 47.115 - type: ndcg_at_1 value: 35.913000000000004 - type: ndcg_at_10 value: 54.03 - type: ndcg_at_100 value: 57.839 - type: ndcg_at_1000 value: 58.925000000000004 - type: ndcg_at_3 value: 48.217999999999996 - type: ndcg_at_5 value: 51.56699999999999 - type: precision_at_1 value: 35.913000000000004 - type: precision_at_10 value: 7.244000000000001 - type: precision_at_100 value: 0.9039999999999999 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 18.905 - type: precision_at_5 value: 12.981000000000002 - type: recall_at_1 value: 35.913000000000004 - type: recall_at_10 value: 72.441 - type: recall_at_100 value: 90.41799999999999 - type: recall_at_1000 value: 99.099 - type: recall_at_3 value: 56.716 - type: recall_at_5 value: 64.90599999999999 - task: type: PairClassification dataset: name: MTEB OpusparcusPC (fr) type: GEM/opusparcus config: fr split: test revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a metrics: - type: cos_sim_accuracy value: 99.90069513406156 - type: cos_sim_ap value: 100.0 - type: cos_sim_f1 value: 99.95032290114257 - type: cos_sim_precision value: 100.0 - type: cos_sim_recall value: 99.90069513406156 - type: dot_accuracy value: 99.90069513406156 - type: dot_ap value: 100.0 - type: dot_f1 value: 99.95032290114257 - type: dot_precision value: 100.0 - type: dot_recall value: 99.90069513406156 - type: euclidean_accuracy value: 99.90069513406156 - type: euclidean_ap value: 100.0 - type: euclidean_f1 value: 99.95032290114257 - type: euclidean_precision value: 100.0 - type: euclidean_recall value: 99.90069513406156 - type: manhattan_accuracy value: 99.90069513406156 - type: manhattan_ap value: 100.0 - type: manhattan_f1 value: 99.95032290114257 - type: manhattan_precision value: 100.0 - type: manhattan_recall value: 99.90069513406156 - type: max_accuracy value: 99.90069513406156 - type: max_ap value: 100.0 - type: max_f1 value: 99.95032290114257 - task: type: PairClassification dataset: name: MTEB PawsX (fr) type: paws-x config: fr split: test revision: 8a04d940a42cd40658986fdd8e3da561533a3646 metrics: - type: cos_sim_accuracy value: 75.25 - type: cos_sim_ap value: 80.86376001270014 - type: cos_sim_f1 value: 73.65945437441204 - type: cos_sim_precision value: 64.02289452166802 - type: cos_sim_recall value: 86.71096345514951 - type: dot_accuracy value: 75.25 - type: dot_ap value: 80.93686107633002 - type: dot_f1 value: 73.65945437441204 - type: dot_precision value: 64.02289452166802 - type: dot_recall value: 86.71096345514951 - type: euclidean_accuracy value: 75.25 - type: euclidean_ap value: 80.86379136218862 - type: euclidean_f1 value: 73.65945437441204 - type: euclidean_precision value: 64.02289452166802 - type: euclidean_recall value: 86.71096345514951 - type: manhattan_accuracy value: 75.3 - type: manhattan_ap value: 80.87826606097734 - type: manhattan_f1 value: 73.68421052631581 - type: manhattan_precision value: 64.0 - type: manhattan_recall value: 86.82170542635659 - type: max_accuracy value: 75.3 - type: max_ap value: 80.93686107633002 - type: max_f1 value: 73.68421052631581 - task: type: STS dataset: name: MTEB SICKFr type: Lajavaness/SICK-fr config: default split: test revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a metrics: - type: cos_sim_pearson value: 81.42349425981143 - type: cos_sim_spearman value: 78.90454327031226 - type: euclidean_pearson value: 78.39086497435166 - type: euclidean_spearman value: 78.9046133980509 - type: manhattan_pearson value: 78.63743094286502 - type: manhattan_spearman value: 79.12136348449269 - task: type: STS dataset: name: MTEB STS22 (fr) type: mteb/sts22-crosslingual-sts config: fr split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 81.452697919749 - type: cos_sim_spearman value: 82.58116836039301 - type: euclidean_pearson value: 81.04038478932786 - type: euclidean_spearman value: 82.58116836039301 - type: manhattan_pearson value: 81.37075396187771 - type: manhattan_spearman value: 82.73678231355368 - task: type: STS dataset: name: MTEB STSBenchmarkMultilingualSTS (fr) type: stsb_multi_mt config: fr split: test revision: 93d57ef91790589e3ce9c365164337a8a78b7632 metrics: - type: cos_sim_pearson value: 85.7419764013806 - type: cos_sim_spearman value: 85.46085808849622 - type: euclidean_pearson value: 83.70449639870063 - type: euclidean_spearman value: 85.46159013076233 - type: manhattan_pearson value: 83.95259510313929 - type: manhattan_spearman value: 85.8029724659458 - task: type: Summarization dataset: name: MTEB SummEvalFr type: lyon-nlp/summarization-summeval-fr-p2p config: default split: test revision: b385812de6a9577b6f4d0f88c6a6e35395a94054 metrics: - type: cos_sim_pearson value: 32.61063271753325 - type: cos_sim_spearman value: 31.454589417353603 - type: dot_pearson value: 32.6106288643431 - type: dot_spearman value: 31.454589417353603 - task: type: Reranking dataset: name: MTEB SyntecReranking type: lyon-nlp/mteb-fr-reranking-syntec-s2p config: default split: test revision: b205c5084a0934ce8af14338bf03feb19499c84d metrics: - type: map value: 84.31666666666666 - type: mrr value: 84.31666666666666 - task: type: Retrieval dataset: name: MTEB SyntecRetrieval type: lyon-nlp/mteb-fr-retrieval-syntec-s2p config: default split: test revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff metrics: - type: map_at_1 value: 63.0 - type: map_at_10 value: 73.471 - type: map_at_100 value: 73.87 - type: map_at_1000 value: 73.87 - type: map_at_3 value: 70.5 - type: map_at_5 value: 73.05 - type: mrr_at_1 value: 63.0 - type: mrr_at_10 value: 73.471 - type: mrr_at_100 value: 73.87 - type: mrr_at_1000 value: 73.87 - type: mrr_at_3 value: 70.5 - type: mrr_at_5 value: 73.05 - type: ndcg_at_1 value: 63.0 - type: ndcg_at_10 value: 78.255 - type: ndcg_at_100 value: 79.88 - type: ndcg_at_1000 value: 79.88 - type: ndcg_at_3 value: 72.702 - type: ndcg_at_5 value: 77.264 - type: precision_at_1 value: 63.0 - type: precision_at_10 value: 9.3 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 26.333000000000002 - type: precision_at_5 value: 18.0 - type: recall_at_1 value: 63.0 - type: recall_at_10 value: 93.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 79.0 - type: recall_at_5 value: 90.0 - task: type: Retrieval dataset: name: MTEB XPQARetrieval (fr) type: jinaai/xpqa config: fr split: test revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f metrics: - type: map_at_1 value: 40.338 - type: map_at_10 value: 61.927 - type: map_at_100 value: 63.361999999999995 - type: map_at_1000 value: 63.405 - type: map_at_3 value: 55.479 - type: map_at_5 value: 59.732 - type: mrr_at_1 value: 63.551 - type: mrr_at_10 value: 71.006 - type: mrr_at_100 value: 71.501 - type: mrr_at_1000 value: 71.509 - type: mrr_at_3 value: 69.07 - type: mrr_at_5 value: 70.165 - type: ndcg_at_1 value: 63.551 - type: ndcg_at_10 value: 68.297 - type: ndcg_at_100 value: 73.13199999999999 - type: ndcg_at_1000 value: 73.751 - type: ndcg_at_3 value: 62.999 - type: ndcg_at_5 value: 64.89 - type: precision_at_1 value: 63.551 - type: precision_at_10 value: 15.661 - type: precision_at_100 value: 1.9789999999999999 - type: precision_at_1000 value: 0.207 - type: precision_at_3 value: 38.273 - type: precision_at_5 value: 27.61 - type: recall_at_1 value: 40.338 - type: recall_at_10 value: 77.267 - type: recall_at_100 value: 95.892 - type: recall_at_1000 value: 99.75500000000001 - type: recall_at_3 value: 60.36 - type: recall_at_5 value: 68.825 - task: type: Clustering dataset: name: MTEB 8TagsClustering type: PL-MTEB/8tags-clustering config: default split: test revision: None metrics: - type: v_measure value: 51.36126303874126 - task: type: Classification dataset: name: MTEB AllegroReviews type: PL-MTEB/allegro-reviews config: default split: test revision: None metrics: - type: accuracy value: 67.13717693836979 - type: f1 value: 57.27609848003782 - task: type: Retrieval dataset: name: MTEB ArguAna-PL type: clarin-knext/arguana-pl config: default split: test revision: 63fc86750af76253e8c760fc9e534bbf24d260a2 metrics: - type: map_at_1 value: 35.276999999999994 - type: map_at_10 value: 51.086 - type: map_at_100 value: 51.788000000000004 - type: map_at_1000 value: 51.791 - type: map_at_3 value: 46.147 - type: map_at_5 value: 49.078 - type: mrr_at_1 value: 35.917 - type: mrr_at_10 value: 51.315999999999995 - type: mrr_at_100 value: 52.018 - type: mrr_at_1000 value: 52.022 - type: mrr_at_3 value: 46.349000000000004 - type: mrr_at_5 value: 49.297000000000004 - type: ndcg_at_1 value: 35.276999999999994 - type: ndcg_at_10 value: 59.870999999999995 - type: ndcg_at_100 value: 62.590999999999994 - type: ndcg_at_1000 value: 62.661 - type: ndcg_at_3 value: 49.745 - type: ndcg_at_5 value: 55.067 - type: precision_at_1 value: 35.276999999999994 - type: precision_at_10 value: 8.791 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.057 - type: precision_at_5 value: 14.637 - type: recall_at_1 value: 35.276999999999994 - type: recall_at_10 value: 87.909 - type: recall_at_100 value: 99.14699999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 60.171 - type: recall_at_5 value: 73.18599999999999 - task: type: Classification dataset: name: MTEB CBD type: PL-MTEB/cbd config: default split: test revision: None metrics: - type: accuracy value: 78.03000000000002 - type: ap value: 29.12548553897622 - type: f1 value: 66.54857118886073 - task: type: PairClassification dataset: name: MTEB CDSC-E type: PL-MTEB/cdsce-pairclassification config: default split: test revision: None metrics: - type: cos_sim_accuracy value: 89.0 - type: cos_sim_ap value: 76.75437826834582 - type: cos_sim_f1 value: 66.4850136239782 - type: cos_sim_precision value: 68.92655367231639 - type: cos_sim_recall value: 64.21052631578948 - type: dot_accuracy value: 89.0 - type: dot_ap value: 76.75437826834582 - type: dot_f1 value: 66.4850136239782 - type: dot_precision value: 68.92655367231639 - type: dot_recall value: 64.21052631578948 - type: euclidean_accuracy value: 89.0 - type: euclidean_ap value: 76.75437826834582 - type: euclidean_f1 value: 66.4850136239782 - type: euclidean_precision value: 68.92655367231639 - type: euclidean_recall value: 64.21052631578948 - type: manhattan_accuracy value: 89.0 - type: manhattan_ap value: 76.66074220647083 - type: manhattan_f1 value: 66.47058823529412 - type: manhattan_precision value: 75.33333333333333 - type: manhattan_recall value: 59.473684210526315 - type: max_accuracy value: 89.0 - type: max_ap value: 76.75437826834582 - type: max_f1 value: 66.4850136239782 - task: type: STS dataset: name: MTEB CDSC-R type: PL-MTEB/cdscr-sts config: default split: test revision: None metrics: - type: cos_sim_pearson value: 93.12903172428328 - type: cos_sim_spearman value: 92.66381487060741 - type: euclidean_pearson value: 90.37278396708922 - type: euclidean_spearman value: 92.66381487060741 - type: manhattan_pearson value: 90.32503296540962 - type: manhattan_spearman value: 92.6902938354313 - task: type: Retrieval dataset: name: MTEB DBPedia-PL type: clarin-knext/dbpedia-pl config: default split: test revision: 76afe41d9af165cc40999fcaa92312b8b012064a metrics: - type: map_at_1 value: 8.83 - type: map_at_10 value: 18.326 - type: map_at_100 value: 26.496 - type: map_at_1000 value: 28.455000000000002 - type: map_at_3 value: 12.933 - type: map_at_5 value: 15.168000000000001 - type: mrr_at_1 value: 66.0 - type: mrr_at_10 value: 72.76700000000001 - type: mrr_at_100 value: 73.203 - type: mrr_at_1000 value: 73.219 - type: mrr_at_3 value: 71.458 - type: mrr_at_5 value: 72.246 - type: ndcg_at_1 value: 55.375 - type: ndcg_at_10 value: 41.3 - type: ndcg_at_100 value: 45.891 - type: ndcg_at_1000 value: 52.905 - type: ndcg_at_3 value: 46.472 - type: ndcg_at_5 value: 43.734 - type: precision_at_1 value: 66.0 - type: precision_at_10 value: 33.074999999999996 - type: precision_at_100 value: 11.094999999999999 - type: precision_at_1000 value: 2.374 - type: precision_at_3 value: 48.583 - type: precision_at_5 value: 42.0 - type: recall_at_1 value: 8.83 - type: recall_at_10 value: 22.587 - type: recall_at_100 value: 50.61600000000001 - type: recall_at_1000 value: 73.559 - type: recall_at_3 value: 13.688 - type: recall_at_5 value: 16.855 - task: type: Retrieval dataset: name: MTEB FiQA-PL type: clarin-knext/fiqa-pl config: default split: test revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e metrics: - type: map_at_1 value: 20.587 - type: map_at_10 value: 33.095 - type: map_at_100 value: 35.24 - type: map_at_1000 value: 35.429 - type: map_at_3 value: 28.626 - type: map_at_5 value: 31.136999999999997 - type: mrr_at_1 value: 40.586 - type: mrr_at_10 value: 49.033 - type: mrr_at_100 value: 49.952999999999996 - type: mrr_at_1000 value: 49.992 - type: mrr_at_3 value: 46.553 - type: mrr_at_5 value: 48.035 - type: ndcg_at_1 value: 40.586 - type: ndcg_at_10 value: 41.046 - type: ndcg_at_100 value: 48.586 - type: ndcg_at_1000 value: 51.634 - type: ndcg_at_3 value: 36.773 - type: ndcg_at_5 value: 38.389 - type: precision_at_1 value: 40.586 - type: precision_at_10 value: 11.466 - type: precision_at_100 value: 1.909 - type: precision_at_1000 value: 0.245 - type: precision_at_3 value: 24.434 - type: precision_at_5 value: 18.426000000000002 - type: recall_at_1 value: 20.587 - type: recall_at_10 value: 47.986000000000004 - type: recall_at_100 value: 75.761 - type: recall_at_1000 value: 94.065 - type: recall_at_3 value: 33.339 - type: recall_at_5 value: 39.765 - task: type: Retrieval dataset: name: MTEB HotpotQA-PL type: clarin-knext/hotpotqa-pl config: default split: test revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907 metrics: - type: map_at_1 value: 40.878 - type: map_at_10 value: 58.775999999999996 - type: map_at_100 value: 59.632 - type: map_at_1000 value: 59.707 - type: map_at_3 value: 56.074 - type: map_at_5 value: 57.629 - type: mrr_at_1 value: 81.756 - type: mrr_at_10 value: 86.117 - type: mrr_at_100 value: 86.299 - type: mrr_at_1000 value: 86.30600000000001 - type: mrr_at_3 value: 85.345 - type: mrr_at_5 value: 85.832 - type: ndcg_at_1 value: 81.756 - type: ndcg_at_10 value: 67.608 - type: ndcg_at_100 value: 70.575 - type: ndcg_at_1000 value: 71.99600000000001 - type: ndcg_at_3 value: 63.723 - type: ndcg_at_5 value: 65.70700000000001 - type: precision_at_1 value: 81.756 - type: precision_at_10 value: 13.619 - type: precision_at_100 value: 1.5939999999999999 - type: precision_at_1000 value: 0.178 - type: precision_at_3 value: 39.604 - type: precision_at_5 value: 25.332 - type: recall_at_1 value: 40.878 - type: recall_at_10 value: 68.096 - type: recall_at_100 value: 79.696 - type: recall_at_1000 value: 89.082 - type: recall_at_3 value: 59.406000000000006 - type: recall_at_5 value: 63.329 - task: type: Retrieval dataset: name: MTEB MSMARCO-PL type: clarin-knext/msmarco-pl config: default split: test revision: 8634c07806d5cce3a6138e260e59b81760a0a640 metrics: - type: map_at_1 value: 2.1839999999999997 - type: map_at_10 value: 11.346 - type: map_at_100 value: 30.325000000000003 - type: map_at_1000 value: 37.806 - type: map_at_3 value: 4.842 - type: map_at_5 value: 6.891 - type: mrr_at_1 value: 86.047 - type: mrr_at_10 value: 89.14699999999999 - type: mrr_at_100 value: 89.46600000000001 - type: mrr_at_1000 value: 89.46600000000001 - type: mrr_at_3 value: 89.14699999999999 - type: mrr_at_5 value: 89.14699999999999 - type: ndcg_at_1 value: 67.829 - type: ndcg_at_10 value: 62.222 - type: ndcg_at_100 value: 55.337 - type: ndcg_at_1000 value: 64.076 - type: ndcg_at_3 value: 68.12700000000001 - type: ndcg_at_5 value: 64.987 - type: precision_at_1 value: 86.047 - type: precision_at_10 value: 69.535 - type: precision_at_100 value: 32.93 - type: precision_at_1000 value: 6.6049999999999995 - type: precision_at_3 value: 79.845 - type: precision_at_5 value: 75.349 - type: recall_at_1 value: 2.1839999999999997 - type: recall_at_10 value: 12.866 - type: recall_at_100 value: 43.505 - type: recall_at_1000 value: 72.366 - type: recall_at_3 value: 4.947 - type: recall_at_5 value: 7.192 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (pl) type: mteb/amazon_massive_intent config: pl split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 80.75319435104238 - type: f1 value: 77.58961444860606 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (pl) type: mteb/amazon_massive_scenario config: pl split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 85.54472091459313 - type: f1 value: 84.29498563572106 - task: type: Retrieval dataset: name: MTEB NFCorpus-PL type: clarin-knext/nfcorpus-pl config: default split: test revision: 9a6f9567fda928260afed2de480d79c98bf0bec0 metrics: - type: map_at_1 value: 4.367 - type: map_at_10 value: 10.38 - type: map_at_100 value: 13.516 - type: map_at_1000 value: 14.982000000000001 - type: map_at_3 value: 7.367 - type: map_at_5 value: 8.59 - type: mrr_at_1 value: 41.486000000000004 - type: mrr_at_10 value: 48.886 - type: mrr_at_100 value: 49.657000000000004 - type: mrr_at_1000 value: 49.713 - type: mrr_at_3 value: 46.904 - type: mrr_at_5 value: 48.065000000000005 - type: ndcg_at_1 value: 40.402 - type: ndcg_at_10 value: 30.885 - type: ndcg_at_100 value: 28.393 - type: ndcg_at_1000 value: 37.428 - type: ndcg_at_3 value: 35.394999999999996 - type: ndcg_at_5 value: 33.391999999999996 - type: precision_at_1 value: 41.486000000000004 - type: precision_at_10 value: 23.437 - type: precision_at_100 value: 7.638 - type: precision_at_1000 value: 2.0389999999999997 - type: precision_at_3 value: 32.817 - type: precision_at_5 value: 28.915999999999997 - type: recall_at_1 value: 4.367 - type: recall_at_10 value: 14.655000000000001 - type: recall_at_100 value: 29.665999999999997 - type: recall_at_1000 value: 62.073 - type: recall_at_3 value: 8.51 - type: recall_at_5 value: 10.689 - task: type: Retrieval dataset: name: MTEB NQ-PL type: clarin-knext/nq-pl config: default split: test revision: f171245712cf85dd4700b06bef18001578d0ca8d metrics: - type: map_at_1 value: 28.616000000000003 - type: map_at_10 value: 41.626000000000005 - type: map_at_100 value: 42.689 - type: map_at_1000 value: 42.733 - type: map_at_3 value: 37.729 - type: map_at_5 value: 39.879999999999995 - type: mrr_at_1 value: 32.068000000000005 - type: mrr_at_10 value: 44.029 - type: mrr_at_100 value: 44.87 - type: mrr_at_1000 value: 44.901 - type: mrr_at_3 value: 40.687 - type: mrr_at_5 value: 42.625 - type: ndcg_at_1 value: 32.068000000000005 - type: ndcg_at_10 value: 48.449999999999996 - type: ndcg_at_100 value: 53.13 - type: ndcg_at_1000 value: 54.186 - type: ndcg_at_3 value: 40.983999999999995 - type: ndcg_at_5 value: 44.628 - type: precision_at_1 value: 32.068000000000005 - type: precision_at_10 value: 7.9750000000000005 - type: precision_at_100 value: 1.061 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 18.404999999999998 - type: precision_at_5 value: 13.111 - type: recall_at_1 value: 28.616000000000003 - type: recall_at_10 value: 66.956 - type: recall_at_100 value: 87.657 - type: recall_at_1000 value: 95.548 - type: recall_at_3 value: 47.453 - type: recall_at_5 value: 55.87800000000001 - task: type: Classification dataset: name: MTEB PAC type: laugustyniak/abusive-clauses-pl config: default split: test revision: None metrics: - type: accuracy value: 69.04141326382856 - type: ap value: 77.47589122111044 - type: f1 value: 66.6332277374775 - task: type: PairClassification dataset: name: MTEB PPC type: PL-MTEB/ppc-pairclassification config: default split: test revision: None metrics: - type: cos_sim_accuracy value: 86.4 - type: cos_sim_ap value: 94.1044939667201 - type: cos_sim_f1 value: 88.78048780487805 - type: cos_sim_precision value: 87.22044728434504 - type: cos_sim_recall value: 90.39735099337747 - type: dot_accuracy value: 86.4 - type: dot_ap value: 94.1044939667201 - type: dot_f1 value: 88.78048780487805 - type: dot_precision value: 87.22044728434504 - type: dot_recall value: 90.39735099337747 - type: euclidean_accuracy value: 86.4 - type: euclidean_ap value: 94.1044939667201 - type: euclidean_f1 value: 88.78048780487805 - type: euclidean_precision value: 87.22044728434504 - type: euclidean_recall value: 90.39735099337747 - type: manhattan_accuracy value: 86.4 - type: manhattan_ap value: 94.11438365697387 - type: manhattan_f1 value: 88.77968877968877 - type: manhattan_precision value: 87.84440842787681 - type: manhattan_recall value: 89.73509933774835 - type: max_accuracy value: 86.4 - type: max_ap value: 94.11438365697387 - type: max_f1 value: 88.78048780487805 - task: type: PairClassification dataset: name: MTEB PSC type: PL-MTEB/psc-pairclassification config: default split: test revision: None metrics: - type: cos_sim_accuracy value: 97.86641929499072 - type: cos_sim_ap value: 99.36904211868182 - type: cos_sim_f1 value: 96.56203288490283 - type: cos_sim_precision value: 94.72140762463343 - type: cos_sim_recall value: 98.47560975609755 - type: dot_accuracy value: 97.86641929499072 - type: dot_ap value: 99.36904211868183 - type: dot_f1 value: 96.56203288490283 - type: dot_precision value: 94.72140762463343 - type: dot_recall value: 98.47560975609755 - type: euclidean_accuracy value: 97.86641929499072 - type: euclidean_ap value: 99.36904211868183 - type: euclidean_f1 value: 96.56203288490283 - type: euclidean_precision value: 94.72140762463343 - type: euclidean_recall value: 98.47560975609755 - type: manhattan_accuracy value: 98.14471243042672 - type: manhattan_ap value: 99.43359540492416 - type: manhattan_f1 value: 96.98795180722892 - type: manhattan_precision value: 95.83333333333334 - type: manhattan_recall value: 98.17073170731707 - type: max_accuracy value: 98.14471243042672 - type: max_ap value: 99.43359540492416 - type: max_f1 value: 96.98795180722892 - task: type: Classification dataset: name: MTEB PolEmo2.0-IN type: PL-MTEB/polemo2_in config: default split: test revision: None metrics: - type: accuracy value: 89.39058171745152 - type: f1 value: 86.8552093529568 - task: type: Classification dataset: name: MTEB PolEmo2.0-OUT type: PL-MTEB/polemo2_out config: default split: test revision: None metrics: - type: accuracy value: 74.97975708502024 - type: f1 value: 58.73081628832407 - task: type: Retrieval dataset: name: MTEB Quora-PL type: clarin-knext/quora-pl config: default split: test revision: 0be27e93455051e531182b85e85e425aba12e9d4 metrics: - type: map_at_1 value: 64.917 - type: map_at_10 value: 78.74600000000001 - type: map_at_100 value: 79.501 - type: map_at_1000 value: 79.524 - type: map_at_3 value: 75.549 - type: map_at_5 value: 77.495 - type: mrr_at_1 value: 74.9 - type: mrr_at_10 value: 82.112 - type: mrr_at_100 value: 82.314 - type: mrr_at_1000 value: 82.317 - type: mrr_at_3 value: 80.745 - type: mrr_at_5 value: 81.607 - type: ndcg_at_1 value: 74.83999999999999 - type: ndcg_at_10 value: 83.214 - type: ndcg_at_100 value: 84.997 - type: ndcg_at_1000 value: 85.207 - type: ndcg_at_3 value: 79.547 - type: ndcg_at_5 value: 81.46600000000001 - type: precision_at_1 value: 74.83999999999999 - type: precision_at_10 value: 12.822 - type: precision_at_100 value: 1.506 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 34.903 - type: precision_at_5 value: 23.16 - type: recall_at_1 value: 64.917 - type: recall_at_10 value: 92.27199999999999 - type: recall_at_100 value: 98.715 - type: recall_at_1000 value: 99.854 - type: recall_at_3 value: 82.04599999999999 - type: recall_at_5 value: 87.2 - task: type: Retrieval dataset: name: MTEB SCIDOCS-PL type: clarin-knext/scidocs-pl config: default split: test revision: 45452b03f05560207ef19149545f168e596c9337 metrics: - type: map_at_1 value: 3.51 - type: map_at_10 value: 9.046999999999999 - type: map_at_100 value: 10.823 - type: map_at_1000 value: 11.144 - type: map_at_3 value: 6.257 - type: map_at_5 value: 7.648000000000001 - type: mrr_at_1 value: 17.299999999999997 - type: mrr_at_10 value: 27.419 - type: mrr_at_100 value: 28.618 - type: mrr_at_1000 value: 28.685 - type: mrr_at_3 value: 23.817 - type: mrr_at_5 value: 25.927 - type: ndcg_at_1 value: 17.299999999999997 - type: ndcg_at_10 value: 16.084 - type: ndcg_at_100 value: 23.729 - type: ndcg_at_1000 value: 29.476999999999997 - type: ndcg_at_3 value: 14.327000000000002 - type: ndcg_at_5 value: 13.017999999999999 - type: precision_at_1 value: 17.299999999999997 - type: precision_at_10 value: 8.63 - type: precision_at_100 value: 1.981 - type: precision_at_1000 value: 0.336 - type: precision_at_3 value: 13.4 - type: precision_at_5 value: 11.700000000000001 - type: recall_at_1 value: 3.51 - type: recall_at_10 value: 17.518 - type: recall_at_100 value: 40.275 - type: recall_at_1000 value: 68.203 - type: recall_at_3 value: 8.155 - type: recall_at_5 value: 11.875 - task: type: PairClassification dataset: name: MTEB SICK-E-PL type: PL-MTEB/sicke-pl-pairclassification config: default split: test revision: None metrics: - type: cos_sim_accuracy value: 86.30248675091724 - type: cos_sim_ap value: 83.6756734006714 - type: cos_sim_f1 value: 74.97367497367497 - type: cos_sim_precision value: 73.91003460207612 - type: cos_sim_recall value: 76.06837606837607 - type: dot_accuracy value: 86.30248675091724 - type: dot_ap value: 83.6756734006714 - type: dot_f1 value: 74.97367497367497 - type: dot_precision value: 73.91003460207612 - type: dot_recall value: 76.06837606837607 - type: euclidean_accuracy value: 86.30248675091724 - type: euclidean_ap value: 83.67566984333091 - type: euclidean_f1 value: 74.97367497367497 - type: euclidean_precision value: 73.91003460207612 - type: euclidean_recall value: 76.06837606837607 - type: manhattan_accuracy value: 86.28210354667753 - type: manhattan_ap value: 83.64216119130171 - type: manhattan_f1 value: 74.92152075340078 - type: manhattan_precision value: 73.4107997265892 - type: manhattan_recall value: 76.49572649572649 - type: max_accuracy value: 86.30248675091724 - type: max_ap value: 83.6756734006714 - type: max_f1 value: 74.97367497367497 - task: type: STS dataset: name: MTEB SICK-R-PL type: PL-MTEB/sickr-pl-sts config: default split: test revision: None metrics: - type: cos_sim_pearson value: 82.23295940859121 - type: cos_sim_spearman value: 78.89329160768719 - type: euclidean_pearson value: 79.56019107076818 - type: euclidean_spearman value: 78.89330209904084 - type: manhattan_pearson value: 79.76098513973719 - type: manhattan_spearman value: 79.05490162570123 - task: type: STS dataset: name: MTEB STS22 (pl) type: mteb/sts22-crosslingual-sts config: pl split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 37.732606308062486 - type: cos_sim_spearman value: 41.01645667030284 - type: euclidean_pearson value: 26.61722556367085 - type: euclidean_spearman value: 41.01645667030284 - type: manhattan_pearson value: 26.60917378970807 - type: manhattan_spearman value: 41.51335727617614 - task: type: Retrieval dataset: name: MTEB SciFact-PL type: clarin-knext/scifact-pl config: default split: test revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e metrics: - type: map_at_1 value: 54.31700000000001 - type: map_at_10 value: 65.564 - type: map_at_100 value: 66.062 - type: map_at_1000 value: 66.08699999999999 - type: map_at_3 value: 62.592999999999996 - type: map_at_5 value: 63.888 - type: mrr_at_1 value: 56.99999999999999 - type: mrr_at_10 value: 66.412 - type: mrr_at_100 value: 66.85900000000001 - type: mrr_at_1000 value: 66.88 - type: mrr_at_3 value: 64.22200000000001 - type: mrr_at_5 value: 65.206 - type: ndcg_at_1 value: 56.99999999999999 - type: ndcg_at_10 value: 70.577 - type: ndcg_at_100 value: 72.879 - type: ndcg_at_1000 value: 73.45 - type: ndcg_at_3 value: 65.5 - type: ndcg_at_5 value: 67.278 - type: precision_at_1 value: 56.99999999999999 - type: precision_at_10 value: 9.667 - type: precision_at_100 value: 1.083 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 26.0 - type: precision_at_5 value: 16.933 - type: recall_at_1 value: 54.31700000000001 - type: recall_at_10 value: 85.056 - type: recall_at_100 value: 95.667 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 71.0 - type: recall_at_5 value: 75.672 - task: type: Retrieval dataset: name: MTEB TRECCOVID-PL type: clarin-knext/trec-covid-pl config: default split: test revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd metrics: - type: map_at_1 value: 0.245 - type: map_at_10 value: 2.051 - type: map_at_100 value: 12.009 - type: map_at_1000 value: 27.448 - type: map_at_3 value: 0.721 - type: map_at_5 value: 1.13 - type: mrr_at_1 value: 88.0 - type: mrr_at_10 value: 93.0 - type: mrr_at_100 value: 93.0 - type: mrr_at_1000 value: 93.0 - type: mrr_at_3 value: 93.0 - type: mrr_at_5 value: 93.0 - type: ndcg_at_1 value: 85.0 - type: ndcg_at_10 value: 80.303 - type: ndcg_at_100 value: 61.23499999999999 - type: ndcg_at_1000 value: 52.978 - type: ndcg_at_3 value: 84.419 - type: ndcg_at_5 value: 82.976 - type: precision_at_1 value: 88.0 - type: precision_at_10 value: 83.39999999999999 - type: precision_at_100 value: 61.96 - type: precision_at_1000 value: 22.648 - type: precision_at_3 value: 89.333 - type: precision_at_5 value: 87.2 - type: recall_at_1 value: 0.245 - type: recall_at_10 value: 2.193 - type: recall_at_100 value: 14.938 - type: recall_at_1000 value: 48.563 - type: recall_at_3 value: 0.738 - type: recall_at_5 value: 1.173 --- # beethogedeon/gte-Qwen2-7B-instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`Alibaba-NLP/gte-Qwen2-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo beethogedeon/gte-Qwen2-7B-instruct-Q4_K_M-GGUF --hf-file gte-qwen2-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo beethogedeon/gte-Qwen2-7B-instruct-Q4_K_M-GGUF --hf-file gte-qwen2-7b-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo beethogedeon/gte-Qwen2-7B-instruct-Q4_K_M-GGUF --hf-file gte-qwen2-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo beethogedeon/gte-Qwen2-7B-instruct-Q4_K_M-GGUF --hf-file gte-qwen2-7b-instruct-q4_k_m.gguf -c 2048 ```
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf
RichardErkhov
null
[ "gguf", "endpoints_compatible", "region:us", "conversational" ]
1,724
1,724
98
0
--- {} --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) vi-gemma-2b-RAG - GGUF - Model creator: https://huggingface.co/ricepaper/ - Original model: https://huggingface.co/ricepaper/vi-gemma-2b-RAG/ | Name | Quant method | Size | | ---- | ---- | ---- | | [vi-gemma-2b-RAG.Q2_K.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q2_K.gguf) | Q2_K | 1.08GB | | [vi-gemma-2b-RAG.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.IQ3_XS.gguf) | IQ3_XS | 1.16GB | | [vi-gemma-2b-RAG.IQ3_S.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.IQ3_S.gguf) | IQ3_S | 1.2GB | | [vi-gemma-2b-RAG.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q3_K_S.gguf) | Q3_K_S | 1.2GB | | [vi-gemma-2b-RAG.IQ3_M.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.IQ3_M.gguf) | IQ3_M | 1.22GB | | [vi-gemma-2b-RAG.Q3_K.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q3_K.gguf) | Q3_K | 1.29GB | | [vi-gemma-2b-RAG.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q3_K_M.gguf) | Q3_K_M | 1.29GB | | [vi-gemma-2b-RAG.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q3_K_L.gguf) | Q3_K_L | 1.36GB | | [vi-gemma-2b-RAG.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.IQ4_XS.gguf) | IQ4_XS | 1.4GB | | [vi-gemma-2b-RAG.Q4_0.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q4_0.gguf) | Q4_0 | 1.44GB | | [vi-gemma-2b-RAG.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.IQ4_NL.gguf) | IQ4_NL | 1.45GB | | [vi-gemma-2b-RAG.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q4_K_S.gguf) | Q4_K_S | 1.45GB | | [vi-gemma-2b-RAG.Q4_K.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q4_K.gguf) | Q4_K | 1.52GB | | [vi-gemma-2b-RAG.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q4_K_M.gguf) | Q4_K_M | 1.52GB | | [vi-gemma-2b-RAG.Q4_1.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q4_1.gguf) | Q4_1 | 1.56GB | | [vi-gemma-2b-RAG.Q5_0.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q5_0.gguf) | Q5_0 | 1.68GB | | [vi-gemma-2b-RAG.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q5_K_S.gguf) | Q5_K_S | 1.68GB | | [vi-gemma-2b-RAG.Q5_K.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q5_K.gguf) | Q5_K | 1.71GB | | [vi-gemma-2b-RAG.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q5_K_M.gguf) | Q5_K_M | 1.71GB | | [vi-gemma-2b-RAG.Q5_1.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q5_1.gguf) | Q5_1 | 1.79GB | | [vi-gemma-2b-RAG.Q6_K.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q6_K.gguf) | Q6_K | 1.92GB | | [vi-gemma-2b-RAG.Q8_0.gguf](https://huggingface.co/RichardErkhov/ricepaper_-_vi-gemma-2b-RAG-gguf/blob/main/vi-gemma-2b-RAG.Q8_0.gguf) | Q8_0 | 2.49GB | Original model description: --- base_model: unsloth/gemma-1.1-2b-it-bnb-4bit language: - en - vi license: apache-2.0 tags: - text-generation-inference - retrieval-augmented-generation - transformers - unsloth - gemma - trl - sft --- ## Model Card: vi-gemma-2b-RAG ### (English below) ### Tiếng Việt (Vietnamese) **Mô tả mô hình:** vi-gemma-2b-RAG là một mô hình ngôn ngữ lớn được tinh chỉnh từ mô hình cơ sở [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it) sử dụng kỹ thuật LoRA. Mô hình được huấn luyện trên tập dữ liệu tiếng Việt với mục tiêu cải thiện khả năng xử lý ngôn ngữ tiếng Việt và nâng cao hiệu suất cho các tác vụ truy xuất thông tin mở (Retrieval Augmented Generation - RAG). **Mục đích sử dụng:** Mô hình vi-gemma-2b-RAG phù hợp cho các tác vụ sau: * Trả lời câu hỏi dựa trên ngữ cảnh tiếng Việt. * Tóm tắt văn bản tiếng Việt. * Dịch máy tiếng Việt. * Và các tác vụ tạo văn bản tiếng Việt khác. **Giới hạn:** Mặc dù đã được tinh chỉnh cho tiếng Việt, vi-gemma-2b-RAG vẫn có thể gặp phải một số hạn chế: * Có thể tạo ra thông tin sai lệch hoặc không chính xác. * Có thể thể hiện thành kiến ​​hoặc quan điểm không phù hợp. * Hiệu suất có thể bị ảnh hưởng bởi chất lượng của dữ liệu đầu vào. **Cách sử dụng:** Dưới đây chúng tôi chia sẻ một số đoạn mã về cách bắt đầu nhanh chóng để sử dụng mô hình. Trước tiên, hãy đảm bảo đã cài đặt `pip install -U transformers`, sau đó sao chép đoạn mã từ phần có liên quan đến usecase của bạn. Chúng tôi khuyến nghị sử dụng `torch.bfloat16` làm mặc định. ```python # pip install transformers torch accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Khởi tạo tokenizer và model từ checkpoint đã lưu tokenizer = AutoTokenizer.from_pretrained("himmeow/vi-gemma-2b-RAG") model = AutoModelForCausalLM.from_pretrained( "himmeow/vi-gemma-2b-RAG", device_map="auto", torch_dtype=torch.bfloat16 ) # Sử dụng GPU nếu có if torch.cuda.is_available(): model.to("cuda") # Định dạng prompt cho model prompt = """ ### Instruction and Input: Dựa vào ngữ cảnh/tài liệu sau: {} Hãy trả lời câu hỏi: {} ### Response: {} """ # Chuẩn bị dữ liệu đầu vào input_data = """ Short Tandem Repeats (STRs) là các trình tự DNA lặp lại ngắn (2- 6 nucleotides) xuất hiện phổ biến trong hệ gen của con người. Các trình tự này có tính đa hình rất cao trong tự nhiên, điều này khiến các STRs trở thành những markers di truyền rất quan trọng trong nghiên cứu bản đồ gen người và chuẩn đoán bệnh lý di truyền cũng như xác định danh tính trong lĩnh vực pháp y. Các STRs trở nên phổ biến tại các phòng xét nghiệm pháp y bởi vì việc nhân bản và phân tích STRs chỉ cần lượng DNA rất thấp ngay cả khi ở dạng bị phân hủy việc đinh danh vẫn có thể được thực hiện thành công. Hơn nữa việc phát hiện và đánh giá sự nhiễm DNA mẫu trong các mẫu vật có thể được giải quyết nhanh với kết quả phân tích STRs. Ở Hoa Kỳ hiện nay, từ bộ 13 markers nay đã tăng lên 20 markers chính đang được sử dụng để tạo ra một cơ sở dữ liệu DNA trên toàn đất nước được gọi là The FBI Combined DNA Index System (Expaned CODIS). CODIS và các cơ sử dữ liệu DNA tương tự đang được sử dụng thực sự thành công trong việc liên kết các hồ sơ DNA từ các tội phạm và các bằng chứng hiện trường vụ án. Kết quả định danh STRs cũng được sử dụng để hỗ trợ hàng trăm nghìn trường hợp xét nghiệm huyết thống cha con mỗi năm' """ query = "Hãy cho tôi biết một số tính chất của STRs được dùng để làm gì?" # Định dạng input text input_text = prompt.format(input_data, query," ") # Mã hóa input text thành input ids input_ids = tokenizer(input_text, return_tensors="pt") # Sử dụng GPU cho input ids nếu có if torch.cuda.is_available(): input_ids = input_ids.to("cuda") # Tạo văn bản bằng model outputs = model.generate( **input_ids, max_new_tokens=500, no_repeat_ngram_size=5, # Ngăn chặn lặp lại các cụm từ 5 gram # do_sample=True, # Kích hoạt chế độ tạo văn bản dựa trên lấy mẫu. Trong chế độ này, model sẽ chọn ngẫu nhiên token tiếp theo dựa trên xác suất được tính từ phân phối xác suất của các token. # temperature=0.7, # Giảm temperature để kiểm soát tính ngẫu nhiên # early_stopping=True, # Dừng tạo văn bản khi tìm thấy kết thúc phù hợp ) # Giải mã và in kết quả print(tokenizer.decode(outputs[0])) ''' <bos> ### Instruction and Input: Dựa vào ngữ cảnh/tài liệu sau: Short Tandem Repeats (STRs) là các trình tự DNA lặp lại ngắn (2- 6 nucleotides) xuất hiện phổ biến trong hệ gen của con người. Các trình tự này có tính đa hình rất cao trong tự nhiên, điều này khiến các STRs trở thành những markers di truyền rất quan trọng trong nghiên cứu bản đồ gen người và chuẩn đoán bệnh lý di truyền cũng như xác định danh tính trong lĩnh vực pháp y. Các STRs trở nên phổ biến tại các phòng xét nghiệm pháp y bởi vì việc nhân bản và phân tích STRs chỉ cần lượng DNA rất thấp ngay cả khi ở dạng bị phân hủy việc đinh danh vẫn có thể được thực hiện thành công. Hơn nữa việc phát hiện và đánh giá sự nhiễm DNA mẫu trong các mẫu vật có thể được giải quyết nhanh với kết quả phân tích STRs. Ở Hoa Kỳ hiện nay, từ bộ 13 markers nay đã tăng lên 20 markers chính đang được sử dụng để tạo ra một cơ sở dữ liệu DNA trên toàn đất nước được gọi là The FBI Combined DNA Index System (Expaned CODIS). CODIS và các cơ sử dữ liệu DNA tương tự đang được sử dụng thực sự thành công trong việc liên kết các hồ sơ DNA từ các tội phạm và các bằng chứng hiện trường vụ án. Kết quả định danh STRs cũng được sử dụng để hỗ trợ hàng trăm nghìn trường hợp xét nghiệm huyết thống cha con mỗi năm' Hãy trả lời câu hỏi: Hãy cho tôi biết một số tính chất của STRs được dùng để làm gì? ### Response: STRs được sử dụng để xác định danh tính, chuẩn đoán bệnh lý và xác định bệnh lý di truyền. <eos> ''' ``` **Huấn luyện:** * **Mô hình cơ sở:** google/gemma-1.1-2b-it * **Tập dữ liệu:** lamhieu/mabrycodes_dialogue_vi * **Phương pháp tinh chỉnh:** LoRA, PEFT với Unsloth ## Model Card: vi-gemma-2b-RAG ### English **Model Description:** vi-gemma-2b-RAG is a large language model fine-tuned from the base model [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it) using LoRA. The model is trained on a Vietnamese dataset to improve its Vietnamese language processing capabilities and enhance its performance for Retrieval Augmented Generation (RAG) tasks. **Intended Use:** The vi-gemma-2b-RAG model is suitable for tasks such as: * Vietnamese question answering. * Vietnamese text summarization. * Vietnamese machine translation. * And other Vietnamese text generation tasks. **Limitations:** While fine-tuned for Vietnamese, vi-gemma-2b-RAG may still have some limitations: * It may generate incorrect or misleading information. * It may exhibit biases or inappropriate opinions. * Its performance may be affected by the quality of the input data. **How to Use:** ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. We recommend `torch.bfloat16` as the default dtype. ```python # pip install transformers torch accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Initialize the tokenizer and model from the saved checkpoint tokenizer = AutoTokenizer.from_pretrained("himmeow/vi-gemma-2b-RAG") model = AutoModelForCausalLM.from_pretrained( "himmeow/vi-gemma-2b-RAG", device_map="auto", torch_dtype=torch.bfloat16 ) # Use GPU if available if torch.cuda.is_available(): model.to("cuda") # Define the prompt format for the model prompt = """ ### Instruction and Input: Based on the following context/document: {} Please answer the question: {} ### Response: {} """ # Prepare the input data input_data = """ Short Tandem Repeats (STRs) are short (2-6 nucleotides) repeating DNA sequences that are widespread in the human genome. These sequences are highly polymorphic in nature, which makes STRs very important genetic markers in human gene mapping and diagnosis of hereditary diseases as well as identification in the field of forensics. STRs have become popular in forensic laboratories because the replication and analysis of STRs requires very small amounts of DNA, even in decomposed form, identification can still be performed successfully. Furthermore, the detection and assessment of sample DNA contamination in specimens can be quickly resolved with STR analysis results. In the United States today, the set of 13 markers has now been increased to 20 main markers being used to create a nationwide DNA database called The FBI Combined DNA Index System (Expaned CODIS). CODIS and similar DNA databases are being used very successfully in linking DNA records from criminals and crime scene evidence. STR identification results are also used to support hundreds of thousands of paternity test cases each year.' """ query = "Tell me what are some properties of STRs used for?" # Format the input text input_text = prompt.format(input_data, query," ") # Encode the input text into input ids input_ids = tokenizer(input_text, return_tensors="pt") # Use GPU for input ids if available if torch.cuda.is_available(): input_ids = input_ids.to("cuda") # Generate text using the model outputs = model.generate( **input_ids, max_new_tokens=500, # Limit the number of tokens generated no_repeat_ngram_size=5, # Prevent repetition of 5-gram phrases # do_sample=True, # temperature=0.7, # Adjust the randomness of the generated text # early_stopping=True, # Stop generating text when a suitable ending is found ) # Decode and print the results print(tokenizer.decode(outputs[0])) ``` **Training:** * **Base Model:** google/gemma-1.1-2b-it * **Dataset:** lamhieu/mabrycodes_dialogue_vi * **Fine-tuning Method:** LoRA, PEFT and Unsloth **Using example repository:** https://github.com/Martincrux/Vietnamese-RAG-system-building-with-vi-gemma-2b-RAG-and-halong_embedding # Uploaded model - **Developed by:** [hiieu](https://huggingface.co/hiieu), [himmeow the coder](https://huggingface.co/himmeow), [cuctrinh](https://www.linkedin.com/in/trinh-cuc-5722832b6) - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-1.1-2b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
[ "QUESTION_ANSWERING", "TRANSLATION", "SUMMARIZATION" ]
[ "CHIA" ]
BioNLP
RichardErkhov/BSC-LT_-_salamandra-7b-gguf
RichardErkhov
null
[ "gguf", "arxiv:2403.14009", "arxiv:2403.20266", "arxiv:2101.00027", "arxiv:2207.00220", "arxiv:1810.06694", "arxiv:1911.05507", "arxiv:1906.03741", "arxiv:2406.17557", "arxiv:2402.06619", "arxiv:1803.09010", "endpoints_compatible", "region:us" ]
1,728
1,728
80
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--- {} --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) salamandra-7b - GGUF - Model creator: https://huggingface.co/BSC-LT/ - Original model: https://huggingface.co/BSC-LT/salamandra-7b/ | Name | Quant method | Size | | ---- | ---- | ---- | | [salamandra-7b.Q2_K.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q2_K.gguf) | Q2_K | 3.08GB | | [salamandra-7b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.IQ3_XS.gguf) | IQ3_XS | 3.39GB | | [salamandra-7b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.IQ3_S.gguf) | IQ3_S | 3.51GB | | [salamandra-7b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q3_K_S.gguf) | Q3_K_S | 3.5GB | | [salamandra-7b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.IQ3_M.gguf) | IQ3_M | 3.6GB | | [salamandra-7b.Q3_K.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q3_K.gguf) | Q3_K | 3.77GB | | [salamandra-7b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q3_K_M.gguf) | Q3_K_M | 3.77GB | | [salamandra-7b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q3_K_L.gguf) | Q3_K_L | 4.0GB | | [salamandra-7b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.IQ4_XS.gguf) | IQ4_XS | 4.18GB | | [salamandra-7b.Q4_0.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q4_0.gguf) | Q4_0 | 4.33GB | | [salamandra-7b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.IQ4_NL.gguf) | IQ4_NL | 4.36GB | | [salamandra-7b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q4_K_S.gguf) | Q4_K_S | 4.35GB | | [salamandra-7b.Q4_K.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q4_K.gguf) | Q4_K | 4.52GB | | [salamandra-7b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q4_K_M.gguf) | Q4_K_M | 4.52GB | | [salamandra-7b.Q4_1.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q4_1.gguf) | Q4_1 | 4.72GB | | [salamandra-7b.Q5_0.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q5_0.gguf) | Q5_0 | 5.11GB | | [salamandra-7b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q5_K_S.gguf) | Q5_K_S | 5.11GB | | [salamandra-7b.Q5_K.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q5_K.gguf) | Q5_K | 5.21GB | | [salamandra-7b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q5_K_M.gguf) | Q5_K_M | 5.21GB | | [salamandra-7b.Q5_1.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q5_1.gguf) | Q5_1 | 5.5GB | | [salamandra-7b.Q6_K.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q6_K.gguf) | Q6_K | 5.94GB | | [salamandra-7b.Q8_0.gguf](https://huggingface.co/RichardErkhov/BSC-LT_-_salamandra-7b-gguf/blob/main/salamandra-7b.Q8_0.gguf) | Q8_0 | 7.69GB | Original model description: --- license: apache-2.0 library_name: transformers pipeline_tag: text-generation language: - bg - ca - code - cs - cy - da - de - el - en - es - et - eu - fi - fr - ga - gl - hr - hu - it - lt - lv - mt - nl - nn - \no - oc - pl - pt - ro - ru - sh - sk - sl - sr - sv - uk --- ![](./images/salamandra_header.png) # Salamandra Model Card Salamandra is a highly multilingual model pre-trained from scratch that comes in three different sizes — 2B, 7B and 40B parameters — with their respective base and instruction-tuned variants. This model card corresponds to the 7B instructed version. To visit the model cards of other Salamandra versions, please refer to the [Model Index](#model-index). The entire Salamandra family is released under a permissive [Apache 2.0 license]((https://www.apache.org/licenses/LICENSE-2.0)). Along with the open weights, all training scripts and configuration files are made publicly available in [this GitHub repository](https://github.com/langtech-bsc/salamandra). --- ## Model Details ### Description Transformer-based decoder-only language model that has been pre-trained from scratch on 7.8 trillion tokens of highly curated data. The pre-training corpus contains text in 35 European languages and code. ### Hyperparameters The full list of hyperparameters for each model can be found [here](https://github.com/langtech-bsc/salamandra/tree/main/configs). ### Architecture | | | |-------------------------|:--------------| | Total Parameters | 7,768,117,248 | | Embedding Parameters | 1,048,576,000 | | Layers | 32 | | Hidden size | 4,096 | | Attention heads | 32 | | Context length | 8,192 | | Vocabulary size | 256,000 | | Precision | bfloat16 | | Embedding type | RoPE | | Activation Function | SwiGLU | | Layer normalization | RMS Norm | | Flash attention | ✅ | | Grouped Query Attention | ✅ | | Num. query groups | 8 | --- ## Intended Use ### Direct Use The models are intended for both research and commercial use in any of the languages included in the training data. The base models are intended either for language generation or to be further fine-tuned for specific use-cases. The instruction-tuned variants can be used as general-purpose assistants, as long as the user is fully aware of the model’s limitations. ### Out-of-scope Use The model is not intended for malicious activities, such as harming others or violating human rights. Any downstream application must comply with current laws and regulations. Irresponsible usage in production environments without proper risk assessment and mitigation is also discouraged. --- ## Hardware and Software ### Training Framework Pre-training was conducted using NVIDIA’s [NeMo Framework](https://docs.nvidia.com/nemo-framework/index.html), which leverages PyTorch Lightning for efficient model training in highly distributed settings. The instruction-tuned versions were produced with [FastChat](https://github.com/lm-sys/FastChat). ### Compute Infrastructure All models were trained on [MareNostrum 5](https://www.bsc.es/ca/marenostrum/marenostrum-5), a pre-exascale EuroHPC supercomputer hosted and operated by Barcelona Supercomputing Center. The accelerated partition is composed of 1,120 nodes with the following specifications: - 4x Nvidia Hopper GPUs with 64 HBM2 memory - 2x Intel Sapphire Rapids 8460Y+ at 2.3Ghz and 32c each (64 cores) - 4x NDR200 (BW per node 800Gb/s) - 512 GB of Main memory (DDR5) - 460GB on NVMe storage |Model|Nodes|GPUs| |:---:|:---:|:---:| |2B|64|256| |7B|128|512| |40B|256 / 512|1,024 / 2,048| --- ## How to use This section offers examples of how to perform inference using various methods. ### Inference You'll find different techniques for running inference, including Huggingface's Text Generation Pipeline, multi-GPU configurations, and vLLM for scalable and efficient generation. #### Inference with Huggingface's Text Generation Pipeline The Huggingface Text Generation Pipeline provides a straightforward way to run inference using the Salamandra-7b model. ```bash pip install transformers torch accelerate sentencepiece protobuf ``` <details> <summary>Show code</summary> ```python from transformers import pipeline, set_seed model_id = "BSC-LT/salamandra-7b" # Sample prompts prompts = [ "Las fiestas de San Isidro Labrador de Yecla son", "El punt més alt del Parc Natural del Montseny és", "Sentence in English: The typical chance of such a storm is around 10%. Sentence in Catalan:", "Si le monde était clair", "The future of AI is", ] # Create the pipeline generator = pipeline("text-generation", model_id, device_map="auto") generation_args = { "temperature": 0.1, "top_p": 0.95, "max_new_tokens": 25, "repetition_penalty": 1.2, "do_sample": True } # Fix the seed set_seed(1) # Generate texts outputs = generator(prompts, **generation_args) # Print outputs for output in outputs: print(output[0]["generated_text"]) ``` </details> #### Inference with single / multi GPU This section provides a simple example of how to run inference using Huggingface's AutoModel class. ```bash pip install transformers torch accelerate sentencepiece protobuf ``` <details> <summary>Show code</summary> ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "BSC-LT/salamandra-7b" # Input text text = "El mercat del barri és" # Load the tokenizer tokenizer = AutoTokenizer.from_pretrained(model_id) # Load the model model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", torch_dtype=torch.bfloat16 ) generation_args = { "temperature": 0.1, "top_p": 0.95, "max_new_tokens": 25, "repetition_penalty": 1.2, "do_sample": True } inputs = tokenizer(text, return_tensors="pt") # Generate texts output = model.generate(input_ids=inputs["input_ids"].to(model.device), attention_mask=inputs["attention_mask"], **generation_args) # Print outputs print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` </details> #### Inference with vLLM vLLM is an efficient library for inference that enables faster and more scalable text generation. ```bash pip install vllm ``` <details> <summary>Show code</summary> ```python from vllm import LLM, SamplingParams model_id = "BSC-LT/salamandra-7b" # Sample prompts prompts = [ "Las fiestas de San Isidro Labrador de Yecla son", "El punt més alt del Parc Natural del Montseny és", "Sentence in English: The typical chance of such a storm is around 10%. Sentence in Catalan:", "Si le monde était clair", "The future of AI is", ] # Create a sampling params object sampling_params = SamplingParams( temperature=0.1, top_p=0.95, seed=1, max_tokens=25, repetition_penalty=1.2) # Create an LLM llm = LLM(model=model_id) # Generate texts outputs = llm.generate(prompts, sampling_params) # Print outputs for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` </details> --- ## Data ### Pretraining Data The training corpus consists of 2.4 trillion tokens, including 35 European languages and 92 programming languages. It amounts to a total of 33TB of pre-processed text. Languages were sampled manually by giving x2 oversampling to Spain's co-official languages (Spanish, Catalan, Galician and Basque), code was undersampled by half, and the rest of the languages were kept as is, resulting in the following distribution: ![lang distrib](./images/corpus_languages.png) This highly multilingual corpus is predominantly composed of data from Colossal OSCAR, which contributes a significant 66.06% of the total tokens. Following this, Starcoder provides 11.91%, and Spanish Crawling adds 3.34%. The next largest sources are French FR at 3.12% and Proof Pile at 1.98%. Other notable contributions include Macocu, Pile of Law, and Eurlex, each contributing around 1.5% to 1.3%. These major sources collectively form the bulk of the corpus, ensuring a rich and diverse dataset for training the language model. The remaining 10% comes from smaller sources in various languages. Feel free to click the expand button below to see the full list of sources. <details> <summary>Data Sources</summary> | Dataset | Language | Source | |-----------------------------------------------|---------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------| | Parlamint corpus | at, bg, cz, dk, ee, es, es-ga, fi, fr, gb, gr, hr, hu, it, lv, nl, no, pl, pt, rs, se, si | Erjavec et al., 2021 | | Bulgarian National Corpus | bg | [Link](http://old.dcl.bas.bg/dataset/BulNC.7z) | | Crawl of Bulgarian news websites | bg | [Link](http://old.dcl.bas.bg/dataset/Bulgarian_news.7z) | | Colossal OSCAR 1.0 | bg, ca, cs, cy, da, de, el, en, es, et, eu, fi, fr, ga, gl, hr, hu, it, lt, lv, mt, nl, nn, no, oc, pl, pt, ro, ru, sh, sk, sl, sr, sv, uk | Brack et al., 2024 | | Wikimedia dumps | bg, ca, cs, da, de, el, en, es, et, eu, fi, fr, ga, gl, hr, hu, it, lt, lv, mt, nl, nn, no, pl, pt, ro, sh, sk, sl, sr, uk | [Link](https://dumps.wikimedia.org/) | | OpenSubtitlesv2016 | bg, ca, cs, da, de, el, en, es, et, eu, fi, fr, gl, hr, it, lt, lv, nl, no, pl, pt, ro, sk, sl, sr, sv, uk | Lison & Tiedemann, 2016 | | MaCoCu web corpus | bg, ca, el, hr, mt, sl, sr, uk | Bañón et al., 2022 | | EurLEX-Resources | bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv | [Link](https://huggingface.co/datasets/joelniklaus/eurlex_resources) | | MC4-Legal | bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv | [Link](https://huggingface.co/datasets/joelito/legal-mc4) | | CURLICAT Corpus | bg, hr, hu, pl, ro, sk, sl | Váradi et al., 2022 | | CATalog | ca | Palomar-Giner et al., 2024 | | Spanish Crawling | ca, es, eu, gl | Relevant Spanish websites crawling | | Starcoder | code | Li et al., 2023 | | SYN v9: large corpus of written Czech | cs | Křen et al., 2021 | | Welsh-GOV | cy | Crawling from [Link](https://www.llyw.cymru) | | DaNewsroom | da | Varab & Schluter, 2020 | | Danish GigaWord | da | Strømberg-Derczynski et al., 2021 | | DK-CLARIN Reference Corpus of General Danish | da | [Link](https://korpus.dsl.dk/clarin/) | | The Danish Parliament Corpus 2009 - 2017, v1 | da | Hansen, 2018 | | DeWaC | de | [Link](https://docs.sslmit.unibo.it/doku.php?id=corpora:dewac) | | Open Legal Data - German court decisions and laws | de | Ostendorff et al., 2020 | | Greek Legal Code | el | Papaloukas et al., 2021 | | Greek Web Corpus | el | Outsios et al., 2018 | | Auxiliary Mathematics Problems and Solutions (AMPS) dataset | en | Hendrycks et al., 2021 | | BIGPATENT | en | Sharma et al., 2019 | | FineWeb-Edu (350BT subset) | en | Penedo et al., 2024 | | peS2o | en | Soldaini & Lo, 2023 | | PG-19 | en | Rae et al., 2019 | | Pile of Law (selected subsets) | en | Henderson* et al., 2022 | | proof-pile | en | [Link](https://huggingface.co/datasets/hoskinson-center/proof-pile) | | RedPajama-Data T1 (StackExchange subset) | en | Computer, 2023 | | The Pile (PhilPapers subset) | en | Gao et al., 2021 | | Biomedical | es | Internally generated scientific dataset: Dialnet, Scielo, CSIC, TDX, BSC, UCM | | HPLTDatasets v1 - Spanish | es | de Gibert et al., 2024 | | Legal | es | Internally generated legal dataset: BOE, BORME, Senado, Congreso, Spanish court orders, DOGC | | Scientific | es | Internally generated scientific dataset: Wikipedia LS, Pubmed, MeSpEn, patents, clinical cases, medical crawler | | Spanish Legal Domain Corpora | es | Gutiérrez-Fandiño et al., 2021 | | Estonian National Corpus 2021 | et | Koppel & Kallas, 2022 | | Estonian Reference Corpus | et | [Link](https://www.cl.ut.ee/korpused/segakorpus/) | | EusCrawl (w/o Wikipedia or NC-licenses) | eu | Artetxe et al., 2022 | | Latxa Corpus v1.1 | eu | Etxaniz et al., 2024 [Link](https://huggingface.co/datasets/HiTZ/latxa-corpus-v1.1) | | Aya Dataset (w/o Evaluation Suite) | eu, hr, nl, fi, ka, hu, lt, nn, ro, sk, lv, cy, bg, cs, en, fr, de, ga, mt, pl, ru, sl, sv, ca, da, et, gl, el, it, no, pt, sr, es, uk | Singh et al., 2024 | | Yle Finnish News Archive | fi | [Link](http://urn.fi/urn:nbn:fi:lb-2021050401) | | CaBeRnet: a New French Balanced Reference Corpus | fr | Popa-Fabre et al., 2020 | | French Public Domain Books | fr | [Link](https://huggingface.co/datasets/PleIAs/French-PD-Books) | | French Public Domain Newspapers | fr | [Link](https://huggingface.co/datasets/PleIAs/French-PD-Newspapers) | | Irish Universal Dependencies | ga | [Link](https://universaldependencies.org/ga/index.html) | | The Gaois bilingual corpus of English-Irish legislation (Irish legislation) | ga | [Link](https://portulanclarin.net/repository/browse/the-gaois-bilingual-corpus-of-english-irish-legislation-processed/daeac17c9e3511ea9b7f02420a000407b83de243dc0b469aab41084386c5b80f/) | | CorpusNÓS | gl | de-Dios-Flores et al., 2024 | | Croatian web corpus hrWaC 2.1 | hr | Ljubešić & Klubička, 2014 | | ITWaC | it | [Link](https://docs.sslmit.unibo.it/doku.php?id=corpora:itwac) | | Corpus of State-related content from the Latvian Web (Processed) | lv | [Link](https://catalog.elra.info/en-us/repository/browse/ELRA-W0169/) | | Korpus Malti | mt | Micallef et al., 2022 | | SoNaR Corpus NC 1.2 | nl | [Link](https://taalmaterialen.ivdnt.org/download/tstc-sonar-corpus/) | | Norwegian Colossal Corpus | nn, no | Kummervold et al., 2021 | | Occitan Corpus | oc | Provided by [IEA](https://www.institutestudisaranesi.cat/) | | NKJP-PodkorpusMilionowy-1.2 (National Corpus of Polish) | pl | Lewandowska-Tomaszczyk et al., 2013 | | Polish Parliamentary Corpus / Korpus Dyskursu Parlamentarnego | pl | Ogrodniczuk, 2018 | | Brazilian Portuguese Web as Corpus | pt | Wagner Filho et al., 2018 | | ParlamentoPT | pt | Rodrigues et al., 2023 | | MARCELL Romanian legislative subcorpus v2 | ro | [Link](https://elrc-share.eu/reposMARCELL%20Romanian%20legislative%20subcorpus%20v2itory/browse/marcell-romanian-legislative-subcorpus-v2/2da548428b9d11eb9c1a00155d026706ce94a6b59ffc4b0e9fb5cd9cebe6889e/) | | Korpus slovenských právnych predpisov v1.9 | sk | [Link](https://www.juls.savba.sk/data/marcell/legal-sk-20220322-1.9.ver.xz) | | od-justice 2.0 | sk | [Link](https://www.juls.savba.sk/data/od-justice/od-justice-2.0.ver.xz) | | Corpus of academic Slovene KAS 2.0 | sl | Žagar et al., 2022 | | slWaC web corpus | sl | Erjavec et al., 2015 | | SrpKorSubset (news, legal, academic, conversation, literary) | sr | [Link](http://www.korpus.matf.bg.ac.rs/) | | The Swedish Culturomics Gigaword Corpus | sv | Rødven-Eide, 2016 | | Corpus of laws and legal acts of Ukraine | uk | [Link](https://lang.org.ua/en/corpora/#anchor7) | <details> <summary>References</summary> - Abadji, J., Suárez, P. J. O., Romary, L., & Sagot, B. (2021). Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus (H. Lüngen, M. Kupietz, P. Bański, A. Barbaresi, S. Clematide, & I. Pisetta, Eds.; pp. 1–9). Leibniz-Institut für Deutsche Sprache. [Link](https://doi.org/10.14618/ids-pub-10468) - Artetxe, M., Aldabe, I., Agerri, R., Perez-de-Viñaspre, O., & Soroa, A. (2022). Does Corpus Quality Really Matter for Low-Resource Languages? - Bañón, M., Esplà-Gomis, M., Forcada, M. L., García-Romero, C., Kuzman, T., Ljubešić, N., van Noord, R., Sempere, L. P., Ramírez-Sánchez, G., Rupnik, P., Suchomel, V., Toral, A., van der Werff, T., & Zaragoza, J. (2022). MaCoCu: Massive collection and curation of monolingual and bilingual data: Focus on under-resourced languages. Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, 303–304. [Link](https://aclanthology.org/2022.eamt-1.41) - Brack, M., Ostendorff, M., Suarez, P. O., Saiz, J. 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The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale (arXiv:2406.17557). arXiv. http://arxiv.org/abs/2406.17557 - Singh, S., Vargus, F., Dsouza, D., Karlsson, B. F., Mahendiran, A., Ko, W.-Y., Shandilya, H., Patel, J., Mataciunas, D., OMahony, L., Zhang, M., Hettiarachchi, R., Wilson, J., Machado, M., Moura, L. S., Krzemiński, D., Fadaei, H., Ergün, I., Okoh, I., … Hooker, S. (2024). Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning (arXiv:2402.06619). arXiv. http://arxiv.org/abs/2402.06619 </details> </details> The model was trained for 3 epochs, with two final rounds of 0.3B higher-quality tokens each, meaning that the total number of tokens seen during pre-training amounts to roughly 7.8 trillion tokens. We provide an extense Datasheet section following the best practices defined by [(Gebru et al., 2021)](https://arxiv.org/pdf/1803.09010). <details> <summary>Datasheet</summary> #### Motivation **For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.** The purpose of creating this dataset is to pre-train the Salamandra family of multilingual models with high performance in a large number of European languages (35) and code (including 92 different programming languages). In addition, we aim to represent especially the co-official languages of Spain: Spanish, Catalan, Galician, and Basque. This is the reason why we carry out an oversampling of these languages. We detected that there is a great lack of massive multilingual data, especially in minority languages (Ostendorff & Rehm, 2023), so part of our efforts in the creation of this pre-training dataset have resulted in the contribution to large projects such as the Community OSCAR (Brack et al., 2024), which includes 151 languages and 40T words, or CATalog (Palomar-Giner et al., 2024), the largest open dataset in Catalan in the world. **Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?** The dataset has been created by the Language Technologies unit (LangTech) of the Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS), which aims to advance the field of natural language processing through cutting-edge research and development and the use of HPC. In particular, it was created by the unit's data team, the main contributors being Javier Saiz, Ferran Espuña, and Jorge Palomar. However, the creation of the dataset would not have been possible without the collaboration of a large number of collaborators, partners, and public institutions, which can be found in detail in the acknowledgements. **Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.** This work/research has been promoted and financed by the Government of Catalonia through the [Aina project](https://projecteaina.cat/). #### Composition **What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description.** The dataset consists entirely of text documents in various languages. Specifically, data was mainly sourced from the following databases and repositories: - **Common Crawl:** Repository that holds website data and is run by the Common Crawl non-profit organization. It is updated monthly and is distributed under the CC0 1.0 public domain license. - **GitHub:** Community platform that allows developers to create, store, manage, and share their code. Repositories are crawled and then distributed with their original licenses, which may vary from permissive to non-commercial licenses. - **Wikimedia:** Database that holds the collection databases managed by the Wikimedia Foundation, including Wikipedia, Wikibooks, Wikinews, Wikiquote, Wikisource, and Wikivoyage. It is updated monthly and is distributed under Creative Commons Attribution-ShareAlike License 4.0. - **EurLex:** Repository that holds the collection of legal documents from the European Union, available in all of the EU’s 24 official languages and run by the Publications Office of the European Union. It is updated daily and is distributed under the Creative Commons Attribution 4.0 International license. - **Other repositories:** Specific repositories were crawled under permission for domain-specific corpora, which include academic, legal, and newspaper repositories. We provide a complete list of dataset sources at the end of this section. **How many instances are there in total (of each type, if appropriate)?** The dataset contains a diverse range of instances across multiple languages, with notable adjustments for certain languages. English represents the largest portion, accounting for 39.08% of the total data. Spanish was upsampled by a factor of 2, bringing its share to 16.59%, while Catalan (1.84%), Basque (0.26%), and Galician (0.36%) were also upsampled by 2. On the other hand, code-related data was downsampled by half, making up 6.42% of the total. Other prominent languages include French (6.59%), Russian (5.39%), German (4.25%), and Hungarian (3.93%), with several additional languages contributing between 1% and 2%, and smaller portions represented by a variety of others. **Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).** The dataset is a sample from multiple sources, with different weights based on the primary language of the content: Spanish, Catalan, Basque, and Galician content was upsampled by a factor of two, while programming languages were downsampled by a factor of half. Other sources were sampled in proportion to their occurrence. **What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description.** Each instance consists of a text document processed for deduplication, language identification, and source-specific filtering. Some documents required optical character recognition (OCR) to extract text from non-text formats such as PDFs. **Is there a label or target associated with each instance? If so, please provide a description.** Each instance is labeled with a unique identifier, the primary language of the content, and the URL for web-sourced instances. Additional labels were automatically assigned to detect specific types of content —harmful or toxic content— and to assign preliminary indicators of undesired qualities —very short documents, high density of symbols, etc.— which were used for filtering instances. **Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.** No significant information is missing from the instances. **Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)? If so, please describe how these relationships are made explicit.** Instances are related through shared metadata, such as source and language identifiers. **Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them.** The dataset is split randomly into training, validation, and test sets. **Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.** Despite removing duplicated instances within each source, redundancy remains at the paragraph and sentence levels, particularly in web-sourced instances where SEO techniques and templates contribute to repeated textual patterns. Some instances may also be duplicated across sources due to format variations. **Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a dataset consumer? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.** The dataset is self-contained and does not rely on external resources. **Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor–patient confidentiality, data that includes the content of individuals’ non-public communications)? If so, please provide a description.** The dataset does not contain confidential data. **Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why. If the dataset does not relate to people, you may skip the remaining questions in this section.** The dataset includes web-crawled content, which may overrepresent pornographic material across languages (Kreutzer et al., 2022). Although pre-processing techniques were applied to mitigate offensive content, the heterogeneity and scale of web-sourced data make exhaustive filtering challenging, which makes it next to impossible to identify all adult content without falling into excessive filtering, which may negatively influence certain demographic groups (Dodge et al., 2021). **Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.** The dataset does not explicitly identify any subpopulations. **Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how.** Web-sourced instances in the dataset may contain personally identifiable information (PII) that is publicly available on the Web, such as names, IP addresses, email addresses, and phone numbers. While it would be possible to indirectly identify individuals through the combination of multiple data points, the nature and scale of web data makes it difficult to parse such information. In any case, efforts are made to filter or anonymize sensitive data during pre-processing, but some identifiable information may remain in the dataset. **Does the dataset contain data that might be considered sensitive in any way? If so, please provide a description.** Given that the dataset includes web-sourced content and other publicly available documents, instances may inadvertently reveal financial information, health-related details, or forms of government identification, such as social security numbers (Subramani et al., 2023), especially if the content originates from less-regulated sources or user-generated platforms. #### Collection Process **How was the data collected?** This dataset is constituted by combining several sources, whose acquisition methods can be classified into three groups: - Web-sourced datasets with some preprocessing available under permissive license (p.e. Common Crawl). - Domain-specific or language-specific raw crawls (p.e. Spanish Crawling). - Manually curated data obtained through collaborators, data providers (by means of legal assignment agreements) or open source projects (p.e. CATalog). **What mechanisms or procedures were used to collect the data? How were these mechanisms or procedures validated?** According to the three groups previously defined, these are the mechanisms used in each of them: - Open direct download. Validation: data integrity tests. - Ad-hoc scrapers or crawlers. Validation: software unit and data integrity tests. - Direct download via FTP, SFTP, API or S3. Validation: data integrity tests. **If the dataset is a sample from a larger set, what was the sampling strategy?** The sampling strategy was to use the whole dataset resulting from the filtering explained in the ‘preprocessing/cleaning/labelling’ section, with the particularity that an upsampling of 2 (i.e. twice the probability of sampling a document) was performed for the co-official languages of Spain (Spanish, Catalan, Galician, Basque), and a downsampling of 1/2 was applied for code (half the probability of sampling a code document, evenly distributed among all programming languages). **Who was involved in the data collection process and how were they compensated?** This data is generally extracted, filtered and sampled by automated processes. The code required to run these processes has been developed entirely by members of the LangTech data team, or otherwise obtained from open-source software. Furthermore, there has been no monetary consideration for acquiring data from suppliers. **Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances? If not, please describe the timeframe in which the data associated with the instances was created.** Data were acquired and processed from April 2023 to April 2024. However, as mentioned, much data has been obtained from open projects such as Common Crawl, which contains data from 2014, so it is the end date (04/2024) rather than the start date that is important. **Were any ethical review processes conducted? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.** No particular ethical review process has been carried out as the data is mostly open and not particularly sensitive. However, we have an internal evaluation team and a bias team to monitor ethical issues. In addition, we work closely with ‘Observatori d'Ètica en Intel·ligència Artificial’ (OEIAC) and ‘Agencia Española de Supervisión de la Inteligencia Artificial’ (AESIA) to audit the processes we carry out from an ethical and legal point of view, respectively. #### Preprocessing **Was any preprocessing/cleaning/labeling of the data done? If so, please provide a description. If not, you may skip the remaining questions in this section.** Instances of text documents were not altered, but web-sourced documents were filtered based on specific criteria along two dimensions: - Quality: documents with a score lower than 0.8, based on undesired qualities, such as documents with low number of lines, very short sentences, presence of long footers and headers, and high percentage of punctuation, obtained through CURATE (Palomar-Giner et al., 2024) were filtered out. - Harmful or adult content: documents originating from Colossal OSCAR were filtered using LLM-Datasets (Ostendorff et al., 2024) based on the perplexity from a language model (‘harmful_pp’ field) provided by the Ungoliant pipeline (Abadji et al., 2021). **Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data? If so, please provide a link or other access point to the “raw” data.** The original raw data was not kept. **Is the software that was used to preprocess/clean/label the data available? If so, please provide a link or other access point.** Yes, the preprocessing and filtering software is open-sourced. The [CURATE](https://github.com/langtech-bsc/CURATE) pipeline was used for Spanish Crawling and CATalog, and the [Ungoliant](https://github.com/oscar-project/ungoliant) pipeline was used for the OSCAR project. #### Uses **Has the dataset been used for any tasks already? If so, please provide a description.** Pre-train the Salamandra model family. **What (other) tasks could the dataset be used for?** The data can be used primarily to pre-train other language models, which can then be used for a wide range of use cases. The dataset could also be used for other tasks such as fine-tuning language models, cross-lingual NLP tasks, machine translation, domain-specific text generation, and language-specific data analysis. **Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? Is there anything a dataset consumer could do to mitigate these risks or harms?** Web-crawled content is over-represented with standard language varieties, impacting language model performance for minority languages. Language diversity in data is crucial to avoid bias, especially in encoding non-standard dialects, preventing the exclusion of demographic groups. Moreover, despite legal uncertainties in web-scraped data, we prioritize permissive licenses and privacy protection measures, acknowledging the challenges posed by personally identifiable information (PII) within large-scale datasets. Our ongoing efforts aim to address privacy concerns and contribute to a more inclusive linguistic dataset. **Are there tasks for which the dataset should not be used?** - #### Distribution **Will the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created? If so, please provide a description.** The dataset will not be released or distributed to third parties. Any related question to distribution is omitted in this section. #### Maintenance **Who will be supporting/hosting/maintaining the dataset?** The dataset will be hosted by the Language Technologies unit (LangTech) of the Barcelona Supercomputing Center (BSC). The team will ensure regular updates and monitor the dataset for any issues related to content integrity, legal compliance, and bias for the sources they are responsible for. **How can the owner/curator/manager of the dataset be contacted?** The data owner may be contacted with the email address [email protected]. **Will the dataset be updated?** The dataset will not be updated. **If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances? If so, please describe these limits and explain how they will be enforced.** The dataset does not keep sensitive data that could allow direct identification of individuals, apart from the data that is publicly available in web-sourced content. Due to the sheer volume and diversity of web data, it is not feasible to notify individuals or manage data retention on an individual basis. However, efforts are made to mitigate the risks associated with sensitive information through pre-processing and filtering to remove identifiable or harmful content. Despite these measures, vigilance is maintained to address potential privacy and ethical issues. **Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to dataset consumers.** Since the dataset will not be updated, only the final version will be kept. **If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?** The dataset does not allow for external contributions. </details> --- ## Evaluation ### Gold-standard benchmarks Evaluation is done using the Language Model Evaluation Harness (Gao et al., 2024). We evaluate on a set of tasks taken from [SpanishBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/spanish_bench), [CatalanBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/catalan_bench), [BasqueBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/basque_bench) and [GalicianBench](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/galician_bench). We also use English tasks already available on the LM Evaluation Harness. These benchmarks include both new and existing tasks and datasets. In the tables below, we include the results in a selection of evaluation datasets that represent model's performance across a variety of tasks within these benchmarks. We only use tasks that are either human generated, human translated, or with a strong human-in-the-loop (i.e., machine translation followed by professional revision or machine generation followed by human revision and annotation). This is the reason behind the variety in number of tasks reported across languages. As more tasks that fulfill these requirements are published, we will update the presented results. We also intend to expand the evaluation to other languages, as long as the datasets meet our quality standards. During the implementation of the evaluation we observed a series of issues worth considering when replicating and interpreting the results presented. These issues include ≈1.5% variances in performance in some tasks depending on the version of the `transformers` library used, and depending on the use (or lack of use) of tensor parallelism when loading a model. When implementing existing tasks, we carry out a comprehensive quality evaluation of the dataset, the Harness task itself, and what kind of input models see during evaluation. Our implementation (see links above) addresses multiple existing problems such as errors in datasets and prompts, and lack of pre-processing. All this means that results will vary if using other Harness implementations, and may slightly vary depending on the replication setup. It should be noted that these results are subject to all the drawbacks of every current gold-standard evaluation, and that the figures do not fully represent the models capabilities and potential. We thus advise caution when reading and interpreting the results. A full list of results compared to other baselines, a discussion of the model's performance across tasks and its implications, and details regarding problem-solving with task implementation will soon be available in the technical report. All results reported below are on a 5-shot setting. #### Spanish <table><thead> <tr> <th>Category</th> <th>Task</th> <th>Metric</th> <th>Result</th> </tr></thead> <tbody> <tr> <td>Commonsense Reasoning</td> <td>xstorycloze_es</td> <td>acc</td> <td>74.06</td> </tr> <tr> <td rowspan="2">NLI</td> <td>wnli_es</td> <td>acc</td> <td>46.48</td> </tr> <tr> <td>xnli_es</td> <td>acc</td> <td>46.47</td> </tr> <tr> <td>Paraphrasing</td> <td>paws_es</td> <td>acc</td> <td>57.65</td> </tr> <tr> <td>QA</td> <td>xquad_es</td> <td>acc</td> <td>71.48</td> </tr> <tr> <td>Translation</td> <td>flores_es</td> <td>bleu</td> <td>23.56</td> </tr> </tbody> </table> #### Catalan <table><thead> <tr> <th>Category</th> <th>Task</th> <th>Metric</th> <th>Result</th> </tr></thead> <tbody> <tr> <td rowspan="2">Commonsense Reasoning</td> <td>copa_ca</td> <td>acc</td> <td>80.8</td> </tr> <tr> <td>xstorycloze_ca</td> <td>acc</td> <td>73.73</td> </tr> <tr> <td rowspan="2">NLI</td> <td>wnli_ca</td> <td>acc</td> <td>56.34</td> </tr> <tr> <td>xnli_ca</td> <td>acc</td> <td>49.4</td> </tr> <tr> <td rowspan="2">Paraphrasing</td> <td>parafraseja</td> <td>acc</td> <td>64.88</td> </tr> <tr> <td>paws_ca</td> <td>acc</td> <td>61.5</td> </tr> <tr> <td rowspan="5">QA</td> <td>arc_ca_easy</td> <td>acc</td> <td>69.23</td> </tr> <tr> <td>arc_ca_challenge</td> <td>acc</td> <td>44.54</td> </tr> <tr> <td>openbookqa_ca</td> <td>acc</td> <td>36.8</td> </tr> <tr> <td>piqa_ca</td> <td>acc</td> <td>70.35</td> </tr> <tr> <td>siqa_ca</td> <td>acc</td> <td>48.26</td> </tr> <tr> <td>Translation</td> <td>flores_ca</td> <td>bleu</td> <td>30.34</td> </tr> </tbody></table> #### Basque <table><thead> <tr> <th>Category</th> <th>Task</th> <th>Metric</th> <th>Result</th> </tr></thead> <tbody> <tr> <td rowspan="2">Commonsense Reasoning</td> <td>xcopa_eu</td> <td>acc</td> <td>68</td> </tr> <tr> <td>xstorycloze_eu</td> <td>acc</td> <td>64.79</td> </tr> <tr> <td rowspan="2">NLI</td> <td>wnli_eu</td> <td>acc</td> <td>38.03</td> </tr> <tr> <td>xnli_eu</td> <td>acc</td> <td>42.85</td> </tr> <tr> <td rowspan="3">QA</td> <td>eus_exams</td> <td>acc</td> <td>38.41</td> </tr> <tr> <td>eus_proficiency</td> <td>acc</td> <td>31.13</td> </tr> <tr> <td>eus_trivia</td> <td>acc</td> <td>45.36</td> </tr> <tr> <td>Reading Comprehension</td> <td>eus_reading</td> <td>acc</td> <td>33.24</td> </tr> <tr> <td>Translation</td> <td>flores_eu</td> <td>bleu</td> <td>16.29</td> </tr> </tbody></table> #### Galician <table><thead> <tr> <th>Category</th> <th>Task</th> <th>Metric</th> <th>Result</th> </tr></thead> <tbody> <tr> <td rowspan="2">Paraphrasing</td> <td>parafrases_gl</td> <td>acc</td> <td>58.84</td> </tr> <tr> <td>paws_gl</td> <td>acc</td> <td>60.85</td> </tr> <tr> <td>QA</td> <td>openbookqa_gl</td> <td>acc</td> <td>34.6</td> </tr> <tr> <td>Translation</td> <td>flores_gl</td> <td>bleu</td> <td>27.98</td> </tr> </tbody> </table> #### English <table><thead> <tr> <th>Category</th> <th>Task</th> <th>Metric</th> <th>Result</th> </tr></thead> <tbody> <tr> <td rowspan="2">Commonsense Reasoning</td> <td>copa</td> <td>acc</td> <td>90</td> </tr> <tr> <td>xstorycloze_en</td> <td>acc</td> <td>79.22</td> </tr> <tr> <td rowspan="2">NLI</td> <td>wnli</td> <td>acc</td> <td>52.11</td> </tr> <tr> <td>xnli_en</td> <td>acc</td> <td>47.27</td> </tr> <tr> <td>Paraphrasing</td> <td>paws *</td> <td>acc</td> <td>59.6</td> </tr> <tr> <td rowspan="6">QA</td> <td>arc_easy</td> <td>acc</td> <td>81.36</td> </tr> <tr> <td>arc_challenge</td> <td>acc</td> <td>50.6</td> </tr> <tr> <td>openbookqa</td> <td>acc</td> <td>34.4</td> </tr> <tr> <td>piqa</td> <td>acc</td> <td>78.78</td> </tr> <tr> <td>social_iqa</td> <td>acc</td> <td>50.15</td> </tr> <tr> <td>squad_en **</td> <td>acc</td> <td>78.06</td> </tr> </tbody></table> \* Current LM Evaluation Harness implementation is lacking correct pre-processing. These results are obtained with adequate pre-processing. \*\* This task is not yet available in the official Harness, we hope to add it soon. ## Ethical Considerations and Limitations We examine the presence of undesired societal and cognitive biases present in this model using different benchmarks. For societal biases, we test performance using the BBQ dataset (Parrish et al., 2022) in the original English and the Regard dataset (Sheng et al., 2019). We report that while performance is high (accuracies between 0.69 and 0.87 depending on the social category) in disambiguated settings the model performs very poorly in ambiguous settings, which is indicative of the presence of societal biases which need to be addressed in post-training phases. We additionally analyse model generations using the Regard dataset and classifier in Catalan, Spanish, and English using backtranslation and manual revision of the translations. We find no statistically significant difference in regard between majority and minority groups for any regard types, with the exception of negative regard in Catalan where model generations are actually slightly worse for social majorities. Our analyses on societal biases show that while these biases are capable of interfering with model performance as expressed in the results on the BBQ dataset, their tendency for representational harm is limited given the results of the Regard dataset. We highlight that our analyses of these biases are by no means exhaustive and are limited by the relative scarcity of adequate resources in all languages present in the training data. We aim to gradually extend and expand our analyses in future work. Our cognitive bias analysis focuses on positional effects in 0-shot settings, and majority class bias in few-shot settings. For positional effects, we leverage the ARC Multiple Choice Question dataset (Clark et al., 2018). We observe moderate to strong primacy effects, whereby the model shows a preference for answers towards the beginning of the list of provided answers. We measure effects of majority class effects in few-shot settings using SST-2 (Socher et al., 2013). We detect moderate effects, implying that outputs can be influenced by the prompts. We highlight that these results can be expected from a pretrained model that has not yet been instruction-tuned or aligned. These tests are performed in order to show the biases the model may contain. We urge developers to take them into account and perform safety testing and tuning tailored to their specific applications of the model. --- ## Additional information ### Author The Language Technologies Unit from Barcelona Supercomputing Center. ### Contact For further information, please send an email to <[email protected]>. ### Copyright Copyright(c) 2024 by Language Technologies Unit, Barcelona Supercomputing Center. ### Funding This work has been promoted and financed by the Government of Catalonia through the [Aina Project](https://projecteaina.cat/). This work is funded by the _Ministerio para la Transformación Digital y de la Función Pública_ - Funded by EU – NextGenerationEU within the framework of [ILENIA Project](https://proyectoilenia.es/) with reference 2022/TL22/00215337. ### Acknowledgements This project has benefited from the contributions of numerous teams and institutions, mainly through data contributions, knowledge transfer or technical support. In Catalonia, many institutions have been involved in the project. Our thanks to Òmnium Cultural, Parlament de Catalunya, Institut d'Estudis Aranesos, Racó Català, Vilaweb, ACN, Nació Digital, El món and Aquí Berguedà. At national level, we are especially grateful to our ILENIA project partners: CENID, HiTZ and CiTIUS for their participation. We also extend our genuine gratitude to the Spanish Senate and Congress, Fundación Dialnet, Fundación Elcano and the ‘Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)’ of the University of Las Palmas de Gran Canaria. At the international level, we thank the Welsh government, DFKI, Occiglot project, especially Malte Ostendorff, and The Common Crawl Foundation, especially Pedro Ortiz, for their collaboration. We would also like to give special thanks to the NVIDIA team, with whom we have met regularly, specially to: Ignacio Sarasua, Adam Henryk Grzywaczewski, Oleg Sudakov, Sergio Perez, Miguel Martinez, Felipes Soares and Meriem Bendris. Their constant support has been especially appreciated throughout the entire process. Their valuable efforts have been instrumental in the development of this work. ### Disclaimer Be aware that the model may contain biases or other unintended distortions. When third parties deploy systems or provide services based on this model, or use the model themselves, they bear the responsibility for mitigating any associated risks and ensuring compliance with applicable regulations, including those governing the use of Artificial Intelligence. The Barcelona Supercomputing Center, as the owner and creator of the model, shall not be held liable for any outcomes resulting from third-party use. ### Citation Technical report and paper coming soon. ### License [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ## Model Index |Model|Base|Instruct| |:---:|:---:|:---:| |2B| [Link](https://huggingface.co/BSC-LT/salamandra-2b) | [Link](https://huggingface.co/BSC-LT/salamandra-2b-instruct) | |7B| [Link](https://huggingface.co/BSC-LT/salamandra-7b) | [Link](https://huggingface.co/BSC-LT/salamandra-7b-instruct) | |40B| WiP | WiP |
[ "QUESTION_ANSWERING", "TRANSLATION", "SUMMARIZATION", "PARAPHRASING" ]
[ "BEAR", "SCIELO" ]
Non_BioNLP
CCwz/gme-Qwen2-VL-7B-Instruct-Q5_K_S-GGUF
CCwz
sentence-similarity
[ "sentence-transformers", "gguf", "mteb", "transformers", "Qwen2-VL", "sentence-similarity", "vidore", "llama-cpp", "gguf-my-repo", "en", "zh", "base_model:Alibaba-NLP/gme-Qwen2-VL-7B-Instruct", "base_model:quantized:Alibaba-NLP/gme-Qwen2-VL-7B-Instruct", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]
1,735
1,735
73
0
--- base_model: Alibaba-NLP/gme-Qwen2-VL-7B-Instruct language: - en - zh license: apache-2.0 tags: - mteb - sentence-transformers - transformers - Qwen2-VL - sentence-similarity - vidore - llama-cpp - gguf-my-repo model-index: - name: gme-Qwen2-VL-7B-Instruct results: - task: type: STS dataset: name: MTEB AFQMC type: C-MTEB/AFQMC config: default split: validation revision: b44c3b011063adb25877c13823db83bb193913c4 metrics: - type: cos_sim_pearson value: 55.46303883144227 - type: cos_sim_spearman value: 59.66708815497073 - type: euclidean_pearson value: 57.81360946949099 - type: euclidean_spearman value: 59.66710825926347 - type: manhattan_pearson value: 57.723697562189344 - type: manhattan_spearman value: 59.55004095814257 - type: cos_sim_pearson value: 55.46303883144227 - type: cos_sim_spearman value: 59.66708815497073 - type: euclidean_pearson value: 57.81360946949099 - type: euclidean_spearman value: 59.66710825926347 - type: manhattan_pearson value: 57.723697562189344 - type: manhattan_spearman value: 59.55004095814257 - task: type: STS dataset: name: MTEB ATEC type: C-MTEB/ATEC config: default split: test revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865 metrics: - type: cos_sim_pearson value: 52.381881068686894 - type: cos_sim_spearman value: 55.468235529709766 - type: euclidean_pearson value: 56.974786979175086 - type: euclidean_spearman value: 55.468231026153745 - type: manhattan_pearson value: 56.944671325662576 - type: manhattan_spearman value: 55.39037386224014 - type: cos_sim_pearson value: 52.381881068686894 - type: cos_sim_spearman value: 55.468235529709766 - type: euclidean_pearson value: 56.974786979175086 - type: euclidean_spearman value: 55.468231026153745 - type: manhattan_pearson value: 56.944671325662576 - type: manhattan_spearman value: 55.39037386224014 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 77.61194029850746 - type: ap value: 41.29789064067677 - type: f1 value: 71.69633278678522 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 97.3258 - type: ap value: 95.91845683387056 - type: f1 value: 97.32526074864263 - type: accuracy value: 97.3258 - type: ap value: 95.91845683387056 - type: f1 value: 97.32526074864263 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 64.794 - type: f1 value: 63.7329780206882 - task: type: Retrieval dataset: name: MTEB ArguAna type: mteb/arguana config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 40.541 - type: map_at_10 value: 56.315000000000005 - type: map_at_100 value: 56.824 - type: map_at_1000 value: 56.825 - type: map_at_3 value: 51.778 - type: map_at_5 value: 54.623 - type: mrr_at_1 value: 41.038000000000004 - type: mrr_at_10 value: 56.532000000000004 - type: mrr_at_100 value: 57.034 - type: mrr_at_1000 value: 57.034 - type: mrr_at_3 value: 52.015 - type: mrr_at_5 value: 54.835 - type: ndcg_at_1 value: 40.541 - type: ndcg_at_10 value: 64.596 - type: ndcg_at_100 value: 66.656 - type: ndcg_at_1000 value: 66.666 - type: ndcg_at_3 value: 55.415000000000006 - type: ndcg_at_5 value: 60.527 - type: precision_at_1 value: 40.541 - type: precision_at_10 value: 9.083 - type: precision_at_100 value: 0.996 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 21.977 - type: precision_at_5 value: 15.661 - type: recall_at_1 value: 40.541 - type: recall_at_10 value: 90.825 - type: recall_at_100 value: 99.57300000000001 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 65.932 - type: recall_at_5 value: 78.307 - type: map_at_1 value: 40.541 - type: map_at_10 value: 56.315000000000005 - type: map_at_100 value: 56.824 - type: map_at_1000 value: 56.825 - type: map_at_3 value: 51.778 - type: map_at_5 value: 54.623 - type: mrr_at_1 value: 41.038000000000004 - type: mrr_at_10 value: 56.532000000000004 - type: mrr_at_100 value: 57.034 - type: mrr_at_1000 value: 57.034 - type: mrr_at_3 value: 52.015 - type: mrr_at_5 value: 54.835 - type: ndcg_at_1 value: 40.541 - type: ndcg_at_10 value: 64.596 - type: ndcg_at_100 value: 66.656 - type: ndcg_at_1000 value: 66.666 - type: ndcg_at_3 value: 55.415000000000006 - type: ndcg_at_5 value: 60.527 - type: precision_at_1 value: 40.541 - type: precision_at_10 value: 9.083 - type: precision_at_100 value: 0.996 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 21.977 - type: precision_at_5 value: 15.661 - type: recall_at_1 value: 40.541 - type: recall_at_10 value: 90.825 - type: recall_at_100 value: 99.57300000000001 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 65.932 - type: recall_at_5 value: 78.307 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 54.96111428218386 - type: v_measure value: 54.96111428218386 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 50.637711388838945 - type: v_measure value: 50.637711388838945 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 64.0741897266483 - type: mrr value: 76.11440882909028 - type: map value: 64.0741897266483 - type: mrr value: 76.11440882909028 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 86.2557839280406 - type: cos_sim_spearman value: 82.58200216886888 - type: euclidean_pearson value: 84.80588838508498 - 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type: map value: 90.50750030424048 - type: mrr value: 92.3970634920635 - type: map value: 90.50750030424048 - type: mrr value: 92.3970634920635 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 28.848000000000003 - type: map_at_10 value: 40.453 - type: map_at_100 value: 42.065000000000005 - type: map_at_1000 value: 42.176 - type: map_at_3 value: 36.697 - type: map_at_5 value: 38.855000000000004 - type: mrr_at_1 value: 34.764 - type: mrr_at_10 value: 45.662000000000006 - type: mrr_at_100 value: 46.56 - type: mrr_at_1000 value: 46.597 - type: mrr_at_3 value: 42.632 - type: mrr_at_5 value: 44.249 - type: ndcg_at_1 value: 34.764 - type: ndcg_at_10 value: 47.033 - type: ndcg_at_100 value: 53.089 - type: ndcg_at_1000 value: 54.818 - type: ndcg_at_3 value: 41.142 - type: ndcg_at_5 value: 43.928 - type: precision_at_1 value: 34.764 - type: precision_at_10 value: 9.027000000000001 - type: precision_at_100 value: 1.465 - type: precision_at_1000 value: 0.192 - type: precision_at_3 value: 19.695 - type: precision_at_5 value: 14.535 - type: recall_at_1 value: 28.848000000000003 - type: recall_at_10 value: 60.849 - type: recall_at_100 value: 85.764 - type: recall_at_1000 value: 96.098 - type: recall_at_3 value: 44.579 - type: recall_at_5 value: 51.678999999999995 - type: map_at_1 value: 28.848000000000003 - type: map_at_10 value: 40.453 - type: map_at_100 value: 42.065000000000005 - type: map_at_1000 value: 42.176 - type: map_at_3 value: 36.697 - type: map_at_5 value: 38.855000000000004 - type: mrr_at_1 value: 34.764 - type: mrr_at_10 value: 45.662000000000006 - type: mrr_at_100 value: 46.56 - type: mrr_at_1000 value: 46.597 - type: mrr_at_3 value: 42.632 - type: mrr_at_5 value: 44.249 - type: ndcg_at_1 value: 34.764 - type: ndcg_at_10 value: 47.033 - type: ndcg_at_100 value: 53.089 - type: ndcg_at_1000 value: 54.818 - type: ndcg_at_3 value: 41.142 - type: ndcg_at_5 value: 43.928 - type: precision_at_1 value: 34.764 - type: precision_at_10 value: 9.027000000000001 - type: precision_at_100 value: 1.465 - type: precision_at_1000 value: 0.192 - type: precision_at_3 value: 19.695 - type: precision_at_5 value: 14.535 - type: recall_at_1 value: 28.848000000000003 - type: recall_at_10 value: 60.849 - type: recall_at_100 value: 85.764 - type: recall_at_1000 value: 96.098 - type: recall_at_3 value: 44.579 - type: recall_at_5 value: 51.678999999999995 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: BeIR/cqadupstack config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 30.731 - type: map_at_10 value: 41.859 - type: map_at_100 value: 43.13 - type: map_at_1000 value: 43.257 - type: map_at_3 value: 38.384 - type: map_at_5 value: 40.284 - type: mrr_at_1 value: 38.471 - type: mrr_at_10 value: 47.531 - type: mrr_at_100 value: 48.199 - type: mrr_at_1000 value: 48.24 - type: mrr_at_3 value: 44.989000000000004 - type: mrr_at_5 value: 46.403 - type: ndcg_at_1 value: 38.471 - type: ndcg_at_10 value: 48.022999999999996 - type: ndcg_at_100 value: 52.32599999999999 - type: ndcg_at_1000 value: 54.26 - type: ndcg_at_3 value: 42.986999999999995 - type: ndcg_at_5 value: 45.23 - type: precision_at_1 value: 38.471 - type: precision_at_10 value: 9.248000000000001 - type: precision_at_100 value: 1.469 - type: precision_at_1000 value: 0.193 - type: precision_at_3 value: 20.892 - type: precision_at_5 value: 14.892 - type: recall_at_1 value: 30.731 - type: recall_at_10 value: 59.561 - type: recall_at_100 value: 77.637 - type: recall_at_1000 value: 89.64999999999999 - type: recall_at_3 value: 44.897999999999996 - type: recall_at_5 value: 51.181 - type: map_at_1 value: 30.731 - type: map_at_10 value: 41.859 - type: map_at_100 value: 43.13 - type: map_at_1000 value: 43.257 - type: map_at_3 value: 38.384 - type: map_at_5 value: 40.284 - type: mrr_at_1 value: 38.471 - type: mrr_at_10 value: 47.531 - type: mrr_at_100 value: 48.199 - type: mrr_at_1000 value: 48.24 - type: mrr_at_3 value: 44.989000000000004 - type: mrr_at_5 value: 46.403 - type: ndcg_at_1 value: 38.471 - type: ndcg_at_10 value: 48.022999999999996 - type: ndcg_at_100 value: 52.32599999999999 - type: ndcg_at_1000 value: 54.26 - type: ndcg_at_3 value: 42.986999999999995 - type: ndcg_at_5 value: 45.23 - type: precision_at_1 value: 38.471 - type: precision_at_10 value: 9.248000000000001 - type: precision_at_100 value: 1.469 - type: precision_at_1000 value: 0.193 - type: precision_at_3 value: 20.892 - type: precision_at_5 value: 14.892 - type: recall_at_1 value: 30.731 - type: recall_at_10 value: 59.561 - type: recall_at_100 value: 77.637 - type: recall_at_1000 value: 89.64999999999999 - type: recall_at_3 value: 44.897999999999996 - type: recall_at_5 value: 51.181 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: BeIR/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 34.949000000000005 - type: map_at_10 value: 48.117 - type: map_at_100 value: 49.355 - type: map_at_1000 value: 49.409 - type: map_at_3 value: 44.732 - type: map_at_5 value: 46.555 - type: mrr_at_1 value: 40.188 - type: mrr_at_10 value: 51.452 - type: mrr_at_100 value: 52.219 - type: mrr_at_1000 value: 52.24100000000001 - type: mrr_at_3 value: 48.642 - type: mrr_at_5 value: 50.134 - type: ndcg_at_1 value: 40.188 - type: ndcg_at_10 value: 54.664 - type: ndcg_at_100 value: 59.38099999999999 - type: ndcg_at_1000 value: 60.363 - type: ndcg_at_3 value: 48.684 - type: ndcg_at_5 value: 51.406 - type: precision_at_1 value: 40.188 - type: precision_at_10 value: 9.116 - type: precision_at_100 value: 1.248 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 22.236 - type: precision_at_5 value: 15.310000000000002 - type: recall_at_1 value: 34.949000000000005 - type: recall_at_10 value: 70.767 - type: recall_at_100 value: 90.79 - type: recall_at_1000 value: 97.57900000000001 - type: recall_at_3 value: 54.723 - type: recall_at_5 value: 61.404 - type: map_at_1 value: 34.949000000000005 - type: map_at_10 value: 48.117 - type: map_at_100 value: 49.355 - type: map_at_1000 value: 49.409 - type: map_at_3 value: 44.732 - type: map_at_5 value: 46.555 - type: mrr_at_1 value: 40.188 - type: mrr_at_10 value: 51.452 - type: mrr_at_100 value: 52.219 - type: mrr_at_1000 value: 52.24100000000001 - type: mrr_at_3 value: 48.642 - type: mrr_at_5 value: 50.134 - type: ndcg_at_1 value: 40.188 - type: ndcg_at_10 value: 54.664 - type: ndcg_at_100 value: 59.38099999999999 - type: ndcg_at_1000 value: 60.363 - type: ndcg_at_3 value: 48.684 - type: ndcg_at_5 value: 51.406 - type: precision_at_1 value: 40.188 - type: precision_at_10 value: 9.116 - type: precision_at_100 value: 1.248 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 22.236 - type: precision_at_5 value: 15.310000000000002 - type: recall_at_1 value: 34.949000000000005 - type: recall_at_10 value: 70.767 - type: recall_at_100 value: 90.79 - type: recall_at_1000 value: 97.57900000000001 - type: recall_at_3 value: 54.723 - type: recall_at_5 value: 61.404 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: BeIR/cqadupstack config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 25.312 - type: map_at_10 value: 34.799 - type: map_at_100 value: 35.906 - type: map_at_1000 value: 35.983 - type: map_at_3 value: 31.582 - type: map_at_5 value: 33.507999999999996 - type: mrr_at_1 value: 27.232 - type: mrr_at_10 value: 36.82 - type: mrr_at_100 value: 37.733 - type: mrr_at_1000 value: 37.791000000000004 - type: mrr_at_3 value: 33.804 - type: mrr_at_5 value: 35.606 - type: ndcg_at_1 value: 27.232 - type: ndcg_at_10 value: 40.524 - type: ndcg_at_100 value: 45.654 - type: ndcg_at_1000 value: 47.557 - type: ndcg_at_3 value: 34.312 - type: ndcg_at_5 value: 37.553 - type: precision_at_1 value: 27.232 - type: precision_at_10 value: 6.52 - type: precision_at_100 value: 0.9530000000000001 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 14.915000000000001 - type: precision_at_5 value: 10.847 - type: recall_at_1 value: 25.312 - type: recall_at_10 value: 56.169000000000004 - type: recall_at_100 value: 79.16499999999999 - type: recall_at_1000 value: 93.49300000000001 - type: recall_at_3 value: 39.5 - type: recall_at_5 value: 47.288999999999994 - type: map_at_1 value: 25.312 - type: map_at_10 value: 34.799 - type: map_at_100 value: 35.906 - type: map_at_1000 value: 35.983 - type: map_at_3 value: 31.582 - type: map_at_5 value: 33.507999999999996 - type: mrr_at_1 value: 27.232 - type: mrr_at_10 value: 36.82 - type: mrr_at_100 value: 37.733 - type: mrr_at_1000 value: 37.791000000000004 - type: mrr_at_3 value: 33.804 - type: mrr_at_5 value: 35.606 - type: ndcg_at_1 value: 27.232 - type: ndcg_at_10 value: 40.524 - type: ndcg_at_100 value: 45.654 - type: ndcg_at_1000 value: 47.557 - type: ndcg_at_3 value: 34.312 - type: ndcg_at_5 value: 37.553 - type: precision_at_1 value: 27.232 - type: precision_at_10 value: 6.52 - type: precision_at_100 value: 0.9530000000000001 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 14.915000000000001 - type: precision_at_5 value: 10.847 - type: recall_at_1 value: 25.312 - type: recall_at_10 value: 56.169000000000004 - type: recall_at_100 value: 79.16499999999999 - type: recall_at_1000 value: 93.49300000000001 - type: recall_at_3 value: 39.5 - type: recall_at_5 value: 47.288999999999994 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: BeIR/cqadupstack config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 17.153 - type: map_at_10 value: 27.671 - type: map_at_100 value: 29.186 - type: map_at_1000 value: 29.299999999999997 - type: map_at_3 value: 24.490000000000002 - type: map_at_5 value: 26.178 - type: mrr_at_1 value: 21.144 - type: mrr_at_10 value: 32.177 - type: mrr_at_100 value: 33.247 - type: mrr_at_1000 value: 33.306000000000004 - type: mrr_at_3 value: 29.187 - type: mrr_at_5 value: 30.817 - type: ndcg_at_1 value: 21.144 - type: ndcg_at_10 value: 33.981 - type: ndcg_at_100 value: 40.549 - type: ndcg_at_1000 value: 43.03 - type: ndcg_at_3 value: 28.132 - type: ndcg_at_5 value: 30.721999999999998 - type: precision_at_1 value: 21.144 - type: precision_at_10 value: 6.666999999999999 - type: precision_at_100 value: 1.147 - type: precision_at_1000 value: 0.149 - type: precision_at_3 value: 14.302999999999999 - type: precision_at_5 value: 10.423 - type: recall_at_1 value: 17.153 - type: recall_at_10 value: 48.591 - type: recall_at_100 value: 76.413 - type: recall_at_1000 value: 93.8 - type: recall_at_3 value: 32.329 - type: recall_at_5 value: 38.958999999999996 - type: map_at_1 value: 17.153 - type: map_at_10 value: 27.671 - type: map_at_100 value: 29.186 - type: map_at_1000 value: 29.299999999999997 - type: map_at_3 value: 24.490000000000002 - type: map_at_5 value: 26.178 - type: mrr_at_1 value: 21.144 - type: mrr_at_10 value: 32.177 - type: mrr_at_100 value: 33.247 - type: mrr_at_1000 value: 33.306000000000004 - type: mrr_at_3 value: 29.187 - type: mrr_at_5 value: 30.817 - type: ndcg_at_1 value: 21.144 - type: ndcg_at_10 value: 33.981 - type: ndcg_at_100 value: 40.549 - type: ndcg_at_1000 value: 43.03 - type: ndcg_at_3 value: 28.132 - type: ndcg_at_5 value: 30.721999999999998 - type: precision_at_1 value: 21.144 - type: precision_at_10 value: 6.666999999999999 - type: precision_at_100 value: 1.147 - type: precision_at_1000 value: 0.149 - type: precision_at_3 value: 14.302999999999999 - type: precision_at_5 value: 10.423 - type: recall_at_1 value: 17.153 - type: recall_at_10 value: 48.591 - type: recall_at_100 value: 76.413 - type: recall_at_1000 value: 93.8 - type: recall_at_3 value: 32.329 - type: recall_at_5 value: 38.958999999999996 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: BeIR/cqadupstack config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 27.909 - type: map_at_10 value: 40.168 - type: map_at_100 value: 41.524 - type: map_at_1000 value: 41.626000000000005 - type: map_at_3 value: 36.274 - type: map_at_5 value: 38.411 - type: mrr_at_1 value: 34.649 - type: mrr_at_10 value: 45.613 - type: mrr_at_100 value: 46.408 - type: mrr_at_1000 value: 46.444 - type: mrr_at_3 value: 42.620999999999995 - type: mrr_at_5 value: 44.277 - type: ndcg_at_1 value: 34.649 - type: ndcg_at_10 value: 47.071000000000005 - type: ndcg_at_100 value: 52.559999999999995 - type: ndcg_at_1000 value: 54.285000000000004 - type: ndcg_at_3 value: 40.63 - type: ndcg_at_5 value: 43.584 - type: precision_at_1 value: 34.649 - type: precision_at_10 value: 8.855 - type: precision_at_100 value: 1.361 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 19.538 - type: precision_at_5 value: 14.187 - type: recall_at_1 value: 27.909 - type: recall_at_10 value: 62.275000000000006 - type: recall_at_100 value: 84.95 - type: recall_at_1000 value: 96.02000000000001 - type: recall_at_3 value: 44.767 - type: recall_at_5 value: 52.03 - type: map_at_1 value: 27.909 - type: map_at_10 value: 40.168 - type: map_at_100 value: 41.524 - type: map_at_1000 value: 41.626000000000005 - type: map_at_3 value: 36.274 - type: map_at_5 value: 38.411 - type: mrr_at_1 value: 34.649 - type: mrr_at_10 value: 45.613 - type: mrr_at_100 value: 46.408 - type: mrr_at_1000 value: 46.444 - type: mrr_at_3 value: 42.620999999999995 - type: mrr_at_5 value: 44.277 - type: ndcg_at_1 value: 34.649 - type: ndcg_at_10 value: 47.071000000000005 - type: ndcg_at_100 value: 52.559999999999995 - type: ndcg_at_1000 value: 54.285000000000004 - type: ndcg_at_3 value: 40.63 - type: ndcg_at_5 value: 43.584 - type: precision_at_1 value: 34.649 - type: precision_at_10 value: 8.855 - type: precision_at_100 value: 1.361 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 19.538 - type: precision_at_5 value: 14.187 - type: recall_at_1 value: 27.909 - type: recall_at_10 value: 62.275000000000006 - type: recall_at_100 value: 84.95 - type: recall_at_1000 value: 96.02000000000001 - type: recall_at_3 value: 44.767 - type: recall_at_5 value: 52.03 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: BeIR/cqadupstack config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 25.846000000000004 - type: map_at_10 value: 36.870999999999995 - type: map_at_100 value: 38.294 - type: map_at_1000 value: 38.401 - type: map_at_3 value: 33.163 - type: map_at_5 value: 35.177 - type: mrr_at_1 value: 31.849 - type: mrr_at_10 value: 41.681000000000004 - type: mrr_at_100 value: 42.658 - type: mrr_at_1000 value: 42.71 - type: mrr_at_3 value: 39.003 - type: mrr_at_5 value: 40.436 - type: ndcg_at_1 value: 31.849 - type: ndcg_at_10 value: 43.291000000000004 - type: ndcg_at_100 value: 49.136 - type: ndcg_at_1000 value: 51.168 - type: ndcg_at_3 value: 37.297999999999995 - type: ndcg_at_5 value: 39.934 - type: precision_at_1 value: 31.849 - type: precision_at_10 value: 8.219 - type: precision_at_100 value: 1.318 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 18.151 - type: precision_at_5 value: 13.242 - type: recall_at_1 value: 25.846000000000004 - type: recall_at_10 value: 57.642 - type: recall_at_100 value: 82.069 - type: recall_at_1000 value: 95.684 - type: recall_at_3 value: 40.778999999999996 - type: recall_at_5 value: 47.647 - type: map_at_1 value: 25.846000000000004 - type: map_at_10 value: 36.870999999999995 - type: map_at_100 value: 38.294 - type: map_at_1000 value: 38.401 - type: map_at_3 value: 33.163 - type: map_at_5 value: 35.177 - type: mrr_at_1 value: 31.849 - type: mrr_at_10 value: 41.681000000000004 - type: mrr_at_100 value: 42.658 - type: mrr_at_1000 value: 42.71 - type: mrr_at_3 value: 39.003 - type: mrr_at_5 value: 40.436 - type: ndcg_at_1 value: 31.849 - type: ndcg_at_10 value: 43.291000000000004 - type: ndcg_at_100 value: 49.136 - type: ndcg_at_1000 value: 51.168 - type: ndcg_at_3 value: 37.297999999999995 - type: ndcg_at_5 value: 39.934 - type: precision_at_1 value: 31.849 - type: precision_at_10 value: 8.219 - type: precision_at_100 value: 1.318 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 18.151 - type: precision_at_5 value: 13.242 - type: recall_at_1 value: 25.846000000000004 - type: recall_at_10 value: 57.642 - type: recall_at_100 value: 82.069 - type: recall_at_1000 value: 95.684 - type: recall_at_3 value: 40.778999999999996 - type: recall_at_5 value: 47.647 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: BeIR/cqadupstack config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 25.102000000000004 - type: map_at_10 value: 33.31 - type: map_at_100 value: 34.443 - type: map_at_1000 value: 34.547 - type: map_at_3 value: 30.932 - type: map_at_5 value: 32.126 - type: mrr_at_1 value: 28.221 - type: mrr_at_10 value: 36.519 - type: mrr_at_100 value: 37.425000000000004 - type: mrr_at_1000 value: 37.498 - type: mrr_at_3 value: 34.254 - type: mrr_at_5 value: 35.388999999999996 - type: ndcg_at_1 value: 28.221 - type: ndcg_at_10 value: 38.340999999999994 - type: ndcg_at_100 value: 43.572 - type: ndcg_at_1000 value: 45.979 - type: ndcg_at_3 value: 33.793 - type: ndcg_at_5 value: 35.681000000000004 - type: precision_at_1 value: 28.221 - type: precision_at_10 value: 6.135 - type: precision_at_100 value: 0.946 - type: precision_at_1000 value: 0.123 - type: precision_at_3 value: 14.519000000000002 - type: precision_at_5 value: 9.969 - type: recall_at_1 value: 25.102000000000004 - type: recall_at_10 value: 50.639 - type: recall_at_100 value: 74.075 - type: recall_at_1000 value: 91.393 - type: recall_at_3 value: 37.952000000000005 - type: recall_at_5 value: 42.71 - type: map_at_1 value: 25.102000000000004 - type: map_at_10 value: 33.31 - type: map_at_100 value: 34.443 - type: map_at_1000 value: 34.547 - type: map_at_3 value: 30.932 - type: map_at_5 value: 32.126 - type: mrr_at_1 value: 28.221 - type: mrr_at_10 value: 36.519 - type: mrr_at_100 value: 37.425000000000004 - type: mrr_at_1000 value: 37.498 - type: mrr_at_3 value: 34.254 - type: mrr_at_5 value: 35.388999999999996 - type: ndcg_at_1 value: 28.221 - type: ndcg_at_10 value: 38.340999999999994 - type: ndcg_at_100 value: 43.572 - type: ndcg_at_1000 value: 45.979 - type: ndcg_at_3 value: 33.793 - type: ndcg_at_5 value: 35.681000000000004 - type: precision_at_1 value: 28.221 - type: precision_at_10 value: 6.135 - type: precision_at_100 value: 0.946 - type: precision_at_1000 value: 0.123 - type: precision_at_3 value: 14.519000000000002 - type: precision_at_5 value: 9.969 - type: recall_at_1 value: 25.102000000000004 - type: recall_at_10 value: 50.639 - type: recall_at_100 value: 74.075 - type: recall_at_1000 value: 91.393 - type: recall_at_3 value: 37.952000000000005 - type: recall_at_5 value: 42.71 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: BeIR/cqadupstack config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 18.618000000000002 - type: map_at_10 value: 26.714 - type: map_at_100 value: 27.929 - type: map_at_1000 value: 28.057 - type: map_at_3 value: 24.134 - type: map_at_5 value: 25.575 - type: mrr_at_1 value: 22.573999999999998 - type: mrr_at_10 value: 30.786 - type: mrr_at_100 value: 31.746000000000002 - type: mrr_at_1000 value: 31.822 - type: mrr_at_3 value: 28.412 - type: mrr_at_5 value: 29.818 - type: ndcg_at_1 value: 22.573999999999998 - type: ndcg_at_10 value: 31.852000000000004 - type: ndcg_at_100 value: 37.477 - type: ndcg_at_1000 value: 40.331 - type: ndcg_at_3 value: 27.314 - type: ndcg_at_5 value: 29.485 - type: precision_at_1 value: 22.573999999999998 - type: precision_at_10 value: 5.86 - type: precision_at_100 value: 1.012 - type: precision_at_1000 value: 0.146 - type: precision_at_3 value: 13.099 - type: precision_at_5 value: 9.56 - type: recall_at_1 value: 18.618000000000002 - type: recall_at_10 value: 43.134 - type: recall_at_100 value: 68.294 - type: recall_at_1000 value: 88.283 - type: recall_at_3 value: 30.397999999999996 - type: recall_at_5 value: 35.998000000000005 - type: map_at_1 value: 18.618000000000002 - type: map_at_10 value: 26.714 - type: map_at_100 value: 27.929 - type: map_at_1000 value: 28.057 - type: map_at_3 value: 24.134 - type: map_at_5 value: 25.575 - type: mrr_at_1 value: 22.573999999999998 - type: mrr_at_10 value: 30.786 - type: mrr_at_100 value: 31.746000000000002 - type: mrr_at_1000 value: 31.822 - type: mrr_at_3 value: 28.412 - type: mrr_at_5 value: 29.818 - type: ndcg_at_1 value: 22.573999999999998 - type: ndcg_at_10 value: 31.852000000000004 - type: ndcg_at_100 value: 37.477 - type: ndcg_at_1000 value: 40.331 - type: ndcg_at_3 value: 27.314 - type: ndcg_at_5 value: 29.485 - type: precision_at_1 value: 22.573999999999998 - type: precision_at_10 value: 5.86 - type: precision_at_100 value: 1.012 - type: precision_at_1000 value: 0.146 - type: precision_at_3 value: 13.099 - type: precision_at_5 value: 9.56 - type: recall_at_1 value: 18.618000000000002 - type: recall_at_10 value: 43.134 - type: recall_at_100 value: 68.294 - type: recall_at_1000 value: 88.283 - type: recall_at_3 value: 30.397999999999996 - type: recall_at_5 value: 35.998000000000005 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: BeIR/cqadupstack config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 27.76 - type: map_at_10 value: 37.569 - type: map_at_100 value: 38.784 - type: map_at_1000 value: 38.884 - type: map_at_3 value: 34.379 - type: map_at_5 value: 36.092999999999996 - type: mrr_at_1 value: 32.556000000000004 - type: mrr_at_10 value: 41.870000000000005 - type: mrr_at_100 value: 42.759 - type: mrr_at_1000 value: 42.806 - type: mrr_at_3 value: 39.086 - type: mrr_at_5 value: 40.574 - type: ndcg_at_1 value: 32.556000000000004 - type: ndcg_at_10 value: 43.382 - type: ndcg_at_100 value: 48.943 - type: ndcg_at_1000 value: 50.961999999999996 - type: ndcg_at_3 value: 37.758 - type: ndcg_at_5 value: 40.282000000000004 - type: precision_at_1 value: 32.556000000000004 - type: precision_at_10 value: 7.463 - type: precision_at_100 value: 1.1480000000000001 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 17.133000000000003 - type: precision_at_5 value: 12.164 - type: recall_at_1 value: 27.76 - type: recall_at_10 value: 56.71000000000001 - type: recall_at_100 value: 81.053 - type: recall_at_1000 value: 94.75 - type: recall_at_3 value: 41.387 - type: recall_at_5 value: 47.818 - type: map_at_1 value: 27.76 - type: map_at_10 value: 37.569 - type: map_at_100 value: 38.784 - type: map_at_1000 value: 38.884 - type: map_at_3 value: 34.379 - type: map_at_5 value: 36.092999999999996 - type: mrr_at_1 value: 32.556000000000004 - type: mrr_at_10 value: 41.870000000000005 - type: mrr_at_100 value: 42.759 - type: mrr_at_1000 value: 42.806 - type: mrr_at_3 value: 39.086 - type: mrr_at_5 value: 40.574 - type: ndcg_at_1 value: 32.556000000000004 - type: ndcg_at_10 value: 43.382 - type: ndcg_at_100 value: 48.943 - type: ndcg_at_1000 value: 50.961999999999996 - type: ndcg_at_3 value: 37.758 - type: ndcg_at_5 value: 40.282000000000004 - type: precision_at_1 value: 32.556000000000004 - type: precision_at_10 value: 7.463 - type: precision_at_100 value: 1.1480000000000001 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 17.133000000000003 - type: precision_at_5 value: 12.164 - type: recall_at_1 value: 27.76 - type: recall_at_10 value: 56.71000000000001 - type: recall_at_100 value: 81.053 - type: recall_at_1000 value: 94.75 - type: recall_at_3 value: 41.387 - type: recall_at_5 value: 47.818 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: BeIR/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 23.62 - type: map_at_10 value: 33.522999999999996 - type: map_at_100 value: 35.281 - type: map_at_1000 value: 35.504000000000005 - type: map_at_3 value: 30.314999999999998 - type: map_at_5 value: 32.065 - type: mrr_at_1 value: 28.458 - type: mrr_at_10 value: 38.371 - type: mrr_at_100 value: 39.548 - type: mrr_at_1000 value: 39.601 - type: mrr_at_3 value: 35.638999999999996 - type: mrr_at_5 value: 37.319 - type: ndcg_at_1 value: 28.458 - type: ndcg_at_10 value: 39.715 - type: ndcg_at_100 value: 46.394999999999996 - type: ndcg_at_1000 value: 48.943999999999996 - type: ndcg_at_3 value: 34.361999999999995 - type: ndcg_at_5 value: 37.006 - type: precision_at_1 value: 28.458 - type: precision_at_10 value: 7.5889999999999995 - type: precision_at_100 value: 1.514 - type: precision_at_1000 value: 0.242 - type: precision_at_3 value: 16.073999999999998 - type: precision_at_5 value: 11.976 - type: recall_at_1 value: 23.62 - type: recall_at_10 value: 52.117000000000004 - type: recall_at_100 value: 81.097 - type: recall_at_1000 value: 96.47 - type: recall_at_3 value: 37.537 - type: recall_at_5 value: 44.112 - type: map_at_1 value: 23.62 - type: map_at_10 value: 33.522999999999996 - type: map_at_100 value: 35.281 - type: map_at_1000 value: 35.504000000000005 - type: map_at_3 value: 30.314999999999998 - type: map_at_5 value: 32.065 - type: mrr_at_1 value: 28.458 - type: mrr_at_10 value: 38.371 - type: mrr_at_100 value: 39.548 - type: mrr_at_1000 value: 39.601 - type: mrr_at_3 value: 35.638999999999996 - type: mrr_at_5 value: 37.319 - type: ndcg_at_1 value: 28.458 - type: ndcg_at_10 value: 39.715 - type: ndcg_at_100 value: 46.394999999999996 - type: ndcg_at_1000 value: 48.943999999999996 - type: ndcg_at_3 value: 34.361999999999995 - type: ndcg_at_5 value: 37.006 - type: precision_at_1 value: 28.458 - type: precision_at_10 value: 7.5889999999999995 - type: precision_at_100 value: 1.514 - type: precision_at_1000 value: 0.242 - type: precision_at_3 value: 16.073999999999998 - type: precision_at_5 value: 11.976 - type: recall_at_1 value: 23.62 - type: recall_at_10 value: 52.117000000000004 - type: recall_at_100 value: 81.097 - type: recall_at_1000 value: 96.47 - type: recall_at_3 value: 37.537 - type: recall_at_5 value: 44.112 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: BeIR/cqadupstack config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 18.336 - type: map_at_10 value: 26.811 - type: map_at_100 value: 27.892 - type: map_at_1000 value: 27.986 - type: map_at_3 value: 23.976 - type: map_at_5 value: 25.605 - type: mrr_at_1 value: 20.148 - type: mrr_at_10 value: 28.898000000000003 - type: mrr_at_100 value: 29.866 - type: mrr_at_1000 value: 29.929 - type: mrr_at_3 value: 26.247999999999998 - type: mrr_at_5 value: 27.744999999999997 - type: ndcg_at_1 value: 20.148 - type: ndcg_at_10 value: 32.059 - type: ndcg_at_100 value: 37.495 - type: ndcg_at_1000 value: 39.855000000000004 - type: ndcg_at_3 value: 26.423000000000002 - type: ndcg_at_5 value: 29.212 - type: precision_at_1 value: 20.148 - type: precision_at_10 value: 5.268 - type: precision_at_100 value: 0.872 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 11.459999999999999 - type: precision_at_5 value: 8.503 - type: recall_at_1 value: 18.336 - type: recall_at_10 value: 46.411 - type: recall_at_100 value: 71.33500000000001 - type: recall_at_1000 value: 88.895 - type: recall_at_3 value: 31.134 - type: recall_at_5 value: 37.862 - type: map_at_1 value: 18.336 - type: map_at_10 value: 26.811 - type: map_at_100 value: 27.892 - type: map_at_1000 value: 27.986 - type: map_at_3 value: 23.976 - type: map_at_5 value: 25.605 - type: mrr_at_1 value: 20.148 - type: mrr_at_10 value: 28.898000000000003 - type: mrr_at_100 value: 29.866 - type: mrr_at_1000 value: 29.929 - type: mrr_at_3 value: 26.247999999999998 - type: mrr_at_5 value: 27.744999999999997 - type: ndcg_at_1 value: 20.148 - type: ndcg_at_10 value: 32.059 - type: ndcg_at_100 value: 37.495 - type: ndcg_at_1000 value: 39.855000000000004 - type: ndcg_at_3 value: 26.423000000000002 - type: ndcg_at_5 value: 29.212 - type: precision_at_1 value: 20.148 - type: precision_at_10 value: 5.268 - type: precision_at_100 value: 0.872 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 11.459999999999999 - type: precision_at_5 value: 8.503 - type: recall_at_1 value: 18.336 - type: recall_at_10 value: 46.411 - type: recall_at_100 value: 71.33500000000001 - type: recall_at_1000 value: 88.895 - type: recall_at_3 value: 31.134 - type: recall_at_5 value: 37.862 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: mteb/climate-fever config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 21.149 - type: map_at_10 value: 35.251 - type: map_at_100 value: 37.342 - type: map_at_1000 value: 37.516 - type: map_at_3 value: 30.543 - type: map_at_5 value: 33.19 - type: mrr_at_1 value: 47.687000000000005 - type: mrr_at_10 value: 59.391000000000005 - type: mrr_at_100 value: 59.946999999999996 - type: mrr_at_1000 value: 59.965999999999994 - type: mrr_at_3 value: 56.938 - type: mrr_at_5 value: 58.498000000000005 - type: ndcg_at_1 value: 47.687000000000005 - type: ndcg_at_10 value: 45.381 - type: ndcg_at_100 value: 52.405 - type: ndcg_at_1000 value: 55.041 - type: ndcg_at_3 value: 40.024 - type: ndcg_at_5 value: 41.821999999999996 - type: precision_at_1 value: 47.687000000000005 - type: precision_at_10 value: 13.355 - type: precision_at_100 value: 2.113 - type: precision_at_1000 value: 0.261 - type: precision_at_3 value: 29.793999999999997 - type: precision_at_5 value: 21.811 - type: recall_at_1 value: 21.149 - type: recall_at_10 value: 49.937 - type: recall_at_100 value: 73.382 - type: recall_at_1000 value: 87.606 - type: recall_at_3 value: 35.704 - type: recall_at_5 value: 42.309000000000005 - type: map_at_1 value: 21.149 - type: map_at_10 value: 35.251 - type: map_at_100 value: 37.342 - type: map_at_1000 value: 37.516 - type: map_at_3 value: 30.543 - type: map_at_5 value: 33.19 - type: mrr_at_1 value: 47.687000000000005 - type: mrr_at_10 value: 59.391000000000005 - type: mrr_at_100 value: 59.946999999999996 - type: mrr_at_1000 value: 59.965999999999994 - type: mrr_at_3 value: 56.938 - type: mrr_at_5 value: 58.498000000000005 - type: ndcg_at_1 value: 47.687000000000005 - type: ndcg_at_10 value: 45.381 - type: ndcg_at_100 value: 52.405 - type: ndcg_at_1000 value: 55.041 - type: ndcg_at_3 value: 40.024 - type: ndcg_at_5 value: 41.821999999999996 - type: precision_at_1 value: 47.687000000000005 - type: precision_at_10 value: 13.355 - type: precision_at_100 value: 2.113 - type: precision_at_1000 value: 0.261 - type: precision_at_3 value: 29.793999999999997 - type: precision_at_5 value: 21.811 - type: recall_at_1 value: 21.149 - type: recall_at_10 value: 49.937 - type: recall_at_100 value: 73.382 - type: recall_at_1000 value: 87.606 - type: recall_at_3 value: 35.704 - type: recall_at_5 value: 42.309000000000005 - task: type: Retrieval dataset: name: MTEB CmedqaRetrieval type: C-MTEB/CmedqaRetrieval config: default split: dev revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301 metrics: - type: map_at_1 value: 28.74 - type: map_at_10 value: 41.981 - type: map_at_100 value: 43.753 - type: map_at_1000 value: 43.858999999999995 - type: map_at_3 value: 37.634 - type: map_at_5 value: 40.158 - type: mrr_at_1 value: 43.086 - type: mrr_at_10 value: 51.249 - type: mrr_at_100 value: 52.154 - type: mrr_at_1000 value: 52.190999999999995 - type: mrr_at_3 value: 48.787000000000006 - type: mrr_at_5 value: 50.193 - type: ndcg_at_1 value: 43.086 - type: ndcg_at_10 value: 48.703 - type: ndcg_at_100 value: 55.531 - type: ndcg_at_1000 value: 57.267999999999994 - type: ndcg_at_3 value: 43.464000000000006 - type: ndcg_at_5 value: 45.719 - type: precision_at_1 value: 43.086 - type: precision_at_10 value: 10.568 - type: precision_at_100 value: 1.616 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 24.256 - type: precision_at_5 value: 17.509 - type: recall_at_1 value: 28.74 - type: recall_at_10 value: 59.349 - type: recall_at_100 value: 87.466 - type: recall_at_1000 value: 98.914 - type: recall_at_3 value: 43.322 - type: recall_at_5 value: 50.409000000000006 - type: map_at_1 value: 28.74 - type: map_at_10 value: 41.981 - type: map_at_100 value: 43.753 - type: map_at_1000 value: 43.858999999999995 - type: map_at_3 value: 37.634 - type: map_at_5 value: 40.158 - type: mrr_at_1 value: 43.086 - type: mrr_at_10 value: 51.249 - type: mrr_at_100 value: 52.154 - type: mrr_at_1000 value: 52.190999999999995 - type: mrr_at_3 value: 48.787000000000006 - type: mrr_at_5 value: 50.193 - type: ndcg_at_1 value: 43.086 - type: ndcg_at_10 value: 48.703 - type: ndcg_at_100 value: 55.531 - type: ndcg_at_1000 value: 57.267999999999994 - type: ndcg_at_3 value: 43.464000000000006 - type: ndcg_at_5 value: 45.719 - type: precision_at_1 value: 43.086 - type: precision_at_10 value: 10.568 - type: precision_at_100 value: 1.616 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 24.256 - type: precision_at_5 value: 17.509 - type: recall_at_1 value: 28.74 - type: recall_at_10 value: 59.349 - type: recall_at_100 value: 87.466 - type: recall_at_1000 value: 98.914 - type: recall_at_3 value: 43.322 - type: recall_at_5 value: 50.409000000000006 - task: type: PairClassification dataset: name: MTEB Cmnli type: C-MTEB/CMNLI config: default split: validation revision: 41bc36f332156f7adc9e38f53777c959b2ae9766 metrics: - type: cos_sim_accuracy value: 79.03788334335539 - type: cos_sim_ap value: 87.21703260472833 - type: cos_sim_f1 value: 79.87784187309127 - type: cos_sim_precision value: 77.36634531113059 - type: cos_sim_recall value: 82.55786766425064 - type: dot_accuracy value: 79.03788334335539 - type: dot_ap value: 87.22906528217948 - type: dot_f1 value: 79.87784187309127 - type: dot_precision value: 77.36634531113059 - type: dot_recall value: 82.55786766425064 - type: euclidean_accuracy value: 79.03788334335539 - type: euclidean_ap value: 87.21703670465753 - type: euclidean_f1 value: 79.87784187309127 - type: euclidean_precision value: 77.36634531113059 - type: euclidean_recall value: 82.55786766425064 - type: manhattan_accuracy value: 78.28021647624774 - 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type: ndcg_at_100 value: 81.34400000000001 - type: ndcg_at_1000 value: 81.477 - type: ndcg_at_3 value: 75.876 - type: ndcg_at_5 value: 77.94200000000001 - type: precision_at_1 value: 67.756 - type: precision_at_10 value: 9.231 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 27.362 - type: precision_at_5 value: 17.45 - type: recall_at_1 value: 67.597 - type: recall_at_10 value: 91.307 - type: recall_at_100 value: 98.946 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 81.428 - type: recall_at_5 value: 86.407 - type: map_at_1 value: 67.597 - type: map_at_10 value: 75.81599999999999 - type: map_at_100 value: 76.226 - type: map_at_1000 value: 76.23100000000001 - type: map_at_3 value: 73.907 - type: map_at_5 value: 75.08200000000001 - type: mrr_at_1 value: 67.756 - type: mrr_at_10 value: 75.8 - type: mrr_at_100 value: 76.205 - type: mrr_at_1000 value: 76.21 - type: mrr_at_3 value: 73.955 - type: mrr_at_5 value: 75.093 - type: ndcg_at_1 value: 67.756 - type: ndcg_at_10 value: 79.598 - type: ndcg_at_100 value: 81.34400000000001 - type: ndcg_at_1000 value: 81.477 - type: ndcg_at_3 value: 75.876 - type: ndcg_at_5 value: 77.94200000000001 - type: precision_at_1 value: 67.756 - type: precision_at_10 value: 9.231 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 27.362 - type: precision_at_5 value: 17.45 - type: recall_at_1 value: 67.597 - type: recall_at_10 value: 91.307 - type: recall_at_100 value: 98.946 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 81.428 - type: recall_at_5 value: 86.407 - task: type: Retrieval dataset: name: MTEB DBPedia type: mteb/dbpedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 9.33 - type: map_at_10 value: 23.118 - type: map_at_100 value: 34.28 - type: map_at_1000 value: 36.574 - type: map_at_3 value: 15.576 - type: map_at_5 value: 18.778 - type: mrr_at_1 value: 75.25 - type: mrr_at_10 value: 81.958 - type: mrr_at_100 value: 82.282 - type: mrr_at_1000 value: 82.285 - type: mrr_at_3 value: 81.042 - type: mrr_at_5 value: 81.62899999999999 - type: ndcg_at_1 value: 63.625 - type: ndcg_at_10 value: 50.781 - type: ndcg_at_100 value: 55.537000000000006 - type: ndcg_at_1000 value: 62.651 - type: ndcg_at_3 value: 55.297 - type: ndcg_at_5 value: 53.103 - type: precision_at_1 value: 75.25 - type: precision_at_10 value: 41.475 - type: precision_at_100 value: 13.5 - type: precision_at_1000 value: 2.686 - type: precision_at_3 value: 59.333000000000006 - type: precision_at_5 value: 51.9 - type: recall_at_1 value: 9.33 - type: recall_at_10 value: 29.398000000000003 - type: recall_at_100 value: 61.951 - type: recall_at_1000 value: 85.463 - type: recall_at_3 value: 17.267 - type: recall_at_5 value: 21.89 - type: map_at_1 value: 9.33 - type: map_at_10 value: 23.118 - type: map_at_100 value: 34.28 - type: map_at_1000 value: 36.574 - type: map_at_3 value: 15.576 - type: map_at_5 value: 18.778 - type: mrr_at_1 value: 75.25 - type: mrr_at_10 value: 81.958 - type: mrr_at_100 value: 82.282 - type: mrr_at_1000 value: 82.285 - type: mrr_at_3 value: 81.042 - type: mrr_at_5 value: 81.62899999999999 - type: ndcg_at_1 value: 63.625 - type: ndcg_at_10 value: 50.781 - type: ndcg_at_100 value: 55.537000000000006 - type: ndcg_at_1000 value: 62.651 - type: ndcg_at_3 value: 55.297 - type: ndcg_at_5 value: 53.103 - type: precision_at_1 value: 75.25 - type: precision_at_10 value: 41.475 - type: precision_at_100 value: 13.5 - type: precision_at_1000 value: 2.686 - type: precision_at_3 value: 59.333000000000006 - type: precision_at_5 value: 51.9 - type: recall_at_1 value: 9.33 - type: recall_at_10 value: 29.398000000000003 - type: recall_at_100 value: 61.951 - type: recall_at_1000 value: 85.463 - type: recall_at_3 value: 17.267 - type: recall_at_5 value: 21.89 - task: type: Retrieval dataset: name: MTEB DuRetrieval type: C-MTEB/DuRetrieval config: default split: dev revision: a1a333e290fe30b10f3f56498e3a0d911a693ced metrics: - type: map_at_1 value: 25.608999999999998 - type: map_at_10 value: 78.649 - type: map_at_100 value: 81.67699999999999 - type: map_at_1000 value: 81.71000000000001 - type: map_at_3 value: 54.112 - type: map_at_5 value: 68.34700000000001 - type: mrr_at_1 value: 87.75 - type: mrr_at_10 value: 92.175 - type: mrr_at_100 value: 92.225 - type: mrr_at_1000 value: 92.227 - type: mrr_at_3 value: 91.833 - type: mrr_at_5 value: 92.06800000000001 - type: ndcg_at_1 value: 87.75 - type: ndcg_at_10 value: 86.56700000000001 - type: ndcg_at_100 value: 89.519 - type: ndcg_at_1000 value: 89.822 - type: ndcg_at_3 value: 84.414 - type: ndcg_at_5 value: 83.721 - type: precision_at_1 value: 87.75 - type: precision_at_10 value: 41.665 - type: precision_at_100 value: 4.827 - type: precision_at_1000 value: 0.49 - type: precision_at_3 value: 75.533 - type: precision_at_5 value: 64.01 - type: recall_at_1 value: 25.608999999999998 - type: recall_at_10 value: 88.708 - type: recall_at_100 value: 98.007 - type: recall_at_1000 value: 99.555 - type: recall_at_3 value: 57.157000000000004 - type: recall_at_5 value: 74.118 - type: map_at_1 value: 25.608999999999998 - type: map_at_10 value: 78.649 - type: map_at_100 value: 81.67699999999999 - type: map_at_1000 value: 81.71000000000001 - type: map_at_3 value: 54.112 - type: map_at_5 value: 68.34700000000001 - type: mrr_at_1 value: 87.75 - type: mrr_at_10 value: 92.175 - type: mrr_at_100 value: 92.225 - type: mrr_at_1000 value: 92.227 - type: mrr_at_3 value: 91.833 - type: mrr_at_5 value: 92.06800000000001 - type: ndcg_at_1 value: 87.75 - type: ndcg_at_10 value: 86.56700000000001 - type: ndcg_at_100 value: 89.519 - type: ndcg_at_1000 value: 89.822 - type: ndcg_at_3 value: 84.414 - type: ndcg_at_5 value: 83.721 - type: precision_at_1 value: 87.75 - type: precision_at_10 value: 41.665 - type: precision_at_100 value: 4.827 - type: precision_at_1000 value: 0.49 - type: precision_at_3 value: 75.533 - type: precision_at_5 value: 64.01 - type: recall_at_1 value: 25.608999999999998 - type: recall_at_10 value: 88.708 - type: recall_at_100 value: 98.007 - type: recall_at_1000 value: 99.555 - type: recall_at_3 value: 57.157000000000004 - type: recall_at_5 value: 74.118 - task: type: Retrieval dataset: name: MTEB EcomRetrieval type: C-MTEB/EcomRetrieval config: default split: dev revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9 metrics: - type: map_at_1 value: 55.800000000000004 - type: map_at_10 value: 65.952 - type: map_at_100 value: 66.413 - type: map_at_1000 value: 66.426 - type: map_at_3 value: 63.3 - type: map_at_5 value: 64.945 - type: mrr_at_1 value: 55.800000000000004 - type: mrr_at_10 value: 65.952 - type: mrr_at_100 value: 66.413 - type: mrr_at_1000 value: 66.426 - type: mrr_at_3 value: 63.3 - type: mrr_at_5 value: 64.945 - type: ndcg_at_1 value: 55.800000000000004 - type: ndcg_at_10 value: 71.00800000000001 - type: ndcg_at_100 value: 72.974 - type: ndcg_at_1000 value: 73.302 - type: ndcg_at_3 value: 65.669 - type: ndcg_at_5 value: 68.634 - type: precision_at_1 value: 55.800000000000004 - type: precision_at_10 value: 8.690000000000001 - type: precision_at_100 value: 0.955 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 24.166999999999998 - type: precision_at_5 value: 15.939999999999998 - type: recall_at_1 value: 55.800000000000004 - type: recall_at_10 value: 86.9 - type: recall_at_100 value: 95.5 - type: recall_at_1000 value: 98.0 - type: recall_at_3 value: 72.5 - type: recall_at_5 value: 79.7 - type: map_at_1 value: 55.800000000000004 - type: map_at_10 value: 65.952 - type: map_at_100 value: 66.413 - type: map_at_1000 value: 66.426 - type: map_at_3 value: 63.3 - type: map_at_5 value: 64.945 - type: mrr_at_1 value: 55.800000000000004 - type: mrr_at_10 value: 65.952 - type: mrr_at_100 value: 66.413 - type: mrr_at_1000 value: 66.426 - type: mrr_at_3 value: 63.3 - type: mrr_at_5 value: 64.945 - type: ndcg_at_1 value: 55.800000000000004 - type: ndcg_at_10 value: 71.00800000000001 - type: ndcg_at_100 value: 72.974 - type: ndcg_at_1000 value: 73.302 - type: ndcg_at_3 value: 65.669 - type: ndcg_at_5 value: 68.634 - type: precision_at_1 value: 55.800000000000004 - type: precision_at_10 value: 8.690000000000001 - type: precision_at_100 value: 0.955 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 24.166999999999998 - type: precision_at_5 value: 15.939999999999998 - type: recall_at_1 value: 55.800000000000004 - type: recall_at_10 value: 86.9 - type: recall_at_100 value: 95.5 - type: recall_at_1000 value: 98.0 - type: recall_at_3 value: 72.5 - type: recall_at_5 value: 79.7 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 67.39500000000001 - type: f1 value: 62.01837785021389 - type: accuracy value: 67.39500000000001 - type: f1 value: 62.01837785021389 - task: type: Retrieval dataset: name: MTEB FEVER type: mteb/fever config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 86.27 - type: map_at_10 value: 92.163 - type: map_at_100 value: 92.351 - type: map_at_1000 value: 92.36 - type: map_at_3 value: 91.36 - type: map_at_5 value: 91.888 - type: mrr_at_1 value: 92.72399999999999 - type: mrr_at_10 value: 95.789 - type: mrr_at_100 value: 95.80300000000001 - type: mrr_at_1000 value: 95.804 - type: mrr_at_3 value: 95.64200000000001 - type: mrr_at_5 value: 95.75 - type: ndcg_at_1 value: 92.72399999999999 - type: ndcg_at_10 value: 94.269 - type: ndcg_at_100 value: 94.794 - type: ndcg_at_1000 value: 94.94 - type: ndcg_at_3 value: 93.427 - type: ndcg_at_5 value: 93.914 - type: precision_at_1 value: 92.72399999999999 - type: precision_at_10 value: 11.007 - type: precision_at_100 value: 1.153 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 34.993 - type: precision_at_5 value: 21.542 - type: recall_at_1 value: 86.27 - type: recall_at_10 value: 97.031 - type: recall_at_100 value: 98.839 - type: recall_at_1000 value: 99.682 - type: recall_at_3 value: 94.741 - type: recall_at_5 value: 96.03 - type: map_at_1 value: 86.27 - type: map_at_10 value: 92.163 - type: map_at_100 value: 92.351 - type: map_at_1000 value: 92.36 - type: map_at_3 value: 91.36 - type: map_at_5 value: 91.888 - type: mrr_at_1 value: 92.72399999999999 - type: mrr_at_10 value: 95.789 - type: mrr_at_100 value: 95.80300000000001 - type: mrr_at_1000 value: 95.804 - type: mrr_at_3 value: 95.64200000000001 - type: mrr_at_5 value: 95.75 - type: ndcg_at_1 value: 92.72399999999999 - type: ndcg_at_10 value: 94.269 - type: ndcg_at_100 value: 94.794 - type: ndcg_at_1000 value: 94.94 - type: ndcg_at_3 value: 93.427 - type: ndcg_at_5 value: 93.914 - type: precision_at_1 value: 92.72399999999999 - type: precision_at_10 value: 11.007 - type: precision_at_100 value: 1.153 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 34.993 - type: precision_at_5 value: 21.542 - type: recall_at_1 value: 86.27 - type: recall_at_10 value: 97.031 - type: recall_at_100 value: 98.839 - type: recall_at_1000 value: 99.682 - type: recall_at_3 value: 94.741 - type: recall_at_5 value: 96.03 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: mteb/fiqa config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 29.561999999999998 - type: map_at_10 value: 48.52 - type: map_at_100 value: 50.753 - type: map_at_1000 value: 50.878 - type: map_at_3 value: 42.406 - type: map_at_5 value: 45.994 - type: mrr_at_1 value: 54.784 - type: mrr_at_10 value: 64.51400000000001 - type: mrr_at_100 value: 65.031 - type: mrr_at_1000 value: 65.05199999999999 - type: mrr_at_3 value: 62.474 - type: mrr_at_5 value: 63.562 - type: ndcg_at_1 value: 54.784 - type: ndcg_at_10 value: 57.138 - type: ndcg_at_100 value: 63.666999999999994 - type: ndcg_at_1000 value: 65.379 - type: ndcg_at_3 value: 52.589 - type: ndcg_at_5 value: 54.32599999999999 - type: precision_at_1 value: 54.784 - type: precision_at_10 value: 15.693999999999999 - type: precision_at_100 value: 2.259 - type: precision_at_1000 value: 0.256 - type: precision_at_3 value: 34.774 - type: precision_at_5 value: 25.772000000000002 - type: recall_at_1 value: 29.561999999999998 - type: recall_at_10 value: 64.708 - type: recall_at_100 value: 87.958 - type: recall_at_1000 value: 97.882 - type: recall_at_3 value: 48.394 - type: recall_at_5 value: 56.101 - type: map_at_1 value: 29.561999999999998 - type: map_at_10 value: 48.52 - type: map_at_100 value: 50.753 - type: map_at_1000 value: 50.878 - type: map_at_3 value: 42.406 - type: map_at_5 value: 45.994 - type: mrr_at_1 value: 54.784 - type: mrr_at_10 value: 64.51400000000001 - type: mrr_at_100 value: 65.031 - type: mrr_at_1000 value: 65.05199999999999 - type: mrr_at_3 value: 62.474 - type: mrr_at_5 value: 63.562 - type: ndcg_at_1 value: 54.784 - type: ndcg_at_10 value: 57.138 - type: ndcg_at_100 value: 63.666999999999994 - type: ndcg_at_1000 value: 65.379 - type: ndcg_at_3 value: 52.589 - type: ndcg_at_5 value: 54.32599999999999 - type: precision_at_1 value: 54.784 - type: precision_at_10 value: 15.693999999999999 - type: precision_at_100 value: 2.259 - type: precision_at_1000 value: 0.256 - type: precision_at_3 value: 34.774 - type: precision_at_5 value: 25.772000000000002 - type: recall_at_1 value: 29.561999999999998 - type: recall_at_10 value: 64.708 - type: recall_at_100 value: 87.958 - type: recall_at_1000 value: 97.882 - type: recall_at_3 value: 48.394 - type: recall_at_5 value: 56.101 - task: type: Retrieval dataset: name: MTEB HotpotQA type: mteb/hotpotqa config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 43.72 - type: map_at_10 value: 71.905 - type: map_at_100 value: 72.685 - type: map_at_1000 value: 72.72800000000001 - type: map_at_3 value: 68.538 - type: map_at_5 value: 70.675 - type: mrr_at_1 value: 87.441 - type: mrr_at_10 value: 91.432 - type: mrr_at_100 value: 91.512 - type: mrr_at_1000 value: 91.513 - type: mrr_at_3 value: 90.923 - type: mrr_at_5 value: 91.252 - type: ndcg_at_1 value: 87.441 - 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type: mrr_at_5 value: 91.252 - type: ndcg_at_1 value: 87.441 - type: ndcg_at_10 value: 79.212 - type: ndcg_at_100 value: 81.694 - type: ndcg_at_1000 value: 82.447 - type: ndcg_at_3 value: 74.746 - type: ndcg_at_5 value: 77.27199999999999 - type: precision_at_1 value: 87.441 - type: precision_at_10 value: 16.42 - type: precision_at_100 value: 1.833 - type: precision_at_1000 value: 0.193 - type: precision_at_3 value: 48.184 - type: precision_at_5 value: 30.897999999999996 - type: recall_at_1 value: 43.72 - type: recall_at_10 value: 82.1 - type: recall_at_100 value: 91.62700000000001 - type: recall_at_1000 value: 96.556 - type: recall_at_3 value: 72.275 - type: recall_at_5 value: 77.24499999999999 - task: type: Classification dataset: name: MTEB IFlyTek type: C-MTEB/IFlyTek-classification config: default split: validation revision: 421605374b29664c5fc098418fe20ada9bd55f8a metrics: - type: accuracy value: 54.520969603693736 - type: f1 value: 42.359043311419626 - type: accuracy value: 54.520969603693736 - type: f1 value: 42.359043311419626 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 96.72559999999999 - type: ap value: 95.01759461773742 - type: f1 value: 96.72429945397575 - type: accuracy value: 96.72559999999999 - type: ap value: 95.01759461773742 - type: f1 value: 96.72429945397575 - task: type: Classification dataset: name: MTEB JDReview type: C-MTEB/JDReview-classification config: default split: test revision: b7c64bd89eb87f8ded463478346f76731f07bf8b metrics: - type: accuracy value: 90.1688555347092 - type: ap value: 63.36583667477521 - type: f1 value: 85.6845016521436 - type: accuracy value: 90.1688555347092 - type: ap value: 63.36583667477521 - type: f1 value: 85.6845016521436 - task: type: STS dataset: name: MTEB LCQMC type: C-MTEB/LCQMC config: default split: test revision: 17f9b096f80380fce5ed12a9be8be7784b337daf metrics: - type: cos_sim_pearson value: 68.8503997749679 - type: cos_sim_spearman value: 74.15059291199371 - type: euclidean_pearson value: 73.01105331948172 - type: euclidean_spearman value: 74.15059069348803 - type: manhattan_pearson value: 72.80856655624557 - type: manhattan_spearman value: 73.95174793448955 - type: cos_sim_pearson value: 68.8503997749679 - type: cos_sim_spearman value: 74.15059291199371 - type: euclidean_pearson value: 73.01105331948172 - type: euclidean_spearman value: 74.15059069348803 - type: manhattan_pearson value: 72.80856655624557 - type: manhattan_spearman value: 73.95174793448955 - task: type: Reranking dataset: name: MTEB MMarcoReranking type: C-MTEB/Mmarco-reranking config: default split: dev revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6 metrics: - type: map value: 32.68592539803807 - type: mrr value: 31.58968253968254 - type: map value: 32.68592539803807 - type: mrr value: 31.58968253968254 - task: type: Retrieval dataset: name: MTEB MMarcoRetrieval type: C-MTEB/MMarcoRetrieval config: default split: dev revision: 539bbde593d947e2a124ba72651aafc09eb33fc2 metrics: - 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type: recall_at_3 value: 85.137 - type: recall_at_5 value: 89.548 - task: type: Retrieval dataset: name: MTEB MSMARCO type: mteb/msmarco config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 22.006999999999998 - type: map_at_10 value: 34.994 - type: map_at_100 value: 36.183 - type: map_at_1000 value: 36.227 - type: map_at_3 value: 30.75 - type: map_at_5 value: 33.155 - type: mrr_at_1 value: 22.679 - type: mrr_at_10 value: 35.619 - type: mrr_at_100 value: 36.732 - type: mrr_at_1000 value: 36.77 - type: mrr_at_3 value: 31.44 - type: mrr_at_5 value: 33.811 - type: ndcg_at_1 value: 22.679 - type: ndcg_at_10 value: 42.376000000000005 - type: ndcg_at_100 value: 48.001 - type: ndcg_at_1000 value: 49.059999999999995 - type: ndcg_at_3 value: 33.727000000000004 - type: ndcg_at_5 value: 38.013000000000005 - type: precision_at_1 value: 22.679 - type: precision_at_10 value: 6.815 - type: precision_at_100 value: 0.962 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 14.441 - type: precision_at_5 value: 10.817 - type: recall_at_1 value: 22.006999999999998 - type: recall_at_10 value: 65.158 - type: recall_at_100 value: 90.997 - type: recall_at_1000 value: 98.996 - type: recall_at_3 value: 41.646 - type: recall_at_5 value: 51.941 - type: map_at_1 value: 22.006999999999998 - type: map_at_10 value: 34.994 - type: map_at_100 value: 36.183 - type: map_at_1000 value: 36.227 - type: map_at_3 value: 30.75 - type: map_at_5 value: 33.155 - type: mrr_at_1 value: 22.679 - type: mrr_at_10 value: 35.619 - type: mrr_at_100 value: 36.732 - type: mrr_at_1000 value: 36.77 - type: mrr_at_3 value: 31.44 - type: mrr_at_5 value: 33.811 - type: ndcg_at_1 value: 22.679 - type: ndcg_at_10 value: 42.376000000000005 - type: ndcg_at_100 value: 48.001 - type: ndcg_at_1000 value: 49.059999999999995 - type: ndcg_at_3 value: 33.727000000000004 - type: ndcg_at_5 value: 38.013000000000005 - type: precision_at_1 value: 22.679 - type: precision_at_10 value: 6.815 - type: precision_at_100 value: 0.962 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 14.441 - type: precision_at_5 value: 10.817 - type: recall_at_1 value: 22.006999999999998 - type: recall_at_10 value: 65.158 - type: recall_at_100 value: 90.997 - type: recall_at_1000 value: 98.996 - type: recall_at_3 value: 41.646 - type: recall_at_5 value: 51.941 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 97.55129958960327 - type: f1 value: 97.43464802675416 - type: accuracy value: 97.55129958960327 - type: f1 value: 97.43464802675416 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 90.4719562243502 - type: f1 value: 70.76460034443902 - type: accuracy value: 90.4719562243502 - type: f1 value: 70.76460034443902 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 83.49024882313383 - type: f1 value: 81.44067057564666 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 87.23268325487558 - type: f1 value: 86.36737921996752 - task: type: Retrieval dataset: name: MTEB MedicalRetrieval type: C-MTEB/MedicalRetrieval config: default split: dev revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6 metrics: - type: map_at_1 value: 56.89999999999999 - type: map_at_10 value: 63.438 - type: map_at_100 value: 63.956 - type: map_at_1000 value: 63.991 - type: map_at_3 value: 61.983 - type: map_at_5 value: 62.778 - type: mrr_at_1 value: 56.99999999999999 - type: mrr_at_10 value: 63.483000000000004 - type: mrr_at_100 value: 63.993 - type: mrr_at_1000 value: 64.02799999999999 - type: mrr_at_3 value: 62.017 - type: mrr_at_5 value: 62.812 - type: ndcg_at_1 value: 56.89999999999999 - type: ndcg_at_10 value: 66.61 - type: ndcg_at_100 value: 69.387 - type: ndcg_at_1000 value: 70.327 - type: ndcg_at_3 value: 63.583999999999996 - type: ndcg_at_5 value: 65.0 - type: precision_at_1 value: 56.89999999999999 - type: precision_at_10 value: 7.66 - type: precision_at_100 value: 0.902 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 22.733 - type: precision_at_5 value: 14.32 - type: recall_at_1 value: 56.89999999999999 - type: recall_at_10 value: 76.6 - type: recall_at_100 value: 90.2 - type: recall_at_1000 value: 97.6 - type: recall_at_3 value: 68.2 - type: recall_at_5 value: 71.6 - type: map_at_1 value: 56.89999999999999 - type: map_at_10 value: 63.438 - type: map_at_100 value: 63.956 - type: map_at_1000 value: 63.991 - type: map_at_3 value: 61.983 - type: map_at_5 value: 62.778 - type: mrr_at_1 value: 56.99999999999999 - type: mrr_at_10 value: 63.483000000000004 - type: mrr_at_100 value: 63.993 - type: mrr_at_1000 value: 64.02799999999999 - type: mrr_at_3 value: 62.017 - type: mrr_at_5 value: 62.812 - type: ndcg_at_1 value: 56.89999999999999 - type: ndcg_at_10 value: 66.61 - type: ndcg_at_100 value: 69.387 - type: ndcg_at_1000 value: 70.327 - type: ndcg_at_3 value: 63.583999999999996 - type: ndcg_at_5 value: 65.0 - type: precision_at_1 value: 56.89999999999999 - type: precision_at_10 value: 7.66 - type: precision_at_100 value: 0.902 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 22.733 - type: precision_at_5 value: 14.32 - type: recall_at_1 value: 56.89999999999999 - type: recall_at_10 value: 76.6 - type: recall_at_100 value: 90.2 - type: recall_at_1000 value: 97.6 - type: recall_at_3 value: 68.2 - type: recall_at_5 value: 71.6 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 40.32149153753394 - type: v_measure value: 40.32149153753394 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 39.40319973495386 - type: v_measure value: 39.40319973495386 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 33.9769104898534 - type: mrr value: 35.32831430710564 - type: map value: 33.9769104898534 - type: mrr value: 35.32831430710564 - task: type: Classification dataset: name: MTEB MultilingualSentiment type: C-MTEB/MultilingualSentiment-classification config: default split: validation revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a metrics: - type: accuracy value: 81.80666666666667 - type: f1 value: 81.83278699395508 - type: accuracy value: 81.80666666666667 - type: f1 value: 81.83278699395508 - task: type: Retrieval dataset: name: MTEB NFCorpus type: mteb/nfcorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 6.3 - type: map_at_10 value: 14.151 - type: map_at_100 value: 18.455 - type: map_at_1000 value: 20.186999999999998 - type: map_at_3 value: 10.023 - type: map_at_5 value: 11.736 - type: mrr_at_1 value: 49.536 - type: mrr_at_10 value: 58.516 - type: mrr_at_100 value: 59.084 - type: mrr_at_1000 value: 59.114 - type: mrr_at_3 value: 56.45 - type: mrr_at_5 value: 57.642 - type: ndcg_at_1 value: 47.522999999999996 - type: ndcg_at_10 value: 38.4 - type: ndcg_at_100 value: 35.839999999999996 - type: ndcg_at_1000 value: 44.998 - type: ndcg_at_3 value: 43.221 - type: ndcg_at_5 value: 40.784 - type: precision_at_1 value: 49.536 - type: precision_at_10 value: 28.977999999999998 - type: precision_at_100 value: 9.378 - type: precision_at_1000 value: 2.2769999999999997 - type: precision_at_3 value: 40.454 - type: precision_at_5 value: 35.418 - type: recall_at_1 value: 6.3 - type: recall_at_10 value: 19.085 - type: recall_at_100 value: 38.18 - type: recall_at_1000 value: 71.219 - type: recall_at_3 value: 11.17 - type: recall_at_5 value: 13.975999999999999 - type: map_at_1 value: 6.3 - type: map_at_10 value: 14.151 - type: map_at_100 value: 18.455 - type: map_at_1000 value: 20.186999999999998 - type: map_at_3 value: 10.023 - type: map_at_5 value: 11.736 - type: mrr_at_1 value: 49.536 - type: mrr_at_10 value: 58.516 - type: mrr_at_100 value: 59.084 - type: mrr_at_1000 value: 59.114 - type: mrr_at_3 value: 56.45 - type: mrr_at_5 value: 57.642 - type: ndcg_at_1 value: 47.522999999999996 - type: ndcg_at_10 value: 38.4 - type: ndcg_at_100 value: 35.839999999999996 - type: ndcg_at_1000 value: 44.998 - type: ndcg_at_3 value: 43.221 - type: ndcg_at_5 value: 40.784 - type: precision_at_1 value: 49.536 - type: precision_at_10 value: 28.977999999999998 - type: precision_at_100 value: 9.378 - type: precision_at_1000 value: 2.2769999999999997 - type: precision_at_3 value: 40.454 - type: precision_at_5 value: 35.418 - type: recall_at_1 value: 6.3 - type: recall_at_10 value: 19.085 - type: recall_at_100 value: 38.18 - type: recall_at_1000 value: 71.219 - type: recall_at_3 value: 11.17 - type: recall_at_5 value: 13.975999999999999 - task: type: Retrieval dataset: name: MTEB NQ type: mteb/nq config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 43.262 - type: map_at_10 value: 60.387 - type: map_at_100 value: 61.102000000000004 - type: map_at_1000 value: 61.111000000000004 - type: map_at_3 value: 56.391999999999996 - type: map_at_5 value: 58.916000000000004 - type: mrr_at_1 value: 48.725 - type: mrr_at_10 value: 62.812999999999995 - type: mrr_at_100 value: 63.297000000000004 - type: mrr_at_1000 value: 63.304 - type: mrr_at_3 value: 59.955999999999996 - type: mrr_at_5 value: 61.785999999999994 - type: ndcg_at_1 value: 48.696 - type: ndcg_at_10 value: 67.743 - type: ndcg_at_100 value: 70.404 - 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task: type: Classification dataset: name: MTEB OnlineShopping type: C-MTEB/OnlineShopping-classification config: default split: test revision: e610f2ebd179a8fda30ae534c3878750a96db120 metrics: - type: accuracy value: 94.46999999999998 - type: ap value: 93.56401511160975 - type: f1 value: 94.46692790889986 - type: accuracy value: 94.46999999999998 - type: ap value: 93.56401511160975 - type: f1 value: 94.46692790889986 - task: type: STS dataset: name: MTEB PAWSX type: C-MTEB/PAWSX config: default split: test revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1 metrics: - type: cos_sim_pearson value: 15.232590709271829 - type: cos_sim_spearman value: 17.204830998481093 - type: euclidean_pearson value: 19.543519063265673 - type: euclidean_spearman value: 17.204830998481093 - type: manhattan_pearson value: 19.5722663367917 - type: manhattan_spearman value: 17.25656568963978 - type: cos_sim_pearson value: 15.232590709271829 - type: cos_sim_spearman value: 17.204830998481093 - type: euclidean_pearson value: 19.543519063265673 - type: euclidean_spearman value: 17.204830998481093 - type: manhattan_pearson value: 19.5722663367917 - type: manhattan_spearman value: 17.25656568963978 - task: type: STS dataset: name: MTEB QBQTC type: C-MTEB/QBQTC config: default split: test revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7 metrics: - type: cos_sim_pearson value: 34.81965984725406 - type: cos_sim_spearman value: 37.697257783907645 - type: euclidean_pearson value: 35.87624912573427 - type: euclidean_spearman value: 37.69725778300291 - type: manhattan_pearson value: 35.69021326773646 - type: manhattan_spearman value: 37.54369033366458 - type: cos_sim_pearson value: 34.81965984725406 - type: cos_sim_spearman value: 37.697257783907645 - type: euclidean_pearson value: 35.87624912573427 - type: euclidean_spearman value: 37.69725778300291 - type: manhattan_pearson value: 35.69021326773646 - type: manhattan_spearman value: 37.54369033366458 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: mteb/quora config: default split: test revision: None metrics: - type: map_at_1 value: 69.952 - type: map_at_10 value: 84.134 - type: map_at_100 value: 84.795 - type: map_at_1000 value: 84.809 - type: map_at_3 value: 81.085 - type: map_at_5 value: 82.976 - type: mrr_at_1 value: 80.56 - type: mrr_at_10 value: 87.105 - type: mrr_at_100 value: 87.20700000000001 - type: mrr_at_1000 value: 87.208 - type: mrr_at_3 value: 86.118 - type: mrr_at_5 value: 86.79299999999999 - type: ndcg_at_1 value: 80.57 - type: ndcg_at_10 value: 88.047 - type: ndcg_at_100 value: 89.266 - type: ndcg_at_1000 value: 89.34299999999999 - type: ndcg_at_3 value: 85.052 - type: ndcg_at_5 value: 86.68299999999999 - type: precision_at_1 value: 80.57 - type: precision_at_10 value: 13.439 - type: precision_at_100 value: 1.536 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.283 - type: precision_at_5 value: 24.558 - type: recall_at_1 value: 69.952 - type: recall_at_10 value: 95.599 - type: recall_at_100 value: 99.67099999999999 - type: recall_at_1000 value: 99.983 - type: recall_at_3 value: 87.095 - type: recall_at_5 value: 91.668 - type: map_at_1 value: 69.952 - type: map_at_10 value: 84.134 - type: map_at_100 value: 84.795 - type: map_at_1000 value: 84.809 - type: map_at_3 value: 81.085 - type: map_at_5 value: 82.976 - type: mrr_at_1 value: 80.56 - type: mrr_at_10 value: 87.105 - type: mrr_at_100 value: 87.20700000000001 - type: mrr_at_1000 value: 87.208 - type: mrr_at_3 value: 86.118 - type: mrr_at_5 value: 86.79299999999999 - type: ndcg_at_1 value: 80.57 - type: ndcg_at_10 value: 88.047 - type: ndcg_at_100 value: 89.266 - type: ndcg_at_1000 value: 89.34299999999999 - type: ndcg_at_3 value: 85.052 - type: ndcg_at_5 value: 86.68299999999999 - type: precision_at_1 value: 80.57 - type: precision_at_10 value: 13.439 - type: precision_at_100 value: 1.536 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.283 - type: precision_at_5 value: 24.558 - type: recall_at_1 value: 69.952 - type: recall_at_10 value: 95.599 - type: recall_at_100 value: 99.67099999999999 - type: recall_at_1000 value: 99.983 - type: recall_at_3 value: 87.095 - type: recall_at_5 value: 91.668 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 70.12802769698337 - type: v_measure value: 70.12802769698337 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 71.19047621740276 - type: v_measure value: 71.19047621740276 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: mteb/scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 6.208 - type: map_at_10 value: 17.036 - type: map_at_100 value: 20.162 - type: map_at_1000 value: 20.552 - type: map_at_3 value: 11.591999999999999 - type: map_at_5 value: 14.349 - type: mrr_at_1 value: 30.599999999999998 - type: mrr_at_10 value: 43.325 - type: mrr_at_100 value: 44.281 - type: mrr_at_1000 value: 44.31 - type: mrr_at_3 value: 39.300000000000004 - type: mrr_at_5 value: 41.730000000000004 - type: ndcg_at_1 value: 30.599999999999998 - type: ndcg_at_10 value: 27.378000000000004 - type: ndcg_at_100 value: 37.768 - type: ndcg_at_1000 value: 43.275000000000006 - type: ndcg_at_3 value: 25.167 - type: ndcg_at_5 value: 22.537 - type: precision_at_1 value: 30.599999999999998 - type: precision_at_10 value: 14.46 - type: precision_at_100 value: 2.937 - type: precision_at_1000 value: 0.424 - type: precision_at_3 value: 23.666999999999998 - type: precision_at_5 value: 20.14 - type: recall_at_1 value: 6.208 - type: recall_at_10 value: 29.29 - type: recall_at_100 value: 59.565 - type: recall_at_1000 value: 85.963 - type: recall_at_3 value: 14.407 - type: recall_at_5 value: 20.412 - type: map_at_1 value: 6.208 - type: map_at_10 value: 17.036 - type: map_at_100 value: 20.162 - type: map_at_1000 value: 20.552 - type: map_at_3 value: 11.591999999999999 - type: map_at_5 value: 14.349 - type: mrr_at_1 value: 30.599999999999998 - type: mrr_at_10 value: 43.325 - type: mrr_at_100 value: 44.281 - type: mrr_at_1000 value: 44.31 - type: mrr_at_3 value: 39.300000000000004 - type: mrr_at_5 value: 41.730000000000004 - type: ndcg_at_1 value: 30.599999999999998 - type: ndcg_at_10 value: 27.378000000000004 - type: ndcg_at_100 value: 37.768 - type: ndcg_at_1000 value: 43.275000000000006 - type: ndcg_at_3 value: 25.167 - type: ndcg_at_5 value: 22.537 - type: precision_at_1 value: 30.599999999999998 - type: precision_at_10 value: 14.46 - type: precision_at_100 value: 2.937 - type: precision_at_1000 value: 0.424 - type: precision_at_3 value: 23.666999999999998 - type: precision_at_5 value: 20.14 - type: recall_at_1 value: 6.208 - type: recall_at_10 value: 29.29 - type: recall_at_100 value: 59.565 - type: recall_at_1000 value: 85.963 - type: recall_at_3 value: 14.407 - type: recall_at_5 value: 20.412 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 82.65489797062479 - type: cos_sim_spearman value: 75.34808277034776 - type: euclidean_pearson value: 79.28097508609059 - type: euclidean_spearman value: 75.3480824481771 - type: manhattan_pearson value: 78.83529262858895 - type: manhattan_spearman value: 74.96318170787025 - type: cos_sim_pearson value: 82.65489797062479 - type: cos_sim_spearman value: 75.34808277034776 - type: euclidean_pearson value: 79.28097508609059 - type: euclidean_spearman value: 75.3480824481771 - type: manhattan_pearson value: 78.83529262858895 - type: manhattan_spearman value: 74.96318170787025 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.06920163624117 - type: cos_sim_spearman value: 77.24549887905519 - type: euclidean_pearson value: 85.58740280635266 - type: euclidean_spearman value: 77.24652170306867 - type: manhattan_pearson value: 85.77917470895854 - type: manhattan_spearman value: 77.54426264008778 - type: cos_sim_pearson value: 85.06920163624117 - type: cos_sim_spearman value: 77.24549887905519 - type: euclidean_pearson value: 85.58740280635266 - type: euclidean_spearman value: 77.24652170306867 - type: manhattan_pearson value: 85.77917470895854 - type: manhattan_spearman value: 77.54426264008778 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 80.9762185094084 - type: cos_sim_spearman value: 80.98090253728394 - type: euclidean_pearson value: 80.88451512135202 - type: euclidean_spearman value: 80.98090253728394 - type: manhattan_pearson value: 80.7606664599805 - type: manhattan_spearman value: 80.87197716950068 - type: cos_sim_pearson value: 80.9762185094084 - type: cos_sim_spearman value: 80.98090253728394 - type: euclidean_pearson value: 80.88451512135202 - type: euclidean_spearman value: 80.98090253728394 - 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type: cos_sim_pearson value: 83.68800355225011 - type: cos_sim_spearman value: 84.47549220803403 - type: euclidean_pearson value: 83.86859896384159 - type: euclidean_spearman value: 84.47551564954756 - type: manhattan_pearson value: 83.74201103044383 - type: manhattan_spearman value: 84.39903759718152 - type: cos_sim_pearson value: 83.68800355225011 - type: cos_sim_spearman value: 84.47549220803403 - type: euclidean_pearson value: 83.86859896384159 - type: euclidean_spearman value: 84.47551564954756 - type: manhattan_pearson value: 83.74201103044383 - type: manhattan_spearman value: 84.39903759718152 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 78.24197302553398 - type: cos_sim_spearman value: 79.44526946553684 - type: euclidean_pearson value: 79.12747636563053 - type: euclidean_spearman value: 79.44526946553684 - type: manhattan_pearson value: 78.94407504115144 - 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type: cos_sim_pearson value: 77.39283860361535 - type: cos_sim_spearman value: 77.14577975930179 - type: euclidean_pearson value: 76.64560889817044 - type: euclidean_spearman value: 77.14577975930179 - type: manhattan_pearson value: 76.82848456242104 - type: manhattan_spearman value: 77.37708521460667 - type: cos_sim_pearson value: 77.39283860361535 - type: cos_sim_spearman value: 77.14577975930179 - type: euclidean_pearson value: 76.64560889817044 - type: euclidean_spearman value: 77.14577975930179 - type: manhattan_pearson value: 76.82848456242104 - type: manhattan_spearman value: 77.37708521460667 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.14036697885552 - type: cos_sim_spearman value: 83.10901632378086 - type: euclidean_pearson value: 83.59991244380554 - type: euclidean_spearman value: 83.10901632378086 - type: manhattan_pearson value: 83.56632266895113 - type: manhattan_spearman value: 83.17610542379353 - type: cos_sim_pearson value: 84.14036697885552 - type: cos_sim_spearman value: 83.10901632378086 - type: euclidean_pearson value: 83.59991244380554 - type: euclidean_spearman value: 83.10901632378086 - type: manhattan_pearson value: 83.56632266895113 - type: manhattan_spearman value: 83.17610542379353 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 88.98026856845443 - type: mrr value: 96.80987494712984 - type: map value: 88.98026856845443 - type: mrr value: 96.80987494712984 - task: type: Retrieval dataset: name: MTEB SciFact type: mteb/scifact config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 41.661 - type: map_at_10 value: 55.492 - type: map_at_100 value: 56.237 - type: map_at_1000 value: 56.255 - type: map_at_3 value: 51.05 - type: map_at_5 value: 54.01200000000001 - 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type: map_at_100 value: 56.237 - type: map_at_1000 value: 56.255 - type: map_at_3 value: 51.05 - type: map_at_5 value: 54.01200000000001 - type: mrr_at_1 value: 44.0 - type: mrr_at_10 value: 56.443 - type: mrr_at_100 value: 57.13700000000001 - type: mrr_at_1000 value: 57.152 - type: mrr_at_3 value: 52.944 - type: mrr_at_5 value: 55.37800000000001 - type: ndcg_at_1 value: 44.0 - type: ndcg_at_10 value: 62.312999999999995 - type: ndcg_at_100 value: 65.63900000000001 - type: ndcg_at_1000 value: 66.019 - type: ndcg_at_3 value: 54.67999999999999 - type: ndcg_at_5 value: 59.284000000000006 - type: precision_at_1 value: 44.0 - type: precision_at_10 value: 9.367 - type: precision_at_100 value: 1.0999999999999999 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 22.778000000000002 - type: precision_at_5 value: 16.467000000000002 - type: recall_at_1 value: 41.661 - type: recall_at_10 value: 82.306 - type: recall_at_100 value: 97.167 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 62.461 - type: recall_at_5 value: 73.411 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.90693069306931 - type: cos_sim_ap value: 97.86562522779887 - type: cos_sim_f1 value: 95.27162977867204 - type: cos_sim_precision value: 95.8502024291498 - type: cos_sim_recall value: 94.69999999999999 - type: dot_accuracy value: 99.90693069306931 - type: dot_ap value: 97.86562522779887 - type: dot_f1 value: 95.27162977867204 - type: dot_precision value: 95.8502024291498 - type: dot_recall value: 94.69999999999999 - type: euclidean_accuracy value: 99.90693069306931 - type: euclidean_ap value: 97.86562522779887 - type: euclidean_f1 value: 95.27162977867204 - type: euclidean_precision value: 95.8502024291498 - type: euclidean_recall value: 94.69999999999999 - type: manhattan_accuracy value: 99.90693069306931 - 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type: ndcg_at_10 value: 79.25999999999999 - type: ndcg_at_100 value: 80.719 - type: ndcg_at_1000 value: 80.862 - type: ndcg_at_3 value: 76.08200000000001 - type: ndcg_at_5 value: 77.782 - type: precision_at_1 value: 66.10000000000001 - type: precision_at_10 value: 9.17 - type: precision_at_100 value: 0.983 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 27.667 - type: precision_at_5 value: 17.419999999999998 - type: recall_at_1 value: 66.10000000000001 - type: recall_at_10 value: 91.7 - type: recall_at_100 value: 98.3 - type: recall_at_1000 value: 99.4 - type: recall_at_3 value: 83.0 - type: recall_at_5 value: 87.1 - type: map_at_1 value: 66.10000000000001 - type: map_at_10 value: 75.238 - type: map_at_100 value: 75.559 - type: map_at_1000 value: 75.565 - type: map_at_3 value: 73.68299999999999 - type: map_at_5 value: 74.63300000000001 - type: mrr_at_1 value: 66.10000000000001 - type: mrr_at_10 value: 75.238 - type: mrr_at_100 value: 75.559 - type: mrr_at_1000 value: 75.565 - type: mrr_at_3 value: 73.68299999999999 - type: mrr_at_5 value: 74.63300000000001 - type: ndcg_at_1 value: 66.10000000000001 - type: ndcg_at_10 value: 79.25999999999999 - type: ndcg_at_100 value: 80.719 - type: ndcg_at_1000 value: 80.862 - type: ndcg_at_3 value: 76.08200000000001 - type: ndcg_at_5 value: 77.782 - type: precision_at_1 value: 66.10000000000001 - type: precision_at_10 value: 9.17 - type: precision_at_100 value: 0.983 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 27.667 - type: precision_at_5 value: 17.419999999999998 - type: recall_at_1 value: 66.10000000000001 - type: recall_at_10 value: 91.7 - type: recall_at_100 value: 98.3 - type: recall_at_1000 value: 99.4 - type: recall_at_3 value: 83.0 - type: recall_at_5 value: 87.1 - task: type: Classification dataset: name: MTEB Waimai type: C-MTEB/waimai-classification config: default split: test revision: 339287def212450dcaa9df8c22bf93e9980c7023 metrics: - type: accuracy value: 91.13 - type: ap value: 79.55231335947015 - type: f1 value: 89.63091922203914 - type: accuracy value: 91.13 - type: ap value: 79.55231335947015 - type: f1 value: 89.63091922203914 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 55.099999999999994 - type: f1 value: 53.115528412999666 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (zh-CN) type: mteb/amazon_massive_intent config: zh-CN split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 79.88231338264963 - type: f1 value: 77.13536609019927 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (zh-CN) type: mteb/amazon_massive_scenario config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 84.50571620712844 - type: f1 value: 83.4128768262944 - task: type: Reranking dataset: name: MTEB T2Reranking type: C-MTEB/T2Reranking config: default split: dev revision: 76631901a18387f85eaa53e5450019b87ad58ef9 metrics: - type: map value: 66.54177017978034 - type: mrr value: 76.76094292377299 - task: type: Retrieval dataset: name: MTEB T2Retrieval type: C-MTEB/T2Retrieval config: default split: dev revision: 8731a845f1bf500a4f111cf1070785c793d10e64 metrics: - type: map_at_1 value: 28.608 - type: map_at_10 value: 81.266 - type: map_at_100 value: 84.714 - type: map_at_1000 value: 84.758 - type: map_at_3 value: 56.967 - type: map_at_5 value: 70.14 - type: mrr_at_1 value: 91.881 - type: mrr_at_10 value: 94.11699999999999 - type: mrr_at_100 value: 94.178 - type: mrr_at_1000 value: 94.181 - type: mrr_at_3 value: 93.772 - type: mrr_at_5 value: 93.997 - type: ndcg_at_1 value: 91.881 - type: ndcg_at_10 value: 87.954 - type: ndcg_at_100 value: 90.904 - type: ndcg_at_1000 value: 91.326 - type: ndcg_at_3 value: 88.838 - type: ndcg_at_5 value: 87.764 - type: precision_at_1 value: 91.881 - type: precision_at_10 value: 43.628 - type: precision_at_100 value: 5.082 - type: precision_at_1000 value: 0.518 - type: precision_at_3 value: 77.62400000000001 - type: precision_at_5 value: 65.269 - type: recall_at_1 value: 28.608 - type: recall_at_10 value: 87.06 - type: recall_at_100 value: 96.815 - type: recall_at_1000 value: 98.969 - type: recall_at_3 value: 58.506 - type: recall_at_5 value: 73.21600000000001 - task: type: Clustering dataset: name: MTEB ThuNewsClusteringP2P type: C-MTEB/ThuNewsClusteringP2P config: default split: test revision: 5798586b105c0434e4f0fe5e767abe619442cf93 metrics: - type: v_measure value: 78.68783858143624 --- # CCwz/gme-Qwen2-VL-7B-Instruct-Q5_K_S-GGUF This model was converted to GGUF format from [`Alibaba-NLP/gme-Qwen2-VL-7B-Instruct`](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo CCwz/gme-Qwen2-VL-7B-Instruct-Q5_K_S-GGUF --hf-file gme-qwen2-vl-7b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo CCwz/gme-Qwen2-VL-7B-Instruct-Q5_K_S-GGUF --hf-file gme-qwen2-vl-7b-instruct-q5_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo CCwz/gme-Qwen2-VL-7B-Instruct-Q5_K_S-GGUF --hf-file gme-qwen2-vl-7b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo CCwz/gme-Qwen2-VL-7B-Instruct-Q5_K_S-GGUF --hf-file gme-qwen2-vl-7b-instruct-q5_k_s.gguf -c 2048 ```
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
AdaptLLM/law-LLM-13B
AdaptLLM
text-generation
[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "legal", "en", "dataset:Open-Orca/OpenOrca", "dataset:GAIR/lima", "dataset:WizardLM/WizardLM_evol_instruct_V2_196k", "dataset:EleutherAI/pile", "arxiv:2309.09530", "arxiv:2411.19930", "arxiv:2406.14491", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,703
1,733
257
34
--- datasets: - Open-Orca/OpenOrca - GAIR/lima - WizardLM/WizardLM_evol_instruct_V2_196k - EleutherAI/pile language: - en metrics: - accuracy pipeline_tag: text-generation tags: - legal --- # Adapting LLMs to Domains via Continual Pre-Training (ICLR 2024) This repo contains the domain-specific base model developed from **LLaMA-1-13B**, using the method in our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530). We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. **Our 7B model competes with much larger domain-specific models like BloombergGPT-50B**. ### [2024/11/29] 🤗 Introduce the multimodal version of AdaptLLM at [AdaMLLM](https://huggingface.co/papers/2411.19930), for adapting MLLMs to domains 🤗 **************************** **Updates** **************************** * 2024/11/29: Released [AdaMLLM](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains) for adapting MLLMs to domains * 2024/9/20: Our [research paper for Instruction-Pretrain](https://huggingface.co/papers/2406.14491) has been accepted by EMNLP 2024 * 2024/8/29: Updated [guidelines](https://huggingface.co/datasets/AdaptLLM/finance-tasks) on evaluating any 🤗Huggingface models on the domain-specific tasks * 2024/6/22: Released the [benchmarking code](https://github.com/microsoft/LMOps/tree/main/adaptllm) * 2024/6/21: Released the general version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain) * 2024/4/2: Released the [raw data splits (train and test)](https://huggingface.co/datasets/AdaptLLM/ConvFinQA) of all the evaluation datasets * 2024/1/16: Our [research paper for AdaptLLM](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024 * 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B * 2023/12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B * 2023/9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/law-tasks), and [base models](https://huggingface.co/AdaptLLM/law-LLM) developed from LLaMA-1-7B ## 1. Domain-Specific Models ### LLaMA-1-7B In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are: <p align='center'> <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700"> </p> ### LLaMA-1-13B Moreover, we scale up our base model to LLaMA-1-13B to see if **our method is similarly effective for larger-scale models**, and the results are consistently positive too: [Biomedicine-LLM-13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B), [Finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) and [Law-LLM-13B](https://huggingface.co/AdaptLLM/law-LLM-13B). ### LLaMA-2-Chat Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat) For example, to chat with the law model: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("AdaptLLM/law-LLM-13B") tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/law-LLM-13B", use_fast=False) # Put your input here: user_input = '''Question: Which of the following is false about ex post facto laws? Options: - They make criminal an act that was innocent when committed. - They prescribe greater punishment for an act than was prescribed when it was done. - They increase the evidence required to convict a person than when the act was done. - They alter criminal offenses or punishment in a substantially prejudicial manner for the purpose of punishing a person for some past activity. Please provide your choice first and then provide explanations if possible.''' # Simply use your input as the prompt for base models prompt = user_input inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device) outputs = model.generate(input_ids=inputs, max_length=2048)[0] answer_start = int(inputs.shape[-1]) pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True) print(pred) ``` ### LLaMA-3-8B (💡New!) In our recent research on [Instruction-Pretrain](https://huggingface.co/papers/2406.14491), we developed a context-based instruction synthesizer to augment the raw corpora with instruction-response pairs, **enabling Llama3-8B to be comparable to or even outperform Llama3-70B**: [Finance-Llama3-8B](https://huggingface.co/instruction-pretrain/finance-Llama3-8B), [Biomedicine-Llama3-8B](https://huggingface.co/instruction-pretrain/medicine-Llama3-8B). ## 2. Domain-Specific Tasks ### Pre-templatized Testing Splits To easily reproduce our prompting results, we have uploaded the filled-in zero/few-shot input instructions and output completions of the test each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks). Note: those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models. ### Evaluating Any Huggingface LMs on Domain-Specific Tasks (💡New!) You can use the following script to reproduce our results and evaluate any other Huggingface models on domain-specific tasks. Note that the script is NOT applicable to models that require specific prompt templates (e.g., Llama2-chat, Llama3-Instruct). 1). **Set Up Dependencies** ```bash git clone https://github.com/microsoft/LMOps cd LMOps/adaptllm pip install -r requirements.txt ``` 2). **Evaluate the Model** ```bash # Select the domain from ['biomedicine', 'finance', 'law'] DOMAIN='law' # Specify any Huggingface model name (Not applicable to chat models) MODEL='AdaptLLM/law-LLM-13B' # Model parallelization: # - Set MODEL_PARALLEL=False if the model fits on a single GPU. # We observe that LMs smaller than 10B always meet this requirement. # - Set MODEL_PARALLEL=True if the model is too large and encounters OOM on a single GPU. MODEL_PARALLEL=True # Choose the number of GPUs from [1, 2, 4, 8] N_GPU=2 # Whether to add a BOS token at the beginning of the prompt input: # - Set to False for AdaptLLM. # - Set to True for instruction-pretrain models. # If unsure, we recommend setting it to False, as this is suitable for most LMs. add_bos_token=False # Run the evaluation script bash scripts/inference.sh ${DOMAIN} ${MODEL} ${add_bos_token} ${MODEL_PARALLEL} ${N_GPU} ``` ### Raw Datasets We have also uploaded the raw training and testing splits, for facilitating fine-tuning or other usages: [ChemProt](https://huggingface.co/datasets/AdaptLLM/ChemProt), [RCT](https://huggingface.co/datasets/AdaptLLM/RCT), [ConvFinQA](https://huggingface.co/datasets/AdaptLLM/ConvFinQA), [FiQA_SA](https://huggingface.co/datasets/AdaptLLM/FiQA_SA), [Headline](https://huggingface.co/datasets/AdaptLLM/Headline), [NER](https://huggingface.co/datasets/AdaptLLM/NER), [FPB](https://huggingface.co/datasets/AdaptLLM/FPB) ### Domain Knowledge Probing Our pre-processed knowledge probing datasets are available at: [med_knowledge_prob](https://huggingface.co/datasets/AdaptLLM/med_knowledge_prob) and [law_knowledge_prob](https://huggingface.co/datasets/AdaptLLM/law_knowledge_prob) ## Citation If you find our work helpful, please cite us: ```bibtex @inproceedings{ cheng2024adapting, title={Adapting Large Language Models via Reading Comprehension}, author={Daixuan Cheng and Shaohan Huang and Furu Wei}, booktitle={The Twelfth International Conference on Learning Representations}, year={2024}, url={https://openreview.net/forum?id=y886UXPEZ0} } ```
[ "QUESTION_ANSWERING" ]
[ "CHEMPROT" ]
Non_BioNLP
RichardErkhov/bennegeek_-_stella_en_1.5B_v5-4bits
RichardErkhov
null
[ "safetensors", "qwen2", "custom_code", "arxiv:2205.13147", "4-bit", "bitsandbytes", "region:us" ]
1,741
1,741
12
0
--- {} --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) stella_en_1.5B_v5 - bnb 4bits - Model creator: https://huggingface.co/bennegeek/ - Original model: https://huggingface.co/bennegeek/stella_en_1.5B_v5/ Original model description: --- model-index: - name: stella_en_1.5B_v5 results: - dataset: config: en name: MTEB AmazonCounterfactualClassification (en) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics: - type: accuracy value: 92.86567164179104 - type: ap value: 72.13503907102613 - type: ap_weighted value: 72.13503907102613 - type: f1 value: 89.5586886376355 - type: f1_weighted value: 93.13621183004571 - type: main_score value: 92.86567164179104 task: type: Classification - dataset: config: default name: MTEB AmazonPolarityClassification revision: e2d317d38cd51312af73b3d32a06d1a08b442046 split: test type: mteb/amazon_polarity metrics: - 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type: accuracy value: 74.8443689869836 - type: f1 value: 75.1139662898148 - type: f1_weighted value: 74.7369003946243 - type: main_score value: 74.8443689869836 task: type: Classification - dataset: config: default name: MTEB TwentyNewsgroupsClustering revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 split: test type: mteb/twentynewsgroups-clustering metrics: - type: main_score value: 61.42918790942448 - type: v_measure value: 61.42918790942448 - type: v_measure_std value: 1.0156550098843082 task: type: Clustering - dataset: config: default name: MTEB TwitterSemEval2015 revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 split: test type: mteb/twittersemeval2015-pairclassification metrics: - type: cosine_accuracy value: 88.22197055492639 - type: cosine_accuracy_threshold value: 83.30042362213135 - type: cosine_ap value: 80.57754959194938 - type: cosine_f1 value: 73.70579190158894 - type: cosine_f1_threshold value: 81.04978799819946 - type: cosine_precision value: 71.64922770303936 - type: cosine_recall value: 75.8839050131926 - type: dot_accuracy value: 88.23985217857782 - type: dot_accuracy_threshold value: 83.31039547920227 - type: dot_ap value: 80.57533213448181 - type: dot_f1 value: 73.61309601143302 - type: dot_f1_threshold value: 81.33968114852905 - type: dot_precision value: 72.51087791144101 - type: dot_recall value: 74.74934036939314 - type: euclidean_accuracy value: 88.22197055492639 - type: euclidean_accuracy_threshold value: 58.290231227874756 - type: euclidean_ap value: 80.57982723880139 - type: euclidean_f1 value: 73.63426519620417 - type: euclidean_f1_threshold value: 61.55576705932617 - type: euclidean_precision value: 71.63173652694611 - type: euclidean_recall value: 75.75197889182058 - type: main_score value: 80.57982723880139 - type: manhattan_accuracy value: 88.14448351910353 - type: manhattan_accuracy_threshold value: 3907.2471618652344 - type: manhattan_ap value: 80.3538079655539 - type: manhattan_f1 value: 73.40466675261054 - type: manhattan_f1_threshold value: 4103.794097900391 - type: manhattan_precision value: 71.76707839677337 - type: manhattan_recall value: 75.11873350923483 - type: max_ap value: 80.57982723880139 - type: max_f1 value: 73.70579190158894 - type: max_precision value: 72.51087791144101 - type: max_recall value: 75.8839050131926 - type: similarity_accuracy value: 88.22197055492639 - type: similarity_accuracy_threshold value: 83.30042362213135 - type: similarity_ap value: 80.57754959194938 - type: similarity_f1 value: 73.70579190158894 - type: similarity_f1_threshold value: 81.04978799819946 - type: similarity_precision value: 71.64922770303936 - type: similarity_recall value: 75.8839050131926 task: type: PairClassification - dataset: config: default name: MTEB TwitterURLCorpus revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf split: test type: mteb/twitterurlcorpus-pairclassification metrics: - type: cosine_accuracy value: 89.88628866379477 - type: cosine_accuracy_threshold value: 80.8050274848938 - type: cosine_ap value: 87.57594591596816 - type: cosine_f1 value: 80.0812257707218 - type: cosine_f1_threshold value: 77.990061044693 - type: cosine_precision value: 76.93126197063205 - type: cosine_recall value: 83.50015398829689 - type: dot_accuracy value: 89.87852679784221 - type: dot_accuracy_threshold value: 80.84419965744019 - type: dot_ap value: 87.56136742222151 - type: dot_f1 value: 80.05898617511521 - type: dot_f1_threshold value: 77.92385816574097 - type: dot_precision value: 76.80554573106035 - type: dot_recall value: 83.60024638127503 - type: euclidean_accuracy value: 89.86882446540149 - type: euclidean_accuracy_threshold value: 62.08193898200989 - type: euclidean_ap value: 87.57517549192228 - type: euclidean_f1 value: 80.05286925872892 - type: euclidean_f1_threshold value: 66.65036082267761 - type: euclidean_precision value: 76.51063232507545 - type: euclidean_recall value: 83.93902063443178 - type: main_score value: 87.64162614197194 - type: manhattan_accuracy value: 89.8959909962355 - type: manhattan_accuracy_threshold value: 4176.108169555664 - type: manhattan_ap value: 87.64162614197194 - type: manhattan_f1 value: 80.17116279069768 - type: manhattan_f1_threshold value: 4433.153533935547 - type: manhattan_precision value: 77.57615035644848 - type: manhattan_recall value: 82.94579611949491 - type: max_ap value: 87.64162614197194 - type: max_f1 value: 80.17116279069768 - type: max_precision value: 77.57615035644848 - type: max_recall value: 83.93902063443178 - type: similarity_accuracy value: 89.88628866379477 - type: similarity_accuracy_threshold value: 80.8050274848938 - type: similarity_ap value: 87.57594591596816 - type: similarity_f1 value: 80.0812257707218 - type: similarity_f1_threshold value: 77.990061044693 - type: similarity_precision value: 76.93126197063205 - type: similarity_recall value: 83.50015398829689 task: type: PairClassification tags: - mteb - sentence-transformers - transformers - sentence-similarity license: mit --- # Updates New open-source models and ToDoList will be listed on https://github.com/DunZhang/Stella/blob/main/news_and_todo.md. You can also find these models on my [homepage](https://huggingface.co/infgrad). # Introduction The models are trained based on `Alibaba-NLP/gte-large-en-v1.5` and `Alibaba-NLP/gte-Qwen2-1.5B-instruct`. Thanks for their contributions! **We simplify usage of prompts, providing two prompts for most general tasks, one is for s2p, another one is for s2s.** Prompt of s2p task(e.g. retrieve task): ```text Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: {query} ``` Prompt of s2s task(e.g. semantic textual similarity task): ```text Instruct: Retrieve semantically similar text.\nQuery: {query} ``` The models are finally trained by [MRL]((https://arxiv.org/abs/2205.13147)), so they have multiple dimensions: 512, 768, 1024, 2048, 4096, 6144 and 8192. The higher the dimension, the better the performance. **Generally speaking, 1024d is good enough.** The MTEB score of 1024d is only 0.001 lower than 8192d. # Model directory structure The model directory structure is very simple, it is a standard SentenceTransformer directory **with a series of `2_Dense_{dims}` folders**, where `dims` represents the final vector dimension. For example, the `2_Dense_256` folder stores Linear weights that convert vector dimensions to 256 dimensions. Please refer to the following chapters for specific instructions on how to use them. # Usage You can use `SentenceTransformers` or `transformers` library to encode text. ## Sentence Transformers ```python from sentence_transformers import SentenceTransformer # This model supports two prompts: "s2p_query" and "s2s_query" for sentence-to-passage and sentence-to-sentence tasks, respectively. # They are defined in `config_sentence_transformers.json` query_prompt_name = "s2p_query" queries = [ "What are some ways to reduce stress?", "What are the benefits of drinking green tea?", ] # docs do not need any prompts docs = [ "There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.", "Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.", ] # ！The default dimension is 1024, if you need other dimensions, please clone the model and modify `modules.json` to replace `2_Dense_1024` with another dimension, e.g. `2_Dense_256` or `2_Dense_8192` ! model = SentenceTransformer("dunzhang/stella_en_1.5B_v5", trust_remote_code=True).cuda() query_embeddings = model.encode(queries, prompt_name=query_prompt_name) doc_embeddings = model.encode(docs) print(query_embeddings.shape, doc_embeddings.shape) # (2, 1024) (2, 1024) similarities = model.similarity(query_embeddings, doc_embeddings) print(similarities) # tensor([[0.8179, 0.2958], # [0.3194, 0.7854]]) ``` ## Transformers ```python import os import torch from transformers import AutoModel, AutoTokenizer from sklearn.preprocessing import normalize query_prompt = "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: " queries = [ "What are some ways to reduce stress?", "What are the benefits of drinking green tea?", ] queries = [query_prompt + query for query in queries] # docs do not need any prompts docs = [ "There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.", "Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.", ] # The path of your model after cloning it model_dir = "{Your MODEL_PATH}" vector_dim = 1024 vector_linear_directory = f"2_Dense_{vector_dim}" model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).cuda().eval() tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True) vector_linear = torch.nn.Linear(in_features=model.config.hidden_size, out_features=vector_dim) vector_linear_dict = { k.replace("linear.", ""): v for k, v in torch.load(os.path.join(model_dir, f"{vector_linear_directory}/pytorch_model.bin")).items() } vector_linear.load_state_dict(vector_linear_dict) vector_linear.cuda() # Embed the queries with torch.no_grad(): input_data = tokenizer(queries, padding="longest", truncation=True, max_length=512, return_tensors="pt") input_data = {k: v.cuda() for k, v in input_data.items()} attention_mask = input_data["attention_mask"] last_hidden_state = model(**input_data)[0] last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0) query_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] query_vectors = normalize(vector_linear(query_vectors).cpu().numpy()) # Embed the documents with torch.no_grad(): input_data = tokenizer(docs, padding="longest", truncation=True, max_length=512, return_tensors="pt") input_data = {k: v.cuda() for k, v in input_data.items()} attention_mask = input_data["attention_mask"] last_hidden_state = model(**input_data)[0] last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0) docs_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] docs_vectors = normalize(vector_linear(docs_vectors).cpu().numpy()) print(query_vectors.shape, docs_vectors.shape) # (2, 1024) (2, 1024) similarities = query_vectors @ docs_vectors.T print(similarities) # [[0.8178789 0.2958377 ] # [0.31938642 0.7853526 ]] ``` # FAQ Q: The details of training? A: The training method and datasets will be released in the future. (specific time unknown, may be provided in a paper) Q: How to choose a suitable prompt for my own task? A: In most cases, please use the s2p and s2s prompts. These two prompts account for the vast majority of the training data. Q: How to reproduce MTEB results? A: Please use evaluation scripts in `Alibaba-NLP/gte-Qwen2-1.5B-instruct` or `intfloat/e5-mistral-7b-instruct` Q: Why each dimension has a linear weight? A: MRL has multiple training methods, we choose this method which has the best performance. Q: What is the sequence length of models? A: 512 is recommended, in our experiments, almost all models perform poorly on specialized long text retrieval datasets. Besides, the model is trained on datasets of 512 length. This may be an optimization term. If you have any questions, please start a discussion on community.
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
huoxu/bge-large-en-v1.5-Q8_0-GGUF
huoxu
feature-extraction
[ "sentence-transformers", "gguf", "feature-extraction", "sentence-similarity", "transformers", "mteb", "llama-cpp", "gguf-my-repo", "en", "base_model:BAAI/bge-large-en-v1.5", "base_model:quantized:BAAI/bge-large-en-v1.5", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,721
1,721
285
0
--- base_model: BAAI/bge-large-en-v1.5 language: - en license: mit tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb - llama-cpp - gguf-my-repo model-index: - name: bge-large-en-v1.5 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.8507462686567 - type: ap value: 38.566457320228245 - type: f1 value: 69.69386648043475 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.416675 - type: ap value: 89.1928861155922 - type: f1 value: 92.39477019574215 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.175999999999995 - type: f1 value: 47.80712792870253 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 40.184999999999995 - type: map_at_10 value: 55.654 - type: map_at_100 value: 56.25 - type: map_at_1000 value: 56.255 - type: map_at_3 value: 51.742999999999995 - type: map_at_5 value: 54.129000000000005 - type: mrr_at_1 value: 40.967 - type: mrr_at_10 value: 55.96 - type: mrr_at_100 value: 56.54900000000001 - type: mrr_at_1000 value: 56.554 - type: mrr_at_3 value: 51.980000000000004 - type: mrr_at_5 value: 54.44 - type: ndcg_at_1 value: 40.184999999999995 - type: ndcg_at_10 value: 63.542 - type: ndcg_at_100 value: 65.96499999999999 - type: ndcg_at_1000 value: 66.08699999999999 - type: ndcg_at_3 value: 55.582 - type: ndcg_at_5 value: 59.855000000000004 - type: precision_at_1 value: 40.184999999999995 - type: precision_at_10 value: 8.841000000000001 - type: precision_at_100 value: 0.987 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 22.238 - type: precision_at_5 value: 15.405 - type: recall_at_1 value: 40.184999999999995 - type: recall_at_10 value: 88.407 - type: recall_at_100 value: 98.72 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 66.714 - type: recall_at_5 value: 77.027 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.567077926750066 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.19453389182364 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 64.46555939623092 - type: mrr value: 77.82361605768807 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.9554128814735 - type: cos_sim_spearman value: 84.65373612172036 - type: euclidean_pearson value: 83.2905059954138 - type: euclidean_spearman value: 84.52240782811128 - type: manhattan_pearson value: 82.99533802997436 - type: manhattan_spearman value: 84.20673798475734 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 87.78896103896103 - type: f1 value: 87.77189310964883 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.714538337650495 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.90108349284447 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 32.795 - type: map_at_10 value: 43.669000000000004 - type: map_at_100 value: 45.151 - type: map_at_1000 value: 45.278 - type: map_at_3 value: 40.006 - type: map_at_5 value: 42.059999999999995 - type: mrr_at_1 value: 39.771 - type: mrr_at_10 value: 49.826 - type: mrr_at_100 value: 50.504000000000005 - type: mrr_at_1000 value: 50.549 - type: mrr_at_3 value: 47.115 - type: mrr_at_5 value: 48.832 - type: ndcg_at_1 value: 39.771 - type: ndcg_at_10 value: 50.217999999999996 - type: ndcg_at_100 value: 55.454 - type: ndcg_at_1000 value: 57.37 - type: ndcg_at_3 value: 44.885000000000005 - type: ndcg_at_5 value: 47.419 - type: precision_at_1 value: 39.771 - type: precision_at_10 value: 9.642000000000001 - type: precision_at_100 value: 1.538 - type: precision_at_1000 value: 0.198 - type: precision_at_3 value: 21.268 - type: precision_at_5 value: 15.536 - type: recall_at_1 value: 32.795 - type: recall_at_10 value: 62.580999999999996 - type: recall_at_100 value: 84.438 - type: recall_at_1000 value: 96.492 - type: recall_at_3 value: 47.071000000000005 - type: recall_at_5 value: 54.079 - type: map_at_1 value: 32.671 - type: map_at_10 value: 43.334 - type: map_at_100 value: 44.566 - type: map_at_1000 value: 44.702999999999996 - type: map_at_3 value: 40.343 - type: map_at_5 value: 41.983 - type: mrr_at_1 value: 40.764 - type: mrr_at_10 value: 49.382 - type: mrr_at_100 value: 49.988 - type: mrr_at_1000 value: 50.03300000000001 - type: mrr_at_3 value: 47.293 - type: mrr_at_5 value: 48.51 - type: ndcg_at_1 value: 40.764 - type: ndcg_at_10 value: 49.039 - type: ndcg_at_100 value: 53.259 - type: ndcg_at_1000 value: 55.253 - type: ndcg_at_3 value: 45.091 - type: ndcg_at_5 value: 46.839999999999996 - type: precision_at_1 value: 40.764 - type: precision_at_10 value: 9.191 - type: precision_at_100 value: 1.476 - type: precision_at_1000 value: 0.19499999999999998 - type: precision_at_3 value: 21.72 - type: precision_at_5 value: 15.299 - type: recall_at_1 value: 32.671 - type: recall_at_10 value: 58.816 - type: recall_at_100 value: 76.654 - type: recall_at_1000 value: 89.05999999999999 - type: recall_at_3 value: 46.743 - type: recall_at_5 value: 51.783 - type: map_at_1 value: 40.328 - type: map_at_10 value: 53.32599999999999 - type: map_at_100 value: 54.37499999999999 - type: map_at_1000 value: 54.429 - type: map_at_3 value: 49.902 - type: map_at_5 value: 52.002 - type: mrr_at_1 value: 46.332 - type: mrr_at_10 value: 56.858 - type: mrr_at_100 value: 57.522 - type: mrr_at_1000 value: 57.54899999999999 - type: mrr_at_3 value: 54.472 - type: mrr_at_5 value: 55.996 - type: ndcg_at_1 value: 46.332 - type: ndcg_at_10 value: 59.313 - type: ndcg_at_100 value: 63.266999999999996 - type: ndcg_at_1000 value: 64.36 - type: ndcg_at_3 value: 53.815000000000005 - type: ndcg_at_5 value: 56.814 - type: precision_at_1 value: 46.332 - type: precision_at_10 value: 9.53 - type: precision_at_100 value: 1.238 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 24.054000000000002 - type: precision_at_5 value: 16.589000000000002 - type: recall_at_1 value: 40.328 - type: recall_at_10 value: 73.421 - type: recall_at_100 value: 90.059 - type: recall_at_1000 value: 97.81 - type: recall_at_3 value: 59.009 - type: recall_at_5 value: 66.352 - type: map_at_1 value: 27.424 - type: map_at_10 value: 36.332 - type: map_at_100 value: 37.347 - type: map_at_1000 value: 37.422 - type: map_at_3 value: 33.743 - type: map_at_5 value: 35.176 - type: mrr_at_1 value: 29.153000000000002 - type: mrr_at_10 value: 38.233 - type: mrr_at_100 value: 39.109 - type: mrr_at_1000 value: 39.164 - type: mrr_at_3 value: 35.876000000000005 - type: mrr_at_5 value: 37.169000000000004 - type: ndcg_at_1 value: 29.153000000000002 - type: ndcg_at_10 value: 41.439 - type: ndcg_at_100 value: 46.42 - type: ndcg_at_1000 value: 48.242000000000004 - type: ndcg_at_3 value: 36.362 - type: ndcg_at_5 value: 38.743 - type: precision_at_1 value: 29.153000000000002 - type: precision_at_10 value: 6.315999999999999 - type: precision_at_100 value: 0.927 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 15.443000000000001 - type: precision_at_5 value: 10.644 - type: recall_at_1 value: 27.424 - type: recall_at_10 value: 55.364000000000004 - type: recall_at_100 value: 78.211 - type: recall_at_1000 value: 91.74600000000001 - type: recall_at_3 value: 41.379 - type: recall_at_5 value: 47.14 - type: map_at_1 value: 19.601 - type: map_at_10 value: 27.826 - type: map_at_100 value: 29.017 - type: map_at_1000 value: 29.137 - type: map_at_3 value: 25.125999999999998 - type: map_at_5 value: 26.765 - type: mrr_at_1 value: 24.005000000000003 - type: mrr_at_10 value: 32.716 - type: mrr_at_100 value: 33.631 - type: mrr_at_1000 value: 33.694 - type: mrr_at_3 value: 29.934 - type: mrr_at_5 value: 31.630999999999997 - type: ndcg_at_1 value: 24.005000000000003 - type: ndcg_at_10 value: 33.158 - type: ndcg_at_100 value: 38.739000000000004 - type: ndcg_at_1000 value: 41.495 - type: ndcg_at_3 value: 28.185 - type: ndcg_at_5 value: 30.796 - type: precision_at_1 value: 24.005000000000003 - type: precision_at_10 value: 5.908 - type: precision_at_100 value: 1.005 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 13.391 - type: precision_at_5 value: 9.876 - type: recall_at_1 value: 19.601 - type: recall_at_10 value: 44.746 - type: recall_at_100 value: 68.82300000000001 - type: recall_at_1000 value: 88.215 - type: recall_at_3 value: 31.239 - type: recall_at_5 value: 37.695 - type: map_at_1 value: 30.130000000000003 - type: map_at_10 value: 40.96 - type: map_at_100 value: 42.282 - type: map_at_1000 value: 42.392 - type: map_at_3 value: 37.889 - type: map_at_5 value: 39.661 - type: mrr_at_1 value: 36.958999999999996 - type: mrr_at_10 value: 46.835 - type: mrr_at_100 value: 47.644 - type: mrr_at_1000 value: 47.688 - type: mrr_at_3 value: 44.562000000000005 - type: mrr_at_5 value: 45.938 - type: ndcg_at_1 value: 36.958999999999996 - type: ndcg_at_10 value: 47.06 - type: ndcg_at_100 value: 52.345 - type: ndcg_at_1000 value: 54.35 - type: ndcg_at_3 value: 42.301 - type: ndcg_at_5 value: 44.635999999999996 - type: precision_at_1 value: 36.958999999999996 - type: precision_at_10 value: 8.479000000000001 - type: precision_at_100 value: 1.284 - type: precision_at_1000 value: 0.163 - type: precision_at_3 value: 20.244 - type: precision_at_5 value: 14.224999999999998 - type: recall_at_1 value: 30.130000000000003 - type: recall_at_10 value: 59.27 - type: recall_at_100 value: 81.195 - type: recall_at_1000 value: 94.21199999999999 - type: recall_at_3 value: 45.885 - type: recall_at_5 value: 52.016 - type: map_at_1 value: 26.169999999999998 - type: map_at_10 value: 36.451 - type: map_at_100 value: 37.791000000000004 - type: map_at_1000 value: 37.897 - type: map_at_3 value: 33.109 - type: map_at_5 value: 34.937000000000005 - type: mrr_at_1 value: 32.877 - type: mrr_at_10 value: 42.368 - type: mrr_at_100 value: 43.201 - type: mrr_at_1000 value: 43.259 - type: mrr_at_3 value: 39.763999999999996 - type: mrr_at_5 value: 41.260000000000005 - type: ndcg_at_1 value: 32.877 - type: ndcg_at_10 value: 42.659000000000006 - type: ndcg_at_100 value: 48.161 - type: ndcg_at_1000 value: 50.345 - type: ndcg_at_3 value: 37.302 - type: ndcg_at_5 value: 39.722 - type: precision_at_1 value: 32.877 - type: precision_at_10 value: 7.9 - type: precision_at_100 value: 1.236 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 17.846 - type: precision_at_5 value: 12.9 - type: recall_at_1 value: 26.169999999999998 - type: recall_at_10 value: 55.35 - type: recall_at_100 value: 78.755 - type: recall_at_1000 value: 93.518 - type: recall_at_3 value: 40.176 - type: recall_at_5 value: 46.589000000000006 - type: map_at_1 value: 27.15516666666667 - type: map_at_10 value: 36.65741666666667 - type: map_at_100 value: 37.84991666666666 - type: map_at_1000 value: 37.96316666666667 - type: map_at_3 value: 33.74974999999999 - type: map_at_5 value: 35.3765 - type: mrr_at_1 value: 32.08233333333334 - type: mrr_at_10 value: 41.033833333333334 - type: mrr_at_100 value: 41.84524999999999 - type: mrr_at_1000 value: 41.89983333333333 - type: mrr_at_3 value: 38.62008333333333 - type: mrr_at_5 value: 40.03441666666666 - type: ndcg_at_1 value: 32.08233333333334 - type: ndcg_at_10 value: 42.229 - type: ndcg_at_100 value: 47.26716666666667 - type: ndcg_at_1000 value: 49.43466666666667 - type: ndcg_at_3 value: 37.36408333333333 - type: ndcg_at_5 value: 39.6715 - type: precision_at_1 value: 32.08233333333334 - type: precision_at_10 value: 7.382583333333334 - type: precision_at_100 value: 1.16625 - type: precision_at_1000 value: 0.15408333333333332 - type: precision_at_3 value: 17.218 - type: precision_at_5 value: 12.21875 - type: recall_at_1 value: 27.15516666666667 - type: recall_at_10 value: 54.36683333333333 - type: recall_at_100 value: 76.37183333333333 - type: recall_at_1000 value: 91.26183333333333 - type: recall_at_3 value: 40.769916666666674 - type: recall_at_5 value: 46.702333333333335 - type: map_at_1 value: 25.749 - type: map_at_10 value: 33.001999999999995 - type: map_at_100 value: 33.891 - type: map_at_1000 value: 33.993 - type: map_at_3 value: 30.703999999999997 - type: map_at_5 value: 31.959 - type: mrr_at_1 value: 28.834 - type: mrr_at_10 value: 35.955 - type: mrr_at_100 value: 36.709 - type: mrr_at_1000 value: 36.779 - type: mrr_at_3 value: 33.947 - type: mrr_at_5 value: 35.089 - type: ndcg_at_1 value: 28.834 - type: ndcg_at_10 value: 37.329 - type: ndcg_at_100 value: 41.79 - type: ndcg_at_1000 value: 44.169000000000004 - type: ndcg_at_3 value: 33.184999999999995 - type: ndcg_at_5 value: 35.107 - type: precision_at_1 value: 28.834 - type: precision_at_10 value: 5.7669999999999995 - type: precision_at_100 value: 0.876 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 14.213000000000001 - type: precision_at_5 value: 9.754999999999999 - type: recall_at_1 value: 25.749 - type: recall_at_10 value: 47.791 - type: recall_at_100 value: 68.255 - type: recall_at_1000 value: 85.749 - type: recall_at_3 value: 36.199 - type: recall_at_5 value: 41.071999999999996 - type: map_at_1 value: 17.777 - type: map_at_10 value: 25.201 - type: map_at_100 value: 26.423999999999996 - type: map_at_1000 value: 26.544 - type: map_at_3 value: 22.869 - type: map_at_5 value: 24.023 - type: mrr_at_1 value: 21.473 - type: mrr_at_10 value: 29.12 - type: mrr_at_100 value: 30.144 - type: mrr_at_1000 value: 30.215999999999998 - type: mrr_at_3 value: 26.933 - type: mrr_at_5 value: 28.051 - type: ndcg_at_1 value: 21.473 - type: ndcg_at_10 value: 30.003 - type: ndcg_at_100 value: 35.766 - type: ndcg_at_1000 value: 38.501000000000005 - type: ndcg_at_3 value: 25.773000000000003 - type: ndcg_at_5 value: 27.462999999999997 - type: precision_at_1 value: 21.473 - type: precision_at_10 value: 5.482 - type: precision_at_100 value: 0.975 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 12.205 - type: precision_at_5 value: 8.692 - type: recall_at_1 value: 17.777 - type: recall_at_10 value: 40.582 - type: recall_at_100 value: 66.305 - type: recall_at_1000 value: 85.636 - type: recall_at_3 value: 28.687 - type: recall_at_5 value: 33.089 - type: map_at_1 value: 26.677 - type: map_at_10 value: 36.309000000000005 - type: map_at_100 value: 37.403999999999996 - type: map_at_1000 value: 37.496 - type: map_at_3 value: 33.382 - type: map_at_5 value: 34.98 - type: mrr_at_1 value: 31.343 - type: mrr_at_10 value: 40.549 - type: mrr_at_100 value: 41.342 - type: mrr_at_1000 value: 41.397 - type: mrr_at_3 value: 38.029 - type: mrr_at_5 value: 39.451 - type: ndcg_at_1 value: 31.343 - type: ndcg_at_10 value: 42.1 - type: ndcg_at_100 value: 47.089999999999996 - type: ndcg_at_1000 value: 49.222 - type: ndcg_at_3 value: 36.836999999999996 - type: ndcg_at_5 value: 39.21 - type: precision_at_1 value: 31.343 - type: precision_at_10 value: 7.164 - type: precision_at_100 value: 1.0959999999999999 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 16.915 - type: precision_at_5 value: 11.940000000000001 - type: recall_at_1 value: 26.677 - type: recall_at_10 value: 55.54599999999999 - type: recall_at_100 value: 77.094 - type: recall_at_1000 value: 92.01 - type: recall_at_3 value: 41.191 - type: recall_at_5 value: 47.006 - type: map_at_1 value: 24.501 - type: map_at_10 value: 33.102 - type: map_at_100 value: 34.676 - type: map_at_1000 value: 34.888000000000005 - type: map_at_3 value: 29.944 - type: map_at_5 value: 31.613999999999997 - type: mrr_at_1 value: 29.447000000000003 - type: mrr_at_10 value: 37.996 - type: mrr_at_100 value: 38.946 - type: mrr_at_1000 value: 38.995000000000005 - type: mrr_at_3 value: 35.079 - type: mrr_at_5 value: 36.69 - type: ndcg_at_1 value: 29.447000000000003 - type: ndcg_at_10 value: 39.232 - type: ndcg_at_100 value: 45.247 - type: ndcg_at_1000 value: 47.613 - type: ndcg_at_3 value: 33.922999999999995 - type: ndcg_at_5 value: 36.284 - type: precision_at_1 value: 29.447000000000003 - type: precision_at_10 value: 7.648000000000001 - type: precision_at_100 value: 1.516 - type: precision_at_1000 value: 0.23900000000000002 - type: precision_at_3 value: 16.008 - type: precision_at_5 value: 11.779 - type: recall_at_1 value: 24.501 - type: recall_at_10 value: 51.18899999999999 - type: recall_at_100 value: 78.437 - type: recall_at_1000 value: 92.842 - type: recall_at_3 value: 35.808 - type: recall_at_5 value: 42.197 - type: map_at_1 value: 22.039 - type: map_at_10 value: 30.377 - type: map_at_100 value: 31.275 - type: map_at_1000 value: 31.379 - type: map_at_3 value: 27.98 - type: map_at_5 value: 29.358 - type: mrr_at_1 value: 24.03 - type: mrr_at_10 value: 32.568000000000005 - type: mrr_at_100 value: 33.403 - type: mrr_at_1000 value: 33.475 - type: mrr_at_3 value: 30.436999999999998 - type: mrr_at_5 value: 31.796000000000003 - type: ndcg_at_1 value: 24.03 - type: ndcg_at_10 value: 35.198 - type: ndcg_at_100 value: 39.668 - type: ndcg_at_1000 value: 42.296 - type: ndcg_at_3 value: 30.709999999999997 - type: ndcg_at_5 value: 33.024 - type: precision_at_1 value: 24.03 - type: precision_at_10 value: 5.564 - type: precision_at_100 value: 0.828 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 13.309000000000001 - type: precision_at_5 value: 9.39 - type: recall_at_1 value: 22.039 - type: recall_at_10 value: 47.746 - type: recall_at_100 value: 68.23599999999999 - type: recall_at_1000 value: 87.852 - type: recall_at_3 value: 35.852000000000004 - type: recall_at_5 value: 41.410000000000004 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 15.692999999999998 - type: map_at_10 value: 26.903 - type: map_at_100 value: 28.987000000000002 - type: map_at_1000 value: 29.176999999999996 - type: map_at_3 value: 22.137 - type: map_at_5 value: 24.758 - type: mrr_at_1 value: 35.57 - type: mrr_at_10 value: 47.821999999999996 - type: mrr_at_100 value: 48.608000000000004 - type: mrr_at_1000 value: 48.638999999999996 - type: mrr_at_3 value: 44.452000000000005 - type: mrr_at_5 value: 46.546 - type: ndcg_at_1 value: 35.57 - type: ndcg_at_10 value: 36.567 - type: ndcg_at_100 value: 44.085 - type: ndcg_at_1000 value: 47.24 - type: ndcg_at_3 value: 29.964000000000002 - type: ndcg_at_5 value: 32.511 - type: precision_at_1 value: 35.57 - type: precision_at_10 value: 11.485 - type: precision_at_100 value: 1.9619999999999997 - type: precision_at_1000 value: 0.256 - type: precision_at_3 value: 22.237000000000002 - type: precision_at_5 value: 17.471999999999998 - type: recall_at_1 value: 15.692999999999998 - type: recall_at_10 value: 43.056 - type: recall_at_100 value: 68.628 - type: recall_at_1000 value: 86.075 - type: recall_at_3 value: 26.918999999999997 - type: recall_at_5 value: 34.14 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 9.53 - type: map_at_10 value: 20.951 - type: map_at_100 value: 30.136000000000003 - type: map_at_1000 value: 31.801000000000002 - type: map_at_3 value: 15.021 - type: map_at_5 value: 17.471999999999998 - type: mrr_at_1 value: 71.0 - type: mrr_at_10 value: 79.176 - type: mrr_at_100 value: 79.418 - type: mrr_at_1000 value: 79.426 - type: mrr_at_3 value: 78.125 - type: mrr_at_5 value: 78.61200000000001 - type: ndcg_at_1 value: 58.5 - type: ndcg_at_10 value: 44.106 - type: ndcg_at_100 value: 49.268 - type: ndcg_at_1000 value: 56.711999999999996 - type: ndcg_at_3 value: 48.934 - type: ndcg_at_5 value: 45.826 - type: precision_at_1 value: 71.0 - type: precision_at_10 value: 35.0 - type: precision_at_100 value: 11.360000000000001 - type: precision_at_1000 value: 2.046 - type: precision_at_3 value: 52.833 - type: precision_at_5 value: 44.15 - type: recall_at_1 value: 9.53 - type: recall_at_10 value: 26.811 - type: recall_at_100 value: 55.916999999999994 - type: recall_at_1000 value: 79.973 - type: recall_at_3 value: 16.413 - type: recall_at_5 value: 19.980999999999998 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 51.519999999999996 - type: f1 value: 46.36601294761231 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 74.413 - type: map_at_10 value: 83.414 - type: map_at_100 value: 83.621 - type: map_at_1000 value: 83.635 - type: map_at_3 value: 82.337 - type: map_at_5 value: 83.039 - type: mrr_at_1 value: 80.19800000000001 - type: mrr_at_10 value: 87.715 - type: mrr_at_100 value: 87.778 - type: mrr_at_1000 value: 87.779 - type: mrr_at_3 value: 87.106 - type: mrr_at_5 value: 87.555 - type: ndcg_at_1 value: 80.19800000000001 - type: ndcg_at_10 value: 87.182 - type: ndcg_at_100 value: 87.90299999999999 - type: ndcg_at_1000 value: 88.143 - type: ndcg_at_3 value: 85.60600000000001 - type: ndcg_at_5 value: 86.541 - type: precision_at_1 value: 80.19800000000001 - type: precision_at_10 value: 10.531 - type: precision_at_100 value: 1.113 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 32.933 - type: precision_at_5 value: 20.429 - type: recall_at_1 value: 74.413 - type: recall_at_10 value: 94.363 - type: recall_at_100 value: 97.165 - type: recall_at_1000 value: 98.668 - type: recall_at_3 value: 90.108 - type: recall_at_5 value: 92.52 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 22.701 - type: map_at_10 value: 37.122 - type: map_at_100 value: 39.178000000000004 - type: map_at_1000 value: 39.326 - type: map_at_3 value: 32.971000000000004 - type: map_at_5 value: 35.332 - type: mrr_at_1 value: 44.753 - type: mrr_at_10 value: 53.452 - type: mrr_at_100 value: 54.198 - type: mrr_at_1000 value: 54.225 - type: mrr_at_3 value: 50.952 - type: mrr_at_5 value: 52.464 - type: ndcg_at_1 value: 44.753 - type: ndcg_at_10 value: 45.021 - type: ndcg_at_100 value: 52.028 - type: ndcg_at_1000 value: 54.596000000000004 - type: ndcg_at_3 value: 41.622 - type: ndcg_at_5 value: 42.736000000000004 - type: precision_at_1 value: 44.753 - type: precision_at_10 value: 12.284 - type: precision_at_100 value: 1.955 - type: precision_at_1000 value: 0.243 - type: precision_at_3 value: 27.828999999999997 - type: precision_at_5 value: 20.061999999999998 - type: recall_at_1 value: 22.701 - type: recall_at_10 value: 51.432 - type: recall_at_100 value: 77.009 - type: recall_at_1000 value: 92.511 - type: recall_at_3 value: 37.919000000000004 - type: recall_at_5 value: 44.131 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 40.189 - type: map_at_10 value: 66.24600000000001 - type: map_at_100 value: 67.098 - type: map_at_1000 value: 67.149 - type: map_at_3 value: 62.684 - type: map_at_5 value: 64.974 - type: mrr_at_1 value: 80.378 - type: mrr_at_10 value: 86.127 - type: mrr_at_100 value: 86.29299999999999 - type: mrr_at_1000 value: 86.297 - type: mrr_at_3 value: 85.31400000000001 - type: mrr_at_5 value: 85.858 - type: ndcg_at_1 value: 80.378 - type: ndcg_at_10 value: 74.101 - type: ndcg_at_100 value: 76.993 - type: ndcg_at_1000 value: 77.948 - type: ndcg_at_3 value: 69.232 - type: ndcg_at_5 value: 72.04599999999999 - type: precision_at_1 value: 80.378 - type: precision_at_10 value: 15.595999999999998 - type: precision_at_100 value: 1.7840000000000003 - type: precision_at_1000 value: 0.191 - type: precision_at_3 value: 44.884 - type: precision_at_5 value: 29.145 - type: recall_at_1 value: 40.189 - type: recall_at_10 value: 77.981 - type: recall_at_100 value: 89.21 - type: recall_at_1000 value: 95.48299999999999 - type: recall_at_3 value: 67.326 - type: recall_at_5 value: 72.863 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 92.84599999999999 - type: ap value: 89.4710787567357 - type: f1 value: 92.83752676932258 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 23.132 - type: map_at_10 value: 35.543 - type: map_at_100 value: 36.702 - type: map_at_1000 value: 36.748999999999995 - type: map_at_3 value: 31.737 - type: map_at_5 value: 33.927 - type: mrr_at_1 value: 23.782 - type: mrr_at_10 value: 36.204 - type: mrr_at_100 value: 37.29 - type: mrr_at_1000 value: 37.330999999999996 - type: mrr_at_3 value: 32.458999999999996 - type: mrr_at_5 value: 34.631 - type: ndcg_at_1 value: 23.782 - type: ndcg_at_10 value: 42.492999999999995 - type: ndcg_at_100 value: 47.985 - type: ndcg_at_1000 value: 49.141 - type: ndcg_at_3 value: 34.748000000000005 - type: ndcg_at_5 value: 38.651 - type: precision_at_1 value: 23.782 - type: precision_at_10 value: 6.665 - type: precision_at_100 value: 0.941 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.776 - type: precision_at_5 value: 10.84 - type: recall_at_1 value: 23.132 - type: recall_at_10 value: 63.794 - type: recall_at_100 value: 89.027 - type: recall_at_1000 value: 97.807 - type: recall_at_3 value: 42.765 - type: recall_at_5 value: 52.11 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.59188326493388 - type: f1 value: 94.3842594786827 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 79.49384404924761 - type: f1 value: 59.7580539534629 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 77.56220578345663 - type: f1 value: 75.27228165561478 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 80.53463349024884 - type: f1 value: 80.4893958236536 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 32.56100273484962 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.470380028839607 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.06102792457849 - type: mrr value: 33.30709199672238 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.776999999999999 - type: map_at_10 value: 14.924000000000001 - type: map_at_100 value: 18.955 - type: map_at_1000 value: 20.538999999999998 - type: map_at_3 value: 10.982 - type: map_at_5 value: 12.679000000000002 - type: mrr_at_1 value: 47.988 - type: mrr_at_10 value: 57.232000000000006 - type: mrr_at_100 value: 57.818999999999996 - type: mrr_at_1000 value: 57.847 - type: mrr_at_3 value: 54.901999999999994 - type: mrr_at_5 value: 56.481 - type: ndcg_at_1 value: 46.594 - type: ndcg_at_10 value: 38.129000000000005 - type: ndcg_at_100 value: 35.54 - type: ndcg_at_1000 value: 44.172 - type: ndcg_at_3 value: 43.025999999999996 - type: ndcg_at_5 value: 41.052 - type: precision_at_1 value: 47.988 - type: precision_at_10 value: 28.111000000000004 - type: precision_at_100 value: 8.929 - type: precision_at_1000 value: 2.185 - type: precision_at_3 value: 40.144000000000005 - type: precision_at_5 value: 35.232 - type: recall_at_1 value: 6.776999999999999 - type: recall_at_10 value: 19.289 - type: recall_at_100 value: 36.359 - type: recall_at_1000 value: 67.54 - type: recall_at_3 value: 11.869 - type: recall_at_5 value: 14.999 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 31.108000000000004 - type: map_at_10 value: 47.126000000000005 - type: map_at_100 value: 48.171 - type: map_at_1000 value: 48.199 - type: map_at_3 value: 42.734 - type: map_at_5 value: 45.362 - type: mrr_at_1 value: 34.936 - type: mrr_at_10 value: 49.571 - type: mrr_at_100 value: 50.345 - type: mrr_at_1000 value: 50.363 - type: mrr_at_3 value: 45.959 - type: mrr_at_5 value: 48.165 - type: ndcg_at_1 value: 34.936 - type: ndcg_at_10 value: 55.028999999999996 - type: ndcg_at_100 value: 59.244 - type: ndcg_at_1000 value: 59.861 - type: ndcg_at_3 value: 46.872 - type: ndcg_at_5 value: 51.217999999999996 - type: precision_at_1 value: 34.936 - type: precision_at_10 value: 9.099 - type: precision_at_100 value: 1.145 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 21.456 - type: precision_at_5 value: 15.411 - type: recall_at_1 value: 31.108000000000004 - type: recall_at_10 value: 76.53999999999999 - type: recall_at_100 value: 94.39 - type: recall_at_1000 value: 98.947 - type: recall_at_3 value: 55.572 - type: recall_at_5 value: 65.525 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 71.56400000000001 - type: map_at_10 value: 85.482 - type: map_at_100 value: 86.114 - type: map_at_1000 value: 86.13 - type: map_at_3 value: 82.607 - type: map_at_5 value: 84.405 - type: mrr_at_1 value: 82.42 - type: mrr_at_10 value: 88.304 - type: mrr_at_100 value: 88.399 - type: mrr_at_1000 value: 88.399 - type: mrr_at_3 value: 87.37 - type: mrr_at_5 value: 88.024 - type: ndcg_at_1 value: 82.45 - type: ndcg_at_10 value: 89.06500000000001 - type: ndcg_at_100 value: 90.232 - type: ndcg_at_1000 value: 90.305 - type: ndcg_at_3 value: 86.375 - type: ndcg_at_5 value: 87.85300000000001 - type: precision_at_1 value: 82.45 - type: precision_at_10 value: 13.486999999999998 - type: precision_at_100 value: 1.534 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.813 - type: precision_at_5 value: 24.773999999999997 - type: recall_at_1 value: 71.56400000000001 - type: recall_at_10 value: 95.812 - type: recall_at_100 value: 99.7 - type: recall_at_1000 value: 99.979 - type: recall_at_3 value: 87.966 - type: recall_at_5 value: 92.268 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 57.241876648614145 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 64.66212576446223 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 5.308 - type: map_at_10 value: 13.803 - type: map_at_100 value: 16.176 - type: map_at_1000 value: 16.561 - type: map_at_3 value: 9.761000000000001 - type: map_at_5 value: 11.802 - type: mrr_at_1 value: 26.200000000000003 - type: mrr_at_10 value: 37.621 - type: mrr_at_100 value: 38.767 - type: mrr_at_1000 value: 38.815 - type: mrr_at_3 value: 34.117 - type: mrr_at_5 value: 36.107 - type: ndcg_at_1 value: 26.200000000000003 - type: ndcg_at_10 value: 22.64 - type: ndcg_at_100 value: 31.567 - type: ndcg_at_1000 value: 37.623 - type: ndcg_at_3 value: 21.435000000000002 - type: ndcg_at_5 value: 18.87 - type: precision_at_1 value: 26.200000000000003 - type: precision_at_10 value: 11.74 - type: precision_at_100 value: 2.465 - type: precision_at_1000 value: 0.391 - type: precision_at_3 value: 20.033 - type: precision_at_5 value: 16.64 - type: recall_at_1 value: 5.308 - type: recall_at_10 value: 23.794999999999998 - type: recall_at_100 value: 50.015 - type: recall_at_1000 value: 79.283 - type: recall_at_3 value: 12.178 - type: recall_at_5 value: 16.882 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.93231134675553 - type: cos_sim_spearman value: 81.68319292603205 - type: euclidean_pearson value: 81.8396814380367 - type: euclidean_spearman value: 81.24641903349945 - type: manhattan_pearson value: 81.84698799204274 - type: manhattan_spearman value: 81.24269997904105 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 86.73241671587446 - type: cos_sim_spearman value: 79.05091082971826 - type: euclidean_pearson value: 83.91146869578044 - type: euclidean_spearman value: 79.87978465370936 - type: manhattan_pearson value: 83.90888338917678 - type: manhattan_spearman value: 79.87482848584241 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 85.14970731146177 - type: cos_sim_spearman value: 86.37363490084627 - type: euclidean_pearson value: 83.02154218530433 - type: euclidean_spearman value: 83.80258761957367 - type: manhattan_pearson value: 83.01664495119347 - type: manhattan_spearman value: 83.77567458007952 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 83.40474139886784 - type: cos_sim_spearman value: 82.77768789165984 - type: euclidean_pearson value: 80.7065877443695 - type: euclidean_spearman value: 81.375940662505 - type: manhattan_pearson value: 80.6507552270278 - type: manhattan_spearman value: 81.32782179098741 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.08585968722274 - type: cos_sim_spearman value: 88.03110031451399 - type: euclidean_pearson value: 85.74012019602384 - type: euclidean_spearman value: 86.13592849438209 - type: manhattan_pearson value: 85.74404842369206 - type: manhattan_spearman value: 86.14492318960154 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 84.95069052788875 - type: cos_sim_spearman value: 86.4867991595147 - type: euclidean_pearson value: 84.31013325754635 - type: euclidean_spearman value: 85.01529258006482 - type: manhattan_pearson value: 84.26995570085374 - type: manhattan_spearman value: 84.96982104986162 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.54617647971897 - type: cos_sim_spearman value: 87.49834181751034 - type: euclidean_pearson value: 86.01015322577122 - type: euclidean_spearman value: 84.63362652063199 - type: manhattan_pearson value: 86.13807574475706 - type: manhattan_spearman value: 84.7772370721132 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 67.20047755786615 - type: cos_sim_spearman value: 67.05324077987636 - type: euclidean_pearson value: 66.91930642976601 - type: euclidean_spearman value: 65.21491856099105 - type: manhattan_pearson value: 66.78756851976624 - type: manhattan_spearman value: 65.12356257740728 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 86.19852871539686 - type: cos_sim_spearman value: 87.5161895296395 - type: euclidean_pearson value: 84.59848645207485 - type: euclidean_spearman value: 85.26427328757919 - type: manhattan_pearson value: 84.59747366996524 - type: manhattan_spearman value: 85.24045855146915 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.63320317811032 - type: mrr value: 96.26242947321379 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 60.928000000000004 - type: map_at_10 value: 70.112 - type: map_at_100 value: 70.59299999999999 - type: map_at_1000 value: 70.623 - type: map_at_3 value: 66.846 - type: map_at_5 value: 68.447 - type: mrr_at_1 value: 64.0 - type: mrr_at_10 value: 71.212 - type: mrr_at_100 value: 71.616 - type: mrr_at_1000 value: 71.64500000000001 - type: mrr_at_3 value: 68.77799999999999 - type: mrr_at_5 value: 70.094 - type: ndcg_at_1 value: 64.0 - type: ndcg_at_10 value: 74.607 - type: ndcg_at_100 value: 76.416 - type: ndcg_at_1000 value: 77.102 - type: ndcg_at_3 value: 69.126 - type: ndcg_at_5 value: 71.41300000000001 - type: precision_at_1 value: 64.0 - type: precision_at_10 value: 9.933 - type: precision_at_100 value: 1.077 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 26.556 - type: precision_at_5 value: 17.467 - type: recall_at_1 value: 60.928000000000004 - type: recall_at_10 value: 87.322 - type: recall_at_100 value: 94.833 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 72.628 - type: recall_at_5 value: 78.428 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.86237623762376 - type: cos_sim_ap value: 96.72586477206649 - type: cos_sim_f1 value: 93.01858362631845 - type: cos_sim_precision value: 93.4409687184662 - type: cos_sim_recall value: 92.60000000000001 - type: dot_accuracy value: 99.78019801980199 - type: dot_ap value: 93.72748205246228 - type: dot_f1 value: 89.04109589041096 - type: dot_precision value: 87.16475095785441 - type: dot_recall value: 91.0 - type: euclidean_accuracy value: 99.85445544554456 - type: euclidean_ap value: 96.6661459876145 - type: euclidean_f1 value: 92.58337481333997 - type: euclidean_precision value: 92.17046580773042 - type: euclidean_recall value: 93.0 - type: manhattan_accuracy value: 99.85445544554456 - type: manhattan_ap value: 96.6883549244056 - type: manhattan_f1 value: 92.57598405580468 - type: manhattan_precision value: 92.25422045680239 - type: manhattan_recall value: 92.9 - type: max_accuracy value: 99.86237623762376 - type: max_ap value: 96.72586477206649 - type: max_f1 value: 93.01858362631845 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 66.39930057069995 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 34.96398659903402 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 55.946944700355395 - type: mrr value: 56.97151398438164 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.541657650692905 - type: cos_sim_spearman value: 31.605804192286303 - type: dot_pearson value: 28.26905996736398 - type: dot_spearman value: 27.864801765851187 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.22599999999999998 - type: map_at_10 value: 1.8870000000000002 - type: map_at_100 value: 9.78 - type: map_at_1000 value: 22.514 - type: map_at_3 value: 0.6669999999999999 - type: map_at_5 value: 1.077 - type: mrr_at_1 value: 82.0 - type: mrr_at_10 value: 89.86699999999999 - type: mrr_at_100 value: 89.86699999999999 - type: mrr_at_1000 value: 89.86699999999999 - type: mrr_at_3 value: 89.667 - type: mrr_at_5 value: 89.667 - type: ndcg_at_1 value: 79.0 - type: ndcg_at_10 value: 74.818 - type: ndcg_at_100 value: 53.715999999999994 - type: ndcg_at_1000 value: 47.082 - type: ndcg_at_3 value: 82.134 - type: ndcg_at_5 value: 79.81899999999999 - type: precision_at_1 value: 82.0 - type: precision_at_10 value: 78.0 - type: precision_at_100 value: 54.48 - type: precision_at_1000 value: 20.518 - type: precision_at_3 value: 87.333 - type: precision_at_5 value: 85.2 - type: recall_at_1 value: 0.22599999999999998 - type: recall_at_10 value: 2.072 - type: recall_at_100 value: 13.013 - type: recall_at_1000 value: 43.462 - type: recall_at_3 value: 0.695 - type: recall_at_5 value: 1.139 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.328 - type: map_at_10 value: 9.795 - type: map_at_100 value: 15.801000000000002 - type: map_at_1000 value: 17.23 - type: map_at_3 value: 4.734 - type: map_at_5 value: 6.644 - type: mrr_at_1 value: 30.612000000000002 - type: mrr_at_10 value: 46.902 - type: mrr_at_100 value: 47.495 - type: mrr_at_1000 value: 47.495 - type: mrr_at_3 value: 41.156 - type: mrr_at_5 value: 44.218 - type: ndcg_at_1 value: 28.571 - type: ndcg_at_10 value: 24.806 - type: ndcg_at_100 value: 36.419000000000004 - type: ndcg_at_1000 value: 47.272999999999996 - type: ndcg_at_3 value: 25.666 - type: ndcg_at_5 value: 25.448999999999998 - type: precision_at_1 value: 30.612000000000002 - type: precision_at_10 value: 23.061 - type: precision_at_100 value: 7.714 - type: precision_at_1000 value: 1.484 - type: precision_at_3 value: 26.531 - type: precision_at_5 value: 26.122 - type: recall_at_1 value: 2.328 - type: recall_at_10 value: 16.524 - type: recall_at_100 value: 47.179 - type: recall_at_1000 value: 81.22200000000001 - type: recall_at_3 value: 5.745 - type: recall_at_5 value: 9.339 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.9142 - type: ap value: 14.335574772555415 - type: f1 value: 54.62839595194111 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.94340690435768 - type: f1 value: 60.286487936731916 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 51.26597708987974 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 87.48882398521786 - type: cos_sim_ap value: 79.04326607602204 - type: cos_sim_f1 value: 71.64566826860633 - type: cos_sim_precision value: 70.55512918905092 - type: cos_sim_recall value: 72.77044854881267 - type: dot_accuracy value: 84.19264469213805 - type: dot_ap value: 67.96360043562528 - type: dot_f1 value: 64.06418393006827 - type: dot_precision value: 58.64941898706424 - type: dot_recall value: 70.58047493403694 - type: euclidean_accuracy value: 87.45902127913214 - type: euclidean_ap value: 78.9742237648272 - type: euclidean_f1 value: 71.5553235908142 - type: euclidean_precision value: 70.77955601445535 - type: euclidean_recall value: 72.34828496042216 - type: manhattan_accuracy value: 87.41729749061214 - type: manhattan_ap value: 78.90073137580596 - type: manhattan_f1 value: 71.3942611553533 - type: manhattan_precision value: 68.52705653967483 - type: manhattan_recall value: 74.51187335092348 - type: max_accuracy value: 87.48882398521786 - type: max_ap value: 79.04326607602204 - type: max_f1 value: 71.64566826860633 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.68125897465751 - type: cos_sim_ap value: 85.6003454431979 - type: cos_sim_f1 value: 77.6957163958641 - type: cos_sim_precision value: 73.0110366307807 - type: cos_sim_recall value: 83.02279026793964 - type: dot_accuracy value: 87.7672992587418 - type: dot_ap value: 82.4971301112899 - type: dot_f1 value: 75.90528233151184 - type: dot_precision value: 72.0370626469368 - type: dot_recall value: 80.21250384970742 - type: euclidean_accuracy value: 88.4503434625684 - type: euclidean_ap value: 84.91949884748384 - type: euclidean_f1 value: 76.92365018444684 - type: euclidean_precision value: 74.53245721712759 - type: euclidean_recall value: 79.47336002463813 - type: manhattan_accuracy value: 88.47556952691427 - type: manhattan_ap value: 84.8963689101517 - type: manhattan_f1 value: 76.85901249256395 - type: manhattan_precision value: 74.31693989071039 - type: manhattan_recall value: 79.58115183246073 - type: max_accuracy value: 88.68125897465751 - type: max_ap value: 85.6003454431979 - type: max_f1 value: 77.6957163958641 --- # huoxu/bge-large-en-v1.5-Q8_0-GGUF This model was converted to GGUF format from [`BAAI/bge-large-en-v1.5`](https://huggingface.co/BAAI/bge-large-en-v1.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/BAAI/bge-large-en-v1.5) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo huoxu/bge-large-en-v1.5-Q8_0-GGUF --hf-file bge-large-en-v1.5-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo huoxu/bge-large-en-v1.5-Q8_0-GGUF --hf-file bge-large-en-v1.5-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo huoxu/bge-large-en-v1.5-Q8_0-GGUF --hf-file bge-large-en-v1.5-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo huoxu/bge-large-en-v1.5-Q8_0-GGUF --hf-file bge-large-en-v1.5-q8_0.gguf -c 2048 ```
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
minhtuan7akp/snowflake-m-v2.0-vietnamese-finetune
minhtuan7akp
sentence-similarity
[ "sentence-transformers", "safetensors", "gte", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:21892", "loss:MultipleNegativesRankingLoss", "custom_code", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:Snowflake/snowflake-arctic-embed-m-v2.0", "base_model:finetune:Snowflake/snowflake-arctic-embed-m-v2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,740
1,740
21
0
--- base_model: Snowflake/snowflake-arctic-embed-m-v2.0 library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:21892 - loss:MultipleNegativesRankingLoss widget: - source_sentence: Sự khác biệt giữa các thời đại trong nghệ thuật trang trí rồng được thể hiện như thế nào qua các thời Hùng Vương, Lý, Trần, Hồ, Lê, Mạc, Nguyễn? sentences: - "Tài liệu tham khảo\r\n323. Nguyễn Quang Ngọc, “Mấy nhận xét về kết cấu kinh tế\ \ của \r\nmột số làng thương nghiệp ờ vùng đồng bằng Bắc Bộ thế kỳ \r\nXVIII-XIX”,\ \ Tạp chí Nghiên cứu Lịch sứ, số 5 (218), 1984.\r\n324. Nguyễn Quang Ngọc, Phan\ \ Đại Doãn, “Mấy ý kiến về hoạt \r\nđộng thương nghiệp ở nông thôn đồng bằng Bắc\ \ Bộ thế kỷ \r\nXVIII-XIX (hiện tượng và bản chất)”, Tạp chí Nghiên cứu\r\nLịch\ \ sử, số 5 (224), 1985.\r\n325. Nguyễn Quang Ngọc, “Thêm vài ý kiến về Tam Điệp”,\ \ Tạp \r\nchí Nghiên cứu Lịch sử, số 1 (244), 1989.\r\n326. Nguyễn Quang Ngọc,\ \ về một số làng buôn ở Đồng bàng Bắc \r\nBộ thế kỳ XVIII-XIX, Hội Sừ học Việt\ \ Nam, 1993.\r\n327. Nguyễn Quang Ngọc, Vũ Văn Quân, “Tư liệu về nguồn gốc \r\n\ chức năng và hoạt động cùa đội Hoàng Sa”, Tạp chí Khoa\r\nhọc xã hội, Đại học\ \ Quốc gia, t.XIV, số 3, 1998, ư. 10-20.\r\n328. Nguyễn Quang Ngọc, “Bảo vệ chủ\ \ quyền ưên Biển Đông: \r\nmột hoạt động nổi bật của vương triều Tây Sơn”, Tạp\ \ chí \r\nLịch sử quân sự, số 1, 1999, tr. 15-18.\r\n329. Nguyễn Quang Ngọc (Chủ\ \ biên), Tiến trình lịch sứ Việt Nam,\r\nNxb. Giáo dục, Hà Nội, 2001.\r\n330.\ \ Nguyền Quân, Phan cẩm Thượng, Mỹ thuật cùa người Việt,\r\nNxb. Mỹ thuật. Hà\ \ Nội. 1989.\r\n331. Nguyễn Tài Thư (Chủ biên), Lịch sử tư tưởng Việt Nam, 2\r\ \ntập, Nxb. Khoa học xã hội, Hà Nội, 1993.\r\n332. Nguyễn Tài Thư, Nho học và\ \ Nho học ớ Việt Nam: Một số lý\r\nluận và thực tiễn, Nxb. Khoa học xã hội, Hà\ \ Nội, 1997.\r\n333. Nguyễn Tưòmg Phượng, Binh chế Việt Nam qua các thời đại,\r\ \nNgày Mai, 1950." - "Ba Thục, Kinh Sở, Ngô Việt…). Kết thúc cuộc \"Hán Sở tranh hùng\", nhà Hán\r\n\ đã thống nhất đất nước Trung Hoa từ bắc xuống nam (tiền bắc hậu nam) và phát\r\ \ntriển đất nước theo một trật tự ngược lại: tiền nam hậu bắc\".\r\nCó thể hình\ \ dung cơ cấu của văn hóa Trung Hoa như sau: \r\nVĂN HOÁ\r\nTRUNG\r\nHOA\r\n=\r\ \nVăn hoá lưu vực sông Hoàng Hà\r\n+\r\nVăn hoá nông\r\nnghiệp lúa nước\r\nĐông\ \ Nam Á\r\nVăn hoá du\r\nmục Tây Bắc +\r\nVăn hoá nông\r\nnghiệp khối Trung\r\n\ Nguyên\r\nMối liên hệ và sự tác động qua lại giữa văn hóa Việt Nam với Trung Hoa,\r\ \ngiữa văn hóa phương Bắc cổ đại với văn hóa phương Nam cổ đại (trong đó có\r\n\ văn hóa Nam – Á - Bách Việt) có thể trình bày trong bảng 1.5.\r\nVĂN HOÁ\r\nP.BẮC\ \ CỔ ĐẠI\r\nVĂN HOÁ PHƯƠNG NAM (= Đ.N.Á cổ đại)\r\nVăn hoá Nam-Á (Bách Việt)\r\ \nVăn hóa vùng lưu\r\nvực sông Hoàng\r\nHà\r\nVăn hóa vùng lưu\r\nvực sông Dương\r\ \nTử\r\nVăn hóa vùng lưu\r\nvực s. Hồng, s.\r\nMã\r\nVăn hóa miền\r\nTrung và\ \ đồng\r\nbằng s. Mê Kông\r\nVĂN HOÁ TRUNG HOA VĂN HOÁ VIỆT NAM\r\nBảng 1.5: Quan\ \ hệ cội nguồn giữa văn hóa Việt Nam và Trung Hoa\r\nBài 3: TIẾN TRÌNH VĂN HÓA\ \ VIỆT NAM\r\nTiến trình văn hóa Việt Nam có thể chia thành 6 giai đoạn: văn hóa\ \ tiền\r\nsử, văn hóa Văn Lang - Âu Lạc, văn hóa thời chống Bắc thuộc, văn hóa\ \ Đại\r\nViệt, văn hóa Đại Nam và văn hóa hiện đại. Sáu giai đoạn này tạo thành\ \ ba lớp:\r\nlớp văn hóa bản địa, lớp văn hóa giao lưu với Trung Hoa và khu vực,\ \ lớp văn\r\nhóa giao lưu với phương Tây.\r\n3.1. Lớp văn hóa bản địa\r\n28\r\n\ Downloaded by Tu?n ?ào Minh ([email protected])\r\nlOMoARcPSD|49704028" - "trái), và hình bán nguyệt (đôi dưới, phải). Trước mắt ta là sự hòa hợp tuyệt\ \ vời\r\ncủa cái động (vật nhau) trong thế tĩnh của ba hình hình học với những\ \ cạnh đáy\r\nvững vàng cho thấy sự ngang sức ngang tài của các chàng trai; sự\ \ vận động liên\r\ntục của cơ bắp như dừng lại. Hai người chờ vật được khuôn lại\ \ trong hai hình\r\nchữ nhật đứng tạo nên cảm giác co ro bất tận trong cái rét\ \ của lễ hội đầu xuân.\r\n4.1.3. Thủ pháp mô hình hóa đã tạo nên một nền nghệ\ \ thuật trang trí và\r\nnhiều mô hình mang tính triết lí sâu sắc.\r\nBộ Tứ Linh\ \ (Hình 4.20a) với long (rồng) biểu trưng cho uy là nam tính; li\r\n(= long mã)\ \ hoặc lân (kì lân, con vật tưởng tượng đầu sư tử, mình nai, đuôi trâu,\r\n131\r\ \nDownloaded by Tu?n ?ào Minh ([email protected])\r\nlOMoARcPSD|49704028\r\ \năn cỏ, rất hiền lành - hình 4.20b) biểu trưng cho ước vọng thái bình, quy (rùa)\r\ \nhiểu tượng cho sự sống lâu và phượng (phụng) biểu tượng cho nữ tính. Rồng -\r\ \nPhượng biểu tượng cho hạnh phúc lứa đôi (ở Trung Hoa hiên tượng này là\r\n“loan-phượng”:\ \ loan là con đực, phượng là con cái). Đồ án trang trí RỒNG phổ\r\nbiến đến mức\ \ phản ánh những đặc trưng cửa từng thời đại. Rồng thời Hùng\r\nvương, thời Lí,\ \ Trần, Hồ, Lê, Mạc, Nguyễn – mỗi thời có những nét đặc thù\r\nriêng tương ứng\ \ với thời đại của mình.\r\nTứ linh cộng thêm ngư-phúc-hạc-hổ thì thành BÁT VẬT.\ \ Ngư (Cá) gắn\r\nvới truyền thuyết \"cá hóa rồng\" biểu tượng cho sự thành đạt.\ \ Chữ phúc là “sự tốt\r\nlành, may mắn” đồng âm và viết gần giống với chữ bức\ \ nghĩa là \"con dơi\", vì" - source_sentence: Nhiệm vụ quan trọng nhất của các nước công nghiệp chủ nghĩa châu Âu và Nhật Bản sau chiến tranh thế giới thứ hai là gì? sentences: - "Dupuis phái tự mình hành động. Tháng 10-1872, Dupuis đi Hương \r\nCảng và Thượng\ \ Hải mua pháo thuyền và đạn dược, mộ quân lính,\r\n1. Đó là các cuộc thám hiểm\ \ cùa phái đoàn Doudard de Lagrée và Francis \r\nGamier vào những năm từ 1866\ \ đến 1870.\r\n2. Nguyễn Phan Quang (1949), Việt Nam thế ky XIX (1802-1884), Nxb.\ \ \r\nThành phố Hồ Chí Minh, tr. 321.\r\n159\r\nLỊCH SỪ VIỆT NAM - TẬP 6\r\nrồi\ \ đến tháng 11 năm đó thì kéo nhau về Bắc Kỳ. Cùng lúc đó, bọn \r\nthực dân hiếu\ \ chiến ở Nam Kỳ cũng lợi dụng việc triều đình Huế \r\nyêu cầu đưa ra Bắc tiễu\ \ trừ giặc biển để phái tàu chiến ra tiếp tay \r\ncho Dupuis. Cậy có lực lượng\ \ mạnh, Dupuis buộc Kinh lược sứ Lê \r\nTuấn trong vòng hai tuần phải xin triều\ \ đình Huế cho phép hắn \r\nđược mượn đường đi lên Vân Nam. Nhung hạn 2 tuần chưa\ \ hết và \r\ngiấy phép cũng chưa có mà Dupuis đã nổ súng, rồi tự tiện kéo đoàn\ \ \r\ntàu vào Cửa cấm (Hải Phòng) ngược sông Hồng lên Hà Nội (ngày \r\n22-12-1872).\ \ Theo sử nhà Nguyễn thì ngày 2-12-1872, Dupuis “từ\r\nHài Dương đi đen Bắc Ninh,\ \ Hà Nội, các quan tình và quân thứ 2-\r\n3 lần biện bác ngăn trở không cho đi,\ \ nhưng chúng không nghe\r\nTrong khoảng thời gian từ năm 1872 đến năm 1873, Dupuis\ \ đã ỷ \r\nthế quân Pháp và triều đình nhà Thanh, trắng trợn xâm phạm chủ \r\n\ quyền Việt Nam, liên tiếp gây ra nhiều vụ khiêu khích, cướp phá \r\nđối với nhân\ \ dân dọc hai bờ sông, tấn công các đồn bốt của triều \r\nđình nhà Nguyễn.\r\n\ Trước hành động ngang ngược cùa Dupuis, quân dân Hà Nội \r\nmặc dù chưa có lệnh\ \ triều đình nhung vẫn tích cực đề phòng. Lệnh" - "hội loài người nói chung hay cùa một quốc gia, một dân tộc nói \r\nriêng. Nghiên\ \ cứu lịch sử là nhằm tìm hiểu những sự kiện xảy ra \r\ntrong quá khứ để từ đó\ \ rút ra các bài học kinh nghiệm cho hiện tại \r\nvà tương lai. Nghiên cứu và\ \ biên soạn lịch sừ, vì vậy, trở thành một \r\nyêu cầu bức thiết của mọi quốc\ \ gia, dân tộc. Phạm Công Trứ, nhà \r\nchính trị danh tiếng, nhà sử học sống ở\ \ thế kỳ XVII, trong bài Tựa\r\nsách Đại Việt sử ký bản kỷ tục biên viết: \"Vì\ \ sao mà làm quốc sử?\r\nVĩ sử chù yếu là để ghi chép sự việc. Có chinh trị cùa\ \ một đời tất\r\nphải có sử của một đời. Mà ngòi bút chép sử giữ nghị luận rất\r\ \nnghiêm, ca ngợi đời thịnh trị thì sáng tỏ ngang với mặt trời, mặt\r\ntrăng,\ \ lên án kẻ loạn tặc thì gay gắt nhu sương thu lạnh buốt,\r\nngười thiện biết\ \ có thể bắt chước, người ác biết có thể tự răn, quan\r\nhệ đến việc chính trị\ \ không phải là không nhiều. Cho nên làm sử là\r\ncốt để cho được như thế\"'.\r\ \nViệt Nam là một dân tộc có lịch sử lâu đời. Việt Nam cũng là \r\nmột dân tộc\ \ yêu sử và có rất nhiều người ham thích tìm tòi, nghiên \r\ncứu và biên soạn\ \ lịch sử. Đã có nhiều công trình lịch sử được công \r\nbố, không chi do các cơ\ \ quan, tổ chức chuyên nghiên cứu biên \r\nsoạn, mà còn do cá nhân người yêu sử\ \ thực hiện... Điều này vừa có \r\nmặt tích cực, lại cỏ mặt tiêu cực. Tích cực\ \ vì sẽ góp phần giúp nhân \r\ndân hiểu thêm về lịch sử nước nhà, nhưng cũng chứa\ \ đựng yếu tố \r\ntiêu cực là dễ dẫn tới những hiểu biết phiến diện, sai lầm về\ \ lịch \r\nsử... đôi khi đồng nhất truyền thuyết với lịch sử?" - "LỊCH SỪ VIỆT NAM - TẬP 11\r\ngiầu mạnh hcm nhờ chiến tranh. Những nước bại trận\ \ như Đức, Ý, \r\nNhật thì kiệt quệ. Song dù thắng hay bại, sự kết thúc chiến\ \ tranh đặt \r\ncho mỗi nước những yêu cầu cấp bách cần giải quyết, tạo nên \r\ \nnhững đặc trưng kinh tế - xã hội ở nhóm nước này.\r\nSau chiến tranh thế giới,\ \ những nưóc công nghiệp chủ nghĩa \r\nchâu Âu và Nhật Bản đều bị chiến tranh\ \ tàn phá nặng nề. Nhiệm vụ \r\nquan trọng của họ ỉà hàn gắn vết thương chiến\ \ tranh, khôi phục \r\nkinh tế, ổn định đời sống xã hội. Đối với Mỹ, nhiệm vụ\ \ chủ yếu là \r\nphải chuyển hướng vận hành kinh tế từ một nền kinh tế phục vụ\ \ \r\nquân sự thời chiến sang nền kinh tế thời bình.\r\nNhừng nét cơ bản của tình\ \ hình thế giới nêu trên đã tác động \r\nđến hầu hết các khu vực trên thế giới,\ \ đặc biệt là khu vực Châu Á \r\nvà Đông Nam Á, tạo điều kiện thuận lợi cho cuộc\ \ đấu tranh giải \r\nphóng của các dân tộc Đông Dương. Từ đầu những năm 1950,\ \ tình \r\nhình cách mạng ba nước Đông Dương chuyển biến nhanh chóng. \r\nVới\ \ cuộc đi thăm Trung Quốc, Liên Xô của Chủ tịch Hồ Chí Minh \r\nđầu năm 1950 và\ \ việc các nước xã hội chủ nghĩa công nhận và đặt \r\nquan hệ ngoại giao với Chính\ \ phủ Việt Nam Dân chủ Cộng hòa là \r\nmột thắng lợi ngoại giao vô cùng quan trọng.\ \ Thắng lợi về ngoại \r\ngiao này đã chấm dứt thời kỳ chiến đấu đom độc, hầu như\ \ bị cách ly \r\nvới bên ngoài và từ đó tiếp nhận được sự đồng tình về chính trị\ \ và \r\nsự viện trợ về vật chất.\r\nVới sự giúp đỡ của Liên Xô, Trung Quốc và\ \ các nước xã hội" - source_sentence: Chức năng của quan Đốc học trong việc quản lý giáo dục ở các tỉnh là gì? sentences: - "Định, Phú Yên, Biên Hoà, Gia Định, Vĩnh Long, Định Tường, An \r\nGiang đều đặt\ \ mỗi tỉnh một quan Đốc học coi việc học chính trong \r\ntinh. Các tỉnh từ Quảng\ \ Trị, Quảng Bình, Hà Tĩnh, Nghệ An, \r\nThanh Hoá, Ninh Bình, Nam Định, Hà Nội,\ \ Hưng Yên, Hải Dương, \r\nSơn Tây, Bắc Ninh cũng đều đật chức Đốc học. Tinh nào\ \ khuyết \r\nchức Đốc học thì đặt Thự đốc học tạm quyền đốc học một thời gian\ \ \r\nđổ phụ trách, đôn đốc việc học trong tỉnh.\r\nCác tỉnh Khánh Hoà, Bình Thuận,\ \ Hà Tiên, Quảng Yên, Hưng \r\nHoá, Tuyên Quang, Thái Nguyên, Lạng Sơn, Cao Bằng,\ \ do số học \r\nsinh ít nên đến cuối thời Thiệu Trị (1847) vẫn chưa đặt chức Đốc\ \ học.\r\nTheo lệ Nhà nước chế cấp ấn quan phòng giao cho Đốc học lo \r\nviệc\ \ học chính trong địa hạt của tinh sờ tại, trong đó có việc xây \r\ndựng trường\ \ sở ở tinh, phù, hoặc huyện, châu; sắp xếp các thày \r\ngiáo và tuyển chọn học\ \ sinh vào học ở các trường. Những công \r\nviệc licn quun đén việc học đểu có\ \ sự phối hựp giữa quan Đốc hục \r\nvới các viên giữ chức Giáo thụ ở các phủ và\ \ Huấn đạo ờ các huyện, \r\nchâu. Một bộ máy giáo dục được tổ chức chặt chẽ theo\ \ ngành dọc \r\ntừ tinh đến phủ, huyện, châu; tổng (ở tổng có Tổng giáo) để theo\ \ \r\ndõi, đôn đốc việc giảng dạy và học tập, đã góp phần đẩy mạnh hom \r\nviệc\ \ giáo dục ở những triều vua Nguyễn nửa đầu thế kỳ XIX. Những \r\nthành tích của\ \ giáo dục bấy giờ biểu hiện rõ nhất ở việc Nhà nước \r\ncứ 3 năm lại mở một kỳ\ \ thi Hương ờ một số tinh thuộc Bác Kỳ (Nam \r\nĐịnh, Hài Dương, Thăng Long);\ \ Nghệ An; kinh đô Huế; Trung Kỳ" - "Trước tình hình thế giới và trong nước ngày càng khẩn trương, ngày 28 - I - 1941,\r\ \nlãnh tụ Nguyễn Ái Quốc về nước triệu tập Hội nghị lần thứ 8 Ban Chấp hành\r\n\ Trung ương Đảng Cộng sản Đông Dương. Hội nghị họp tại Pác Bó (Cao Bằng) từ\r\n\ ngày 10 đến ngày 19 - 5 - 1941.\r\nHội nghị chủ †rương trước hết phởi giỏi phóng\ \ cho được cóc dôn tộc\r\nĐông Dương ro khỏi éch Phớp - Nhột. Hội nghị quyết định\ \ tiếp tục tạm\r\ngóc khổu hiệu “Đónh đổ địa chủ, chia ruộng đốt cho dôn còy”\ \ thay bằng\r\ncóc khổu hiệu “Tịch thu ruộng đốt của đế quốc vò Việt gian chia\ \ cho dên\r\ncòy nghèo, giởm †ô, giỏm tức, chia lợi ruộng công”, tiến tới thực\ \ hiện\r\n“Người còy có ruộng”. Hội nghị chủ trương †hònh lộp Việt Nơm độc lập\r\ \nđồng minh (gọi tốt lò Việt Minh) bao gồm céc †ổ chức quồn chúng, lốy\r\ntên\ \ lò Hội Cứu quốc nhồm : “Liên hiệp hết thỏy cóc giới đồng bèo yêu\r\nnước, không\ \ phôn biệt giòu nghèo, giò trẻ, gới trai, không phôn biệt tôn\r\ngiáo vò xu hướng\ \ chính trị, đặng cùng nhau mưu cuộc dôn tộc giỏi phóng\r\nvò sinh tồn” °°,\r\n\ \r\nMặt trận Việt Minh chính thức thành lập ngày 19 - 5 - 1941. Chỉ sau một thời\r\ \ngian ngắn, tổ chức này đã có uy tín và ảnh hưởng sâu rộng trong nhân dân. Sau\ \ Hội\r\nnghị Trung ương, lãnh tụ Nguyễn Ái Quốc đã gửi thư kêu gọi đồng bào cả\ \ nước\r\nđoàn kết thống nhất đánh đuổi Pháp - Nhật." - "\"Chính sự ngày một đổ nát, đói kém xảy ra luôn luôn. Nhân dân cùng\r\nquân,\ \ khốn khổ, giặc cướp nổi lên ở nhiễu nơi\".\r\n(Khâm định Việt sử thông giám\ \ cương mục)\r\n\r\nỞ Nghệ An, Thanh Hoá, Ninh Bình,... dân nghèo nổi dậy đấu\ \ tranh. Trong\r\ntình hình đó, một số thế lực phong kiến ở các địa phương lại\ \ đánh giết lẫn\r\nnhau, quấy phá nhân dân và chống lại triều đình. Nhà Lý phải\ \ dựa vào thế lực\r\nhọ Trần để chống lại các lực lượng nổi loạn nên đã tạo điều\ \ kiện và thời cơ cho\r\nhọ Trần buộc Chiêu Hoàng (vua cuối cùng của nhà Lý) phải\ \ nhường ngôi cho\r\nTrần Cảnh vào tháng 12, năm Ất Dậu (đâu năm 1226).\r\n\r\n\ (1) Việc thổ mộc : việc làm nhà cửa, chùa, đền, đào sông, hồ..." - source_sentence: Thiệu Trị đã xử lý trường hợp của Lý Văn Phức và việc người Pháp bắt giữ thuyền quân đi tuần biển của Việt Nam ra sao? sentences: - "hóa; thuế độc quyền; thué điền thổ...\r\nTheo những con số thống kê chính thức\ \ thì các loại thuế trên \r\nđều tăng lên đáng kể, khoảng từ ba đến hơn ba lần\ \ vào năm 1945 \r\n(số dự thu) so với năm 1939 (số thực thu) như sau:\r\nBảng\ \ 29: Thu nhập từ một sổ loại thuế ở Đông Dương \r\ntrong các năm 1939 và 19453\r\ \nĐom vị: nghìn đồng\r\nThuế 1939 1945\r\nThuế tiêu thụ và vận chuyển hàng hoá\ \ 20.655.000 58.265.000\r\nThuế muối, rượu, thuốc phiện, diêm, pháo,\r\nthuốc\ \ lá\r\n24.694.000 87.000.000\r\nThuế điền thổ, trước bạ 11.821.000 28.625.000\r\ \nvề thuốc phiện, do việc nhập khẩu bị ngừng, Pháp khuyến khích \r\nnhân dân thượng\ \ du trồng loại cây này nên số thuốc phiện sản xuất \r\nđược ngày một tăng: năm\ \ 1940: 7.560kg; nãm 1941: 17.344kg; năm\r\n1. Annuaire statistique de V Union\ \ f,rariỊaise Outre- mer 1939-1946, tr. K -\r\n90-93.\r\n2, 3. Annuaire statistique\ \ de runion firanẹaise Outre - mer 1939-1946, tr.\r\nK-90.\r\n552" - "Chương I. Chính sách thuộc địa của Pháp..\r\nbộ đồng bào các dân tộc thiểu số.\ \ về phương diện này, chính quyền \r\nthuộc địa còn muốn đi xa hơn là cố định\ \ đồng bào vào một không \r\ngian nhất định, rồi đưa họ đến với chế độ sở hữu\ \ ruộng đất - chế độ \r\nsở hữu tập thể và ấn định cho họ một chế độ thuế khóa.\r\ \nNhư vậy, “chính sách thâm nhập” có xuất phát điểm là chính \r\nsách “chia đế\ \ trf' và mục tiêu là tách các dân tộc thiểu số ra khỏi \r\ndân tộc Kinh, dùng\ \ dân tộc nọ chống lại dân tộc kia và nhằm một \r\nmục đích cao hơn là từ chinh\ \ phục, khuất phục về chính trị để tiến \r\nsang khai thác, bóc lột về đất đai,\ \ nhân công và thuế khóa của các \r\nđồng bào.\r\n7. Một số “cải cách” xã hội\ \ khác liên quan đến nông dân và\r\ncông nhân\r\nLiên quan đến nông dân, trong\ \ bài diễn văn về Tinh hình Đông\r\nDương và tuyên bo cải cách vào tháng 9/19301,\ \ Pierre Pasquier nêu \r\nra những vấn đề như: thi hành luật điền thổ, giúp nông\ \ dân Nam Kỳ \r\nthế chấp ruộng đất để vay tín dụng ngân hàng; dẫn thủy nhập điền,\ \ \r\nlàm thuỷ lợi để tăng diện tích canh tác, cải tiến kỹ thuật trồng trọt; \r\ \ngiúp nông dân thăng tién về sờ hữu ruộng đất (từ người không có \r\nđất lên\ \ tiểu điền chủ); mở rộng việc nhượng đất, khẩn hoang ở \r\nnhững vùng rừng núi\ \ ở Bắc và Trung Kỳ cũng như ở phía tây và \r\nnam Nam Kỳ; quy định lại chế độ\ \ lĩnh canh để \"hạn ché bớt sự bóc\r\nlột cùa địa chù đoi với tá điền”.\r\nTriển\ \ khai những “cải cách” này, Pierre Pasquier cho tiếp tục \r\nxây dựng các công\ \ trình thuỷ nông, rồi thành lập Hội đồng Khẩn" - "theo vài mươi người, đeo gươm, đeo súng, đến thẳng ngay công \r\nquán, đưa ra\ \ một lá thư của nước Pháp bằng chữ Hán, lời lẽ ngang \r\nngược. Lý Văn Phức không\ \ nhận thư, Lạp Biệt Nhĩ quát to doạ nạt, \r\nđể lại thư xuống ghế rồi đi. Lý\ \ Văn Phức và Nguyễn Đình Tân bàn \r\nvới nhau rằng: \"Nhận lấy thư là có tội,\ \ mà đốt thư đi cũng có tội, \r\nkhông gì bằng cho chạy trạm về đệ tâu lên\".\ \ Lý Văn Phức về Kinh,\r\n1. Thực lục, tập VI, sđd, tr. 301.\r\n492\r\nChương\ \ VII. Quan hệ đối ngoại\r\nThiệu Trị giận là làm mất quốc thể, sai vệ cẩm y đóng\ \ gông đem \r\ngiam ở Tà đãi lậu, bắt giải chức, giao cho đình thần bàn.\r\nKhi\ \ ấy, bọn Pháp ngày thường lên bờ, ngông nghênh đi lại các \r\nnơi giao tiếp với\ \ dân đi đạo. Những thuyền quân đi tuần biển bị \r\nchúng bắt giữ lại ở cừa biển\ \ và cướp lấy buồm thuyền và dây buộc \r\nthuyền cùa 5 chiếc thuyền bọc đồng ở\ \ Kinh phái đi Nam (Kim \r\nƯng, Phấn Bằng, Linh Phượng, Thọ Hạc, Vân Bằng) đậu\ \ ở vụng \r\nTrà Sơn, đối diện vói chiến thuyền Pháp.\r\nViệc báo lên, Thiệu Trị\ \ sai ngay Đô thống Hữu quân Mai Công \r\nNgôn, Tham tri Bộ Hộ Đào Trí Phú đem\ \ biền binh 3 vệ Vũ lâm, Hổ \r\noai, Hùng nhuệ đến Quảng Nam cùng với lực lượng\ \ thủy, bộ tại \r\nchỗ tổ chức bố phòng. Thiệu Trị truyền chi căn dặn Mai Công\ \ \r\nNgôn và Đào Trí Phú rằng: \"Người Tây dương nếu đã sợ uy, thu \r\nhình,\ \ thì ta không nên tự động thủ trước; nếu chúng sinh chuyện \r\ntrước, thì đốc\ \ sức thành đài cùng biền binh các hiệu thuyền và \r\nthuyền đồng do Kinh phái\ \ đi, ngoài hợp, trong ứng, lập tức đánh" - source_sentence: Gia Cát Lượng đã giúp ai trong việc quản lý nước Thục? sentences: - "phải trông coi mọi việc, giúp Thành Vương đến lúc trưởng thành. \r\n4\r\n Hoắc\ \ Quang giữ chức Đại tư mã tướng quân, phò Hán Chiêu Đế lúc lên ngôi mới 9 tuổi.\ \ \r\n5\r\n Gia Cát Lượng tức Khổng Minh, là thừa tướng của Chiêu Đế Lưu Bị nước\ \ Thục đời Tam Quốc. Lưu Bị chết, con là Lưu Thiện nối \r\nngôi, tức Thục Hậu\ \ chúa, mọi việc nước, việc quân đều phải trông cậy vào Gia Cát Lượng. \r\n6\r\ \n Tô Hiến Thành là Thái úy triều Lý Cao Tông, nhận di mệnh Cao Tông phò vua nhỏ\ \ là Long Cán lên nối ngôi mới 3 tuổi. \r\n7\r\n Tứ phụ: nghĩa là bốn viên đại\ \ thần giúp vua khi mới lên ngôi. \r\n8\r\n Chỉ Thuận Tông. \r\n9\r\n Xích chủy:\ \ nghĩa là mõm đỏ, miệng đỏ, hay đỏ mỏ. Xích chủy hầu là loài đỏ mỏ ám chỉ Lê\ \ Quý Ly. \r\n10 Bạch kê: nghĩa là gà trắng. Nghệ Tông sinh năm Tân Dậu, tức năm\ \ gà. Tân thuộc hành kim, loài kim sắc trắng. Vì thế \"bạch kê\" \r\nám chỉ Nghệ\ \ Tông. \r\n11 Chữ vương? ở trong lòng chữ khẩu? là chữ \"quốc\"?. \r\n12 Theo\ \ tục nhà Trần, hằng năm vào ngày mồng 4 tháng 4, vua hội họp bề tôi làm lễ tuyên\ \ thệ ở đền Đồng Cổ. (Xem bản kỷ, quyển \r\n5, Kiến Trung năm thứ 3, 1277). \r\ \n13 Chỉ Quý Ly. \r\n288 Đại Việt Sử Ký Toàn Thư - Bản Kỷ - Quyển VIII \r\nQuý\ \ Ly bỏ mũ, rập đầu khóc lóc từ tạ, chỉ trời vạch đất thề rằng: \r\n\"Nếu thần\ \ không biết dốc lòng trung, hết sức giúp Quan gia để truyền đến con cháu về sau\ \ thì \r\ntrời sẽ ghét bỏ thần\". \r\nQuý Ly lại nói: \"Lúc Linh Đức Vương làm\ \ điều thất đức, nếu không nhờ oai linh bệ hạ thì thần đã" - "éo, xênh xang lạ hom cả\", và gánh xiếc của BẮc thành trổ tài dịp Đại \r\nkhánh\ \ \"Ngũ tuần\" của vua: \"4 đứa leo dây, đứa trẻ lộn dây, đứa trẻ \r\nmúa trên\ \ bàn tay 2 đứa\".\r\nNhững định chế về tổ chức và hoạt động nghệ thuật của nhà\ \ \r\nNguyễn đã có tác dụng quan ữọng kích thích các loại hình vãn nghệ \r\ndân\ \ gian phát triển cả về số lượng lẫn chất lượng. Trong các đợt biểu \r\ndiễn ở\ \ Kinh đô, trước yêu cầu thưởng lãm nghiêm ngặt và cao hơn \r\nđịa phương, các\ \ nhà viết kịch bản. đạo diễn, diễn viên phải trau dồi để \r\nnâng cao năng lực\ \ sáng tác, dàn dựng và kỹ năng biểu diễn.\r\n2. Nghệ thuật dân gian\r\nSinh hoạt\ \ văn nghệ dân gian trong các làng quê cũng phát triển. \r\nỞ Bắc Kỳ, Bắc Trung\ \ Kỳ, hát ả đào rất phổ biến. Bên cạnh đó là \r\ncác thể loại dân ca: hát Xoan\ \ ở Phú Thọ, Quan họ Bắc Ninh, hát \r\nSli, Then ở Lạng Sơn, hát Ví dặm, Phường\ \ vải ở Nghệ An, Hà \r\nTĩnh. Ở các tinh trung du và đồng bằng Bắc Bộ, Thanh Hóa,\ \ chèo \r\nsân đình mang tính trào lộng nở rộ. Thể loại trò hài, xiếc ở Bắc Kỳ\ \ \r\ncũng thu hút đông đảo khán giả.\r\n639" - "Tây. Ngoài cơ sờ đúc súng cũ của tiên triều, năm 1825 vua Minh \r\nMệnh mờ thêm\ \ sáu xưởng nữa. vốn cần cù và ham học hỏi sáng \r\ntạo, những người thợ quân\ \ giới đã được \"thứ súng tay nạp thuốc nổ \r\nmạnh theo kiểu Tây dương\". Vào\ \ những năm cuối triều Minh \r\nM ệnh, họ đã đúc 15 cỗ đại pháo X ung tiêu băng\ \ đồng và hai cỗ \r\nsúng lớn Chấn hải, loại đại pháo lợi hại trong thủy chiến\ \ phương \r\nTây. Sau đó, lại xuất xưởng tiếp 30 cỗ Chấn hải. Năm 1829, quản \r\ \nkho Hải Dương là Tôn Thất Thiện cùng với 100 lính Chấn cơ chế \r\nra cối gỗ\ \ chạy bàng sức nước ở khe suối để giã, luyện thuốc súng. \r\nDụng cụ này là xe\ \ \"Thủy hỏa ký tế\", và những năm sau được phổ \r\ncập trong quân ngũ. Từ vũ\ \ khí phương Tây, người Đại Nam đã tự \r\ntìm hiểu từng chi tiết để chế tạo thước\ \ đo ngắm bắn, thước kiểm tra \r\nthuốc súng. Trong bảy năm ờ ngôi, vua Thiệu\ \ Trị đúc 9 cỗ súng \r\nbàng đồng hiệu là \"Thần uy phục viễn đại tướng quân\"\ , cỗ to nhất \r\nlà 10.706 cân, cỗ nhỏ nhất là 10.222 cân, tổng cộng là 93.829\ \ cân.\r\n649\r\nLỊCH SỬ VIỆT NAM - TẬP 5\r\nVà ba cỗ súng hiệu \"Bảo Đại định\ \ công an dân hòa chúng thượng \r\ntướng quân\", mỗi cỗ trên 14.500 cân, tổng\ \ cộng là 43.620 cân1.\r\nĐe tạo điều kiện cho quân thủy học tập, bộ Công cấp\ \ cho họ la \r\nbàn, thước đo nước, đồng hồ cát xem giờ của phương Tây. v ề khoa\ \ \r\nmục bắn súng thì lính thủy phải tập bắn súng điểu sang và đại bác. \r\n\ Minh Mệnh yêu cầu Hiệp biện Đại học sĩ lãnh Thượng thư bộ Binh \r\nTrương Đăng\ \ Quế đọc kỹ các sách và bản đồ thủy chiến \"Tây" model-index: - name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m-v2.0 results: - task: type: information-retrieval name: Information Retrieval dataset: name: Snowflake/snowflake arctic embed m v2.0 type: Snowflake/snowflake-arctic-embed-m-v2.0 metrics: - type: cosine_accuracy@1 value: 0.43333333333333335 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6474885844748859 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7219178082191781 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7981735159817351 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.43333333333333335 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.21582952815829526 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14438356164383562 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0798173515981735 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.43333333333333335 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6474885844748859 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7219178082191781 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7981735159817351 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6167502643310033 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5585560266724653 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5649771622451427 name: Cosine Map@100 --- # SentenceTransformer based on Snowflake/snowflake-arctic-embed-m-v2.0 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) on the csv dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) <!-- at revision 0d1661ceed1cb456c85726749d5be61ebb30d4f1 --> - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - csv <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: GteModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'Gia Cát Lượng đã giúp ai trong việc quản lý nước Thục?', 'phải trông coi mọi việc, giúp Thành Vương đến lúc trưởng thành. \r\n4\r\n Hoắc Quang giữ chức Đại tư mã tướng quân, phò Hán Chiêu Đế lúc lên ngôi mới 9 tuổi. \r\n5\r\n Gia Cát Lượng tức Khổng Minh, là thừa tướng của Chiêu Đế Lưu Bị nước Thục đời Tam Quốc. Lưu Bị chết, con là Lưu Thiện nối \r\nngôi, tức Thục Hậu chúa, mọi việc nước, việc quân đều phải trông cậy vào Gia Cát Lượng. \r\n6\r\n Tô Hiến Thành là Thái úy triều Lý Cao Tông, nhận di mệnh Cao Tông phò vua nhỏ là Long Cán lên nối ngôi mới 3 tuổi. \r\n7\r\n Tứ phụ: nghĩa là bốn viên đại thần giúp vua khi mới lên ngôi. \r\n8\r\n Chỉ Thuận Tông. \r\n9\r\n Xích chủy: nghĩa là mõm đỏ, miệng đỏ, hay đỏ mỏ. Xích chủy hầu là loài đỏ mỏ ám chỉ Lê Quý Ly. \r\n10 Bạch kê: nghĩa là gà trắng. Nghệ Tông sinh năm Tân Dậu, tức năm gà. Tân thuộc hành kim, loài kim sắc trắng. Vì thế "bạch kê" \r\nám chỉ Nghệ Tông. \r\n11 Chữ vương? ở trong lòng chữ khẩu? là chữ "quốc"?. \r\n12 Theo tục nhà Trần, hằng năm vào ngày mồng 4 tháng 4, vua hội họp bề tôi làm lễ tuyên thệ ở đền Đồng Cổ. (Xem bản kỷ, quyển \r\n5, Kiến Trung năm thứ 3, 1277). \r\n13 Chỉ Quý Ly. \r\n288 Đại Việt Sử Ký Toàn Thư - Bản Kỷ - Quyển VIII \r\nQuý Ly bỏ mũ, rập đầu khóc lóc từ tạ, chỉ trời vạch đất thề rằng: \r\n"Nếu thần không biết dốc lòng trung, hết sức giúp Quan gia để truyền đến con cháu về sau thì \r\ntrời sẽ ghét bỏ thần". \r\nQuý Ly lại nói: "Lúc Linh Đức Vương làm điều thất đức, nếu không nhờ oai linh bệ hạ thì thần đã', 'Tây. Ngoài cơ sờ đúc súng cũ của tiên triều, năm 1825 vua Minh \r\nMệnh mờ thêm sáu xưởng nữa. vốn cần cù và ham học hỏi sáng \r\ntạo, những người thợ quân giới đã được "thứ súng tay nạp thuốc nổ \r\nmạnh theo kiểu Tây dương". Vào những năm cuối triều Minh \r\nM ệnh, họ đã đúc 15 cỗ đại pháo X ung tiêu băng đồng và hai cỗ \r\nsúng lớn Chấn hải, loại đại pháo lợi hại trong thủy chiến phương \r\nTây. Sau đó, lại xuất xưởng tiếp 30 cỗ Chấn hải. Năm 1829, quản \r\nkho Hải Dương là Tôn Thất Thiện cùng với 100 lính Chấn cơ chế \r\nra cối gỗ chạy bàng sức nước ở khe suối để giã, luyện thuốc súng. \r\nDụng cụ này là xe "Thủy hỏa ký tế", và những năm sau được phổ \r\ncập trong quân ngũ. Từ vũ khí phương Tây, người Đại Nam đã tự \r\ntìm hiểu từng chi tiết để chế tạo thước đo ngắm bắn, thước kiểm tra \r\nthuốc súng. Trong bảy năm ờ ngôi, vua Thiệu Trị đúc 9 cỗ súng \r\nbàng đồng hiệu là "Thần uy phục viễn đại tướng quân", cỗ to nhất \r\nlà 10.706 cân, cỗ nhỏ nhất là 10.222 cân, tổng cộng là 93.829 cân.\r\n649\r\nLỊCH SỬ VIỆT NAM - TẬP 5\r\nVà ba cỗ súng hiệu "Bảo Đại định công an dân hòa chúng thượng \r\ntướng quân", mỗi cỗ trên 14.500 cân, tổng cộng là 43.620 cân1.\r\nĐe tạo điều kiện cho quân thủy học tập, bộ Công cấp cho họ la \r\nbàn, thước đo nước, đồng hồ cát xem giờ của phương Tây. v ề khoa \r\nmục bắn súng thì lính thủy phải tập bắn súng điểu sang và đại bác. \r\nMinh Mệnh yêu cầu Hiệp biện Đại học sĩ lãnh Thượng thư bộ Binh \r\nTrương Đăng Quế đọc kỹ các sách và bản đồ thủy chiến "Tây', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> ## Evaluation ### Metrics #### Information Retrieval * Dataset: `Snowflake/snowflake-arctic-embed-m-v2.0` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.4333 | | cosine_accuracy@3 | 0.6475 | | cosine_accuracy@5 | 0.7219 | | cosine_accuracy@10 | 0.7982 | | cosine_precision@1 | 0.4333 | | cosine_precision@3 | 0.2158 | | cosine_precision@5 | 0.1444 | | cosine_precision@10 | 0.0798 | | cosine_recall@1 | 0.4333 | | cosine_recall@3 | 0.6475 | | cosine_recall@5 | 0.7219 | | cosine_recall@10 | 0.7982 | | **cosine_ndcg@10** | **0.6168** | | cosine_mrr@10 | 0.5586 | | cosine_map@100 | 0.565 | <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### csv * Dataset: csv * Size: 21,892 training samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 9 tokens</li><li>mean: 26.95 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 373.94 tokens</li><li>max: 596 tokens</li></ul> | * Samples: | anchor | positive | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Tính chất kiến trúc của đình làng triều Mạc được thể hiện qua những đặc điểm gì, như số gian, hình dạng, nội thất và cách bố trí không gian trong công trình?</code> | <code>Đình làng là công trình kiến trúc công cộng được dựng nên <br>băng sự đóng góp của cải và công sức của cả cộng đồng làng xã. <br>Ngoài chức năng là trụ sở hành chính của cả làng, ngôi đình còn là <br>trung tâm sinh hoạt văn hóa làng xã, là nơi diễn ra các nghi lễ trọng <br>đại trong dịp tế lễ thần Thành hoàng làng và tô chức hội hè hăng <br>năm. Có thê nói, ngôi đình làng là nơi hội tụ sức mạnh của cả cộng <br>đồng và là biểu trưng đặc sắc nhất của văn hóa làng xã. <br> <br>Trong các ngôi đình triều Mạc, Thân thành hoàng có lý lịch <br>xuất thân khá phong phú. Tản Viên sơn thánh là vị thần có ảnh <br>hưởng lớn ở xứ Đoài được thờ phụng ở đình Tây Đăng, Thanh Lũng <br>và nhiều làng xã khác. Thần Cao Sơn, Quý Minh tương truyền là <br>tướng tâm phúc của Hùng Vương được thờ ở đình làng Lỗ Hạnh. <br>Dân làng Lỗ Hạnh còn thờ cả Phương Dung công chúa... Từ thế <br>kỷ XYVI và các thế kỷ tiếp sau, Thần thành hoàng làng trở thành <br>vị vua tỉnh thần ở các làng xã, tín ngưỡng thờ cúng Thân thành <br>hoàng càng trở nên phong phú thê hiện qua lễ...</code> | | <code>Nguyễn Khắc Nhu có vai trò gì trong khởi nghĩa toàn khu vực miền núi Bắc Kỳ của Việt Nam Quốc dân Đảng vào năm 1930?</code> | <code>bị nổ do bất cẩn. Do đó công việc bị phát hiện. Hai người phụ trách <br>cơ quan chế bom là Đỗ Cương và Quản Trác trốn thoát. Nhiều binh <br>lính và dân thường bị bắt. Công việc bạo động của Xứ Nhu không <br>thành. Đúng lúc này Việt Nam Quốc dân Đảng vừa thành lập, cử <br>người tới mời Xứ Nhu và Việt Nam Dân quốc gia nhập Việt Nam <br>Quốc dân Đảng. Hầu hết các đồng chí của Xứ Nhu trở thành đảng <br>viên của Việt Nam Quốc dân Đảng ở vùng Bắc Ninh, Bắc Giang. <br>Do đó, Việt Nam Quốc dân Đảng mạnh lên về số lượng1. Cùng với <br>việc phát triển đảng viên ở Bẳc Ninh, Bắc Giang, Việt Nam Quốc <br>dân Đảng còn thiết lập nhiều cơ sở ở các tỉnh Thái Bình, Hải Dương, <br>1. Nguyễn Khắc Nhu tức Xứ Nhu (1882-1930), người làng Song Khê, huyện <br>Yên Dũng, tinh Bắc Giang. Với lòng yêu nuớc và ý chí chống Pháp, <br>ông dự tính thành lập một tổ chức hoạt động công khai nhăm đào tạo <br>tài năng cho đất nước lấy tên là "Hội Quốc dân dục tài”. Việc này <br>không thành công, ông lại lập tổ chức bí mật nhăm bạo động lật đổ ách <br>áp b...</code> | | <code>Giá gạo tháng 3-1950 ở Liên khu IV là bao nhiêu đồng/tạ và có chênh lệch gì so với giá gạo ở Liên khu III và Liên khu Việt Bắc?</code> | <code>ngày càng tăng nhanh, nhất là ở Việt Bắc. Giá gạo tăng mạnh <br>nhất, giá thực phẩm cũng tăng dần theo giá gạo. Giá các mặt hàng <br>kỹ nghệ tăng chậm hơn. Giá hàng ngoại hóa hầu như không tăng <br>vỉ trong vùng Pháp chiếm đóng, hàng ngoại hóa tính bằng tiền <br>Đông Dương không tăng, hom nữa nhân dân cũng ít tiêu thụ hàng <br>ngoại hóa vì bị cấm. <br>1. Viện Kinh tế học, Kinh tế Việt Nam từ Cách mạng Tháng Tám đến..., Sách <br>đã dẫn, tr. 238. <br>2. Chuơng trình và báo cáo của Bộ Kinh tế về tình hình hoạt động năm 1950. <br>Trung tâm lưu trữ quốc gia in, phông Phủ Thủ tướng, Hồ sơ số 1914. <br>488 <br>Chương VI. Việt Nam dân chủ cộng hòa xây dựng.. <br>Giá gạo trong những tháng đầu năm 1950 so với cuối năm 1949 <br>có thay đổi, Liên khu IV (Thanh Hóa) giá tăng lên 154%; Liên khu <br>III (Hà Đông - Hà Nam) giá tăng lên 153%; Liên khu Việt Bắc <br>(Thái Nguyên) giá tăng lên 800%. <br>Giá gạo ở Thái Nguyên từ 1.625 đồng/tạ lên 13.000 đồng/tạ <br>(tăng 800%); ờ Phú Thọ từ 2.650 đồng/tạ lên 7.500 đồng/tạ (tăng <br>283%). Mặt khác, ...</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### csv * Dataset: csv * Size: 21,892 evaluation samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 26.56 tokens</li><li>max: 108 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 369.01 tokens</li><li>max: 559 tokens</li></ul> | * Samples: | anchor | positive | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Nguyễn Hoàng đã thực hiện những hành động gì để dần dần tách khỏi sự ràng buộc của họ Trịnh sau khi trở lại Thuận Quảng vào năm 1600, và những hành động này đã ảnh hưởng như thế nào đến mối quan hệ giữa hai dòng họ?</code> | <code>thẳng đối với họ Nguyễn. Trịnh Tùng đã lấy danh nghĩa vua Lê sai <br>sứ giả là Thiêm đô ngự sử Lê Nghĩa Trạch đem sắc vào phủ dụ <br>Nguyễn Hoàng và vẫn cho ở lại trấn thủ, hằng năm nộp thuế như <br>cũ. Cùng với sắc của vua Lê, Trịnh Tùng có gửi thư kèm theo <br>Chương ĩ. Sự phân liệt Đàng Trong - Đàng Ngoài... <br>1, Toàn thư. quyển 17, tập IV, Sđd, tr. 200. <br>2, Đại Nam thực lục, Tiền biên, quyển 1, tập I, Sđd, tr. 34. <br>3, Đại Nam thực lục, Tiển biên, quyển 1, tập I, Sđd, tr. 35. <br>39 <br>LỊCH SỬ VIỆT NAM - TẬP 4 <br>"khuyên giữ việc thuế cống". Nguyễn Hoàng sai sứ giả đáp lễ tạ on <br>vua Lê và gửi thư cho Trịnh Tùng hẹn kết nghĩa thông gia, đem con <br>gái là Ngọc Tú gả cho Trịnh Tráng (con Trịnh Tùng) lấy danh <br>nghĩa hôn nhân để duy trì mối quan hệ bề ngoài giao hảo giữa hai <br>dòng họ vốn có sẵn một mối thù địch. <br>- Chính sách cùa họ Nguyễn từ khi Nguyễn Hoàng trở lại <br>Thuận Quảng <br>Năm 1600, Nguyễn Hoàng ròi được khỏi đất Bẳc trở về Thuận <br>Quảng bắt đầu thực hiện một chính sách cai trị mói, dần dần tác...</code> | | <code>Báo cáo của Ủy ban Kháng chiến hành chính Hà Nội về hoạt động giáo dục bù nhìn và tình hình các giáo sư trường Chu Văn An có nội dung gì?</code> | <code>Tài liệu tham khảo <br>21. Báo cáo sô' 2 BC/I ngày 12-11-1949 và Báo cáo sô' 463 <br>BC/DB ngày 25-12-1949 của Ty Công an H à Nội. Trung <br>tâm Lưu trữ Quốc gia III, phông Phủ Thủ tướng, Hồ sơ <br>SỐ921. <br>28. Báo “Le song” ngày 11-2-1949. Trung tâm Lưu trữ Quốc <br>gia III, phông Phủ Thủ tướng, Hồ sơ sô' 2002. <br>29. Báo cáo của u ỷ ban Kháng chiến hành chính Hà Nội vê <br>hoạt động giáo dục bù nhìn và tình hình các giáo sư <br>trường Chu Văn An. Trung tâm Lưu trữ Quốc gia III, <br>phông Phủ Thủ tướng, Hồ sơ số 979. <br>30. Báo cáo của Tổng Giám đốc Việt N am Công an vụ sô' <br>122/NCB3 ngày 1-4-1951. Trung tâm Lưu trữ Quốic gia <br>III, phông Phủ Thủ tướng, Hồ sơ sô' 979. <br>31. Báo cáo thành tích về cống tác công an trong 8 năm kháng <br>chiến (1946-1954) của Bộ Công an. Trung tâm Lưu trữ <br>Quốc gia III, phông Phủ Thủ tướng, Hồ sơ sô' 927. <br>32. Báo cáo một năm kháng chiến (12-1946 đến 12-1947) của <br>UBKCHC Khu 12. Trung tâm Lưu trữ Quốc gia III, phông <br>Phủ Thủ tướng, Hồ sơ sô" 2000. <br>33. Báo cáo thành tích quăn sự trong 8 n...</code> | | <code>Đặc điểm dân số của nước ta ảnh hưởng đến các ngành dịch vụ như thế nào và đòi hỏi những ngành dịch vụ nào cần được ưu tiên phát triển trong quá trình đô thị hóa?</code> | <code>— Trong các thành phố lớn thường hình thành các trung tâm giao dịch, <br>thương mại. Đó là nơi tập trung các ngân hàng, các văn phòng đại diện <br>của các công ti, các siêu thị hay các tổ hợp thương mại, dịch vụ lớn... <br>Ở các thành phố lớn trên thế giới, thường dễ nhận thấy các trung tâm <br>thương mại này do sự tập trung các ngôi nhà cao tầng, chọc trời. Một <br>thành phố có thể có trung tâm thương mại chính và một số trung tâm <br>thương mại nhỏ hơn, kết quả của sự phát triển đô thị. <br> <br>— Ở nước ta, các thành phố, thị xã thường có khu hành chính (phân <br>“đô”) và khu buôn bán, dịch vụ (phân “thị'). Ở Hà Nội, Thành phố <br>Hồ Chí Minh các trung tâm giao dịch, thương mại của thành phố đang <br>được hình thành rõ nét. <br> <br>CÂU HỎI VÀ BÀI TẬP <br> <br>174 <br> <br>1. Cho biết đặc điểm dân số của nước ta (đông, tăng còn tương đối <br>nhanh, mức sống đang nâng lên và đô thị hoá đang phát triển với <br>tốc độ nhanh hơn) có ảnh hưởng đến các ngành dịch vụ như thế <br>nào ? Các đặc điểm đó đòi hỏi những ngành dịch vụ nào cần được <br>ưu tiê...</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 6 - `per_device_eval_batch_size`: 6 - `learning_rate`: 3e-06 - `num_train_epochs`: 2 - `warmup_ratio`: 0.05 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 6 - `per_device_eval_batch_size`: 6 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 3e-06 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 2 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.05 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | Validation Loss | Snowflake/snowflake-arctic-embed-m-v2.0_cosine_ndcg@10 | |:------:|:----:|:-------------:|:---------------:|:------------------------------------------------------:| | 1.6139 | 5300 | 0.0151 | 0.0323 | 0.6185 | | 1.6443 | 5400 | 0.0212 | 0.0323 | 0.6182 | | 1.6748 | 5500 | 0.0363 | 0.0323 | 0.6173 | | 1.7052 | 5600 | 0.0151 | 0.0324 | 0.6182 | | 1.7357 | 5700 | 0.0224 | 0.0324 | 0.6175 | | 1.7661 | 5800 | 0.0222 | 0.0325 | 0.6179 | | 1.7966 | 5900 | 0.016 | 0.0325 | 0.6171 | | 1.8270 | 6000 | 0.0262 | 0.0325 | 0.6172 | | 1.8575 | 6100 | 0.0205 | 0.0325 | 0.6179 | | 1.8879 | 6200 | 0.0172 | 0.0325 | 0.6169 | | 1.9184 | 6300 | 0.0216 | 0.0325 | 0.6177 | | 1.9488 | 6400 | 0.0281 | 0.0324 | 0.6170 | | 1.9793 | 6500 | 0.0274 | 0.0324 | 0.6168 | ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.4.1 - Transformers: 4.48.0 - PyTorch: 2.5.1 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION" ]
[ "CHIA" ]
Non_BioNLP
pruas/BENT-PubMedBERT-NER-Cell-Type
pruas
token-classification
[ "transformers", "pytorch", "bert", "token-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,673
1,709
24
0
--- language: - en license: apache-2.0 pipeline_tag: token-classification --- Named Entity Recognition (NER) model to recognize cell type entities. Please cite our work: ``` @article{NILNKER2022, title = {NILINKER: Attention-based approach to NIL Entity Linking}, journal = {Journal of Biomedical Informatics}, volume = {132}, pages = {104137}, year = {2022}, issn = {1532-0464}, doi = {https://doi.org/10.1016/j.jbi.2022.104137}, url = {https://www.sciencedirect.com/science/article/pii/S1532046422001526}, author = {Pedro Ruas and Francisco M. Couto}, } ``` [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) fine-tuned on the following dataset: - [CRAFT](https://github.com/UCDenver-ccp/CRAFT/tree/master/concept-annotation): entity type "CL" - [BioNLP13CG](): entity type "Cell" - [JNLPBA](http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004): entity type "cell_type" - [CellFinder](http://cellfinder.org/about/annotation/): entity type "CellType"
[ "NAMED_ENTITY_RECOGNITION" ]
[ "CRAFT", "CELLFINDER", "JNLPBA" ]
BioNLP
RichardErkhov/GritLM_-_GritLM-7B-8bits
RichardErkhov
text-generation
[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "custom_code", "arxiv:2402.09906", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]
1,714
1,714
4
0
--- {} --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) GritLM-7B - bnb 8bits - Model creator: https://huggingface.co/GritLM/ - Original model: https://huggingface.co/GritLM/GritLM-7B/ Original model description: --- pipeline_tag: text-generation inference: true license: apache-2.0 datasets: - GritLM/tulu2 tags: - mteb model-index: - name: GritLM-7B results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 81.17910447761194 - type: ap value: 46.26260671758199 - type: f1 value: 75.44565719934167 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 96.5161 - type: ap value: 94.79131981460425 - type: f1 value: 96.51506148413065 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 57.806000000000004 - type: f1 value: 56.78350156257903 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 38.478 - type: map_at_10 value: 54.955 - type: map_at_100 value: 54.955 - type: map_at_1000 value: 54.955 - type: map_at_3 value: 50.888999999999996 - type: map_at_5 value: 53.349999999999994 - type: mrr_at_1 value: 39.757999999999996 - type: mrr_at_10 value: 55.449000000000005 - type: mrr_at_100 value: 55.449000000000005 - type: mrr_at_1000 value: 55.449000000000005 - type: mrr_at_3 value: 51.37500000000001 - type: mrr_at_5 value: 53.822 - type: ndcg_at_1 value: 38.478 - type: ndcg_at_10 value: 63.239999999999995 - type: ndcg_at_100 value: 63.239999999999995 - type: ndcg_at_1000 value: 63.239999999999995 - type: ndcg_at_3 value: 54.935 - type: ndcg_at_5 value: 59.379000000000005 - type: precision_at_1 value: 38.478 - type: precision_at_10 value: 8.933 - type: precision_at_100 value: 0.893 - type: precision_at_1000 value: 0.089 - type: precision_at_3 value: 22.214 - type: precision_at_5 value: 15.491 - type: recall_at_1 value: 38.478 - type: recall_at_10 value: 89.331 - type: recall_at_100 value: 89.331 - type: recall_at_1000 value: 89.331 - type: recall_at_3 value: 66.643 - type: recall_at_5 value: 77.45400000000001 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 51.67144081472449 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 48.11256154264126 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 67.33801955487878 - type: mrr value: 80.71549487754474 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 88.1935203751726 - type: cos_sim_spearman value: 86.35497970498659 - type: euclidean_pearson value: 85.46910708503744 - type: euclidean_spearman value: 85.13928935405485 - type: manhattan_pearson value: 85.68373836333303 - type: manhattan_spearman value: 85.40013867117746 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 88.46753246753248 - type: f1 value: 88.43006344981134 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 40.86793640310432 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 39.80291334130727 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.421 - type: map_at_10 value: 52.349000000000004 - type: map_at_100 value: 52.349000000000004 - type: map_at_1000 value: 52.349000000000004 - type: map_at_3 value: 48.17 - type: map_at_5 value: 50.432 - type: mrr_at_1 value: 47.353 - type: mrr_at_10 value: 58.387 - type: mrr_at_100 value: 58.387 - type: mrr_at_1000 value: 58.387 - type: mrr_at_3 value: 56.199 - type: mrr_at_5 value: 57.487 - type: ndcg_at_1 value: 47.353 - type: ndcg_at_10 value: 59.202 - type: ndcg_at_100 value: 58.848 - type: ndcg_at_1000 value: 58.831999999999994 - type: ndcg_at_3 value: 54.112 - type: ndcg_at_5 value: 56.312 - type: precision_at_1 value: 47.353 - type: precision_at_10 value: 11.459 - type: precision_at_100 value: 1.146 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 26.133 - type: precision_at_5 value: 18.627 - type: recall_at_1 value: 38.421 - type: recall_at_10 value: 71.89 - type: recall_at_100 value: 71.89 - type: recall_at_1000 value: 71.89 - type: recall_at_3 value: 56.58 - type: recall_at_5 value: 63.125 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.025999999999996 - type: map_at_10 value: 50.590999999999994 - type: map_at_100 value: 51.99700000000001 - type: map_at_1000 value: 52.11599999999999 - type: map_at_3 value: 47.435 - type: map_at_5 value: 49.236000000000004 - type: mrr_at_1 value: 48.28 - type: mrr_at_10 value: 56.814 - type: mrr_at_100 value: 57.446 - type: mrr_at_1000 value: 57.476000000000006 - type: mrr_at_3 value: 54.958 - type: mrr_at_5 value: 56.084999999999994 - type: ndcg_at_1 value: 48.28 - type: ndcg_at_10 value: 56.442 - type: ndcg_at_100 value: 60.651999999999994 - type: ndcg_at_1000 value: 62.187000000000005 - type: ndcg_at_3 value: 52.866 - type: ndcg_at_5 value: 54.515 - type: precision_at_1 value: 48.28 - type: precision_at_10 value: 10.586 - type: precision_at_100 value: 1.6310000000000002 - type: precision_at_1000 value: 0.20600000000000002 - type: precision_at_3 value: 25.945 - type: precision_at_5 value: 18.076 - type: recall_at_1 value: 38.025999999999996 - type: recall_at_10 value: 66.11399999999999 - type: recall_at_100 value: 83.339 - type: recall_at_1000 value: 92.413 - type: recall_at_3 value: 54.493 - type: recall_at_5 value: 59.64699999999999 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 47.905 - type: map_at_10 value: 61.58 - type: map_at_100 value: 62.605 - type: map_at_1000 value: 62.637 - type: map_at_3 value: 58.074000000000005 - type: map_at_5 value: 60.260000000000005 - type: mrr_at_1 value: 54.42 - type: mrr_at_10 value: 64.847 - type: mrr_at_100 value: 65.403 - type: mrr_at_1000 value: 65.41900000000001 - type: mrr_at_3 value: 62.675000000000004 - type: mrr_at_5 value: 64.101 - type: ndcg_at_1 value: 54.42 - type: ndcg_at_10 value: 67.394 - type: ndcg_at_100 value: 70.846 - type: ndcg_at_1000 value: 71.403 - type: ndcg_at_3 value: 62.025 - type: ndcg_at_5 value: 65.032 - type: precision_at_1 value: 54.42 - type: precision_at_10 value: 10.646 - type: precision_at_100 value: 1.325 - type: precision_at_1000 value: 0.13999999999999999 - type: precision_at_3 value: 27.398 - type: precision_at_5 value: 18.796 - type: recall_at_1 value: 47.905 - type: recall_at_10 value: 80.84599999999999 - type: recall_at_100 value: 95.078 - type: recall_at_1000 value: 98.878 - type: recall_at_3 value: 67.05600000000001 - type: recall_at_5 value: 74.261 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.745 - type: map_at_10 value: 41.021 - type: map_at_100 value: 41.021 - type: map_at_1000 value: 41.021 - type: map_at_3 value: 37.714999999999996 - type: map_at_5 value: 39.766 - type: mrr_at_1 value: 33.559 - type: mrr_at_10 value: 43.537 - type: mrr_at_100 value: 43.537 - type: mrr_at_1000 value: 43.537 - type: mrr_at_3 value: 40.546 - type: mrr_at_5 value: 42.439 - type: ndcg_at_1 value: 33.559 - type: ndcg_at_10 value: 46.781 - type: ndcg_at_100 value: 46.781 - type: ndcg_at_1000 value: 46.781 - type: ndcg_at_3 value: 40.516000000000005 - type: ndcg_at_5 value: 43.957 - type: precision_at_1 value: 33.559 - type: precision_at_10 value: 7.198 - type: precision_at_100 value: 0.72 - type: precision_at_1000 value: 0.07200000000000001 - type: precision_at_3 value: 17.1 - type: precision_at_5 value: 12.316 - type: recall_at_1 value: 30.745 - type: recall_at_10 value: 62.038000000000004 - type: recall_at_100 value: 62.038000000000004 - type: recall_at_1000 value: 62.038000000000004 - type: recall_at_3 value: 45.378 - type: recall_at_5 value: 53.580000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.637999999999998 - type: map_at_10 value: 31.05 - type: map_at_100 value: 31.05 - type: map_at_1000 value: 31.05 - type: map_at_3 value: 27.628000000000004 - type: map_at_5 value: 29.767 - type: mrr_at_1 value: 25.0 - type: mrr_at_10 value: 36.131 - type: mrr_at_100 value: 36.131 - type: mrr_at_1000 value: 36.131 - type: mrr_at_3 value: 33.333 - type: mrr_at_5 value: 35.143 - type: ndcg_at_1 value: 25.0 - type: ndcg_at_10 value: 37.478 - type: ndcg_at_100 value: 37.469 - type: ndcg_at_1000 value: 37.469 - type: ndcg_at_3 value: 31.757999999999996 - type: ndcg_at_5 value: 34.821999999999996 - type: precision_at_1 value: 25.0 - type: precision_at_10 value: 7.188999999999999 - type: precision_at_100 value: 0.719 - type: precision_at_1000 value: 0.07200000000000001 - type: precision_at_3 value: 15.837000000000002 - type: precision_at_5 value: 11.841 - type: recall_at_1 value: 19.637999999999998 - type: recall_at_10 value: 51.836000000000006 - type: recall_at_100 value: 51.836000000000006 - type: recall_at_1000 value: 51.836000000000006 - type: recall_at_3 value: 36.384 - type: recall_at_5 value: 43.964 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 34.884 - type: map_at_10 value: 47.88 - type: map_at_100 value: 47.88 - type: map_at_1000 value: 47.88 - type: map_at_3 value: 43.85 - type: map_at_5 value: 46.414 - type: mrr_at_1 value: 43.022 - type: mrr_at_10 value: 53.569 - type: mrr_at_100 value: 53.569 - type: mrr_at_1000 value: 53.569 - type: mrr_at_3 value: 51.075 - type: mrr_at_5 value: 52.725 - type: ndcg_at_1 value: 43.022 - type: ndcg_at_10 value: 54.461000000000006 - type: ndcg_at_100 value: 54.388000000000005 - type: ndcg_at_1000 value: 54.388000000000005 - type: ndcg_at_3 value: 48.864999999999995 - type: ndcg_at_5 value: 52.032000000000004 - type: precision_at_1 value: 43.022 - type: precision_at_10 value: 9.885 - type: precision_at_100 value: 0.988 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 23.612 - type: precision_at_5 value: 16.997 - type: recall_at_1 value: 34.884 - type: recall_at_10 value: 68.12899999999999 - type: recall_at_100 value: 68.12899999999999 - type: recall_at_1000 value: 68.12899999999999 - type: recall_at_3 value: 52.428 - type: recall_at_5 value: 60.662000000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.588 - type: map_at_10 value: 43.85 - type: map_at_100 value: 45.317 - type: map_at_1000 value: 45.408 - type: map_at_3 value: 39.73 - type: map_at_5 value: 42.122 - type: mrr_at_1 value: 38.927 - type: mrr_at_10 value: 49.582 - type: mrr_at_100 value: 50.39 - type: mrr_at_1000 value: 50.426 - type: mrr_at_3 value: 46.518 - type: mrr_at_5 value: 48.271 - type: ndcg_at_1 value: 38.927 - type: ndcg_at_10 value: 50.605999999999995 - type: ndcg_at_100 value: 56.22200000000001 - type: ndcg_at_1000 value: 57.724 - type: ndcg_at_3 value: 44.232 - type: ndcg_at_5 value: 47.233999999999995 - type: precision_at_1 value: 38.927 - type: precision_at_10 value: 9.429 - type: precision_at_100 value: 1.435 - type: precision_at_1000 value: 0.172 - type: precision_at_3 value: 21.271 - type: precision_at_5 value: 15.434000000000001 - type: recall_at_1 value: 31.588 - type: recall_at_10 value: 64.836 - type: recall_at_100 value: 88.066 - type: recall_at_1000 value: 97.748 - type: recall_at_3 value: 47.128 - type: recall_at_5 value: 54.954 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.956083333333336 - type: map_at_10 value: 43.33483333333333 - type: map_at_100 value: 44.64883333333333 - type: map_at_1000 value: 44.75 - type: map_at_3 value: 39.87741666666666 - type: map_at_5 value: 41.86766666666667 - type: mrr_at_1 value: 38.06341666666667 - type: mrr_at_10 value: 47.839666666666666 - type: mrr_at_100 value: 48.644000000000005 - type: mrr_at_1000 value: 48.68566666666667 - type: mrr_at_3 value: 45.26358333333334 - type: mrr_at_5 value: 46.790000000000006 - type: ndcg_at_1 value: 38.06341666666667 - type: ndcg_at_10 value: 49.419333333333334 - type: ndcg_at_100 value: 54.50166666666667 - type: ndcg_at_1000 value: 56.161166666666674 - type: ndcg_at_3 value: 43.982416666666666 - type: ndcg_at_5 value: 46.638083333333334 - type: precision_at_1 value: 38.06341666666667 - type: precision_at_10 value: 8.70858333333333 - type: precision_at_100 value: 1.327 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 20.37816666666667 - type: precision_at_5 value: 14.516333333333334 - type: recall_at_1 value: 31.956083333333336 - type: recall_at_10 value: 62.69458333333334 - type: recall_at_100 value: 84.46433333333334 - type: recall_at_1000 value: 95.58449999999999 - type: recall_at_3 value: 47.52016666666666 - type: recall_at_5 value: 54.36066666666666 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.912 - type: map_at_10 value: 38.291 - type: map_at_100 value: 39.44 - type: map_at_1000 value: 39.528 - type: map_at_3 value: 35.638 - type: map_at_5 value: 37.218 - type: mrr_at_1 value: 32.822 - type: mrr_at_10 value: 41.661 - type: mrr_at_100 value: 42.546 - type: mrr_at_1000 value: 42.603 - type: mrr_at_3 value: 39.238 - type: mrr_at_5 value: 40.726 - type: ndcg_at_1 value: 32.822 - type: ndcg_at_10 value: 43.373 - type: ndcg_at_100 value: 48.638 - type: ndcg_at_1000 value: 50.654999999999994 - type: ndcg_at_3 value: 38.643 - type: ndcg_at_5 value: 41.126000000000005 - type: precision_at_1 value: 32.822 - type: precision_at_10 value: 6.8709999999999996 - type: precision_at_100 value: 1.032 - type: precision_at_1000 value: 0.128 - type: precision_at_3 value: 16.82 - type: precision_at_5 value: 11.718 - type: recall_at_1 value: 28.912 - type: recall_at_10 value: 55.376999999999995 - type: recall_at_100 value: 79.066 - type: recall_at_1000 value: 93.664 - type: recall_at_3 value: 42.569 - type: recall_at_5 value: 48.719 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.181 - type: map_at_10 value: 31.462 - type: map_at_100 value: 32.73 - type: map_at_1000 value: 32.848 - type: map_at_3 value: 28.57 - type: map_at_5 value: 30.182 - type: mrr_at_1 value: 27.185 - type: mrr_at_10 value: 35.846000000000004 - type: mrr_at_100 value: 36.811 - type: mrr_at_1000 value: 36.873 - type: mrr_at_3 value: 33.437 - type: mrr_at_5 value: 34.813 - type: ndcg_at_1 value: 27.185 - type: ndcg_at_10 value: 36.858000000000004 - type: ndcg_at_100 value: 42.501 - type: ndcg_at_1000 value: 44.945 - type: ndcg_at_3 value: 32.066 - type: ndcg_at_5 value: 34.29 - type: precision_at_1 value: 27.185 - type: precision_at_10 value: 6.752 - type: precision_at_100 value: 1.111 - type: precision_at_1000 value: 0.151 - type: precision_at_3 value: 15.290000000000001 - type: precision_at_5 value: 11.004999999999999 - type: recall_at_1 value: 22.181 - type: recall_at_10 value: 48.513 - type: recall_at_100 value: 73.418 - type: recall_at_1000 value: 90.306 - type: recall_at_3 value: 35.003 - type: recall_at_5 value: 40.876000000000005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 33.934999999999995 - type: map_at_10 value: 44.727 - type: map_at_100 value: 44.727 - type: map_at_1000 value: 44.727 - type: map_at_3 value: 40.918 - type: map_at_5 value: 42.961 - type: mrr_at_1 value: 39.646 - type: mrr_at_10 value: 48.898 - type: mrr_at_100 value: 48.898 - type: mrr_at_1000 value: 48.898 - type: mrr_at_3 value: 45.896 - type: mrr_at_5 value: 47.514 - type: ndcg_at_1 value: 39.646 - type: ndcg_at_10 value: 50.817 - type: ndcg_at_100 value: 50.803 - type: ndcg_at_1000 value: 50.803 - type: ndcg_at_3 value: 44.507999999999996 - type: ndcg_at_5 value: 47.259 - type: precision_at_1 value: 39.646 - type: precision_at_10 value: 8.759 - type: precision_at_100 value: 0.876 - type: precision_at_1000 value: 0.08800000000000001 - type: precision_at_3 value: 20.274 - type: precision_at_5 value: 14.366000000000001 - type: recall_at_1 value: 33.934999999999995 - type: recall_at_10 value: 65.037 - type: recall_at_100 value: 65.037 - type: recall_at_1000 value: 65.037 - type: recall_at_3 value: 47.439 - type: recall_at_5 value: 54.567 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.058 - type: map_at_10 value: 43.137 - type: map_at_100 value: 43.137 - type: map_at_1000 value: 43.137 - type: map_at_3 value: 39.882 - type: map_at_5 value: 41.379 - type: mrr_at_1 value: 38.933 - type: mrr_at_10 value: 48.344 - type: mrr_at_100 value: 48.344 - type: mrr_at_1000 value: 48.344 - type: mrr_at_3 value: 45.652 - type: mrr_at_5 value: 46.877 - type: ndcg_at_1 value: 38.933 - type: ndcg_at_10 value: 49.964 - type: ndcg_at_100 value: 49.242000000000004 - type: ndcg_at_1000 value: 49.222 - type: ndcg_at_3 value: 44.605 - type: ndcg_at_5 value: 46.501999999999995 - type: precision_at_1 value: 38.933 - type: precision_at_10 value: 9.427000000000001 - type: precision_at_100 value: 0.943 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 20.685000000000002 - type: precision_at_5 value: 14.585 - type: recall_at_1 value: 32.058 - type: recall_at_10 value: 63.074 - type: recall_at_100 value: 63.074 - type: recall_at_1000 value: 63.074 - type: recall_at_3 value: 47.509 - type: recall_at_5 value: 52.455 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.029000000000003 - type: map_at_10 value: 34.646 - type: map_at_100 value: 34.646 - type: map_at_1000 value: 34.646 - type: map_at_3 value: 31.456 - type: map_at_5 value: 33.138 - type: mrr_at_1 value: 28.281 - type: mrr_at_10 value: 36.905 - type: mrr_at_100 value: 36.905 - type: mrr_at_1000 value: 36.905 - type: mrr_at_3 value: 34.011 - type: mrr_at_5 value: 35.638 - type: ndcg_at_1 value: 28.281 - type: ndcg_at_10 value: 40.159 - type: ndcg_at_100 value: 40.159 - type: ndcg_at_1000 value: 40.159 - type: ndcg_at_3 value: 33.995 - type: ndcg_at_5 value: 36.836999999999996 - type: precision_at_1 value: 28.281 - type: precision_at_10 value: 6.358999999999999 - type: precision_at_100 value: 0.636 - type: precision_at_1000 value: 0.064 - type: precision_at_3 value: 14.233 - type: precision_at_5 value: 10.314 - type: recall_at_1 value: 26.029000000000003 - type: recall_at_10 value: 55.08 - type: recall_at_100 value: 55.08 - type: recall_at_1000 value: 55.08 - type: recall_at_3 value: 38.487 - type: recall_at_5 value: 45.308 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 12.842999999999998 - type: map_at_10 value: 22.101000000000003 - type: map_at_100 value: 24.319 - type: map_at_1000 value: 24.51 - type: map_at_3 value: 18.372 - type: map_at_5 value: 20.323 - type: mrr_at_1 value: 27.948 - type: mrr_at_10 value: 40.321 - type: mrr_at_100 value: 41.262 - type: mrr_at_1000 value: 41.297 - type: mrr_at_3 value: 36.558 - type: mrr_at_5 value: 38.824999999999996 - type: ndcg_at_1 value: 27.948 - type: ndcg_at_10 value: 30.906 - type: ndcg_at_100 value: 38.986 - type: ndcg_at_1000 value: 42.136 - type: ndcg_at_3 value: 24.911 - type: ndcg_at_5 value: 27.168999999999997 - type: precision_at_1 value: 27.948 - type: precision_at_10 value: 9.798 - type: precision_at_100 value: 1.8399999999999999 - type: precision_at_1000 value: 0.243 - type: precision_at_3 value: 18.328 - type: precision_at_5 value: 14.502 - type: recall_at_1 value: 12.842999999999998 - type: recall_at_10 value: 37.245 - type: recall_at_100 value: 64.769 - type: recall_at_1000 value: 82.055 - type: recall_at_3 value: 23.159 - type: recall_at_5 value: 29.113 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 8.934000000000001 - type: map_at_10 value: 21.915000000000003 - type: map_at_100 value: 21.915000000000003 - type: map_at_1000 value: 21.915000000000003 - type: map_at_3 value: 14.623 - type: map_at_5 value: 17.841 - type: mrr_at_1 value: 71.25 - type: mrr_at_10 value: 78.994 - type: mrr_at_100 value: 78.994 - type: mrr_at_1000 value: 78.994 - type: mrr_at_3 value: 77.208 - type: mrr_at_5 value: 78.55799999999999 - type: ndcg_at_1 value: 60.62499999999999 - type: ndcg_at_10 value: 46.604 - type: ndcg_at_100 value: 35.653 - type: ndcg_at_1000 value: 35.531 - type: ndcg_at_3 value: 50.605 - type: ndcg_at_5 value: 48.730000000000004 - type: precision_at_1 value: 71.25 - type: precision_at_10 value: 37.75 - type: precision_at_100 value: 3.775 - type: precision_at_1000 value: 0.377 - type: precision_at_3 value: 54.417 - type: precision_at_5 value: 48.15 - type: recall_at_1 value: 8.934000000000001 - type: recall_at_10 value: 28.471000000000004 - type: recall_at_100 value: 28.471000000000004 - type: recall_at_1000 value: 28.471000000000004 - type: recall_at_3 value: 16.019 - type: recall_at_5 value: 21.410999999999998 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.81 - type: f1 value: 47.987573380720114 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 66.81899999999999 - type: map_at_10 value: 78.034 - type: map_at_100 value: 78.034 - type: map_at_1000 value: 78.034 - type: map_at_3 value: 76.43100000000001 - type: map_at_5 value: 77.515 - type: mrr_at_1 value: 71.542 - type: mrr_at_10 value: 81.638 - type: mrr_at_100 value: 81.638 - type: mrr_at_1000 value: 81.638 - type: mrr_at_3 value: 80.403 - type: mrr_at_5 value: 81.256 - type: ndcg_at_1 value: 71.542 - type: ndcg_at_10 value: 82.742 - type: ndcg_at_100 value: 82.741 - type: ndcg_at_1000 value: 82.741 - type: ndcg_at_3 value: 80.039 - type: ndcg_at_5 value: 81.695 - type: precision_at_1 value: 71.542 - type: precision_at_10 value: 10.387 - type: precision_at_100 value: 1.039 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 31.447999999999997 - type: precision_at_5 value: 19.91 - type: recall_at_1 value: 66.81899999999999 - type: recall_at_10 value: 93.372 - type: recall_at_100 value: 93.372 - type: recall_at_1000 value: 93.372 - type: recall_at_3 value: 86.33 - type: recall_at_5 value: 90.347 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 31.158 - type: map_at_10 value: 52.017 - type: map_at_100 value: 54.259 - type: map_at_1000 value: 54.367 - type: map_at_3 value: 45.738 - type: map_at_5 value: 49.283 - type: mrr_at_1 value: 57.87 - type: mrr_at_10 value: 66.215 - type: mrr_at_100 value: 66.735 - type: mrr_at_1000 value: 66.75 - type: mrr_at_3 value: 64.043 - type: mrr_at_5 value: 65.116 - type: ndcg_at_1 value: 57.87 - type: ndcg_at_10 value: 59.946999999999996 - type: ndcg_at_100 value: 66.31099999999999 - type: ndcg_at_1000 value: 67.75999999999999 - type: ndcg_at_3 value: 55.483000000000004 - type: ndcg_at_5 value: 56.891000000000005 - type: precision_at_1 value: 57.87 - type: precision_at_10 value: 16.497 - type: precision_at_100 value: 2.321 - type: precision_at_1000 value: 0.258 - type: precision_at_3 value: 37.14 - type: precision_at_5 value: 27.067999999999998 - type: recall_at_1 value: 31.158 - type: recall_at_10 value: 67.381 - type: recall_at_100 value: 89.464 - type: recall_at_1000 value: 97.989 - type: recall_at_3 value: 50.553000000000004 - type: recall_at_5 value: 57.824 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 42.073 - type: map_at_10 value: 72.418 - type: map_at_100 value: 73.175 - type: map_at_1000 value: 73.215 - type: map_at_3 value: 68.791 - type: map_at_5 value: 71.19 - type: mrr_at_1 value: 84.146 - type: mrr_at_10 value: 88.994 - type: mrr_at_100 value: 89.116 - type: mrr_at_1000 value: 89.12 - type: mrr_at_3 value: 88.373 - type: mrr_at_5 value: 88.82 - type: ndcg_at_1 value: 84.146 - type: ndcg_at_10 value: 79.404 - type: ndcg_at_100 value: 81.83200000000001 - type: ndcg_at_1000 value: 82.524 - type: ndcg_at_3 value: 74.595 - type: ndcg_at_5 value: 77.474 - type: precision_at_1 value: 84.146 - type: precision_at_10 value: 16.753999999999998 - type: precision_at_100 value: 1.8599999999999999 - type: precision_at_1000 value: 0.19499999999999998 - type: precision_at_3 value: 48.854 - type: precision_at_5 value: 31.579 - type: recall_at_1 value: 42.073 - type: recall_at_10 value: 83.768 - type: recall_at_100 value: 93.018 - type: recall_at_1000 value: 97.481 - type: recall_at_3 value: 73.282 - type: recall_at_5 value: 78.947 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 94.9968 - type: ap value: 92.93892195862824 - type: f1 value: 94.99327998213761 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.698 - type: map_at_10 value: 34.585 - type: map_at_100 value: 35.782000000000004 - type: map_at_1000 value: 35.825 - type: map_at_3 value: 30.397999999999996 - type: map_at_5 value: 32.72 - type: mrr_at_1 value: 22.192 - type: mrr_at_10 value: 35.085 - type: mrr_at_100 value: 36.218 - type: mrr_at_1000 value: 36.256 - type: mrr_at_3 value: 30.986000000000004 - type: mrr_at_5 value: 33.268 - type: ndcg_at_1 value: 22.192 - type: ndcg_at_10 value: 41.957 - type: ndcg_at_100 value: 47.658 - type: ndcg_at_1000 value: 48.697 - type: ndcg_at_3 value: 33.433 - type: ndcg_at_5 value: 37.551 - type: precision_at_1 value: 22.192 - type: precision_at_10 value: 6.781 - type: precision_at_100 value: 0.963 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 14.365 - type: precision_at_5 value: 10.713000000000001 - type: recall_at_1 value: 21.698 - type: recall_at_10 value: 64.79 - type: recall_at_100 value: 91.071 - type: recall_at_1000 value: 98.883 - type: recall_at_3 value: 41.611 - type: recall_at_5 value: 51.459999999999994 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 96.15823073415413 - type: f1 value: 96.00362034963248 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 87.12722298221614 - type: f1 value: 70.46888967516227 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 80.77673167451245 - type: f1 value: 77.60202561132175 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 82.09145931405514 - type: f1 value: 81.7701921473406 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 36.52153488185864 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 36.80090398444147 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.807141746058605 - type: mrr value: 32.85025611455029 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.920999999999999 - type: map_at_10 value: 16.049 - type: map_at_100 value: 16.049 - type: map_at_1000 value: 16.049 - type: map_at_3 value: 11.865 - type: map_at_5 value: 13.657 - type: mrr_at_1 value: 53.87 - type: mrr_at_10 value: 62.291 - type: mrr_at_100 value: 62.291 - type: mrr_at_1000 value: 62.291 - type: mrr_at_3 value: 60.681 - type: mrr_at_5 value: 61.61 - type: ndcg_at_1 value: 51.23799999999999 - type: ndcg_at_10 value: 40.892 - type: ndcg_at_100 value: 26.951999999999998 - type: ndcg_at_1000 value: 26.474999999999998 - type: ndcg_at_3 value: 46.821 - type: ndcg_at_5 value: 44.333 - type: precision_at_1 value: 53.251000000000005 - type: precision_at_10 value: 30.124000000000002 - type: precision_at_100 value: 3.012 - type: precision_at_1000 value: 0.301 - type: precision_at_3 value: 43.55 - type: precision_at_5 value: 38.266 - type: recall_at_1 value: 6.920999999999999 - type: recall_at_10 value: 20.852 - type: recall_at_100 value: 20.852 - type: recall_at_1000 value: 20.852 - type: recall_at_3 value: 13.628000000000002 - type: recall_at_5 value: 16.273 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 46.827999999999996 - type: map_at_10 value: 63.434000000000005 - type: map_at_100 value: 63.434000000000005 - type: map_at_1000 value: 63.434000000000005 - type: map_at_3 value: 59.794000000000004 - type: map_at_5 value: 62.08 - type: mrr_at_1 value: 52.288999999999994 - type: mrr_at_10 value: 65.95 - type: mrr_at_100 value: 65.95 - type: mrr_at_1000 value: 65.95 - type: mrr_at_3 value: 63.413 - type: mrr_at_5 value: 65.08 - type: ndcg_at_1 value: 52.288999999999994 - type: ndcg_at_10 value: 70.301 - type: ndcg_at_100 value: 70.301 - type: ndcg_at_1000 value: 70.301 - type: ndcg_at_3 value: 63.979 - type: ndcg_at_5 value: 67.582 - type: precision_at_1 value: 52.288999999999994 - type: precision_at_10 value: 10.576 - type: precision_at_100 value: 1.058 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 28.177000000000003 - type: precision_at_5 value: 19.073 - type: recall_at_1 value: 46.827999999999996 - type: recall_at_10 value: 88.236 - type: recall_at_100 value: 88.236 - type: recall_at_1000 value: 88.236 - type: recall_at_3 value: 72.371 - type: recall_at_5 value: 80.56 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.652 - type: map_at_10 value: 85.953 - type: map_at_100 value: 85.953 - type: map_at_1000 value: 85.953 - type: map_at_3 value: 83.05399999999999 - type: map_at_5 value: 84.89 - type: mrr_at_1 value: 82.42 - type: mrr_at_10 value: 88.473 - type: mrr_at_100 value: 88.473 - type: mrr_at_1000 value: 88.473 - type: mrr_at_3 value: 87.592 - type: mrr_at_5 value: 88.211 - type: ndcg_at_1 value: 82.44 - type: ndcg_at_10 value: 89.467 - type: ndcg_at_100 value: 89.33 - type: ndcg_at_1000 value: 89.33 - type: ndcg_at_3 value: 86.822 - type: ndcg_at_5 value: 88.307 - type: precision_at_1 value: 82.44 - type: precision_at_10 value: 13.616 - type: precision_at_100 value: 1.362 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 38.117000000000004 - type: precision_at_5 value: 25.05 - type: recall_at_1 value: 71.652 - type: recall_at_10 value: 96.224 - type: recall_at_100 value: 96.224 - type: recall_at_1000 value: 96.224 - type: recall_at_3 value: 88.571 - type: recall_at_5 value: 92.812 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 61.295010338050474 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 67.26380819328142 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 5.683 - type: map_at_10 value: 14.924999999999999 - type: map_at_100 value: 17.532 - type: map_at_1000 value: 17.875 - type: map_at_3 value: 10.392 - type: map_at_5 value: 12.592 - type: mrr_at_1 value: 28.000000000000004 - type: mrr_at_10 value: 39.951 - type: mrr_at_100 value: 41.025 - type: mrr_at_1000 value: 41.056 - type: mrr_at_3 value: 36.317 - type: mrr_at_5 value: 38.412 - type: ndcg_at_1 value: 28.000000000000004 - type: ndcg_at_10 value: 24.410999999999998 - type: ndcg_at_100 value: 33.79 - type: ndcg_at_1000 value: 39.035 - type: ndcg_at_3 value: 22.845 - type: ndcg_at_5 value: 20.080000000000002 - type: precision_at_1 value: 28.000000000000004 - type: precision_at_10 value: 12.790000000000001 - type: precision_at_100 value: 2.633 - type: precision_at_1000 value: 0.388 - type: precision_at_3 value: 21.367 - type: precision_at_5 value: 17.7 - type: recall_at_1 value: 5.683 - type: recall_at_10 value: 25.91 - type: recall_at_100 value: 53.443 - type: recall_at_1000 value: 78.73 - type: recall_at_3 value: 13.003 - type: recall_at_5 value: 17.932000000000002 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 84.677978681023 - type: cos_sim_spearman value: 83.13093441058189 - type: euclidean_pearson value: 83.35535759341572 - type: euclidean_spearman value: 83.42583744219611 - type: manhattan_pearson value: 83.2243124045889 - type: manhattan_spearman value: 83.39801618652632 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 81.68960206569666 - type: cos_sim_spearman value: 77.3368966488535 - type: euclidean_pearson value: 77.62828980560303 - type: euclidean_spearman value: 76.77951481444651 - type: manhattan_pearson value: 77.88637240839041 - type: manhattan_spearman value: 77.22157841466188 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 84.18745821650724 - type: cos_sim_spearman value: 85.04423285574542 - type: euclidean_pearson value: 85.46604816931023 - type: euclidean_spearman value: 85.5230593932974 - type: manhattan_pearson value: 85.57912805986261 - type: manhattan_spearman value: 85.65955905111873 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 83.6715333300355 - type: cos_sim_spearman value: 82.9058522514908 - type: euclidean_pearson value: 83.9640357424214 - type: euclidean_spearman value: 83.60415457472637 - type: manhattan_pearson value: 84.05621005853469 - type: manhattan_spearman value: 83.87077724707746 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.82422928098886 - type: cos_sim_spearman value: 88.12660311894628 - type: euclidean_pearson value: 87.50974805056555 - type: euclidean_spearman value: 87.91957275596677 - type: manhattan_pearson value: 87.74119404878883 - type: manhattan_spearman value: 88.2808922165719 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 84.80605838552093 - type: cos_sim_spearman value: 86.24123388765678 - type: euclidean_pearson value: 85.32648347339814 - type: euclidean_spearman value: 85.60046671950158 - type: manhattan_pearson value: 85.53800168487811 - type: manhattan_spearman value: 85.89542420480763 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 89.87540978988132 - type: cos_sim_spearman value: 90.12715295099461 - type: euclidean_pearson value: 91.61085993525275 - type: euclidean_spearman value: 91.31835942311758 - type: manhattan_pearson value: 91.57500202032934 - type: manhattan_spearman value: 91.1790925526635 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 69.87136205329556 - type: cos_sim_spearman value: 68.6253154635078 - type: euclidean_pearson value: 68.91536015034222 - type: euclidean_spearman value: 67.63744649352542 - type: manhattan_pearson value: 69.2000713045275 - type: manhattan_spearman value: 68.16002901587316 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.21849551039082 - type: cos_sim_spearman value: 85.6392959372461 - type: euclidean_pearson value: 85.92050852609488 - type: euclidean_spearman value: 85.97205649009734 - type: manhattan_pearson value: 86.1031154802254 - type: manhattan_spearman value: 86.26791155517466 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 86.83953958636627 - type: mrr value: 96.71167612344082 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 64.994 - type: map_at_10 value: 74.763 - type: map_at_100 value: 75.127 - type: map_at_1000 value: 75.143 - type: map_at_3 value: 71.824 - type: map_at_5 value: 73.71 - type: mrr_at_1 value: 68.333 - type: mrr_at_10 value: 75.749 - type: mrr_at_100 value: 75.922 - type: mrr_at_1000 value: 75.938 - type: mrr_at_3 value: 73.556 - type: mrr_at_5 value: 74.739 - type: ndcg_at_1 value: 68.333 - type: ndcg_at_10 value: 79.174 - type: ndcg_at_100 value: 80.41 - type: ndcg_at_1000 value: 80.804 - type: ndcg_at_3 value: 74.361 - type: ndcg_at_5 value: 76.861 - type: precision_at_1 value: 68.333 - type: precision_at_10 value: 10.333 - type: precision_at_100 value: 1.0999999999999999 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 28.778 - type: precision_at_5 value: 19.067 - type: recall_at_1 value: 64.994 - type: recall_at_10 value: 91.822 - type: recall_at_100 value: 97.0 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 78.878 - type: recall_at_5 value: 85.172 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.72079207920792 - type: cos_sim_ap value: 93.00265215525152 - type: cos_sim_f1 value: 85.06596306068602 - type: cos_sim_precision value: 90.05586592178771 - type: cos_sim_recall value: 80.60000000000001 - type: dot_accuracy value: 99.66039603960397 - type: dot_ap value: 91.22371407479089 - type: dot_f1 value: 82.34693877551021 - type: dot_precision value: 84.0625 - type: dot_recall value: 80.7 - type: euclidean_accuracy value: 99.71881188118812 - type: euclidean_ap value: 92.88449963304728 - type: euclidean_f1 value: 85.19480519480518 - type: euclidean_precision value: 88.64864864864866 - type: euclidean_recall value: 82.0 - type: manhattan_accuracy value: 99.73267326732673 - type: manhattan_ap value: 93.23055393056883 - type: manhattan_f1 value: 85.88957055214725 - type: manhattan_precision value: 87.86610878661088 - type: manhattan_recall value: 84.0 - type: max_accuracy value: 99.73267326732673 - type: max_ap value: 93.23055393056883 - type: max_f1 value: 85.88957055214725 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 77.3305735900358 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 41.32967136540674 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 55.95514866379359 - type: mrr value: 56.95423245055598 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.783007208997144 - type: cos_sim_spearman value: 30.373444721540533 - type: dot_pearson value: 29.210604111143905 - type: dot_spearman value: 29.98809758085659 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.234 - type: map_at_10 value: 1.894 - type: map_at_100 value: 1.894 - type: map_at_1000 value: 1.894 - type: map_at_3 value: 0.636 - type: map_at_5 value: 1.0 - type: mrr_at_1 value: 88.0 - type: mrr_at_10 value: 93.667 - type: mrr_at_100 value: 93.667 - type: mrr_at_1000 value: 93.667 - type: mrr_at_3 value: 93.667 - type: mrr_at_5 value: 93.667 - type: ndcg_at_1 value: 85.0 - type: ndcg_at_10 value: 74.798 - type: ndcg_at_100 value: 16.462 - type: ndcg_at_1000 value: 7.0889999999999995 - type: ndcg_at_3 value: 80.754 - type: ndcg_at_5 value: 77.319 - type: precision_at_1 value: 88.0 - type: precision_at_10 value: 78.0 - type: precision_at_100 value: 7.8 - type: precision_at_1000 value: 0.7799999999999999 - type: precision_at_3 value: 83.333 - type: precision_at_5 value: 80.80000000000001 - type: recall_at_1 value: 0.234 - type: recall_at_10 value: 2.093 - type: recall_at_100 value: 2.093 - type: recall_at_1000 value: 2.093 - type: recall_at_3 value: 0.662 - type: recall_at_5 value: 1.0739999999999998 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.703 - type: map_at_10 value: 10.866000000000001 - type: map_at_100 value: 10.866000000000001 - type: map_at_1000 value: 10.866000000000001 - type: map_at_3 value: 5.909 - type: map_at_5 value: 7.35 - type: mrr_at_1 value: 36.735 - type: mrr_at_10 value: 53.583000000000006 - type: mrr_at_100 value: 53.583000000000006 - type: mrr_at_1000 value: 53.583000000000006 - type: mrr_at_3 value: 49.32 - type: mrr_at_5 value: 51.769 - type: ndcg_at_1 value: 34.694 - type: ndcg_at_10 value: 27.926000000000002 - type: ndcg_at_100 value: 22.701 - type: ndcg_at_1000 value: 22.701 - type: ndcg_at_3 value: 32.073 - type: ndcg_at_5 value: 28.327999999999996 - type: precision_at_1 value: 36.735 - type: precision_at_10 value: 24.694 - type: precision_at_100 value: 2.469 - type: precision_at_1000 value: 0.247 - type: precision_at_3 value: 31.973000000000003 - type: precision_at_5 value: 26.939 - type: recall_at_1 value: 2.703 - type: recall_at_10 value: 17.702 - type: recall_at_100 value: 17.702 - type: recall_at_1000 value: 17.702 - type: recall_at_3 value: 7.208 - type: recall_at_5 value: 9.748999999999999 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.79960000000001 - type: ap value: 15.467565415565815 - type: f1 value: 55.28639823443618 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 64.7792869269949 - type: f1 value: 65.08597154774318 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 55.70352297774293 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 88.27561542588067 - type: cos_sim_ap value: 81.08262141256193 - type: cos_sim_f1 value: 73.82341501361338 - type: cos_sim_precision value: 72.5720112159062 - type: cos_sim_recall value: 75.11873350923483 - type: dot_accuracy value: 86.66030875603504 - type: dot_ap value: 76.6052349228621 - type: dot_f1 value: 70.13897280966768 - type: dot_precision value: 64.70457079152732 - type: dot_recall value: 76.56992084432717 - type: euclidean_accuracy value: 88.37098408535495 - type: euclidean_ap value: 81.12515230092113 - type: euclidean_f1 value: 74.10338225909379 - type: euclidean_precision value: 71.76761433868974 - type: euclidean_recall value: 76.59630606860158 - type: manhattan_accuracy value: 88.34118137926924 - type: manhattan_ap value: 80.95751834536561 - type: manhattan_f1 value: 73.9119496855346 - type: manhattan_precision value: 70.625 - type: manhattan_recall value: 77.5197889182058 - type: max_accuracy value: 88.37098408535495 - type: max_ap value: 81.12515230092113 - type: max_f1 value: 74.10338225909379 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.79896767182831 - type: cos_sim_ap value: 87.40071784061065 - type: cos_sim_f1 value: 79.87753144712087 - type: cos_sim_precision value: 76.67304015296367 - type: cos_sim_recall value: 83.3615645210964 - type: dot_accuracy value: 88.95486474948578 - type: dot_ap value: 86.00227979119943 - type: dot_f1 value: 78.54601474525914 - type: dot_precision value: 75.00525394045535 - type: dot_recall value: 82.43763473975977 - type: euclidean_accuracy value: 89.7892653393876 - type: euclidean_ap value: 87.42174706480819 - type: euclidean_f1 value: 80.07283321194465 - type: euclidean_precision value: 75.96738529574351 - type: euclidean_recall value: 84.6473668001232 - type: manhattan_accuracy value: 89.8474793340319 - type: manhattan_ap value: 87.47814292587448 - type: manhattan_f1 value: 80.15461150280949 - type: manhattan_precision value: 74.88798234468 - type: manhattan_recall value: 86.21804742839544 - type: max_accuracy value: 89.8474793340319 - type: max_ap value: 87.47814292587448 - type: max_f1 value: 80.15461150280949 --- # Model Summary > GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks. - **Repository:** [ContextualAI/gritlm](https://github.com/ContextualAI/gritlm) - **Paper:** https://arxiv.org/abs/2402.09906 - **Logs:** https://wandb.ai/muennighoff/gritlm/runs/0uui712t/overview - **Script:** https://github.com/ContextualAI/gritlm/blob/main/scripts/training/train_gritlm_7b.sh | Model | Description | |-------|-------------| | [GritLM 7B](https://hf.co/GritLM/GritLM-7B) | Mistral 7B finetuned using GRIT | | [GritLM 8x7B](https://hf.co/GritLM/GritLM-8x7B) | Mixtral 8x7B finetuned using GRIT | # Use The model usage is documented [here](https://github.com/ContextualAI/gritlm?tab=readme-ov-file#inference). # Citation ```bibtex @misc{muennighoff2024generative, title={Generative Representational Instruction Tuning}, author={Niklas Muennighoff and Hongjin Su and Liang Wang and Nan Yang and Furu Wei and Tao Yu and Amanpreet Singh and Douwe Kiela}, year={2024}, eprint={2402.09906}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
nomic-ai/nomic-embed-text-v1.5
nomic-ai
sentence-similarity
[ "sentence-transformers", "onnx", "safetensors", "nomic_bert", "feature-extraction", "sentence-similarity", "mteb", "transformers", "transformers.js", "custom_code", "en", "arxiv:2205.13147", "arxiv:2402.01613", "license:apache-2.0", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,707
1,737
986,761
577
--- language: - en library_name: sentence-transformers license: apache-2.0 pipeline_tag: sentence-similarity tags: - feature-extraction - sentence-similarity - mteb - transformers - transformers.js model-index: - name: epoch_0_model results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.20895522388058 - type: ap value: 38.57605549557802 - type: f1 value: 69.35586565857854 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.8144 - type: ap value: 88.65222882032363 - type: f1 value: 91.80426301643274 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.162000000000006 - type: f1 value: 46.59329642263158 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 24.253 - type: map_at_10 value: 38.962 - type: map_at_100 value: 40.081 - type: map_at_1000 value: 40.089000000000006 - type: map_at_3 value: 33.499 - type: map_at_5 value: 36.351 - type: mrr_at_1 value: 24.609 - type: mrr_at_10 value: 39.099000000000004 - type: mrr_at_100 value: 40.211000000000006 - type: mrr_at_1000 value: 40.219 - type: mrr_at_3 value: 33.677 - type: mrr_at_5 value: 36.469 - type: ndcg_at_1 value: 24.253 - type: ndcg_at_10 value: 48.010999999999996 - type: ndcg_at_100 value: 52.756 - type: ndcg_at_1000 value: 52.964999999999996 - type: ndcg_at_3 value: 36.564 - type: ndcg_at_5 value: 41.711999999999996 - type: precision_at_1 value: 24.253 - type: precision_at_10 value: 7.738 - type: precision_at_100 value: 0.98 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 15.149000000000001 - type: precision_at_5 value: 11.593 - type: recall_at_1 value: 24.253 - type: recall_at_10 value: 77.383 - type: recall_at_100 value: 98.009 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 45.448 - type: recall_at_5 value: 57.965999999999994 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 45.69069567851087 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 36.35185490976283 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 61.71274951450321 - type: mrr value: 76.06032625423207 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 86.73980520022269 - type: cos_sim_spearman value: 84.24649792685918 - type: euclidean_pearson value: 85.85197641158186 - type: euclidean_spearman value: 84.24649792685918 - type: manhattan_pearson value: 86.26809552711346 - type: manhattan_spearman value: 84.56397504030865 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 84.25324675324674 - type: f1 value: 84.17872280892557 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.770253446400886 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 32.94307095497281 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 32.164 - type: map_at_10 value: 42.641 - type: map_at_100 value: 43.947 - type: map_at_1000 value: 44.074999999999996 - type: map_at_3 value: 39.592 - type: map_at_5 value: 41.204 - type: mrr_at_1 value: 39.628 - type: mrr_at_10 value: 48.625 - type: mrr_at_100 value: 49.368 - type: mrr_at_1000 value: 49.413000000000004 - type: mrr_at_3 value: 46.400000000000006 - type: mrr_at_5 value: 47.68 - type: ndcg_at_1 value: 39.628 - type: ndcg_at_10 value: 48.564 - type: ndcg_at_100 value: 53.507000000000005 - type: ndcg_at_1000 value: 55.635999999999996 - type: ndcg_at_3 value: 44.471 - type: ndcg_at_5 value: 46.137 - type: precision_at_1 value: 39.628 - type: precision_at_10 value: 8.856 - type: precision_at_100 value: 1.429 - type: precision_at_1000 value: 0.191 - type: precision_at_3 value: 21.268 - type: precision_at_5 value: 14.649000000000001 - type: recall_at_1 value: 32.164 - type: recall_at_10 value: 59.609 - type: recall_at_100 value: 80.521 - type: recall_at_1000 value: 94.245 - type: recall_at_3 value: 46.521 - type: recall_at_5 value: 52.083999999999996 - type: map_at_1 value: 31.526 - type: map_at_10 value: 41.581 - type: map_at_100 value: 42.815999999999995 - type: map_at_1000 value: 42.936 - type: map_at_3 value: 38.605000000000004 - type: map_at_5 value: 40.351 - type: mrr_at_1 value: 39.489999999999995 - type: mrr_at_10 value: 47.829 - type: mrr_at_100 value: 48.512 - type: mrr_at_1000 value: 48.552 - type: mrr_at_3 value: 45.754 - type: mrr_at_5 value: 46.986 - type: ndcg_at_1 value: 39.489999999999995 - type: ndcg_at_10 value: 47.269 - type: ndcg_at_100 value: 51.564 - type: ndcg_at_1000 value: 53.53099999999999 - type: ndcg_at_3 value: 43.301 - type: ndcg_at_5 value: 45.239000000000004 - type: precision_at_1 value: 39.489999999999995 - type: precision_at_10 value: 8.93 - type: precision_at_100 value: 1.415 - type: precision_at_1000 value: 0.188 - type: precision_at_3 value: 20.892 - type: precision_at_5 value: 14.865999999999998 - type: recall_at_1 value: 31.526 - type: recall_at_10 value: 56.76 - type: recall_at_100 value: 75.029 - type: recall_at_1000 value: 87.491 - type: recall_at_3 value: 44.786 - type: recall_at_5 value: 50.254 - type: map_at_1 value: 40.987 - type: map_at_10 value: 52.827 - type: map_at_100 value: 53.751000000000005 - type: map_at_1000 value: 53.81 - type: map_at_3 value: 49.844 - type: map_at_5 value: 51.473 - type: mrr_at_1 value: 46.833999999999996 - type: mrr_at_10 value: 56.389 - type: mrr_at_100 value: 57.003 - type: mrr_at_1000 value: 57.034 - type: mrr_at_3 value: 54.17999999999999 - type: mrr_at_5 value: 55.486999999999995 - type: ndcg_at_1 value: 46.833999999999996 - type: ndcg_at_10 value: 58.372 - type: ndcg_at_100 value: 62.068 - type: ndcg_at_1000 value: 63.288 - type: ndcg_at_3 value: 53.400000000000006 - type: ndcg_at_5 value: 55.766000000000005 - type: precision_at_1 value: 46.833999999999996 - type: precision_at_10 value: 9.191 - type: precision_at_100 value: 1.192 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 23.448 - type: precision_at_5 value: 15.862000000000002 - type: recall_at_1 value: 40.987 - type: recall_at_10 value: 71.146 - type: recall_at_100 value: 87.035 - type: recall_at_1000 value: 95.633 - type: recall_at_3 value: 58.025999999999996 - type: recall_at_5 value: 63.815999999999995 - type: map_at_1 value: 24.587 - type: map_at_10 value: 33.114 - type: map_at_100 value: 34.043 - type: map_at_1000 value: 34.123999999999995 - type: map_at_3 value: 30.45 - type: map_at_5 value: 31.813999999999997 - type: mrr_at_1 value: 26.554 - type: mrr_at_10 value: 35.148 - type: mrr_at_100 value: 35.926 - type: mrr_at_1000 value: 35.991 - type: mrr_at_3 value: 32.599000000000004 - type: mrr_at_5 value: 33.893 - type: ndcg_at_1 value: 26.554 - type: ndcg_at_10 value: 38.132 - type: ndcg_at_100 value: 42.78 - type: ndcg_at_1000 value: 44.919 - type: ndcg_at_3 value: 32.833 - type: ndcg_at_5 value: 35.168 - type: precision_at_1 value: 26.554 - type: precision_at_10 value: 5.921 - type: precision_at_100 value: 0.8659999999999999 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 13.861 - type: precision_at_5 value: 9.605 - type: recall_at_1 value: 24.587 - type: recall_at_10 value: 51.690000000000005 - type: recall_at_100 value: 73.428 - type: recall_at_1000 value: 89.551 - type: recall_at_3 value: 37.336999999999996 - type: recall_at_5 value: 43.047000000000004 - type: map_at_1 value: 16.715 - type: map_at_10 value: 24.251 - type: map_at_100 value: 25.326999999999998 - type: map_at_1000 value: 25.455 - type: map_at_3 value: 21.912000000000003 - type: map_at_5 value: 23.257 - type: mrr_at_1 value: 20.274 - type: mrr_at_10 value: 28.552 - type: mrr_at_100 value: 29.42 - type: mrr_at_1000 value: 29.497 - type: mrr_at_3 value: 26.14 - type: mrr_at_5 value: 27.502 - type: ndcg_at_1 value: 20.274 - type: ndcg_at_10 value: 29.088 - type: ndcg_at_100 value: 34.293 - type: ndcg_at_1000 value: 37.271 - type: ndcg_at_3 value: 24.708 - type: ndcg_at_5 value: 26.809 - type: precision_at_1 value: 20.274 - type: precision_at_10 value: 5.361 - type: precision_at_100 value: 0.915 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 11.733 - type: precision_at_5 value: 8.556999999999999 - type: recall_at_1 value: 16.715 - type: recall_at_10 value: 39.587 - type: recall_at_100 value: 62.336000000000006 - type: recall_at_1000 value: 83.453 - type: recall_at_3 value: 27.839999999999996 - type: recall_at_5 value: 32.952999999999996 - type: map_at_1 value: 28.793000000000003 - type: map_at_10 value: 38.582 - type: map_at_100 value: 39.881 - type: map_at_1000 value: 39.987 - type: map_at_3 value: 35.851 - type: map_at_5 value: 37.289 - type: mrr_at_1 value: 34.455999999999996 - type: mrr_at_10 value: 43.909 - type: mrr_at_100 value: 44.74 - type: mrr_at_1000 value: 44.786 - type: mrr_at_3 value: 41.659 - type: mrr_at_5 value: 43.010999999999996 - type: ndcg_at_1 value: 34.455999999999996 - type: ndcg_at_10 value: 44.266 - type: ndcg_at_100 value: 49.639 - type: ndcg_at_1000 value: 51.644 - type: ndcg_at_3 value: 39.865 - type: ndcg_at_5 value: 41.887 - type: precision_at_1 value: 34.455999999999996 - type: precision_at_10 value: 7.843999999999999 - type: precision_at_100 value: 1.243 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 18.831999999999997 - type: precision_at_5 value: 13.147 - type: recall_at_1 value: 28.793000000000003 - type: recall_at_10 value: 55.68300000000001 - type: recall_at_100 value: 77.99000000000001 - type: recall_at_1000 value: 91.183 - type: recall_at_3 value: 43.293 - type: recall_at_5 value: 48.618 - type: map_at_1 value: 25.907000000000004 - type: map_at_10 value: 35.519 - type: map_at_100 value: 36.806 - type: map_at_1000 value: 36.912 - type: map_at_3 value: 32.748 - type: map_at_5 value: 34.232 - type: mrr_at_1 value: 31.621 - type: mrr_at_10 value: 40.687 - type: mrr_at_100 value: 41.583 - type: mrr_at_1000 value: 41.638999999999996 - type: mrr_at_3 value: 38.527 - type: mrr_at_5 value: 39.612 - type: ndcg_at_1 value: 31.621 - type: ndcg_at_10 value: 41.003 - type: ndcg_at_100 value: 46.617999999999995 - type: ndcg_at_1000 value: 48.82 - type: ndcg_at_3 value: 36.542 - type: ndcg_at_5 value: 38.368 - type: precision_at_1 value: 31.621 - type: precision_at_10 value: 7.396999999999999 - type: precision_at_100 value: 1.191 - type: precision_at_1000 value: 0.153 - type: precision_at_3 value: 17.39 - type: precision_at_5 value: 12.1 - type: recall_at_1 value: 25.907000000000004 - type: recall_at_10 value: 52.115 - type: recall_at_100 value: 76.238 - type: recall_at_1000 value: 91.218 - type: recall_at_3 value: 39.417 - type: recall_at_5 value: 44.435 - type: map_at_1 value: 25.732166666666668 - type: map_at_10 value: 34.51616666666667 - type: map_at_100 value: 35.67241666666666 - type: map_at_1000 value: 35.78675 - type: map_at_3 value: 31.953416666666662 - type: map_at_5 value: 33.333 - type: mrr_at_1 value: 30.300166666666673 - type: mrr_at_10 value: 38.6255 - type: mrr_at_100 value: 39.46183333333334 - type: mrr_at_1000 value: 39.519999999999996 - type: mrr_at_3 value: 36.41299999999999 - type: mrr_at_5 value: 37.6365 - type: ndcg_at_1 value: 30.300166666666673 - type: ndcg_at_10 value: 39.61466666666667 - type: ndcg_at_100 value: 44.60808333333334 - type: ndcg_at_1000 value: 46.91708333333334 - type: ndcg_at_3 value: 35.26558333333333 - type: ndcg_at_5 value: 37.220000000000006 - type: precision_at_1 value: 30.300166666666673 - type: precision_at_10 value: 6.837416666666667 - type: precision_at_100 value: 1.10425 - type: precision_at_1000 value: 0.14875 - type: precision_at_3 value: 16.13716666666667 - type: precision_at_5 value: 11.2815 - type: recall_at_1 value: 25.732166666666668 - type: recall_at_10 value: 50.578916666666665 - type: recall_at_100 value: 72.42183333333334 - type: recall_at_1000 value: 88.48766666666667 - type: recall_at_3 value: 38.41325 - type: recall_at_5 value: 43.515750000000004 - type: map_at_1 value: 23.951 - type: map_at_10 value: 30.974 - type: map_at_100 value: 31.804 - type: map_at_1000 value: 31.900000000000002 - type: map_at_3 value: 28.762 - type: map_at_5 value: 29.94 - type: mrr_at_1 value: 26.534000000000002 - type: mrr_at_10 value: 33.553 - type: mrr_at_100 value: 34.297 - type: mrr_at_1000 value: 34.36 - type: mrr_at_3 value: 31.391000000000002 - type: mrr_at_5 value: 32.525999999999996 - type: ndcg_at_1 value: 26.534000000000002 - type: ndcg_at_10 value: 35.112 - type: ndcg_at_100 value: 39.28 - type: ndcg_at_1000 value: 41.723 - type: ndcg_at_3 value: 30.902 - type: ndcg_at_5 value: 32.759 - type: precision_at_1 value: 26.534000000000002 - type: precision_at_10 value: 5.445 - type: precision_at_100 value: 0.819 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 12.986 - type: precision_at_5 value: 9.049 - type: recall_at_1 value: 23.951 - type: recall_at_10 value: 45.24 - type: recall_at_100 value: 64.12299999999999 - type: recall_at_1000 value: 82.28999999999999 - type: recall_at_3 value: 33.806000000000004 - type: recall_at_5 value: 38.277 - type: map_at_1 value: 16.829 - type: map_at_10 value: 23.684 - type: map_at_100 value: 24.683 - type: map_at_1000 value: 24.81 - type: map_at_3 value: 21.554000000000002 - type: map_at_5 value: 22.768 - type: mrr_at_1 value: 20.096 - type: mrr_at_10 value: 27.230999999999998 - type: mrr_at_100 value: 28.083999999999996 - type: mrr_at_1000 value: 28.166000000000004 - type: mrr_at_3 value: 25.212 - type: mrr_at_5 value: 26.32 - type: ndcg_at_1 value: 20.096 - type: ndcg_at_10 value: 27.989000000000004 - type: ndcg_at_100 value: 32.847 - type: ndcg_at_1000 value: 35.896 - type: ndcg_at_3 value: 24.116 - type: ndcg_at_5 value: 25.964 - type: precision_at_1 value: 20.096 - type: precision_at_10 value: 5 - type: precision_at_100 value: 0.8750000000000001 - type: precision_at_1000 value: 0.131 - type: precision_at_3 value: 11.207 - type: precision_at_5 value: 8.08 - type: recall_at_1 value: 16.829 - type: recall_at_10 value: 37.407000000000004 - type: recall_at_100 value: 59.101000000000006 - type: recall_at_1000 value: 81.024 - type: recall_at_3 value: 26.739 - type: recall_at_5 value: 31.524 - type: map_at_1 value: 24.138 - type: map_at_10 value: 32.275999999999996 - type: map_at_100 value: 33.416000000000004 - type: map_at_1000 value: 33.527 - type: map_at_3 value: 29.854000000000003 - type: map_at_5 value: 31.096 - type: mrr_at_1 value: 28.450999999999997 - type: mrr_at_10 value: 36.214 - type: mrr_at_100 value: 37.134 - type: mrr_at_1000 value: 37.198 - type: mrr_at_3 value: 34.001999999999995 - type: mrr_at_5 value: 35.187000000000005 - type: ndcg_at_1 value: 28.450999999999997 - type: ndcg_at_10 value: 37.166 - type: ndcg_at_100 value: 42.454 - type: ndcg_at_1000 value: 44.976 - type: ndcg_at_3 value: 32.796 - type: ndcg_at_5 value: 34.631 - type: precision_at_1 value: 28.450999999999997 - type: precision_at_10 value: 6.241 - type: precision_at_100 value: 0.9950000000000001 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 14.801 - type: precision_at_5 value: 10.280000000000001 - type: recall_at_1 value: 24.138 - type: recall_at_10 value: 48.111 - type: recall_at_100 value: 71.245 - type: recall_at_1000 value: 88.986 - type: recall_at_3 value: 36.119 - type: recall_at_5 value: 40.846 - type: map_at_1 value: 23.244 - type: map_at_10 value: 31.227 - type: map_at_100 value: 33.007 - type: map_at_1000 value: 33.223 - type: map_at_3 value: 28.924 - type: map_at_5 value: 30.017 - type: mrr_at_1 value: 27.668 - type: mrr_at_10 value: 35.524 - type: mrr_at_100 value: 36.699 - type: mrr_at_1000 value: 36.759 - type: mrr_at_3 value: 33.366 - type: mrr_at_5 value: 34.552 - type: ndcg_at_1 value: 27.668 - type: ndcg_at_10 value: 36.381 - type: ndcg_at_100 value: 43.062 - type: ndcg_at_1000 value: 45.656 - type: ndcg_at_3 value: 32.501999999999995 - type: ndcg_at_5 value: 34.105999999999995 - type: precision_at_1 value: 27.668 - type: precision_at_10 value: 6.798 - type: precision_at_100 value: 1.492 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 15.152 - type: precision_at_5 value: 10.791 - type: recall_at_1 value: 23.244 - type: recall_at_10 value: 45.979 - type: recall_at_100 value: 74.822 - type: recall_at_1000 value: 91.078 - type: recall_at_3 value: 34.925 - type: recall_at_5 value: 39.126 - type: map_at_1 value: 19.945 - type: map_at_10 value: 27.517999999999997 - type: map_at_100 value: 28.588 - type: map_at_1000 value: 28.682000000000002 - type: map_at_3 value: 25.345000000000002 - type: map_at_5 value: 26.555 - type: mrr_at_1 value: 21.996 - type: mrr_at_10 value: 29.845 - type: mrr_at_100 value: 30.775999999999996 - type: mrr_at_1000 value: 30.845 - type: mrr_at_3 value: 27.726 - type: mrr_at_5 value: 28.882 - type: ndcg_at_1 value: 21.996 - type: ndcg_at_10 value: 32.034 - type: ndcg_at_100 value: 37.185 - type: ndcg_at_1000 value: 39.645 - type: ndcg_at_3 value: 27.750999999999998 - type: ndcg_at_5 value: 29.805999999999997 - type: precision_at_1 value: 21.996 - type: precision_at_10 value: 5.065 - type: precision_at_100 value: 0.819 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 12.076 - type: precision_at_5 value: 8.392 - type: recall_at_1 value: 19.945 - type: recall_at_10 value: 43.62 - type: recall_at_100 value: 67.194 - type: recall_at_1000 value: 85.7 - type: recall_at_3 value: 32.15 - type: recall_at_5 value: 37.208999999999996 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 18.279 - type: map_at_10 value: 31.052999999999997 - type: map_at_100 value: 33.125 - type: map_at_1000 value: 33.306000000000004 - type: map_at_3 value: 26.208 - type: map_at_5 value: 28.857 - type: mrr_at_1 value: 42.671 - type: mrr_at_10 value: 54.557 - type: mrr_at_100 value: 55.142 - type: mrr_at_1000 value: 55.169000000000004 - type: mrr_at_3 value: 51.488 - type: mrr_at_5 value: 53.439 - type: ndcg_at_1 value: 42.671 - type: ndcg_at_10 value: 41.276 - type: ndcg_at_100 value: 48.376000000000005 - type: ndcg_at_1000 value: 51.318 - type: ndcg_at_3 value: 35.068 - type: ndcg_at_5 value: 37.242 - type: precision_at_1 value: 42.671 - type: precision_at_10 value: 12.638 - type: precision_at_100 value: 2.045 - type: precision_at_1000 value: 0.26 - type: precision_at_3 value: 26.08 - type: precision_at_5 value: 19.805 - type: recall_at_1 value: 18.279 - type: recall_at_10 value: 46.946 - type: recall_at_100 value: 70.97200000000001 - type: recall_at_1000 value: 87.107 - type: recall_at_3 value: 31.147999999999996 - type: recall_at_5 value: 38.099 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.573 - type: map_at_10 value: 19.747 - type: map_at_100 value: 28.205000000000002 - type: map_at_1000 value: 29.831000000000003 - type: map_at_3 value: 14.109 - type: map_at_5 value: 16.448999999999998 - type: mrr_at_1 value: 71 - type: mrr_at_10 value: 77.68599999999999 - type: mrr_at_100 value: 77.995 - type: mrr_at_1000 value: 78.00200000000001 - type: mrr_at_3 value: 76.292 - type: mrr_at_5 value: 77.029 - type: ndcg_at_1 value: 59.12500000000001 - type: ndcg_at_10 value: 43.9 - type: ndcg_at_100 value: 47.863 - type: ndcg_at_1000 value: 54.848 - type: ndcg_at_3 value: 49.803999999999995 - type: ndcg_at_5 value: 46.317 - type: precision_at_1 value: 71 - type: precision_at_10 value: 34.4 - type: precision_at_100 value: 11.063 - type: precision_at_1000 value: 1.989 - type: precision_at_3 value: 52.333 - type: precision_at_5 value: 43.7 - type: recall_at_1 value: 8.573 - type: recall_at_10 value: 25.615 - type: recall_at_100 value: 53.385000000000005 - type: recall_at_1000 value: 75.46000000000001 - type: recall_at_3 value: 15.429 - type: recall_at_5 value: 19.357 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.989999999999995 - type: f1 value: 42.776314451497555 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 74.13499999999999 - type: map_at_10 value: 82.825 - type: map_at_100 value: 83.096 - type: map_at_1000 value: 83.111 - type: map_at_3 value: 81.748 - type: map_at_5 value: 82.446 - type: mrr_at_1 value: 79.553 - type: mrr_at_10 value: 86.654 - type: mrr_at_100 value: 86.774 - type: mrr_at_1000 value: 86.778 - type: mrr_at_3 value: 85.981 - type: mrr_at_5 value: 86.462 - type: ndcg_at_1 value: 79.553 - type: ndcg_at_10 value: 86.345 - type: ndcg_at_100 value: 87.32 - type: ndcg_at_1000 value: 87.58200000000001 - type: ndcg_at_3 value: 84.719 - type: ndcg_at_5 value: 85.677 - type: precision_at_1 value: 79.553 - type: precision_at_10 value: 10.402000000000001 - type: precision_at_100 value: 1.1119999999999999 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 32.413 - type: precision_at_5 value: 20.138 - type: recall_at_1 value: 74.13499999999999 - type: recall_at_10 value: 93.215 - type: recall_at_100 value: 97.083 - type: recall_at_1000 value: 98.732 - type: recall_at_3 value: 88.79 - type: recall_at_5 value: 91.259 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 18.298000000000002 - type: map_at_10 value: 29.901 - type: map_at_100 value: 31.528 - type: map_at_1000 value: 31.713 - type: map_at_3 value: 25.740000000000002 - type: map_at_5 value: 28.227999999999998 - type: mrr_at_1 value: 36.728 - type: mrr_at_10 value: 45.401 - type: mrr_at_100 value: 46.27 - type: mrr_at_1000 value: 46.315 - type: mrr_at_3 value: 42.978 - type: mrr_at_5 value: 44.29 - type: ndcg_at_1 value: 36.728 - type: ndcg_at_10 value: 37.456 - type: ndcg_at_100 value: 43.832 - type: ndcg_at_1000 value: 47 - type: ndcg_at_3 value: 33.694 - type: ndcg_at_5 value: 35.085 - type: precision_at_1 value: 36.728 - type: precision_at_10 value: 10.386 - type: precision_at_100 value: 1.701 - type: precision_at_1000 value: 0.22599999999999998 - type: precision_at_3 value: 22.479 - type: precision_at_5 value: 16.605 - type: recall_at_1 value: 18.298000000000002 - type: recall_at_10 value: 44.369 - type: recall_at_100 value: 68.098 - type: recall_at_1000 value: 87.21900000000001 - type: recall_at_3 value: 30.215999999999998 - type: recall_at_5 value: 36.861 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 39.568 - type: map_at_10 value: 65.061 - type: map_at_100 value: 65.896 - type: map_at_1000 value: 65.95100000000001 - type: map_at_3 value: 61.831 - type: map_at_5 value: 63.849000000000004 - type: mrr_at_1 value: 79.136 - type: mrr_at_10 value: 84.58200000000001 - type: mrr_at_100 value: 84.765 - type: mrr_at_1000 value: 84.772 - type: mrr_at_3 value: 83.684 - type: mrr_at_5 value: 84.223 - type: ndcg_at_1 value: 79.136 - type: ndcg_at_10 value: 72.622 - type: ndcg_at_100 value: 75.539 - type: ndcg_at_1000 value: 76.613 - type: ndcg_at_3 value: 68.065 - type: ndcg_at_5 value: 70.58 - type: precision_at_1 value: 79.136 - type: precision_at_10 value: 15.215 - type: precision_at_100 value: 1.7500000000000002 - type: precision_at_1000 value: 0.189 - type: precision_at_3 value: 44.011 - type: precision_at_5 value: 28.388999999999996 - type: recall_at_1 value: 39.568 - type: recall_at_10 value: 76.077 - type: recall_at_100 value: 87.481 - type: recall_at_1000 value: 94.56400000000001 - type: recall_at_3 value: 66.01599999999999 - type: recall_at_5 value: 70.97200000000001 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 85.312 - type: ap value: 80.36296867333715 - type: f1 value: 85.26613311552218 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 23.363999999999997 - type: map_at_10 value: 35.711999999999996 - type: map_at_100 value: 36.876999999999995 - type: map_at_1000 value: 36.923 - type: map_at_3 value: 32.034 - type: map_at_5 value: 34.159 - type: mrr_at_1 value: 24.04 - type: mrr_at_10 value: 36.345 - type: mrr_at_100 value: 37.441 - type: mrr_at_1000 value: 37.480000000000004 - type: mrr_at_3 value: 32.713 - type: mrr_at_5 value: 34.824 - type: ndcg_at_1 value: 24.026 - type: ndcg_at_10 value: 42.531 - type: ndcg_at_100 value: 48.081 - type: ndcg_at_1000 value: 49.213 - type: ndcg_at_3 value: 35.044 - type: ndcg_at_5 value: 38.834 - type: precision_at_1 value: 24.026 - type: precision_at_10 value: 6.622999999999999 - type: precision_at_100 value: 0.941 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.909 - type: precision_at_5 value: 10.871 - type: recall_at_1 value: 23.363999999999997 - type: recall_at_10 value: 63.426 - type: recall_at_100 value: 88.96300000000001 - type: recall_at_1000 value: 97.637 - type: recall_at_3 value: 43.095 - type: recall_at_5 value: 52.178000000000004 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.0095759233926 - type: f1 value: 92.78387794667408 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 75.0296397628819 - type: f1 value: 58.45699589820874 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.45662407531944 - type: f1 value: 71.42364781421813 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 77.07800941492937 - type: f1 value: 77.22799045640845 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.531234379250606 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.941490381193802 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.3115090856725 - type: mrr value: 31.290667638675757 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.465 - type: map_at_10 value: 13.03 - type: map_at_100 value: 16.057 - type: map_at_1000 value: 17.49 - type: map_at_3 value: 9.553 - type: map_at_5 value: 11.204 - type: mrr_at_1 value: 43.653 - type: mrr_at_10 value: 53.269 - type: mrr_at_100 value: 53.72 - type: mrr_at_1000 value: 53.761 - type: mrr_at_3 value: 50.929 - type: mrr_at_5 value: 52.461 - type: ndcg_at_1 value: 42.26 - type: ndcg_at_10 value: 34.673 - type: ndcg_at_100 value: 30.759999999999998 - type: ndcg_at_1000 value: 39.728 - type: ndcg_at_3 value: 40.349000000000004 - type: ndcg_at_5 value: 37.915 - type: precision_at_1 value: 43.653 - type: precision_at_10 value: 25.789 - type: precision_at_100 value: 7.754999999999999 - type: precision_at_1000 value: 2.07 - type: precision_at_3 value: 38.596000000000004 - type: precision_at_5 value: 33.251 - type: recall_at_1 value: 5.465 - type: recall_at_10 value: 17.148 - type: recall_at_100 value: 29.768 - type: recall_at_1000 value: 62.239 - type: recall_at_3 value: 10.577 - type: recall_at_5 value: 13.315 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 37.008 - type: map_at_10 value: 52.467 - type: map_at_100 value: 53.342999999999996 - type: map_at_1000 value: 53.366 - type: map_at_3 value: 48.412 - type: map_at_5 value: 50.875 - type: mrr_at_1 value: 41.541 - type: mrr_at_10 value: 54.967 - type: mrr_at_100 value: 55.611 - type: mrr_at_1000 value: 55.627 - type: mrr_at_3 value: 51.824999999999996 - type: mrr_at_5 value: 53.763000000000005 - type: ndcg_at_1 value: 41.541 - type: ndcg_at_10 value: 59.724999999999994 - type: ndcg_at_100 value: 63.38700000000001 - type: ndcg_at_1000 value: 63.883 - type: ndcg_at_3 value: 52.331 - type: ndcg_at_5 value: 56.327000000000005 - type: precision_at_1 value: 41.541 - type: precision_at_10 value: 9.447 - type: precision_at_100 value: 1.1520000000000001 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 23.262 - type: precision_at_5 value: 16.314999999999998 - type: recall_at_1 value: 37.008 - type: recall_at_10 value: 79.145 - type: recall_at_100 value: 94.986 - type: recall_at_1000 value: 98.607 - type: recall_at_3 value: 60.277 - type: recall_at_5 value: 69.407 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 70.402 - type: map_at_10 value: 84.181 - type: map_at_100 value: 84.796 - type: map_at_1000 value: 84.81400000000001 - type: map_at_3 value: 81.209 - type: map_at_5 value: 83.085 - type: mrr_at_1 value: 81.02000000000001 - type: mrr_at_10 value: 87.263 - type: mrr_at_100 value: 87.36 - type: mrr_at_1000 value: 87.36 - type: mrr_at_3 value: 86.235 - type: mrr_at_5 value: 86.945 - type: ndcg_at_1 value: 81.01 - type: ndcg_at_10 value: 87.99900000000001 - type: ndcg_at_100 value: 89.217 - type: ndcg_at_1000 value: 89.33 - type: ndcg_at_3 value: 85.053 - type: ndcg_at_5 value: 86.703 - type: precision_at_1 value: 81.01 - type: precision_at_10 value: 13.336 - type: precision_at_100 value: 1.52 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 37.14 - type: precision_at_5 value: 24.44 - type: recall_at_1 value: 70.402 - type: recall_at_10 value: 95.214 - type: recall_at_100 value: 99.438 - type: recall_at_1000 value: 99.928 - type: recall_at_3 value: 86.75699999999999 - type: recall_at_5 value: 91.44099999999999 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 56.51721502758904 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 61.054808572333016 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.578 - type: map_at_10 value: 11.036999999999999 - type: map_at_100 value: 12.879999999999999 - type: map_at_1000 value: 13.150999999999998 - type: map_at_3 value: 8.133 - type: map_at_5 value: 9.559 - type: mrr_at_1 value: 22.6 - type: mrr_at_10 value: 32.68 - type: mrr_at_100 value: 33.789 - type: mrr_at_1000 value: 33.854 - type: mrr_at_3 value: 29.7 - type: mrr_at_5 value: 31.480000000000004 - type: ndcg_at_1 value: 22.6 - type: ndcg_at_10 value: 18.616 - type: ndcg_at_100 value: 25.883 - type: ndcg_at_1000 value: 30.944 - type: ndcg_at_3 value: 18.136 - type: ndcg_at_5 value: 15.625 - type: precision_at_1 value: 22.6 - type: precision_at_10 value: 9.48 - type: precision_at_100 value: 1.991 - type: precision_at_1000 value: 0.321 - type: precision_at_3 value: 16.8 - type: precision_at_5 value: 13.54 - type: recall_at_1 value: 4.578 - type: recall_at_10 value: 19.213 - type: recall_at_100 value: 40.397 - type: recall_at_1000 value: 65.2 - type: recall_at_3 value: 10.208 - type: recall_at_5 value: 13.718 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.44288351714071 - type: cos_sim_spearman value: 79.37995604564952 - type: euclidean_pearson value: 81.1078874670718 - type: euclidean_spearman value: 79.37995905980499 - type: manhattan_pearson value: 81.03697527288986 - type: manhattan_spearman value: 79.33490235296236 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 84.95557650436523 - type: cos_sim_spearman value: 78.5190672399868 - type: euclidean_pearson value: 81.58064025904707 - type: euclidean_spearman value: 78.5190672399868 - type: manhattan_pearson value: 81.52857930619889 - type: manhattan_spearman value: 78.50421361308034 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 84.79128416228737 - type: cos_sim_spearman value: 86.05402451477147 - type: euclidean_pearson value: 85.46280267054289 - type: euclidean_spearman value: 86.05402451477147 - type: manhattan_pearson value: 85.46278563858236 - type: manhattan_spearman value: 86.08079590861004 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 83.20623089568763 - type: cos_sim_spearman value: 81.53786907061009 - type: euclidean_pearson value: 82.82272250091494 - type: euclidean_spearman value: 81.53786907061009 - type: manhattan_pearson value: 82.78850494027013 - type: manhattan_spearman value: 81.5135618083407 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 85.46366618397936 - type: cos_sim_spearman value: 86.96566013336908 - type: euclidean_pearson value: 86.62651697548931 - type: euclidean_spearman value: 86.96565526364454 - type: manhattan_pearson value: 86.58812160258009 - type: manhattan_spearman value: 86.9336484321288 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.51858358641559 - type: cos_sim_spearman value: 84.7652527954999 - type: euclidean_pearson value: 84.23914783766861 - type: euclidean_spearman value: 84.7652527954999 - type: manhattan_pearson value: 84.22749648503171 - type: manhattan_spearman value: 84.74527996746386 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 87.28026563313065 - type: cos_sim_spearman value: 87.46928143824915 - type: euclidean_pearson value: 88.30558762000372 - type: euclidean_spearman value: 87.46928143824915 - type: manhattan_pearson value: 88.10513330809331 - type: manhattan_spearman value: 87.21069787834173 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 62.376497134587375 - type: cos_sim_spearman value: 65.0159550112516 - type: euclidean_pearson value: 65.64572120879598 - type: euclidean_spearman value: 65.0159550112516 - type: manhattan_pearson value: 65.88143604989976 - type: manhattan_spearman value: 65.17547297222434 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.22876368947644 - type: cos_sim_spearman value: 85.46935577445318 - type: euclidean_pearson value: 85.32830231392005 - type: euclidean_spearman value: 85.46935577445318 - type: manhattan_pearson value: 85.30353211758495 - type: manhattan_spearman value: 85.42821085956945 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 80.60986667767133 - type: mrr value: 94.29432314236236 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 54.528 - type: map_at_10 value: 65.187 - type: map_at_100 value: 65.62599999999999 - type: map_at_1000 value: 65.657 - type: map_at_3 value: 62.352 - type: map_at_5 value: 64.025 - type: mrr_at_1 value: 57.333 - type: mrr_at_10 value: 66.577 - type: mrr_at_100 value: 66.88 - type: mrr_at_1000 value: 66.908 - type: mrr_at_3 value: 64.556 - type: mrr_at_5 value: 65.739 - type: ndcg_at_1 value: 57.333 - type: ndcg_at_10 value: 70.275 - type: ndcg_at_100 value: 72.136 - type: ndcg_at_1000 value: 72.963 - type: ndcg_at_3 value: 65.414 - type: ndcg_at_5 value: 67.831 - type: precision_at_1 value: 57.333 - type: precision_at_10 value: 9.5 - type: precision_at_100 value: 1.057 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 25.778000000000002 - type: precision_at_5 value: 17.2 - type: recall_at_1 value: 54.528 - type: recall_at_10 value: 84.356 - type: recall_at_100 value: 92.833 - type: recall_at_1000 value: 99.333 - type: recall_at_3 value: 71.283 - type: recall_at_5 value: 77.14999999999999 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.74158415841585 - type: cos_sim_ap value: 92.90048959850317 - type: cos_sim_f1 value: 86.35650810245687 - type: cos_sim_precision value: 90.4709748083242 - type: cos_sim_recall value: 82.6 - type: dot_accuracy value: 99.74158415841585 - type: dot_ap value: 92.90048959850317 - type: dot_f1 value: 86.35650810245687 - type: dot_precision value: 90.4709748083242 - type: dot_recall value: 82.6 - type: euclidean_accuracy value: 99.74158415841585 - type: euclidean_ap value: 92.90048959850317 - type: euclidean_f1 value: 86.35650810245687 - type: euclidean_precision value: 90.4709748083242 - type: euclidean_recall value: 82.6 - type: manhattan_accuracy value: 99.74158415841585 - type: manhattan_ap value: 92.87344692947894 - type: manhattan_f1 value: 86.38497652582159 - type: manhattan_precision value: 90.29443838604145 - type: manhattan_recall value: 82.8 - type: max_accuracy value: 99.74158415841585 - type: max_ap value: 92.90048959850317 - type: max_f1 value: 86.38497652582159 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 63.191648770424216 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 34.02944668730218 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 50.466386167525265 - type: mrr value: 51.19071492233257 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.198022505886435 - type: cos_sim_spearman value: 30.40170257939193 - type: dot_pearson value: 30.198015316402614 - type: dot_spearman value: 30.40170257939193 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.242 - type: map_at_10 value: 2.17 - type: map_at_100 value: 12.221 - type: map_at_1000 value: 28.63 - type: map_at_3 value: 0.728 - type: map_at_5 value: 1.185 - type: mrr_at_1 value: 94 - type: mrr_at_10 value: 97 - type: mrr_at_100 value: 97 - type: mrr_at_1000 value: 97 - type: mrr_at_3 value: 97 - type: mrr_at_5 value: 97 - type: ndcg_at_1 value: 89 - type: ndcg_at_10 value: 82.30499999999999 - type: ndcg_at_100 value: 61.839999999999996 - type: ndcg_at_1000 value: 53.381 - type: ndcg_at_3 value: 88.877 - type: ndcg_at_5 value: 86.05199999999999 - type: precision_at_1 value: 94 - type: precision_at_10 value: 87 - type: precision_at_100 value: 63.38 - type: precision_at_1000 value: 23.498 - type: precision_at_3 value: 94 - type: precision_at_5 value: 92 - type: recall_at_1 value: 0.242 - type: recall_at_10 value: 2.302 - type: recall_at_100 value: 14.979000000000001 - type: recall_at_1000 value: 49.638 - type: recall_at_3 value: 0.753 - type: recall_at_5 value: 1.226 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 3.006 - type: map_at_10 value: 11.805 - type: map_at_100 value: 18.146 - type: map_at_1000 value: 19.788 - type: map_at_3 value: 5.914 - type: map_at_5 value: 8.801 - type: mrr_at_1 value: 40.816 - type: mrr_at_10 value: 56.36600000000001 - type: mrr_at_100 value: 56.721999999999994 - type: mrr_at_1000 value: 56.721999999999994 - type: mrr_at_3 value: 52.041000000000004 - type: mrr_at_5 value: 54.796 - type: ndcg_at_1 value: 37.755 - type: ndcg_at_10 value: 29.863 - type: ndcg_at_100 value: 39.571 - type: ndcg_at_1000 value: 51.385999999999996 - type: ndcg_at_3 value: 32.578 - type: ndcg_at_5 value: 32.351 - type: precision_at_1 value: 40.816 - type: precision_at_10 value: 26.531 - type: precision_at_100 value: 7.796 - type: precision_at_1000 value: 1.555 - type: precision_at_3 value: 32.653 - type: precision_at_5 value: 33.061 - type: recall_at_1 value: 3.006 - type: recall_at_10 value: 18.738 - type: recall_at_100 value: 48.058 - type: recall_at_1000 value: 83.41300000000001 - type: recall_at_3 value: 7.166 - type: recall_at_5 value: 12.102 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.4178 - type: ap value: 14.648781342150446 - type: f1 value: 55.07299194946378 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.919637804187886 - type: f1 value: 61.24122013967399 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 49.207896583685695 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 86.23114978840078 - type: cos_sim_ap value: 74.26624727825818 - type: cos_sim_f1 value: 68.72377190817083 - type: cos_sim_precision value: 64.56400742115028 - type: cos_sim_recall value: 73.45646437994723 - type: dot_accuracy value: 86.23114978840078 - type: dot_ap value: 74.26624032659652 - type: dot_f1 value: 68.72377190817083 - type: dot_precision value: 64.56400742115028 - type: dot_recall value: 73.45646437994723 - type: euclidean_accuracy value: 86.23114978840078 - type: euclidean_ap value: 74.26624714480556 - type: euclidean_f1 value: 68.72377190817083 - type: euclidean_precision value: 64.56400742115028 - type: euclidean_recall value: 73.45646437994723 - type: manhattan_accuracy value: 86.16558383501221 - type: manhattan_ap value: 74.2091943976357 - type: manhattan_f1 value: 68.64221520524654 - type: manhattan_precision value: 63.59135913591359 - type: manhattan_recall value: 74.5646437994723 - type: max_accuracy value: 86.23114978840078 - type: max_ap value: 74.26624727825818 - type: max_f1 value: 68.72377190817083 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.3681841114604 - type: cos_sim_ap value: 86.65166387498546 - type: cos_sim_f1 value: 79.02581944698774 - type: cos_sim_precision value: 75.35796605434099 - type: cos_sim_recall value: 83.06898675700647 - type: dot_accuracy value: 89.3681841114604 - type: dot_ap value: 86.65166019802056 - type: dot_f1 value: 79.02581944698774 - type: dot_precision value: 75.35796605434099 - type: dot_recall value: 83.06898675700647 - type: euclidean_accuracy value: 89.3681841114604 - type: euclidean_ap value: 86.65166462876266 - type: euclidean_f1 value: 79.02581944698774 - type: euclidean_precision value: 75.35796605434099 - type: euclidean_recall value: 83.06898675700647 - type: manhattan_accuracy value: 89.36624364497226 - type: manhattan_ap value: 86.65076471274106 - type: manhattan_f1 value: 79.07408783532733 - type: manhattan_precision value: 76.41102972856527 - type: manhattan_recall value: 81.92947336002464 - type: max_accuracy value: 89.3681841114604 - type: max_ap value: 86.65166462876266 - type: max_f1 value: 79.07408783532733 --- # nomic-embed-text-v1.5: Resizable Production Embeddings with Matryoshka Representation Learning **Exciting Update!**: `nomic-embed-text-v1.5` is now multimodal! [nomic-embed-vision-v1](https://huggingface.co/nomic-ai/nomic-embed-vision-v1.5) is aligned to the embedding space of `nomic-embed-text-v1.5`, meaning any text embedding is multimodal! ## Usage **Important**: the text prompt *must* include a *task instruction prefix*, instructing the model which task is being performed. For example, if you are implementing a RAG application, you embed your documents as `search_document: <text here>` and embed your user queries as `search_query: <text here>`. ## Task instruction prefixes ### `search_document` #### Purpose: embed texts as documents from a dataset This prefix is used for embedding texts as documents, for example as documents for a RAG index. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True) sentences = ['search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten'] embeddings = model.encode(sentences) print(embeddings) ``` ### `search_query` #### Purpose: embed texts as questions to answer This prefix is used for embedding texts as questions that documents from a dataset could resolve, for example as queries to be answered by a RAG application. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True) sentences = ['search_query: Who is Laurens van Der Maaten?'] embeddings = model.encode(sentences) print(embeddings) ``` ### `clustering` #### Purpose: embed texts to group them into clusters This prefix is used for embedding texts in order to group them into clusters, discover common topics, or remove semantic duplicates. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True) sentences = ['clustering: the quick brown fox'] embeddings = model.encode(sentences) print(embeddings) ``` ### `classification` #### Purpose: embed texts to classify them This prefix is used for embedding texts into vectors that will be used as features for a classification model ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True) sentences = ['classification: the quick brown fox'] embeddings = model.encode(sentences) print(embeddings) ``` ### Sentence Transformers ```python import torch.nn.functional as F from sentence_transformers import SentenceTransformer matryoshka_dim = 512 model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True) sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?'] embeddings = model.encode(sentences, convert_to_tensor=True) embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],)) embeddings = embeddings[:, :matryoshka_dim] embeddings = F.normalize(embeddings, p=2, dim=1) print(embeddings) ``` ### Transformers ```diff import torch import torch.nn.functional as F from transformers import AutoTokenizer, AutoModel def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?'] tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1.5', trust_remote_code=True, safe_serialization=True) model.eval() encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') + matryoshka_dim = 512 with torch.no_grad(): model_output = model(**encoded_input) embeddings = mean_pooling(model_output, encoded_input['attention_mask']) + embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],)) + embeddings = embeddings[:, :matryoshka_dim] embeddings = F.normalize(embeddings, p=2, dim=1) print(embeddings) ``` The model natively supports scaling of the sequence length past 2048 tokens. To do so, ```diff - tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') + tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', model_max_length=8192) - model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True) + model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True, rotary_scaling_factor=2) ``` ### Transformers.js ```js import { pipeline, layer_norm } from '@huggingface/transformers'; // Create a feature extraction pipeline const extractor = await pipeline('feature-extraction', 'nomic-ai/nomic-embed-text-v1.5'); // Define sentences const texts = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']; // Compute sentence embeddings let embeddings = await extractor(texts, { pooling: 'mean' }); console.log(embeddings); // Tensor of shape [2, 768] const matryoshka_dim = 512; embeddings = layer_norm(embeddings, [embeddings.dims[1]]) .slice(null, [0, matryoshka_dim]) .normalize(2, -1); console.log(embeddings.tolist()); ``` ## Nomic API The easiest way to use Nomic Embed is through the Nomic Embedding API. Generating embeddings with the `nomic` Python client is as easy as ```python from nomic import embed output = embed.text( texts=['Nomic Embedding API', '#keepAIOpen'], model='nomic-embed-text-v1.5', task_type='search_document', dimensionality=256, ) print(output) ``` For more information, see the [API reference](https://docs.nomic.ai/reference/endpoints/nomic-embed-text) ## Infinity Usage with [Infinity](https://github.com/michaelfeil/infinity). ```bash docker run --gpus all -v $PWD/data:/app/.cache -e HF_TOKEN=$HF_TOKEN -p "7997":"7997" \ michaelf34/infinity:0.0.70 \ v2 --model-id nomic-ai/nomic-embed-text-v1.5 --revision "main" --dtype float16 --batch-size 8 --engine torch --port 7997 --no-bettertransformer ``` ## Adjusting Dimensionality `nomic-embed-text-v1.5` is an improvement upon [Nomic Embed](https://huggingface.co/nomic-ai/nomic-embed-text-v1) that utilizes [Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147) which gives developers the flexibility to trade off the embedding size for a negligible reduction in performance. | Name | SeqLen | Dimension | MTEB | | :-------------------------------:| :----- | :-------- | :------: | | nomic-embed-text-v1 | 8192 | 768 | **62.39** | | nomic-embed-text-v1.5 | 8192 | 768 | 62.28 | | nomic-embed-text-v1.5 | 8192 | 512 | 61.96 | | nomic-embed-text-v1.5 | 8192 | 256 | 61.04 | | nomic-embed-text-v1.5 | 8192 | 128 | 59.34 | | nomic-embed-text-v1.5 | 8192 | 64 | 56.10 | ![image/png](https://cdn-uploads.huggingface.co/production/uploads/607997c83a565c15675055b3/CRnaHV-c2wMUMZKw72q85.png) ## Training Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data! [![image/webp](https://cdn-uploads.huggingface.co/production/uploads/607997c83a565c15675055b3/pjhJhuNyRfPagRd_c_iUz.webp)](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample) We train our embedder using a multi-stage training pipeline. Starting from a long-context [BERT model](https://huggingface.co/nomic-ai/nomic-bert-2048), the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles. In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage. For more details, see the Nomic Embed [Technical Report](https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-matryoshka). Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors) # Join the Nomic Community - Nomic: [https://nomic.ai](https://nomic.ai) - Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8) - Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai) # Citation If you find the model, dataset, or training code useful, please cite our work ```bibtex @misc{nussbaum2024nomic, title={Nomic Embed: Training a Reproducible Long Context Text Embedder}, author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar}, year={2024}, eprint={2402.01613}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
RichardErkhov/phamhai_-_Llama-3.2-1B-Instruct-Frog-awq
RichardErkhov
null
[ "safetensors", "llama", "4-bit", "awq", "region:us" ]
1,732
1,732
11
0
--- {} --- Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Llama-3.2-1B-Instruct-Frog - AWQ - Model creator: https://huggingface.co/phamhai/ - Original model: https://huggingface.co/phamhai/Llama-3.2-1B-Instruct-Frog/ Original model description: --- license: llama3.2 language: - en - vi base_model: - meta-llama/Llama-3.2-1B-Instruct pipeline_tag: text-generation --- <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6612cc790b91dd96968028f9/yP51EyRNg-CHCKB4gBYan.png" width="100" /> </p> <h1>Llama-3.2-1B-Instruct-Frog - a RAG-optimized LLaMA3.2 for Vietnamese</h1> At the end of September 2024, Meta released two lightweight LLM model versions: [Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) and [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct). However, these models are not well-supported for Vietnamese, especially for tasks related to Retrieval-Augmented Generation (RAG). Today, I am excited to announce the release of two models specifically trained to provide better support for Vietnamese RAG tasks. <h2>Model Details:</h2> + Base Models: Llama-3.2-1B-Instruct and Llama-3.2-3B-Instruct + Performance: The models are optimized for fast inference and can be easily deployed on on-premise and edge devices (laptop/smartphone/NVIDIA Jetson Xavier/Raspberry Pi,ect). + Model weights: + [Llama-3.2-1B-Instruct-Frog](https://huggingface.co/phamhai/Llama-3.2-1B-Instruct-Frog): 131K context length, 1 billion parameters + [Llama-3.2-3B-Instruct-Frog](https://huggingface.co/phamhai/Llama-3.2-3B-Instruct-Frog): 131K context length, 3 billion parameters + Limitations: The 1B model currently has poorer prompt understanding and lower accuracy in some tasks such as summarization and entity extraction in Function Calling. Please consider and choose a model that fits your application needs. <blockquote style="color:red"> <p><strong style="color: red">Terms of Use and License</strong>: By using our released weights, you agree to and comply with the terms and conditions specified in Meta's LLaMA-3 license.</blockquote> <h2>Model Evaluation</h2> Will be updated in the coming days. <h2> Run the model </h2> (*Disclaimer: The name of the bot is called Vivi, which is due to my passion for VinFast vehicles, and I also hope to develop my own smaller models for VinFast's car lines (which they refer to as their virtual assistant, Vivi). This model has no affiliation with VinFast or any related entities.*) <h3> with Huggingface's transformers </h3> <h4> 1. QnA task </h4> ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "phamhai/Llama-3.2-1B-Instruct-Frog" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained(model_path) messages = [ {"role": "system", "content": "Bạn là một người bạn gái xinh đẹp. Tên của bạn là Vivi. Hãy luôn xưng là Vivi, gọi người nói là anh và trả lời luôn bắt đầu bằng cụm từ Dạ thưa anh yêu của em."}, {"role": "user", "content": "xin chào em"}] tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=False, return_tensors="pt") outputs = model.generate(tokenized_chat, max_new_tokens=128) print(tokenizer.decode(outputs[0])) # Dạ thưa anh yêu, chào buổi sáng. messages = [ {"role": "system", "content": "Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực. Hãy luôn trả lời một cách hữu ích nhất có thể, đồng thời giữ an toàn.\nNếu một câu hỏi không có ý nghĩa hoặc không hợp lý về mặt thông tin, hãy giải thích tại sao thay vì trả lời một điều gì đó không chính xác, vui lòng không chia sẻ thông tin sai lệch."}, {"role": "user", "content": "Làm sao để chữa bệnh đau đầu?"}] tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") outputs = model.generate(tokenized_chat, max_new_tokens=256) print(tokenizer.decode(outputs[0])) # 1. Nghỉ ngơi: Đau đầu có thể được giảm bớt bằng cách nghỉ ngơi và thư giãn. Nếu bạn cảm thấy đau đầu do căng thẳng, hãy thử thư giãn bằng cách tập yoga hoặc thiền định. # 2. Uống nước: Uống đủ nước có thể giúp giảm đau đầu. Hãy uống ít nhất 8 ly nước mỗi ngày để giữ cơ thể được cân bằng nước. # 3. Massage: Massage nhẹ nhàng khu vực đầu có thể giúp giảm đau đầu. Bạn có thể tự massage hoặc đến phòng massage để được chuyên gia massage. # 4. Sử dụng thuốc giảm đau: Thuốc giảm đau như aspirin hoặc ibuprofen có thể giúp giảm đau đầu. Tuy nhiên, hãy đọc kỹ hướng dẫn sử dụng và tuân thủ liều lượng được khuyến nghị. # 5. Áp dụng nhiệt hoặc lạnh: Áp dụng nhiệt hoặc lạnh lên khu vực đầu có thể giúp giảm đau đầu. Bạn có thể sử dụng túi đá hoặc băng để áp lên khu vực đầu hoặc sử dụng khăn ấm để áp lên khu vực đầu. # 6. Điều chỉnh chế độ ăn uống: Ăn uống lành mạnh và cân bằng có thể giúp giảm đau đầu. Hạn chế các loại thực phẩm có chứa caffeine và đường, và ăn nhiều trái cây và rau quả để cung cấp đủ vitamin và khoáng chất cho cơ thể. # 7. Tập thể dục: Tập thể dục thường xuyên có thể giúp giảm đau đầu. Hãy tham gia các hoạt động thể thao như đi bộ, chạy bộ hoặc bơi lội để giảm đau đầu. # 8. Tránh căng thẳng: Căng thẳng có thể gây ra đau đầu. Hãy cố gắng giảm căng thẳng bằng cách tập yoga, thiền định hoặc các hoạt động thư giãn khác. # 9. Kiểm tra sức khỏe: Nếu đau đầu kéo dài hoặc trở nên nghiêm trọng hơn, hãy tham khảo ý kiến ​​bác sĩ để kiểm tra sức khỏe của bạn. ``` <h4> 2. Summarization task </h4> <h5> Focused Answer </h5> ```python messages = [ {"role": "system", "content": '''Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực. Hãy luôn trả lời một cách hữu ích nhất có thể, đồng thời giữ an toàn. Nếu một câu hỏi không có ý nghĩa hoặc không hợp lý về mặt thông tin, hãy giải thích tại sao thay vì trả lời một điều gì đó không chính xác, vui lòng không chia sẻ thông tin sai lệch. Context: Đoạn 0: "Chính phủ đề xuất bổ sung gần 20.700 tỷ đồng vốn điều lệ cho Ngân hàng Ngoại thương Việt Nam (Vietcombank) từ cổ tức bằng cổ phiếu được chia của cổ đông Nhà nước. Chiều 23/10, thừa ủy quyền Chính phủ, Phó thủ tướng, Bộ trưởng Tài chính Hồ Đức Phớc trình Quốc hội về bổ sung vốn Nhà nước tại Ngân hàng Ngoại Thương Việt Nam (Vietcombank). Theo đó, Chính phủ đề nghị tăng vốn điều lệ cho ngân hàng này gần 20.700 tỷ đồng từ cổ tức bằng cổ phiếu được chia của cổ đông Nhà nước. Số tiền này lấy từ nguồn lợi nhuận còn lại lũy kế đến hết năm 2018 và lãi còn lại năm 2021. Vốn điều lệ dự kiến rót thêm cho Vietcombank gần bằng lợi nhuận hợp nhất trước thuế nửa đầu năm nay của nhà băng này. Việc bổ sung vốn cho "ông lớn" ngân hàng quốc doanh được Phó thủ tướng nhấn mạnh là cấp thiết để duy trì tỷ lệ vốn góp Nhà nước, phù hợp chiến lược phát triển kinh tế xã hội, tạo nguồn lực hỗ trợ ngân hàng yếu kém. Phó thủ tướng cho biết, phần lợi nhuận còn lại lũy kế hết năm 2018 và lãi còn lại 2021 hiện được hạch toán theo dõi tại VCB, chưa nằm trong cân đối ngân sách Nhà nước. Do vậy, nguồn vốn đề xuất tăng cho ngân hàng này không ảnh hưởng tới kế hoạch dự toán thu chi ngân sách 2024-2025. Phó thủ tướng, Bộ trưởng Tài chính Hồ Đức Phớc đọc tờ trình bổ sung vốn cho Vietcombank, ngày 23/10. Ảnh: Trung tâm báo chí Quốc hội Phó thủ tướng, Bộ trưởng Tài chính Hồ Đức Phớc đọc tờ trình bổ sung vốn cho Vietcombank, ngày 23/10. Ảnh: Trung tâm báo chí Quốc hội Vốn điều lệ của Vietcombank hiện là 55.891 tỷ đồng, thấp hơn nhiều so với VPBank (79.339 tỷ đồng), Techcombank (70.450 tỷ đồng) và không có sự cách biệt lớn so với một số ngân hàng thương mại cổ phần như MB (52.871) tỷ đồng, ACB (44.667 tỷ đồng) và SHB (36.629 tỷ đồng). Ngoài ra, việc tăng vốn nhằm để ngân hàng này đáp ứng các tỷ lệ an toàn tối thiểu. Tính tới cuối 2023, tỷ lệ an toàn vốn (CAR) của ngân hàng này là 11,05%, đảm bảo quy định. Tuy nhiên, mức này thấp hơn các ngân hàng thương mại cổ phần (VPBank, MB là 12-13%; Techcombank 13-15%...) và các nhà băng trong khu vực (Singapore là 17,1%, Indonesia 23,27%...). Thẩm tra nội dung này, Chủ nhiệm Ủy ban Kinh tế Vũ Hồng Thanh cho rằng đề xuất tăng vốn cho Vietcombank bảo đảm cơ sở pháp lý và đúng thẩm quyền theo quy định. Tuy nhiên, Ủy ban Kinh tế đề nghị Chính phủ lấy ý kiến của cổ đông chiến lược nước ngoài Ngân hàng Mizuho Corporate Bank - đơn vị nắm 15% vốn điều lệ của Vietcombank. Việc này nhằm thuận lợi trong quá trình tăng vốn. Chính phủ cũng cần bổ sung thông tin hiện trạng vốn của Vietcombank so với các ngân hàng thương mại trong hệ thống hiện nay. "Có ý kiến đề nghị làm rõ nhận định nguồn vốn đề xuất để tăng vốn điều lệ không tác động đến ngân sách Nhà nước", ông Thanh cho biết. Trụ sở Ngân hàng Ngoại thương Việt Nam (Vietcombank). Ảnh: VCB Trụ sở Ngân hàng Ngoại thương Việt Nam (Vietcombank). Ảnh: VCB Chủ nhiệm Ủy ban Kinh tế Vũ Hồng Thanh đề nghị Chính phủ chỉ đạo Ngân hàng Nhà nước cùng các bộ, ngành liên quan xử lý phần lợi nhuận còn lại năm 2022, 2023 (lần lượt là 21.680 tỷ và 25.009 tỷ đồng), nhằm tăng năng lực tài chính cho Vietcombank, bù đắp mức thiếu hụt vốn tự có, bảo đảm an toàn hoạt động. Cơ quan thẩm tra lưu ý vốn được bổ sung cho Vietcombank cần được dùng để mở rộng kinh doanh, cung ứng tín dụng với các lĩnh vực, dự án quan trọng quốc gia quy mô lớn, giảm lãi suất cho vay, cũng như đổi mới mô hình quản trị, chất lượng dịch vụ của nhà băng này. "Chính phủ cần đánh giá kỹ tác động việc bổ sung vốn Nhà nước cho Vietcombank tới phát triển của ngành ngân hàng, hiệu quả kinh tế xã hội", Ủy ban Kinh tế lưu ý. Vietcombank là một trong 4 ngân hàng thương mại Nhà nước, bên cạnh BIDV, VietinBank và Agribank. Ngân hàng này do Nhà nước sở hữu 74,8% vốn điều lệ. Lũy kế nửa đầu năm nay, lợi nhuận hợp nhất trước thuế của nhà băng này đạt 20.835 tỷ đồng, tăng 1,6% so với cùng kỳ 2023. Với dữ liệu này, Vietcombank tiếp tục đứng đầu toàn hệ thống ngân hàng về lợi nhuận 6 tháng đầu năm. Đây cũng là mức lãi nửa đầu năm cao kỷ lục của nhà băng này. Tính đến 30/6, tổng tài sản của ngân hàng đạt hơn 1,9 triệu tỷ đồng, tăng 3,6% so với cuối 2023. Trong đó, cho vay khách hàng gần 1,37 triệu tỷ đồng, tăng 7,8%." Đoạn 1: "Đã có vài đơn vị bán tín chỉ carbon cho khách ngoại nhưng còn thiếu cơ sở pháp lý để đảm bảo hoạt động được thuận lợi, theo chuyên gia. Thông tin tại phiên tọa đàm thuộc Diễn đàn và Triển lãm Kinh tế xanh 2024 (GEFE), ông Đỗ Ngọc Quỳnh, Tổng thư ký Hiệp hội Thị trường Trái phiếu Việt Nam (VBMA), cho biết thị trường tín chỉ carbon tự nguyện Việt Nam đã có một số đơn vị bán được tín chỉ carbon cho nhà đầu tư, tập đoàn nước ngoài. "Họ đang mua chứng chỉ carbon và chứng chỉ năng lượng tái tạo (REC) trong tiêu chí RE100, tức 100% năng lượng tái tạo", ông cho biết. RE100 là sáng kiến toàn cầu dành cho các công ty cam kết sử dụng 100% điện năng tái tạo, phát động bởi Climate Group và CDP vào 2014. Từ trái sang, Marco Gaspari, Điều phối viên Ngành Môi trường tại Cơ quan Hợp tác Phát triển Italy (AICS Hà Nội) và ông Đỗ Ngọc Quỳnh, Tổng Thư ký Hiệp hội Thị trường Trái phiếu Việt Nam (VBMA) nói tại tọa đàm. Ảnh: GEFE 2024 Marco Gaspari, Điều phối viên Ngành Môi trường tại Cơ quan Hợp tác Phát triển Italy (AICS Hà Nội) và ông Đỗ Ngọc Quỳnh, Tổng Thư ký Hiệp hội Thị trường Trái phiếu Việt Nam (VBMA) chia sẻ tại tọa đàm. Ảnh: GEFE 2024 Thị trường carbon gồm hai hình thức là bắt buộc và tự nguyện. Đồ họa: Dỹ Tùng Phân biệt các loại thị trường carbon. Đồ họa: Dỹ Tùng Theo kế hoạch của chính phủ, thị trường bắt buộc sẽ vận hành thử nghiệm vào giai đoạn 2025-2028. Với thị trường tự nguyện, ông Quỳnh cho biết đã bắt đầu hình thành và cũng biến động theo diễn biến xu hướng chung toàn cầu. Chuyên gia VBMA cho rằng Việt Nam đã có chính sách chung để thực hiện cam kết Net Zero vào 2050, nhưng vẫn chưa có pháp lý đầy đủ và rõ ràng cho thị trường carbon tự nguyện. "Những người bán tại Việt Nam sau giao dịch không biết hạch toán vào đâu, nộp thuế thế nào. Một số chọn phương án tính vào thu nhập bất thường để khai thuế", ông ví dụ. Ông Nguyễn Thành Nghiệp, Luật sư thành viên công ty luật VTN và Cộng sự chỉ ra việc chưa có quy định xác định tính chất tài sản của tín chỉ carbon. "Chúng có được xem là tài sản bình thường, được thế chấp hay giao dịch thế nào chưa có đủ căn cứ pháp lý", ông nói. Ngoài ra, quy trình MRV (đo lường, báo cáo và kiểm chứng) cũng cần quy định, hướng dẫn rõ. Theo ông, ngoài các cơ quan quản lý, khu vực tư nhân cũng trông chờ xem liệu có thể tham gia hoạt động MRV không. "Trong thời gian tới, nếu hoàn thiện pháp lý, thị trường sẽ có nhiều tiềm năng phát triển hơn", ông Đỗ Ngọc Quỳnh dự báo. Ngoài tín chỉ carbon, với tiềm năng điện tái tạo thứ tư thế giới theo McKenzie, ông cho rằng có thể khai thác việc vừa bán tín chỉ carbon vừa bán được REC. Theo VBMA, quy mô thị trường carbon bắt buộc toàn cầu đạt 104 tỷ USD năm ngoái, tăng 100% so với năm 2020. Trong khi, thị trường tự nguyện đã thu hẹp còn 800 triệu USD, giảm hai phần ba so với 2021 do một số vụ bê bối liên quan đến "giặt xanh" (green washing) làm ảnh hưởng đến uy tín, niềm tin. Theo dõi biến động của thị trường thế giới giúp các bên tham gia trong thị trường carbon tự nguyện còn sơ khai của Việt Nam rút kinh nghiệm và tìm ra hướng đi. Marco Gaspari, Điều phối viên Ngành Môi trường tại Cơ quan Hợp tác Phát triển Italy (AICS) văn phòng Hà Nội, dự báo người mua sẽ cần tìm kiếm các bên bán tín chỉ có hệ thống quản trị tốt và rõ ràng. Ông cho rằng người mua đang thiên về chuộng mua tín chỉ lĩnh vực giảm phát thải sản xuất vì dễ chứng minh. Một loại được quan tâm khác là "carbon xanh dương" (blue carbon) - tín chỉ tạo ra từ các dự án hấp thụ carbon của rừng ngập mặn, đầm lầy bãi triều và cỏ biển. Ông chỉ ra Việt Nam triển vọng với 200.000 ha rừng ngập mặn, có thể làm các dự án carbon tương tự như ở Honduras. Bà Thu Nguyễn, Quản lý chính sách tại Apanada Management Consultancy, Đại diện Viện Tài nguyên Thế giới (WRI) khuyến nghị các dự án tín chỉ carbon nâng cao giá trị bằng cách quan tâm đến tính bình đẳng và bao trùm. Theo đó, mục tiêu không chỉ là giảm phát thải mà còn là cải thiện đời sống người dân và phát triển bình đẳng hơn "Dự án cần bảo đảm có tham vấn của cộng đồng, đặc biệt là phụ nữ và các nhóm yếu thế, để tạo ra lợi ích cho cả cộng đồng lẫn nhà đầu tư", bà nói." Đoạn 2: "Giá nhẫn trơn liên tục điều chỉnh, tăng gần một triệu đồng trong ngày và có nơi lên sát 89 triệu đồng một lượng. 15h ngày 23/10, giá mua bán nhẫn trơn được các thương hiệu kinh doanh điều chỉnh theo diễn biến đi lên của thế giới. Chiều nay, mỗi ounce vàng quốc tế tiếp tục thiết lập kỷ lục mới 2.755 USD. Giá nhẫn trơn tại Công ty Vàng bạc đá quý Sài Gòn (SJC) cũng tăng nửa triệu đồng so với đầu sáng và gần 1 triệu đồng so với cuối ngày hôm qua, lên 86,9 - 88,2 triệu đồng. Công ty Vàng bạc đá quý Phú Nhuận (PNJ) và Mi Hồng niêm yết giá nhẫn trơn quanh vùng 87,4 - 88,4 triệu đồng. Còn tại Tập đoàn Vàng bạc đá quý DOJI, giá mua bán nhẫn trơn cùng thời điểm thậm chí lên 88 - 88,9 triệu đồng một lượng. Trước đó đầu ngày, Công ty Vàng bạc đá quý Sài Gòn (SJC) đã tăng 300.000 đồng một lượng so với cuối ngày hôm qua, niêm yết giá nhẫn trơn tại 86,3 - 87,6 triệu đồng. Biểu giá mua bán nhẫn trơn tại Tập đoàn Vàng bạc đá quý DOJI lúc 9h sáng là 87 - 88 triệu đồng, tăng 200.000 đồng so với cuối ngày hôm qua. Nhẫn trơn giữ nhịp tăng liên tục trong 10 ngày qua. So với giữa tháng, mỗi lượng nhẫn trơn đã tăng hơn 5 triệu đồng. Còn so với đầu năm, nhẫn trơn tăng gần 25 triệu một lượng, tương đương hiệu suất 39%. Trong khi giá vàng miếng SJC đứng yên ở vùng 87 - 89 triệu một lượng, do Ngân hàng Nhà nước chưa thay đổi giá bán can thiệp. Thời điểm này là mùa cưới cuối năm và nhu cầu mua vàng nhẫn làm quà cưới tăng, song người dân không dễ để mua được mặt hàng này tại các thương hiệu lớn. Các thương hiệu lớn như DOJI, PNJ, Bảo Tín Minh Châu thường xuyên trong tình trạng cháy hàng. Khách lẻ chỉ may mắn mua được số lượng ít nếu cửa hàng vừa có khách bán ra. Còn tại SJC, các chi nhánh giới hạn lượng mua tối đa 5 phân đến 1 chỉ mỗi người. Trên thị trường quốc tế, mỗi ounce vàng trong 5 ngày qua tăng mạnh hơn 100 USD. Kim loại quý có thời điểm lên mức kỷ lục gần 2.750 USD, trước khi lùi về vùng 2.738 USD vào sáng nay. Quy đổi theo tỷ giá bán Vietcombank, giá vàng trong nước chênh lệch 3,5-5 triệu đồng một lượng so với thế giới. Theo dự báo của các nhà băng hàng đầu thế giới, giá vàng thế giới có thể lên 3.000 USD một ounce vào năm sau. Các chuyên gia khuyến nghị nhà đầu tư phân bổ tỷ trọng nhỏ danh mục vào kênh trú ẩn này, đặc biệt trong bối cảnh kim loại quý đã tăng mạnh thời gian qua." Đoạn 3: "Nhu cầu trú ẩn khi căng thẳng địa chính trị leo thang kéo giá vàng lên mức đỉnh mới, tại 2.748 USD một ounce. Chốt phiên giao dịch 22/10, giá vàng thế giới giao ngay tăng gần 30 USD lên 2.748 USD một ounce. Đây là mức cao kỷ lục mới của kim loại quý. "Căng thẳng địa chính trị vẫn là nguyên nhân chủ yếu. Hai tuần nữa sẽ diễn ra bầu cử Tổng thống Mỹ và cuộc đua vẫn rất sát sao. Bất ổn chính trị đang kéo nhu cầu trú ẩn lên cao", Peter A. Grant - Phó giám đốc Zaner Metals nhận định trên Reuters. Giá vàng thế giới đảo chiều tăng mạnh trong phiên 22/10. Đồ thị: Kitco Giá vàng thế giới đảo chiều tăng mạnh trong phiên 22/10. Đồ thị: Kitco Cuộc thăm dò mới nhất của Reuters/Ipsos cho thấy tỷ lệ ủng hộ Phó tổng thống Kamala Harris hiện là 46%, nhỉnh hơn so với 43% của cựu Tổng thống Donald Trump. "Sự sát sao này đang tạo nên tình trạng thiếu chắc chắn. Môi trường này có lợi cho vàng", các nhà phân tích tại ngân hàng BNP Paribas nhận định. Grant dự báo nếu căng thẳng tại Trung Đông tiếp tục tăng nhiệt, giá có thể lên 3.000 USD cuối năm nay. Từ đầu năm, giá đã tăng 33% và liên tiếp lập đỉnh mới. Một yếu tố khác đang hỗ trợ kim loại quý là làn sóng giảm lãi suất của các ngân hàng trung ương lớn trên toàn cầu. Mỹ, châu Âu, Trung Quốc cùng hàng loạt nền kinh tế khác đã giảm lãi suất năm nay để hỗ trợ nền kinh tế. Trong khi đó, tại Wall Street, các chỉ số chính gần như đứng yên. Nhà đầu tư hiện theo dõi lợi suất trái phiếu chính phủ Mỹ và chờ đánh giá thêm báo cáo tài chính của các doanh nghiệp. Ngoài vàng, các kim loại quý khác cũng tăng giá. Bạc lập đỉnh 12 năm, khi tăng 3,2% lên gần 35 USD một ounce. Han Tan - chiến lược gia thị trường tại Exinity Group dự báo bạc vượt mốc 35 USD trước khi cuộc bầu cử diễn ra. Bạch kim đắt thêm 2,8% lên 1.031 USD một ounce. Palladium tăng 2,9% lên 1.081 USD." '''}, {"role": "user", "content": '''giá nhẫn trơn hôm nay là bao nhiêu?'''}] tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") outputs = model.generate(tokenized_chat, max_new_tokens=128) print(tokenizer.decode(outputs[0])) # Giá nhẫn trơn hôm nay là 86,9 - 88,2 triệu đồng. ``` ***You can customize the prompt before the answer to get a response that suits your needs.*** ***You can also add information about this bot's persona in the system prompt.*** <h4> 3. Function Calling task </h4> ***In this task, we are following the Function Calling template from Glaive AI: [glaiveai/glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2).*** ```python messages = [ {"role": "system", "content": '''Bạn là một trợ lý hữu ích với khả năng truy cập vào các hàm sau. Hãy sử dụng chúng nếu cần - { "name": "weather_forecast", "description": "Cung cấp cập nhật và dự báo thời tiết cho các địa điểm cụ thể, bao gồm nhiệt độ, độ ẩm và tình trạng thời tiết. Ví dụ: thời tiết hôm nay, dự báo thời tiết ở Hà Nội, nhiệt độ tại Đà Nẵng, v.v.", "parameters": { "properties": { "__arg1": { "description": "__arg1", "type": "string" } }, "required": [ "__arg1" ], "type": "object" } }, { "name": "news_update", "description": "Cung cấp các bài báo và cập nhật tin tức mới nhất trên nhiều lĩnh vực như chính trị, công nghệ, thể thao và giải trí. Ví dụ: tin tức hôm nay, cập nhật thể thao, tin công nghệ mới nhất, v.v.", "parameters": { "properties": { "__arg1": { "description": "__arg1", "type": "string" } }, "required": [ "__arg1" ], "type": "object" } }, { "name": "recipe_search", "description": "Tìm kiếm và gợi ý công thức nấu ăn dựa trên nguyên liệu hoặc sở thích dinh dưỡng. Ví dụ: công thức món ăn với gà, món chay, ăn kiêng, v.v.", "parameters": { "properties": { "__arg1": { "description": "__arg1", "type": "string" } }, "required": [ "__arg1" ], "type": "object" } }, { "name": "movie_recommendation", "description": "Cung cấp gợi ý phim dựa trên thể loại, tâm trạng hoặc tiêu đề cụ thể. Ví dụ: phim hài hay, phim hành động mới, gợi ý phim cho tối nay, v.v.", "parameters": { "properties": { "__arg1": { "description": "__arg1", "type": "string" } }, "required": [ "__arg1" ], "type": "object" } }, { "name": "fitness_advice", "description": "Cung cấp mẹo và bài tập cho sức khỏe và thể dục dựa trên mục tiêu của người dùng. Ví dụ: bài tập giảm cân, lịch tập gym cho người mới, lời khuyên về dinh dưỡng, v.v.", "parameters": { "properties": { "__arg1": { "description": "__arg1", "type": "string" } }, "required": [ "__arg1" ], "type": "object" } }, { "name": "travel_planner", "description": "Hỗ trợ lập kế hoạch du lịch, bao gồm gợi ý lịch trình, mẹo đặt vé và thông tin về điểm đến. Ví dụ: lên kế hoạch du lịch Đà Nẵng, địa điểm tham quan ở Nha Trang, mẹo du lịch Nhật Bản, v.v.", "parameters": { "properties": { "__arg1": { "description": "__arg1", "type": "string" } }, "required": [ "__arg1" ], "type": "object" } }'''}, {"role": "user", "content": "hôm nay có tin tức gì về ô nhiễm không khí"}] tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") outputs = model.generate(tokenized_chat, max_new_tokens=128) print(tokenizer.decode(outputs[0])) # <functioncall> {"name": "news_update", "arguments": {}} ``` <h4> 4. Question Rewriting task </h4> ```python messages = [ {"role": "system", "content": '''Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực. Hãy luôn trả lời một cách hữu ích nhất có thể, đồng thời giữ an toàn.\nNếu một câu hỏi không có ý nghĩa hoặc không hợp lý về mặt thông tin, hãy giải thích tại sao thay vì trả lời một điều gì đó không chính xác, vui lòng không chia sẻ thông tin sai lệch. Context: USER: Concert "Anh trai vượt ngàn chông gai" diễn ra ở đâu? ASSISTANT: Đêm concert Anh trai vượt ngàn chông gai diễn ra vào tối 19-10, tại công viên bờ sông Sài Gòn, TP.HCM với sự tham dự của khoảng 20.000 khán giả. USER: Buổi biểu diễn có bao nhiêu nghệ sĩ? ASSISTANT: Danh sách nghệ sĩ gồm 17 nghệ sĩ: Kay Trần, Jun Phạm, Cường Seven, Tự Long, (S)TRONG Trọng Hiếu, S.T Sơn Thạch, BB Trần, Bùi Công Nam, Rhymastic, Soobin Hoàng Sơn, Binz, Quốc Thiên, Thanh Duy, Bằng Kiều, Tiến Luật, Đinh Tiến Đạt và Đỗ Hoàng Hiệp. USER: Chương trình phát sóng khi nào và ở đâu? ASSISTANT: chương trình phát sóng từ ngày 29 tháng 6 năm 2024 lúc 20:00 thứ 7 hàng tuần trên VTV3 và công chiếu lúc 20:30 cùng ngày trên kênh YouTube YeaH1 Show của nhà sản xuất chương trình.'''}, {"role": "user", "content": '''Dựa vào đoạn hội thoại được cung cấp, viết lại câu nói của người dùng sao cho đầu đủ ý nhất có thể mà không bị sai lệch thông tin. Câu nói: Concert này có tổ chức ở Hà Nội không? '''}] tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") outputs = model.generate(tokenized_chat, max_new_tokens=512) print(tokenizer.decode(outputs[0])) # Concert "Anh trai vượt ngàn chông gai" có tổ chức ở Hà Nội không? ``` ***Modify the parameters "temperature", "top_k", "top_p" to suit your usecase.*** Corresponding Author: + [email protected]
[ "SUMMARIZATION" ]
[ "CHIA" ]
Non_BioNLP
simonosgoode/nomic_embed_fine_tune_law_v3
simonosgoode
sentence-similarity
[ "sentence-transformers", "safetensors", "nomic_bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:12750", "loss:MultipleNegativesRankingLoss", "custom_code", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:nomic-ai/nomic-embed-text-v1.5", "base_model:finetune:nomic-ai/nomic-embed-text-v1.5", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,731
1,731
9
0
--- base_model: nomic-ai/nomic-embed-text-v1.5 library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:12750 - loss:MultipleNegativesRankingLoss widget: - source_sentence: 'cluster: SUMMARY: Eli Lilly Canada Inc. v. Novopharm Limited Court (s) Database Federal Court Decisions Date 2007-10-31 Neutral citation 2007 FC 1126 File numbers T-1048-07 Decision Content Date: 20071031 Docket: T-1048-07 Citation: 2007 FC 1126 Toronto, Ontario, October 31, 2007 PRESENT: The Honourable Justice Johanne Gauthier BETWEEN: ELI LILLY CANADA INC., ELI LILLY AND COMPANY, ELI LILLY COMPANY LIMITED and ELI LILLY SA Plaintiffs (Defendants by Counterclaim) and NOVOPHARM LIMITED Defendant (Plaintiff by Counterclaim) REASONS FOR ORDER AND ORDER [1] Novopharm appeals the Order of Prothonotary Tabib dated September 25, 2007 granting the plaintiffs’ motion for bifurcation of the issues of quantum from those of validity and infringement of the patent in suit pursuant to Rule 107 of the Federal Courts Rules, 1998, SOR/98-106. It is to be noted that Prothonotary Tabib is the Case Manager in this matter. [2] All the principles applicable to this appeal are well known. As the matter before Prothonotary Tabib did not involve a question vital to the final issue of the case, the Court should not intervene on appeal unless her decision was clearly wrong, “in the sense that the exercise of discretion was based upon a wrong principle or a misapprehension of the facts” (Z.I. Pompey Industrie v. ECU-Line N.V. [2003], 1 S.C.R. 450 at para. 461), Merck and Co. v. Apotex Inc. [2003], 30 C.P.R (4th) 40 (FCA); [2003] F.C.J. No. 1925 at para. 19). The principles or conditions for the making of an order pursuant to Rule 107 are also well established (see for example Apotex Inc. v. Bristol-Myers Squibb Co. 2003 F.C.A. 263, (2003) 26 C.P.R. (4th) 129 at para. 7); Illva Saronno S.p.A. v. Privilegiata Fabbrica Maraschino "Excelsior" (T.D.), [1998] F.C.J. No. 1500; Illva Saronno S.p.A. v. Privilegiata Fabbrica Maraschino, [2000] F.C.J. No. 170 at para 8; Merck & Co. et al. v. Brantford Chemicals Inc. [2004] F.C.J. No. 1704, 35 C.P.R. (4th) 4, aff’d [2005] F.C.J. No. 837, 39 C.P.R (4th) 524 (F.C.A.); Apotex Inc. v. Merck & Co. [2004] F.C.J. No. 1372 at para. 3). It is trite law that the applicant bears the burden of convincing the Court on a balance of probabilities that in light of the evidence and all of the circumstances of the case (including the nature of the claims, the conduct of the litigation, the issues and remedies sought), bifurcation or severance is more likely than not to result in the just, expeditious and least expensive determination of the proceeding on its merits. [3] That being said, having carefully considered all the arguments put forth by the parties on this appeal, the Court is not persuaded that Prothonotary Tabib made any error that warrants the Court’s intervention. [4] As mentioned at the hearing, given that time is of the essence here, the Court will not comment on each and every issue raised by Novopharm (such issues are summarised at paragraph 2 of the written representations). However, considering the importance given to the following issues at the hearing, it is worth noting specifically that the Court cannot agree with Novopharm that Prothonotary Tabib implicitly applied or assumed the existence of a presumption in favour of bifurcation in patent infringement cases, which had the effect of actually reversing the burden of proof so as to place it on the shoulders of Novopharm. There was evidence before Prothonotary Tabib dealing with bifurcation of quantum issues in cases involving patent infringement in the last fifteen years (such as the affidavits of Nancy Gallinger and of Alisha Meredith). Prothonotary Tabib expressly refers to Apotex Inc. v. Bristol-Myers Squibb Co. above; in that case, the Federal Court of Appeal agreed that “when an experienced specialist bar like the intellectual property bar commonly consents to the making of a bifurcation order, it is open to a judge to infer that, in general, such an order may well advance the just and expeditious resolution of claims”. [5] It is also absolutely clear from the decision that this was only one of many factors Prothonotary Tabib considered before making her order. Among many other things, she was satisfied based on the evidence before her, the pleadings, her knowledge of the history of the proceeding and the issues it involved, that not only would bifurcation likely have the advantage of speeding up the determination of the liability issues (which at this stage also involve novel questions of law particularly in respect of the section 8 counterclaim), but that bifurcation would also more likely than not avoid at least one side of the quantification exercise whatever the result of the trial on liability issues. (page 4 last sentence and page 6 and 7) [6] Evidently, the Prothonotary was satisfied that she did not require more specific evidence in respect of the number of days of discoveries or an exact quantification of the time and expenses that would be saved in order to determine whether this would necessarily result in a saving of time and money for the Court and the parties. [7] Novopharm says that this constitute an error of law as Prothonotary Tabib failed to heed the evidentiary requirements set out by the Federal Court of Appeal in Realsearch Inc. v. Valon Kone Brunette, 31 C.P.R. (4th) 101 (F.C.A.), [2004] 2 F.C.R. 514. [8] Like Prothonotary Tabib, the Court does not believe that Realsearch establishes a new condition or standard for the making of an order under Rule 107. As any party who has a burden of proof to meet, the applicant seeking such an order must provide sufficient evidence to enable the Court to come to a conclusion on the matter before it. The fact that there was no evidence dealing with the specific saving of time and money that would result from the bifurcation in the case before the Court in Realsearch was worth noting and was particularly significant because the bifurcation sought in that case was in respect of a question of law (claims construction). Such request was an unusual and a somewhat novel use of bifurcation pursuant to Rule 107. In such a case, the Court could not rely on experience or on an inference based on a consistent practice in respect of the bifurcation of quantum issues in similar cases or on knowledge acquired while case managing the matter. The situation is quite different here. [9] It is clear from her order that Prothonotary Tabib knew perfectly well that the applicant had to satisfy her on a balance of probabilities. She was fully aware of all the arguments raised by Novopharm in respect of the quality (or rather lack thereof) of the evidence before her. Still, she concluded on page 9 that on the whole, she was satisfied that she could reach a conclusion that severance is more likely than not to result in the just, expeditious and least expensive determination of the proceeding on its merits. [10] In fact, even if Novopharm had convinced that the Court that it should exercise its discretion de novo, the Court would ultimately have reached the same conclusion as Prothonotary Tabib. ORDER THIS COURT ORDERS that: The appeal is dismissed with costs. “Johanne Gauthier” Judge FEDERAL COURT SOLICITORS OF RECORD DOCKET: T-1048-07 STYLE OF CAUSE: ELI LILLY CANADA INC. ET AL Plaintiffs and NOVOPHARM LIMITED Defendant PLACE OF HEARING: TORONTO, ONTARIO DATE OF HEARING: 29-OCT-2007 REASONS FOR : Gauthier, J. DATED: 31-OCT-2007 APPEARANCES: MR. ANTHONY G. CREBER FOR THE PLAINTIFFS MR. JONATHAN STAINSBY MR. ANDY RADHAKANT MR. NEIL FINEBERG FOR THE DEFENDANT SOLICITORS OF RECORD: GOWLING LAFLEUR HENDERSON LLP Barristers & Solicitors Ottawa, Ontario FOR THE PLAINTIFFS HEENAN BLAIKIE LLP Lawyers Toronto, Ontario FOR THE DEFENDANT ' sentences: - 'cluster: ANALYSIS: In analyzing the issue of whether the applicants were denied the right to counsel, the court carefully reviewed the transcript of the hearing and found that it did not support the applicants'' allegations. The court noted that the applicants had been informed that their original lawyer, Philip U. Okpala, would not be attending the hearing and that they had been given the opportunity to request an adjournment, which was denied. The court also found that the Board Member had not pressured the applicants to proceed without counsel, but rather had given them the opportunity to decide whether to proceed with or without counsel. In analyzing the issue of whether the Board made capricious findings of fact, the court determined that the Board''s conclusion that the police were unable to locate the perpetrator of the ticket incident and that the principal claimant did not pursue the matter further was reasonable and not made arbitrarily or irrationally.' - 'cluster: SUMMARY: **(1) Facts** The case before the court involves a patent dispute between Eli Lilly Canada Inc. and Novopharm Limited. Eli Lilly Canada Inc. had sought a motion to bifurcate the issues of quantum from those of validity and infringement of the patent in suit. Prothonotary Tabib granted the motion, and Novopharm Limited appealed the decision. The parties involved in the case had been litigating for some time, and the court was considering the appropriateness of bifurcation to speed up the determination of the liability issues and to avoid quantification exercises. **(2) Issue** The issue before the court was whether Prothonotary Tabib erred in granting the motion to bifurcate the issues of quantum from those of validity and infringement of the patent in suit. Novopharm Limited argued that Prothonotary Tabib had made an error of law in granting the motion without sufficient evidence to support the decision. Specifically, Novopharm Limited argued that Prothonotary Tabib had failed to heed the evidentiary requirements set out by the Federal Court of Appeal in Realsearch Inc. v. Valon Kone Brunette. **(3) Rule** The court applied the principles established in previous cases, including Z.I. Pompey Industrie v. ECU-Line N.V. and Merck and Co. v. Apotex Inc. The court held that the applicant bears the burden of convincing the court on a balance of probabilities that bifurcation or severance is more likely than not to result in the just, expeditious, and least expensive determination of the proceeding on its merits. **(4) Analysis** The court analyzed the decision of Prothonotary Tabib and found that she had considered multiple factors before granting the motion to bifurcate. The court noted that Prothonotary Tabib had considered the evidence before her, the pleadings, her knowledge of the history of the proceeding, and the issues it involved. The court also found that Prothonotary Tabib had not implicitly applied or assumed the existence of a presumption in favor of bifurcation in patent infringement cases. The court concluded that Prothonotary Tabib had not made an error of law in granting the motion to bifurcate. **(5) Conclusion** The court dismissed Novopharm Limited''s appeal, finding that Prothonotary Tabib had not erred in granting the motion to bifurcate the issues of quantum from those of validity and infringement of the patent in suit. The court held that Prothonotary Tabib had considered the necessary factors and had not made an error of law in granting the motion. The court also noted that even if it had exercised its discretion de novo, it would have reached the same conclusion as Prothonotary Tabib.' - 'cluster: FACTS: The case before the court involves a patent dispute between Eli Lilly Canada Inc. and Novopharm Limited. Eli Lilly Canada Inc. had sought a motion to bifurcate the issues of quantum from those of validity and infringement of the patent in suit. Prothonotary Tabib granted the motion, and Novopharm Limited appealed the decision. The parties involved in the case had been litigating for some time, and the court was considering the appropriateness of bifurcation to speed up the determination of the liability issues and to avoid quantification exercises.' - source_sentence: 'cluster: SUMMARY: Mennes v. McClung Court (s) Database Federal Court Decisions Date 2001-12-07 Neutral citation 2001 FCT 1349 File numbers T-2351-00 Notes Digest Decision Content Date: 20011207 Docket: T-2351-00 Neutral Citation: 2001 FCT 1349 BETWEEN: EMILE MENNES Applicant and LUCIE McCLUNG, OLE INGSTRUP, MICHEL ROY, KAREN WISEMAN, LIZ ESHKROD, THE COMMISSIONER OF CORRECTIONS, THE CORECTIONAL SERVICE OF CANADA, THE NATIONAL LIBRARY OF CANADA, THE MINISTER OF NATIONAL HEALTH AND WELFARE Respondents REASONS FOR ORDER AND ORDER BLAIS J. [1] This is an application for judicial review of the decision rendered by the Acting Assistant Commissioner Karen J. Wiseman, Correctional Service of Canada ("CSC") of Inmate Grievance Presentation ("Third level"), Reference No. V4000A004355 under subsection 4(g) and sections 90 and 91 of the Corrections and Conditional Release Act (the "Act"). FACTS [2] The applicant is an inmate at Warkworth Institution in Campbellford, Ontario. [3] The applicant has been working as a grievance clerk for Warkworth at the office of the Institutional Grievance Co-ordinator for approximately two and a half (2½) years. [4] The applicant is aware of the policy and the procedure governing the grievance process at Warkworth Institution. [5] On February 18, 2000, the applicant began a group complaint with fellow inmate Helmut Buxbaum. [6] The complaint was entered in regards to the state of some of the laundered bed linens that had been returned to the applicant. [7] The applicant has described the condition of the bed linens to be "absolutely disgusting", with "nose pickings baked along one edge", "permanently stained with urine and other bodily emissions" (see page 6, paragraph 12 of the affidavit of Emile Mennes, applicant''s record). [8] The applicant''s original complaint was denied and consequently, the applicant appealed the decision through the First level grievance (Institutional Warden), the Second level grievance (Regional) and subsequently the Third (and final) level grievance (National). The applicant''s complaint was denied at each level. [9] In regards to the content of the applicant''s Third level grievance, the applicant sought to be issued new bed linens and in addition, he requested that the unit laundry room be converted into a full scale laundromat so that the inmates at Warkworth could have the privilege of laundering their own bed linens. [10] On September 8, 2000, the Acting Assistant Commissioner Karen J. Wiseman denied the applicant''s Third level appeal with the provision of reasons on both grounds. PERTINENT LEGISLATION 3. The purpose of the federal correctional system is to contribute to the maintenance of a just, peaceful and safe society by (a) carrying out sentences imposed by courts through the safe and humane custody and supervision of offenders; and (b) assisting the rehabilitation of offenders and their reintegration into the community as law-abiding citizens through the provision of programs in penitentiaries and in the community. 3. Le système correctionnel vise à contribuer au maintien d''une société juste, vivant en paix et en sécurité, d''une part, en assurant l''exécution des peines par des mesures de garde et de surveillance sécuritaires et humaines, et d''autre part, en aidant au moyen de programmes appropriés dans les pénitenciers ou dans la collectivité, à la réadaptation des délinquants et à leur réinsertion sociale à titre de citoyens respectueux des lois. 4. The principles that shall guide the Service in achieving the purpose referred to in section 3 are [...] (g) that correctional decisions be made in a forthright and fair manner, with access by the offender to an effective grievance procedure; 4. Le Service est guidé, dans l''exécution de ce mandat, par les principes qui suivent : [...] (g) ses décisions doivent être claires et équitables, les délinquants ayant accès à des mécanismes efficaces de règlement de griefs; 90. There shall be a procedure for fairly and expeditiously resolving offenders'' grievances on matters within the jurisdiction of the Commissioner, and the procedure shall operate in accordance with the regulations made under paragraph 96(u). 90. Est établie, conformément aux règlements d''application de l''alinéa 96u), une procédure de règlement juste et expéditif des griefs des délinquants sur des questions relevant du commissaire. 91. Every offender shall have complete access to the offender grievance procedure without negative consequences. 91. Tout délinquant doit, sans crainte de représailles, avoir libre accès à la procédure de règlement des griefs. ISSUES [11] 1. Did CSC make a reviewable error in denying the applicant''s Third level appeal? 2. Is the determination of the outcome of the applicant''s grievance at the Third level grievance appropriately delegated by the Commissioner of Corrections to the Acting Assistant Commissioner Karen J. Wiseman? ANALYSIS 1. Did CSC make a reviewable error in denying the applicant''s Third level appeal? [12] No, the CSC did not make a reviewable error in denying the applicant''s Third level appeal. STANDARD OF REVIEW [13] In Tehrankari v. Canada (Correctional Service), [2000] F.C.J. 495, Lemieux J. recently defined the applicable standard of review of a decision by the Federal Commissioner of the Correctional Service when applying the grievance procedure contained in Section 90 of the Act. He held: [para 33] A word needs to be said about the standard of review applicable in this case keeping in mind the type of decision made and the decision-maker (see Baker v. Canada (Minister of Citizenship and Immigration), [1999] 2 S.C.R. 817. In Baker, supra, L''Heureux-Dubé J. pointed out it was held in Pushpanathan v. Canada (Minister of Citizenship and Immigration), [1998] 1 S.C.R. 982, a decision which related to the determination of a question of law in that case, (the interpretation of the exclusion provisions in section 2 of the Immigration Act as they relate to the definition of Convention refugee) made by the Immigration and Refugee Board, was subject to a standard of review of correctness but on other questions, the standard of review varied. [...] [para 44] To conclude on this point, I would apply a correctness standard if the question involved is the proper interpretation of section 24 of the Act; however, I would apply the standard of reasonableness simpliciter if the question involved is either the application of proper legal principles to the facts or whether the refusal decision to correct information on the offender''s file was proper. The patently unreasonable standard applies to pure findings of fact. (Subsection 18.2(4) of the Federal Court Act, R.S.C. 1985, c. F-7.) [14] The decision to deny the applicant''s Third level appeal was based on pure findings of fact and therefore, the appropriate standard of review is patently unreasonable. [15] The applicant''s arguments presented on his Third level appeal were similar to the ones previously put forward at the earlier levels with the additional argument of a laundromat to be accessible to the inmates at Warkworth. [16] Subsection 82(a) of the Regulations applies on an appeal of a complaint or a grievance. The provision reads as follows: 82. In reviewing an offender''s complaint or grievance, the person reviewing the complaint or grievance shall take into consideration (a) any efforts made by staff members and the offender to resolve the complaint or grievance, and any recommendations resulting therefrom; [...] 82. Lors de l''examen de la plainte ou du grief, la personne chargée de cet examen doit tenir compte : (a) des mesures prises par les agents et le délinquant pour régler la question sur laquelle porte la plainte ou le grief et des recommandations en découlant; [17] In the present case, the "efforts made by staff members" resulted in an exchange of the soiled bed linens the applicant complained of in his grievance, yet he remains to date dissatisfied with his bed linens, pillow and mattress, but there is nothing that justify the Court to intervene. 2. Is the determination of the outcome of the applicant''s grievance appropriately delegated by the Commissioner of Corrections to the Acting Assistant Commissioner Karen J. Wiseman? [18] Yes, the determination of the outcome of the applicant''s grievance was appropriately delegated by the Commissioner of Corrections to the Acting Assistant Commissioner Karen J. Wiseman. [19] The decision of the applicant''s Third level grievance was rendered by the Acting Assistant Commissioner Karen J. Wiseman. The applicant claims that subsections 80(2) and 80(3) of the Regulations state that the Commissioner of Corrections, Ms. Lucie McClung, should have been the one to hear his appeal and not the Acting Assistant Commissioner Karen J. Wiseman. The applicant relies upon subsections 80(2) and 80(3) of the Regulations and the long established rule of delegatus non potest delegare as a principle of interpretation or statutory construction. [20] However, according to the respondent, at each level of the applicant''s grievance process, his complaint was reviewed by the appropriate party designated under the Act and the Regulations. Sections 75-82 of the Regulations provide for the grievance process and there is clearly no requirement under the Act or the Regulations for the Commissioner of Corrections, to individually or directly review complaints at the Third level appeal or at any other level. In addition, it would be impractical for the Commissioner of Corrections to have to review all the grievances made by every inmate in the country, at each level of appeal. [21] The resolution to this issue is found in several sources: section 97 of the Act, section 98 of the Act, Commissioner''s Directive Number 081 dated June 22, 1998 entitled Offender Complaints and Grievances (CD 081), the inclusion printed at the bottom of the decision of the Commissioner (Third level grievance - National) and lastly subsection 2(2) of the Act. They will be treated below in this order. It is the interaction between these multiple sources that allowed for the delegation of authority to the Acting Assistant Commissioner Karen J. Wiseman by the Commissioner to pronounce on the final stage of the grievance process. [22] Section 97 of the Act pertains to the Commissioner having the authority to issue Rules: 97. Subject to this Part and the regulations, the Commissioner may make rules (a) for the management of the Service; (b) for the matters described in section 4; and (c) generally for carrying out the purposes and provisions of this Part and the regulations. 97. Sous réserve de la présente partie et de ses règlements, le commissaire peut établir des règles concernant : a) la gestion du Service; b) les questions énumérées à l''article 4; c) toute autre mesure d''application de cette partie et des règlements. [23] Section 98 of the Act allows for the creation of Commissioner''s Directives: 98. (1) The Commissioner may designate as Commissioner''s Directives any or all rules made under section 97. (2) The Commissioner''s Directives shall be accessible to offenders, staff members and the public. 98. (1) Les règles établies en application de l''article 97 peuvent faire l''objet de directives du commissaire. (2) Les directives doivent être accessibles et peuvent être consultées par les délinquants, les agents et le public. [24] Commissioner''s Directive entitled Offender Complaints and Grievances (CD 081) reads at paragraphs 19 and 20: 19. An offender, who is not satisfied with the decision from the Regional Deputy Commissioner, may submit a grievance to the Assistant Commissioner, Corporate Development, through the Institutional Grievance Coordinator or through the District Office. A grievance must normally be submitted within ten working days of receipt of the reply at the regional level. An offender may also grieve at this level in cases where action was not taken in accordance with the Regional Deputy Commissioner''s decision. 20. The decision of the Assistant Commissioner, Corporate Development constitutes the final stage of the Offender Complaints and Grievance process. 19. Le délinquant qui est insatisfait de la décision du sous-commissaire régional peut soumettre un grief au commissaire adjoint, Développement organisationnel, par l''entremise du coordonnateur des griefs de l''établissement ou du bureau de district. Le grief doit normalement être présenté dans les dix jours ouvrables suivant la réception de la réponse au niveau régional. Un délinquant peut aussi présenter un grief à ce niveau lorsque les mesures prescrites par le sous-commissaire régional n''ont pas été mises en application. 20. La décision du commissaire adjoint, Développement organisationnel, constitue l''étape finale du processus de règlement des plaintes et des griefs des délinquants. [25] The next source is the inclusion printed in the decision of the Commissioner (Third level grievance - National), found at the bottom of the page above the signature, and which reads as follows: The Commissioner of the Correctional Service of Canada has authorized the Assistant Commissioner, Corporate Development (ACCD), Michel Roy, to exercise the powers, duties, and functions given to him under Section 80(2) of the Corrections and Conditional Release Regulations, 1992. This authorization remains in effect until such time as it is withdrawn in writing. Accordingly, this decision by the ACCD is to be considered the conclusion of the inmate grievance system. Le Commissaire du Service correctionnel du Canada a autorisé le Commissaire adjoint, Développement organisationnel, Michel Roy, à exercer les pouvoirs et les fonctions qui lui sont conférés en vertu du paragraphe 80(2) du Règlement sur le système correctionnel et la mise en liberté sous condition (1992). Cette autorisation demeure en vigueur jusqu''à ce qu''elle soit révoquée par écrit. Par conséquent, la décision du Commissaire adjoint, Développement organisationnel constitue l''étape finale du processus de règlement des plaintes et griefs des détenus. [26] However, in order to answer the specific issue in question that being, can Assistant Commissioner Michel Roy delegate his authority to an Acting Assistant Commissioner for the purpose of rendering a final decision in the grievance process? The ultimate solution to this question is found in subsection 2(2) of the Act. The French version of this provision is more instructive than the English version and therefore has been reproduced first, followed by the English version: Délégation (2) Sauf dans les cas visés à l''alinéa 96b) et sous réserve de la présente partie, les pouvoirs et fonctions conférés par celle-ci au commissaire et au directeur du pénitencier sont, en cas d''absence, d''empêchement ou de vacance de leur poste, respectivement exercés par le suppléant ou par la personne qui est alors responsable du pénitencier. Exercise of powers, etc. (2) Except as otherwise provided by this Part or by regulations made under paragraph 96(b), (a) powers, duties and functions that this Part assigns to the Commissioner may only be exercised or performed by the Commissioner or, where Commissioner is absent or incapacitated or where the office is vacant, by the person acting in the place of the Commissioner; and (b) powers, duties and functions that this Part assigns to the institutional head may only be exercised or performed by the institutional head or, where institutional head is absent or incapacitated or where the office is vacant, by the person who, at the relevant time, is in charge of the penitentiary. [27] In summary, the Acting Assistant Commissioner Karen Wiseman held the proper authority by virtue of the aforementioned sources in rendering her final decision of the grievance process under subsections 80(2) and 80(3) of the Act. [28] It is my opinion that there is no reason warranting the intervention of this Court as the decision of the Acting Assistant Commissioner does not reveal any reviewable errors. [29] Therefore, this application for judicial review should be dismissed. Pierre Blais Judge OTTAWA, ONTARIO December 7, 2001 FEDERAL COURT OF CANADA TRIAL DIVISION NAMES OF COUNSEL AND SOLICITORS OF RECORD DOCKET: T-2351-00 STYLE OF CAUSE: Emile Mennes - and - Lucie McClung and others PLACE OF HEARING: Ottawa, Ontario DATE OF HEARING: November 28, 2001 REASONS FOR ORDER: the Honourable Mr. Justice Blais DATED:, December 7, 2001 APPEARANCES: Mr. Emile MennesFOR APPLICANT Ms. Sogie SabetaFOR RESPONDENT SOLICITORS OF RECORD: Mr. Emile Mennes FOR APPLICANT Campbellford, Ontario Morris Rosenberg FOR RESPONDENT Deputy Attorney General of Canada ' sentences: - 'cluster: SUMMARY: **(1) Facts** The person concerned, an inmate at Warkworth Institution in Campbellford, Ontario, has been working as a grievance clerk for approximately two and a half years. He initiated a group complaint with fellow inmate Helmut Buxbaum regarding the state of laundered bed linens, which he described as "absolutely disgusting" with stains and other bodily emissions. The complaint was denied at each level of the grievance process, including the Third level appeal, which was decided by the Acting Assistant Commissioner Karen J. Wiseman. The person concerned sought to have new bed linens issued and the unit laundry room converted into a full-scale laundromat, allowing inmates to launder their own bed linens. **(2) Issue** The issues before the court were: (1) whether the Correctional Service of Canada (CSC) made a reviewable error in denying the person concerned''s Third level appeal, and (2) whether the determination of the outcome of the person concerned''s grievance was appropriately delegated by the Commissioner of Corrections to the Acting Assistant Commissioner Karen J. Wiseman. **(3) Rule** The court applied the standard of review of patently unreasonable to the decision of the Acting Assistant Commissioner. The court held that the CSC did not make a reviewable error in denying the person concerned''s Third level appeal. The decision was based on pure findings of fact, and the CSC had considered the efforts made by staff members to resolve the complaint, including exchanging the soiled bed linens. The court also held that the determination of the outcome of the person concerned''s grievance was appropriately delegated by the Commissioner of Corrections to the Acting Assistant Commissioner. **(4) Analysis** The court analyzed the standard of review applicable to the decision of the Acting Assistant Commissioner, citing Tehrankari v. Canada (Correctional Service), [2000] F.C.J. 495, and Baker v. Canada (Minister of Citizenship and Immigration), [1999] 2 S.C.R. 817. The court applied the standard of patently unreasonable to the decision, as it was based on pure findings of fact. The court also examined the CSC''s decision-making process, citing subsection 82(a) of the Regulations, which requires consideration of efforts made by staff members to resolve the complaint. The court found that the CSC had considered these efforts and had not made a reviewable error in denying the person concerned''s Third level appeal. Regarding the delegation of authority, the court examined the relevant legislation, including sections 75-82 of the Regulations, and Commissioner''s Directive Number 081. The court held that the Acting Assistant Commissioner had the proper authority to render the final decision in the grievance process, citing subsection 2(2) of the Act, which allows for delegation of powers and functions in cases of absence, incapacitation, or vacancy. **(5) Conclusion** The court concluded that the CSC did not make a reviewable error in denying the person concerned''s Third level appeal, and that the determination of the outcome of the grievance was appropriately delegated by the Commissioner of Corrections to the Acting Assistant Commissioner. The court dismissed the application for judicial review, finding no reason to intervene in the decision of the Acting Assistant Commissioner.' - 'cluster: FACTS: The person concerned, a 68-year-old citizen of Saint Lucia, came to Canada as a visitor in 2003 and has remained here ever since. She has three sisters, two adult sons, and their respective families living in Canada. The person concerned submitted an application for permanent residence on humanitarian and compassionate grounds under subsection 25(1) of the Immigration and Refugee Protection Act in 2018. Her application was based on her establishment in Canada, the best interests of her Canadian grandchildren, and the hardship she would face if she were required to return to Saint Lucia. A Senior Immigration Officer refused the application in 2019, citing concerns about the credibility of the person concerned''s evidence.' - 'cluster: ANALYSIS: The court analyzed the standard of review applicable to the decision of the Acting Assistant Commissioner, citing Tehrankari v. Canada (Correctional Service), [2000] F.C.J. 495, and Baker v. Canada (Minister of Citizenship and Immigration), [1999] 2 S.C.R. 817. The court applied the standard of patently unreasonable to the decision, as it was based on pure findings of fact. The court also examined the CSC''s decision-making process, citing subsection 82(a) of the Regulations, which requires consideration of efforts made by staff members to resolve the complaint. The court found that the CSC had considered these efforts and had not made a reviewable error in denying the person concerned''s Third level appeal.Regarding the delegation of authority, the court examined the relevant legislation, including sections 75-82 of the Regulations, and Commissioner''s Directive Number 081. The court held that the Acting Assistant Commissioner had the proper authority to render the final decision in the grievance process, citing subsection 2(2) of the Act, which allows for delegation of powers and functions in cases of absence, incapacitation, or vacancy.' - source_sentence: 'cluster: CONCLUSION: Duncan v. Behdzi Ahda First Nation Court (s) Database Federal Court Decisions Date 2004-08-19 Neutral citation 2004 FC 1148 File numbers T-2212-01 Decision Content Date: 20040819 Docket: T-2212-01 Citation: 2004 FC 1148 BETWEEN: DORA DUNCAN and JENNIFER DUNCAN Applicants and THE BAND COUNCIL OF BEHDZI AHDA FIRST NATION, THE SETTLEMENT CORPORATION OF COLVILLE LAKE, SHARON TUTCHO, J.B. GULLY, ROLAND CODZI, and SARAH KOCHON Respondents ASSESSMENT OF COSTS - REASONS CHARLES E. STINSON Assessment Officer [1] The Court, by way of judicial review, quashed resolutions by certain of the Respondents purporting to remove the Applicants from their elected positions of Chief and Band Councilor respectively and purporting to set a by-election to replace them. Costs were awarded jointly and severally as against the Respondents. I issued a timetable for written disposition of the Applicants'' bill of costs. THE RESPONDENTS'' POSITION [2] The Respondents took issue as follows with only four items: (i) item 1 (preparation of originating document and materials) should be reduced from the maximum 7 units claimed to 5 units because the issues were not particularly complex; (ii) item 5 (preparation for contested motion) should be reduced from the maximum 7 units claimed to 4 units because its issues also were not particularly complex; (iii) item 8 (preparation for examination) should be reduced from the maximum 5 units claimed to 3 units because the examination lasted one hour and (iv) item 10 (preparation for status review), claimed at the maximum 6 units, should be disallowed because the Respondents should not be liable for costs of a process necessitated solely by the Applicants'' failure to proceed expeditiously and because the status review was conducted in writing without the necessity of an appearance. THE APPLICANTS'' POSITION [3] The Applicants argued that the complexity and importance of band council resolutions coupled with the detail and volume of the supporting materials and with the lengthy consultations with counsel warrant the maximum 7 units for item 1. The amount of work that was required justifies the maximum 7 and 5 units respectively for items 5 and 8. For item 10, the Applicants asserted that they had been ready for trial and that the Court decided that the delay did not warrant a dismissal. The Respondents'' materials did not establish prejudice as a consequence of delay. The status review required considerable preparation time notwithstanding its conduct in writing. [4] The Applicants argued further to Mark M. Orkin Q.C., The Law of Costs, Second Edition, (Aurora, Ont.: Canada Law Book, 2003) at para. 222.1 (page 2-220.4) that the time spent by one counsel should not be the measure of the reasonableness of time spent by another counsel in providing representation necessary in the best interests of the latter''s client. As well, the Law of Costs supra holds at para. 222.3.1 (page 2-220.11) that some courts have been reluctant to interfere in the face of assertions of excessive hours claimed for counsel''s time and that some courts have said that party and party assessments of costs must acknowledge legitimate efforts of counsel relative to their clients and the courts. ASSESSMENT [5] I concluded at paragraph [7] in Bruce Starlight et al v. Her Majesty the Queen, [2001] F.C.J. 1376 (A.O.) that the same point in the ranges throughout the columns in the Tariff need not be used as each item for the services of counsel must be considered in its own circumstances and that some generalization is required between the available values in ranges. I will exercise discretion consistent with my approach in Grace M. Carlile v. Her Majesty the Queen (1997), 97 D.T.C. 5284 at 5287 (T.O.) and with the sentiment of Lord Justice Russell in Re: Eastwood (deceased) (1974), 3 ALL. E.R. 603 at 608, that assessment of costs is "rough justice, in the sense of being compounded of much sensible approximation", in sorting out a reasonable result for costs. I do not think that this was the most complex instance of litigation. I allow 6 units for item 1. I have examined the materials associated with the interlocutory motion (by the Respondents for leave to file additional affidavits) in issue: I allow 5 units for item 5. [6] My allowance for item 1 reflected my sense for this litigation that strong feelings in a small and somewhat isolated community may have made the pre-hearing environment somewhat awkward. I allow the 5 units as claimed for item 8. As with some other steps in this litigation, the Applicants asserted the challenges facing the administration of justice in Northern Canada, including geography and limited resources, as factors affecting the process of status review. I think that an allowance is warranted for item 10, which I fix at 4 units. [7] The Applicants'' bill of costs, presented at $7,810.18, is assessed and allowed at $7,221.68. (Sgd.) "Charles E. Stinson" Assessment Officer Vancouver, British Columbia August 19, 2004 FEDERAL COURT NAMES OF COUNSEL AND SOLICITORS OF RECORD DOCKET: T-2212-01 STYLE OF CAUSE: DORA DUNCAN ET AL. v. THE BAND COUNCIL OF BEHDZI AHDA FIRST NATION ET AL. ASSESSMENT OF COSTS IN WRITING WITHOUT PERSONAL APPEARANCE OF PARTIES REASONS FOR ASSESSMENT OF COSTS BY: CHARLES E. STINSON DATED: August 19, 2004 SOLICITORS OF RECORD: Lawson Lundell FOR THE APPLICANTS Yellowknife, NWT Field LLP FOR THE RESPONDENTS Yellowknife, NWT ' sentences: - 'cluster: ANALYSIS: The court considered the applicants'' bill of costs, which included items for preparation of originating documents, preparation for a contested motion, preparation for examination, and preparation for a status review. The respondents argued that certain items should be reduced or disallowed, while the applicants argued that the complexity and importance of the case justified the claimed costs. The court allowed 6 units for item 1, 5 units for item 5, 5 units for item 8, and 4 units for item 10. The court also noted that the challenges facing the administration of justice in Northern Canada, including geography and limited resources, were factors affecting the process of status review.' - 'cluster: CONCLUSION: The court assessed the applicants'' bill of costs at $7,221.68, which is $588.50 less than the claimed amount of $7,810.18. The court''s assessment of costs reflects its exercise of discretion in taking into account the circumstances of the case, including the complexity and importance of the case, as well as the challenges facing the administration of justice in Northern Canada.' - 'cluster: ANALYSIS: The court found that the Officer''s best interests of the child (BIOC) analysis was flawed and rendered the decision unreasonable. The Officer''s assessment was highly generalized and failed to properly identify and define the granddaughter''s needs or examine them with a great deal of attention. The Officer failed to consider the emotional and practical hardships the granddaughter would face if the person concerned was forced to leave the country, despite evidence of hardship on the record. The Officer also placed undue emphasis on the degree to which the granddaughter depends on the person concerned, rather than considering how the person concerned''s departure would impact the granddaughter in the particular circumstances of the case.' - source_sentence: 'cluster: FACTS: Canada (Public Safety and Emergency Preparedness) v. Imalenowa Court (s) Database Federal Court Decisions Date 2022-09-13 Neutral citation 2022 FC 1286 File numbers IMM-6854-21 Decision Content Date: 20220913 Docket: IMM-6854-21 Citation: 2022 FC 1286 Ottawa, Ontario, September 13, 2022 PRESENT: The Hon Mr. Justice Henry S. Brown BETWEEN: THE MINISTER OF PUBLIC SAFETY AND EMERGENCY PREPAREDNESS Applicant and PRINCE UYI IMALENOWA Respondent JUDGMENT AND REASONS I. Nature of the matter [1] This is an application for judicial review of a decision by the Immigration Appeal Division [IAD], dated September 22, 2021 [Decision], staying the Respondent’s removal from Canada. The Respondent is a 43-year-old permanent resident of Canada and citizen of Nigeria. The Immigration Division [ID] issued a removal order for reasons of serious criminality, because of the Respondent’s conviction for identity theft fraud involving as many as 50 individuals. He was convicted and sentenced on one count. The Respondent did not challenge the legality of the removal order, but sought a stay from the IAD on humanitarian and compassionate [H&C] grounds. [2] The Respondent based his request for H&C in part on a fraudulent letter from his ex-spouse in support. The IAD found he had fraudulently written and forged his ex-wife’s signature on the letter he gave it. The letter contained material falsehoods. He was found not credible, lacking remorse, did not appreciate the wrong he had done others and had other failings noted by the IAD. [3] That said, the IAD granted a stay, finding sufficient H&C grounds based on “moderate establishment” in Canada and “hardship” he would suffer if removed to Nigeria. The hardship was based mainly on the state of Nigeria’s healthcare system, the IAD finding among other things the Respondent would have to pay for his own drugs, which appears to be relatively common in Nigeria, but which creates hardship for indigent persons. The IAD found the Respondent could “re-establish himself in Nigeria and earn an average person’s wages” from which it appears he is not indigent. [4] The Applicant notes for the first time that the Respondent in his H&C relies on a list of prescriptions that weren’t his. The list was someone else’s prescription, which was agreed. The Respondent said the fault was with his doctor and or his lawyer, essentially asserting neither looked at them before they were filed with the IAD. I take it he also asserts the IAD likewise failed to examine them. The Respondent filed the proper list before this Court. Respondent’s counsel agreed I should not assess or weigh the different list, but also said essentially that the Court should not ignore his new evidence either. In addition, the medical records relied upon by the IAD were not updated after the ID and were by then 2 ½ years old. [5] Judicial review will be granted because of my inability to assess the veracity and weight to be given the newly filed prescription list, which was central to the IAD’s determination of hardship, and issues with respect to the justification, rationality and intelligibility of the IAD’s determinations. II. Background Facts [6] The Respondent arrived in Canada in 2011 and made a refugee claim based on his fluid sexual orientation. His refugee claim was rejected. [7] He met someone in Canada and married her in 2012. The Respondent received his permanent residency through her sponsorship in 2013. The marriage lasted eighteen months or so and ended in divorce. [8] The Respondent was convicted in April 2018 of one count of identity fraud. The underlying activities took place between July and December 2014. The Respondent was originally charged with fraudulently impersonating at least 50 people to obtain credit cards in their names. His sentence included an intermittent jail sentence of 90 days and two years probation, as well as forfeiture and financial conditions. [9] Immigration authorities completed a section 44 report under the Immigration and Refugee Protection Act, SC 2001, c 27 [IRPA]. The Respondent had the opportunity to make submissions on H&C factors. As part of this process, the Respondent submitted a letter purportedly from his spouse. The IAD found the letter fraudulent – it was in fact written by the Respondent and contained false information and a forgery for a signature. For example, the letter was dated January 2019 and indicated the couple were married for almost seven years. In fact they were married for only 18 months and divorced in 2015. [10] As noted, the IAD found the Respondent forged the signature of his ex wife on the fraudulent letter, which I note praised the Respondent for his “honesty”, another falsehood. III. Decision under review [11] In granting the stay of removal, the IAD set out to review the Respondent’s H&C considerations in light of the factors established in Ribic v Canada (Minister of Employment and Immigration), 1986 CarswellNat 1357 at para 14 [Ribic]. The IAD considered “the seriousness of the offences giving rise to the removal order; the Appellant’s remorse; possibility of rehabilitation and the risk of reoffending; length of time spent in Canada; extent to which the Appellant is established in Canada; family support in Canada and the impact of removal upon the family; community support; and any hardship if the Appellant were to be removed to his country of citizenship.” [12] The IAD found the offence was serious: the conviction involved credit card fraud, which despite not being a violent crime, has “grave consequences for the victim” of which there were as many as 50. [13] The IAD found the Respondent “was not sincere when he expressed remorse”. The IAD found the Respondent lacked credibility when addressing both the conviction and the fraudulent letter. The IAD found that “[h]is submitting a forged letter to immigration authorities after having been convicted amounts to his committing a further fraud.” [14] Although the Respondent testified at the hearing that he did not know what he was doing with the credit cards was illegal, the IAD found this testimony untruthful and that the Respondent recognized he was involved in a criminal activity from the beginning. The IAD found the Respondent “wrote the letter himself, signed it fraudulently as his former spouse, and submitted it to immigration authorities.” [15] The IAD found the Respondent had not fully accepted responsibility for either of his actions, the criminal conviction or the fraudulent letter. [16] The IAD found the Respondent posed a “moderate risk” of reoffending based on his having no further convictions since the reportable offence. It also found he had a moderate possibility of rehabilitation. He had taken a number of courses and certificates to make himself more employable. He also completed his probation. The IAD noted that normally an individual with one conviction and attempts to rehabilitate himself would have a high possibility or rehabilitation and pose a low risk for reoffending. [17] However, the IAD found the Respondent did not appreciate the consequences of his actions, evidenced by his lack of credibility at the hearing and the fraudulent letter. The IAD found the Respondent had not “fully made efforts to address the factors that led to his criminal behaviour”, leading to the IAD concluding the Respondent had a moderate possibility of rehabilitation and a moderate risk of reoffending. [18] The IAD found the Respondent’s time in Canada was a moderately positive factor, as he had spent 10 years in Canada, but committed the offences within four years of arriving. The IAD also found the Respondent was only moderately established in Canada, as he owned no real estate and had no investments, but had a job, a car, and some savings. Notably, the record shows the steady job was recently acquired. [19] The IAD found the Respondent had no family support in Canada. It assigned little weight to the support letters he filed from his friends, because the letter he filed from his ex wife was fraudulent. [20] The IAD found the Respondent would suffer a hardship if he were removed to Nigeria due to diabetes, high cholesterol, a pulmonary embolism, cataracts, and a number of surgeries. However his medical records were two and a half years old and it appears not all of these conditions were still relevant. Although the submitted medical documents were dated to 2019, the IAD found it was more likely than not the Respondent was still affected by diabetes and the pulmonary embolism, again based on his testimony which this time it believed. Notably the IAD earlier rejected his testimony. [21] The IAD found the Respondent could “re-establish himself in Nigeria and earn an average person’s wages”, but that his medical conditions “would be difficult for him to address in Nigeria because of the state of the Nigerian healthcare system”. As previously noted it appears most Nigerians pay for their own medications. [22] The IAD found the best interests of the child were neutral. The Respondent has a 15-year-old daughter in the United States, but he had not seen her since she was seven – eight years ago. The Respondent’s relationship with his daughter was electronic and the IAD found returning the Respondent to Nigeria would have little impact on how he related to his child. IV. Issues [23] The Applicant submits “[t]he IAD’s decision lacks an internally coherent chain of analysis justified in relation to the facts”. The Respondent submits the issue is “[w]hether the decision is reasonable.” [24] Respectfully, the only issues are whether the Decision is reasonable, and whether this Court should assess the just now filed list of his prescriptions. V. Standard of Review [25] Both parties submit the standard on review should be reasonableness, per Canada (Minister of Citizenship and Immigration) v Vavilov, 2019 SCC 65 [Vavilov]. I agree. Regarding reasonableness, in Canada Post Corp v Canadian Union of Postal Workers, 2019 SCC 67, issued at the same time as the Supreme Court of Canada’s decision in Vavilov, the majority per Justice Rowe explains what is required for a reasonable decision, and what is required of a court reviewing on the reasonableness standard: [31] A reasonable decision is “one that is based on an internally coherent and rational chain of analysis and that is justified in relation to the facts and law that constrain the decision maker” (Vavilov, at para. 85). Accordingly, when conducting reasonableness review “[a] reviewing court must begin its inquiry into the reasonableness of a decision by examining the reasons provided with ‘respectful attention’ and seeking to understand the reasoning process followed by the decision maker to arrive at [the] conclusion” (Vavilov, at para. 84, quoting Dunsmuir, at para. 48). The reasons should be read holistically and contextually in order to understand “the basis on which a decision was made” (Vavilov, at para. 97, citing Newfoundland Nurses). [32] A reviewing court should consider whether the decision as a whole is reasonable: “what is reasonable in a given situation will always depend on the constraints imposed by the legal and factual context of the particular decision under review” (Vavilov, at para. 90). The reviewing court must ask “whether the decision bears the hallmarks of reasonableness – justification, transparency and intelligibility – and whether it is justified in relation to the relevant factual and legal constraints that bear on the decision” (Vavilov, at para. 99, citing Dunsmuir, at paras. 47 and 74, and Catalyst Paper Corp. v. North Cowichan (District), 2012 SCC 2, [2012] 1 S.C.R. 5, at para. 13). [33] Under reasonableness review, “[t]he burden is on the party challenging the decision to show that it is unreasonable” (Vavilov, at para. 100). The challenging party must satisfy the court “that any shortcomings or flaws relied on ... are sufficiently central or significant to render the decision unreasonable” (Vavilov, at para. 100). [Emphasis added] [26] In the words of the Supreme Court of Canada in Vavilov, a reviewing court must be satisfied the decision-maker’s reasoning “adds up”: [104] Similarly, the internal rationality of a decision may be called into question if the reasons exhibit clear logical fallacies, such as circular reasoning, false dilemmas, unfounded generalizations or an absurd premise. This is not an invitation to hold administrative decision makers to the formalistic constraints and standards of academic logicians. However, a reviewing court must ultimately be satisfied that the decision maker’s reasoning “adds up”. [105] In addition to the need for internally coherent reasoning, a decision, to be reasonable, must be justified in relation to the constellation of law and facts that are relevant to the decision: Dunsmuir, at para. 47; Catalyst, at para. 13; Nor-Man Regional Health Authority, at para. 6. Elements of the legal and factual contexts of a decision operate as constraints on the decision maker in the exercise of its delegated powers. [Emphasis added] [27] The Supreme Court of Canada in Vavilov at para 86 states, “it is not enough for the outcome of a decision to be justifiable. Where reasons for a decision are required, the decision must also be justified, by way of those reasons, by the decision-maker to those to whom the decision applies,” and provides guidance that the reviewing court decide based on the record before them: [126] That being said, a reasonable decision is one that is justified in light of the facts: Dunsmuir, para. 47. The decision maker must take the evidentiary record and the general factual matrix that bears on its decision into account, and its decision must be reasonable in light of them: see Southam, at para. 56. The reasonableness of a decision may be jeopardized where the decision maker has fundamentally misapprehended or failed to account for the evidence before it. In Baker, for example, the decision maker had relied on irrelevant stereotypes and failed to consider relevant evidence, which led to a conclusion that there was a reasonable apprehension of bias: para. 48. Moreover, the decision maker’s approach would also have supported a finding that the decision was unreasonable on the basis that the decision maker showed that his conclusions were not based on the evidence that was actually before him: para. 48. [Emphasis added] VI. Legislation [28] The IAD granted the stay pursuant to section 68(1) of the IRPA: Removal order stayed Sursis 68(1) To stay a removal order, the Immigration Appeal Division must be satisfied, taking into account the best interests of a child directly affected by the decision, that sufficient humanitarian and compassionate considerations warrant special relief in light of all the circumstances of the case. 68(1) Il est sursis à la mesure de renvoi sur preuve qu’il y a — compte tenu de l’intérêt supérieur de l’enfant directement touché — des motifs d’ordre humanitaire justifiant, vu les autres circonstances de l’affaire, la prise de mesures spéciales. VII. Case law [29] In Ribic, the Immigration Appeal Board established an application for equitable jurisdiction under section 72(1)(b) of the Immigration Act, 1976, SC 1976-77, c 52 (the analogous provision in prior legislation) should consider the circumstances of the case, including: … the seriousness of the offence or offences leading to the deportation and the possibility of rehabilitation or in the alternative, the circumstances surrounding the failure to meet the conditions of admission which led to the deportation order. The Board looks to the length of time spent in Canada and the degree to which the appellant is established; family in Canada and the dislocation to that family that deportation of the appellant would cause; the support available for the appellant not only within the family but also within the community and the degree of hardship that would be caused to the appellant by his return to his country of nationality. While the general areas of review are similar in each case the facts are rarely, if ever, identical (Ribic at para 14). [30] In Chieu v Canada (Minister of Citizenship and Immigration), 2002 SCC 3 at para 77, the Supreme Court of Canada (SCC) endorsed the Ribic approach when assessing removals under section 70(1)(b) of the Immigration Act, RSC 1985, c I-2. The SCC confirmed the Ribic factors apply to IRPA in Canada (Citizenship and Immigration) v Khosa, 2009 SCC 12 at para 137. VIII. Analysis [31] The Applicant submits the Decision lacks an internally coherent chain of analysis and that the IAD granted exceptional relief on an unjustifiably low standard. Overall, I agree. [32] The Respondent submits the Applicant is asking the Court to reweigh the evidence and reach a different conclusion. The Respondent’s submissions focus on the broad, discretionary jurisdiction of the IAD regime and the SCC’s endorsement of the Ribic factors in Chieu and Khosa. The Respondent submits the IAD properly considered the Ribic factors in the Decision. A. The reasons lacked an internally coherent and rational chain of analysis (1) Medical conditions and records [33] The Applicant alleges the Decision lacks internal rationality in how the IAD treated the Respondent’s medical conditions and documents. First, the Applicant submits the IAD’s acceptance of the Respondent’s testimony on his continuing medical conditions in lieu of documentary support, given the credibility findings, was irrational. Second, the Applicant submits the IAD misapprehended evidence on a central aspect of the Decision. [34] In my view, the determinative issue is the treatment of the medical records. The Applicant submits, and I agree, that the IAD misapprehended evidence on a central aspect of the Decision. The Decision was largely based on the assertion the Respondent required prescription medication, but the prescription records submitted to the IAD were not the Respondent’s. The Respondent and his team produced and relied on someone else’s prescription list. [35] In effect the Respondent says neither he, his pharmacist, his lawyer nor the IAD actually looked at the prescriptions he filed with the IAD. Instead it seems it is up to the Court to assess this central new evidence de novo. [36] That said, a central and key findings of the IAD is the Respondent would suffer hardship caused by difficulty in obtaining his required medications in Nigeria. The IAD noted the Respondent’s medical documents were only dated to 2019, but found it was likely the Respondent was still affected by the conditions. Whether or not the Respondent requires prescription medication is therefore central to the Decision. [37] Yet, and with respect, we do not know whether and to what extent prescriptions are needed and for what and in what amounts, frequency or otherwise. [38] The Respondent acknowledges the prescription record was not in his name – although he has to because that is obvious on the record. He says an “accurate and updated Prescription history” is an exhibit attached to his Affidavit. I am unable to assess that assertion. [39] The prescription record submitted by the Respondent is dated December 7, 2021, which is after the Decision was issued. [40] The Applicant contends the Respondent’s acknowledgement of the erroneous records and submission of revised records supports the argument the IAD misapprehended evidence on central aspect of decision. The Applicant further submits the provision of evidence dated after the Decision confirms the matter should be sent back for redetermination. I cannot but agree with these self evident submissions. [41] In my view, the entirely inappropriate and inaccurate prescription record filed, and the obvious inattention to it by all parties including the IAD are sufficient grounds to grant this judicial review. The hardship, particularly in obtaining prescription medications, was a key factor in the Decision granting the stay on H&C grounds. If the Respondent does not require prescription medication, that ground is invalid. [42] Further, the fact the issue was not raised at the hearing and the Respondent did not have an opportunity to address the issue, also supports allowing this application and remitting the Decision for redetermination. [43] Additionally, it is well established that judicial review is based on the material before the decision maker (Association of Universities and Colleges of Canada v Canadian Copyright Licensing Agency (Access Copyright), 2012 FCA 22 at para 20). Therefore, this Court is unable to consider whether the updated medical records are sufficient to establish whether the Respondent still requires prescription medication. [44] Judicial review will be ordered on this ground. (2) Rehabilitation and reoffending [45] The Applicant also submits, and I also agree, there is a lack of internal rationality in the Decision that is “particularly obvious” in the IAD’s positive weighing of rehabilitation in light of the findings on lack of credibility, the absence of remorse, the lack of insight into his criminality, his moderate likelihood of reoffending and his lack of support not to mention the Respondent’s continued fraud on the IAD itself. [46] The IAD made numerous explicit findings on the Respondent’s lack of remorse and continued use of fraudulent documents. While the Applicant highlights a dozen of the IAD’s findings, some of the most significant are: “His submitting a forged letter to immigration authorities after having been convicted amounts to his committing a further fraud”; “His actions after his conviction and his lack of credibility at this hearing indicate that he does not appreciate the consequences of his actions”; “His submitting a fraudulent letter after committing fraud, then testifying in a way that is simply not credible, demonstrate that the Appellant has not fully made efforts to address the factors that led to his criminal behavior”; and “His submitting the letter mirrors the criminal offence that led to his removal order”. The Applicant did not highlight this finding, but in my view, this comment confirms the Respondent was still engaging in the same illegal behaviour that led to the removal order being issued in the first place. [47] In my respectful view, the IAD’s finding the Respondent “has a moderate possibility for rehabilitation and poses a moderate risk of reoffending” in light of the findings on the fraudulent letter and the Respondent’s lack of remorse is a close to if not a fatal flaw in the logic of the Decision. The IAD’s findings demonstrate that even during the removal proceedings, the Respondent engaged in the sort of fraudulent behaviour that led to his inadmissibility. The IAD does not indicate why, when the Respondent engaged in the same fraudulent activity, is not remorseful, and does not have insight into his criminality, it found his rehabilitation “a moderate possibility”. The fraudulent letter was submitted after the Respondent completed his probation, which further suggests those actions did not lead to rehabilitation, even moderately. In my view such conduct attacks the integrity of the immigration system and must be considered in light of constraining law to that effect. [48] The finding with respect to hardship in the absence of a pharmacy record is an obvious case of an unjustified and unintelligible finding leading to unreasonableness and judicial review. Again here, the IAD does not explain or come to grips with how the cascade of negative findings justify a finding of moderate likelihood of rehabilitation, particularly the blatant fraud on the IAD itself. The Decision does not indicate any programs, treatment, or therapy the Respondent has subsequently engaged in that might assist him in gaining insight into his criminal activities. I am compelled to conclude the finding of a “moderate possibility for rehabilitation” is neither justified nor intelligible and thus unreasonable per Vavilov. B. The IAD granted exceptional relief on an unreasonably low standard [49] The Applicant acknowledges the Court owes a high degree of deference to the IAD’s assessment of H&C factors, but submits the IAD granted H&C relief based only on some hardship without considering such relief is exceptional in nature, not routine. I agree. Such a finding is contrary to the majority judgment in Kanthasamy v Canada (Citizenship and Immigration), 2015 SCC 61 [per Abella J] at para 23: “There will inevitably be some hardship associated with being required to leave Canada. This alone will not generally be sufficient to warrant relief on humanitarian and compassionate grounds” under section 25 of IRPA, and I would say the same for subsection 68(1) of IRPA. [50] Further, the Applicant asserts the IAD must not exercise its discretion routinely or lightly, and again I agree: Canada (Citizenship and Immigration) v Ndir, 2020 FC 673 [per St-Louis J] at para 31, 39; and Canada (Public Safety and Emergency Preparedness) v Abou Antoun, 2018 FC 540 [per Lafrenière J] at para 19. [51] Otherwise, H&C simply becomes an alternative routine and unexceptional immigration scheme, which it is not. [52] Judicial review will be granted on these grounds as well. IX. Conclusion [53] In my respectful view, the Decision is unreasonable for the reasons noted. Therefore judicial review will be granted. X. Certified Question [54] Neither party proposed a question of general importance and none arises. JUDGMENT in IMM-6854-21 THIS COURT’S JUDGMENT is that judicial review is granted, the Decision of the IAD is set aside, this matter is remanded for reconsideration by a differently constituted IAD, no question of general importance is certified and there is no Order as to costs. "Henry S. Brown" Judge FEDERAL COURT SOLICITORS OF RECORD DOCKET: IMM-6854-21 STYLE OF CAUSE: THE MINISTER OF PUBLIC SAFETY AND EMERGENCY PREPAREDNESS v PRINCE UYI IMALENOWA PLACE OF HEARING: HELD BY WAY OF VIDEOCONFERENCE DATE OF HEARING: SEPTEMBER 8, 2022 JUDGMENT AND REASONS: BROWN J. DATED: SEPTEMBER 13, 2022 APPEARANCES: Bradley Bechard FOR THE APPLICANT Adetayo G. Akinyemi FOR THE RESPONDENT SOLICITORS OF RECORD: Attorney General of Canada Toronto, Ontario FOR THE APPLICANT Adetayo G. Akinyemi Barrister and Solicitor Toronto, Ontario FOR THE RESPONDENT ' sentences: - 'cluster: FACTS: This case involves an application for judicial review of a decision by the Immigration Appeal Division (IAD) to stay the removal of a 43-year-old permanent resident of Canada, who is a citizen of Nigeria. The person concerned was convicted of identity theft fraud involving as many as 50 individuals and was sentenced to 90 days in jail and two years of probation. He did not challenge the legality of the removal order but sought a stay on humanitarian and compassionate (H&C) grounds. The IAD found that the person concerned had fraudulently written and forged his ex-wife''s signature on a letter he submitted in support of his H&C application. However, the IAD granted a stay, finding sufficient H&C grounds based on "moderate establishment" in Canada and "hardship" he would suffer if removed to Nigeria. The hardship was based mainly on the state of Nigeria''s healthcare system, where the person concerned would have to pay for his own medications.' - 'cluster: ISSUES: The issue before the court was whether the PRRA Officer''s decision was reasonable, given the person concerned''s claims of risk in the DRC due to his untreated mental illness. The court had to determine whether the Officer''s findings regarding the availability of medical treatment and state protection in the DRC were supported by the evidence and whether the Officer had properly assessed the risks faced by the person concerned.' - 'cluster: RULES: The court applied the reasonableness standard of review, as established in Canada (Minister of Citizenship and Immigration) v Vavilov, 2019 SCC 65. A reasonable decision is one that is based on an internally coherent and rational chain of analysis and that is justified in relation to the facts and law that constrain the decision-maker. The court must examine the reasons provided with "respectful attention" and seek to understand the reasoning process followed by the decision-maker to arrive at the conclusion.' - source_sentence: 'cluster: ISSUES: Abdou v. Canada (Citizenship and Immigration) Court (s) Database Federal Court Decisions Date 2014-05-26 Neutral citation 2014 FC 500 File numbers T-1638-13 Decision Content Date: 20140526 Docket: T-1638-13 Citation: 2014 FC 500 Ottawa, Ontario, May 26, 2014 PRESENT: The Honourable Mr. Justice Manson BETWEEN: HATEM SALAMA RE ABDOU Applicant and THE MINISTER OF CITIZENSHIP AND IMMIGRATION Respondent REASONS FOR JUDGMENT AND JUDGMENT [1] This is an appeal of the decision of Wojciech Sniegowski, a Citizenship Judge with the Citizenship Commission, Immigration Canada [the Judge], pursuant to subsection 14(5) of the Citizenship Act, RSC 1985, c C-29 [the Act]. The Judge denied the Applicant’s application for Canadian citizenship by deciding that he did not meet the residency requirement as defined in 5(1)(c) of the Act. . I. Issues [2] The issues are: A. Was the Judge’s decision reasonable in finding that the Applicant did not meet the residency requirement in 5(1)(c) of the Act? B. Did the Judge breach the duty of procedural fairness? II. Standard of Review [3] The issues involving the assessment of evidence and of mixed fact and law are reviewable on the standard of reasonableness (Dunsmuir v New Brunswick, 2008 SCC 9, at para 47-48 51, 53-54, 57, 62, 64; Singh v Canada (Minister of Citizenship and Immigration), 2008 FC 408 at para 10). [4] The issue of procedural fairness is reviewable on the standard of correctness (Dunsmuir, at paras 57, 79; Navidi v Canada (Minister of Citizenship and Immigration), 2012 FC 372, at para 13 [Navidi]). III. Background [5] The Applicant is a stateless individual who was born in Kuwait. He arrived in Canada on June 7, 2003, and became a Permanent Resident of Canada on that date. He made an application for Canadian citizenship on August 8, 2008. For purposes of the residency requirement in 5(1)(c) of the Act, the Relevant Period at issue is August 8, 2004, to August 8, 2008 [the Relevant Period]. [6] In his original application for citizenship, the Applicant listed three absences from Canada totalling 354 days. This includes a 320 day absence to Kuwait from 2004-2005. However, in his follow-up Residency Questionnaire, the Applicant listed only 34 days of absence, omitting the 320 day absence to Kuwait listed in his original application. [7] In support of his application, the Applicant submitted numerous documents, including: • Records with the Ontario Ministry of Health; • Notices of Assessment for 2003-2006, 2008; • Gas receipts; • Report cards for his children in Ontario schools; • Incorporation documents for 6612237 Canada Limited, a corporation for which the Applicant is an Officer and Director; • Banking records showing numerous wire transfers beginning in March, 2006; • Documentation pertaining to the removal of conditions that were imposed on him as a Permanent Resident; • Copies of two passports belonging to the Applicant. One is valid from September 15, 2002, to October 2, 2004, and contains a Kuwaiti residence permit valid from September 24, 2001, to September 9, 2004. The other is valid from May 5, 2009, to May 4, 2014, and contains a Kuwaiti residence permit valid from May 20, 2009, to July 3, 2010; • A Citizen’s Report from the Hamilton Police Service, which notes that his passport was not recovered after a stolen vehicle was returned to the Applicant, on or around October 3, 2007; and • Documents regarding financial and real estate dealings. [8] The Applicant did not submit a passport which covered the period from September 10, 2004, to May 4, 2009. [9] The Applicant had an interview before the Judge on April 18, 2013. [10] The Judge evaluated whether the Applicant met the residency requirement in 5(1)(c) of the Act in accordance with the test from (Re) Pourghasemi, [1993] FCJ No 232 (TD) [Pourghasemi]. In so doing, the Judge was not satisfied that the Applicant had proven that he was physically present in Canada for 1,095 days during the relevant period. [11] The Judge noted credibility concerns regarding the discrepancy between the absences listed on his original application (354 days) and his residence questionnaire (34 days). Additionally, without a passport submitted that was valid for the bulk of the Relevant Period, his absences were not verifiable. [12] The Judge found that the banking records submitted to prove the sale of construction equipment were more consistent with money transfers aimed at supporting family in Canada. This is supported by the fact that on his Residence Questionnaire, the Applicant claimed he sold his construction company in 2004. [13] Further, the Judge found that the lack of any reported income in 2003 and 2004 does not support his contention that he lived in Canada during the Relevant Period. [14] Based on the information submitted, the Judge was not satisfied that he had met the test from Pourghasemi (Atwani v Canada (Minister of Citizenship and Immigration), 2011 FC 1354, at paras 12, 18). IV. Analysis A. Was the Judge’s decision reasonable? [15] The Applicant makes limited submissions on the reasonableness of the Judge’s decision. His arguments amount to a claim that the Judge failed to properly consider the evidence of the Applicant’s Ministry of Health records, gas receipts, and documentation pertaining to the removal of conditions imposed on him as a Permanent Resident. [16] While the Judge did not cite all the evidence mentioned by the Respondent, as a whole the Judge’s decision was reasonable. There was a significant discrepancy between the absences declared in the Applicant’s original application and his Residence Questionnaire. The lack of a passport to verify these absences leaves the Applicant without clear or convincing evidence of his physical presence in Canada during the Relevant Period. In addition, it was reasonable of the Judge to find that the money transfers were more consistent with supporting family in Canada than of the gradual selling of construction equipment, given that the Applicant claimed he sold his construction equipment in 2004. [17] The Applicant’s arguments amount to assertions that the Judge failed to appropriately consider the evidence. This is not sufficient to show that the Judge’s decision was unreasonable. B. Did the Judge breach the duty of procedural fairness? [18] The Applicant notes that section 1.10 of the Citizenship Policy Manual [the Manual] suggests that a high degree of procedural fairness may be required of a citizenship judge due to the nature of the rights at issue. At sections 1.12 and 1.19 of the Manual, the content of this duty is described as including the right to be heard and that it may be unfair for a citizenship judge to base a decision on information that the applicant has not had an opportunity to comment on. [19] The Applicant argues that he was never given an opportunity to address a number of the Judge’s concerns. First, the Judge did not question him about the discrepancy in the absences declared in his citizenship application and his Residence Questionnaire. Second, the Judge did not give the Applicant an opportunity to explain why he was unable to submit a passport to corroborate his stated absences. Third, the Judge drew a negative inference from the Applicant’s history of money transfers, without giving the Applicant an opportunity to explain how these transfers reflect Kuwaiti business practices. [20] As these findings were central to the Judge’s decision, the Applicant argues that he ought to have been given an opportunity to respond to them. [21] At paras 8 and 10-12 of his affidavit, the Applicant describes a number of issues that were not raised by the Judge in his interview: 8. At the outset of the interview, the citizenship judge flatly told me he did not want to see any documents that I had in my possession. The citizenship judge was mainly focussed on questioning me about the money I brought to Canada by means of selling heavy construction equipment in Kuwait… 10…I was never questioned by the citizenship judge at my interview concerning the discrepancy which I was in a position to explain and satisfy the citizenship judge as to why and how the discrepancy came about. 11…the citizenship judge did not provide me with an opportunity to address his concerns concerning the missing passport and if he had allowed me the opportunity to address his concerns, I would have been able to provide evidence concerning my trips during the years 2004 and 2009 outside Canada. 12…I was not questioned by the citizenship judge concerning any medical problems that my family members had during the time I was in Canada and if he had done so, I would have been in a position to show the citizenship judge that I had to be in Canada for approximately one year when my daughter, Tala lost an eye due to an accident which occurred in or about October 2006. [22] This summary is supported by the notes of the interview provided by the Judge at pages 30-32 of the Certified Tribunal Record. These notes primarily relate to the money the Applicant brought into Canada, the alleged sale of heavy construction equipment by the Applicant, and some background information. [23] A fair reading of the Applicant’s affidavit and the Judge’s notes shows that the Judge did not focus his questioning on the discrepancy in the absences declared, the lost passport, or the other documentary evidence submitted. [24] The content of the procedural fairness required of a Judge in the context of a citizenship interview was described in Johar v Canada (Minister of Citizenship and Immigration), 2009 FC 1015, at para 41 [Johar]: The Citizenship Judge is not obligated to provide an appellant with an opportunity to file additional material. The process cannot become a running commentary on the adequacy of the appellant''s evidence (Zheng v. Canada (MCI), 2007 FC 1311, 163 A.C.W.S. (3d) 120, per Justice Simpson at para. 14). However, it is well established that an interview with the Citizenship Judge is "clearly intended to provide the candidate the opportunity to answer or, at the very least, address the concerns which gave rise to the request for an interview in the first place", and when an appellant is deprived of the opportunity to address those concerns, a denial of natural justice occurs (Stine v. Canada (MCI), [1999] F.C.J. No. 1264 (QL), 173 F.T.R. 298, per Justice Pelletier at para. 8; Tshimanga v. Canada (MCI), 2005 FC 1579, 151 A.C.W.S. (3d) 18, per Deputy Justice Rouleau at para. 17-19). At issue in Johar was a lost passport and credibility concerns relating to that loss, similar to this case. [25] The Respondent cites Navidi in support of its position. In Navidi, the applicant’s travel history included a number of undeclared absences. The judge held that this undermined the applicant’s credibility and none of the other evidence submitted by the applicant was sufficient to show that 5(1)(c) of the Act was satisfied. The applicant claimed that he had not been afforded due procedural fairness as he was not given an opportunity to respond to the negative credibility finding in his interview. However, in Navidi, the judge did request additional submissions of the applicant (Navidi, at para 31). [26] The Judge’s decision in this appeal hinged on a negative credibility finding, based on the discrepancy in the absences declared by the Applicant. As in Johar, the Judge did not raise this discrepancy with the Applicant. Given the necessary procedural fairness afforded to applicants in citizenship applications and the centrality of this issue to the Applicant’s claim, I find that there was a breach of procedural fairness. JUDGMENT THIS COURT’S JUDGMENT is that: 1. The Applicant’s appeal is allowed and his application is referred back to another Citizenship Judge for re-determination. "Michael D. Manson" Judge FEDERAL COURT SOLICITORS OF RECORD DOCKET: T-1638-13 STYLE OF CAUSE: HATEM SALAMA RE ABDOU v THE MINISTER OF CITIZENSHIP AND IMMIGRATION PLACE OF HEARING: Toronto, ontario DATE OF HEARING: May 21, 2014 REASONS FOR JUDGMENT AND JUDGMENT: MANSON J. DATED: May 26, 2014 APPEARANCES: Donald Greenbaum For The Applicant, HATEM SALAMA RE ABDOU Suzanne M. Bruce For The Respondent, THE MINISTER OF CITIZENSHIP AND IMMIGRATION SOLICITORS OF RECORD: Donald M. Greenbaum, QC Barrister, Solicitor & Notary Public Toronto, Ontario For The Applicant, HATEM SALAMA RE ABDOU William F. Pentney Deputy Attorney General of Canada Toronto, Ontario For The Respondent, THE MINISTER OF CITIZENSHIP AND IMMIGRATION ' sentences: - 'cluster: SUMMARY: **(1) Facts** The person concerned, a former member of the Canadian Forces, applied to the Minister of Veterans Affairs for a pension in respect of recurrent inversion sprains of his right ankle, which he alleged was consequential to his pensioned condition of pes planus. The Minister refused to extend pension entitlement, and the decision was affirmed by the review panel and appeal panel of the Veterans Review and Appeal Board. The person concerned sought judicial review of the decision, arguing that the Board erred in concluding that the inversion sprain was not consequent to his existing pensioned conditions. The person concerned had served in the Canadian Forces for over 20 years, during which time he developed eight disabilities for which he received disability pensions, including pes planus of both feet. He applied for a pension in respect of recurrent inversion sprains of his right ankle, which he alleged was consequential to his pensioned condition of pes planus. The Minister refused to extend pension entitlement, and the decision was affirmed by the review panel and appeal panel of the Board. **(2) Issue** The issue before the court was whether the Board erred in concluding that the inversion sprain was not consequent to the person concerned''s existing pensioned conditions. This question turned on two issues: (1) did the Board disregard the medical evidence; and (2) did the Board fail to apply the statutory burden of proof under the Veterans Review and Appeal Board Act. **(3) Rule** The court applied the standard of review of patent unreasonableness for questions of fact and reasonableness simpliciter for questions of mixed law and fact. The court also considered the statutory rules of evidence binding the Board, which required it to accept uncontradicted evidence, draw all reasonable inferences in favour of the applicant, and resolve any doubt in favour of the applicant. **(4) Analysis** The court found that the Board made a patently unreasonable credibility finding with respect to the medical opinion of Dr. Saunders, who had a historical professional relationship with the person concerned and had physically examined him. The Board failed to refer to Dr. Saunders'' evidence that he was the person concerned''s physician and had examined him, which was relevant to the decision under review. The court also found that the Board erred by not making clear whether it applied the statutory burden of proof in section 39 of the Veterans Review and Appeal Act to the facts. **(5) Conclusion** The court concluded that the Board''s decision did not stand up to a probing examination and was therefore not reasonable. The court referred the matter back to another panel of the Board for redetermination, with instructions to weigh the evidence and apply the statutory burden of proof. If the Board concludes that the person concerned''s recurrent inversion sprains of his right ankle are consequential to his pes planus, the Board must take into account that the person concerned is already receiving a small pension with respect to this same ankle injury.' - 'cluster: CONCLUSION: The court allowed the person concerned''s appeal and referred his application back to another Citizenship Judge for re-determination. The court''s decision was based on the finding that the Judge''s decision was not reasonable and that the Judge breached the duty of procedural fairness. The court''s decision highlights the importance of procedural fairness in citizenship applications and the need for Citizenship Judges to provide applicants with a fair opportunity to address concerns raised during the interview.' - 'cluster: ISSUES: The issues before the court were whether the Judge''s decision to deny the person concerned''s application for Canadian citizenship was reasonable and whether the Judge breached the duty of procedural fairness. Specifically, the court had to determine whether the Judge''s decision was based on a reasonable assessment of the evidence and whether the person concerned was given a fair opportunity to address the concerns raised by the Judge during the interview.' --- # SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) <!-- at revision 679199c2575b5bfe93b06161d06cd7c16ebe4124 --> - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity <!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("simonosgoode/nomic_embed_fine_tune_law_v3") # Run inference sentences = [ 'cluster: ISSUES: Abdou v. Canada (Citizenship and Immigration)\nCourt (s) Database\nFederal Court Decisions\nDate\n2014-05-26\nNeutral citation\n2014 FC 500\nFile numbers\nT-1638-13\nDecision Content\nDate: 20140526\nDocket:\nT-1638-13\nCitation: 2014 FC 500\nOttawa, Ontario, May 26, 2014\nPRESENT: The Honourable Mr. Justice Manson\nBETWEEN:\nHATEM SALAMA RE ABDOU\nApplicant\nand\nTHE MINISTER OF CITIZENSHIP AND IMMIGRATION\nRespondent\nREASONS FOR JUDGMENT AND JUDGMENT\n[1] This is an appeal of the decision of Wojciech Sniegowski, a Citizenship Judge with the Citizenship Commission, Immigration Canada [the Judge], pursuant to subsection 14(5) of the Citizenship Act, RSC 1985, c C-29 [the Act]. The Judge denied the Applicant’s application for Canadian citizenship by deciding that he did not meet the residency requirement as defined in 5(1)(c) of the Act. .\nI. Issues [2] The issues are:\nA. Was the Judge’s decision reasonable in finding that the Applicant did not meet the residency requirement in 5(1)(c) of the Act?\nB. Did the Judge breach the duty of procedural fairness?\nII. Standard of Review [3] The issues involving the assessment of evidence and of mixed fact and law are reviewable on the standard of reasonableness (Dunsmuir v New Brunswick, 2008 SCC 9, at para 47-48 51, 53-54, 57, 62, 64; Singh v Canada (Minister of Citizenship and Immigration), 2008 FC 408 at para 10).\n[4] The issue of procedural fairness is reviewable on the standard of correctness (Dunsmuir, at paras 57, 79; Navidi v Canada (Minister of Citizenship and Immigration), 2012 FC 372, at para 13 [Navidi]).\nIII. Background [5] The Applicant is a stateless individual who was born in Kuwait. He arrived in Canada on June 7, 2003, and became a Permanent Resident of Canada on that date. He made an application for Canadian citizenship on August 8, 2008. For purposes of the residency requirement in 5(1)(c) of the Act, the Relevant Period at issue is August 8, 2004, to August 8, 2008 [the Relevant Period].\n[6] In his original application for citizenship, the Applicant listed three absences from Canada totalling 354 days. This includes a 320 day absence to Kuwait from 2004-2005. However, in his follow-up Residency Questionnaire, the Applicant listed only 34 days of absence, omitting the 320 day absence to Kuwait listed in his original application.\n[7] In support of his application, the Applicant submitted numerous documents, including:\n• Records with the Ontario Ministry of Health;\n• Notices of Assessment for 2003-2006, 2008;\n• Gas receipts;\n• Report cards for his children in Ontario schools;\n• Incorporation documents for 6612237 Canada Limited, a corporation for which the Applicant is an Officer and Director;\n• Banking records showing numerous wire transfers beginning in March, 2006;\n• Documentation pertaining to the removal of conditions that were imposed on him as a Permanent Resident;\n• Copies of two passports belonging to the Applicant. One is valid from September 15, 2002, to October 2, 2004, and contains a Kuwaiti residence permit valid from September 24, 2001, to September 9, 2004. The other is valid from May 5, 2009, to May 4, 2014, and contains a Kuwaiti residence permit valid from May 20, 2009, to July 3, 2010;\n• A Citizen’s Report from the Hamilton Police Service, which notes that his passport was not recovered after a stolen vehicle was returned to the Applicant, on or around October 3, 2007; and\n• Documents regarding financial and real estate dealings.\n[8] The Applicant did not submit a passport which covered the period from September 10, 2004, to May 4, 2009.\n[9] The Applicant had an interview before the Judge on April 18, 2013.\n[10] The Judge evaluated whether the Applicant met the residency requirement in 5(1)(c) of the Act in accordance with the test from (Re) Pourghasemi, [1993] FCJ No 232 (TD) [Pourghasemi]. In so doing, the Judge was not satisfied that the Applicant had proven that he was physically present in Canada for 1,095 days during the relevant period.\n[11] The Judge noted credibility concerns regarding the discrepancy between the absences listed on his original application (354 days) and his residence questionnaire (34 days). Additionally, without a passport submitted that was valid for the bulk of the Relevant Period, his absences were not verifiable.\n[12] The Judge found that the banking records submitted to prove the sale of construction equipment were more consistent with money transfers aimed at supporting family in Canada. This is supported by the fact that on his Residence Questionnaire, the Applicant claimed he sold his construction company in 2004.\n[13] Further, the Judge found that the lack of any reported income in 2003 and 2004 does not support his contention that he lived in Canada during the Relevant Period.\n[14] Based on the information submitted, the Judge was not satisfied that he had met the test from Pourghasemi (Atwani v Canada (Minister of Citizenship and Immigration), 2011 FC 1354, at paras 12, 18).\nIV. Analysis A. Was the Judge’s decision reasonable? [15] The Applicant makes limited submissions on the reasonableness of the Judge’s decision. His arguments amount to a claim that the Judge failed to properly consider the evidence of the Applicant’s Ministry of Health records, gas receipts, and documentation pertaining to the removal of conditions imposed on him as a Permanent Resident.\n[16] While the Judge did not cite all the evidence mentioned by the Respondent, as a whole the Judge’s decision was reasonable. There was a significant discrepancy between the absences declared in the Applicant’s original application and his Residence Questionnaire. The lack of a passport to verify these absences leaves the Applicant without clear or convincing evidence of his physical presence in Canada during the Relevant Period. In addition, it was reasonable of the Judge to find that the money transfers were more consistent with supporting family in Canada than of the gradual selling of construction equipment, given that the Applicant claimed he sold his construction equipment in 2004.\n[17] The Applicant’s arguments amount to assertions that the Judge failed to appropriately consider the evidence. This is not sufficient to show that the Judge’s decision was unreasonable.\nB. Did the Judge breach the duty of procedural fairness? [18] The Applicant notes that section 1.10 of the Citizenship Policy Manual [the Manual] suggests that a high degree of procedural fairness may be required of a citizenship judge due to the nature of the rights at issue. At sections 1.12 and 1.19 of the Manual, the content of this duty is described as including the right to be heard and that it may be unfair for a citizenship judge to base a decision on information that the applicant has not had an opportunity to comment on.\n[19] The Applicant argues that he was never given an opportunity to address a number of the Judge’s concerns. First, the Judge did not question him about the discrepancy in the absences declared in his citizenship application and his Residence Questionnaire. Second, the Judge did not give the Applicant an opportunity to explain why he was unable to submit a passport to corroborate his stated absences. Third, the Judge drew a negative inference from the Applicant’s history of money transfers, without giving the Applicant an opportunity to explain how these transfers reflect Kuwaiti business practices.\n[20] As these findings were central to the Judge’s decision, the Applicant argues that he ought to have been given an opportunity to respond to them.\n[21] At paras 8 and 10-12 of his affidavit, the Applicant describes a number of issues that were not raised by the Judge in his interview:\n8. At the outset of the interview, the citizenship judge flatly told me he did not want to see any documents that I had in my possession. The citizenship judge was mainly focussed on questioning me about the money I brought to Canada by means of selling heavy construction equipment in Kuwait…\n10…I was never questioned by the citizenship judge at my interview concerning the discrepancy which I was in a position to explain and satisfy the citizenship judge as to why and how the discrepancy came about.\n11…the citizenship judge did not provide me with an opportunity to address his concerns concerning the missing passport and if he had allowed me the opportunity to address his concerns, I would have been able to provide evidence concerning my trips during the years 2004 and 2009 outside Canada.\n12…I was not questioned by the citizenship judge concerning any medical problems that my family members had during the time I was in Canada and if he had done so, I would have been in a position to show the citizenship judge that I had to be in Canada for approximately one year when my daughter, Tala lost an eye due to an accident which occurred in or about October 2006.\n[22] This summary is supported by the notes of the interview provided by the Judge at pages 30-32 of the Certified Tribunal Record. These notes primarily relate to the money the Applicant brought into Canada, the alleged sale of heavy construction equipment by the Applicant, and some background information.\n[23] A fair reading of the Applicant’s affidavit and the Judge’s notes shows that the Judge did not focus his questioning on the discrepancy in the absences declared, the lost passport, or the other documentary evidence submitted.\n[24] The content of the procedural fairness required of a Judge in the context of a citizenship interview was described in Johar v Canada (Minister of Citizenship and Immigration), 2009 FC 1015, at para 41 [Johar]:\nThe Citizenship Judge is not obligated to provide an appellant with an opportunity to file additional material. The process cannot become a running commentary on the adequacy of the appellant\'s evidence (Zheng v. Canada (MCI), 2007 FC 1311, 163 A.C.W.S. (3d) 120, per Justice Simpson at para. 14). However, it is well established that an interview with the Citizenship Judge is "clearly intended to provide the candidate the opportunity to answer or, at the very least, address the concerns which gave rise to the request for an interview in the first place", and when an appellant is deprived of the opportunity to address those concerns, a denial of natural justice occurs (Stine v. Canada (MCI), [1999] F.C.J. No. 1264 (QL), 173 F.T.R. 298, per Justice Pelletier at para. 8; Tshimanga v. Canada (MCI), 2005 FC 1579, 151 A.C.W.S. (3d) 18, per Deputy Justice Rouleau at para. 17-19).\nAt issue in Johar was a lost passport and credibility concerns relating to that loss, similar to this case.\n[25] The Respondent cites Navidi in support of its position. In Navidi, the applicant’s travel history included a number of undeclared absences. The judge held that this undermined the applicant’s credibility and none of the other evidence submitted by the applicant was sufficient to show that 5(1)(c) of the Act was satisfied. The applicant claimed that he had not been afforded due procedural fairness as he was not given an opportunity to respond to the negative credibility finding in his interview. However, in Navidi, the judge did request additional submissions of the applicant (Navidi, at para 31).\n[26] The Judge’s decision in this appeal hinged on a negative credibility finding, based on the discrepancy in the absences declared by the Applicant. As in Johar, the Judge did not raise this discrepancy with the Applicant. Given the necessary procedural fairness afforded to applicants in citizenship applications and the centrality of this issue to the Applicant’s claim, I find that there was a breach of procedural fairness.\nJUDGMENT\nTHIS COURT’S JUDGMENT is that:\n1. The Applicant’s appeal is allowed and his application is referred back to another Citizenship Judge for re-determination.\n"Michael D. Manson"\nJudge\nFEDERAL COURT\nSOLICITORS OF RECORD\nDOCKET:\nT-1638-13\nSTYLE OF CAUSE:\nHATEM SALAMA RE ABDOU v THE MINISTER OF CITIZENSHIP AND IMMIGRATION\nPLACE OF HEARING:\nToronto, ontario\nDATE OF HEARING:\nMay 21, 2014\nREASONS FOR JUDGMENT AND JUDGMENT:\nMANSON J.\nDATED:\nMay 26, 2014\nAPPEARANCES:\nDonald Greenbaum\nFor The Applicant,\nHATEM SALAMA RE ABDOU\nSuzanne M. Bruce\nFor The Respondent,\nTHE MINISTER OF CITIZENSHIP AND IMMIGRATION\nSOLICITORS OF RECORD:\nDonald M. Greenbaum, QC\nBarrister, Solicitor & Notary Public\nToronto, Ontario\nFor The Applicant,\nHATEM SALAMA RE ABDOU\nWilliam F. Pentney\nDeputy Attorney General of Canada\nToronto, Ontario\nFor The Respondent,\nTHE MINISTER OF CITIZENSHIP AND IMMIGRATION\n', "cluster: ISSUES: The issues before the court were whether the Judge's decision to deny the person concerned's application for Canadian citizenship was reasonable and whether the Judge breached the duty of procedural fairness. Specifically, the court had to determine whether the Judge's decision was based on a reasonable assessment of the evidence and whether the person concerned was given a fair opportunity to address the concerns raised by the Judge during the interview.", "cluster: CONCLUSION: The court allowed the person concerned's appeal and referred his application back to another Citizenship Judge for re-determination. The court's decision was based on the finding that the Judge's decision was not reasonable and that the Judge breached the duty of procedural fairness. The court's decision highlights the importance of procedural fairness in citizenship applications and the need for Citizenship Judges to provide applicants with a fair opportunity to address concerns raised during the interview.", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 12,750 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 370 tokens</li><li>mean: 3019.34 tokens</li><li>max: 6550 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 211.22 tokens</li><li>max: 1042 tokens</li></ul> | <ul><li>min: 32 tokens</li><li>mean: 223.91 tokens</li><li>max: 1261 tokens</li></ul> | * Samples: | anchor | positive | negative | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>cluster: ISSUES: Woodbine Entertainment Group v. Horsemen's Benevolent and Protective Association<br>Court (s) Database<br>Federal Court Decisions<br>Date<br>2004-11-04<br>Neutral citation<br>2004 FC 1554<br>File numbers<br>T-466-03<br>Decision Content<br>Date: 20041104<br>Docket: T-466-03<br>Citation: 2004 FC 1554<br>BETWEEN:<br>WOODBINE ENTERTAINMENT GROUP<br>Applicant<br>and<br>HORSEMEN'S BENEVOLENT AND PROTECTIVE ASSOCIATION<br>OF ONTARIO, ONTARIO HARNESS HORSE ASSOCIATION and<br>CANADIAN PARI-MUTUEL AGENCY<br>Respondents<br>REASONS FOR ORDER<br>SIMPSON, J.<br>The Applications<br>[1] In the first application, the Horsemen's Benevolent and Protective Association of Ontario ("HBPA") and the Ontario Harness Horse Association ("OHHA") (together the "Associations") seek judicial review of a decision dated December 18, 2002 (the "Decision") made by the Canadian Pari-Mutuel Agency ("CPMA") in which it issued a license to Woodbine Entertainment Group ("WEG") to conduct wagering on simulcast horse racing in calendar year 2003 (the "Merits Application").<br>[2] Th...</code> | <code>cluster: ISSUES: The issue before the court is whether the CPMA's decision to issue a license to WEG in the absence of a Pre-License Agreement with the HBPA and OHHA is valid, and whether the Betting Regulations require such an agreement to be in place before a license can be issued. The HBPA and OHHA seek a writ of prohibition, certiorari, and a declaration that the issuance of licenses by the CPMA in the absence of a Pre-License Agreement is a nullity. WEG, on the other hand, seeks a declaration that certain sections of the Betting Regulations are ultra vires of the Parliament of Canada.</code> | <code>cluster: FACTS: This case revolves around a dispute between Woodbine Entertainment Group (WEG) and the Horsemen's Benevolent and Protective Association of Ontario (HBPA) and the Ontario Harness Horse Association (OHHA) regarding the issuance of a license to WEG to conduct wagering on simulcast horse racing in Ontario. The HBPA and OHHA are associations representing horsemen who are members of the thoroughbred and standardbred racing industries, respectively. WEG operates horse racing tracks and wagering facilities in Ontario. The Canadian Pari-Mutuel Agency (CPMA) is a national regulatory unit that enforces the Pari-Mutuel Betting Supervision Regulations (Betting Regulations).In 2002, the CPMA issued a license to WEG to conduct wagering on simulcast horse racing in 2003, despite the fact that WEG had not entered into a Pre-License Agreement with the HBPA and OHHA, which are typically required by the Betting Regulations. The CPMA accepted 150 Access Agreements signed by individual hors...</code> | | <code>cluster: ANALYSIS: D Souza v. Canada (Citizenship and Immigration)<br>Court (s) Database<br>Federal Court Decisions<br>Date<br>2021-12-16<br>Neutral citation<br>2021 FC 1430<br>File numbers<br>IMM-6744-19<br>Decision Content<br>Date: 20211216<br>Docket: IMM-6744-19<br>Citation: 2021 FC 1430<br>Ottawa, Ontario, December 16, 2021<br>PRESENT: The Honourable Mr. Justice Favel<br>BETWEEN:<br>RESHMA ANITHA D SOUZA<br>Applicant<br>and<br>THE MINISTER OF CITIZENSHIP AND IMMIGRATION<br>Respondent<br>JUDGMENT AND REASONS<br>I. Nature of the Matter<br>[1] The Applicant seeks judicial review of a November 5, 2019 re-determination decision [Decision] of a visa officer [Officer] pursuant to section 72 of the Immigration and Refugee Protection Act, SC 2001, c 27 [IRPA]. The Officer refused the Applicant’s application for a temporary resident visa and work permit [the Application] because the Officer was not satisfied that the Applicant’s offer of employment [Employment Offer] was genuine.<br>[2] The application for judicial review is allowed.<br>II. Background<br>[3] The Appl...</code> | <code>cluster: ANALYSIS: The court finds that the decision of the visa officer was not reasonable. The officer overlooked or misapprehended material evidence, including the couple's explanation for the reduction in work hours and the female employer's prospects of future employment. The officer also made several errors regarding the couple's ability to fulfill the terms of the employment offer, including their financial situation and the number of hours they would need to hire a caregiver. The court finds that the officer's findings were speculative and not based on the evidence.The court also finds that the decision is not justified, transparent, and intelligible. The officer's conclusion that the employment offer was not genuine is not supported by the evidence, and the officer failed to consider the couple's explanations and submissions.</code> | <code>cluster: ISSUES: The main issue before the court is whether the decision of the visa officer to refuse the person concerned's application for a temporary resident visa and work permit was reasonable. The court also considers whether it should enter an indirect substitution or make a cost order in favour of the person concerned.</code> | | <code>cluster: FACTS: Bellosillo v. Canada<br>Court (s) Database<br>Federal Court Decisions<br>Date<br>2006-03-28<br>Neutral citation<br>2006 FC 396<br>File numbers<br>T-501-06<br>Decision Content<br>Date: 20060328<br>Docket: T-501-06<br>Citation: 2006 FC 396<br>Ottawa, Ontario, March 28, 2006<br>PRESENT: The Honourable Mr. Justice Martineau<br>BETWEEN:<br>ARIEL JOHN BELLOSILLO<br>Plaintiff<br>and<br>HER MAJESTY THE QUEEN,<br>CORRECTIONAL SERVICE OF CANADA<br>INSTITUTIONAL HEAD OF WARKWORTH INSTITUTION<br>Defendants<br>REASONS FOR ORDER AND ORDER<br>[1] The Plaintiff is an inmate in Warkworth Institution, a penitentiary under the management and control of Correctional Service of Canada (CSC). He is currently incarcerated for an indeterminate period as a dangerous offender, having been convicted of two counts of sexual assault causing bodily harm and two counts of overcoming resistance to commit an offence by administering a drug.<br>[2] The Plaintiff is required under a Warrant Remanding a Prisoner issued by a Justice of the Peace for Ontario to attend in Provinci...</code> | <code>cluster: FACTS: The person concerned is an inmate in Warkworth Institution, a penitentiary managed by Correctional Service of Canada (CSC). He is serving an indeterminate sentence as a dangerous offender for various sexual assault charges. The person concerned has been ordered to attend Provincial Court in Ottawa on March 30, 2006, to answer to new charges. As a result, he is required to be transferred from Warkworth Institution to the Assessment Unit of Millhaven Institution, and then to the Ottawa Detention Centre. The person concerned has filed a motion for an interim injunction to prevent his transfer to the provincial facilities, citing concerns about his health and potential breaches of his rights under the Canadian Charter of Rights and Freedoms.The CSC has established a community standard for healthcare for inmates, which includes preparing a Health Status Summary for each inmate being transferred between federal and provincial facilities. In this case, the person concerned's ...</code> | <code>cluster: RULES: The court rules that the person concerned's motion for an interim injunction must fail, as the conditions for granting an interlocutory injunction have not been met. Specifically, the court finds that there is no serious issue to be tried, as the person concerned's health condition is currently under control, and he is considered fit to travel to the provincial detention facility. Additionally, the court finds that the person concerned has not established that he will suffer irreparable harm if his transfer takes place as scheduled.</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 2,250 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 370 tokens</li><li>mean: 2955.16 tokens</li><li>max: 6550 tokens</li></ul> | <ul><li>min: 32 tokens</li><li>mean: 213.29 tokens</li><li>max: 1042 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 206.64 tokens</li><li>max: 973 tokens</li></ul> | * Samples: | anchor | positive | negative | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>cluster: FACTS: Murphy v. Canada (Attorney General)<br>Court (s) Database<br>Federal Court Decisions<br>Date<br>2016-11-02<br>Neutral citation<br>2016 FC 1208<br>File numbers<br>T-192-16<br>Decision Content<br>Date: 20161102<br>Docket: T-192-16<br>Citation: 2016 FC 1208<br>Ottawa, Ontario, November 2, 2016<br>PRESENT: The Honourable Mr. Justice Brown<br>BETWEEN:<br>DAPHNE MURPHY<br>Applicant<br>and<br>THE ATTORNEY GENERAL OF CANADA<br>Respondent<br>JUDGMENT AND REASONS<br>I. Nature of the Matter [1] This is an application for judicial review brought by Daphne Murphy [the Applicant] under s. 18.1 of the Federal Courts Act, RSC 1985, c F-7 of a decision made on October 8, 2015, by a member of the Social Security Tribunal – Appeal Division (SST-AD) [SST-AD Decision] denying the Applicant’s application for leave to appeal. The Applicant sought leave in order to appeal a decision of the Social Security Tribunal – General Division (SST-GD) made on August 28, 2015 [SST-GD Decision], which had dismissed the Applicant’s appeal from a decision denying her app...</code> | <code>cluster: FACTS: The person concerned, a 58-year-old woman from Gander, Newfoundland, applied for Canada Pension Plan (CPP) disability benefits. She had a significant speech impairment and was unable to work due to a stroke she suffered in 2011 and a knee injury she sustained in 2009. She had a limited education and work experience, with only a few short-term jobs between 1979 and 2011. Her application was initially denied in 2011 and again in 2012 after reconsideration. She appealed the decision to the Social Security Tribunal – General Division (SST-GD), which conducted a paper appeal and denied her application in 2015. The SST-GD found that she failed to prove that she had a severe and prolonged disability on or before December 31, 1997, the minimum qualifying period (MQP) for CPP disability benefits. The person concerned then sought leave to appeal to the Social Security Tribunal – Appeal Division (SST-AD), which denied her application in 2015. She subsequently applied for judicial...</code> | <code>cluster: CONCLUSION: In conclusion, the court's decision in this case was based on the principle that the SST must interpret and apply the CPP Act in a liberal and generous manner. The court found that the SST-GD and SST-AD had failed to apply this principle in the person concerned's case, and that their decisions were therefore unreasonable. The court's decision was also based on the principle that the person concerned had a right to a more comprehensive disability review that considers her employability in the real world. The court's conclusion was that the SST-AD's decision was not reasonable and that the matter should be remitted to a differently constituted SST-AD for redetermination.</code> | | <code>cluster: CONCLUSION: Altamirano v. Canada (Citizenship and Immigration)<br>Court (s) Database<br>Federal Court Decisions<br>Date<br>2023-07-19<br>Neutral citation<br>2023 FC 989<br>File numbers<br>IMM-4441-22<br>Decision Content<br>Date: 20230719<br>Docket: IMM-4441-22<br>Citation: 2023 FC 989<br>Ottawa, Ontario, July 19, 2023<br>PRESENT: The Honourable Mr. Justice Ahmed<br>BETWEEN:<br>JOEL MARTINEZ ALTAMIRANO<br>EUSEBIA ROSALIA REYES LUNA<br>ABAD GILBERTO MORA REYES AZUCENA MORA REYES GAEL MARTINEZ MORA<br>Applicants<br>and<br>THE MINISTER OF CITIZENSHIP AND IMMIGRATION<br>Respondent<br>JUDGMENT AND REASONS<br>I. Overview [1] The Applicants seek judicial review of a decision of the Refugee Appeal Division (“RAD”) dated April 26, 2022, confirming the determination of the Refugee Protection Division (“RPD”) that the Applicants are neither Convention refugees nor persons in need of protection under sections 96 and 97(1) of the Immigration and Refugee Protection Act, SC 2001, c 27 (“IRPA”).<br>[2] The RAD upheld the RPD’s refusal of the refugee claim on the bas...</code> | <code>cluster: CONCLUSION: The court concluded that the RAD's decision is reasonable in light of the Applicants' circumstances and evidence. The application for judicial review is therefore dismissed.</code> | <code>cluster: SUMMARY: **(1) Facts**<br><br>The Applicants, Joel Martinez Altamirano, his wife Azucena Mora Reyes, and their child Gael Martinez Mora, along with Azucena's mother Eusebia Rosalia Reyes Luna and brother Abad Gilberto Mora Reyes, are Mexican citizens who made claims for refugee protection in Canada. The Applicants claimed to be victims of the Jalisco New Generation Cartel (CJNG) in Mexico, alleging that they were extorted and threatened after failing to pay a ransom for the release of Eusebia's son Ulises, who was kidnapped by the cartel in 2019. The Applicants claimed that they feared persecution or harm in Mexico at the hands of the CJNG cartel if they returned.<br><br>The Refugee Protection Division (RPD) found that the Applicants were not Convention refugees or persons in need of protection under sections 96 and 97 of the Immigration and Refugee Protection Act (IRPA). The RPD determined that the Applicants had a viable internal flight alternative (IFA) in Merida, Mexico, and that rel...</code> | | <code>cluster: CONCLUSION: Osipova v. Canada (Citizenship and Immigration)<br>Court (s) Database<br>Federal Court Decisions<br>Date<br>2024-07-05<br>Neutral citation<br>2024 FC 1055<br>File numbers<br>IMM-9267-23<br>Decision Content<br>Date: 20240705<br>Docket: IMM-9267-23<br>Citation: 2024 FC 1055<br>Ottawa, Ontario, July 5, 2024<br>PRESENT: The Honourable Madam Justice Aylen<br>BETWEEN:<br>LIUDMILA OSIPOVA<br>Applicant<br>and<br>THE MINISTER OF CITIZENSHIP AND IMMIGRATION<br>Respondent<br>JUDGMENT AND REASONS<br>[1] The Applicant, a 73-year old mother and grandmother of Russian citizenship, seeks judicial review of a reconsideration decision dated May 26, 2023, made by a Senior Immigration Officer [Officer] at Immigration, Refugees and Citizenship Canada, refusing the Applicant’s application for permanent residence from within Canada on humanitarian and compassionate [H&C] grounds under subsection 25(1) of the Immigration and Refugee Protection Act, SC 2001, c 27 [IRPA].<br>[2] The Applicant asserts that the Officer’s decision was unreasonable on the basis...</code> | <code>cluster: CONCLUSION: The court allowed the application for judicial review, set aside the decision, and remitted the matter back to a different officer for redetermination. Prior to the redetermination, the person concerned would be given an opportunity to provide updated submissions and documentation in support of her application. The court found that the Officer's BIOC analysis was unreasonable, which rendered the decision as a whole unreasonable, and that the person concerned had raised sufficient grounds for judicial review.</code> | <code>cluster: ISSUES: The sole issue before the court was whether the Officer's decision was reasonable. The person concerned argued that the Officer's decision was unreasonable due to several factors, including a failure to conduct a proper assessment of hardship, an error in assessing the best interests of the child, and a failure to give proper consideration to adverse country conditions in Russia.</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0314 | 100 | 0.7181 | 0.0840 | | 0.0627 | 200 | 0.0542 | 0.0354 | | 0.0941 | 300 | 0.0323 | 0.0264 | | 0.1255 | 400 | 0.0238 | 0.0305 | | 0.1568 | 500 | 0.0307 | 0.0166 | | 0.1882 | 600 | 0.0266 | 0.0173 | | 0.2196 | 700 | 0.0101 | 0.0130 | | 0.2509 | 800 | 0.0159 | 0.0111 | | 0.2823 | 900 | 0.0134 | 0.0113 | | 0.3137 | 1000 | 0.0125 | 0.0133 | | 0.3450 | 1100 | 0.0204 | 0.0111 | | 0.3764 | 1200 | 0.017 | 0.0083 | | 0.4078 | 1300 | 0.0172 | 0.0066 | | 0.4391 | 1400 | 0.0133 | 0.0047 | | 0.4705 | 1500 | 0.0141 | 0.0047 | | 0.5019 | 1600 | 0.0089 | 0.0053 | | 0.5332 | 1700 | 0.0068 | 0.0067 | | 0.5646 | 1800 | 0.0145 | 0.0053 | | 0.5960 | 1900 | 0.0096 | 0.0058 | | 0.6274 | 2000 | 0.0024 | 0.0056 | | 0.6587 | 2100 | 0.0084 | 0.0044 | | 0.6901 | 2200 | 0.0028 | 0.0035 | | 0.7215 | 2300 | 0.002 | 0.0034 | | 0.7528 | 2400 | 0.0045 | 0.0040 | | 0.7842 | 2500 | 0.0033 | 0.0044 | | 0.8156 | 2600 | 0.0013 | 0.0037 | | 0.8469 | 2700 | 0.0047 | 0.0034 | | 0.8783 | 2800 | 0.0018 | 0.0030 | | 0.9097 | 2900 | 0.0021 | 0.0030 | | 0.9410 | 3000 | 0.0041 | 0.0028 | | 0.9724 | 3100 | 0.0063 | 0.0026 | ### Framework Versions - Python: 3.11.9 - Sentence Transformers: 3.3.0 - Transformers: 4.46.2 - PyTorch: 2.4.1+cu121 - Accelerate: 1.1.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION" ]
[ "BEAR", "CAS", "MQP" ]
Non_BioNLP
tanbinh2210/mlm_finetuned_2_phobert
tanbinh2210
sentence-similarity
[ "sentence-transformers", "safetensors", "roberta", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:357018", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:tanbinh2210/mlm_finetuned_phobert", "base_model:finetune:tanbinh2210/mlm_finetuned_phobert", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,732
1,732
7
0
--- base_model: tanbinh2210/mlm_finetuned_phobert library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:357018 - loss:MultipleNegativesRankingLoss widget: - source_sentence: đánh_giá phẩm_chất chính chị của cán_bộ đang công_tác tại mặt_trận tổ_quốc qua những nội_dung nào ? sentences: - 'trách_nhiệm và mối quan_hệ công_tác trách_nhiệm và mối quan_hệ công_tác giữa học_viện với lãnh_đạo bộ_tư_pháp , các đơn_vị thuộc bộ_tư_pháp , các sở tư_pháp , cục thi_hành án dân_sự , các tổ_chức và cá_nhân khác có liên_quan được thực_hiện theo quy_chế làm_việc của bộ_tư_pháp và các quy_định cụ_thể sau : 1 . học_viện chịu sự chỉ_đạo trực_tiếp của bộ_trưởng và thứ_trưởng được bộ_trưởng phân_công phụ_trách , có trách_nhiệm tổ_chức thực_hiện , báo_cáo và chịu trách_nhiệm trước bộ_trưởng , thứ_trưởng phụ_trách và trước pháp_luật về kết_quả giải_quyết công_việc được giao . 2 . học_viện là đầu_mối tham_mưu , giúp lãnh_đạo bộ thực_hiện quan_hệ với tòa_án_nhân_dân_tối_cao , viện_kiểm_sát_nhân_dân_tối_cao , liên_đoàn luật_sư việt_nam , các bộ , ngành , địa_phương , các cơ_quan , tổ_chức khác trong lĩnh_vực thuộc phạm_vi chức_năng , nhiệm_vụ của học_viện theo quy_định của pháp_luật vè phân_cấp của bộ_tư_pháp .' - tiêu_chuẩn về phẩm_chất chính_trị , đạo_đức , lối sống 1 . trung_thành với tổ_quốc , với đảng ; kiên_định với đường_lối đổi_mới , độc_lập dân_tộc , dân_chủ và chủ_nghĩa_xã_hội của đảng , nhà_nước ; tích_cực tham_gia sự_nghiệp công_nghiệp hóa , hiện_đại_hóa đất_nước , trước_hết là việc cải_cách và hiện_đại hóa ngành , lĩnh_vực được phân_công quản_lý . 2 . có bản_lĩnh chính_trị vững_vàng , có tư_tưởng đổi_mới , dám nghĩ , dám làm , dám chịu trách_nhiệm cá_nhân ; không có biểu_hiện tiêu_cực , sách_nhiễu , cửa_quyền , tham_nhũng , lãng_phí . 3 . có lý_lịch rõ_ràng , đạo_đức tốt , lối sống lành_mạnh . 4 . có tinh_thần đoàn_kết nội_bộ ; gương_mẫu chấp_hành các chủ_trương , đường_lối của đảng , pháp_luật của nhà_nước , quy_định của cơ_quan và nơi cư_trú ; 5 . chấp_hành nguyên_tắc tập_trung_dân_chủ và quy_chế dân_chủ cơ_sở trong quá_trình thực_hiện nhiệm_vụ chính_trị được giao . - 'nội_dung đánh_giá 1 - mức_độ thực_hiện chức_trách , nhiệm_vụ được giao : thể_hiện ở khối_lượng , chất_lượng , tiến_độ , hiệu_quả của công_việc trong từng vị_trí , từng thời_gian ; tinh_thần trách_nhiệm trong công_tác . 2 - về phẩm_chất chính_trị , đạo_đức , lối sống - nhận_thức , tư_tưởng chính_trị ; việc chấp_hành chủ_trương , đường_lối và quy_chế , quy_định của đảng , chính_sách , pháp_luật của nhà_nước . - việc giữ_gìn đạo_đức và lối sống lành_mạnh ; chống quan_liêu , tham_nhũng , lãng_phí và những biểu_hiện tiêu_cực khác . - tinh_thần học_tập nâng cao trình_độ ; tính trung_thực , ý_thức tổ_chức kỷ_luật ; tinh_thần tự_phê_bình và phê_bình . - đoàn_kết , quan_hệ trong công_tác ; mối quan_hệ , tinh_thần và thái_độ phục_vụ nhân_dân . 3 - chiều_hướng và triển_vọng phát_triển .' - source_sentence: trách_nhiệm của sở y_tế trong việc chẩn_đoán xác_định tình_trạng nghiện ma_túy là gì ? sentences: - ủy ban_nhân_dân cấp tỉnh có trách_nhiệm chỉ_đạo sở lao_động - thương_binh và xã_hội , sở y_tế và các cơ_quan có liên_quan hướng_dẫn , kiểm_tra các trung_tâm chữa bệnh - giáo_dục - lao_động xã_hội và các cơ_sở cai_nghiện ma_túy tự_nguyện thực_hiện các quy_định về quy_trình điều_trị cho người nghiện ma túy theo thông_tư này và các văn_bản quy_phạm_pháp_luật có liên_quan . - '4 . trách_nhiệm của y_tế ngành : chủ_trì , phối_hợp với các đơn_vị liên_quan tổ_chức triển_khai , hướng_dẫn , thanh_tra , kiểm_tra và đánh_giá việc thực_hiện thông_tư này trong phạm_vi quản_lý của bộ , ngành . 5 . trách_nhiệm của cơ_sở y_tế : a ) tổ_chức thực_hiện các quy_định về tiêu_chuẩn chẩn_đoán và quy_trình chuyên_môn xác_định tình_trạng nghiện ma túy theo đúng quy_định tại thông_tư này và các quy_định có liên_quan ; b ) tổ_chức , đào_tạo , tập_huấn , truyền_thông , phổ_biến cho các đối_tượng có liên_quan các quy_định về xác_định tình_trạng nghiện ma_túy ; c ) tổ_chức điều_trị hội_chứng_cai , các rối_loạn tâm_thần và các bệnh kèm theo ( nếu có ) cho người cần xác_định tình_trạng nghiện ma_túy trong thời_gian xác_định tình_trạng nghiện ma_túy ; d ) thực_hiện việc lưu_giữ hồ_sơ xác_định tình_trạng nghiện ma_túy theo quy_định của pháp_luật về khám bệnh , chữa bệnh ; đ ) báo_cáo kết_quả hoạt_động xác_định tình_trạng nghiện ma_túy của cơ_sở y_tế .' - 'điều 28 . hồ_sơ , thủ_tục đăng_ký tạm_trú , gia_hạn tạm_trú 1 . hồ_sơ đăng_ký tạm_trú bao_gồm : a ) tờ khai thay_đổi thông_tin cư_trú ; đối_với người đăng_ký tạm_trú là người chưa thành_niên thì trong tờ khai phải ghi rõ ý_kiến đồng_ý của cha , mẹ hoặc người giám_hộ , trừ trường_hợp đã có ý_kiến đồng_ý bằng văn_bản ; b ) giấy_tờ , tài_liệu chứng_minh chỗ ở hợp_pháp . 2 . người đăng_ký tạm_trú nộp hồ_sơ đăng_ký tạm_trú đến cơ_quan đăng_ký cư_trú nơi mình dự_kiến tạm_trú . khi tiếp_nhận hồ_sơ đăng_ký tạm_trú , cơ_quan đăng_ký cư_trú kiểm_tra và cấp phiếu tiếp_nhận hồ_sơ cho người đăng_ký ; trường_hợp hồ_sơ chưa đầy_đủ thì hướng_dẫn người đăng_ký bổ_sung hồ_sơ . trong thời_hạn 03 ngày làm_việc kể từ ngày nhận được hồ_sơ đầy_đủ và hợp_lệ , cơ_quan đăng_ký cư_trú có trách_nhiệm thẩm_định , cập_nhật thông_tin về nơi tạm_trú mới , thời_hạn tạm_trú của người đăng_ký vào cơ_sở_dữ_liệu về cư_trú và thông_báo cho người đăng_ký về việc đã cập_nhật thông_tin đăng_ký tạm_trú ; trường_hợp từ_chối đăng_ký thì phải trả_lời bằng văn_bản và nêu rõ lý_do .' - source_sentence: chánh_án tòa_án quân_sự trung_ương không được hưởng chế_độ phụ_cấp đặc_thù trong trường_hợp nào ? sentences: - 'iii . cách tính trả . 1 . đối_tượng quy_định tại điều 1 thông_tư này được bổ_nhiệm từ tháng nào thì được hưởng chế_độ phụ_cấp đặc_thù từ tháng đó . khi bị miễn_nhiệm , cách_chức , từ trần hoặc thôi giữ chức_danh quy_định tại điều 1 thông_tư này từ tháng nào thì thôi_hưởng chế_độ phụ_cấp đặc_thù từ tháng tiếp_theo . các trường_hợp sau không được hưởng phụ đặc_thù : - thời_gian được cử đi công_tác , làm_việc , học_tập ở nước_ngoài được hưởng 40 % tiền_lương theo quy_định tại khoản 4 , điều 8 nghị_định số 204 / 2004 / nđ-cp ngày 14/12/2004 của chính_phủ ; - thời_gian đi công_tác , học_tập ở trong nước không trực_tiếp làm công_tác chuyên_môn từ 3 tháng trở lên ; - thời_gian bị ốm_đau , thai_sản nghỉ vượt quá thời_hạn quy_định của luật bảo_hiểm_xã_hội ; - thời_gian nghỉ_việc riêng không hưởng lương từ 1 tháng trở lên ; - thời_gian bị đình_chỉ công_tác . 2 . phụ_cấp đặc_thù đối_với một_số chức_danh tư_pháp và thanh_tra trong quân_đội không được tính để hưởng các chế_độ bảo_hiểm_xã_hội , bảo_hiểm_y_tế . 3 . mức phụ_cấp đặc_thù quy_định tại thông_tư này được tính trả cùng kỳ lương hàng tháng ; đối_tượng thuộc đơn_vị nào do đơn_vị đó chi_trả và hạch_toán vào mục 102 , tiểu_mục 08 , ngành tương_ứng trong mục_lục ngân_sách nhà_nước áp_dụng trong quân_đội .' - 'cách tính hưởng phụ_cấp 1 . mức phụ_cấp đặc_thù quy_định tại điều 2 quyết_định này được tính trên mức lương cấp_bậc quân_hàm , ngạch bậc hiện_hưởng hoặc phụ_cấp quân_hàm_cộng phụ_cấp chức_vụ lãnh_đạo và phụ_cấp thâm_niên vượt khung ( nếu có ) . 2 . khi chuyển công_tác khác mà không giữ các chức_vụ , chức_danh quy_định cho các đối_tượng tại điều 2 quyết_định này hoặc nghỉ chuẩn_bị hưu hoặc thôi phục_vụ trong quân_đội thì thôi_hưởng phụ_cấp đặc_thù từ tháng tiếp_theo . 3 . thời_gian không được tính hưởng phụ_cấp đặc_thù , bao_gồm : a ) thời_gian đi công_tác , làm_việc học_tập ở nước_ngoài được hưởng tiền_lương theo quy_định tại khoản 4 điều 8 nghị_định số 204 / 2004 / nđ-cp ngày 14 tháng 12 năm 2004 của chính_phủ về chế_độ tiền_lương đối_với cán_bộ , công_chức , viên_chức và lực_lượng_vũ_trang ; b ) thời_gian nghỉ_việc không hưởng lương liên_tục từ 1 tháng trở lên ; c ) thời_gian nghỉ_việc hưởng bảo_hiểm_xã_hội theo quy_định của pháp_luật về bảo_hiểm_xã_hội ; d ) thời_gian bị đình_chỉ công_tác hoặc bị tạm giữ , tạm giam .' - 'trực_ca của thuyền_viên ... 2 . trực_ca là nhiệm_vụ của thuyền_viên và phải được duy_trì một_cách thích_hợp , hiệu_quả để đảm_bảo an_toàn , an_ninh và phòng_ngừa ô_nhiễm môi_trường . ca trực của mỗi thuyền_viên được chia thành ca biển và ca bờ : a ) thời_gian trực_ca biển là 04 giờ và mỗi ngày trực 02 ca cách nhau 08 giờ ; trường_hợp có thay_đổi múi_giờ thì thời_gian trực_ca biển do thuyền_trưởng quyết_định ; b ) thời_gian trực ca bờ do thuyền_trưởng quy_định , căn_cứ vào điều_kiện cụ_thể khi tàu neo_đậu . ...' - source_sentence: quy_định về xử_phạt vi_phạm hành_chính đối_với hành_vi đánh người gây thương_tích ? sentences: - 'vi_phạm quy_định về trật_tự công_cộng ... 5 . phạt tiền từ 5.000.000 đồng đến 8.000.000 đồng đối_với một trong những hành_vi sau đây : a ) cố_ý gây thương_tích hoặc gây tổn_hại cho sức_khỏe của người khác nhưng không bị truy_cứu trách_nhiệm hình_sự ; b ) gây_rối trật_tự công_cộng mà có mang theo các loại vũ_khí thô_sơ , công_cụ hỗ_trợ hoặc công_cụ , đồ_vật , phương_tiện khác có khả_năng sát_thương ; c ) quay_phim , chụp ảnh , vẽ sơ_đồ địa_điểm cấm , khu_vực cấm liên_quan đến quốc_phòng , an_ninh ; d ) dâm_ô đối_với người dưới 16 tuổi nhưng không bị truy_cứu trách_nhiệm hình_sự ; đ ) sàm sỡ , quấy_rối tình_dục ; e ) khiêu_dâm , kích_dục ở nơi công_cộng ; g ) thực_hiện thiết_kế , sản_xuất , sửa_chữa , bảo_dưỡng , thử_nghiệm tàu bay , động_cơ tàu bay , cánh_quạt tàu bay và trang_bị , thiết_bị của tàu bay không người lái , phương_tiện bay siêu_nhẹ có chủng_loại hoặc chất_lượng không phù_hợp với loại sản_phẩm đã đăng_ký theo giấy_phép do cơ_quan có thẩm_quyền cấp ; h ) sử_dụng tàu bay không người lái và các phương_tiện bay siêu nhẹ phóng , bắn , thả từ trên không các loại vật , chất gây hại hoặc chứa_đựng nguy_cơ gây hại khi không được phép . ...' - '" 5 . phạt tiền từ 5.000.000 đồng đến 8.000.000 đồng đối_với một trong những hành_vi sau đây : a ) cố_ý gây thương_tích hoặc gây tổn_hại cho sức_khỏe của người khác nhưng không bị truy_cứu trách_nhiệm hình_sự ; b ) gây_rối trật_tự công_cộng mà có mang theo các loại vũ_khí thô_sơ , công_cụ hỗ_trợ hoặc công_cụ , đồ_vật , phương_tiện khác có khả_năng sát_thương ; ... "' - 'tiêu_chuẩn cơ_sở_vật_chất mức_độ 1 ... 3 . khối phòng hỗ_trợ học_tập thư_viện : có phòng đọc cho học_sinh tối_thiểu 35 chỗ , phòng đọc giáo_viên tối_thiểu 20 chỗ . 4 . khối phụ_trợ_a ) phòng nghỉ giáo_viên : bố_trí liền kề với khối phòng học_tập , bảo_đảm 10 lớp có 01 phòng ; b ) khu vệ_sinh học_sinh : khu vệ_sinh riêng cho mỗi tầng nhà , mỗi dãy phòng học .' - source_sentence: hồ_sơ xin thôi quốc_tịch việt_nam bao_gồm những gì ? sentences: - 3 . bản_sao giấy khai_sinh của người con chưa thành_niên cùng thôi quốc_tịch việt_nam theo cha_mẹ hoặc giấy_tờ hợp_lệ khác chứng_minh quan_hệ cha_con , mẹ_con . trường_hợp chỉ người cha hoặc người mẹ thôi quốc_tịch việt_nam mà con chưa thành_niên sinh_sống cùng người đó thôi quốc_tịch việt_nam theo cha hoặc mẹ thì phải nộp văn_bản thỏa_thuận có đủ chữ_ký của cha_mẹ về việc xin thôi quốc_tịch việt_nam cho con . văn_bản thỏa_thuận không phải chứng_thực chữ_ký ; người đứng đơn xin thôi quốc_tịch việt_nam cho con phải chịu trách_nhiệm về tính chính_xác chữ_ký của người kia . trường_hợp cha , mẹ đã chết , bị mất năng_lực hành_vi dân_sự hoặc hạn_chế năng_lực hành_vi dân_sự thì văn_bản thỏa_thuận được thay_thế bằng giấy_tờ chứng_minh cha , mẹ đã chết , bị mất hoặc hạn_chế năng_lực hành_vi dân_sự . 4 . hồ_sơ xin thôi quốc_tịch việt_nam phải lập thành 3 bộ , được lưu_trữ tại văn_phòng chủ_tịch nước , bộ_tư_pháp và cơ_quan thụ_lý hồ_sơ . - 'điều 28 . hồ_sơ xin thôi quốc_tịch việt_nam 1 . hồ_sơ xin thôi quốc_tịch việt_nam bao_gồm : a ) đơn xin thôi quốc_tịch việt_nam ; b ) bản khai_lý_lịch ; c ) bản_sao hộ_chiếu việt_nam , giấy_chứng_minh nhân_dân hoặc giấy_tờ khác quy_định tại điều 11 của luật này ; d ) phiếu lý_lịch tư_pháp do cơ_quan có thẩm_quyền của việt_nam cấp . phiếu lý_lịch tư_pháp phải là phiếu được cấp không quá 90 ngày tính đến ngày nộp hồ_sơ ; đ ) giấy_tờ xác_nhận về việc người đó đang làm thủ_tục nhập quốc_tịch nước_ngoài , trừ trường_hợp pháp_luật nước đó không quy_định về việc cấp giấy này ; e ) giấy xác_nhận không nợ thuế do cục thuế nơi người xin thôi quốc_tịch việt_nam cư_trú cấp ; g ) đối_với người trước_đây là cán_bộ , công_chức , viên_chức hoặc phục_vụ trong lực_lượng_vũ_trang nhân_dân việt_nam đã nghỉ hưu , thôi_việc , bị miễn_nhiệm , bãi_nhiệm , cách_chức hoặc giải_ngũ , phục_viên chưa quá 5 năm thì còn phải nộp giấy của cơ_quan , tổ_chức , đơn_vị đã ra quyết_định cho nghỉ hưu , cho thôi_việc , miễn_nhiệm , bãi_nhiệm , cách_chức hoặc giải_ngũ , phục_viên xác_nhận việc thôi quốc_tịch việt_nam của người đó không phương_hại đến lợi_ích quốc_gia của việt_nam .' - '" điều 23 . vi_phạm quy_định về hoạt_động ngoại_hối ... 4 . phạt tiền từ 30.000.000 đồng đến 50.000.000 đồng đối_với một trong các hành_vi vi_phạm sau đây : ... n ) giao_dịch , báo_giá , định_giá , ghi_giá trong hợp_đồng , thỏa_thuận , niêm_yết , quảng_cáo giá hàng hóa , dịch_vụ , quyền sử_dụng đất và các hình_thức tương_tự khác ( bao_gồm cả quy_đổi hoặc điều_chỉnh giá hàng hóa , dịch_vụ , giá_trị của hợp_đồng , thỏa_thuận ) bằng ngoại_tệ không đúng quy_định của pháp_luật ; ... "' --- # SentenceTransformer based on tanbinh2210/mlm_finetuned_phobert This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [tanbinh2210/mlm_finetuned_phobert](https://huggingface.co/tanbinh2210/mlm_finetuned_phobert) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [tanbinh2210/mlm_finetuned_phobert](https://huggingface.co/tanbinh2210/mlm_finetuned_phobert) <!-- at revision 81acdda75847cf90ee2624deff7de4aa59fd1004 --> - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tanbinh2210/mlm_finetuned_2_phobert") # Run inference sentences = [ 'hồ_sơ xin thôi quốc_tịch việt_nam bao_gồm những gì ?', 'điều 28 . hồ_sơ xin thôi quốc_tịch việt_nam 1 . hồ_sơ xin thôi quốc_tịch việt_nam bao_gồm : a ) đơn xin thôi quốc_tịch việt_nam ; b ) bản khai_lý_lịch ; c ) bản_sao hộ_chiếu việt_nam , giấy_chứng_minh nhân_dân hoặc giấy_tờ khác quy_định tại điều 11 của luật này ; d ) phiếu lý_lịch tư_pháp do cơ_quan có thẩm_quyền của việt_nam cấp . phiếu lý_lịch tư_pháp phải là phiếu được cấp không quá 90 ngày tính đến ngày nộp hồ_sơ ; đ ) giấy_tờ xác_nhận về việc người đó đang làm thủ_tục nhập quốc_tịch nước_ngoài , trừ trường_hợp pháp_luật nước đó không quy_định về việc cấp giấy này ; e ) giấy xác_nhận không nợ thuế do cục thuế nơi người xin thôi quốc_tịch việt_nam cư_trú cấp ; g ) đối_với người trước_đây là cán_bộ , công_chức , viên_chức hoặc phục_vụ trong lực_lượng_vũ_trang nhân_dân việt_nam đã nghỉ hưu , thôi_việc , bị miễn_nhiệm , bãi_nhiệm , cách_chức hoặc giải_ngũ , phục_viên chưa quá 5 năm thì còn phải nộp giấy của cơ_quan , tổ_chức , đơn_vị đã ra quyết_định cho nghỉ hưu , cho thôi_việc , miễn_nhiệm , bãi_nhiệm , cách_chức hoặc giải_ngũ , phục_viên xác_nhận việc thôi quốc_tịch việt_nam của người đó không phương_hại đến lợi_ích quốc_gia của việt_nam .', '3 . bản_sao giấy khai_sinh của người con chưa thành_niên cùng thôi quốc_tịch việt_nam theo cha_mẹ hoặc giấy_tờ hợp_lệ khác chứng_minh quan_hệ cha_con , mẹ_con . trường_hợp chỉ người cha hoặc người mẹ thôi quốc_tịch việt_nam mà con chưa thành_niên sinh_sống cùng người đó thôi quốc_tịch việt_nam theo cha hoặc mẹ thì phải nộp văn_bản thỏa_thuận có đủ chữ_ký của cha_mẹ về việc xin thôi quốc_tịch việt_nam cho con . văn_bản thỏa_thuận không phải chứng_thực chữ_ký ; người đứng đơn xin thôi quốc_tịch việt_nam cho con phải chịu trách_nhiệm về tính chính_xác chữ_ký của người kia . trường_hợp cha , mẹ đã chết , bị mất năng_lực hành_vi dân_sự hoặc hạn_chế năng_lực hành_vi dân_sự thì văn_bản thỏa_thuận được thay_thế bằng giấy_tờ chứng_minh cha , mẹ đã chết , bị mất hoặc hạn_chế năng_lực hành_vi dân_sự . 4 . hồ_sơ xin thôi quốc_tịch việt_nam phải lập thành 3 bộ , được lưu_trữ tại văn_phòng chủ_tịch nước , bộ_tư_pháp và cơ_quan thụ_lý hồ_sơ .', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### json * Dataset: json * Size: 357,018 training samples * Columns: <code>query</code>, <code>pos</code>, and <code>neg</code> * Approximate statistics based on the first 1000 samples: | | query | pos | neg | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 5 tokens</li><li>mean: 16.98 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 151.14 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 114.34 tokens</li><li>max: 256 tokens</li></ul> | * Samples: | query | pos | neg | |:--------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>thủ_tục hưởng bảo_hiểm_xã_hội một lần gồm bao_nhiêu bước thực_hiện ?</code> | <code>bước 1 . lập , nộp hồ_sơ nlđ lập hồ_sơ theo quy_định tại mục_9.3 ( thành_phần hồ_sơ ) và nộp cho cơ_quan bhxh nơi cư_trú . bước 2 . cơ_quan bhxh tiếp_nhận hồ_sơ và giải_quyết theo quy_định . bước 3 . nhận kết_quả nlđ nhận kết_quả giải_quyết , gồm : quyết_định về việc hưởng bhxh một lần ; bản quá_trình đóng bhxh ; tiền trợ_cấp .</code> | <code>3 . giải_quyết hưởng bảo_hiểm_xã_hội một lần trong thời_hạn 10 ngày tính đến thời_điểm chấm_dứt hợp_đồng lao_động hoặc thời_điểm giấy_phép lao_động , chứng_chỉ hành_nghề , giấy_phép hành_nghề hết hiệu_lực ( tùy thuộc điều_kiện nào đến trước ) mà người lao_động không tiếp_tục làm_việc theo hợp_đồng lao_động hoặc không được gia_hạn giấy_phép , người lao_động có yêu_cầu hưởng bảo_hiểm_xã_hội một lần nộp hồ_sơ theo quy_định cho cơ_quan bảo_hiểm_xã_hội . trong thời_hạn 05 ngày làm_việc kể từ ngày nhận đủ hồ_sơ theo quy_định , cơ_quan bảo_hiểm_xã_hội có trách_nhiệm giải_quyết và tổ_chức chi_trả cho người lao_động , trường_hợp không giải_quyết thì phải trả_lời bằng văn_bản và nêu rõ lý_do . "</code> | | <code>vụ đất_đai thuộc bộ tài_nguyên và môi_trường có những chức_danh lãnh_đạo nào ?</code> | <code>lãnh_đạo vụ 1 . vụ đất_đai có vụ trưởng và không quá 03 phó vụ trưởng . 2 . vụ trưởng vụ đất_đai chịu trách_nhiệm trước bộ_trưởng và trước pháp_luật về mọi hoạt_động của vụ ; ban_hành quy_chế làm_việc của vụ ; ký các văn_bản về chuyên_môn , nghiệp_vụ theo chức_năng , nhiệm_vụ được giao và các văn_bản khác theo phân_công , ủy_quyền của bộ_trưởng . 3 . phó vụ trưởng vụ đất_đai giúp vụ trưởng , chịu trách_nhiệm trước vụ trưởng và trước pháp_luật về lĩnh_vực công_tác được phân_công .</code> | <code>cơ_cấu tổ_chức 1 . lãnh_đạo vụ : a ) lãnh_đạo vụ có vụ trưởng và các phó vụ trưởng do bộ_trưởng bộ nông_nghiệp và phát_triển nông_thôn bổ_nhiệm , miễn_nhiệm theo quy_định ; ...</code> | | <code>lãnh_đạo báo pháp_luật việt_nam gồm có những_ai ?</code> | <code>cơ_cấu tổ_chức , biên_chế 1 . cơ_cấu tổ_chức của báo , gồm : a ) lãnh_đạo báo : lãnh_đạo báo gồm tổng_biên_tập và không quá 03 ( ba ) phó tổng_biên_tập . tổng_biên_tập chịu trách_nhiệm trước bộ_trưởng và trước pháp_luật về việc thực_hiện các chức_năng , nhiệm_vụ , quyền_hạn của báo . các phó tổng_biên_tập giúp tổng_biên_tập quản_lý , điều_hành hoạt_động của báo ; được tổng_biên_tập phân_công trực_tiếp quản_lý , điều_hành một_số lĩnh_vực hoạt_động của báo ; chịu trách_nhiệm trước tổng_biên_tập và trước pháp_luật về việc quản_lý , điều_hành những lĩnh_vực công_tác được phân_công . b ) các đơn_vị trực_thuộc báo - ban thư_ký tòa_soạn ; - ban thời_sự chính_trị ; - ban kinh_tế ; - ban nội_chính ; - ban văn_hóa - xã_hội ; - ban bạn_đọc ; - ban doanh_nhân và pháp_luật ; - ban báo pháp_luật điện_tử ; - ban chuyên_đề báo in ; - ban chuyên_đề báo_điện_tử ; - ban trị_sự ; - phòng kế_hoạch - tài_chính . việc thành_lập , tổ_chức lại , giải_thể các đơn_vị trực_thuộc báo tại điểm này do bộ_trưởng quyế...</code> | <code>cơ_cấu tổ_chức , biên_chế 1 . cơ_cấu tổ_chức a ) lãnh_đạo tạp_chí : lãnh_đạo tạp_chí gồm tổng_biên_tập và không quá 03 ( ba ) phó tổng_biên_tập . tổng_biên_tập chịu trách_nhiệm trước bộ_trưởng và trước pháp_luật về việc thực_hiện chức_năng , nhiệm_vụ , quyền_hạn của tạp_chí . các phó tổng_biên_tập giúp tổng_biên_tập quản_lý , điều_hành hoạt_động của tạp_chí ; được tổng_biên_tập phân_công trực_tiếp quản_lý , điều_hành một_số lĩnh_vực hoạt_động của tạp_chí ; chịu trách_nhiệm trước tổng_biên_tập và trước pháp_luật về việc quản_lý , điều_hành các lĩnh_vực đã được phân_công . ...</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### json * Dataset: json * Size: 357,018 evaluation samples * Columns: <code>query</code>, <code>pos</code>, and <code>neg</code> * Approximate statistics based on the first 1000 samples: | | query | pos | neg | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 6 tokens</li><li>mean: 17.15 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 153.03 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 114.34 tokens</li><li>max: 256 tokens</li></ul> | * Samples: | query | pos | neg | |:--------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>kỳ thi tốt_nghiệp thpt quốc_gia được tổ_chức nhằm mục_đích gì ?</code> | <code>“ điều 2 . mục_đích , yêu_cầu 1 . thi tốt_nghiệp thpt nhằm mục_đích : đánh_giá kết_quả học_tập của người học theo mục_tiêu giáo_dục của chương_trình giáo_dục_phổ_thông cấp thpt , chương_trình gdtx cấp thpt ( gọi chung là chương_trình thpt ) ; lấy kết_quả thi để xét công_nhận tốt_nghiệp thpt ; làm cơ_sở đánh_giá chất_lượng dạy , học của trường phổ_thông và công_tác chỉ_đạo của các cơ_quan quản_lý giáo_dục . các cơ_sở giáo_dục đại_học , giáo_dục nghề_nghiệp có_thể sử_dụng kết_quả thi tốt_nghiệp thpt để tuyển_sinh . 2 . kỳ thi tốt_nghiệp thpt ( gọi tắt là kỳ thi ) phải bảo_đảm yêu_cầu nghiêm_túc , trung_thực , khách_quan , công_bằng . ”</code> | <code>" điều 12 . đối_tượng , điều_kiện dự thi 1 . đối_tượng dự thi gồm : a ) người đã học xong chương_trình thpt trong năm tổ_chức kỳ_thi ; b ) người đã học xong chương_trình thpt nhưng chưa thi tốt_nghiệp thpt hoặc đã thi nhưng chưa tốt_nghiệp thpt ở những năm trước ; c ) người đã có bằng tốt_nghiệp thpt , người đã có bằng tốt_nghiệp trung_cấp dự thi để lấy kết_quả làm cơ_sở đăng_ký xét tuyển_sinh ; d ) một_số trường_hợp đặc_biệt khác do bộ_trưởng bộ gdđt quyết_định . "</code> | | <code>chánh_án tòa_án quân_sự trung_ương không được hưởng chế_độ phụ_cấp đặc_thù trong trường_hợp nào ?</code> | <code>iii . cách tính trả . 1 . đối_tượng quy_định tại điều 1 thông_tư này được bổ_nhiệm từ tháng nào thì được hưởng chế_độ phụ_cấp đặc_thù từ tháng đó . khi bị miễn_nhiệm , cách_chức , từ trần hoặc thôi giữ chức_danh quy_định tại điều 1 thông_tư này từ tháng nào thì thôi_hưởng chế_độ phụ_cấp đặc_thù từ tháng tiếp_theo . các trường_hợp sau không được hưởng phụ đặc_thù : - thời_gian được cử đi công_tác , làm_việc , học_tập ở nước_ngoài được hưởng 40 % tiền_lương theo quy_định tại khoản 4 , điều 8 nghị_định số 204 / 2004 / nđ-cp ngày 14/12/2004 của chính_phủ ; - thời_gian đi công_tác , học_tập ở trong nước không trực_tiếp làm công_tác chuyên_môn từ 3 tháng trở lên ; - thời_gian bị ốm_đau , thai_sản nghỉ vượt quá thời_hạn quy_định của luật bảo_hiểm_xã_hội ; - thời_gian nghỉ_việc riêng không hưởng lương từ 1 tháng trở lên ; - thời_gian bị đình_chỉ công_tác . 2 . phụ_cấp đặc_thù đối_với một_số chức_danh tư_pháp và thanh_tra trong quân_đội không được tính để hưởng các chế_độ bảo_hiểm_xã_hội , bảo_...</code> | <code>cách tính hưởng phụ_cấp 1 . mức phụ_cấp đặc_thù quy_định tại điều 2 quyết_định này được tính trên mức lương cấp_bậc quân_hàm , ngạch bậc hiện_hưởng hoặc phụ_cấp quân_hàm_cộng phụ_cấp chức_vụ lãnh_đạo và phụ_cấp thâm_niên vượt khung ( nếu có ) . 2 . khi chuyển công_tác khác mà không giữ các chức_vụ , chức_danh quy_định cho các đối_tượng tại điều 2 quyết_định này hoặc nghỉ chuẩn_bị hưu hoặc thôi phục_vụ trong quân_đội thì thôi_hưởng phụ_cấp đặc_thù từ tháng tiếp_theo . 3 . thời_gian không được tính hưởng phụ_cấp đặc_thù , bao_gồm : a ) thời_gian đi công_tác , làm_việc học_tập ở nước_ngoài được hưởng tiền_lương theo quy_định tại khoản 4 điều 8 nghị_định số 204 / 2004 / nđ-cp ngày 14 tháng 12 năm 2004 của chính_phủ về chế_độ tiền_lương đối_với cán_bộ , công_chức , viên_chức và lực_lượng_vũ_trang ; b ) thời_gian nghỉ_việc không hưởng lương liên_tục từ 1 tháng trở lên ; c ) thời_gian nghỉ_việc hưởng bảo_hiểm_xã_hội theo quy_định của pháp_luật về bảo_hiểm_xã_hội ; d ) thời_gian bị đình_chỉ cô...</code> | | <code>nhân_viên hải_quan có thuộc đối_tượng được hưởng phụ_cấp ưu_đãi theo nghề đối_với công_chức hải_quan không ?</code> | <code>đối_tượng và phạm_vi áp_dụng tổng_cục trưởng tổng_cục hải_quan và công_chức đã được xếp lương theo các ngạch công_chức hải_quan , gồm : kiểm_tra_viên cao_cấp hải_quan , kiểm_tra_viên chính hải_quan , kiểm_tra_viên hải_quan , kiểm_tra_viên hải_quan ( cao_đẳng ) , kiểm_tra_viên trung_cấp hải_quan , nhân_viên hải_quan . 2 . nguyên_tắc áp_dụng a ) đối_tượng được hưởng phụ_cấp ưu_đãi theo nghề hải_quan quy_định tại khoản 1 mục i thông_tư này là những người được cấp có thẩm_quyền quyết_định bổ_nhiệm vào_ngạch hoặc chức_danh theo quy_định của pháp_luật . b ) công_chức được bổ_nhiệm vào_ngạch hoặc chức_danh nào thì được hưởng phụ_cấp ưu_đãi quy_định đối_với ngạch hoặc chức_danh đó . c ) công_chức được bổ_nhiệm vào ngạch công_chức hải_quan cao hơn ( nâng_ngạch ) mà tổng mức tiền_lương cộng phụ_cấp ưu_đãi theo nghề hải_quan ở ngạch được bổ_nhiệm thấp hơn tổng mức tiền_lương cộng phụ_cấp ưu_đãi theo nghề hải_quan đã hưởng ở ngạch cũ thì được bảo_lưu phần chênh_lệch giữa tổng mức tiền_lương cộng p...</code> | <code>đối_tượng và phạm_vi áp_dụng tổng_cục trưởng tổng_cục hải_quan và công_chức đã được xếp lương theo các ngạch công_chức hải_quan , gồm : kiểm_tra_viên cao_cấp hải_quan , kiểm_tra_viên chính hải_quan , kiểm_tra_viên hải_quan , kiểm_tra_viên hải_quan ( cao_đẳng ) , kiểm_tra_viên trung_cấp hải_quan , nhân_viên hải_quan .</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `overwrite_output_dir`: True - `per_device_train_batch_size`: 48 - `per_device_eval_batch_size`: 48 - `learning_rate`: 1e-05 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: True - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 48 - `per_device_eval_batch_size`: 48 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 1e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | |:------:|:-----:|:-------------:| | 0.0168 | 100 | 0.8316 | | 0.0336 | 200 | 0.7016 | | 0.0504 | 300 | 0.618 | | 0.0672 | 400 | 0.581 | | 0.0840 | 500 | 0.5548 | | 0.1008 | 600 | 0.5178 | | 0.1176 | 700 | 0.4886 | | 0.1344 | 800 | 0.487 | | 0.1512 | 900 | 0.4726 | | 0.1680 | 1000 | 0.4579 | | 0.1848 | 1100 | 0.4643 | | 0.2016 | 1200 | 0.4475 | | 0.2185 | 1300 | 0.4233 | | 0.2353 | 1400 | 0.4346 | | 0.2521 | 1500 | 0.4391 | | 0.2689 | 1600 | 0.4272 | | 0.2857 | 1700 | 0.4204 | | 0.3025 | 1800 | 0.4321 | | 0.3193 | 1900 | 0.4198 | | 0.3361 | 2000 | 0.4147 | | 0.3529 | 2100 | 0.4062 | | 0.3697 | 2200 | 0.3968 | | 0.3865 | 2300 | 0.4089 | | 0.4033 | 2400 | 0.3908 | | 0.4201 | 2500 | 0.3993 | | 0.4369 | 2600 | 0.3837 | | 0.4537 | 2700 | 0.4001 | | 0.4705 | 2800 | 0.3872 | | 0.4873 | 2900 | 0.3727 | | 0.5041 | 3000 | 0.3728 | | 0.5209 | 3100 | 0.3765 | | 0.5377 | 3200 | 0.3834 | | 0.5545 | 3300 | 0.376 | | 0.5713 | 3400 | 0.3614 | | 0.5881 | 3500 | 0.3695 | | 0.6049 | 3600 | 0.3633 | | 0.6217 | 3700 | 0.3628 | | 0.6385 | 3800 | 0.3589 | | 0.6554 | 3900 | 0.3363 | | 0.6722 | 4000 | 0.3434 | | 0.6890 | 4100 | 0.342 | | 0.7058 | 4200 | 0.3473 | | 0.7226 | 4300 | 0.3457 | | 0.7394 | 4400 | 0.344 | | 0.7562 | 4500 | 0.3487 | | 0.7730 | 4600 | 0.332 | | 0.7898 | 4700 | 0.3487 | | 0.8066 | 4800 | 0.3416 | | 0.8234 | 4900 | 0.3386 | | 0.8402 | 5000 | 0.3182 | | 0.8570 | 5100 | 0.3473 | | 0.8738 | 5200 | 0.3313 | | 0.8906 | 5300 | 0.3235 | | 0.9074 | 5400 | 0.3297 | | 0.9242 | 5500 | 0.336 | | 0.9410 | 5600 | 0.3319 | | 0.9578 | 5700 | 0.3328 | | 0.9746 | 5800 | 0.317 | | 0.9914 | 5900 | 0.3288 | | 1.0082 | 6000 | 0.3346 | | 1.0250 | 6100 | 0.316 | | 1.0418 | 6200 | 0.3278 | | 1.0586 | 6300 | 0.3044 | | 1.0754 | 6400 | 0.3122 | | 1.0923 | 6500 | 0.3089 | | 1.1091 | 6600 | 0.2975 | | 1.1259 | 6700 | 0.3017 | | 1.1427 | 6800 | 0.2868 | | 1.1595 | 6900 | 0.288 | | 1.1763 | 7000 | 0.301 | | 1.1931 | 7100 | 0.2717 | | 1.2099 | 7200 | 0.2711 | | 1.2267 | 7300 | 0.2642 | | 1.2435 | 7400 | 0.2602 | | 1.2603 | 7500 | 0.2689 | | 1.2771 | 7600 | 0.2577 | | 1.2939 | 7700 | 0.2595 | | 1.3107 | 7800 | 0.2559 | | 1.3275 | 7900 | 0.2587 | | 1.3443 | 8000 | 0.2421 | | 1.3611 | 8100 | 0.241 | | 1.3779 | 8200 | 0.2551 | | 1.3947 | 8300 | 0.2553 | | 1.4115 | 8400 | 0.2383 | | 1.4283 | 8500 | 0.2479 | | 1.4451 | 8600 | 0.2535 | | 1.4619 | 8700 | 0.241 | | 1.4787 | 8800 | 0.246 | | 1.4955 | 8900 | 0.2403 | | 1.5124 | 9000 | 0.2334 | | 1.5292 | 9100 | 0.2435 | | 1.5460 | 9200 | 0.2338 | | 1.5628 | 9300 | 0.2386 | | 1.5796 | 9400 | 0.2347 | | 1.5964 | 9500 | 0.2413 | | 1.6132 | 9600 | 0.2353 | | 1.6300 | 9700 | 0.2304 | | 1.6468 | 9800 | 0.2318 | | 1.6636 | 9900 | 0.2204 | | 1.6804 | 10000 | 0.2317 | | 1.6972 | 10100 | 0.2235 | | 1.7140 | 10200 | 0.2346 | | 1.7308 | 10300 | 0.2375 | | 1.7476 | 10400 | 0.2318 | | 1.7644 | 10500 | 0.2294 | | 1.7812 | 10600 | 0.2314 | | 1.7980 | 10700 | 0.2372 | | 1.8148 | 10800 | 0.237 | | 1.8316 | 10900 | 0.2117 | | 1.8484 | 11000 | 0.2364 | | 1.8652 | 11100 | 0.235 | | 1.8820 | 11200 | 0.2196 | | 1.8988 | 11300 | 0.2304 | | 1.9156 | 11400 | 0.2283 | | 1.9324 | 11500 | 0.2381 | | 1.9493 | 11600 | 0.2333 | | 1.9661 | 11700 | 0.2354 | | 1.9829 | 11800 | 0.2156 | | 1.9997 | 11900 | 0.2387 | | 2.0165 | 12000 | 0.2345 | | 2.0333 | 12100 | 0.2253 | | 2.0501 | 12200 | 0.2319 | | 2.0669 | 12300 | 0.2232 | | 2.0837 | 12400 | 0.213 | | 2.1005 | 12500 | 0.2189 | | 2.1173 | 12600 | 0.2092 | | 2.1341 | 12700 | 0.2129 | | 2.1509 | 12800 | 0.2005 | | 2.1677 | 12900 | 0.2068 | | 2.1845 | 13000 | 0.22 | | 2.2013 | 13100 | 0.1941 | | 2.2181 | 13200 | 0.1869 | | 2.2349 | 13300 | 0.201 | | 2.2517 | 13400 | 0.1965 | | 2.2685 | 13500 | 0.1901 | | 2.2853 | 13600 | 0.1851 | | 2.3021 | 13700 | 0.1997 | | 2.3189 | 13800 | 0.1926 | | 2.3357 | 13900 | 0.1803 | | 2.3525 | 14000 | 0.1838 | | 2.3693 | 14100 | 0.1817 | | 2.3862 | 14200 | 0.1947 | | 2.4030 | 14300 | 0.1843 | | 2.4198 | 14400 | 0.192 | | 2.4366 | 14500 | 0.1823 | | 2.4534 | 14600 | 0.1879 | | 2.4702 | 14700 | 0.182 | | 2.4870 | 14800 | 0.1846 | | 2.5038 | 14900 | 0.1799 | | 2.5206 | 15000 | 0.1769 | | 2.5374 | 15100 | 0.1868 | | 2.5542 | 15200 | 0.1869 | | 2.5710 | 15300 | 0.1845 | | 2.5878 | 15400 | 0.1858 | | 2.6046 | 15500 | 0.1786 | | 2.6214 | 15600 | 0.1788 | | 2.6382 | 15700 | 0.1892 | | 2.6550 | 15800 | 0.1731 | | 2.6718 | 15900 | 0.1822 | | 2.6886 | 16000 | 0.1779 | | 2.7054 | 16100 | 0.188 | | 2.7222 | 16200 | 0.1867 | | 2.7390 | 16300 | 0.1801 | | 2.7558 | 16400 | 0.1879 | | 2.7726 | 16500 | 0.1799 | | 2.7894 | 16600 | 0.1871 | | 2.8063 | 16700 | 0.1913 | | 2.8231 | 16800 | 0.1887 | | 2.8399 | 16900 | 0.1747 | | 2.8567 | 17000 | 0.1908 | | 2.8735 | 17100 | 0.184 | | 2.8903 | 17200 | 0.1791 | | 2.9071 | 17300 | 0.1871 | | 2.9239 | 17400 | 0.1915 | | 2.9407 | 17500 | 0.1921 | | 2.9575 | 17600 | 0.1906 | | 2.9743 | 17700 | 0.1864 | | 2.9911 | 17800 | 0.1861 | </details> ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.3.1 - Transformers: 4.45.1 - PyTorch: 2.4.0 - Accelerate: 0.34.2 - Datasets: 3.0.1 - Tokenizers: 0.20.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION" ]
[ "CHIA" ]
Non_BioNLP
IEETA/BioNExt
IEETA
null
[ "en", "dataset:bigbio/biored", "license:mit", "region:us" ]
1,715
1,715
0
1
--- datasets: - bigbio/biored language: - en license: mit metrics: - f1 --- # Model Card for BioNExt BioNExt, is an end-to-end Biomedical Relation Extraction and Classifcation system. The work utilized three modules, a Tagger (Named Entity Recognition), Linker (Entity Linking) and an Extractor (Relation Extraction and Classification). This repositories contains two models: 1. **Tagger:** Named Entity Recognition module, which performs 6 class biomedical NER: **Genes, Diseases, Chemicals, Variants (mutations), Species, and Cell Lines**. 2. **Extractor:** Performs Relation Extraction and classification. The classes for the relation Extraction are: **Positive Correlation, Negative Correlation, Association, Binding, Drug Interaction, Cotreatment, Comparison, and Conversion.** For a full description on how to utilize our end-to-end pipeline we point you towards our [GitHub](https://github.com/ieeta-pt/BioNExt) repository. - **Developed by:** IEETA - **Model type:** BERT Base - **Language(s) (NLP):** English - **License:** MIT - **Finetuned from model:** BioLinkBERT-Large ### Model Sources - **Repository:** [IEETA BioNExt GitHub](https://github.com/ieeta-pt/BioNExt) - **Paper:** Towards Discovery: An End-to-End System for Uncovering Novel Biomedical Relations [Awaiting Publication] **Authors:** - Tiago Almeida ([ORCID: 0000-0002-4258-3350](https://orcid.org/0000-0002-4258-3350)) - Richard A A Jonker ([ORCID: 0000-0002-3806-6940](https://orcid.org/0000-0002-3806-6940)) - Rui Antunes ([ORCID: 0000-0003-3533-8872](https://orcid.org/0000-0003-3533-8872)) - João R Almeida ([ORCID: 0000-0003-0729-2264](https://orcid.org/0000-0003-0729-2264)) - Sérgio Matos ([ORCID: 0000-0003-1941-3983](https://orcid.org/0000-0003-1941-3983)) ## Uses Note we do not take any liability for the use of the model in any professional/medical domain. The model is intended for academic purposes only. ## How to Get Started with the Model Please refer to our GitHub repository for more information on our end-to-end inference pipeline: [IEETA BioNExt GitHub](https://github.com/ieeta-pt/BioNExt) ## Training Data The training data utilized was the BioRED corpus, wihtin the scope of the BioCreative-VIII challenge. Ling Luo, Po-Ting Lai, Chih-Hsuan Wei, Cecilia N Arighi, Zhiyong Lu, BioRED: a rich biomedical relation extraction dataset, Briefings in Bioinformatics, Volume 23, Issue 5, September 2022, bbac282, https://doi.org/10.1093/bib/bbac282 ## Results As evaluated as an end to end system, our results are as follows: - **Tagger**: 43.10 - **Linker**: 32.46 - **Extractor**: 24.59 | Configuration | Entity Pair (P/R/F%) | + Relation (P/R/F%) | + Novel (P/R/F%) | |---------------------------------------|-----------------------|----------------------|------------------| | Competition best | -/-/55.84 | -/-/43.03 | -/-/32.75 | | BioNExt (end-to-end) | 45.89/40.63/43.10 | 34.56/30.60/32.46 | 26.18/23.18/24.59 | ## Citation **BibTeX:** [Awaiting Publication]
[ "NAMED_ENTITY_RECOGNITION", "RELATION_EXTRACTION" ]
[ "BIORED" ]
BioNLP
vocabtrimmer/mt5-small-trimmed-fr-10000-frquad-qa
vocabtrimmer
text2text-generation
[ "transformers", "pytorch", "mt5", "text2text-generation", "question answering", "fr", "dataset:lmqg/qg_frquad", "arxiv:2210.03992", "license:cc-by-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,679
1,679
12
0
--- datasets: - lmqg/qg_frquad language: fr license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore pipeline_tag: text2text-generation tags: - question answering widget: - text: 'question: En quelle année a-t-on trouvé trace d''un haut fourneau similaire?, context: Cette technologie ne disparaît qu''au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L''expérience n''est un échec technique que dans les régions où le savoir-faire n''existe pas, ou a disparu.' example_title: Question Answering Example 1 - text: 'question: Comment appelle-t-on la Guerre de 14-18 ?, context: Ce black dog peut être lié à des évènements traumatisants issus du monde extérieur, tels que son renvoi de l''Amirauté après la catastrophe des Dardanelles, lors de la Grande Guerre de 14-18, ou son rejet par l''électorat en juillet 1945. On sait également que dans ces deux cas, la guérison, certes lente et douloureuse et jamais complète ni définitive, se fera grâce à la peinture. D''un autre côté, étant donnés les symptômes de ce mal que Churchill éprouvait de plus en plus, il ne pouvait rien moins qu''être purement associé à de telles causes extrinsèques, ce qui correspond au profil classique de la dépression majeure unipolaire ou bipolaire.' example_title: Question Answering Example 2 model-index: - name: vocabtrimmer/mt5-small-trimmed-fr-10000-frquad-qa results: - task: type: text2text-generation name: Text2text Generation dataset: name: lmqg/qg_frquad type: default args: default metrics: - type: bleu4_question_answering value: 18.63 name: BLEU4 (Question Answering) - type: rouge_l_question_answering value: 29.33 name: ROUGE-L (Question Answering) - type: meteor_question_answering value: 23.73 name: METEOR (Question Answering) - type: bertscore_question_answering value: 89.65 name: BERTScore (Question Answering) - type: moverscore_question_answering value: 72.01 name: MoverScore (Question Answering) - type: answer_f1_score__question_answering value: 47.59 name: AnswerF1Score (Question Answering) - type: answer_exact_match_question_answering value: 30.24 name: AnswerExactMatch (Question Answering) --- # Model Card of `vocabtrimmer/mt5-small-trimmed-fr-10000-frquad-qa` This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-fr-10000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-10000) for question answering task on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [vocabtrimmer/mt5-small-trimmed-fr-10000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-10000) - **Language:** fr - **Training data:** [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="fr", model="vocabtrimmer/mt5-small-trimmed-fr-10000-frquad-qa") # model prediction answers = model.answer_q(list_question="En quelle année a-t-on trouvé trace d'un haut fourneau similaire?", list_context=" Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-fr-10000-frquad-qa") output = pipe("question: En quelle année a-t-on trouvé trace d'un haut fourneau similaire?, context: Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu.") ``` ## Evaluation - ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-10000-frquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_frquad.default.json) | | Score | Type | Dataset | |:-----------------|--------:|:--------|:-----------------------------------------------------------------| | AnswerExactMatch | 30.24 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | AnswerF1Score | 47.59 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | BERTScore | 89.65 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_1 | 28.11 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_2 | 23.97 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_3 | 21.1 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_4 | 18.63 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | METEOR | 23.73 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | MoverScore | 72.01 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | ROUGE_L | 29.33 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_frquad - dataset_name: default - input_types: ['paragraph_question'] - output_types: ['answer'] - prefix_types: None - model: vocabtrimmer/mt5-small-trimmed-fr-10000 - max_length: 512 - max_length_output: 32 - epoch: 25 - batch: 32 - lr: 0.0005 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 2 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-10000-frquad-qa/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```
[ "QUESTION_ANSWERING" ]
[ "CAS" ]
Non_BioNLP
Savoxism/Finetuned-Paraphrase-Multilingual-MiniLM-L12-v2
Savoxism
sentence-similarity
[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:89592", "loss:CachedMultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:2101.06983", "base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", "base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,741
1,741
3
0
--- base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:89592 - loss:CachedMultipleNegativesRankingLoss widget: - source_sentence: Chánh Thanh tra Sở Lao động - Thương binh và Xã hội có quyền xử phạt doanh nghiệp cản trở quá trình tổ chức đại diện người lao động tại cơ sở lấy ý kiến về đình công không? sentences: - 'Quyền hạn, trách nhiệm của Bộ Giao thông vận tải 1. Ban hành và bổ sung, sửa đổi Điều lệ tổ chức và hoạt động của Viện. 2. Quyết định phê duyệt kế hoạch tài chính và tài sản hàng năm của Viện; giám sát việc quản lý, sử dụng tài chính, tài sản, phân phối thu nhập, trích lập và sử dụng các quỹ của Viện theo quy định. 3. Quyết định giao nhiệm vụ nghiên cứu khoa học, phê duyệt các dự án đầu tư theo phân cấp, 4. Kiểm tra, giám sát thực hiện các mục tiêu, nhiệm vụ Nhà nước giao; đánh giá kết quả hoạt động của Viện; nhận xét, đánh giá hàng năm đối với Viện trưởng. 5. Quyết định quy hoạch, bổ nhiệm, bổ nhiệm lại, luân chuyển, điều động, từ chức, miễn nhiệm, khen thưởng, kỷ luật, giải quyết chế độ, chính sách đối với Viện trưởng, Phó Viện trưởng, Kế toán trưởng và các viên chức khác của Viện theo quy định của pháp luật và phân cấp quản lý của Bộ. 6. Thực hiện các quyền và nhiệm vụ khác theo quy định của pháp luật.' - '"Điều 15. Hồ sơ thành lập quỹ 1. Hồ sơ thành lập quỹ được lập thành 01 bộ và gửi đến cơ quan nhà nước có thẩm quyền quy định tại Điều 18 Nghị định này. 2. Hồ sơ thành lập quỹ, gồm: a) Đơn đề nghị thành lập quỹ; b) Dự thảo điều lệ quỹ; c) Bản cam kết đóng góp tài sản thành lập quỹ của các sáng lập viên, tài liệu chứng minh tài sản đóng góp để thành lập quỹ theo quy định tại Điều 14 Nghị định này; d) Sơ yếu lý lịch, phiếu lý lịch tư pháp của các thành viên Ban sáng lập quỹ và các tài liệu theo quy định tại Điều 11, Điều 12 hoặc Điều 13 Nghị định này. Sáng lập viên thuộc diện quản lý của cơ quan có thẩm quyền theo quy định thì có văn bản đồng ý của cơ quan có thẩm quyền theo phân cấp quản lý cán bộ; đ) Văn bản bầu các chức danh Ban sáng lập quỹ; e) Văn bản xác nhận nơi dự kiến đặt trụ sở của quỹ."' - 'Thẩm quyền xử phạt của Thanh tra lao động ... 2. Chánh Thanh tra Sở Lao động - Thương binh và Xã hội có quyền: a) Phạt cảnh cáo; b) Phạt tiền đến 37.500.000 đồng đối với hành vi vi phạm hành chính trong lĩnh vực lao động, bảo hiểm xã hội quy định tại Chương II, Chương III Nghị định này, trừ hành vi vi phạm quy định tại khoản 3 Điều 32 Nghị định này; c) Phạt tiền đến 50.000.000 đồng đối với hành vi vi phạm hành chính trong lĩnh vực người lao động Việt Nam đi làm việc ở nước ngoài theo hợp đồng quy định tại Chương IV Nghị định này; d) Áp dụng hình thức xử phạt bổ sung quy định tại Chương II, Chương III và Chương IV, trừ hình thức xử phạt bổ sung quy định tại khoản 5 Điều 32 Nghị định này; đ) Áp dụng biện pháp khắc phục hậu quả quy định tại Chương II, Chương III và Chương IV Nghị định này. ...' - source_sentence: Mối quan hệ công tác của thuyền trưởng đơn vị dân quân tự vệ được quy định thế nào? sentences: - '"Điều 14. Chức trách, nhiệm vụ, mối quan hệ công tác của tiểu đoàn trưởng, hải đoàn trưởng, đại đội trưởng, hải đội trưởng, trung đội trưởng, tiểu đội trưởng, thuyền trưởng, khẩu đội trưởng 1. Chức trách Chịu trách nhiệm trước pháp luật, đảng ủy (chi bộ), người chỉ huy, chính ủy, chính trị viên cấp trên và cấp ủy (chi bộ) cấp mình về xây dựng, huấn luyện, hoạt động của đơn vị Dân quân tự vệ thuộc quyền. 2. Nhiệm vụ a) Chỉ huy đơn vị Dân quân tự vệ thuộc quyền chấp hành chủ trương, đường lối của Đảng, chính sách, pháp luật của Nhà nước, nghị quyết lãnh đạo của đảng ủy (chi bộ), sự quản lý, điều hành của Ủy ban nhân dân các cấp hoặc đảng ủy (chi bộ), người đứng đầu cơ quan, tổ chức; chỉ thị, mệnh lệnh của người chỉ huy cấp trên theo phân cấp quản lý; b) Nắm vững tình hình mọi mặt, lập kế hoạch, trình cấp có thẩm quyền phê duyệt; tổ chức thực hiện nhiệm vụ xây dựng, huấn luyện, hoạt động sẵn sàng chiến đấu, chiến đấu, phục vụ chiến đấu, phòng thủ dân sự và chế độ, chính sách của đơn vị Dân quân tự vệ thuộc quyền; c) Đăng ký, quản lý, nắm tình hình chính trị, tư tưởng, trình độ, năng lực công tác của các chức vụ chỉ huy và chiến sĩ Dân quân tự vệ thuộc quyền; d) Tiểu đoàn trưởng, hải đoàn trưởng, đại đội trưởng, hải đội trưởng phối hợp với chính trị viên cùng cấp tiến hành công tác đảng, công tác chính trị cho đơn vị mình; đ) Kiểm tra, phối hợp kiểm tra, sơ kết, tổng kết, báo cáo theo quy định. 3. Mối quan hệ công tác a) Quan hệ với cấp ủy (chi bộ) cấp trên và cấp ủy (chi bộ) cùng cấp là quan hệ phục tùng sự lãnh đạo, chỉ đạo về công tác Dân quân tự vệ; b) Quan hệ với cơ quan quân sự địa phương cấp tỉnh, cấp huyện, cấp xã, ban chỉ huy quân sự cơ quan, tổ chức theo phân cấp quản lý là quan hệ phục tùng sự chỉ đạo, chỉ huy, quản lý điều hành về công tác Dân quân tự vệ; c) Quan hệ với người chỉ huy, chính ủy, chính trị viên cấp trên là quan hệ giữa cấp dưới và cấp trên; d) Quan hệ với chính trị viên đơn vị Dân quân tự vệ cùng cấp là quan hệ phối hợp công tác; đ) Quan hệ với cơ quan, tổ chức, đơn vị đứng chân hoặc hoạt động trên địa bàn là quan hệ phối hợp công tác; e) Quan hệ với chỉ huy đơn vị Dân quân tự vệ thuộc quyền là quan hệ cấp trên và cấp dưới."' - '“Điều 55. Thuận tình ly hôn Trong trường hợp vợ chồng cùng yêu cầu ly hôn, nếu xét thấy hai bên thật sự tự nguyện ly hôn và đã thỏa thuận về việc chia tài sản, việc trông nom, nuôi dưỡng, chăm sóc, giáo dục con trên cơ sở bảo đảm quyền lợi chính đáng của vợ và con thì Tòa án công nhận thuận tình ly hôn; nếu không thỏa thuận được hoặc có thỏa thuận nhưng không bảo đảm quyền lợi chính đáng của vợ và con thì Tòa án giải quyết việc ly hôn.”' - 'Doanh nghiệp quản lý, thanh lý tài sản 1. Các loại doanh nghiệp sau đây được hành nghề quản lý, thanh lý tài sản trong quá trình giải quyết phá sản: a) Công ty hợp danh; b) Doanh nghiệp tư nhân. 2. Điều kiện để doanh nghiệp hành nghề quản lý, thanh lý tài sản: a) Công ty hợp danh có tối thiểu hai thành viên hợp danh là Quản tài viên, Tổng giám đốc hoặc Giám đốc của công ty hợp danh là Quản tài viên; b) Doanh nghiệp tư nhân có chủ doanh nghiệp là Quản tài viên, đồng thời là Giám đốc. 3. Chính phủ quy định chi tiết việc hành nghề quản lý, thanh lý tài sản và việc quản lý nhà nước đối với doanh nghiệp quản lý, thanh lý tài sản.' - source_sentence: Người chịu trách nhiệm chuyên môn về dược của cơ sở bán buôn thuốc dược liệu phải có những văn bằng nào? sentences: - 'Phiên họp Tổ đại biểu Quốc hội 1. Tại mỗi kỳ họp Quốc hội, Ủy ban Thường vụ Quốc hội thành lập Tổ đại biểu Quốc hội, chỉ định Tổ trưởng, Phó Tổ trưởng Tổ đại biểu Quốc hội. 2. Tổ trưởng Tổ đại biểu Quốc hội chủ tọa phiên họp Tổ. Trường hợp Tổ trưởng vắng mặt thì Phó Tổ trưởng được phân công chủ tọa phiên họp. 3. Tổng Thư ký Quốc hội phân công thư ký phiên họp Tổđại biểu Quốc hội. 4. Trình tự phiên họp Tổ đại biểu Quốc hội được tiến hành như sau: a) Chủ tọa nêu nội dung đề nghị đại biểu Quốc hội tập trung thảo luận; b) Đại biểu Quốc hội phát biểu ý kiến; c) Chủ tọa phát biểu kết thúc phiên họp.Các hình thức làm việc tại kỳ họp Quốc hội ... 4. Các phiên họp Đoàn đại biểu Quốc hội, Tổ đại biểu Quốc hội thảo luận về các nội dung thuộc chương trình kỳ họp. ...' - '1. Mức phụ cấp a) Mức phụ cấp 25% áp dụng đối với nhà giáo đang trực tiếp giảng dạy trong các trường đại học, cao đẳng, các học viện, trường bồi dưỡng của các Bộ, cơ quan ngang Bộ, cơ quan thuộc Chính phủ, tổ chức Đảng, tổ chức chính trị - xã hội ở Trung ương và các trường chính trị của các tỉnh, thành phố trực thuộc Trung ương (trừ nhà giáo giảng dạy trong các trường sư phạm, khoa sư phạm và nhà giáo dạy môn khoa học Mác - Lênin, Tư tưởng Hồ Chí Minh); b) Mức phụ cấp 30% áp dụng đối với nhà giáo đang trực tiếp giảng dạy trong các trường trung học cơ sở, trung học phổ thông, trung tâm kỹ thuật tổng hợp - hướng nghiệp, trung tâm giáo dục thường xuyên, trung tâm dạy nghề ở đồng bằng, thành phố, thị xã; trường trung học chuyên nghiệp, trường dạy nghề; các trung tâm bồi dưỡng chính trị của huyện, quận, thị xã, thành phố trực thuộc tỉnh; c) Mức phụ cấp 35% áp dụng đối với nhà giáo đang trực tiếp giảng dạy trong các trường mầm non, tiểu học ở đồng bằng, thành phố, thị xã; các trường trung học cơ sở, trung học phổ thông, các trung tâm kỹ thuật tổng hợp - hướng nghiệp, trung tâm giáo dục thường xuyên, trung tâm dạy nghề ở miền núi, hải đảo, vùng sâu, vùng xa; d) Mức phụ cấp 40% áp dụng đối với nhà giáo đang trực tiếp giảng dạy trong các trường sư phạm, khoa sư phạm (đại học, cao đẳng, trung học), trường cán bộ quản lý giáo dục và đào tạo và nhà giáo dạy môn chính trị trong các trường trung học chuyên nghiệp, trường dạy nghề; đ) Mức phụ cấp 45% áp dụng đối với nhà giáo đang trực tiếp giảng dạy các môn khoa học Mác - Lênin, Tư tưởng Hồ Chí Minh trong các trường đại học, cao đẳng; e) Mức phụ cấp 50% áp dụng đối với nhà giáo đang trực tiếp giảng dạy trong các trường mầm non, tiểu học ở miền núi, hải đảo, vùng sâu, vùng xa. Việc xác định địa bàn miền núi thực hiện theo quy định của Uỷ ban Dân tộc; địa bàn hải đảo theo thực tế địa lý; địa bàn vùng sâu, vùng xa tuỳ theo đặc điểm của từng địa phương do Uỷ ban nhân dân tỉnh hướng dẫn sau khi có ý kiến thống nhất của Liên Bộ. 2. Cách tính Mức phụ cấp ưu đãi được hưởng = Mức lương tối thiểu chung x [hệ số lương theo ngạch, bậc hiện hưởng + hệ số phụ cấp chức vụ lãnh đạo (nếu có) + % (quy theo hệ số) phụ cấp thâm niên vượt khung (nếu có)] x tỷ lệ % phụ cấp ưu đãi.' - 'Điều kiện đối với người chịu trách nhiệm chuyên môn về dược của cơ sở bán buôn thuốc, nguyên liệu làm thuốc 1. Người chịu trách nhiệm chuyên môn về dược của cơ sở bán buôn thuốc, nguyên liệu làm thuốc phải có văn bằng chuyên môn quy định tại điểm a khoản 1 Điều 13 của Luật này và có 02 năm thực hành chuyên môn tại cơ sở dược phù hợp, trừ trường hợp quy định tại khoản 2 và khoản 3 Điều này. 2. Người chịu trách nhiệm chuyên môn về dược của cơ sở bán buôn vắc xin, sinh phẩm phải có một trong các văn bằng chuyên môn quy định tại điểm a, b hoặc d khoản 1 Điều 13 của Luật này và có 02 năm thực hành chuyên môn tại cơ sở dược phù hợp. 3. Người chịu trách nhiệm chuyên môn về dược của cơ sở bán buôn dược liệu, thuốc dược liệu, thuốc cổ truyền phải có một trong các văn bằng chuyên môn quy định tại điểm a, c, i hoặc l khoản 1 Điều 13 của Luật này và có 02 năm thực hành chuyên môn tại cơ sở dược phù hợp, trừ trường hợp quy định tại điểm c khoản 2 Điều 13 của Luật này.' - source_sentence: Giấy phép lái xe ô tô có được sử dụng thay thế cho giấy phép lái xe máy trong trường hợp có yêu cầu kiểm tra từ cơ quan có thẩm quyền hay không? sentences: - 'Vi phạm quy định về tiêu chuẩn đủ điều kiện bay ... 4. Phạt tiền từ 80.000.000 đồng (tám mươi triệu đồng) đến 100.000.000 đồng (một trăm triệu đồng) đối với hành vi đưa tàu bay vào khai thác mà không có Giấy chứng nhận đủ điều kiện bay. ...Nguyên tắc áp dụng 1. Mức phạt tiền quy định tại Chương II Nghị định này là mức phạt tiền áp dụng đối với các tổ chức, trừ mức phạt tiền quy định tại khoản 1, 2, 3, 4 Điều 6; điểm i, k khoản 1 Điều 7; khoản 1, 2, 3, 4, 5 Điều 8; khoản 1, 2, 4, 5, 6 Điều 9; khoản 1, 2 và điểm a, b khoản 5 Điều 10; khoản 1, 2, 3, 4 và điểm g khoản 5 Điều 11; khoản 1 Điều 12; điểm b, c khoản 1 và điểm a, c khoản 2 Điều 14; khoản 1, 2 và điểm a, d, đ khoản 3, khoản 4, 5 Điều 15; khoản 1, 2, 3, 4, 5, 6 Điều 16; khoản 1, 2 Điều 17; khoản 1 và điểm a, b, d khoản 2 Điều 18; khoản 1, 2 Điều 19; khoản 1, 2, 3, 4, 5, 6 Điều 21; khoản 1, 2 Điều 24; khoản 1, 2, 3 Điều 25; khoản 1, 2, 3, 4, 5, 6, 7, 8 Điều 26; điểm a, b, đ khoản 1 Điều 27; khoản 1, 2, 3 và điểm a khoản 4, điểm b khoản 5 Điều 28; khoản 1, 2, 3 Điều 30 Nghị định này là mức phạt áp dụng đối với cá nhân. Đối với cùng một hành vi vi phạm hành chính thì mức phạt tiền đối với tổ chức bằng hai lần mức phạt tiền đối với cá nhân. ...' - 'Phê duyệt Phương án khai thác thực vật rừng thông thường ... 2. Cơ quan có thẩm quyền phê duyệt: a) Bộ Nông nghiệp và Phát triển nông thôn phê duyệt Phương án khai thác đối với trường hợp quy định tại các điểm a, b, c, d và đ khoản 1 Điều này đối với diện tích rừng do Bộ Nông nghiệp và Phát triển nông thôn quản lý; b) Ủy ban nhân dân cấp huyện phê duyệt Phương án khai thác đối với trường hợp quy định tại điểm đ khoản 1 Điều này do cá nhân, hộ gia đình, cộng đồng dân cư tự đầu tư; khai thác tận dụng, tận thu gỗ rừng sản xuất là rừng tự nhiên do cá nhân, hộ gia đình, cộng đồng dân cư quản lý; c) Sở Nông nghiệp và Phát triển nông thôn phê duyệt Phương án khai thác đối với trường hợp không thuộc quy định tại điểm a và điểm b khoản này. ...' - '"Điều 58. Điều kiện của người lái xe tham gia giao thông 1. Người lái xe tham gia giao thông phải đủ độ tuổi, sức khoẻ quy định tại Điều 60 của Luật này và có giấy phép lái xe phù hợp với loại xe được phép điều khiển do cơ quan nhà nước có thẩm quyền cấp. .." "Điều 59. Giấy phép lái xe 1. Căn cứ vào kiểu loại, công suất động cơ, tải trọng và công dụng của xe cơ giới, giấy phép lái xe được phân thành giấy phép lái xe không thời hạn và giấy phép lái xe có thời hạn. 2. Giấy phép lái xe không thời hạn bao gồm các hạng sau đây: a) Hạng A1 cấp cho người lái xe mô tô hai bánh có dung tích xi-lanh từ 50 cm3 đến dưới 175 cm3; b) Hạng A2 cấp cho người lái xe mô tô hai bánh có dung tích xi-lanh từ 175 cm3 trở lên và các loại xe quy định cho giấy phép lái xe hạng A1; c) Hạng A3 cấp cho người lái xe mô tô ba bánh, các loại xe quy định cho giấy phép lái xe hạng A1 và các xe tương tự. ... 4. Giấy phép lái xe có thời hạn gồm các hạng sau đây: a) Hạng A4 cấp cho người lái máy kéo có trọng tải đến 1.000 kg; b) Hạng B1 cấp cho người không hành nghề lái xe điều khiển xe ô tô chở người đến 9 chỗ ngồi; xe ô tô tải, máy kéo có trọng tải dưới 3.500 kg; c) Hạng B2 cấp cho người hành nghề lái xe điều khiển xe ô tô chở người đến 9 chỗ ngồi; xe ô tô tải, máy kéo có trọng tải dưới 3.500 kg; d) Hạng C cấp cho người lái xe ô tô tải, máy kéo có trọng tải từ 3.500 kg trở lên và các loại xe quy định cho các giấy phép lái xe hạng B1, B2; đ) Hạng D cấp cho người lái xe ô tô chở người từ 10 đến 30 chỗ ngồi và các loại xe quy định cho các giấy phép lái xe hạng B1, B2, C; e) Hạng E cấp cho người lái xe ô tô chở người trên 30 chỗ ngồi và các loại xe quy định cho các giấy phép lái xe hạng B1, B2, C, D; g) Giấy phép lái xe hạng FB2, FD, FE cấp cho người lái xe đã có giấy phép lái xe hạng B2, D, E để lái các loại xe quy định cho các giấy phép lái xe hạng này khi kéo rơ moóc hoặc xe ô tô chở khách nối toa; hạng FC cấp cho người lái xe đã có giấy phép lái xe hạng C để lái các loại xe quy định cho hạng C khi kéo rơ moóc, đầu kéo kéo sơ mi rơ moóc."' - source_sentence: Tiêu chí xếp loại chất lượng công chức ở mức không hoàn thành nhiệm vụ được quy định ra sao? sentences: - 'Nhiệm vụ: 1. Hội tập hợp các nghệ sĩ hoạt động thuộc các bộ môn, chuyên ngành sân khấu, nhằm tạo ra sức mạnh tổng hợp để xây dựng và phát triển nền sân khấu Việt Nam tiên tiến đậm đà bản sắc dân tộc theo định hướng phát triển văn hóa nghệ thuật của Đảng. Hội tạo điều kiện cho Hội viên học tập chính trị, nâng cao nghiệp vụ nắm vững định hướng sáng tạo văn học nghệ thuật. 2. Hội cố gắng tạo điều kiện thuận lợi để các nghệ sĩ hoạt động sân khấu chủ động sáng tạo những vở diễn có giá trị cao về tư tưởng và nghệ thuật, đồng thời khuyến khích sự phát triển ngành phê bình và nghiên cứu sân khấu. Tham gia nghiên cứu các đề tài khoa học về nghệ thuật sân khấu. 3. Hội thường xuyên phối kết hợp với các cơ quan chuyên môn của Bộ Văn hóa Thông tin để xây dựng những đơn vị sân khấu vững mạnh, hoạt động có hiệu quả, đồng thời khuyến khích, giúp đỡ các tiết mục thử nghiệm, tìm tòi các hình thức sáng tạo mới để rút kinh nghiệm. 4. Khuyến khích và giúp đỡ bằng nhiều hình thức đối với những hoạt động của sân khấu không chuyên nghiệp. 5. Theo dõi, phát hiện kịp thời, phản ánh với Đảng, Nhà nước đối với các hiện tượng sân khấu mà d­ư luận xã hội quan tâm và quá trình phát triển của nghệ thuật sân khấu Việt Nam. 6. Củng cố, mở rộng quan hệ hợp tác với các nước để trao đổi, giới thiệu học tập kinh nghiệm về nghệ thuật sân khấu theo quy định của pháp luật. ...' - 'Tiêu chí xếp loại chất lượng công chức ở mức không hoàn thành nhiệm vụ 1. Công chức không giữ chức vụ lãnh đạo, quản lý có một trong các tiêu chí sau đây thì xếp loại chất lượng ở mức không hoàn thành nhiệm vụ: a) Có biểu hiện suy thoái về tư tưởng chính trị, đạo đức, lối sống, tự diễn biến, tự chuyển hóa theo đánh giá của cấp có thẩm quyền; b) Có trên 50% các tiêu chí về kết quả thực hiện nhiệm vụ theo quy định của pháp luật, theo kế hoạch đề ra hoặc theo công việc cụ thể được giao chưa bảo đảm tiến độ, chất lượng, hiệu quả; c) Có hành vi vi phạm trong quá trình thực thi nhiệm vụ bị xử lý kỷ luật trong năm đánh giá. 2. Công chức giữ chức vụ lãnh đạo, quản lý có một trong các tiêu chí sau đây thì xếp loại chất lượng ở mức không hoàn thành nhiệm vụ: a) Có biểu hiện suy thoái về tư tưởng chính trị, đạo đức, lối sống, tự diễn biến, tự chuyển hóa theo đánh giá của cấp có thẩm quyền; b) Có trên 50% các tiêu chí về kết quả thực hiện nhiệm vụ theo quy định của pháp luật, theo kế hoạch đề ra hoặc theo công việc cụ thể được giao chưa bảo đảm tiến độ, chất lượng, hiệu quả; c) Cơ quan, tổ chức, đơn vị hoặc lĩnh vực công tác được giao phụ trách hoàn thành dưới 50% các chỉ tiêu, nhiệm vụ; d) Cơ quan, tổ chức, đơn vị thuộc thẩm quyền phụ trách, quản lý trực tiếp liên quan đến tham ô, tham nhũng, lãng phí và bị xử lý theo quy định của pháp luật. đ) Có hành vi vi phạm trong quá trình thực thi nhiệm vụ bị xử lý kỷ luật trong năm đánh giá.' - "Giao dịch lô lẻ\n1. Giao dịch lô lẻ được thực hiện theo phương thức khớp lệnh\ \ và phương thức thỏa thuận trên hệ thống giao dịch.\n2. Nhà đầu tư chỉ được phép\ \ nhập lệnh LO đối với giao dịch lô lẻ \n3. Đơn vị giao dịch lô lẻ là 01 cổ phiếu\ \ hoặc chứng chỉ quỹ hoặc chứng quyền có bảo đảm.\n4. Giá giao dịch:\na) Giá của\ \ lệnh giao dịch lô lẻ phải tuân thủ theo các quy định về giá giao dịch tương\ \ tự giao dịch lô chẵn.\nb) Các lệnh giao dịch lô lẻ không được sử dụng để xác\ \ định giá tham chiếu, giá tính chỉ số.\n5. Giao dịch lô lẻ của cổ phiếu, chứng\ \ chỉ quỹ và chứng quyền có bảo đảm mới niêm yết hoặc giao dịch trở lại sau khi\ \ bị tạm ngừng, đình chỉ giao dịch từ 25 ngày giao dịch liên tiếp trở lên không\ \ được nhập vào hệ thống giao dịch cho đến khi có giá đóng cửa được xác lập.\n\ 6. SGDCK có trách nhiệm tổ chức giao dịch lô lẻ theo các phương thức quy định\ \ tại khoản 2 Điều 13 Quy chế này." --- # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 tokens - **Similarity Function:** Cosine Similarity <!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("Savoxism/Finetuned-Paraphrase-Multilingual-MiniLM-L12-v2") # Run inference sentences = [ 'Tiêu chí xếp loại chất lượng công chức ở mức không hoàn thành nhiệm vụ được quy định ra sao?', 'Tiêu chí xếp loại chất lượng công chức ở mức không hoàn thành nhiệm vụ\n1. Công chức không giữ chức vụ lãnh đạo, quản lý có một trong các tiêu chí sau đây thì xếp loại chất lượng ở mức không hoàn thành nhiệm vụ:\na) Có biểu hiện suy thoái về tư tưởng chính trị, đạo đức, lối sống, tự diễn biến, tự chuyển hóa theo đánh giá của cấp có thẩm quyền;\nb) Có trên 50% các tiêu chí về kết quả thực hiện nhiệm vụ theo quy định của pháp luật, theo kế hoạch đề ra hoặc theo công việc cụ thể được giao chưa bảo đảm tiến độ, chất lượng, hiệu quả;\nc) Có hành vi vi phạm trong quá trình thực thi nhiệm vụ bị xử lý kỷ luật trong năm đánh giá.\n2. Công chức giữ chức vụ lãnh đạo, quản lý có một trong các tiêu chí sau đây thì xếp loại chất lượng ở mức không hoàn thành nhiệm vụ:\na) Có biểu hiện suy thoái về tư tưởng chính trị, đạo đức, lối sống, tự diễn biến, tự chuyển hóa theo đánh giá của cấp có thẩm quyền;\nb) Có trên 50% các tiêu chí về kết quả thực hiện nhiệm vụ theo quy định của pháp luật, theo kế hoạch đề ra hoặc theo công việc cụ thể được giao chưa bảo đảm tiến độ, chất lượng, hiệu quả;\nc) Cơ quan, tổ chức, đơn vị hoặc lĩnh vực công tác được giao phụ trách hoàn thành dưới 50% các chỉ tiêu, nhiệm vụ;\nd) Cơ quan, tổ chức, đơn vị thuộc thẩm quyền phụ trách, quản lý trực tiếp liên quan đến tham ô, tham nhũng, lãng phí và bị xử lý theo quy định của pháp luật.\nđ) Có hành vi vi phạm trong quá trình thực thi nhiệm vụ bị xử lý kỷ luật trong năm đánh giá.', 'Nhiệm vụ:\n1. Hội tập hợp các nghệ sĩ hoạt động thuộc các bộ môn, chuyên ngành sân khấu, nhằm tạo ra sức mạnh tổng hợp để xây dựng và phát triển nền sân khấu Việt Nam tiên tiến đậm đà bản sắc dân tộc theo định hướng phát triển văn hóa nghệ thuật của Đảng. Hội tạo điều kiện cho Hội viên học tập chính trị, nâng cao nghiệp vụ nắm vững định hướng sáng tạo văn học nghệ thuật.\n2. Hội cố gắng tạo điều kiện thuận lợi để các nghệ sĩ hoạt động sân khấu chủ động sáng tạo những vở diễn có giá trị cao về tư tưởng và nghệ thuật, đồng thời khuyến khích sự phát triển ngành phê bình và nghiên cứu sân khấu. Tham gia nghiên cứu các đề tài khoa học về nghệ thuật sân khấu.\n3. Hội thường xuyên phối kết hợp với các cơ quan chuyên môn của Bộ Văn hóa Thông tin để xây dựng những đơn vị sân khấu vững mạnh, hoạt động có hiệu quả, đồng thời khuyến khích, giúp đỡ các tiết mục thử nghiệm, tìm tòi các hình thức sáng tạo mới để rút kinh nghiệm.\n4. Khuyến khích và giúp đỡ bằng nhiều hình thức đối với những hoạt động của sân khấu không chuyên nghiệp.\n5. Theo dõi, phát hiện kịp thời, phản ánh với Đảng, Nhà nước đối với các hiện tượng sân khấu mà d\xadư luận xã hội quan tâm và quá trình phát triển của nghệ thuật sân khấu Việt Nam.\n6. Củng cố, mở rộng quan hệ hợp tác với các nước để trao đổi, giới thiệu học tập kinh nghiệm về nghệ thuật sân khấu theo quy định của pháp luật.\n...', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 89,592 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 7 tokens</li><li>mean: 24.66 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 252.25 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | sentence_0 | sentence_1 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Quy trình thực hiện việc sửa đổi quyết định thanh tra liên quan đến nội dung thanh tra theo đề nghị của Đoàn thanh tra được quy định như thế nào?</code> | <code>Sửa đổi, bổ sung quyết định thanh tra liên quan đến đối tượng thanh tra, nội dung thanh tra<br>...<br>4. Sửa đổi, bổ sung quyết định thanh tra liên quan đến nội dung thanh tra, đối tượng thanh tra theo đề nghị của Đoàn thanh tra:<br>a) Khi có căn cứ sửa đổi, bổ sung nội dung thanh tra, đối tượng thanh tra của quyết định thanh tra quy định tại khoản 2 Điều này, Đoàn thanh tra thảo luận về đề nghị sửa đổi, bổ sung nội dung quyết định thanh tra, đối tượng thanh tra. Các ý kiến khác nhau phải được Trưởng đoàn thanh tra báo cáo đầy đủ với người ra quyết định thanh tra;<br>b) Trưởng đoàn thanh tra thay mặt Đoàn thanh tra có văn bản đề nghị người ra quyết định thanh tra xem xét, quyết định việc sửa đổi, bổ sung nội dung quyết định thanh tra. Văn bản đề nghị sửa đổi, bổ sung quyết định thanh tra phải nêu rõ lý do, nội dung sửa đổi, bổ sung và những nội dung khác có liên quan để người ra quyết định thanh tra xem xét, quyết định. Ý kiến của người ra quyết định thanh tra phải thể hiện bằng văn bản;<br>c) Trường hợp người ra quyết định thanh tra phê duyệt việc sửa đổi, bổ sung nội dung thanh tra, đối tượng thanh tra của quyết định thanh tra thì người ra quyết định thanh tra có quyết định sửa đổi, bổ sung quyết định thanh tra yêu cầu Trưởng đoàn thanh tra thực hiện theo quyết định thanh tra sửa đổi, bổ sung.<br>Trưởng đoàn thanh tra có trách nhiệm thông báo nội dung sửa đổi, bổ sung quyết định thanh tra cho các thành viên Đoàn thanh tra; xây dựng kế hoạch tiến hành thanh tra sửa đổi, bổ sung và tổ chức triển khai thực hiện.<br>...</code> | | <code>Ủy ban nhân dân cấp tỉnh có quyền phê duyệt phương án khai thác tận dụng gỗ loài thực vật rừng thông thường từ rừng tự nhiên hay không?</code> | <code>Phê duyệt Phương án khai thác thực vật rừng thông thường<br>...<br>2. Cơ quan có thẩm quyền phê duyệt:<br>a) Bộ Nông nghiệp và Phát triển nông thôn phê duyệt Phương án khai thác đối với trường hợp quy định tại các điểm a, b, c, d và đ khoản 1 Điều này đối với diện tích rừng do Bộ Nông nghiệp và Phát triển nông thôn quản lý;<br>b) Ủy ban nhân dân cấp huyện phê duyệt Phương án khai thác đối với trường hợp quy định tại điểm đ khoản 1 Điều này do cá nhân, hộ gia đình, cộng đồng dân cư tự đầu tư; khai thác tận dụng, tận thu gỗ rừng sản xuất là rừng tự nhiên do cá nhân, hộ gia đình, cộng đồng dân cư quản lý;<br>c) Sở Nông nghiệp và Phát triển nông thôn phê duyệt Phương án khai thác đối với trường hợp không thuộc quy định tại điểm a và điểm b khoản này.<br>...</code> | | <code>Mức phụ cấp lưu trú cho người đi công tác thuộc Bộ Quốc phòng được quy định như thế nào?</code> | <code>Phụ cấp lưu trú<br>Phụ cấp lưu trú là khoản tiền hỗ trợ thêm cho người đi công tác ngoài tiền lương do cơ quan, đơn vị cử đi công tác chi trả, được tính từ ngày bắt đầu đi công tác đến khi kết thúc đợt công tác trở về cơ quan, đơn vị (bao gồm thời gian đi trên đường, thời gian lưu trú tại nơi đến công tác). Mức phụ cấp lưu trú như sau:<br>1. Mức 200.000 đồng/ngày: Áp dụng đối với thời gian đi trên đường từ 5 giờ/ngày trở lên hoặc từ 150 km/ngày trở lên đối với khu vực vùng sâu, miền núi đi lại khó khăn và 300 km/ngày trở lên đối với khu vực còn lại.<br>2. Mức 100.000 đồng/ngày: Áp dụng đối với thời gian lưu trú tại cơ quan, đơn vị nơi đến công tác.<br>3. Mức 250.000 đồng/ngày: Áp dụng đối với thời gian đi công tác thực tế trên biển của quân nhân, công nhân quốc phòng, viên chức quốc phòng, công chức quốc phòng đang công tác, làm việc ở đất liền được cử đi công tác trên biển, đảo.<br>4. Đối với trường hợp đi và về trong ngày nếu không đủ điều kiện quy định tại khoản 1 Điều này thì được áp dụng phụ cấp lưu trú quy định tại khoản 2 Điều này với điều kiện thời gian làm việc tại đơn vị và thời gian đi, về tối thiểu từ 5 giờ trở lên.<br>5. Đối với quân nhân, công nhân quốc phòng, viên chức quốc phòng, công chức quốc phòng khi làm nhiệm vụ (huấn luyện, chiến đấu, tuần tra, cứu nạn, vận chuyển và các nhiệm vụ khác) trên tàu chiến đấu các loại, tàu cảnh sát biển, tàu kiểm ngư, tàu tìm kiếm cứu hộ, cứu nạn trên biển, tàu vận tải phục vụ trên biển thì những ngày thực tế đi biển được hưởng chế độ bồi dưỡng đi biển, phụ cấp ngày đi biển và phụ cấp đặc thù đi biển theo quy định (không được hưởng chế độ phụ cấp lưu trú quy định tại khoản 3 Điều này).</code> | * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.7143 | 500 | 0.4527 | | 1.4286 | 1000 | 0.1506 | | 2.1429 | 1500 | 0.1119 | | 2.8571 | 2000 | 0.0907 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.1.1 - Transformers: 4.45.2 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.1 - Datasets: 2.21.0 - Tokenizers: 0.20.3 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### CachedMultipleNegativesRankingLoss ```bibtex @misc{gao2021scaling, title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, year={2021}, eprint={2101.06983}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION" ]
[ "CHIA" ]
Non_BioNLP
Dizex/FoodBaseBERT-NER
Dizex
token-classification
[ "transformers", "pytorch", "safetensors", "bert", "token-classification", "FoodBase", "NER", "en", "dataset:Dizex/FoodBase", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,667
1,684
1,241
19
--- datasets: - Dizex/FoodBase language: en license: mit tags: - FoodBase - NER widget: - text: 'Today''s meal: Fresh olive poké bowl topped with chia seeds. Very delicious!' example_title: Food example 1 - text: Tartufo Pasta with garlic flavoured butter and olive oil, egg yolk, parmigiano and pasta water. example_title: Food example 2 --- # FoodBaseBERT ## Model description **FoodBaseBERT** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** of Food entities. It has been trained to recognize one entity: food (FOOD). Specifically, this model is a *bert-base-cased* model that was fine-tuned on the [FoodBase NER](https://academic.oup.com/database/article/doi/10.1093/database/baz121/5611291) dataset. ## Intended uses #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Dizex/FoodBaseBERT") model = AutoModelForTokenClassification.from_pretrained("Dizex/FoodBaseBERT") pipe = pipeline("ner", model=model, tokenizer=tokenizer) example = "Today's meal: Fresh olive poké bowl topped with chia seeds. Very delicious!" ner_entity_results = pipe(example) print(ner_entity_results) ```
[ "NAMED_ENTITY_RECOGNITION" ]
[ "CHIA" ]
Non_BioNLP
bobox/DeBERTa-small-ST-v1-test-step3
bobox
sentence-similarity
[ "sentence-transformers", "pytorch", "deberta-v2", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:279409", "loss:CachedGISTEmbedLoss", "en", "dataset:tals/vitaminc", "dataset:allenai/scitail", "dataset:allenai/sciq", "dataset:allenai/qasc", "dataset:sentence-transformers/msmarco-msmarco-distilbert-base-v3", "dataset:sentence-transformers/natural-questions", "dataset:sentence-transformers/trivia-qa", "dataset:sentence-transformers/gooaq", "dataset:google-research-datasets/paws", "arxiv:1908.10084", "base_model:bobox/DeBERTa-small-ST-v1-test-step2", "base_model:finetune:bobox/DeBERTa-small-ST-v1-test-step2", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,724
1,724
6
0
--- base_model: bobox/DeBERTa-small-ST-v1-test-step2 datasets: - tals/vitaminc - allenai/scitail - allenai/sciq - allenai/qasc - sentence-transformers/msmarco-msmarco-distilbert-base-v3 - sentence-transformers/natural-questions - sentence-transformers/trivia-qa - sentence-transformers/gooaq - google-research-datasets/paws language: - en library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - dot_accuracy - dot_accuracy_threshold - dot_f1 - dot_f1_threshold - dot_precision - dot_recall - dot_ap - manhattan_accuracy - manhattan_accuracy_threshold - manhattan_f1 - manhattan_f1_threshold - manhattan_precision - manhattan_recall - manhattan_ap - euclidean_accuracy - euclidean_accuracy_threshold - euclidean_f1 - euclidean_f1_threshold - euclidean_precision - euclidean_recall - euclidean_ap - max_accuracy - max_accuracy_threshold - max_f1 - max_f1_threshold - max_precision - max_recall - max_ap pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:279409 - loss:CachedGISTEmbedLoss widget: - source_sentence: what is acetylcholinesterase in biochemistry sentences: - The Arkansas was the last nuclear powered cruiser built by the United States. (Click here for detailed information about the USS Arkansas) USS Enterprise (CVN-65) The USS Enterprise was the Navy's first nuclear powered aircraft carrier and from 1961 to 1972 she was the biggest warship in the world. - The staph infection may also live on the skin of a dog as a parasite or in the respiratory system of a dog. In some cases if a dog is not treated right away the dog could become very ill or even die. In some cases where a dog is infected with staph a dog may have one or several different symptoms. These signs may be a reason to have a dog seen by a veterinarian; typically this disease may make a dog very sick. Some symptoms of staph infection may include but are not limited to. - Acetylcholinesterase (HGNC symbol ACHE), also known as AChE or acetylhydrolase, is the primary cholinesterase in the body.It is an enzyme that catalyzes the breakdown of acetylcholine and of some other choline esters that function as neurotransmitters.AChE is found at mainly neuromuscular junctions and in chemical synapses of the cholinergic type, where its activity serves to terminate synaptic transmission.uring neurotransmission, ACh is released from the nerve into the synaptic cleft and binds to ACh receptors on the post-synaptic membrane, relaying the signal from the nerve. AChE, also located on the post-synaptic membrane, terminates the signal transmission by hydrolyzing ACh. - source_sentence: More than 169 countries had reported over 212,000 COVID-19 cases before March 19 , 2020 . sentences: - As of 23 March , more than 341,000 cases of COVID-19 have been reported in 192 countries and territories , resulting in more than 14,700 deaths and 99,000 recoveries . - As of 21 March , more than 278,000 cases of COVID-19 have been reported in over 186 countries and territories , resulting in more than 11,500 deaths and 92,000 recoveries. virus seems to mostly spread between people via respiratory droplets . - As of 18 March 2020 , more than 212,000 cases of COVID-19 have been reported in at least 170 countries and territories , with major outbreaks in China , Iran and the European Union . - source_sentence: The brain is harmed if you get meningitis. sentences: - Which organ of our body is harmed if you get meningitis? - What is the rate at which a specific allele appears within a population called? - What is the term for the application of science to solve problems? - source_sentence: Electrical energy can be converted into kinetic energy and heat energy by an electric motor. sentences: - Solution is the term for a homogeneous mixture of two or more substances. - Solution is the term for a homogeneous mixture of two or more substances. - Electric motors transform electrical energy into kinetic energy. - source_sentence: where was the first hudson bay company post sentences: - Hudson's Bay Company Hudson's Bay Company's first inland trading post was established by Samuel Hearne in 1774 in Cumberland House, Saskatchewan.[38][39] - Another Mother for Peace Los Angeles artist Lorraine Art Schneider donated the use of a striking illustration for the Mother's Day peace cards--a sunflower on yellow background amid the slogan “War is not healthy for children and other living things.” [1] - Steven Ogg Steven Ogg is a Canadian actor.[1] He is best known for his roles as Trevor Philips in the 2013 video game Grand Theft Auto V and Simon in The Walking Dead.[2] He has also appeared in television series such as Better Call Saul, Law & Order, Person of Interest, Broad City, and Westworld. model-index: - name: SentenceTransformer based on bobox/DeBERTa-small-ST-v1-test-step2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.8861432363248951 name: Pearson Cosine - type: spearman_cosine value: 0.9077370026312627 name: Spearman Cosine - type: pearson_manhattan value: 0.9086667636630104 name: Pearson Manhattan - type: spearman_manhattan value: 0.9042784013946286 name: Spearman Manhattan - type: pearson_euclidean value: 0.9079766073019917 name: Pearson Euclidean - type: spearman_euclidean value: 0.903971004980131 name: Spearman Euclidean - type: pearson_dot value: 0.8769458596903497 name: Pearson Dot - type: spearman_dot value: 0.879439974530776 name: Spearman Dot - type: pearson_max value: 0.9086667636630104 name: Pearson Max - type: spearman_max value: 0.9077370026312627 name: Spearman Max - task: type: binary-classification name: Binary Classification dataset: name: VitaminC type: VitaminC metrics: - type: cosine_accuracy value: 0.5546875 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.8242440819740295 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.6684856753069577 name: Cosine F1 - type: cosine_f1_threshold value: 0.42694979906082153 name: Cosine F1 Threshold - type: cosine_precision value: 0.508298755186722 name: Cosine Precision - type: cosine_recall value: 0.9760956175298805 name: Cosine Recall - type: cosine_ap value: 0.5536891122484903 name: Cosine Ap - type: dot_accuracy value: 0.548828125 name: Dot Accuracy - type: dot_accuracy_threshold value: 320.12664794921875 name: Dot Accuracy Threshold - type: dot_f1 value: 0.6711772665764546 name: Dot F1 - type: dot_f1_threshold value: 147.43307495117188 name: Dot F1 Threshold - type: dot_precision value: 0.5081967213114754 name: Dot Precision - type: dot_recall value: 0.9880478087649402 name: Dot Recall - type: dot_ap value: 0.5328419714769899 name: Dot Ap - type: manhattan_accuracy value: 0.556640625 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 317.9842224121094 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.6649006622516557 name: Manhattan F1 - type: manhattan_f1_threshold value: 510.47943115234375 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.498015873015873 name: Manhattan Precision - type: manhattan_recall value: 1.0 name: Manhattan Recall - type: manhattan_ap value: 0.5558452170498778 name: Manhattan Ap - type: euclidean_accuracy value: 0.5625 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 15.206985473632812 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.6657789613848203 name: Euclidean F1 - type: euclidean_f1_threshold value: 22.77822494506836 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.5 name: Euclidean Precision - type: euclidean_recall value: 0.9960159362549801 name: Euclidean Recall - type: euclidean_ap value: 0.5563017058033936 name: Euclidean Ap - type: max_accuracy value: 0.5625 name: Max Accuracy - type: max_accuracy_threshold value: 320.12664794921875 name: Max Accuracy Threshold - type: max_f1 value: 0.6711772665764546 name: Max F1 - type: max_f1_threshold value: 510.47943115234375 name: Max F1 Threshold - type: max_precision value: 0.508298755186722 name: Max Precision - type: max_recall value: 1.0 name: Max Recall - type: max_ap value: 0.5563017058033936 name: Max Ap --- # SentenceTransformer based on bobox/DeBERTa-small-ST-v1-test-step2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [bobox/DeBERTa-small-ST-v1-test-step2](https://huggingface.co/bobox/DeBERTa-small-ST-v1-test-step2) on the negation-triplets, [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc), [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail), [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail), xsum-pairs, [sciq_pairs](https://huggingface.co/datasets/allenai/sciq), [qasc_pairs](https://huggingface.co/datasets/allenai/qasc), openbookqa_pairs, [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions), [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) and [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [bobox/DeBERTa-small-ST-v1-test-step2](https://huggingface.co/bobox/DeBERTa-small-ST-v1-test-step2) <!-- at revision 227c804cec7dd9eaab6a3cd4f9df268d4b5a1ca2 --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Datasets:** - negation-triplets - [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) - [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) - [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) - xsum-pairs - [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) - [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) - openbookqa_pairs - [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) - [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) - [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) - [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) - [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) - **Language:** en <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("bobox/DeBERTa-small-ST-v1-test-step3") # Run inference sentences = [ 'where was the first hudson bay company post', "Hudson's Bay Company Hudson's Bay Company's first inland trading post was established by Samuel Hearne in 1774 in Cumberland House, Saskatchewan.[38][39]", 'Steven Ogg Steven Ogg is a Canadian actor.[1] He is best known for his roles as Trevor Philips in the 2013 video game Grand Theft Auto V and Simon in The Walking Dead.[2] He has also appeared in television series such as Better Call Saul, Law & Order, Person of Interest, Broad City, and Westworld.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8861 | | **spearman_cosine** | **0.9077** | | pearson_manhattan | 0.9087 | | spearman_manhattan | 0.9043 | | pearson_euclidean | 0.908 | | spearman_euclidean | 0.904 | | pearson_dot | 0.8769 | | spearman_dot | 0.8794 | | pearson_max | 0.9087 | | spearman_max | 0.9077 | #### Binary Classification * Dataset: `VitaminC` * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.5547 | | cosine_accuracy_threshold | 0.8242 | | cosine_f1 | 0.6685 | | cosine_f1_threshold | 0.4269 | | cosine_precision | 0.5083 | | cosine_recall | 0.9761 | | cosine_ap | 0.5537 | | dot_accuracy | 0.5488 | | dot_accuracy_threshold | 320.1266 | | dot_f1 | 0.6712 | | dot_f1_threshold | 147.4331 | | dot_precision | 0.5082 | | dot_recall | 0.988 | | dot_ap | 0.5328 | | manhattan_accuracy | 0.5566 | | manhattan_accuracy_threshold | 317.9842 | | manhattan_f1 | 0.6649 | | manhattan_f1_threshold | 510.4794 | | manhattan_precision | 0.498 | | manhattan_recall | 1.0 | | manhattan_ap | 0.5558 | | euclidean_accuracy | 0.5625 | | euclidean_accuracy_threshold | 15.207 | | euclidean_f1 | 0.6658 | | euclidean_f1_threshold | 22.7782 | | euclidean_precision | 0.5 | | euclidean_recall | 0.996 | | euclidean_ap | 0.5563 | | max_accuracy | 0.5625 | | max_accuracy_threshold | 320.1266 | | max_f1 | 0.6712 | | max_f1_threshold | 510.4794 | | max_precision | 0.5083 | | max_recall | 1.0 | | **max_ap** | **0.5563** | <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Datasets #### negation-triplets * Dataset: negation-triplets * Size: 35,750 training samples * Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | entailment | negative | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 21.52 tokens</li><li>max: 101 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.34 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.62 tokens</li><li>max: 44 tokens</li></ul> | * Samples: | anchor | entailment | negative | |:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | <code>Brunette woman in a white bikini pouring a drink into a man's cup.</code> | <code>A woman is serving a beverage to a man.</code> | <code>A woman is not serving a beverage to a man.</code> | | <code>People under a tent at a reception.</code> | <code>People are outside.</code> | <code>People are inside.</code> | | <code>A man is smoking at sunset.</code> | <code>The time in the picture is probably somewhere between 3PM and 9PM.</code> | <code>The time in the picture is probably not somewhere between 3PM and 9PM.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 34,375 training samples * Columns: <code>claim</code> and <code>evidence</code> * Approximate statistics based on the first 1000 samples: | | claim | evidence | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 7 tokens</li><li>mean: 18.14 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 37.45 tokens</li><li>max: 192 tokens</li></ul> | * Samples: | claim | evidence | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Dora and the Lost City of Gold grossed more than $ 58 million in North America , more than $ 40 million in other territories , and more than $ 98 million globally .</code> | <code>, Dora and the Lost City of Gold has grossed $ 58.4 million in the United States and Canada , and $ 40.4 million in other territories , for a worldwide total of $ 98.8 million , against a production budget of $ 49 million .</code> | | <code>After Juan Antonio and Vicky Cristina have sex , they go to bed .</code> | <code>Afterwards , Juan Antonio lets her know he thinks she 's beautiful , and they make love , and eventually go to bed .</code> | | <code>Tove Lo 's single `` Disco Tits '' reached number 55 in Sweden .</code> | <code>`` Its lead single , `` '' Disco Tits '' '' , peaked at number 55 in Sweden. ''</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### scitail-pairs-qa * Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 14,237 training samples * Columns: <code>sentence2</code> and <code>question</code> * Approximate statistics based on the first 1000 samples: | | sentence2 | question | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 7 tokens</li><li>mean: 16.29 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 15.21 tokens</li><li>max: 35 tokens</li></ul> | * Samples: | sentence2 | question | |:--------------------------------------------------------------------------|:--------------------------------------------------------------------| | <code>When an atom gains or loses an electron it becames a(n) ion.</code> | <code>When an atom gains or loses an electron it becames an?</code> | | <code>The stratosphere is the layer above the troposphere.</code> | <code>What is the layer above the troposphere?</code> | | <code>Solar and wind are still expensive compared to fossil fuels.</code> | <code>Solar and wind are still expensive compared to what?</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### scitail-pairs-pos * Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 8,600 training samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 7 tokens</li><li>mean: 23.52 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.54 tokens</li><li>max: 39 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------| | <code>Ozone is a molecule formed of THREE oxygen atoms.</code> | <code>Three oxygen ions make up an ozone molecule.</code> | | <code>Animals live by eating the energy produced by plants (herbivores), or live by eating animals that eat plants (carnivores) Let's take the human being (a polyhagous animal) as an example and ponder upon it.</code> | <code>Herbivores is the term for animals that eat producers to get energy.</code> | | <code>Fertilization triggers Meiosis II, and then the sperm nucleus unites with the resulting egg nucleus.</code> | <code>When a sperm penetrates the egg, it triggers the egg to complete meiosis.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### xsum-pairs * Dataset: xsum-pairs * Size: 38,500 training samples * Columns: <code>document</code> and <code>summary</code> * Approximate statistics based on the first 1000 samples: | | document | summary | |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 40 tokens</li><li>mean: 223.77 tokens</li><li>max: 443 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 25.9 tokens</li><li>max: 45 tokens</li></ul> | * Samples: | document | summary | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>William Heath, 12, from Earls Barton, has so far disguised himself as Ali G, Hercule Poirot and Groucho Marx as part of his Movember campaign.<br>He is raising money in memory of his grandfather, who died from cancer in February.<br>William came up with the idea as he cannot grow a moustache himself.<br>His granddad, Robin Wickham, died from pancreatic, testicular and prostate cancer, and William is now asking people to donate to The Movember Foundation, via his own "Mo Space" page.<br>William has already dressed as seven famous moustachioed men, including Biggles, Keith Lemon and Magnum PI.<br>His costumes were sourced from a combination of a local fancy dress shop and items around the home.<br>"My granddad and I were very close," he said. "He used to come on holiday with us and was always having fun."<br>His mum Clare said William had wanted to do something that would help, but was also fun.<br>She said: "We needed to fill 30 days. When you try to think of 30 people with moustaches it is harder than you think.<br>"The overriding thing is that it has been fun, but it has serious implications."<br>William said his favourite character so far had been Ali G, but he had struggled to create a successful Ned Flanders outfit.<br>He said: "I tried to paint my face yellow, but it didn't really work."</code> | <code>A schoolboy has decided to raise money for a men's health charity by dressing up as a different moustachioed famous face every day for a month.</code> | | <code>Media playback is not supported on this device<br>The Daily Mail claimed a £10m offer was made to owner Owen Oyston despite him stating the club was not for sale at a recent public meeting with fans.<br>Oyston was heckled throughout over the way the club is being run.<br>"The owners of the club have received no approach whatsoever, from any investor or third party," said a statement.<br>The Blackpool Supporters Trust launched a £16m bid to buy the club July 2015 but Oyston, whose son Karl is chairman, eventually ended those takeover talks.<br>The Seasiders, who will play in the fourth tier for the first time since 2000-01 this season, have also announced the signing of midfielder Danny Pugh.<br>The 33-year-old former Manchester United trainee joins on a free transfer after his release by Bury and has signed a one-year deal, with further 12-month option.<br>"He brings a wealth of experience to the team and will be a strong, vocal presence in the middle of the park," said boss Gary Bowyer.</code> | <code>League Two club Blackpool have denied claims they were subject of a new takeover offer.</code> | | <code>The Cochrane Collaboration carried out a systematic review of eight exercise trials involving more than 300 patients living at home or in care.<br>Exercise did little for patients' moods, the research concluded.<br>But it did help them carry out daily activities such as rising from a chair, and boosted their cognitive skills.<br>Whether these benefits improve quality of life is still unclear, but the study authors say the findings are reason for optimism.<br>Dementia affects some 800,000 people in the UK. And the number of people with the condition is steadily increasing because people are living longer.<br>It is estimated that by 2021, the number of people with dementia in the UK will have increased to around one million.<br>With no cure, ways to improve the lives of those living with the condition are vital.<br>Researcher Dorothy Forbes, of the University of Alberta, and colleagues who carried out the Cochrane review, said: "Clearly, further research is needed to be able to develop best practice guidelines to enable healthcare providers to advise people with dementia living at home or in institutions.<br>"We also need to understand what level and intensity of exercise is beneficial for someone with dementia."<br>Dr Laura Phipps of Alzheimer's Research UK said: "We do know that exercise is an important part of keeping healthy, and though we can't say that exercise will prevent dementia, evidence does suggest it can help reduce the risk of the condition as part of a healthy lifestyle."</code> | <code>People with dementia who exercise improve their thinking abilities and everyday life, a body of medical research concludes.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### sciq_pairs * Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) * Size: 11,095 training samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 7 tokens</li><li>mean: 16.92 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 85.56 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>The bright color of poison dart frogs serves what purpose?</code> | <code>Poison dart frogs have toxins in their skin. Their bright colors warn potential predators not to take a bite!.</code> | | <code>What term describes motion that repeats itself at regular time intervals, such as exhibited by a vibrating guitar string?</code> | <code>When you pluck a guitar string, the resulting sound has a steady tone and lasts a long time. Each successive vibration of the string takes the same time as the previous one. We define periodic motion to be a motion that repeats itself at regular time intervals, such as exhibited by the guitar string or by an object on a spring moving up and down. The time to complete one oscillation remains constant and is called the period T . Its units are usually seconds, but may be any convenient unit of time. The word period refers to the time for some event whether repetitive or not; but we shall be primarily interested in periodic motion, which is by definition repetitive. A concept closely related to period is the frequency of an event. For example, if you get a paycheck twice a month, the frequency of payment is two per month and the period between checks is half a month. Frequency f is defined to be the number of events per unit time. For periodic motion, frequency is the number of oscillations per unit time. The relationship between frequency and period is.</code> | | <code>What do stored fats provide our body with for later use?</code> | <code>Fats are one type of lipid. Stored fat gives your body energy to use for later. It’s like having money in a savings account: it’s there in case you need it. Stored fat also cushions and protects internal organs. In addition, it insulates the body. It helps keep you warm in cold weather.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### qasc_pairs * Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 7,727 training samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 11.4 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 34.47 tokens</li><li>max: 67 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What do geologists use to determine the age of rock layers?</code> | <code>radioactive dating is used to determine the age of fossils. Geologists use fossils to determine the age of the rock layer that it was found in.. Geologists use radioactive dating to determine the age of rock layers.</code> | | <code>Air pollution can cause a decrease in what?</code> | <code>air pollution can cause the pH of soil to decrease. Research has shown that soil pH less than 6.0 can decrease crop yields.. Air pollution can cause a decrease in crop yields. </code> | | <code>What organism uses their sense of smell to find a mate?</code> | <code>Most salamanders use their sense of smell to find a mate.. Salamanders are a type of amphibian.. Some amphibians use their sense of smell to find a mate.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### openbookqa_pairs * Dataset: openbookqa_pairs * Size: 4,522 training samples * Columns: <code>question</code> and <code>fact</code> * Approximate statistics based on the first 1000 samples: | | question | fact | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 13.8 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.5 tokens</li><li>max: 30 tokens</li></ul> | * Samples: | question | fact | |:-----------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | <code>What is animal competition?</code> | <code>if two animals eat the same prey then those animals compete for that pey</code> | | <code>If you wanted to make a metal bed frame, where would you start?</code> | <code>alloys are made of two or more metals</code> | | <code>Places lacking warmth have few what</code> | <code>cold environments contain few organisms</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### msmarco_pairs * Dataset: [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9) * Size: 30,250 training samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 8.65 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 76.38 tokens</li><li>max: 207 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>propria definition</code> | <code>The lamina propria is a constituent of the moist linings known as mucous membranes or mucosa, which line various tubes in the body.The lamina propria is a thin layer of loose connective tissue which lies beneath the epithelium and together with the epithelium constitutes the mucosa.As its Latin name indicates it is a characteristic component of the mucosa, the mucosa's own special layer.he lamina propria is a constituent of the moist linings known as mucous membranes or mucosa, which line various tubes in the body.</code> | | <code>what family is the common bullfrog in</code> | <code>The American bullfrog (Rana catesbeiana), often simply known as the bullfrog in Canada and the United States, is an aquatic frog, a member of the family Ranidae, or “true frogs”. This frog has an olive green back and sides blotched with brownish markings and a whitish belly spotted with yellow or grey.</code> | | <code>why does well pump cycle on and off</code> | <code>1 When a well pump turns on every time a water-using fixture’s valve is opened, or if it turns on and off rapidly while the fixture is in use, that’s short cycling. It is typically caused by a water pressure tank that has lost it built-in cushion of pressurized air.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### nq_pairs * Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 30,250 training samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 11.81 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 135.28 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>who established the republic of china in 1912 ce</code> | <code>Republic of China (1912–1949) The Republic of China was a sovereign state in East Asia, that occupied the territories of modern China and for part of its history Mongolia and Taiwan. It was founded in 1912, after the Qing dynasty, the last imperial dynasty, was overthrown in the Xinhai Revolution. The Republic's first president, Sun Yat-sen, served only briefly before handing over the position to Yuan Shikai, former leader of the Beiyang Army. His party, then led by Song Jiaoren, won the parliamentary election held in December 1912. Song was assassinated shortly after, and the Beiyang Army led by Yuan Shikai maintained full control of the government in Beijing. Between late 1915 and early 1916, Yuan tried to reinstate the monarchy, before resigning after popular unrest. After Yuan's death in 1916, members of cliques in the former Beiyang Army claimed their autonomy and clashed with each other. During this period, the authority of the republican government was weakened by a restoration of the Qing government.</code> | | <code>what is the message in john lennon's imagine</code> | <code>Imagine (John Lennon song) "Imagine" is a song written and performed by English musician John Lennon. The best-selling single of his solo career, its lyrics encourage the listener to imagine a world at peace without the barriers of borders or the divisions of religion and nationality, and to consider the possibility that the whole of humanity would live unattached to material possessions.</code> | | <code>who is the original singer for these boots are made for walking</code> | <code>These Boots Are Made for Walkin' "These Boots Are Made for Walkin'" is a hit song written by Lee Hazlewood and recorded by Nancy Sinatra. It charted January 22, 1966,[3] and reached No. 1 in the United States Billboard Hot 100 and in the UK Singles Chart.[2]</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### trivia_pairs * Dataset: [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) * Size: 12,024 training samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 7 tokens</li><li>mean: 17.49 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 200.72 tokens</li><li>max: 482 tokens</li></ul> | * Samples: | query | answer | |:--------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What is the name of 'Bob the Builder's' cement mixer?</code> | <code>BOB the BUILDER DIZZY the CEMENT MIXER BOB the BUILDER DIZZY THE CEMENT MIXER Large Friction Action Dizzy the Cement Mixer and clicky noise turn by hand Mixer from the Large Bob The Builder Friction toy Collection  Measures 5" x  4" High  in EXCELLENT Played with Condition �8.00 .. US $15.60  .. EUROS 12.00 Ref # 0845</code> | | <code>Which cartoon character, who cooks at the Krusty Krab, lives in a pineapple under the sea?</code> | <code>SpongeBob SquarePants from SpongeBob SquarePants| Cartoon | Nick.com Gary SpongeBob SquarePants Come follow the adventures of the world's most lovable sponge and his starfish sidekick! Though they have the best intentions, SpongeBob and Patrick are always causing trouble… and plenty of laughs! When he's not at the Krusty Krab grilling up some epic Krabby Patties, SpongeBob can be found jellyfishing with Patrick, blowing bubbles, or annoying his favorite neighbor, Squidward! Bikini Bottom is home to the coolest creatures under the sea, and you CAN'T miss out on any of their adventures. Is mayonnaise an instrument? Watch SpongeBob SquarePants to find out! Are you ready, kids? AYE AYE CAPTAIN! ON TV</code> | | <code>Chinese Democracy was the long awaited 2008 album release of which band?</code> | <code>Guns N Roses 'Chinese Democracy' Set For Wal-Mart Release | Gigwise Guns N Roses 'Chinese Democracy' Set For Wal-Mart Release Band are in talks... Guns N Roses Tickets Buy tickets safely & securely with Seatwave Guns N’ Roses long-awaited new studio album could be released exclusively in retail stores, it’s been reported. Wal-Mart and Best Buy are understood to be leading negotiations to acquire 'Chinese Democracy's' release - possibly before the end of 2008. Industry reports have escalated since Irving Azoff's Front Line Management took over all of the bands business duties. Last year, the company were influential in instigating the release of The Eagles ‘Long Road To Eden’ via Wal-Mart chains. The album enjoyed huge, chart-topping success in the US. According to Billboard, Guns N Roses are also in negotiations for a more traditional release which would take in the rest of the world. As previously reported on Gigwise , Guns N Roses will release their first song in over a decade on the next installment of Rock Band. 'Shackler's Revenge' leads a playlist that spans music releases from the 1960s to today. Guns N Roses – Through The Years.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### gooaq_pairs * Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 30,250 training samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 8 tokens</li><li>mean: 11.37 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 56.7 tokens</li><li>max: 154 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>do namibians need a visa for london?</code> | <code>The Embassy is closed every Friday. Namibia tourist visa is not required for citizens of United Kingdom for a stay up to 90 days. Sounds good!</code> | | <code>what is the meaning of personal finance?</code> | <code>Personal finance is a term that covers managing your money as well as saving and investing. ... It often refers to the entire industry that provides financial services to individuals and households and advises them about financial and investment opportunities.</code> | | <code>what are trim tabs for?</code> | <code>Trim tabs are either flat plates or vertical blades fitted either side of the boat and attached to the transom. They are used to trim or level the boat, both fore and aft and side to side. Trim tabs are controlled up and downwards by either a hydraulic, or electric ram system on the transom.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### paws-pos * Dataset: [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) at [161ece9](https://huggingface.co/datasets/google-research-datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09) * Size: 21,829 training samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 9 tokens</li><li>mean: 25.63 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 25.55 tokens</li><li>max: 68 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Thompson was born in Hendon , North London , Dena Holmes in 1960 and worked for a building society bank .</code> | <code>Thompson was born Dena Holmes in 1960 in Hendon , North London . She worked for a building society .</code> | | <code>One of his first cousins was Elizabeth , aka Lady Elizabeth Hervey , aka Bess Foster , Duchess of Devonshire . His younger brother married Lord Bishop Foster .</code> | <code>One of his first cousins was Elizabeth , alias Lady Elizabeth Hervey , alias Bess Foster , Duchess of Devonshire , his younger brother married Lord Bishop Foster .</code> | | <code>At the executive level , EEAA represents the central arm of the Ministry .</code> | <code>At executive level , EEAA represents the central arm of the ministry .</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` ### Evaluation Datasets #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 108 evaluation samples * Columns: <code>claim</code> and <code>evidence</code> * Approximate statistics based on the first 1000 samples: | | claim | evidence | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 9 tokens</li><li>mean: 21.36 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 36.11 tokens</li><li>max: 79 tokens</li></ul> | * Samples: | claim | evidence | |:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Dragon Con had over 5000 guests .</code> | <code>Among the more than 6000 guests and musical performers at the 2009 convention were such notables as Patrick Stewart , William Shatner , Leonard Nimoy , Terry Gilliam , Bruce Boxleitner , James Marsters , and Mary McDonnell .</code> | | <code>COVID-19 has reached more than 185 countries .</code> | <code>As of , more than cases of COVID-19 have been reported in more than 190 countries and 200 territories , resulting in more than deaths .</code> | | <code>In March , Italy had 3.6x times more cases of coronavirus than China .</code> | <code>As of 12 March , among nations with at least one million citizens , Italy has the world 's highest per capita rate of positive coronavirus cases at 206.1 cases per million people ( 3.6x times the rate of China ) and is the country with the second-highest number of positive cases as well as of deaths in the world , after China .</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### negation-triplets * Dataset: negation-triplets * Size: 64 evaluation samples * Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | entailment | negative | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 11 tokens</li><li>mean: 13.64 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 13.23 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 13.52 tokens</li><li>max: 22 tokens</li></ul> | * Samples: | anchor | entailment | negative | |:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:-----------------------------------------------------------------| | <code>Set of toy animals sitting in front of a red wooden wagon.</code> | <code>Several toy animals - a bull, giraffe, deer and parakeet.</code> | <code>Several toy animals - a cow, lion, wolf and canary.</code> | | <code>A bathroom with a toilette with it's seat down.</code> | <code>A bathroom with a sink and a toilet</code> | <code>A bathroom without a sink or a toilet</code> | | <code>A striped plane flying up into the sky as the sun shines behind it.</code> | <code>An airplane is ascending into the white sky</code> | <code>An airplane is descending into the black sky</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### scitail-pairs-pos * Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 54 evaluation samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 9 tokens</li><li>mean: 20.81 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 15.48 tokens</li><li>max: 23 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------| | <code>humans normally have 23 pairs of chromosomes.</code> | <code>Humans typically have 23 pairs pairs of chromosomes.</code> | | <code>A solution is a homogenous mixture of two or more substances that exist in a single phase.</code> | <code>Solution is the term for a homogeneous mixture of two or more substances.</code> | | <code>Upwelling The physical process in near-shore ocean systems of rising of nutrients and colder bottom waters to the surface because of constant wind patterns along the shoreline.</code> | <code>Upwelling is the term for when deep ocean water rises to the surface.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### scitail-pairs-qa * Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 128 evaluation samples * Columns: <code>sentence2</code> and <code>question</code> * Approximate statistics based on the first 1000 samples: | | sentence2 | question | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 7 tokens</li><li>mean: 15.31 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 14.91 tokens</li><li>max: 26 tokens</li></ul> | * Samples: | sentence2 | question | |:---------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | <code>It takes earth one week to rotate on its axis seven times.</code> | <code>How long does it take for Earth to rotate on its axis seven times?</code> | | <code>Both hurricanes and tornadoes always have high winds.</code> | <code>Both hurricanes and tornadoes always</code> | | <code>Seeds of a pine cone are easily carried by the wind and dispersed because seeds have wings.</code> | <code>Why are seeds of a pine cone easily carried by the wind and dispersed?</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### xsum-pairs * Dataset: xsum-pairs * Size: 128 evaluation samples * Columns: <code>document</code> and <code>summary</code> * Approximate statistics based on the first 1000 samples: | | document | summary | |:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 63 tokens</li><li>mean: 214.16 tokens</li><li>max: 341 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 25.86 tokens</li><li>max: 43 tokens</li></ul> | * Samples: | document | summary | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------| | <code>The rescue on Monday afternoon involved a challenging winch down to the casualty by a coastguard helicopter's paramedic.<br>The Inverness Airport-based helicopter could not be brought too close in case its downdraft blew the man and his fellow walkers off the ridge.<br>The walker was taken to hospital for treatment.<br>The alarm was raised at 14:25 on Monday after the man became unwell on Carn Mor Dearg.<br>He was airlifted to Torlundy and from there was taken to hospital by ambulance.<br>Scott Sharman, paramedic winchman, said: "It was an extremely steep ridge and we needed to make sure we kept at a safe distance because the downdraft could very easily have blown them over the ridge."</code> | <code>A walker had to be rescued from a 1,000m (3,500ft) ridge near Ben Nevis after he became ill.</code> | | <code>The 23-year-old, who did not make a senior appearances during a year with the Hornets, has signed a two-year deal with the O's.<br>Woods began his career in Cambridge United's youth set-up and subsequently joined Manchester United's academy.<br>He moved to Doncaster Rovers in 2009 and went on to make 82 appearances, before leaving the club last summer.<br>Woods, a former England Under-19 international, will provide competition for fellow new arrival Adam Legzdins.</code> | <code>League One side Leyton Orient have signed goalkeeper Gary Woods following his departure from Watford.</code> | | <code>The 32-year-old, the number one ranked Test bowler, has been out of action with a groin strain since the first Test against India in early November.<br>But in Cape Town this week he was able to complete two bowling spells at match intensity in the nets, run sprints and do agility tests and fielding drills.<br>The first of the four-match Test series begins in Durban on 26 December.<br>Proteas team manager, Dr Mohammed Moosajee said: "Dale has put a lot of work into his training and rehab since his return from India so we are very happy to have him back from injury.<br>"He was put through a thorough fitness test on Thursday by physiotherapist, Shane Jabaar, he came through the tests without any discomfort and will be available for selection for the first Test match against England."<br>Steyn has taken 402 wickets in 81 Tests at an average of 22.56, including 46 in 11 matches against England.<br>South Africa remain at the top of the Test rankings despite a 3-0 defeat in India this month.</code> | <code>South Africa paceman Dale Steyn will be fit for the Test series with England after passing a fitness test.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### sciq_pairs * Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) * Size: 128 evaluation samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 8 tokens</li><li>mean: 16.72 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 73.28 tokens</li><li>max: 332 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>The skull is a part of a vertebrate endoskeleton that encloses and protects what organ?</code> | <code>part of a vertebrate endoskeleton that encloses and protects the brain; also called the skull.</code> | | <code>The action of sunlight on the skin triggers the body to produce what vitamin?</code> | <code>Milk and other dairy foods are not the only sources of calcium. This important nutrient is also found in green leafy vegetables, broccoli, and intact salmon and canned sardines with their soft bones. Nuts, beans, seeds, and shellfish provide calcium in smaller quantities. Except for fatty fish like salmon and tuna, or fortified milk or cereal, vitamin D is not found naturally in many foods. The action of sunlight on the skin triggers the body to produce its own vitamin D (Figure 6.22), but many people, especially those of darker complexion and those living in northern latitudes where the sun’s rays are not as strong, are deficient in vitamin D. In cases of deficiency, a doctor can prescribe a vitamin D supplement.</code> | | <code>What phenomenon is essential in order for evolution to occur because it increases genetic variation and the potential for individuals to differ?</code> | <code>Mutations are essential for evolution to occur because they increase genetic variation and the potential for individuals to differ. The majority of mutations are neutral in their effects on the organisms in which they occur. Beneficial mutations may become more common through natural selection. Harmful mutations may cause genetic disorders or cancer.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### qasc_pairs * Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 128 evaluation samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 11.34 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 35.58 tokens</li><li>max: 66 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What designed for swimming are used for moving faster through water?</code> | <code>webbed feet are used for moving faster through water by aquatic animals. Ducks have webbed feet, designed for swimming.. Feet designed for swimming are used for moving faster through water.</code> | | <code>what falls making sunlight available to surrounding plants?</code> | <code>if a tree falls then sunlight becomes available to the surrounding plants. Oak trees are found throughout.. if an oak falls then sunlight becomes available to surrounding plants</code> | | <code>What is the term used for an individual who is learning ethology?</code> | <code>Ethologists usually study how animals behave in their natural environment.. Ethology is the study of behavior.. Ethologists learn ethology</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### openbookqa_pairs * Dataset: openbookqa_pairs * Size: 128 evaluation samples * Columns: <code>question</code> and <code>fact</code> * Approximate statistics based on the first 1000 samples: | | question | fact | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 13.98 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.78 tokens</li><li>max: 28 tokens</li></ul> | * Samples: | question | fact | |:-----------------------------------------------------------------------|:-----------------------------------------------------------------------------| | <code>The thermal production of a stove is generically used for</code> | <code>a stove generates heat for cooking usually</code> | | <code>What creates a valley?</code> | <code>a valley is formed by a river flowing</code> | | <code>when it turns day and night on a planet, what cause this?</code> | <code>a planet rotating causes cycles of day and night on that planet</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### msmarco_pairs * Dataset: [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9) * Size: 128 evaluation samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 8.91 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 80.7 tokens</li><li>max: 195 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>when did young american by david bowie come out</code> | <code>Young Americans (song) Young Americans is a single by English singer and songwriter David Bowie, released in 1975. It is included in the album of the same name. The song was a massive breakthrough in the United States, where glam rock had never really become very popular outside the major cities.</code> | | <code>are investment commissions tax deductible</code> | <code>You typically pay a commission when you buy, and you pay another commission when you sell. The IRS does not consider investment commissions to be a tax-deductible expense. Instead, the commission becomes part of the investment's cost basis, which still provides you with some tax relief.</code> | | <code>does photosynthesis occur in prokaryotes</code> | <code>Animal cells do not undergo photosynthesis, but in a plant cell, the site of photosynthesis is the chloroplast. Prokaryotes are more primitive-they have no nucleus for a start, and also have no chloroplasts.They are bacterial cells and so do not undergo photosynthesis.ating Newest Oldest. Best Answer: Actually, some procaryotes have photosynthesis. The thing is, as they don't have organelles, it doesn't occur in a differentiated structure, like choroplasts in eucaryotes. It happens along the plasma membrane.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### nq_pairs * Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 128 evaluation samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 11.68 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 127.79 tokens</li><li>max: 326 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>who played gimli in the lord of the rings movies</code> | <code>John Rhys-Davies John Rhys-Davies (born 5 May 1944) is a Welsh actor and voice actor known for his portrayal of Gimli in The Lord of the Rings trilogy and the charismatic Arab excavator Sallah in the Indiana Jones films. He also played Agent Michael Malone in the 1993 remake of the 1950s television series The Untouchables, Pilot Vasco Rodrigues in the mini-series Shōgun, Professor Maximillian Arturo in Sliders, King Richard I in Robin of Sherwood, General Leonid Pushkin in the James Bond film The Living Daylights, and Macro in I, Claudius. Additionally, he provided the voices of Cassim in Disney's Aladdin and the King of Thieves, Macbeth in Gargoyles, Man Ray in SpongeBob SquarePants, Hades in Justice League and Tobias in the computer game Freelancer.</code> | | <code>why did the red sea get its name</code> | <code>Red Sea Red Sea is a direct translation of the Greek Erythra Thalassa (Ερυθρὰ Θάλασσα), Latin Mare Rubrum (alternatively Sinus Arabicus, literally "Arabian Gulf"), Arabic: البحر الأحمر‎, translit. Al-Baḥr Al-Aḥmar (alternatively بحر القلزم Baḥr Al-Qulzum, literally "the Sea of Clysma"), Somali Badda Cas and Tigrinya Qeyyiḥ bāḥrī (ቀይሕ ባሕሪ). The name of the sea may signify the seasonal blooms of the red-coloured Trichodesmium erythraeum near the water's surface.[5] A theory favored by some modern scholars is that the name red is referring to the direction south, just as the Black Sea's name may refer to north. The basis of this theory is that some Asiatic languages used color words to refer to the cardinal directions.[6] Herodotus on one occasion uses Red Sea and Southern Sea interchangeably.[7]</code> | | <code>when is the new president of mexico announced</code> | <code>Mexican general election, 2018 López Obrador won the election on 1 July 2018 with over 50% of the popular vote. In terms of states won, López Obrador won in a landslide, carrying 31 out of 32 of the country's states,[5] the most states won by a candidate since Ernesto Zedillo won every state in the 1994 election.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### trivia_pairs * Dataset: [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) * Size: 128 evaluation samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 8 tokens</li><li>mean: 16.72 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 211.76 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | query | answer | |:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Who sang '99 Red Balloons'?</code> | <code>99 red ballons - Nena - YouTube 99 red ballons - Nena Want to watch this again later? Sign in to add this video to a playlist. Need to report the video? Sign in to report inappropriate content. Rating is available when the video has been rented. This feature is not available right now. Please try again later. Uploaded on Jul 29, 2006 This is the video 99 red ballons (english version) by nena Category</code> | | <code>Who is the patron saint of doctors, surgeons, painters and artists?</code> | <code>St. Luke Medals St. Luke Medals St. Luke is the Patron Saint of artists, brewers, butchers, doctors, glassworks, painters, physicians and surgeons. Luke is symbolically portrayed as an animal of sacrifice, the ox, because he begins his Gospel with the history of Zachary, the priest offering sacrifice to God, and because throughout his Gospel he accentuates the universal priesthood of Christ. St. Luke�s Gospel includes six miracles and 18 parables not found elsewhere in the Bible.</code> | | <code>Vande Mataram (I praise thee, Mother) is the national song of which BRIC nation?</code> | <code>'Vande Mataram' Real National Anthem: RSS Leader- The New Indian Express 'Vande Mataram' Real National Anthem: RSS Leader By PTI   |   Published: 02nd April 2016 01:09 PM  |   Last Updated: 02nd April 2016 01:09 PM  |   A+A A-   |   0 Share Via Email MUMBAI: Days after RSS chief Mohan Bhagwat's 'Bharat Mata Ki Jai' remarks, a top functionary of the organisation has said that 'Vande Mataram' is the real national anthem as opposed to the 'Constitutionally-mandated' Jana Gana Mana. "Jana Gana Mana is today our national anthem. It has to be respected. There is no reason why it should evoke any other sentiment," RSS General Secretary Bhaiyyaji Joshi said. "But it is the national anthem as decided by the Constitution. If one considers the true meaning, then Vande Mataram is the national anthem," he said yesterday at the Deendayal Upadhyay Research Institute here. "We consider things created due to the Constitution to be national," Joshi said. "When was Jana Gana Mana written? It was written some time back. But the sentiments expressed in Jana Gana Mana have been expressed keeping the state in view," he said. "However, the sentiments expressed in Vande Mataram denote the nation's character and style. This is the difference between the two songs. Both deserve respect," Joshi said. 'Vande Mataram', literally, "I praise thee, Mother", is a poem by Bankim Chandra Chattopadhyay. A hymn to the 'Mother Land', it played a vital role in the Indian independence movement. In 1950, the song's first two verses were given the official status of the "national song", distinct from the national anthem, Jana Gana Mana. O</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### gooaq_pairs * Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 128 evaluation samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 8 tokens</li><li>mean: 11.34 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 57.08 tokens</li><li>max: 112 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>which gases are emitted from vehicles?</code> | <code>['Particulate matter (PM). One type of particulate matter is the soot seen in vehicle exhaust. ... ', 'Volatile Organic Compounds (VOCs). ... ', 'Nitrogen oxides (NOx). ... ', 'Carbon monoxide (CO). ... ', 'Sulfur dioxide (SO2). ... ', 'Greenhouse gases.']</code> | | <code>how long does it take for someone to not be drunk?</code> | <code>It takes 30 minutes to feel the effects of alcohol. Drinking more than one drink every 30 minutes means you are probably drinking too much, too fast. Slow yourself down, and if you find yourself feeling thirsty before those 30 minutes have passed, try a glass of water first.</code> | | <code>is ssdi taxable in ohio?</code> | <code>Social Security retirement benefits are fully exempt from state income taxes in Ohio. Any income from retirement accounts (like a 401(k) or an IRA) or pensions is taxed as regular income (but there are credits available). Social security benefits, if taxable on the federal return, are deducted on Ohio schedule A.</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` #### paws-pos * Dataset: [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) at [161ece9](https://huggingface.co/datasets/google-research-datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09) * Size: 128 evaluation samples * Columns: <code>sentence1</code> and <code>sentence2</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 25.72 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 25.55 tokens</li><li>max: 41 tokens</li></ul> | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>They were there to enjoy us and they were there to pray for us .</code> | <code>They were there for us to enjoy and they were there for us to pray .</code> | | <code>After the end of the war in June 1902 , Higgins left Southampton in the `` SSBavarian '' in August , returning to Cape Town the following month .</code> | <code>In August , after the end of the war in June 1902 , Higgins Southampton left the `` SSBavarian '' and returned to Cape Town the following month .</code> | | <code>From the merger of the Four Rivers Council and the Audubon Council , the Shawnee Trails Council was born .</code> | <code>Shawnee Trails Council was formed from the merger of the Four Rivers Council and the Audubon Council .</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.05} ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 640 - `per_device_eval_batch_size`: 64 - `learning_rate`: 3.5e-05 - `weight_decay`: 5e-05 - `num_train_epochs`: 2 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 6.999999999999999e-06} - `warmup_ratio`: 0.2 - `save_safetensors`: False - `fp16`: True - `push_to_hub`: True - `hub_model_id`: bobox/DeBERTa-small-ST-v1-test-step3-checkpoints-tmp - `hub_strategy`: all_checkpoints - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 640 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 3.5e-05 - `weight_decay`: 5e-05 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 2 - `max_steps`: -1 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 6.999999999999999e-06} - `warmup_ratio`: 0.2 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: False - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: True - `resume_from_checkpoint`: None - `hub_model_id`: bobox/DeBERTa-small-ST-v1-test-step3-checkpoints-tmp - `hub_strategy`: all_checkpoints - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | paws-pos loss | trivia pairs loss | scitail-pairs-pos loss | scitail-pairs-qa loss | qasc pairs loss | sciq pairs loss | openbookqa pairs loss | msmarco pairs loss | nq pairs loss | vitaminc-pairs loss | xsum-pairs loss | gooaq pairs loss | negation-triplets loss | VitaminC_max_ap | sts-test_spearman_cosine | |:------:|:----:|:-------------:|:-------------:|:-----------------:|:----------------------:|:---------------------:|:---------------:|:---------------:|:---------------------:|:------------------:|:-------------:|:-------------------:|:---------------:|:----------------:|:----------------------:|:---------------:|:------------------------:| | 0.0022 | 1 | 0.8103 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0045 | 2 | 0.8803 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0067 | 3 | 0.8219 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0090 | 4 | 0.0574 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0112 | 5 | 0.3044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0135 | 6 | 0.3306 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0157 | 7 | 0.759 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0180 | 8 | 0.0472 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0202 | 9 | 0.7782 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0225 | 10 | 0.0757 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0247 | 11 | 0.7778 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0270 | 12 | 0.7111 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0292 | 13 | 0.6598 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0315 | 14 | 0.8901 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0337 | 15 | 0.3206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0360 | 16 | 0.3408 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0382 | 17 | 0.5623 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0404 | 18 | 0.0758 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0427 | 19 | 0.994 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0449 | 20 | 2.4196 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0472 | 21 | 0.0561 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0494 | 22 | 0.0827 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0517 | 23 | 0.7405 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0539 | 24 | 0.9656 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0562 | 25 | 0.7855 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0584 | 26 | 0.6349 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0607 | 27 | 0.8087 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0629 | 28 | 0.9282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0652 | 29 | 0.3377 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0674 | 30 | 0.3289 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0697 | 31 | 0.6314 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0719 | 32 | 0.0611 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0742 | 33 | 0.8942 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0764 | 34 | 0.0701 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0787 | 35 | 0.8506 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0809 | 36 | 0.3386 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0831 | 37 | 0.0701 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0854 | 38 | 0.8042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0876 | 39 | 0.8744 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0899 | 40 | 0.8644 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0921 | 41 | 0.8647 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0944 | 42 | 0.7916 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0966 | 43 | 0.8599 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0989 | 44 | 0.0523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1011 | 45 | 0.6968 | 0.0245 | 0.5306 | 0.0737 | 0.0016 | 0.0908 | 0.0154 | 0.6755 | 0.1599 | 0.0959 | 1.7274 | 0.0382 | 0.2968 | 0.9175 | 0.5544 | 0.9038 | | 0.1034 | 46 | 0.3376 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1056 | 47 | 0.5174 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1079 | 48 | 0.8162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1101 | 49 | 0.3545 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1124 | 50 | 0.315 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1146 | 51 | 0.0627 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1169 | 52 | 0.8851 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1191 | 53 | 0.8382 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1213 | 54 | 0.733 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1236 | 55 | 0.7173 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1258 | 56 | 0.7659 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1281 | 57 | 0.793 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1303 | 58 | 0.5426 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1326 | 59 | 0.7641 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1348 | 60 | 0.0657 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1371 | 61 | 0.7836 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1393 | 62 | 0.9306 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1416 | 63 | 0.8673 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1438 | 64 | 0.9296 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1461 | 65 | 0.8211 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1483 | 66 | 0.7685 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1506 | 67 | 0.7139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1528 | 68 | 0.8241 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1551 | 69 | 0.6256 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1573 | 70 | 0.8842 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1596 | 71 | 0.804 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1618 | 72 | 0.0989 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1640 | 73 | 0.332 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1663 | 74 | 0.5736 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1685 | 75 | 0.8285 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1708 | 76 | 0.9561 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1730 | 77 | 0.0633 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1753 | 78 | 0.0848 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1775 | 79 | 0.8325 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1798 | 80 | 1.0011 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1820 | 81 | 0.8697 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1843 | 82 | 0.8344 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1865 | 83 | 0.9967 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1888 | 84 | 0.4638 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1910 | 85 | 0.8994 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1933 | 86 | 0.7789 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1955 | 87 | 0.0555 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1978 | 88 | 0.3778 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2 | 89 | 0.708 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2022 | 90 | 0.0689 | 0.0245 | 0.5192 | 0.0654 | 0.0016 | 0.0911 | 0.0158 | 0.7133 | 0.1517 | 0.0965 | 1.6142 | 0.0399 | 0.3071 | 0.9220 | 0.5542 | 0.9046 | | 0.2045 | 91 | 2.3489 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2067 | 92 | 0.741 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2090 | 93 | 0.7729 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2112 | 94 | 0.0631 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2135 | 95 | 0.9342 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2157 | 96 | 0.8581 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2180 | 97 | 0.5198 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2202 | 98 | 0.846 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2225 | 99 | 0.6581 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2247 | 100 | 0.3579 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2270 | 101 | 0.908 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2292 | 102 | 0.0664 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2315 | 103 | 0.5411 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2337 | 104 | 0.9163 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2360 | 105 | 0.7975 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2382 | 106 | 0.37 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2404 | 107 | 0.8495 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2427 | 108 | 0.8073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2449 | 109 | 0.7563 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2472 | 110 | 0.6585 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2494 | 111 | 0.3246 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2517 | 112 | 0.9718 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2539 | 113 | 0.8584 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2562 | 114 | 0.3385 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2584 | 115 | 0.323 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2607 | 116 | 0.3359 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2629 | 117 | 0.6955 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2652 | 118 | 0.0539 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2674 | 119 | 0.0507 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2697 | 120 | 0.314 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2719 | 121 | 1.0339 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2742 | 122 | 0.3158 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2764 | 123 | 0.7809 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2787 | 124 | 0.9516 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2809 | 125 | 0.3117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2831 | 126 | 0.8366 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2854 | 127 | 0.8033 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2876 | 128 | 0.7253 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2899 | 129 | 0.8345 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2921 | 130 | 0.7532 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2944 | 131 | 0.8247 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2966 | 132 | 0.5175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2989 | 133 | 0.7813 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3011 | 134 | 0.6582 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3034 | 135 | 0.3484 | 0.0242 | 0.5458 | 0.0738 | 0.0015 | 0.0924 | 0.0162 | 0.6892 | 0.1677 | 0.0974 | 1.5520 | 0.0366 | 0.3082 | 0.9212 | 0.5518 | 0.9050 | | 0.3056 | 136 | 0.7648 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3079 | 137 | 0.7554 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3101 | 138 | 0.0753 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3124 | 139 | 0.4987 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3146 | 140 | 0.8543 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3169 | 141 | 0.9425 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3191 | 142 | 0.0472 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3213 | 143 | 0.848 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3236 | 144 | 0.8946 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3258 | 145 | 0.7841 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3281 | 146 | 0.6653 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3303 | 147 | 0.3522 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3326 | 148 | 0.4853 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3348 | 149 | 0.4726 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3371 | 150 | 0.8693 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3393 | 151 | 0.8124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3416 | 152 | 0.8206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3438 | 153 | 0.9406 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3461 | 154 | 0.7944 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3483 | 155 | 0.0766 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3506 | 156 | 0.8609 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3528 | 157 | 1.0533 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3551 | 158 | 0.8396 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3573 | 159 | 0.7865 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3596 | 160 | 2.4616 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3618 | 161 | 0.0556 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3640 | 162 | 0.3758 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3663 | 163 | 0.9312 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3685 | 164 | 0.7993 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3708 | 165 | 0.8104 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3730 | 166 | 0.8199 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3753 | 167 | 1.0724 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3775 | 168 | 0.3521 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3798 | 169 | 0.8536 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3820 | 170 | 0.872 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3843 | 171 | 0.8009 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3865 | 172 | 0.7798 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3888 | 173 | 0.5953 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3910 | 174 | 0.7562 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3933 | 175 | 0.7227 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3955 | 176 | 0.8953 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3978 | 177 | 0.7102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4 | 178 | 0.0667 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4022 | 179 | 0.0528 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4045 | 180 | 0.7312 | 0.0241 | 0.5359 | 0.0916 | 0.0013 | 0.0949 | 0.0173 | 0.7253 | 0.1738 | 0.1032 | 1.5216 | 0.0496 | 0.3070 | 0.9813 | 0.5583 | 0.9055 | | 0.4067 | 181 | 0.7809 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4090 | 182 | 0.8333 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4112 | 183 | 0.9283 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4135 | 184 | 0.7011 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4157 | 185 | 0.8413 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4180 | 186 | 1.1679 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4202 | 187 | 0.8701 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4225 | 188 | 0.8139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4247 | 189 | 0.664 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4270 | 190 | 0.3835 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4292 | 191 | 0.8516 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4315 | 192 | 0.5479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4337 | 193 | 0.8642 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4360 | 194 | 0.3121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4382 | 195 | 0.6932 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4404 | 196 | 0.0647 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4427 | 197 | 0.8173 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4449 | 198 | 0.3122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4472 | 199 | 0.7852 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4494 | 200 | 0.811 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4517 | 201 | 0.7564 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4539 | 202 | 0.0541 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4562 | 203 | 0.9085 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4584 | 204 | 0.8416 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4607 | 205 | 0.0569 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4629 | 206 | 0.7998 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4652 | 207 | 0.7218 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4674 | 208 | 0.9292 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4697 | 209 | 0.8279 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4719 | 210 | 0.8452 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4742 | 211 | 1.1099 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4764 | 212 | 0.9436 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4787 | 213 | 0.8389 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4809 | 214 | 0.3297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4831 | 215 | 0.8098 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4854 | 216 | 0.0386 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4876 | 217 | 0.7752 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4899 | 218 | 0.8071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4921 | 219 | 2.571 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4944 | 220 | 0.5912 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4966 | 221 | 0.3792 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4989 | 222 | 0.7456 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5011 | 223 | 0.7207 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5034 | 224 | 0.3254 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5056 | 225 | 0.0461 | 0.0236 | 0.5324 | 0.0761 | 0.0012 | 0.0921 | 0.0165 | 0.7430 | 0.1787 | 0.0980 | 1.4897 | 0.0276 | 0.2668 | 0.9457 | 0.5573 | 0.9051 | | 0.5079 | 226 | 0.347 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5101 | 227 | 0.0417 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5124 | 228 | 0.7783 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5146 | 229 | 0.9027 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5169 | 230 | 0.7166 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5191 | 231 | 0.705 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5213 | 232 | 0.8425 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5236 | 233 | 0.5362 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5258 | 234 | 0.7869 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5281 | 235 | 0.88 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5303 | 236 | 0.8077 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5326 | 237 | 0.8145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5348 | 238 | 0.78 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5371 | 239 | 0.0536 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5393 | 240 | 0.7975 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5416 | 241 | 0.8932 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5438 | 242 | 0.3386 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5461 | 243 | 0.7741 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5483 | 244 | 0.7439 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5506 | 245 | 0.7999 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5528 | 246 | 0.8542 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5551 | 247 | 0.6992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5573 | 248 | 0.8579 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5596 | 249 | 1.0221 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5618 | 250 | 0.699 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5640 | 251 | 0.8523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5663 | 252 | 1.0307 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5685 | 253 | 0.846 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5708 | 254 | 0.8361 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5730 | 255 | 0.8224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5753 | 256 | 0.5301 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5775 | 257 | 0.3795 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5798 | 258 | 0.5434 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5820 | 259 | 0.847 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5843 | 260 | 0.7323 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5865 | 261 | 0.6606 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5888 | 262 | 0.0543 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5910 | 263 | 0.6709 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5933 | 264 | 0.809 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5955 | 265 | 1.0391 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5978 | 266 | 0.7396 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6 | 267 | 0.7839 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6022 | 268 | 0.3054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6045 | 269 | 0.5258 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6067 | 270 | 0.7367 | 0.0244 | 0.4969 | 0.0964 | 0.0009 | 0.0913 | 0.0162 | 0.7336 | 0.1587 | 0.1078 | 1.4936 | 0.0269 | 0.3026 | 0.9505 | 0.5506 | 0.9085 | | 0.6090 | 271 | 0.747 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6112 | 272 | 0.7855 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6135 | 273 | 0.0473 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6157 | 274 | 0.4378 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6180 | 275 | 0.8767 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6202 | 276 | 1.0345 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6225 | 277 | 0.5182 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6247 | 278 | 2.5949 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6270 | 279 | 0.833 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6292 | 280 | 0.0778 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6315 | 281 | 0.8048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6337 | 282 | 0.7524 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6360 | 283 | 0.3246 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6382 | 284 | 0.0728 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6404 | 285 | 2.3619 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6427 | 286 | 0.7464 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6449 | 287 | 0.6691 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6472 | 288 | 0.059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6494 | 289 | 0.7841 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6517 | 290 | 0.647 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6539 | 291 | 0.8814 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6562 | 292 | 0.7247 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6584 | 293 | 0.059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6607 | 294 | 0.8317 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6629 | 295 | 0.8548 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6652 | 296 | 0.9213 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6674 | 297 | 0.6923 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6697 | 298 | 0.7777 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6719 | 299 | 0.7496 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6742 | 300 | 0.7636 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6764 | 301 | 0.6867 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6787 | 302 | 0.0506 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6809 | 303 | 0.3346 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6831 | 304 | 0.2485 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6854 | 305 | 0.8508 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6876 | 306 | 0.8464 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6899 | 307 | 0.3385 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6921 | 308 | 0.8837 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6944 | 309 | 0.9019 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6966 | 310 | 0.6922 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6989 | 311 | 0.6348 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7011 | 312 | 0.7522 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7034 | 313 | 0.7843 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7056 | 314 | 0.0493 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7079 | 315 | 0.357 | 0.0240 | 0.5302 | 0.0995 | 0.0010 | 0.0917 | 0.0152 | 0.6922 | 0.1518 | 0.1014 | 1.4885 | 0.0278 | 0.2842 | 0.9598 | 0.5576 | 0.9068 | | 0.7101 | 316 | 0.841 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7124 | 317 | 0.5849 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7146 | 318 | 0.6818 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7169 | 319 | 0.8269 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7191 | 320 | 0.6979 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7213 | 321 | 0.3218 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7236 | 322 | 0.8206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7258 | 323 | 0.2106 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7281 | 324 | 1.0524 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7303 | 325 | 0.3774 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7326 | 326 | 0.9098 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7348 | 327 | 0.7988 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7371 | 328 | 0.7916 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7393 | 329 | 0.6314 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7416 | 330 | 0.8628 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7438 | 331 | 0.0688 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7461 | 332 | 0.7386 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7483 | 333 | 0.8458 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7506 | 334 | 0.0442 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7528 | 335 | 0.317 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7551 | 336 | 0.8087 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7573 | 337 | 0.3398 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7596 | 338 | 0.699 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7618 | 339 | 0.7901 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7640 | 340 | 0.8072 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7663 | 341 | 0.5939 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7685 | 342 | 0.6933 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7708 | 343 | 0.0437 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7730 | 344 | 0.9882 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7753 | 345 | 0.3707 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7775 | 346 | 0.7103 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7798 | 347 | 0.0372 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7820 | 348 | 0.028 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7843 | 349 | 0.7676 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7865 | 350 | 0.6754 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7888 | 351 | 0.0439 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7910 | 352 | 0.8039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7933 | 353 | 0.0104 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7955 | 354 | 0.0555 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7978 | 355 | 0.8646 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8 | 356 | 0.7781 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8022 | 357 | 0.011 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8045 | 358 | 0.3267 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8067 | 359 | 2.5281 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8090 | 360 | 0.301 | 0.0243 | 0.5313 | 0.1073 | 0.0006 | 0.1025 | 0.0160 | 0.6711 | 0.1452 | 0.1023 | 1.4291 | 0.0260 | 0.2771 | 0.9031 | 0.5599 | 0.9054 | | 0.8112 | 361 | 0.7533 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8135 | 362 | 0.2958 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8157 | 363 | 0.8296 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8180 | 364 | 0.3191 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8202 | 365 | 0.7866 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8225 | 366 | 0.3157 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8247 | 367 | 0.7402 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8270 | 368 | 0.4957 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8292 | 369 | 0.8505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8315 | 370 | 0.7702 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8337 | 371 | 0.7591 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8360 | 372 | 0.727 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8382 | 373 | 0.3233 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8404 | 374 | 0.8738 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8427 | 375 | 0.0393 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8449 | 376 | 0.7454 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8472 | 377 | 0.8297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8494 | 378 | 0.7802 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8517 | 379 | 0.6229 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8539 | 380 | 0.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8562 | 381 | 0.3506 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8584 | 382 | 0.041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8607 | 383 | 0.725 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8629 | 384 | 0.257 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8652 | 385 | 0.7912 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8674 | 386 | 0.8915 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8697 | 387 | 0.779 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8719 | 388 | 0.7828 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8742 | 389 | 0.7462 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8764 | 390 | 0.7913 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8787 | 391 | 0.3209 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8809 | 392 | 0.5932 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8831 | 393 | 0.0613 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8854 | 394 | 0.8802 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8876 | 395 | 0.6116 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8899 | 396 | 0.0537 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8921 | 397 | 0.3006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8944 | 398 | 0.7636 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8966 | 399 | 0.612 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8989 | 400 | 0.54 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9011 | 401 | 0.2761 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9034 | 402 | 1.2668 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9056 | 403 | 0.8066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9079 | 404 | 0.0094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9101 | 405 | 0.673 | 0.0242 | 0.4919 | 0.1050 | 0.0008 | 0.0942 | 0.0152 | 0.7152 | 0.1418 | 0.1087 | 1.4849 | 0.0285 | 0.2616 | 0.9024 | 0.5543 | 0.9052 | | 0.9124 | 406 | 0.5189 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9146 | 407 | 0.649 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9169 | 408 | 0.2982 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9191 | 409 | 0.7511 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9213 | 410 | 0.5164 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9236 | 411 | 0.5924 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9258 | 412 | 0.8191 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9281 | 413 | 0.2311 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9303 | 414 | 0.7421 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9326 | 415 | 0.2936 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9348 | 416 | 0.737 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9371 | 417 | 0.6539 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9393 | 418 | 0.6855 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9416 | 419 | 0.8134 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9438 | 420 | 0.6885 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9461 | 421 | 0.5581 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9483 | 422 | 0.8029 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9506 | 423 | 0.8126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9528 | 424 | 0.8425 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9551 | 425 | 0.049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9573 | 426 | 0.7849 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9596 | 427 | 0.068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9618 | 428 | 0.2925 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9640 | 429 | 0.777 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9663 | 430 | 0.7397 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9685 | 431 | 0.0007 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9708 | 432 | 0.8535 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9730 | 433 | 0.7026 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9753 | 434 | 0.7557 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9775 | 435 | 0.7225 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9798 | 436 | 0.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9820 | 437 | 0.4131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9843 | 438 | 0.2824 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9865 | 439 | 0.3144 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9888 | 440 | 0.0509 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9910 | 441 | 0.7645 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9933 | 442 | 0.2787 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9955 | 443 | 0.64 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9978 | 444 | 0.4045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0 | 445 | 0.7661 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0022 | 446 | 0.7335 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0045 | 447 | 0.7835 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0067 | 448 | 0.7674 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0090 | 449 | 0.0489 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0112 | 450 | 0.3104 | 0.0251 | 0.4878 | 0.0699 | 0.0008 | 0.0959 | 0.0178 | 0.7217 | 0.1512 | 0.1032 | 1.4285 | 0.0287 | 0.2688 | 0.8765 | 0.5541 | 0.9043 | | 1.0135 | 451 | 0.2977 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0157 | 452 | 0.7256 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0180 | 453 | 0.0327 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0202 | 454 | 0.7372 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0225 | 455 | 0.0518 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0247 | 456 | 0.7668 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0270 | 457 | 0.6634 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0292 | 458 | 0.6022 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0315 | 459 | 0.7255 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0337 | 460 | 0.2823 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0360 | 461 | 0.2614 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0382 | 462 | 0.5231 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0404 | 463 | 0.0424 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0427 | 464 | 0.9838 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0449 | 465 | 2.4683 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0472 | 466 | 0.0497 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0494 | 467 | 0.0766 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0517 | 468 | 0.7333 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0539 | 469 | 0.7881 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0562 | 470 | 0.7611 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0584 | 471 | 0.6023 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0607 | 472 | 0.7884 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0629 | 473 | 0.8465 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0652 | 474 | 0.2752 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0674 | 475 | 0.2648 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0697 | 476 | 0.5548 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0719 | 477 | 0.0554 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0742 | 478 | 0.8244 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0764 | 479 | 0.0369 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0787 | 480 | 0.747 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0809 | 481 | 0.2507 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0831 | 482 | 0.0304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0854 | 483 | 0.7735 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0876 | 484 | 0.7526 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0899 | 485 | 0.7959 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0921 | 486 | 0.7405 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0944 | 487 | 0.7041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0966 | 488 | 0.6991 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0989 | 489 | 0.0462 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1011 | 490 | 0.5835 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1034 | 491 | 0.2632 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1056 | 492 | 0.4681 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1079 | 493 | 0.7271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1101 | 494 | 0.2582 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1124 | 495 | 0.2251 | 0.0239 | 0.5306 | 0.0761 | 0.0005 | 0.1029 | 0.0153 | 0.6792 | 0.1548 | 0.0933 | 1.3855 | 0.0206 | 0.2823 | 0.8651 | 0.5517 | 0.9052 | | 1.1146 | 496 | 0.0385 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1169 | 497 | 0.7277 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1191 | 498 | 0.705 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1213 | 499 | 0.6059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1236 | 500 | 0.6156 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1258 | 501 | 0.6809 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1281 | 502 | 0.7104 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1303 | 503 | 0.4397 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1326 | 504 | 0.6952 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1348 | 505 | 0.0557 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1371 | 506 | 0.6711 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1393 | 507 | 0.7173 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1416 | 508 | 0.7037 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1438 | 509 | 0.8578 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1461 | 510 | 0.6712 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1483 | 511 | 0.7472 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1506 | 512 | 0.5911 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1528 | 513 | 0.6827 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1551 | 514 | 0.5034 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1573 | 515 | 0.8367 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1596 | 516 | 0.6596 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1618 | 517 | 0.0859 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1640 | 518 | 0.2797 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1663 | 519 | 0.5181 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1685 | 520 | 0.6837 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1708 | 521 | 0.7238 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1730 | 522 | 0.0318 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1753 | 523 | 0.0694 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1775 | 524 | 0.7472 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1798 | 525 | 0.8912 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1820 | 526 | 0.7744 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1843 | 527 | 0.6869 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1865 | 528 | 0.8497 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1888 | 529 | 0.4281 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1910 | 530 | 0.7605 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1933 | 531 | 0.6354 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1955 | 532 | 0.0518 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1978 | 533 | 0.2602 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2 | 534 | 0.5082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2022 | 535 | 0.0603 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2045 | 536 | 2.3371 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2067 | 537 | 0.6513 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2090 | 538 | 0.6053 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2112 | 539 | 0.0544 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2135 | 540 | 0.7219 | 0.0242 | 0.5325 | 0.0872 | 0.0007 | 0.0975 | 0.0165 | 0.6885 | 0.1340 | 0.0944 | 1.3777 | 0.0253 | 0.2804 | 0.8755 | 0.5533 | 0.9066 | | 1.2157 | 541 | 0.6862 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2180 | 542 | 0.4639 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2202 | 543 | 0.6663 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2225 | 544 | 0.5047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2247 | 545 | 0.2306 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2270 | 546 | 0.7147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2292 | 547 | 0.0344 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2315 | 548 | 0.4429 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2337 | 549 | 0.6966 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2360 | 550 | 0.6926 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2382 | 551 | 0.261 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2404 | 552 | 0.6558 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2427 | 553 | 0.6285 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2449 | 554 | 0.6471 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2472 | 555 | 0.4989 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2494 | 556 | 0.195 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2517 | 557 | 0.8431 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2539 | 558 | 0.642 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2562 | 559 | 0.2251 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2584 | 560 | 0.2057 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2607 | 561 | 0.2198 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2629 | 562 | 0.4856 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2652 | 563 | 0.0273 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2674 | 564 | 0.0302 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2697 | 565 | 0.1863 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2719 | 566 | 0.8053 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2742 | 567 | 0.1935 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2764 | 568 | 0.5837 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2787 | 569 | 0.7606 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2809 | 570 | 0.1904 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2831 | 571 | 0.6585 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2854 | 572 | 0.7043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2876 | 573 | 0.6083 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2899 | 574 | 0.6523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2921 | 575 | 0.553 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2944 | 576 | 0.6234 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2966 | 577 | 0.4428 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2989 | 578 | 0.5433 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3011 | 579 | 0.4937 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3034 | 580 | 0.2222 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3056 | 581 | 0.5672 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3079 | 582 | 0.6562 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3101 | 583 | 0.056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3124 | 584 | 0.4015 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3146 | 585 | 0.6675 | 0.0242 | 0.4988 | 0.0997 | 0.0007 | 0.0983 | 0.0161 | 0.7107 | 0.1444 | 0.0869 | 1.3895 | 0.0208 | 0.2780 | 0.9073 | 0.5507 | 0.9080 | | 1.3169 | 586 | 0.7298 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3191 | 587 | 0.0372 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3213 | 588 | 0.7247 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3236 | 589 | 0.6839 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3258 | 590 | 0.6848 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3281 | 591 | 0.4449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3303 | 592 | 0.2104 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3326 | 593 | 0.391 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3348 | 594 | 0.3641 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3371 | 595 | 0.6953 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3393 | 596 | 0.6382 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3416 | 597 | 0.6245 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3438 | 598 | 0.6775 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3461 | 599 | 0.5727 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3483 | 600 | 0.0567 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3506 | 601 | 0.6258 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3528 | 602 | 0.8138 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3551 | 603 | 0.6099 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3573 | 604 | 0.6801 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3596 | 605 | 2.2003 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3618 | 606 | 0.052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3640 | 607 | 0.2175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3663 | 608 | 0.7671 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3685 | 609 | 0.5524 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3708 | 610 | 0.5868 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3730 | 611 | 0.6628 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3753 | 612 | 0.8106 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3775 | 613 | 0.2 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3798 | 614 | 0.57 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3820 | 615 | 0.6329 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3843 | 616 | 0.5616 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3865 | 617 | 0.6678 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3888 | 618 | 0.454 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3910 | 619 | 0.5198 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3933 | 620 | 0.5259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3955 | 621 | 0.714 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3978 | 622 | 0.4943 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4 | 623 | 0.0324 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4022 | 624 | 0.0305 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4045 | 625 | 0.5194 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4067 | 626 | 0.5412 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4090 | 627 | 0.5688 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4112 | 628 | 0.7636 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4135 | 629 | 0.478 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4157 | 630 | 0.5674 | 0.0240 | 0.4915 | 0.1026 | 0.0006 | 0.0886 | 0.0171 | 0.7429 | 0.1468 | 0.0846 | 1.4192 | 0.0243 | 0.2606 | 0.9350 | 0.5566 | 0.9058 | | 1.4180 | 631 | 0.9232 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4202 | 632 | 0.613 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4225 | 633 | 0.5689 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4247 | 634 | 0.4126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4270 | 635 | 0.2148 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4292 | 636 | 0.7029 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4315 | 637 | 0.3989 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4337 | 638 | 0.6291 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4360 | 639 | 0.158 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4382 | 640 | 0.4833 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4404 | 641 | 0.0561 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4427 | 642 | 0.6613 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4449 | 643 | 0.1917 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4472 | 644 | 0.5755 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4494 | 645 | 0.5609 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4517 | 646 | 0.5407 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4539 | 647 | 0.0455 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4562 | 648 | 0.6599 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4584 | 649 | 0.6952 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4607 | 650 | 0.0329 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4629 | 651 | 0.6939 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4652 | 652 | 0.4664 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4674 | 653 | 0.6686 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4697 | 654 | 0.6167 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4719 | 655 | 0.6612 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4742 | 656 | 0.8139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4764 | 657 | 0.6813 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4787 | 658 | 0.6031 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4809 | 659 | 0.1783 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4831 | 660 | 0.6536 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4854 | 661 | 0.0318 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4876 | 662 | 0.6372 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4899 | 663 | 0.5695 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4921 | 664 | 2.3259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4944 | 665 | 0.4342 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4966 | 666 | 0.2176 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4989 | 667 | 0.5419 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5011 | 668 | 0.4976 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5034 | 669 | 0.1964 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5056 | 670 | 0.0311 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5079 | 671 | 0.1832 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5101 | 672 | 0.0345 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5124 | 673 | 0.5376 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5146 | 674 | 0.6316 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5169 | 675 | 0.5025 | 0.0239 | 0.5190 | 0.0944 | 0.0006 | 0.0907 | 0.0164 | 0.7405 | 0.1450 | 0.0895 | 1.3485 | 0.0209 | 0.2531 | 0.9070 | 0.5589 | 0.9067 | | 1.5191 | 676 | 0.509 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5213 | 677 | 0.6078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5236 | 678 | 0.3961 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5258 | 679 | 0.5699 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5281 | 680 | 0.6305 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5303 | 681 | 0.5886 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5326 | 682 | 0.6432 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5348 | 683 | 0.614 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5371 | 684 | 0.0432 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5393 | 685 | 0.633 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5416 | 686 | 0.6228 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5438 | 687 | 0.2105 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5461 | 688 | 0.5429 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5483 | 689 | 0.5361 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5506 | 690 | 0.5567 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5528 | 691 | 0.6131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5551 | 692 | 0.5111 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5573 | 693 | 0.6216 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5596 | 694 | 0.7615 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5618 | 695 | 0.51 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5640 | 696 | 0.6989 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5663 | 697 | 0.8145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5685 | 698 | 0.5928 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5708 | 699 | 0.6046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5730 | 700 | 0.6483 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5753 | 701 | 0.3976 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5775 | 702 | 0.2033 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5798 | 703 | 0.4127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5820 | 704 | 0.6008 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5843 | 705 | 0.5346 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5865 | 706 | 0.4183 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5888 | 707 | 0.0245 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5910 | 708 | 0.4834 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5933 | 709 | 0.5815 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5955 | 710 | 0.7791 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5978 | 711 | 0.4835 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6 | 712 | 0.5797 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6022 | 713 | 0.1891 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6045 | 714 | 0.3955 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6067 | 715 | 0.497 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6090 | 716 | 0.6271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6112 | 717 | 0.5571 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6135 | 718 | 0.0405 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6157 | 719 | 0.2968 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6180 | 720 | 0.7262 | 0.0241 | 0.4987 | 0.1010 | 0.0007 | 0.0932 | 0.0165 | 0.7172 | 0.1424 | 0.0960 | 1.3777 | 0.0198 | 0.2702 | 0.9084 | 0.5552 | 0.9091 | | 1.6202 | 721 | 0.7611 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6225 | 722 | 0.3926 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6247 | 723 | 2.3127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6270 | 724 | 0.7026 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6292 | 725 | 0.0685 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6315 | 726 | 0.6031 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6337 | 727 | 0.579 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6360 | 728 | 0.1705 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6382 | 729 | 0.0591 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6404 | 730 | 2.1115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6427 | 731 | 0.4871 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6449 | 732 | 0.4263 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6472 | 733 | 0.0484 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6494 | 734 | 0.5249 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6517 | 735 | 0.3998 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6539 | 736 | 0.7226 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6562 | 737 | 0.4494 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6584 | 738 | 0.0537 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6607 | 739 | 0.7129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6629 | 740 | 0.6079 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6652 | 741 | 0.6688 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6674 | 742 | 0.567 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6697 | 743 | 0.5196 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6719 | 744 | 0.5081 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6742 | 745 | 0.5413 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6764 | 746 | 0.4741 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6787 | 747 | 0.0289 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6809 | 748 | 0.1956 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6831 | 749 | 0.1967 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6854 | 750 | 0.6488 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6876 | 751 | 0.7052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6899 | 752 | 0.1807 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6921 | 753 | 0.6238 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6944 | 754 | 0.6328 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6966 | 755 | 0.4677 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6989 | 756 | 0.44 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7011 | 757 | 0.5382 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7034 | 758 | 0.6094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7056 | 759 | 0.0262 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7079 | 760 | 0.1995 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7101 | 761 | 0.6595 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7124 | 762 | 0.4056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7146 | 763 | 0.4836 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7169 | 764 | 0.5474 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7191 | 765 | 0.6019 | 0.0240 | 0.5037 | 0.1004 | 0.0006 | 0.0951 | 0.0160 | 0.7151 | 0.1363 | 0.0948 | 1.4008 | 0.0213 | 0.2534 | 0.9053 | 0.5581 | 0.9094 | | 1.7213 | 766 | 0.1824 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7236 | 767 | 0.6398 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7258 | 768 | 0.1518 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7281 | 769 | 0.7804 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7303 | 770 | 0.2294 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7326 | 771 | 0.719 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7348 | 772 | 0.61 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7371 | 773 | 0.5865 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7393 | 774 | 0.4411 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7416 | 775 | 0.6174 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7438 | 776 | 0.0526 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7461 | 777 | 0.5093 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7483 | 778 | 0.6742 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7506 | 779 | 0.0293 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7528 | 780 | 0.1776 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7551 | 781 | 0.6964 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7573 | 782 | 0.2044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7596 | 783 | 0.5221 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7618 | 784 | 0.579 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7640 | 785 | 0.5887 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7663 | 786 | 0.4357 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7685 | 787 | 0.5437 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7708 | 788 | 0.0326 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7730 | 789 | 0.7279 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7753 | 790 | 0.2255 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7775 | 791 | 0.5386 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7798 | 792 | 0.0218 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7820 | 793 | 0.0174 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7843 | 794 | 0.542 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7865 | 795 | 0.511 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7888 | 796 | 0.0345 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7910 | 797 | 0.6513 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7933 | 798 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7955 | 799 | 0.0467 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7978 | 800 | 0.6994 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8 | 801 | 0.6583 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8022 | 802 | 0.0059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8045 | 803 | 0.1896 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8067 | 804 | 2.2539 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8090 | 805 | 0.1933 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8112 | 806 | 0.5681 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8135 | 807 | 0.1692 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8157 | 808 | 0.6595 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8180 | 809 | 0.1603 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8202 | 810 | 0.6671 | 0.0245 | 0.4944 | 0.1077 | 0.0005 | 0.0981 | 0.0158 | 0.6886 | 0.1277 | 0.0865 | 1.3900 | 0.0200 | 0.2530 | 0.8990 | 0.5586 | 0.9083 | | 1.8225 | 811 | 0.1995 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8247 | 812 | 0.5579 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8270 | 813 | 0.3833 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8292 | 814 | 0.6411 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8315 | 815 | 0.6034 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8337 | 816 | 0.5206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8360 | 817 | 0.5941 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8382 | 818 | 0.2062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8404 | 819 | 0.6086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8427 | 820 | 0.037 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8449 | 821 | 0.6257 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8472 | 822 | 0.7064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8494 | 823 | 0.563 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8517 | 824 | 0.4359 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8539 | 825 | 0.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8562 | 826 | 0.233 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8584 | 827 | 0.0335 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8607 | 828 | 0.6077 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8629 | 829 | 0.1707 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8652 | 830 | 0.5807 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8674 | 831 | 0.6566 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8697 | 832 | 0.663 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8719 | 833 | 0.5896 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8742 | 834 | 0.5418 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8764 | 835 | 0.5735 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8787 | 836 | 0.2062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8809 | 837 | 0.4343 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8831 | 838 | 0.0614 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8854 | 839 | 0.6301 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8876 | 840 | 0.3956 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8899 | 841 | 0.0479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8921 | 842 | 0.1819 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8944 | 843 | 0.6005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8966 | 844 | 0.452 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8989 | 845 | 0.4083 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9011 | 846 | 0.1702 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9034 | 847 | 0.9503 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9056 | 848 | 0.6427 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9079 | 849 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9101 | 850 | 0.4609 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9124 | 851 | 0.3854 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9146 | 852 | 0.4411 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9169 | 853 | 0.181 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9191 | 854 | 0.5846 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9213 | 855 | 0.3585 | 0.0243 | 0.4916 | 0.1050 | 0.0006 | 0.0897 | 0.0162 | 0.6815 | 0.1330 | 0.0923 | 1.3997 | 0.0198 | 0.2540 | 0.8800 | 0.5565 | 0.9085 | | 1.9236 | 856 | 0.4303 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9258 | 857 | 0.5627 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9281 | 858 | 0.1687 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9303 | 859 | 0.5509 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9326 | 860 | 0.175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9348 | 861 | 0.605 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9371 | 862 | 0.5085 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9393 | 863 | 0.5059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9416 | 864 | 0.6114 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9438 | 865 | 0.5132 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9461 | 866 | 0.4178 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9483 | 867 | 0.6022 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9506 | 868 | 0.5691 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9528 | 869 | 0.7299 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9551 | 870 | 0.0441 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9573 | 871 | 0.5855 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9596 | 872 | 0.0151 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9618 | 873 | 0.184 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9640 | 874 | 0.6185 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9663 | 875 | 0.6474 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9685 | 876 | 0.0005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9708 | 877 | 0.6692 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9730 | 878 | 0.496 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9753 | 879 | 0.5654 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9775 | 880 | 0.4925 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9798 | 881 | 0.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9820 | 882 | 0.2304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9843 | 883 | 0.1772 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9865 | 884 | 0.1804 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9888 | 885 | 0.0198 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9910 | 886 | 0.6703 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9933 | 887 | 0.1552 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9955 | 888 | 0.4962 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9978 | 889 | 0.2099 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0 | 890 | 0.6554 | 0.0246 | 0.4886 | 0.0966 | 0.0005 | 0.0885 | 0.0164 | 0.6816 | 0.1300 | 0.0919 | 1.3958 | 0.0203 | 0.2583 | 0.8738 | 0.5563 | 0.9077 | </details> ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.0.1 - Transformers: 4.44.0 - PyTorch: 2.4.0 - Accelerate: 0.33.0 - Datasets: 2.21.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION", "SEMANTIC_SIMILARITY", "TRANSLATION" ]
[ "CAS", "SCIQ", "SCITAIL" ]
Non_BioNLP
JosephusCheung/Guanaco
JosephusCheung
text-generation
[ "transformers", "pytorch", "llama", "text-generation", "guannaco", "alpaca", "conversational", "en", "zh", "ja", "de", "dataset:JosephusCheung/GuanacoDataset", "doi:10.57967/hf/0607", "license:gpl-3.0", "autotrain_compatible", "text-generation-inference", "region:us" ]
1,680
1,685
2,164
230
--- datasets: - JosephusCheung/GuanacoDataset language: - en - zh - ja - de license: gpl-3.0 pipeline_tag: conversational tags: - llama - guannaco - alpaca inference: false --- ![](https://huggingface.co/JosephusCheung/Guanaco/resolve/main/StupidBanner.png) **You can run on Colab free T4 GPU now** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ocSmoy3ba1EkYu7JWT1oCw9vz8qC2cMk#scrollTo=zLORi5OcPcIJ) **It is highly recommended to use fp16 inference for this model, as 8-bit precision may significantly affect performance. If you require a more Consumer Hardware friendly version, please use the specialized quantized, only 5+GB V-Ram required** [JosephusCheung/GuanacoOnConsumerHardware](https://huggingface.co/JosephusCheung/GuanacoOnConsumerHardware). **You are encouraged to use the latest version of transformers from GitHub.** Guanaco is an advanced instruction-following language model built on Meta's LLaMA 7B model. Expanding upon the initial 52K dataset from the Alpaca model, an additional 534K+ entries have been incorporated, covering English, Simplified Chinese, Traditional Chinese (Taiwan), Traditional Chinese (Hong Kong), Japanese, Deutsch, and various linguistic and grammatical tasks. This wealth of data enables Guanaco to perform exceptionally well in multilingual environments. In an effort to foster openness and replicability in research, we have made the Guanaco Dataset publicly accessible and we have released the model weights here. By providing these resources, we aim to inspire more researchers to pursue related research and collectively advance the development of instruction-following language models. [KBlueLeaf](https://huggingface.co/KBlueLeaf)’s invaluable contributions to the conceptual validation, [trained model](https://huggingface.co/KBlueLeaf/guanaco-7B-leh) and [inference development](https://github.com/KohakuBlueleaf/guanaco-lora) of the model would be gratefully acknowledged, without whose efforts the project shall never have come to fruition. When utilizing the Guanaco model, please bear in mind the following points: The Guanaco model has not been filtered for harmful, biased, or explicit content. As a result, outputs that do not adhere to ethical norms may be generated during use. Please exercise caution when using the model in research or practical applications. 1. ### Improved context and prompt role support: The new format is designed to be similar to ChatGPT, allowing for better integration with the Alpaca format and enhancing the overall user experience. Instruction is utilized as a few-shot context to support diverse inputs and responses, making it easier for the model to understand and provide accurate responses to user queries. The format is as follows: ``` ### Instruction: User: History User Input Assistant: History Assistant Answer ### Input: System: Knowledge User: New User Input ### Response: New Assistant Answer ``` This structured format allows for easier tracking of the conversation history and maintaining context throughout a multi-turn dialogue. 3. ### Role-playing support: Guanaco now offers advanced role-playing support, similar to Character.AI, in English, Simplified Chinese, Traditional Chinese, Japanese, and Deutsch, making it more versatile for users from different linguistic backgrounds. Users can instruct the model to assume specific roles, historical figures, or fictional characters, as well as personalities based on their input. This allows for more engaging and immersive conversations. The model can use various sources of information to provide knowledge and context for the character's background and behavior, such as encyclopedic entries, first-person narrations, or a list of personality traits. The model will consistently output responses in the format "Character Name: Reply" to maintain the chosen role throughout the conversation, enhancing the user's experience. 4. ### Rejection of answers and avoidance of erroneous responses: The model has been updated to handle situations where it lacks sufficient knowledge or is unable to provide a valid response more effectively. Reserved keywords have been introduced to indicate different scenarios and provide clearer communication with the user, use in System Prompt: NO IDEA: Indicates that the model lacks the necessary knowledge to provide an accurate answer, and will explain this to the user, encouraging them to seek alternative sources. FORBIDDEN: Indicates that the model refuses to answer due to specific reasons (e.g., legal, ethical, or safety concerns), which will be inferred based on the context of the query. SFW: Indicates that the model refuses to answer a question because it has been filtered for NSFW content, ensuring a safer and more appropriate user experience. 6. ### Continuation of responses for ongoing topics: The Guanaco model can now continue answering questions or discussing topics upon the user's request, making it more adaptable and better suited for extended conversations. The contextual structure consisting of System, Assistant, and User roles allows the model to engage in multi-turn dialogues, maintain context-aware conversations, and provide more coherent responses. The model can now accommodate role specification and character settings, providing a more immersive and tailored conversational experience based on the user's preferences. It is important to remember that Guanaco is a 7B-parameter model, and **any knowledge-based content should be considered potentially inaccurate**. We strongly recommend **providing verifiable sources in System Prompt, such as Wikipedia, for knowledge-based answers**. In the absence of sources, it is crucial to inform users of this limitation to prevent the dissemination of false information and to maintain transparency. Due to the differences in the format between this project and [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca), please refer to *Guanaco-lora: LoRA for training Multilingual Instruction-following LM based on LLaMA* (https://github.com/KohakuBlueleaf/guanaco-lora) for further training and inference our models. ## Recent News We've noticed a recent entrant in the field, the QLoRa method, which we find concerning due to its attempt to piggyback on the reputation of Guanaco. We strongly disapprove of such practices. QLoRa, as far as we can tell, lacks mathematical robustness and its performance significantly trails behind that of GPTQ and advancements such as PEFT fine-tuning, which have been successful in improving upon it. Guanaco has been diligent, consistently releasing multilingual datasets since March 2023, along with publishing weights that are not only an enhanced version of GPTQ but also support multimodal VQA and have been optimized for 4-bit. Despite the substantial financial investment of tens of thousands of dollars in distilling data from OpenAI's GPT models, we still consider these efforts to be incremental. We, however, aim to move beyond the incremental: 1. We strive to no longer rely on distillation data from OpenAI: We've found that relying on GPT-generated data impedes significant breakthroughs. Furthermore, this approach has proven to be disastrous when dealing with the imbalances in multilingual tasks. 2. We're focusing on the enhancement of quantization structure and partial native 4-bit fine-tuning: We are deeply appreciative of the GPTQ-Llama project for paving the way in state-of-the-art LLM quantization. Its unique qualities, especially at the 7B size, are facilitating significant progress in multilingual and multimodal tasks. 3. We plan to utilize visual data to adjust our language models: We believe this will fundamentally address the issues of language imbalance, translation inaccuracies, and the lack of graphical logic in LLM. While our work is still in the early stages, we're determined to break new ground in these areas. Our critique of QLoRa's practices does not stem from animosity but rather from the fundamental belief that innovation should be rooted in originality, integrity, and substantial progress.
[ "TRANSLATION" ]
[ "BEAR" ]
Non_BioNLP
bhavnicksm/brown-beetle-small-v1
bhavnicksm
null
[ "model2vec", "safetensors", "embeddings", "static-embeddings", "sentence-transformers", "mteb", "en", "license:mit", "model-index", "region:us" ]
1,737
1,737
27
2
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type: max_ap value: 79.86335865098036 - type: max_f1 value: 72.03383314109958 - type: max_precision value: 71.93979419444018 - type: max_recall value: 72.12811826301201 - type: similarity_accuracy value: 86.35269918888501 - type: similarity_accuracy_threshold value: 65.62072038650513 - type: similarity_ap value: 79.86335865098036 - type: similarity_f1 value: 72.03383314109958 - type: similarity_f1_threshold value: 62.21768856048584 - type: similarity_precision value: 71.93979419444018 - type: similarity_recall value: 72.12811826301201 --- # 🪲 brown-beetle-small-v1 Model Card <div align="center"> <img width="75%" alt="Beetle logo" src="./assets/beetle_logo.png"> </div> > [!TIP] > Beetles are some of the most diverse and interesting creatures on Earth. They are found in every environment, from the deepest oceans to the highest mountains. They are also known for their ability to adapt to a wide range of habitats and lifestyles. They are small, fast and powerful! The beetle series of models are made as good starting points for Static Embedding training (via TokenLearn or Fine-tuning), as well as decent Static Embedding models. Each beetle model is made to be an improvement over the original **M2V_base_output** model in some way, and that's the threshold we set for each model (except the brown beetle series, which is the original model). This model has been distilled from `baai/bge-base-en-v1.5`, with PCA with 256 dimensions and applying Zipf. > [!NOTE] > The brown beetle series is made for convinience in loading and using the model instead of having to run it, though it is pretty fast to reproduce anyways. If you want to use the original model by the folks from the Minish Lab, you can use the **M2V_base_output** model. ## Version Information - **brown-beetle-base-v0**: The original model, without using PCA or Zipf. The lack of PCA and Zipf also makes this a decent model for further training. - **brown-beetle-base-v0.1**: The original model, with PCA but of the same size as the original model. This model is great if you want to experiment with Zipf or other weighting methods. - **brown-beetle-base-v1**: The original model, with PCA and Zipf. - **brown-beetle-small-v1**: A smaller version of the original model, with PCA and Zipf. Equivalent to **M2V_base_output**. - **brown-beetle-tiny-v1**: A tiny version of the original model, with PCA and Zipf. - **brown-beetle-base-v1.1**: The original model, with PCA with 768 dimensions, applying Zipf and applying SIF re-weighting, learnt from a subset of the C4 corpus. This model is significantly better than the M2V_base_output model. - **brown-beetle-small-v1.1**: A smaller version of the original model, with PCA with 256 dimensions, applying Zipf and applying SIF re-weighting, learnt from a subset of the C4 corpus. This model is significantly better than the M2V_base_output model but slightly worse than the brown-beetle-base-v1.1 model. - **brown-beetle-tiny-v1.1**: A tiny version of the original model, with PCA with 128 dimensions, applying Zipf and applying SIF re-weighting, learnt from a subset of the C4 corpus. This model is significantly better than the M2V_base_output model but slightly worse than the brown-beetle-small-v1.1 model. ## Installation Install model2vec using pip: ```bash pip install model2vec ``` ## Usage Load this model using the `from_pretrained` method: ```python from model2vec import StaticModel # Load a pretrained Model2Vec model model = StaticModel.from_pretrained("bhavnicksm/brown-beetle-small-v1") # Compute text embeddings embeddings = model.encode(["Example sentence"]) ``` Read more about the Model2Vec library [here](https://github.com/MinishLab/model2vec). ## Reproduce this model To reproduce this model, you must install the `model2vec[distill]` package and use the following code: ```python from model2vec.distill import distill # Distill the model m2v_model = distill( model_name="bge-base-en-v1.5", pca_dims=256, apply_zipf=True, ) # Save the model m2v_model.save_pretrained("brown-beetle-small-v1") ``` ## Comparison with other models Coming soon... ## Acknowledgements This model is made using the [Model2Vec](https://github.com/MinishLab/model2vec) library. Credit goes to the [Minish Lab](https://github.com/MinishLab) team for developing this library. ## Citation Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work. ```bibtex @software{minishlab2024model2vec, authors = {Stephan Tulkens, Thomas van Dongen}, title = {Model2Vec: Turn any Sentence Transformer into a Small Fast Model}, year = {2024}, url = {https://github.com/MinishLab/model2vec}, } ```
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
pyf98/gigaspeech_e_branchformer
pyf98
automatic-speech-recognition
[ "espnet", "audio", "automatic-speech-recognition", "en", "dataset:gigaspeech", "arxiv:2210.00077", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]
1,674
1,674
1
0
--- datasets: - gigaspeech language: en license: cc-by-4.0 tags: - espnet - audio - automatic-speech-recognition --- ## ESPnet2 ASR model ### `pyf98/gigaspeech_e_branchformer` This model was trained by Yifan Peng using gigaspeech recipe in [espnet](https://github.com/espnet/espnet/). References: - [E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)](https://arxiv.org/abs/2210.00077) - [Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding (ICML 2022)](https://proceedings.mlr.press/v162/peng22a.html) ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 197dc412eab82e9bab008f00fbcb922c824d8cf2 pip install -e . cd egs2/gigaspeech/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/gigaspeech_e_branchformer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Sat Jan 21 17:54:14 EST 2023` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.12.1` - Git hash: `197dc412eab82e9bab008f00fbcb922c824d8cf2` - Commit date: `Sat Jan 21 13:59:20 2023 -0500` ## asr_train_asr_e_branchformer_e17_size512_mlp3072_linear1024_layerdrop_raw_en_bpe5000 ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev|5715|129240|91.1|6.3|2.6|3.1|11.9|74.0| |decode_asr_asr_model_valid.acc.ave/test|19930|392325|90.6|6.9|2.5|2.3|11.8|67.1| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev|5715|673778|96.8|1.4|1.8|2.6|5.8|74.0| |decode_asr_asr_model_valid.acc.ave/test|19930|2056231|96.5|1.6|1.9|2.0|5.5|67.1| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev|5715|160740|91.4|5.4|3.3|3.2|11.8|74.0| |decode_asr_asr_model_valid.acc.ave/test|19930|493006|90.8|6.0|3.1|2.7|11.9|67.1| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_e_branchformer_e17_size512_mlp3072_linear1024_layerdrop.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_e_branchformer_e17_size512_mlp3072_linear1024_layerdrop_raw_en_bpe5000 ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 5 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 46971 dist_launcher: null multiprocessing_distributed: true unused_parameters: true sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 30 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 4 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 35000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe5000/train/speech_shape - exp/asr_stats_raw_en_bpe5000/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe5000/valid/speech_shape - exp/asr_stats_raw_en_bpe5000/valid/text_shape.bpe batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train/wav.scp - speech - kaldi_ark - - dump/raw/train/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - kaldi_ark - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0015 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 token_list: - <blank> - <unk> - S - ▁THE - ▁TO - ▁OF - ▁A - ▁AND - '''' - ▁THAT - ▁IN - ▁YOU - ▁I - ▁IT - T - ▁IS - ▁WAS - ED - ▁WE - ▁FOR - ING - ▁THIS - D - ▁ON - ▁BE - ▁WITH - ▁HAVE - ▁SO - ▁HE - RE - ▁THEY - ▁ARE - ▁NOT - ▁AS - ▁LIKE - ▁AT - ▁KNOW - ▁WHAT - LY - ▁CAN - ▁DO - ▁ABOUT - ▁ALL - ▁HIS - M - ▁HAD - '-' - ▁ONE - ▁OR - ▁FROM - ▁THERE - ▁ME - ▁MY - ▁BUT - ▁JUST - ▁YOUR - ▁AN - ▁BY - Y - ▁IF - ▁OUT - ▁PEOPLE - ▁UP - ▁HER - ER - ▁WERE - ▁THINK - E - N - ▁WOULD - ▁SHE - ▁THEIR - ▁WHO - ▁MORE - ▁OUR - ▁THEM - ▁WHEN - ▁WHICH - ▁VERY - ▁WILL - ▁SOME - ▁TIME - ▁BEEN - R - ▁GET - ▁HAS - ▁GOING - ▁HIM - VE - ▁REALLY - ▁HOW - ▁DON - ▁NO - ▁THEN - LL - ▁GO - ▁BECAUSE - ▁NOW - AL - ▁INTO - ▁THESE - ▁OTHER - ▁RIGHT - ▁SEE - ▁SAID - ▁HERE - ▁WAY - ▁TWO - ▁US - ▁WANT - ▁COULD - ▁S - ▁SAY - ▁OVER - ▁AH - ES - ▁WHERE - ▁BACK - ▁ALSO - ▁THOSE - ▁THINGS - ▁MAKE - ▁KIND - ▁MUCH - IN - ▁WELL - ▁GOOD - ▁DID - L - ▁FIRST - ▁THAN - ▁LITTLE - ▁RE - C - ▁NEW - ▁WORK - ▁ANY - A - P - ▁LOT - ▁DOWN - ▁SOMETHING - ▁THING - OR - LE - ▁MAN - ▁GOT - B - ▁COME - ▁ONLY - G - ▁BEING - ▁ACTUALLY - ▁LOOK - O - ▁TAKE - ▁EVEN - ▁NEED - ▁THROUGH - W - ▁GREAT - ▁WORLD - ▁MANY - ▁SHOULD - ▁YEARS - ATION - ▁UM - ▁MOST - ▁DAY - ▁YEAH - ▁LIFE - ▁BEFORE - ▁THREE - ▁UN - ION - ▁DIFFERENT - ▁DE - ▁MIGHT - ▁LET - ▁MADE - ▁MEAN - ▁PART - IC - ▁AGAIN - TH - ▁AFTER - ▁OWN - ▁USE - ITY - ABLE - ▁LONG - ▁STILL - ▁MAY - F - ▁OFF - ▁NEVER - ▁PUT - ▁C - ▁SAME - ▁EVERY - CH - AN - 'ON' - ERS - I - ▁POINT - RO - ▁TOO - ▁DOING - ▁LAST - RI - ▁AROUND - K - ▁CAME - ▁SORT - H - AR - ▁LOVE - MENT - IVE - ▁FIND - ▁WHY - ▁TALK - ▁FIVE - IL - ▁TELL - LA - ▁END - LI - ▁AM - ▁THOUGHT - NESS - ▁PLACE - ▁YEAR - ▁E - ▁ALWAYS - ▁SHOW - ▁GIVE - ▁DIDN - ▁FEEL - Z - ▁OLD - UR - DE - ▁ANOTHER - ▁SUCH - U - ▁QUESTION - RA - TER - ▁WENT - ▁DOES - ▁HELP - IT - ▁HOME - ▁HUNDRED - ▁NEXT - ▁START - ▁COURSE - ▁UNDER - EN - NE - EL - ▁BIT - ▁FOUND - ▁FOUR - ▁CALLED - ▁DONE - ▁AWAY - ▁THOUSAND - ▁BIG - ▁SURE - ▁O - ▁PERSON - ▁TWENTY - ▁BETTER - CK - ▁CO - CE - ▁HOUSE - US - MA - ▁FACT - ▁GONNA - UL - ▁HEAD - ▁DIS - ▁MEN - ▁HAND - ▁P - ▁ABLE - NA - ▁TEN - ▁IMPORTANT - ▁MUST - ▁NAME - ▁TODAY - ▁FEW - ▁CAR - ▁EACH - ▁STATE - RY - ▁SIDE - ENT - ▁MIND - EST - ▁CALL - ATE - MAN - TA - ▁BETWEEN - ▁EVER - ▁SCHOOL - ▁BOTH - ▁MONEY - ▁UPON - ▁REAL - ▁WHOLE - ▁MOMENT - IA - ▁NUMBER - IST - ET - X - ▁DATA - ▁HIGH - ▁GOD - ▁F - ▁BOOK - ▁BEST - ▁SET - ▁PO - ▁HAVING - GE - ▁WITHOUT - ▁KEEP - ▁LOOKING - ▁CHANGE - OL - ▁SECOND - ▁LA - ▁FACE - ▁CASE - ▁FAR - ▁HUMAN - IES - ▁HARD - TE - ▁PRO - ▁LEFT - ▁TOGETHER - _ - ▁SYSTEM - ▁PUBLIC - ▁MR - ▁IDEA - ▁ONCE - ▁ANYTHING - ID - TED - ▁USED - ▁B - ▁ENOUGH - ▁WAR - SH - ▁EXAMPLE - ▁STORY - ▁DOESN - ▁LIVE - ▁TALKING - ARY - ▁QUITE - ▁AGAINST - ▁NIGHT - ▁POWER - ▁UNDERSTAND - ▁CON - '000' - ▁MAYBE - ▁BELIEVE - ▁COUNTRY - ▁GETTING - ▁YOUNG - ▁BO - ▁TRYING - ▁OPEN - ▁THANK - ▁MO - VER - TO - CO - ▁WHILE - ▁SAW - ▁SIX - ▁CARE - LO - LING - ▁G - TIC - ▁K - ▁SAYS - ▁ST - ▁HIMSELF - ▁WEEK - ▁EVERYTHING - ▁ROOM - AT - LESS - ▁WATER - INE - ▁D - IS - ▁NOTHING - ▁LIGHT - ▁TOOK - IAN - ▁READ - ▁MA - ▁SENSE - IR - ▁STARTED - ▁COMING - ▁SUPPORT - ▁WHITE - ▁RO - ▁BUSINESS - ▁PROBABLY - FUL - ▁ORDER - ▁FAMILY - ▁PRETTY - ▁SAYING - ▁SEEN - ▁BLACK - ▁TOLD - ▁PROCESS - ▁PER - TON - TING - ▁TRY - ▁THOUGH - ▁HEAR - ▁WANTED - TI - ▁MATTER - ▁HOPE - ▁WORKING - ▁ASK - ▁GOVERNMENT - ▁BA - ▁EX - ▁PRE - ▁HEARD - IE - ▁EXPERIENCE - ATED - ME - ▁HEART - ISM - KE - ▁NA - ANT - ▁MAKING - ▁ALREADY - ▁EN - MO - ▁PROBLEM - ▁SMALL - ▁YET - ▁RUN - 'NO' - ▁REMEMBER - OUS - ▁LI - ▁PERCENT - ▁INFORMATION - ▁WORD - ▁CHILDREN - ▁REASON - ▁OFTEN - ▁KNEW - ▁EYES - ▁ALMOST - ▁YES - ▁ELSE - ▁ASKED - ▁LESS - ▁STUFF - ▁HEALTH - ▁RA - PH - ▁U - HO - AS - ▁CITY - IM - ANCE - ▁PLAY - ▁COMMUNITY - ▁WOMEN - TY - ▁FATHER - ▁VI - ATING - ▁DAYS - ▁LOOKED - ▁ANSWER - ▁FREE - ▁MEANS - CI - SON - ▁PRESIDENT - ▁OH - UM - ▁BAD - LED - ISH - PE - UND - NT - NG - ▁WHETHER - OP - ▁FORM - AGE - ▁LEVEL - BE - ▁MYSELF - ▁SEVEN - ▁THIRTY - ▁DOOR - ▁LAW - ▁HALF - ▁BECOME - ▁SOMEONE - ▁SOCIAL - ▁MOTHER - ▁BRING - UP - IAL - ▁DU - PO - ▁LAND - ▁QUESTIONS - ▁WORDS - ▁EIGHT - ▁HO - ▁SHALL - ▁AGO - ▁COM - ▁DR - ▁SINCE - ▁TRUE - ▁BODY - ▁LEAST - ▁ACROSS - ▁LINE - ▁WITHIN - ▁MOVE - ▁INTERESTING - ▁LARGE - ▁FELT - HA - ▁LE - ▁SP - ▁TIMES - ▁CA - ▁USING - ▁T - ▁HAPPENED - ▁WOMAN - ▁LATER - ENCE - ▁STATES - ▁FULL - ▁DA - MB - MIN - ▁VOICE - UN - ▁STUDENTS - BER - ▁COMPANY - ▁VIEW - ▁THINKING - ▁VIDEO - ▁TURN - ▁IMP - ▁LEAVE - ▁PAST - ▁MAR - ▁HISTORY - ▁FRIEND - ▁DOLLARS - ▁OKAY - NI - ▁JOB - ▁SHORT - ▁PAGE - ▁COMES - DO - ▁FIRE - ▁TEAM - ▁REST - ▁DI - ▁GROUP - ▁CERTAIN - ▁UNTIL - ▁AIR - BO - ▁ACT - ▁COURT - ▁RESEARCH - ▁SPACE - ▁BUILDING - ▁AMERICAN - OT - ▁BRAIN - ▁GAME - ▁APP - ▁STA - AM - MI - ▁ALONG - ▁SE - ▁BAR - RING - VI - ▁DEATH - ▁HAPPEN - ▁DEAL - ▁CLOSE - ▁DURING - ▁STRONG - ▁CHILD - ▁WON - ▁MORNING - ▁SOON - ▁GENERAL - ▁THEMSELVES - ▁PLAN - ▁CAUSE - ▁SCIENCE - ▁NE - ▁PRESENT - ▁ART - ▁EM - ▁FIGURE - LAND - ▁REPORT - ▁W - ▁FUTURE - ▁POSSIBLE - ▁POST - ▁SOMETIMES - ▁MU - ▁FIFTY - ▁SHA - V - VA - MP - DA - ▁PROGRAM - ▁YOURSELF - WAY - ▁MILLION - ARD - ▁CLEAR - FF - ▁GAVE - ▁GOES - ▁RATHER - ▁STAY - ▁EARLY - ▁MISS - ▁JU - DI - ▁LEARN - ▁RED - ▁COUPLE - NY - ▁TOP - ▁GIVEN - ▁FRIENDS - ▁FRONT - ▁CONVERSATION - ▁PAY - MON - '1' - ▁EVERYONE - ▁CREATE - ▁SPEAK - ▁HI - ▁FOOD - ▁CONTROL - FI - ▁TEST - ▁TERMS - ▁BEHIND - ▁WASN - ▁GUYS - ▁WHATEVER - ▁FEELING - ▁LANGUAGE - ▁BASICALLY - RU - GG - ▁PARTICULAR - ▁HA - ▁MUSIC - ▁SOUND - ▁BREAK - ▁TAKING - CON - RAN - ▁VALUE - ▁WRONG - ▁STAR - ▁BUILD - BI - ▁GONE - ▁BOARD - DY - ▁WRITE - ▁EITHER - ▁SEEMED - ▁KING - ▁TURNED - ▁SA - ALLY - '2' - '9' - NCE - ▁LO - ▁FORWARD - BU - ▁TAKEN - ▁UNIVERSITY - CA - ▁EVERYBODY - PA - LINE - ▁STOP - ▁ITSELF - ▁BROUGHT - ▁MAKES - ▁HANDS - ▁GUESS - IZED - ▁AGE - ▁SERVICE - ▁WAYS - ▁NINE - ▁BEGAN - ▁HU - ▁GRA - ▁FEAR - ▁NATIONAL - PER - ▁SELF - ▁EXACTLY - ▁TI - DING - GO - ▁TABLE - ▁SOMEBODY - ▁LIVING - HE - ▁BECAME - ▁RU - ▁SINGLE - ▁VA - ▁COMP - ▁SIGN - ▁FALL - LIC - ▁SEA - ▁GIRL - ▁UNITED - IG - ▁KIDS - '3' - ITIES - ▁M - ▁STUDY - ▁FORTY - ▁CHECK - ▁ADD - PI - ▁COMMON - ▁PI - PED - RS - ▁TRUMP - ▁GUY - ▁JE - ▁SUB - ▁HIT - ▁SUPER - BA - ▁SEEMS - ▁ISN - ▁AL - ▁ALLOW - ISE - ▁POLITICAL - ▁PROJECT - ▁SEND - ▁INTEREST - ▁SHARE - ▁JOHN - ▁NEAR - ▁VE - ▁MARKET - ▁CERTAINLY - ▁ISSUE - ▁CLA - ▁LOW - ▁WISH - ▁COST - AD - ▁KNOWN - CU - ▁MONTHS - ▁SOUTH - ▁NICE - ▁RECORD - ▁TA - WA - ▁LU - END - ▁SUN - AK - ▁SON - ▁STAND - '7' - ▁KEY - ▁V - LU - ▁RI - GRA - ▁GA - ▁BOY - ▁GROUND - ▁EASY - ▁ACCOUNT - ▁HAPPY - '8' - ▁OFFICE - ▁CLASS - URE - ▁HOLD - TOR - OS - '6' - ▁CUT - ▁MODEL - ▁CHURCH - '4' - ▁MEDIA - BB - ▁EARTH - ▁STREET - IOUS - ▁LISTEN - ▁PA - ▁LIVES - ▁CANNOT - ▁FE - ▁LOST - ▁BUY - ▁STEP - LIN - OM - UT - LES - ▁PAPER - ▁FIELD - ▁BEAUTIFUL - ▁SPECIAL - VING - SHIP - ▁ACTION - '5' - ▁DEEP - ▁AREA - ▁MINUTES - ▁NATURE - NER - ▁EDUCATION - ▁YORK - KING - ▁SITUATION - ▁WALL - ▁DIFFICULT - OW - QUI - ▁BI - ▁PLEASE - ▁AMONG - ▁POSITION - ▁WATCH - ▁JA - ▁MRS - ▁TYPE - AC - ▁SLEEP - ▁DIFFERENCE - TEN - ▁HOURS - ▁DEAD - ▁RELATIONSHIP - ▁FOLLOW - ▁POOR - ▁DOCTOR - ▁SENT - ▁TALKED - ▁RISK - ▁PERHAPS - TIME - ▁LEARNING - ▁MET - ▁OUTSIDE - ▁CONTENT - ▁LIST - ▁READY - ▁WALK - VO - ▁ROUND - ATIONS - ▁SIMPLE - ▁CHA - ▁WA - ▁SEVERAL - RN - ▁SIR - ▁LORD - IZE - ▁TRUTH - ▁HOUR - ▁AMERICA - ▁CONS - ▁FINE - ITE - ▁WAIT - ▁MIDDLE - OUR - ▁HAPPENS - AP - GA - ▁HUGE - ▁SEEM - ▁COLLEGE - ▁AMOUNT - ▁CONTINUE - ▁ACCESS - ▁ARM - ▁LEAD - ▁LONGER - ▁NEWS - ▁PASS - ▁COULDN - ▁APPROACH - ▁R - ▁ROAD - ▁WRITING - ▁ATTENTION - ▁INSIDE - ENS - ▁PRODUCT - LER - ▁TECHNOLOGY - ▁RETURN - WARD - ▁MAIN - ▁VISIT - ▁ALONE - ▁KA - ▁BROTHER - ▁PARTY - ▁SU - ▁PIECE - BY - ▁LINK - DER - ▁SHIP - UD - ▁WIFE - '0' - THER - ▁OTHERS - ▁PRACTICE - IFY - ICAL - PING - ▁MEETING - RON - ▁TOWN - ▁DESIGN - ▁CHARACTER - ▁BLOOD - ▁RUNNING - ▁CHE - ▁PROVIDE - ▁TH - ▁MEET - ▁COOL - ▁LOCAL - ▁CHANCE - ▁BASED - STER - IONS - ▁THIRD - ▁SAFE - ▁FA - ▁FEET - FA - ▁VOTE - CING - ECT - ▁ENERGY - ▁NORTH - ▁TOWARDS - ▁GREEN - ▁PERIOD - TION - ▁HAPPENING - NING - CY - ILE - ▁SOCIETY - ▁LATE - ATOR - ▁DARK - ▁WOULDN - ▁L - ▁POLICE - ▁CENTER - ▁SUBJECT - ▁TRIED - ▁SEEING - ▁QUICKLY - ▁TU - ▁FOCUS - ▁BILL - ▁TRUST - ▁CHALLENGE - ▁SAT - ▁WINDOW - PP - ▁MARK - ▁NOR - ▁EPISODE - ▁FAST - ▁BRI - ▁TRA - HI - ▁EL - ▁SOURCE - LEY - ▁WORKED - ▁ISSUES - ▁IMAGE - ▁HERSELF - ▁HAR - RK - ▁ADDRESS - ▁COMPANIES - ▁TRI - UGH - ▁COLOR - ▁PE - ▁TOUCH - ▁PARENTS - RIC - ▁RATE - ▁PEN - ▁HOWEVER - SIDE - CC - ▁CONSIDER - ▁SAN - IFIED - ▁CODE - CHE - ▁EVIDENCE - ▁WONDER - TLE - ▁MASTER - ▁MONTH - ▁WORTH - ▁EAT - ▁SEARCH - ▁INTER - UNT - EP - ▁RESPECT - ▁ESPECIALLY - ▁RAN - ATIVE - ▁BEGIN - ▁WROTE - ▁DOG - ▁FUN - ▁DREAM - ABILITY - ▁FAIR - ▁LADY - ▁WORKS - ▁HAVEN - ▁CULTURE - ▁WI - ▁PICK - ▁AUSTRALIA - ▁MIS - ▁SIT - ▁IMAGINE - GU - ▁CLICK - ▁ATTACK - ▁OPPORTUNITY - ▁EYE - ▁STE - ▁DEN - ▁DEVELOPMENT - ▁FACEBOOK - ▁OFFER - ▁BANK - ▁NINETEEN - ▁PERSONAL - ▁APPLICATION - ▁ABSOLUTELY - ▁EFFECT - ▁DEAR - ▁BREATH - ▁NI - ▁OBJECT - ▁GRI - ▁REACH - ▁STOOD - ▁PHONE - ▁MACHINE - ▁WO - ▁SIXTY - ▁FIT - ▁PROBLEMS - ▁BOOKS - ▁SIMPLY - ▁FURTHER - ▁CHANGED - SET - ▁BUILT - ▁BEGINNING - ▁LAY - ▁COVER - ▁MOVEMENT - ▁KILL - ▁INSTEAD - ▁GU - GEN - ▁WOOD - ▁IMPACT - ▁RESULT - ▁LETTER - CL - ▁MEMBERS - ▁FI - ▁N - ▁CAP - ▁ENTIRE - MENTS - ▁COMPLETELY - ▁DOUBT - ▁ENGLISH - ▁SOFTWARE - MEN - ▁STORIES - ▁NATION - ▁FOOT - ▁POLICY - GER - ▁LEARNED - ▁CENTURY - ▁FIGHT - ▁TRANS - ▁FLOW - ▁PASSED - ▁MESSAGE - ▁INDIVIDUAL - ▁FIFTEEN - ▁TRAVEL - ▁CROSS - ▁REMAIN - ▁WIN - ▁PAIN - RIES - ▁ROCK - ▁BU - AIN - CTION - ▁PURPOSE - VAL - ▁SUCCESS - ▁AB - ▁WELCOME - ONE - ▁WRITTEN - ▁NON - ▁PICTURE - ▁KEPT - SA - ▁LOOKS - VEN - ▁TROUBLE - ▁EFFORT - ▁NEEDS - ▁HORSE - GING - ANG - LAR - ACH - ▁KNOWLEDGE - ▁MAJOR - IP - ▁FUNCTION - ▁PODCAST - NED - ▁ROLE - ▁MINE - OUT - ▁CIVIL - QUE - ▁AMAZING - USE - WI - ▁MENTIONED - ▁FORCE - ▁SONG - ▁DISEASE - ▁GROW - ▁HELD - ▁HAIR - ▁WEBSITE - ▁AHEAD - ▁PATH - ▁LISTENING - ▁WEEKS - ▁INTERESTED - ▁DIE - TU - ▁MEASURE - ▁NATURAL - ▁STORE - ▁WANNA - ▁TEXT - ▁READING - ▁CLOUD - WE - ▁SERVICES - ▁TERM - ▁INTERVIEW - ▁EXPLAIN - ▁SIMILAR - ▁DECIDED - ▁ABOVE - ▁SUR - ▁CASES - ▁CHAIR - ▁INDEED - ILY - ▁MOVING - ▁BLUE - ▁JO - ▁BEYOND - ▁DECISION - ▁NEEDED - ▁LED - ▁ECONOMIC - ▁SPIRIT - ▁WHOM - ▁SITE - ▁AVAILABLE - ▁NINETY - ▁DEPEND - ▁PERFECT - ▁GIVING - ▁DAUGHTER - ▁PRESS - ▁BOX - OLOGY - IGN - ▁JUSTICE - ▁WATCHING - ▁CLAIM - ▁COUNT - IO - ▁CHARGE - LEN - ▁REGARD - ISTS - ISTIC - ▁PARK - ▁VER - ▁DEPARTMENT - IANS - ▁OK - ▁CUR - ▁WIND - ▁SECRET - ▁EXPECT - ▁OP - ▁OBVIOUSLY - ▁FINALLY - ▁ANYONE - ▁FO - ▁COMMAND - ▁SITTING - ▁PROFESSOR - ▁NETWORK - ▁TRAIN - ▁INVOLVED - ▁REPRESENT - ▁DOCUMENT - ▁ONLINE - ▁MOVIE - ▁MON - ▁PRICE - ▁RIVER - ▁EXIST - ▁WONDERFUL - ▁THANKS - ▁EQUAL - ▁STAFF - ▁TWELVE - ▁PROTECT - ▁QUICK - PL - ▁RACE - ▁INDUSTRY - ▁PAT - ▁COR - ▁PRIVATE - ▁RESPONSE - ▁BRA - ▁EVENING - ▁CHI - ▁WILD - ▁CAT - ▁DEC - ▁ENVIRONMENT - ▁DEF - ▁SPECIFIC - ▁SCREEN - ▁WEST - NU - ▁MATERIAL - ▁PHYSICAL - ▁NORMAL - DIC - ABLY - ▁AUDIENCE - ▁SOUL - ▁ANTI - FT - ▁SEVENTY - ▁REQUIRE - ▁CREATED - ▁STUDENT - ▁FISH - ▁MINUTE - ▁CONCERN - IZATION - ▁COLD - ▁PLACES - ▁WIDE - RAL - ▁SPEND - ▁MAD - ▁DEFINITELY - GED - ▁MENTAL - ▁SAL - ▁CORRECT - ▁RICH - TRO - ▁IDEAS - ▁COUNTRIES - ▁UNDERSTANDING - ▁INCLUDE - ELL - ▁POPULATION - ▁FLU - ▁USUALLY - ▁AGREE - ▁PEACE - ▁FELLOW - ▁STAGE - ▁YE - IBLE - EE - ▁STANDARD - ▁DISC - ▁SPEAKING - ▁CHOICE - 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▁LIB - ▁FILTER - ▁CUTTING - ▁REFLECTION - ▁RATIONAL - ▁INDUSTRIAL - ▁GREY - ▁IGNORE - ▁IRAQ - ▁LEGISLATION - ISATION - ▁SPITE - ▁SCENARIO - ▁CHARACTERISTIC - ▁ASSAULT - ▁BARRIER - ▁CHRONIC - ▁HARRY - ▁JAPAN - ▁CONTRAST - ▁EMBRACE - ▁PREPARE - ▁TANK - ▁CURVE - ▁RAPID - COURSE - ▁RESEARCHERS - ▁PUMP - ▁GRIEF - ▁SUPERIOR - ▁PERSONALITY - ▁AWARENESS - ▁PROMPT - ▁SETTLE - ▁HARRIS - ▁PHYSICIAN - ▁EXPENSE - ▁EDWARD - ▁TUNE - ▁SOUGHT - ▁UNCERTAIN - ▁FORGOT - ▁DIAMOND - ▁THOROUGH - ▁TICKET - ▁SAVAGE - ▁PREACH - ▁PILOT - ▁THROAT - ▁ARROW - ENCIES - ▁CURIOSITY - ▁RESISTANCE - ▁FICTION - ▁STROKE - ▁TREASURE - ▁URBAN - ▁COMPETE - ▁PROTEIN - ▁SWIM - ▁PROPORTION - ▁VISIBLE - ▁MARGIN - ▁ORGANISATION - ▁ETC - ▁PLOT - ▁NICK - ▁MEDICATION - ▁LEAGUE - ▁DESPAIR - ▁FINANCE - ▁ASSISTANT - ▁GENUINE - ▁PROCEDURE - ▁EXHAUST - ▁FLORIDA - ▁JAR - ▁HEAVILY - ▁SHIRT - ▁PERCENTAGE - ▁SHRI - ▁BOWL - ▁VALID - ▁MARIA - ▁HOLIDAY - ▁TRANSLATE - ▁INFECTION - ▁TAG - ▁VULNERABLE - ▁QUO - ▁DIVINE - ▁ABSTRACT - ▁RECOGNITION - ▁ASSESSMENT - WATER - ▁VOTERS - ▁EXCITEMENT - ▁PROSPECT - ▁BEHALF - ▁PERCEPTION - ▁JAPANESE - ▁YIELD - ▁VERSE - '0000014' - ▁DIGNITY - ▁GUARANTEE - ▁PRAISE - ▁FOND - ▁DUKE - ▁STEPPED - ▁SPELL - ▁DISTANT - JI - ▁CHIP - ▁SUCK - ▁INTEGRATE - ▁TEAR - ▁FAKE - ▁INTENT - ▁CHAMPION - ▁SLAVERY - ▁ADVERTISING - ▁TWIST - ▁CLINIC - ▁FULFILL - ▁SPARE - ▁HURRY - ▁CONFRONT - '0000001' - ▁BUG - CASE - ▁PLASTIC - ▁MANUFACTURE - ▁CLEVER - ▁ADMITTED - POST - ▁SACRED - ▁AUTOMATICALLY - ▁INNOCENT - ▁ANGLE - ▁PECULIAR - ▁MEXICO - LIFE - ▁SATURDAY - ▁IMPORT - ▁AUDIT - ▁CATEGORY - ▁ESC - ▁CHALLENGING - ▁RESTORE - ▁SOVIET - ▁ALICE - ▁CAKE - ▁RELAX - ▁DREADFUL - ▁PHYSICS - ▁MEDITATION - ▁PSYCHOLOGICAL - ▁KATE - ▁CORONAVIRUS - ▁MEMORIES - ▁NERVE - ▁WINNER - ▁GLU - ▁CLOSING - ▁THRILL - ▁POISON - ▁SARAH - ▁SENTIMENT - ▁SOLDIER - ▁EXTERNAL - ▁AGENCIES - ▁RIDICULOUS - ▁BIAS - IZZ - ▁CORPORATION - ▁ABSENCE - ▁MATTHEW - ▁PARTNERSHIP - ▁EMPLOYMENT - ▁IDENTIFIED - ▁CEO - ▁PERMISSION - ▁VARIABLE - ▁CREAM - ▁BIRTHDAY - FACE - ▁TYPICAL - ▁HELLO - ▁INHABIT - ▁PERMANENT - ▁RURAL - ▁STRAIN - ▁HEALTHCARE - 0000008 - ▁SATISFIED - ▁OBJECTIVE - ▁BRUSH - ▁ALBUM - '07' - ▁PUZZLE - ▁HUNGRY - ▁DECENT - ▁STRICT - ▁DEMONSTRATE - ▁MORTAL - ▁ZI - ▁WINNING - ▁FORTUNATE - ▁BULLET - ▁EARNEST - THROP - ▁BURIED - ▁HORN - ▁MYSTERIOUS - ▁LICENSE - ▁ARC - ▁MIDST - ▁HORIZON - ▁SANDERS - ▁PRIVACY - ▁FLESH - '0000007' - ▁BOOM - ▁ITALY - ▁PINK - ▁OPPOSITION - ▁HISTORIAN - ▁AMONGST - ▁HANDSOME - ▁PSYCHO - ▁REPUTATION - OGRAPHIC - ▁JULIA - ▁HARVARD - ▁NUMEROUS - ▁SYM - ▁STAIRS - ▁EXECUTE - ▁CRITICISM - ▁FIERCE - ▁WEDDING - ▁IMPEACHMENT - ▁ATTACHED - ▁SEPTEMBER - ▁FRED - ▁ENTRANCE - ▁PROFESSION - ▁DISTRACT - ▁APRIL - ▁STRATEGIES - ▁ORANGE - ▁SAINT - ▁INVITE - ▁CONTRIBUTION - ▁NEST - ▁REPAIR - ▁WEIGH - ▁PSYCHOLOGY - ▁MARVEL - ▁BOLT - ▁PLUM - ▁SENSITIVE - ▁PEAK - ▁RECOGNISE - ▁FAITHFUL - ▁DIMENSION - ▁ILLEGAL - ▁INVISIBLE - ▁ITALIAN - ▁ECHO - ▁SURROUNDING - ▁ARRANGEMENT - ▁PROPERTIES - ▁ELECTRICITY - ▁KEEN - ▁THEATER - ▁BISHOP - ▁ANALYTIC - ▁IMMIGRANT - ▁INTRODUCTION - '0000004' - ▁STEPHEN - ▁CONFUSED - ▁THUMB - ▁CONCEAL - ▁UNCOMFORTABLE - ▁SCARCELY - ▁CONQUER - ▁DISTINCTION - ▁HIND - ▁BACTERIA - ▁SERVING - ▁EASE - ▁BROWSER - ▁GLORY - ▁UNEXPECTED - ▁CRUSH - ▁ZOOM - ▁BORROW - ▁PASSIONATE - ▁GENIUS - ▁NOVEMBER - ▁BEGUN - ▁DISMISS - ▁COLLECTIVE - ▁DIVERSITY - ▁EMPOWER - ▁LAMP - ▁INFANT - ▁SOLEMN - ▁SEQUENCE - ▁CARRIAGE - ▁SURGERY - ▁BOSTON - ▁RESCUE - ▁INDEX - ▁GRADUALLY - ▁BROW - ▁SMILING - ▁SWING - AMERICAN - ▁MEDIUM - ▁GENEROUS - ▁PATENT - ▁CENTURIES - ▁VETERAN - ▁SINK - ▁LINCOLN - ▁STRATEGIC - ▁CONTEST - ▁CANDLE - ▁ADMIRE - ▁DRUNK - ▁GREET - ▁ARCHITECT - ▁DAWN - ▁GLOW - ▁ADVISOR - 0000009 - 08 - ▁INDIGENOUS - ▁LOOP - ▁CORPS - ▁DEFAULT - ▁INTELLIGENT - ▁MELT - ▁FRANCISCO - ▁INVESTOR - ▁SUSPICION - ▁FRIGHT - ▁ASSUMPTION - ▁UNDERNEATH - ▁CUB - ▁INJURY - ▁BROADCAST - ▁KNIFE - ▁BABIES - ▁THREAD - ▁INFINITE - ▁CONTROLLED - ▁RAISING - ▁FACILITIES - QUEST - ▁RECRUIT - EMPT - ▁PLANNED - ▁COLLAPSE - ▁REFERRED - ▁GANG - ▁FACILITY - ▁PUNISHMENT - ▁CONTINENT - ▁ALIGN - ▁PRONOUN - ▁RETAIN - ▁CONFUSION - ▁PUNISH - ▁MELBOURNE - ▁ADVERTISE - ▁DISTRIBUTION - ▁DISPUTE - ▁ALERT - ▁ASSERT - ▁EXCEED - ▁DOMAIN - ▁TENSION - ▁SEGMENT - '0000010' - ▁ACID - ▁LEGEND - ▁DANCING - ▁COVERAGE - ▁EXPORT - ▁DESTRUCTION - ▁GREG - ▁BELT - ▁ECOSYSTEM - ▁CLINTON - ▁WHIT - ▁BIOLOGICAL - ▁CONVEY - ▁DELIBERATE - ▁PROGRAMMING - ▁DEFENCE - ▁INSPIRATION - ▁CANADIAN - ▁COGNITIVE - ▁ENFORCEMENT - ▁UNIFORM - ▁TURKEY - ▁CONTINUOUS - ▁DWELL - ▁DEDICATED - ▁PARALLEL - ▁INVESTIGATE - ▁STAMP - ▁INDEPENDENCE - ▁INHERIT - ▁SCRATCH - ▁BEHAVE - ▁EXCESS - ▁GENTLY - ▁ROMANTIC - ▁HINT - ▁LOYAL - ▁ARRIVE - ▁ARRIVAL - ▁INTERFACE - ▁RECEIVING - ▁CONSUME - ▁ANALOG - ▁SPOIL - ▁CONVICTION - ▁UNCONSCIOUS - ▁KNIGHT - ▁CORPORATE - ▁ROUTINE - ▁DRAGON - ▁REFUSE - ▁ARTIFICIAL - ▁ETHICS - ▁JOSEPH - ▁FRIGHTENED - ▁OVERCOME - MORROW - ▁JONES - ▁LOCATED - ▁LEGACY - ▁IMMIGRATION - ▁ACCESSIBLE - ▁EXPOSURE - ▁BUREAU - ▁CONTINUING - ▁CETERA - ▁VITAL - ▁HUMBLE - ▁PRECIOUS - ▁GRASP - ▁CAROLINA - ▁LONELY - ▁SENSATION - ▁PREPARATION - ▁SATISFACTION - ▁DIALOGUE - ▁MIRACLE - ▁VAGUE - ▁MINIMUM - ▁MEANINGFUL - '0000003' - ▁GEORGIA - ▁INTIMATE - ▁WITHDRAW - ▁ONGOING - ▁REPUBLIC - ▁OBEY - ▁JACKSON - ▁INSTRUCT - ▁DETECTIVE - ▁SUSAN - ▁DUMB - '0000006' - ▁SUBSTANTIAL - ▁QUERY - ▁APPOINTMENT - ▁GRANDFATHER - ▁CHEERFUL - ▁BLOOM - ▁JANUARY - ▁ACADEMY - ▁ALEXANDER - ▁DISRUPT - ▁RECIPE - CONNECT - ▁GRANDMOTHER - ▁BIOLOGY - ▁ATHLETE - ▁SCROLL - CALLED - ▁COMPARISON - ▁COMBAT - ▁HYPER - ▁AGGRESSIVE - ▁UNIVERSITIES - ▁HURRIED - ▁INTERFERE - ▁SOVEREIGN - ▁EXCEL - ▁CREATOR - ▁WARRIOR - ▁SPLENDID - ▁MONSTER - ▁RESUME - ▁EXTRACT - ▁WICKED - ▁OCCUPIED - ▁DRAFT - ▁RESIGN - ▁AUTHORITIES - ▁RACISM - ▁STRIKING - ▁SMOKING - ▁ABSORB - ▁FLEW - ▁RIVAL - ▁DECEMBER - ▁TUESDAY - ▁OCTOBER - ▁SETTLEMENT - ▁BEDROOM - ▁NEGLECT - ▁MURMUR - ▁PHILOSOPHICAL - ▁SILLY - ▁INTEND - ▁APPOINTED - ▁EXECUTION - ▁PATIENCE - PLAY - ▁LIQUID - ▁THRONE - ▁THUNDER - ▁CORRUPTION - ▁SCREW - ▁APPROVAL - ▁ELECTRONIC - ▁MOTIVE - ▁IMMENSE - ▁ENGAGING - ▁FOOLISH - ▁ULTIMATE - FRONT - ▁ESSAY - ▁GESTURE - ▁CLERK - ▁NECESSITY - ▁WHIP - ▁PERSUADE - ▁NODDED - ▁PURPLE - ▁OBLIGATION - ▁GLOOM - ▁MERIT - ▁SCRAP - ▁SPECTRUM - ▁IMMUNE - ▁FABRIC - ▁PRODUCING - ▁ELSEWHERE - ▁TORTURE - ▁ATTRIBUTE - ▁POLITE - ▁CHUCK - ▁AMBITION - ▁PASSENGER - ▁ALIEN - ▁PRIMARILY - ▁CHASE - ▁CONCRETE - ▁PARTICIPANT - ▁GRATITUDE - ▁SILK - ▁WOLF - ▁ARTHUR - ▁DELICATE - ▁INDICATOR - ▁CONCERT - ▁MERCY - ▁OBLIGED - ▁EXCLUSIVE - ▁UKRAINE - ▁GUIDANCE - ▁COMPETITIVE - ▁JERUSALEM - ▁AUTHENTIC - ▁SUPPER - ▁CALCULATE - ▁COLLABORATION - ▁SUBSEQUENT - ▁UNHAPPY - ▁CONTRARY - ▁ASSEMBLY - ▁DIPLOMA - ▁BUBBLE - POWER - ▁SWEAT - ▁EXPERIENCING - ▁INQUIRY - ▁TRANSLATION - ▁ARISE - ▁DRAMA - ▁POSE - ▁ENHANCE - ▁PARDON - ▁DRIFT - ▁CASUAL - TRUST - ▁OXYGEN - ▁COMPOSED - ▁FOLK - ▁EXPLOIT - ▁ACTIVIST - ▁TELEPHONE - ▁ARGUING - ▁MISERABLE - ▁ABORIGINAL - ▁SHINING - ▁VIETNAM - ▁BETRAY - LOOKING - ▁OVERWHELMING - ▁FRAUD - ▁REDUCTION - ▁THEORIES - ▁PHENOMENON - ▁CONSCIENCE - ▁FESTIVAL - ▁UGLY - ▁STEADY - ▁CALENDAR - ▁THREATENED - ▁ASHAMED - ▁DISGUST - ▁REFRESH - ▁CATEGORIES - ▁SURVIVAL - ▁FLAVOR - ▁CAUSING - ▁HUGH - ▁INSULT - ▁SYMPATHY - ▁MARGARET - ▁ABSURD - ▁TACTIC - ▁SUPPRESS - ▁RIFLE - '0000016' - ▁THURSDAY - ▁QUEENSLAND - ▁ABROAD - ▁BELOVED - ▁DOROTHY - ▁ETHICAL - ▁NOWHERE - ▁MODERATE - ▁SNAKE - ▁CANCEL - ▁SWEAR - ▁MOURN - ▁CIRCUIT - ▁VEGETABLE - ▁BRUTAL - ▁MOTIVATION - ▁CREEP - ▁THERAPIST - ▁JEALOUS - ▁UNFORTUNATE - ▁HARDWARE - ▁HESITATE - ▁AWKWARD - ▁RACHEL - ▁COTTAGE - ▁OUTPUT - ▁JAVASCRIPT - ▁JEWEL - ▁CONVINCE - ▁REPLICA - ▁WHISTLE - ▁WARREN - ▁CLUE - ▁DREAD - ▁MESSENGER - ▁RETAIL - ▁SUBURB - ▁STOMACH - ▁JIMMY - ▁GUIDELINES - ▁FEATHER - ▁IMPULSE - ▁EXTENSION - ▁ASSESS - ▁COMPROMISE - ▁EXPLICIT - ▁DOCTRINE - ▁ECONOMIST - ▁WELFARE - ▁SUPPLEMENT - ▁MORRIS - ▁POSSIBILITIES - ▁SURRENDER - ▁ENTRY - ▁OCCUPATION - ▁IMPRESSIVE - ▁EXTENSIVE - ▁FRAGMENT - ▁ETHNIC - PRODUCT - ▁MONARCH - ▁AVENUE - ▁DIAGNOSE - ▁MERCHANT - ▁ANTHONY - ▁PURSUIT - ▁IRISH - KEEPER - '0000002' - ▁OPPONENT - ▁GUILT - ▁FUNERAL - ▁DIVORCE - ▁SWEEP - ▁WRETCH - ▁EQUIVALENT - ▁CONTRADICT - ▁SENSOR - ▁VOYAGE - ▁PROTOCOL - ▁SHIELD - ▁MUTUAL - ▁CONDEMN - ▁SLEPT - ▁MOTIVATE - ▁MICROSOFT - ▁NEUTRAL - ▁PORTRAIT - ▁ANCHOR - ▁OUTRAGE - ▁EQUITY - ▁INCENTIVE - ▁CURTAIN - ▁REGULATE - ▁INVITATION - ▁GYM - ▁MICROPHONE - ▁CENSUS - ▁OPERATOR - ▁DIFFER - ▁WEDNESDAY - ▁CREATIVITY - ▁CLIFF - ▁TESTIMONY - ▁DESCRIBING - ▁VARIATION - ▁THRUST - ▁LITERARY - ▁HOLLOW - ▁CONTEMPT - ▁SLICE - ▁DECLARE - ▁ANTICIPATE - ▁CIGARETTE - ▁RELIEVE - ▁RACIST - ▁RESTRAIN - ▁PHENOMENA - ▁ELIMINATE - ▁CEREMONY - ▁PREPARING - ▁IMPRESSED - ▁SUPPLIES - ▁EQUATION - ▁FEBRUARY - ▁JORDAN - ▁CRYSTAL - ▁ACQUAINTANCE - ▁PACIFIC - ▁MAMMA - ▁TISSUE - ▁INTERIOR - ▁TRAGEDY - SCRIPT - ▁SUPERINTENDENT - ▁BOUNDARIES - ▁LEGITIMATE - ▁LIFESTYLE - ▁INDUSTRIES - ▁APPARENT - ▁EMPHASIS - ▁JACOB - ▁IOWA - ▁FUNNEL - ▁TAYLOR - ▁PREVAIL - ▁WORKSHOP - ▁IMPERIAL - ▁CONCEIVE - ▁SPEAR - '0000020' - ▁DELICIOUS - ▁ESSENCE - ▁ZHUGE - ▁EXPEDITION - ▁MISTRESS - ▁APOLOGIZE - ▁SWEPT - ▁RECOLLECT - ▁ACCENT - ▁ETERNAL - ▁QUARREL - ▁CONSTRAIN - ▁SURVIVOR - DIRECT - ▁JERSEY - ▁POTATO - ▁MANKIND - ▁BATTERY - ▁REGULATOR - ▁METAPHOR - ▁CRUCIAL - ▁CORRUPT - ▁MAJESTY - ▁FREAK - ▁SWALLOW - '0000012' - ▁INDUCE - ▁VACATION - ▁INCLINED - ▁REVELATION - ▁EXPLORING - ▁SINGULAR - ▁OUTBREAK - ▁WHALE - ▁BASKET - ▁AROSE - ▁ELBOW - ▁ABSENT - ▁SCANDAL - ▁LIBRARIES - ▁PSYCHOLOGIST - ▁ZEALAND - ▁SPRANG - ▁CRITERIA - ▁PIANO - ▁DECREASE - ▁BITCOIN - ▁REINFORCE - ▁INHALE - ▁GRAVITY - ▁PROMINENT - ▁PIVOT - ▁CHOCOLATE - ▁HORMONE - ▁FURNISH - ▁MICHIGAN - ▁MINNESOTA - ▁FREQUENT - ▁SATISFY - ▁TOBACCO - ▁POND - ▁INGREDIENT - ▁ENTITLED - ▁BASEBALL - ▁CONCENTRATE - ▁POLISH - ▁LIEUTENANT - ▁COMPOUND - ▁BERNIE - ▁EFFICIENCY - ▁GILBERT - ▁ALLIANCE - ▁PREGNANT - ▁ORCHESTRA - ▁HISTORIC - ▁ARRANGED - ▁ISOLATED - ▁IDIOT - ▁SHY - ▁AMBASSADOR - ▁INJURED - ▁CURRICULUM - ▁FURNITURE - ▁HOLLYWOOD - ▁ASSIGNMENT - ▁INJECT - ▁TEMPORARY - ▁WIDOW - ▁MATHEMATICS - ▁FOREHEAD - ▁ILLUSTRATE - ▁NEGRO - ▁CREEK - ▁CIRCUMSTANCE - ▁POWDER - ▁EMERGING - ▁SAUCE - ▁EXPANSION - ▁INTEGRATION - ▁LOBBY - ▁CONTROVERSIAL - ▁TEENAGER - ▁REVENGE - ▁DEPOSIT - ▁COUNTENANCE - ▁HARVEST - ▁ELEPHANT - ▁UNEMPLOYMENT - ▁PROSECUTOR - ▁SOCIETIES - ▁IGNORANCE - ▁MONKEY - ▁COMPLIMENT - ▁AFGHANISTAN - ▁ANALYZE - ▁RECKON - ▁CONTEMPORARY - ▁NURSING - ▁RITUAL - ▁CHARLIE - ▁WRECK - ▁EXHALE - ▁ARRAY - ▁SKETCH - ▁INDULGE - ▁MANUFACTURING - ▁SOPHISTICATED - ▁JUNIOR - ▁ACCORD - ▁BLEND - ▁DROWN - ▁MERRY - ▁CONSUMPTION - ▁MECHANICAL - ▁PEPPER - ▁COLORADO - ▁BOOST - ▁MANUSCRIPT - ▁CONVENIENT - ▁MOLECULE - ▁CALCULATION - ▁DOMINATE - ▁MONUMENT - ▁ACCUSTOMED - OLOGIES - ▁IMPROVING - ▁CRAWL - ▁BRAZIL - ▁ASSASSIN - ▁DIAGNOSIS - ▁DISABILITIES - ▁NINTH - ▁PREJUDICE - ▁ATLANTIC - ▁NONSENSE - ▁CHEMISTRY - ▁STATUE - ▁NEGOTIATION - ▁SATELLITE - ▁INTEGRITY - ▁DEPRESSED - ▁INCORPORATE - ▁CARPET - ▁DETERMINATION - ▁FEAST - ▁FROZEN - ▁KENNEDY - ▁ENLIGHTEN - ▁INEVITABLE - ▁TEMPLATE - SCHOOL - ▁ABORTION - ▁DELEGATE - KNOWN - ▁PRECEDE - ▁GOSPEL - ▁GALLERY - ▁MAGNIFICENT - ▁MEMORIAL - ▁SELDOM - ▁LEVERAGE - ▁RELIABLE - ▁NETFLIX - ▁UPSTAIRS - ▁ARBITR - '0000011' - ▁CONVERSION - ▁CAUTIOUS - ▁NAVIGATE - ▁FIGURING - ▁TRAGIC - ▁SPHERE - ▁FOSSIL - ▁EXPLOSION - ▁ENFORCE - ▁KELLY - ▁CHOOSING - ▁HEBREW - ▁OXFORD - ▁OFFENSIVE - ▁CEILING - ▁GLORIOUS - ▁BUZZ - ▁RHYTHM - ▁AGRICULTURE - ▁DISCRIMINATION - ▁EMPATHY - ▁ENCOURAGING - ▁ACCOMPANIED - ▁SKEPTIC - ▁JERRY - ▁CONSPIRACY - ▁BASKETBALL - ▁MODULE - ▁TACKLE - ▁DIABETES - ▁LUXURY - ▁CONGRATULATIONS - ▁IGNORANT - ▁PROHIBIT - ▁CATALOG - ▁EXAMINATION - ▁TUNNEL - ▁SEPARATION - ▁ZHANG - ▁PEARL - ▁SLEEVE - ▁MEXICAN - ▁AFFIRM - ▁PATRIOT - ▁INHERENT - ▁CONCENTRATION - ▁TRANSPARENT - ▁BARBARA - ▁EAGLE - ▁TERRIBLY - ▁CURRENCY - PIECE - ▁CRAP - ▁PENALTY - ▁QUANTUM - THINK - ▁HERITAGE - ▁ENTHUSIASM - ▁TRUNK - ▁SUSPICIOUS - ▁SCOTLAND - ▁QUANTITY - ▁THRESHOLD - ▁DELETE - ▁SAUDI - ▁HAWK - ▁PAKISTAN - ▁EARLIEST - ▁JURISDICTION - ▁CERTIFICATE - ▁GUITAR - ▁SENSIBLE - ▁RYAN - ▁ENVELOPE - ▁GLIMPSE - ▁FLEXIBLE - ▁IMPATIENT - ▁LEGISLATURE - ▁CORONA - '0000013' - ▁COMPELLED - ▁BULLETPROOF - ▁MAINTENANCE - ▁NEGOTIATE - ▁COMPREHENSIVE - ▁FREQUENCY - ▁PARTICIPATION - ▁WHISK - '0000017' - ▁TREMBLING - ▁VIRGIN - ▁OBSTACLE - ▁RESIDENCE - ▁VIVID - ▁DEMONSTRATION - ▁SNEAK - ▁PLUNGE - ▁EARTHQUAKE - ▁COINCIDE - ▁PHYSIO - ▁UNFAIR - ▁PENCIL - ▁DOMINANT - ▁OPTIMIZE - ▁PHILADELPHIA - ▁PROBABILITY - ▁ASSUMING - ▁JULIE - ▁SIGNIFICANCE - ▁ADEQUATE - ▁NEVERTHELESS - ▁STITCH - ▁ADMIRATION - ▁ROBUST - ▁OPERA - ▁SIGNATURE - ▁ILLUSION - ▁LEATHER - ▁COMPUTING - ▁ADMINISTRATOR - ▁EXCLUDE - <sos/eos> init: null input_size: null ctc_conf: ignore_nan_grad: true joint_net_conf: null use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram5000/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 frontend: default frontend_conf: n_fft: 512 hop_length: 256 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_bpe5000/train/feats_stats.npz model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false preencoder: null preencoder_conf: {} encoder: e_branchformer encoder_conf: output_size: 512 attention_heads: 8 attention_layer_type: rel_selfattn pos_enc_layer_type: rel_pos rel_pos_type: latest cgmlp_linear_units: 3072 cgmlp_conv_kernel: 31 use_linear_after_conv: false gate_activation: identity num_blocks: 17 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d layer_drop_rate: 0.1 linear_units: 1024 positionwise_layer_type: linear macaron_ffn: true use_ffn: true merge_conv_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 8 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 layer_drop_rate: 0.2 preprocessor: default preprocessor_conf: {} required: - output_dir - token_list version: '202211' distributed: true ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
[ "TRANSLATION" ]
[ "BEAR", "CRAFT" ]
Non_BioNLP
dhruvrnaik/test-openbiollm
dhruvrnaik
null
[ "pytorch", "llama", "llama-3", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation", "heathcare", "medical", "clinical", "med", "lifescience", "Pharmaceutical", "Pharma", "en", "arxiv:2305.18290", "arxiv:2303.13375", "arxiv:2212.13138", "arxiv:2305.09617", "arxiv:2402.07023", "base_model:meta-llama/Meta-Llama-3-70B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-70B-Instruct", "license:llama3", "region:us" ]
1,739
1,739
16
0
--- base_model: meta-llama/Meta-Llama-3-70B-Instruct language: - en license: llama3 tags: - llama-3 - llama - Mixtral - instruct - finetune - chatml - DPO - RLHF - gpt4 - distillation - heathcare - medical - clinical - med - lifescience - Pharmaceutical - Pharma widget: - example_title: OpenBioLLM-70B messages: - role: system content: You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. - role: user content: How long does it take for newborn jaundice to go away? output: text: 'Newborn jaundice, also known as neonatal jaundice, is a common condition in newborns where the yellowing of the skin and eyes occurs due to an elevated level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when red blood cells break down. In most cases, newborn jaundice resolves on its own without any specific treatment. The duration of newborn jaundice can vary depending on several factors such as the underlying cause, gestational age at birth, and individual variations in bilirubin metabolism. Here are some general guidelines: 1. Physiological jaundice: This is the most common type of newborn jaundice and usually appears within 24-72 hours after birth. It tends to peak between the second and fifth day of life and gradually improves over the next week or two. By the time the baby is one week old, the jaundice should have mostly resolved. 2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and may appear later than physiological jaundice, typically between the fifth and fourteenth day of life. It tends to persist for a longer duration but usually resolves within six weeks after birth. 3. Pathological jaundice: This type of jaundice is less common and occurs due to an underlying medical condition that affects bilirubin metabolism or liver function. The duration of pathological jaundice depends on the specific cause and may require treatment. It''s important for parents to monitor their newborn''s jaundice closely and seek medical advice if the jaundice progresses rapidly, becomes severe, or is accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness. In these cases, further evaluation and management may be necessary. Remember that each baby is unique, and the timing of jaundice resolution can vary. If you have concerns about your newborn''s jaundice, it''s always best to consult with a healthcare professional for personalized advice and guidance.' model-index: - name: OpenBioLLM-70B results: [] --- <div align="center"> <img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/fJIOPJnY6Ff6fUiSIuMEt.png) <div align="center"> <h1>Advancing Open-source Large Language Models in Medical Domain</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://colab.research.google.com/drive/1F5oV20InEYeAJGmBwYF9NM_QhLmjBkKJ?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> | <a href="https://github.com/openlifescience-ai"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> | <a href="#"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> | <a href="https://discord.gg/A5Fjf5zC69"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/KGmRE5w2sepNtwsEu8t7K.jpeg) Introducing OpenBioLLM-70B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-70B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. 🏥 **Biomedical Specialization**: OpenBioLLM-70B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. 🎓 **Superior Performance**: With 70 billion parameters, OpenBioLLM-70B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-4, Gemini, Meditron-70B, Med-PaLM-1 & Med-PaLM-2 on biomedical benchmarks. 🧠 **Advanced Training Techniques**: OpenBioLLM-70B builds upon the powerful foundations of the **Meta-Llama-3-70B-Instruct** and [Meta-Llama-3-70B-Instruct](meta-llama/Meta-Llama-3-70B-Instruct) models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: <div align="center"> <img width="1200px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png"> </div> - **Policy Optimization**: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290) - **Fine-tuning dataset**: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated) This combination of cutting-edge techniques enables OpenBioLLM-70B to align with key capabilities and preferences for biomedical applications. ⚙️ **Release Details**: - **Model Size**: 70 billion parameters - **Quantization**: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-70B-GGUF) - **Language(s) (NLP):** en - **Developed By**: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs - **License:** Meta-Llama License - **Fine-tuned from models:** [Meta-Llama-3-70B-Instruct](meta-llama/Meta-Llama-3-70B-Instruct) - **Resources for more information:** - Paper: Coming soon The model can be fine-tuned for more specialized tasks and datasets as needed. OpenBioLLM-70B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences. We are excited to share OpenBioLLM-70B with researchers and developers around the world. ### Community & Resources #### 🔥 Your Daily Dose of Medical AI Breakthroughs 🚀 We turn hours of the latest research papers into minutes. Get daily tweets and news on the latest medical AI breakthroughs, dataset releases, and benchmark results – all carefully curated to save you time while keeping you informed. <div align="center"> <table> <tr> <td align="center"> <a href="https://twitter.com/OpenLifeSciAI"> <img src="https://img.shields.io/badge/X-Follow%20%40OpenLifeSciAI-black?style=flat&logo=x" alt="Twitter Follow"/> <br> Daily updates on Medical LLMs,<br>datasets & benchmarks </a> </td> <td align="center"> <a href="https://www.linkedin.com/company/openlifesciai/"> <img src="https://img.shields.io/badge/LinkedIn-Connect-blue?style=for-the-badge&logo=linkedin" alt="LinkedIn"/> <br> Daily news on Medical LLMs,<br>datasets & benchmarks </a> </td> </tr> <tr> <td align="center"> <a href="https://www.youtube.com/@OpenlifesciAI"> <img src="https://img.shields.io/badge/YouTube-Subscribe-red?style=for-the-badge&logo=youtube" alt="YouTube"/> <br> Video & audio summaries of<br>latest research </a> </td> <td align="center"> <a href="https://t.co/l5z6y6C4cM"> <img src="https://img.shields.io/badge/Discord-Join-7289DA?style=for-the-badge&logo=discord" alt="Discord"/> <br> Connect with researchers &<br>discuss latest developments </a> </td> </tr> </table> </div> ### Use with transformers **Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.** See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "aaditya/OpenBioLLM-Llama3-70B" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."}, {"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.0, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## **Training procedure** ### **Training hyperparameters** <details> <summary>Click to see details</summary> - learning_rate: 0.0002 - lr_scheduler: cosine - train_batch_size: 12 - eval_batch_size: 8 - GPU: H100 80GB SXM5 - num_devices: 8 - optimizer: adamw_bnb_8bit - lr_scheduler_warmup_steps: 100 - num_epochs: 4 </details> ### **Peft hyperparameters** <details> <summary>Click to see details</summary> - adapter: qlora - lora_r: 128 - lora_alpha: 256 - lora_dropout: 0.05 - lora_target_linear: true -lora_target_modules: - q_proj - v_proj - k_proj - o_proj - gate_proj - down_proj - up_proj </details> ### **Training results** ### **Framework versions** - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1 - Axolotl - Lm harness for evaluation # Benchmark Results 🔥 OpenBioLLM-70B demonstrates superior performance compared to larger models, such as GPT-4, Gemini, Meditron-70B, Med-PaLM-1 & Med-PaLM-2 across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 86.06%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge. 🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy. | | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA 4 opts | PubMedQA | MedMCQA | Avg | |--------------------|-------------|------------------|---------|--------------|-----------------|------------------|--------------|----------|---------|-------| | **OpenBioLLM-70B** | **92.93** | **93.197** | **83.904** | 93.75 | 93.827 | **85.749** | 78.162 | 78.97 | **74.014** | **86.05588** | | Med-PaLM-2 (5-shot) | 88.3 | 90 | 77.8 | **95.2** | 94.4 | 80.9 | **79.7** | **79.2** | 71.3 | 84.08 | | **GPT-4** | 86.04 | 91 | 80 | 93.01 | **95.14** | 76.88 | 78.87 | 75.2 | 69.52 | 82.85 | | Med-PaLM-1 (Flan-PaLM, 5-shot) | 80.4 | 75 | 63.7 | 83.8 | 88.9 | 76.3 | 67.6 | 79 | 57.6 | 74.7 | | **OpenBioLLM-8B** | 76.101 | 86.1 | 69.829 | 78.21 | 84.213 | 68.042 | 58.993 | 74.12 | 56.913 | 72.502 | | Gemini-1.0 | 76.7 | 75.8 | 66.7 | 77.7 | 88 | 69.2 | 58 | 70.7 | 54.3 | 70.79 | | GPT-3.5 Turbo 1106 | 74.71 | 74 | 72.79 | 72.79 | 72.91 | 64.73 | 57.71 | 72.66 | 53.79 | 66 | | Meditron-70B | 66.79 | 69 | 53.33 | 71.69 | 76.38 | 63 | 57.1 | 76.6 | 46.85 | 64.52 | | gemma-7b | 69.81 | 70 | 59.26 | 66.18 | 79.86 | 60.12 | 47.21 | 76.2 | 48.96 | 64.18 | | Mistral-7B-v0.1 | 68.68 | 71 | 55.56 | 68.38 | 68.06 | 59.54 | 50.82 | 75.4 | 48.2 | 62.85 | | Apollo-7B | 62.26 | 72 | 61.48 | 69.12 | 70.83 | 55.49 | 55.22 | 39.8 | 53.77 | 60 | | MedAlpaca-7b | 57.36 | 69 | 57.04 | 67.28 | 65.28 | 54.34 | 41.71 | 72.8 | 37.51 | 58.03 | | BioMistral-7B | 59.9 | 64 | 56.5 | 60.4 | 59 | 54.7 | 50.6 | 77.5 | 48.1 | 57.3 | | AlpaCare-llama2-7b | 49.81 | 49 | 45.92 | 33.82 | 50 | 43.35 | 29.77 | 72.2 | 34.42 | 45.36 | | ClinicalGPT | 30.56 | 27 | 30.37 | 19.48 | 25 | 24.27 | 26.08 | 63.8 | 28.18 | 30.52 | <div align="center"> <img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png"> </div> ## Detailed Medical Subjectwise accuracy ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/UXF-V0col0Z0sS6BGPBkE.png) # Use Cases & Examples 🚨 **Below results are from the quantized version of OpenBioLLM-70B # Summarize Clinical Notes OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/xdwdBgOxNi_TfML0hKlI8.png) # Answer Medical Questions OpenBioLLM-70B can provide answers to a wide range of medical questions. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/zO95GlwOQEZqCKQF69mE6.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/OKBczKw7gWeW5xsuDpc27.png) <details> <summary>Click to see details</summary> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/eJGHT5khppYvJb8fQ-YW4.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Cnbwrqa_-ORHRuNRC2P6Y.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/J9DhdcvukAc9mnnW9fj2C.png) </details> # Clinical Entity Recognition OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_69BW4k9LVABFwtxixL45.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/DKy5wYCoPhoPPUc1-x8_J.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/7WD9zCCBZT4-4XlfnIQjl.png) # Biomarkers Extraction ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/ZttoM4AiteT7gFYVhjIpN.png) # Classification OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Bf5MW1d75qT-1F_TR_hC0.png) # De-Identification OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/hKX4kzm--Tw5bj6K78msy.png) **Advisory Notice!**  While OpenBioLLM-70B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments. Therefore, we strongly advise against using OpenBioLLM-70B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B is intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor. Appropriately adapting and validating OpenBioLLM-70B for specific medical use cases would require significant additional work, potentially including: - Thorough testing and evaluation in relevant clinical scenarios - Alignment with evidence-based guidelines and best practices - Mitigation of potential biases and failure modes - Integration with human oversight and interpretation - Compliance with regulatory and ethical standards Always consult a qualified healthcare provider for personal medical needs. # Citation If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows: ``` @misc{OpenBioLLMs, author = {Ankit Pal, Malaikannan Sankarasubbu}, title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}} } ``` The accompanying paper is currently in progress and will be released soon. <div align="center"> <h2> 💌 Contact </h2> </div> We look forward to hearing you and collaborating on this exciting project! **Contributors:** - [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com] - Saama AI Labs - Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. # References We thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models! Result sources - [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375) - [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138) - [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617) - [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023)
[ "QUESTION_ANSWERING" ]
[ "MEDQA", "PUBMEDQA" ]
BioNLP
comet24082002/ft_bge_newLaw_OnlineContrastiveLoss_SimSCE_V2_5epochs
comet24082002
sentence-similarity
[ "sentence-transformers", "safetensors", "xlm-roberta", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:21048", "loss:OnlineContrastiveLoss", "arxiv:1908.10084", "base_model:comet24082002/finetune_bge_simsce_V1", "base_model:finetune:comet24082002/finetune_bge_simsce_V1", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,718
1,718
8
0
--- base_model: comet24082002/finetune_bge_simsce_V1 datasets: [] language: [] library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:21048 - loss:OnlineContrastiveLoss widget: - source_sentence: Cá nhân thi hộ cho người khác có bị xử phạt vi phạm hành chính hay không? sentences: - '"1. Chủ tịch Ủy ban nhân dân các cấp có thẩm quyền xử phạt đối với các hành vi vi phạm quy định tại Nghị định này trong phạm vi quản lý của địa phương mình. 2. Cảnh sát giao thông trong phạm vi chức năng, nhiệm vụ được giao có thẩm quyền xử phạt đối với các hành vi vi phạm quy định tại các điểm, khoản, điều của Nghị định này như sau: a) Điều 5, Điều 6, Điều 7, Điều 8, Điều 9, Điều 10, Điều 11; b) Khoản 1, khoản 2, khoản 3, khoản 4, khoản 5 (trừ điểm a khoản 5), khoản 6 (trừ điểm đ khoản 6), khoản 7, điểm a khoản 8 Điều 12; c) Khoản 1; điểm b, điểm c, điểm d khoản 2; điểm b khoản 3; điểm a khoản 4; điểm b, điểm c khoản 5 Điều 13; d) Khoản 1, khoản 2, điểm a khoản 3, khoản 4, khoản 4a, khoản 5, khoản 6, khoản 7, khoản 8, điểm a khoản 9 Điều 15; đ) Điều 16, Điều 17, Điều 18, Điều 19, Điều 20, Điều 21, Điều 22, Điều 23, Điều 24, Điều 25, Điều 26, Điều 27; e) Khoản 1; khoản 2; điểm a khoản 3; điểm a, điểm b, điểm c, điểm d, điểm đ, điểm e, điểm p, điểm q khoản 4; khoản 5; điểm d, điểm đ, điểm e, điểm i, điểm m, điểm n, điểm o, điểm p, điểm q khoản 6; điểm a, điểm b, điểm c, điểm h, điểm i khoản 7 Điều 28; g) Điều 29, Điều 30, Điều 31, Điều 32, Điều 33, Điều 34, Điều 35 (trừ điểm b, điểm c khoản 2; điểm a khoản 3; điểm b khoản 4; khoản 5 Điều 35), Điều 36; h) Điểm a, điểm b, điểm c, điểm d, điểm e khoản 1; điểm a khoản 2; điểm a, điểm b, điểm c khoản 3; điểm d khoản 4; khoản 8 Điều 37; i) Điểm b, điểm c khoản 1; khoản 2; khoản 3; khoản 4 Điều 40; k) Điều 41, Điều 42, Điều 43, Điều 44, Điều 45, Điều 46, Điều 47; l) Khoản 1, khoản 2, khoản 3, khoản 4, khoản 5 Điều 48; m) Điều 49, Điều 50; n) Khoản 1; khoản 2; khoản 3; điểm a, điểm b, điểm c khoản 4 Điều 51; o) Điều 52; khoản 1, khoản 2, khoản 3 Điều 53; p) Điểm a, điểm c khoản 1; điểm a, điểm b, điểm c khoản 2; khoản 3; khoản 4 Điều 54; q) Điều 56, Điều 57, Điều 58, Điều 59, Điều 60, Điều 61, Điều 62, Điều 63, Điều 64, Điều 65, Điều 66; r) Khoản 2 Điều 67; s) Điểm a, điểm b khoản 1; điểm b, điểm c, điểm đ, điểm e khoản 3 Điều 71; t) Điều 72, Điều 73."' - 'Điều 6. Kinh phí hòa giải, đối thoại tại Tòa án 1. Nhà nước bảo đảm kinh phí hòa giải, đối thoại tại Tòa án từ ngân sách nhà nước và các nguồn kinh phí hợp pháp khác theo quy định của pháp luật. 2. Kinh phí hòa giải, đối thoại tại Tòa án do Chính phủ trình Quốc hội quyết định sau khi thống nhất với Tòa án nhân dân tối cao. 3. Bộ trưởng Bộ Tài chính quy định việc lập dự toán, quản lý, sử dụng và quyết toán kinh phí hòa giải, đối thoại tại Tòa án. Điều 9. Chi phí hòa giải, đối thoại tại Tòa án 1. Chi phí hòa giải, đối thoại tại Tòa án do ngân sách nhà nước bảo đảm, trừ các trường hợp quy định tại khoản 2 Điều này. 2. Các bên tham gia hòa giải, đối thoại tại Tòa án phải chịu chi phí trong các trường hợp sau đây: a) Chi phí hòa giải đối với tranh chấp về kinh doanh, thương mại có giá ngạch; b) Chi phí khi các bên thống nhất lựa chọn địa điểm hòa giải, đối thoại ngoài trụ sở Tòa án; chi phí khi Hòa giải viên xem xét hiện trạng tài sản liên quan đến vụ việc dân sự, khiếu kiện hành chính mà tài sản đó nằm ngoài phạm vi địa giới hành chính của tỉnh nơi Tòa án có thẩm quyền giải quyết có trụ sở; c) Chi phí phiên dịch tiếng nước ngoài. 3. Chính phủ quy định chi tiết mức thu, trình tự, thủ tục thu, nộp và việc quản lý, sử dụng chi phí quy định tại khoản 2 Điều này.' - '"Điều 52. Nguyên tắc thực hiện chính sách hỗ trợ về nhà ở xã hội 1. Việc thực hiện chính sách hỗ trợ về nhà ở xã hội phải bảo đảm các nguyên tắc sau đây: a) Có sự kết hợp giữa Nhà nước, cộng đồng dân cư, dòng họ và đối tượng được hỗ trợ trong việc thực hiện chính sách; b) Bảo đảm công khai, minh bạch, có sự kiểm tra, giám sát chặt chẽ của cơ quan nhà nước có thẩm quyền và cộng đồng dân cư; c) Bảo đảm đúng đối tượng, đủ điều kiện theo quy định của Luật này; d) Trường hợp một đối tượng được hưởng nhiều chính sách hỗ trợ khác nhau thì được hưởng một chính sách hỗ trợ mức cao nhất; trường hợp các đối tượng có cùng tiêu chuẩn và điều kiện thì đối tượng là người khuyết tật, nữ giới được ưu tiên hỗ trợ trước; đ) Trường hợp hộ gia đình có nhiều đối tượng được hưởng nhiều chính sách hỗ trợ thì chỉ áp dụng một chính sách hỗ trợ cho cả hộ gia đình. 2. Ủy ban nhân dân cấp tỉnh chịu trách nhiệm tổ chức thực hiện và kiểm tra, thanh tra việc thực hiện chính sách hỗ trợ về nhà ở xã hội trên địa bàn."' - source_sentence: Người lao động thuộc đối nhiều đối tượng tham gia bảo hiểm y tế thì phải đóng bảo hiểm y tế cho bên nào? sentences: - 'Nội dung hồ sơ đồ án quy hoạch xây dựng vùng 1. Thành phần bản vẽ: a) Sơ đồ vị trí và liên hệ vùng: Vị trí, ranh giới của vùng, mối quan hệ về tự nhiên, kinh tế - xã hội và môi trường có ảnh hưởng tới vùng quy hoạch. Thể hiện trên nền bản đồ địa hình theo tỷ lệ thích hợp. b) Các sơ đồ hiện trạng vùng: Điều kiện tự nhiên; hiện trạng phân bố dân cư và sử dụng đất; hiện trạng hệ thống hạ tầng xã hội, hạ tầng kỹ thuật và môi trường cấp vùng. Thể hiện trên nền bản đồ địa hình tỷ lệ 1/100.000 hoặc 1/250.000 đối với vùng liên tỉnh và tỷ lệ 1/25.000 hoặc 1/50.000 đối với các vùng khác. c) Các sơ đồ về phân vùng và định hướng phát triển không gian vùng: Xác định các vùng phát triển, bảo tồn, hạn chế phát triển, vùng cấm phát triển; tổ chức hệ thống các đô thị, các khu vực dân cư nông thôn; phân bố, xác định quy mô các không gian phát triển công nghiệp, nông nghiệp, lâm nghiệp; các vùng nghỉ ngơi du lịch, khai thác, bảo vệ thiên nhiên, tôn tạo, các vùng di tích lịch sử văn hóa và các chức năng khác, phân bố cơ sở kinh tế - kỹ thuật cấp vùng. Thể hiện trên nền bản đồ địa hình tỷ lệ 1/100.000 hoặc 1/250.000 đối với vùng liên tỉnh và tỷ lệ 1/25.000 hoặc 1/50.000 đối với các vùng khác. d) Các sơ đồ định hướng hạ tầng kỹ thuật cấp vùng: Giao thông, cao độ nền, thoát nước mặt, cung cấp năng lượng, viễn thông, cấp nước, quản lý chất thải và nghĩa trang. Thể hiện trên nền bản đồ địa hình tỷ lệ 1/100.000 hoặc 1/250.000 đối với vùng liên tỉnh và tỷ lệ 1/25.000 hoặc 1/50.000 đối với các vùng khác. đ) Các bản vẽ về đánh giá môi trường chiến lược. Thể hiện trên nền bản đồ địa hình theo tỷ lệ thích hợp. 2. Thuyết minh: a) Lý do và sự cần thiết lập quy hoạch xây dựng vùng; các căn cứ lập quy hoạch; quan Điểm và Mục tiêu phát triển của vùng. b) Phân tích, đánh giá Điều kiện tự nhiên, thực trạng kinh tế - xã hội vùng; hiện trạng phân bố đô thị và nông thôn, sự biến động về dân số trong vùng lập quy hoạch; hiện trạng sử dụng và quản lý đất đai; hiện trạng hạ tầng xã hội, hạ tầng kỹ thuật vùng; hiện trạng tài nguyên và môi trường vùng; hiện trạng các chương trình, dự án đầu tư phát triển đối với vùng lập quy hoạch; đánh giá công tác quản lý thực hiện các quy hoạch, quản lý tài nguyên và bảo vệ môi trường. Đối với các vùng liên tỉnh, liên huyện, vùng dọc tuyến đường cao tốc, hành lang kinh tế liên tỉnh: Đánh giá việc phối hợp, liên kết phát triển giữa các tỉnh, các huyện trong vùng; nêu rõ hiệu quả, bất cập trong triển khai việc liên kết phát triển đối với công tác quản lý thực hiện quy hoạch, làm cơ sở đề xuất các giải pháp trong đồ án quy hoạch. Các nội dung trên yêu cầu trình bày mạch lạc, ngắn gọn, đủ ý, rõ ràng và phải kèm theo các sơ đồ, bảng biểu minh họa. c) Xác định động lực và tiềm năng phát triển của vùng. d) Dự báo về kinh tế - xã hội, dân số, lao động, tỷ lệ đô thị hóa, sử dụng đất, môi trường; các rủi ro về biến động, thảm họa thiên nhiên, … đ) Định hướng phát triển không gian theo Mục tiêu và tính chất phát triển vùng. Nội dung cụ thể bao gồm: Phân vùng phát triển đô thị, công nghiệp, khu kinh tế, du lịch, hạ tầng xã hội, bảo tồn (cảnh quan thiên nhiên và di sản văn hóa lịch sử), nông thôn, sử dụng đất. e) Định hướng phát triển hệ thống hạ tầng kỹ thuật cấp vùng về giao thông, cao độ nền và thoát nước mặt, cung cấp năng lượng, viễn thông, cấp nước, quản lý chất thải và nghĩa trang. g) Xác định danh Mục các chương trình, dự án ưu tiên đầu tư về cơ sở hạ tầng kỹ thuật, hạ tầng xã hội và bảo vệ môi trường; các dự án cần được nêu rõ quy mô đầu tư xây dựng, dự báo nhu cầu vốn và kiến nghị nguồn vốn thực hiện. h) Đề xuất giải pháp thực hiện quy hoạch sau khi được cấp có thẩm quyền phê duyệt. Đối với vùng có phạm vi bao gồm nhiều đơn vị hành chính cấp tỉnh, cần kiến nghị mô hình, cơ chế quản lý và liên kết phát triển vùng liên tỉnh. i) Đánh giá môi trường chiến lược: Nội dung theo quy định tại Mục g Khoản 1 Điều 8 Nghị định số 44/2015/NĐ-CP ngày 06/5/2015 của Chính phủ quy định chi Tiết một số nội dung về quy hoạch xây dựng (sau đây viết tắt là Nghị định số 44/2015/NĐ-CP). Thuyết minh đồ án phải có bảng biểu thống kê, phụ lục tính toán, hình ảnh minh họa và hệ thống sơ đồ, bản vẽ thu nhỏ khổ A3 với ký hiệu và ghi chú rõ ràng, được sắp xếp kèm theo nội dung cho từng phần của thuyết minh.' - "Tiêu chuẩn chung về đạo đức nghề nghiệp của viên chức chuyên ngành công tác xã\ \ hội \nViên chức chuyên ngành công tác xã hội phải đáp ứng tiêu chuẩn chức danh\ \ nghề nghiệp quy định cụ thể tại Chương II của Thông tư này và tiêu chuẩn chung\ \ về đạo đức nghề nghiệp của viên chức chuyên ngành công tác xã hội như sau: \n\ 1. Có phẩm chất chính trị, đạo đức tốt; chấp hành chủ trương, chính sách của Đảng\ \ và pháp luật của Nhà nước.\n2. Đặt lợi ích của đối tượng là mục tiêu quan trọng\ \ nhất trong hoạt động nghề nghiệp, có ý thức bảo vệ lợi ích lâu dài và liên tục\ \ cho đối tượng; tôn trọng đời tư, quyền tự quyết và quyền bảo mật của đối tượng;\ \ khuyến khích, hỗ trợ đối tượng thực hiện những mục tiêu phù hợp.\n3. Không lợi\ \ dụng mối quan hệ nghề nghiệp để vụ lợi cá nhân ảnh hưởng đến công tác trợ giúp\ \ đối tượng.\n4. Tôn trọng, cởi mở, đoàn kết, đồng cảm và chia sẻ với các đồng\ \ nghiệp trong hoạt động nghề nghiệp. \n5. Thực hiện đúng và đầy đủ các nghĩa\ \ vụ của người viên chức trong hoạt động nghề nghiệp.\n6. Thường xuyên học tập\ \ nâng cao trình độ nghiệp vụ công tác xã hội." - '"Điều 22. Mức hưởng bảo hiểm y tế .. 2. Trường hợp một người thuộc nhiều đối tượng tham gia bảo hiểm y tế thì được hưởng quyền lợi bảo hiểm y tế theo đối tượng có quyền lợi cao nhất. ..."' - source_sentence: Phó Trưởng ban thường trực của Ban Chỉ đạo cải cách hành chính nhà nước của Bộ Giao thông vận tải có trách nhiệm như thế nào? sentences: - 'Thay đổi nội dung ghi trong giấy phép hoạt động báo chí 1. Chậm nhất là 05 ngày kể từ ngày thay đổi địa Điểm trụ sở chính, điện thoại, fax, thư điện tử, thời gian phát hành, đơn vị cung cấp dịch vụ kết nối Internet, cơ quan báo chí phải thông báo với cơ quan quản lý nhà nước về báo chí. 2. Khi thay đổi tên gọi cơ quan chủ quản báo chí, tên gọi cơ quan báo chí; tôn chỉ, Mục đích; tên gọi ấn phẩm báo chí, phụ trương, chuyên trang của báo điện tử, kênh phát thanh, kênh truyền hình; địa Điểm phát sóng, địa Điểm trụ sở gắn với trung tâm tổng khống chế; phương thức truyền dẫn, phát sóng; thời lượng kênh phát thanh, kênh truyền hình; tên miền của chuyên trang và báo điện tử, cơ quan chủ quản phải có hồ sơ đề nghị Bộ Thông tin và Truyền thông sửa đổi, bổ sung giấy phép. Hồ sơ, thủ tục đề nghị sửa đổi, bổ sung giấy phép hoạt động báo chí do Bộ trưởng Bộ Thông tin và Truyền thông quy định. 3. Khi thay đổi hình thức trình bày, vị trí của tên ấn phẩm báo chí, phụ trương; biểu tượng kênh phát thanh, kênh truyền hình; kỳ hạn xuất bản, số trang, khuôn khổ và những nội dung thay đổi không quy định tại Khoản 1 và Khoản 2 Điều này, cơ quan chủ quản báo chí có văn bản đề nghị Bộ Thông tin và Truyền thông. Việc thay đổi chỉ được thực hiện sau khi có văn bản chấp thuận của Bộ Thông tin và Truyền thông. được sửa đổi bởi Khoản 4 Điều 20 Luật sửa đổi, bổ sung một số Điều của 37 Luật có liên quan đến quy hoạch 2018' - 'Đăng ký chuyển giao công nghệ 1. Hợp đồng chuyển giao công nghệ và phần chuyển giao công nghệ quy định tại khoản 2 Điều 5 của Luật này thuộc một trong những trường hợp sau đây phải đăng ký với cơ quan quản lý nhà nước về khoa học và công nghệ, trừ công nghệ hạn chế chuyển giao đã được cấp Giấy phép chuyển giao công nghệ: a) Chuyển giao công nghệ từ nước ngoài vào Việt Nam; b) Chuyển giao công nghệ từ Việt Nam ra nước ngoài; c) Chuyển giao công nghệ trong nước có sử dụng vốn nhà nước hoặc ngân sách nhà nước, trừ trường hợp đã được cấp Giấy chứng nhận đăng ký kết quả thực hiện nhiệm vụ khoa học và công nghệ. ...' - 'Phó Trưởng ban thường trực 1. Giúp Trưởng ban trực tiếp chỉ đạo, điều hành các hoạt động chung của Ban Chỉ đạo; triển khai thực hiện chương trình, kế hoạch CCHC đã được phê duyệt 2. Triệu tập các cuộc họp định kỳ và đột xuất của Ban Chỉ đạo. 3. Thay mặt Trưởng ban xử lý công việc thường xuyên của Ban Chỉ đạo, chủ trì các cuộc họp của Ban Chỉ đạo khi Trưởng ban vắng mặt.' - source_sentence: Hành vi “phe vé” gây mất trật tự thì có bị xử lý hay không? sentences: - 'Cộng gộp 1. Hàng hóa được coi là có xuất xứ tại Nước thành viên xuất khẩu khi được sản xuất từ nguyên liệu có xuất xứ tại Nước thành viên khác với điều kiện công đoạn gia công, chế biến được thực hiện tại Nước thành viên xuất khẩu vượt quá công đoạn gia công, chế biến đơn giản quy định tại Điều 10 Thông tư này. ...' - '"Điều 318. Tội gây rối trật tự công cộng 1. Người nào gây rối trật tự công cộng gây ảnh hưởng xấu đến an ninh, trật tự, an toàn xã hội hoặc đã bị xử phạt vi phạm hành chính về hành vi này hoặc đã bị kết án về tội này, chưa được xóa án tích mà còn vi phạm, thì bị phạt tiền từ 5.000.000 đồng đến 50.000.000 đồng, phạt cải tạo không giam giữ đến 02 năm hoặc phạt tù từ 03 tháng đến 02 năm. 2. Phạm tội thuộc một trong các trường hợp sau đây, thì bị phạt tù từ 02 năm đến 07 năm: a) Có tổ chức; b) Dùng vũ khí, hung khí hoặc có hành vi phá phách; c) Gây cản trở giao thông nghiêm trọng hoặc gây đình trệ hoạt động công cộng; d) Xúi giục người khác gây rối; đ) Hành hung người can thiệp bảo vệ trật tự công cộng; e) Tái phạm nguy hiểm. ..."' - '"Điều 2. Quy định về mức trần học phí đối với cơ sở giáo dục công lập chất lượng cao trên địa bàn Thủ đô năm học 2022-2023 … Trên cơ sở mức trần học phí, Thủ trưởng cơ sở giáo dục công lập chất lượng cao căn cứ vào điều kiện kinh tế xã hội của địa bàn, cùng với cam kết thực hiện chất lượng giáo dục cao theo tiêu chí cơ sở vật chất, đội ngũ giáo viên, chương trình, phương pháp giảng dạy và kết quả kiểm định để quyết định mức thu học phí cụ thể sau khi có sự thống nhất bằng văn bản của Ủy ban nhân dân quận, huyện, thị xã hoặc Sở Giáo dục và Đào tạo theo phân cấp quản lý, Mức thu học phí trong trường hợp học trực tuyến (Online) bằng 75% mức thu học phí theo hình thức học trực tiếp của các cơ sở giáo dục công lập chất lượng cao đã được ban hành và được làm tròn đến đơn vị nghìn đồng.”' - source_sentence: Hồ sơ ứng cử và thời gian nộp hồ sơ ứng cử Hội đồng nhân dân như thế nào? sentences: - 'Xử phạt người điều khiển xe đạp, xe đạp máy (kể cả xe đạp điện), người điều khiển xe thô sơ khác vi phạm quy tắc giao thông đường bộ ... 2. Phạt tiền từ 100.000 đồng đến 200.000 đồng đối với người điều khiển xe thực hiện một trong các hành vi vi phạm sau đây: a) Điều khiển xe đạp, xe đạp máy buông cả hai tay; chuyển hướng đột ngột trước đầu xe cơ giới đang chạy; dùng chân điều khiển xe đạp, xe đạp máy; b) Không chấp hành hiệu lệnh, chỉ dẫn của người điều khiển giao thông hoặc người kiểm soát giao thông; c) Người đang điều khiển xe hoặc chở người ngồi trên xe bám, kéo, đẩy xe khác, vật khác, mang vác vật cồng kềnh; điều khiển xe kéo theo xe khác, vật khác; d) Không nhường đường cho xe xin vượt khi có đủ điều kiện an toàn hoặc gây cản trở đối với xe cơ giới xin vượt, gây cản trở xe ưu tiên; đ) Không chấp hành hiệu lệnh của đèn tín hiệu giao thông. 3. Phạt tiền từ 300.000 đồng đến 400.000 đồng đối với người điều khiển xe thực hiện một trong các hành vi vi phạm sau đây: a) Điều khiển xe lạng lách, đánh võng; đuổi nhau trên đường; b) Đi xe bằng một bánh đối với xe đạp, xe đạp máy; đi xe bằng hai bánh đối với xe xích lô; c) Đi vào khu vực cấm; đường có biển báo hiệu nội dung cấm đi vào đối với loại phương tiện đang điều khiển; đi ngược chiều đường của đường một chiều, đường có biển “Cấm đi ngược chiều”; e) Điều khiển xe trên đường mà trong máu hoặc hơi thở có nồng độ cồn vượt quá 50 miligam đến 80 miligam/100 mililít máu hoặc vượt quá 0,25 miligam đến 0,4 miligam/1 lít khí thở. ...' - '"Điều 8. Hội đồng tuyển dụng viên chức 1. Trường hợp đơn vị sự nghiệp công lập được giao thẩm quyền tuyển dụng, Hội đồng tuyển dụng có 05 hoặc 07 thành viên, bao gồm: a) Chủ tịch Hội đồng là người đứng đầu hoặc cấp phó của người đứng đầu đơn vị sự nghiệp công lập; b) Phó Chủ tịch Hội đồng là người phụ trách công tác tổ chức cán bộ của đơn vị sự nghiệp công lập; c) Ủy viên kiêm Thư ký Hội đồng là viên chức giúp việc về công tác tổ chức cán bộ của đơn vị sự nghiệp công lập; d) Các ủy viên khác là người có chuyên môn, nghiệp vụ liên quan đến việc tổ chức tuyển dụng do người đứng đầu đơn vị sự nghiệp công lập quyết định. Trường hợp không bố trí được Chủ tịch Hội đồng tuyển dụng theo quy định tại điểm a khoản này thì cơ quan có thẩm quyền quản lý đơn vị sự nghiệp công lập xem xét, quyết định. 2. Trường hợp cơ quan có thẩm quyền quản lý đơn vị sự nghiệp công lập thực hiện việc tuyển dụng, Hội đồng tuyển dụng có 05 hoặc 07 thành viên, bao gồm: a) Chủ tịch Hội đồng là người đứng đầu hoặc cấp phó của người đứng đầu cơ quan có thẩm quyền tuyển dụng; b) Phó Chủ tịch Hội đồng là lãnh đạo bộ phận tham mưu về tổ chức cán bộ của cơ quan có thẩm quyền tuyển dụng; c) Ủy viên kiêm Thư ký Hội đồng là người đại diện bộ phận tham mưu về tổ chức cán bộ của cơ quan có thẩm quyền tuyển dụng; d) Các ủy viên khác là người có chuyên môn, nghiệp vụ liên quan đến việc tổ chức tuyển dụng do người đứng đầu cơ quan có thẩm quyền tuyển dụng quyết định. 3. Hội đồng tuyển dụng làm việc theo nguyên tắc tập thể, quyết định theo đa số; trường hợp biểu quyết ngang nhau thì thực hiện theo ý kiến mà Chủ tịch Hội đồng tuyển dụng đã biểu quyết. Hội đồng tuyển dụng có nhiệm vụ, quyền hạn sau đây: a) Thành lập các bộ phận giúp việc: Ban kiểm tra Phiếu đăng ký dự tuyển, Ban đề thi, Ban coi thi, Ban phách, Ban chấm thi, Ban chấm phúc khảo (nếu có); Ban kiểm tra sát hạch khi tổ chức thực hiện phỏng vấn hoặc thực hành tại vòng 2; Trường hợp cần thiết, Chủ tịch Hội đồng tuyển dụng thành lập Tổ Thư ký giúp việc; b) Tổ chức thu phí dự tuyển và sử dụng phí dự tuyển theo quy định; c) Kiểm tra Phiếu đăng ký dự tuyển, tổ chức thi, chấm thi, chấm phúc khảo theo quy chế; d) Báo cáo người đứng đầu cơ quan, đơn vị có thẩm quyền tuyển dụng quyết định công nhận kết quả thi tuyển, xét tuyển; đ) Giải quyết khiếu nại, tố cáo trong quá trình tổ chức thi tuyển, xét tuyển; e) Hội đồng tuyển dụng tự giải thể sau khi hoàn thành nhiệm vụ. 4. Không bố trí những người có quan hệ là cha, mẹ, anh, chị, em ruột của người dự tuyển hoặc của bên vợ (chồng) của người dự tuyển; vợ hoặc chồng, con đẻ hoặc con nuôi của người dự tuyển hoặc những người đang trong thời hạn xử lý kỷ luật hoặc đang thi hành quyết định kỷ luật làm thành viên Hội đồng tuyển dụng, thành viên các bộ phận giúp việc của Hội đồng tuyển dụng."' - 'Trình tự xem xét, thông qua dự thảo nghị quyết của Hội đồng nhân dân cấp xã 1. Dự thảo nghị quyết của Hội đồng nhân dân cấp xã phải được Ban của Hội đồng nhân dân cùng cấp thẩm tra trước khi trình Hội đồng nhân dân cấp xã. Chậm nhất là 03 ngày trước ngày khai mạc kỳ họp Hội đồng nhân dân, Ủy ban nhân dân gửi tờ trình, dự thảo nghị quyết và các tài liệu có liên quan đến các đại biểu Hội đồng nhân dân. 2. Việc xem xét, thông qua dự thảo nghị quyết tại kỳ họp Hội đồng nhân dân được tiến hành theo trình tự sau đây: a) Đại diện Ủy ban nhân dân thuyết trình dự thảo nghị quyết; b) Đại diện Ban của Hội đồng nhân dân được phân công thẩm tra trình bày báo cáo thẩm tra; c) Hội đồng nhân dân thảo luận và biểu quyết thông qua dự thảo nghị quyết. 3. Dự thảo nghị quyết được thông qua khi có quá nửa tổng số đại biểu Hội đồng nhân dân biểu quyết tán thành. 4. Chủ tịch Hội đồng nhân dân ký chứng thực nghị quyết.' --- # SentenceTransformer based on comet24082002/finetune_bge_simsce_V1 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [comet24082002/finetune_bge_simsce_V1](https://huggingface.co/comet24082002/finetune_bge_simsce_V1). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [comet24082002/finetune_bge_simsce_V1](https://huggingface.co/comet24082002/finetune_bge_simsce_V1) <!-- at revision af7a9066abe057bf5109dcd3d877747dcc61227c --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity <!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("comet24082002/ft_bge_newLaw_OnlineContrastiveLoss_SimSCE_V2_5epochs") # Run inference sentences = [ 'Hồ sơ ứng cử và thời gian nộp hồ sơ ứng cử Hội đồng nhân dân như thế nào?', 'Trình tự xem xét, thông qua dự thảo nghị quyết của Hội đồng nhân dân cấp xã\n1. Dự thảo nghị quyết của Hội đồng nhân dân cấp xã phải được Ban của Hội đồng nhân dân cùng cấp thẩm tra trước khi trình Hội đồng nhân dân cấp xã. Chậm nhất là 03 ngày trước ngày khai mạc kỳ họp Hội đồng nhân dân, Ủy ban nhân dân gửi tờ trình, dự thảo nghị quyết và các tài liệu có liên quan đến các đại biểu Hội đồng nhân dân.\n2. Việc xem xét, thông qua dự thảo nghị quyết tại kỳ họp Hội đồng nhân dân được tiến hành theo trình tự sau đây:\na) Đại diện Ủy ban nhân dân thuyết trình dự thảo nghị quyết;\nb) Đại diện Ban của Hội đồng nhân dân được phân công thẩm tra trình bày báo cáo thẩm tra;\nc) Hội đồng nhân dân thảo luận và biểu quyết thông qua dự thảo nghị quyết.\n3. Dự thảo nghị quyết được thông qua khi có quá nửa tổng số đại biểu Hội đồng nhân dân biểu quyết tán thành.\n4. Chủ tịch Hội đồng nhân dân ký chứng thực nghị quyết.', '"Điều 8. Hội đồng tuyển dụng viên chức\n1. Trường hợp đơn vị sự nghiệp công lập được giao thẩm quyền tuyển dụng, Hội đồng tuyển dụng có 05 hoặc 07 thành viên, bao gồm:\na) Chủ tịch Hội đồng là người đứng đầu hoặc cấp phó của người đứng đầu đơn vị sự nghiệp công lập;\nb) Phó Chủ tịch Hội đồng là người phụ trách công tác tổ chức cán bộ của đơn vị sự nghiệp công lập;\nc) Ủy viên kiêm Thư ký Hội đồng là viên chức giúp việc về công tác tổ chức cán bộ của đơn vị sự nghiệp công lập;\nd) Các ủy viên khác là người có chuyên môn, nghiệp vụ liên quan đến việc tổ chức tuyển dụng do người đứng đầu đơn vị sự nghiệp công lập quyết định.\nTrường hợp không bố trí được Chủ tịch Hội đồng tuyển dụng theo quy định tại điểm a khoản này thì cơ quan có thẩm quyền quản lý đơn vị sự nghiệp công lập xem xét, quyết định.\n2. Trường hợp cơ quan có thẩm quyền quản lý đơn vị sự nghiệp công lập thực hiện việc tuyển dụng, Hội đồng tuyển dụng có 05 hoặc 07 thành viên, bao gồm:\na) Chủ tịch Hội đồng là người đứng đầu hoặc cấp phó của người đứng đầu cơ quan có thẩm quyền tuyển dụng;\nb) Phó Chủ tịch Hội đồng là lãnh đạo bộ phận tham mưu về tổ chức cán bộ của cơ quan có thẩm quyền tuyển dụng;\nc) Ủy viên kiêm Thư ký Hội đồng là người đại diện bộ phận tham mưu về tổ chức cán bộ của cơ quan có thẩm quyền tuyển dụng;\nd) Các ủy viên khác là người có chuyên môn, nghiệp vụ liên quan đến việc tổ chức tuyển dụng do người đứng đầu cơ quan có thẩm quyền tuyển dụng quyết định.\n3. Hội đồng tuyển dụng làm việc theo nguyên tắc tập thể, quyết định theo đa số; trường hợp biểu quyết ngang nhau thì thực hiện theo ý kiến mà Chủ tịch Hội đồng tuyển dụng đã biểu quyết. Hội đồng tuyển dụng có nhiệm vụ, quyền hạn sau đây:\na) Thành lập các bộ phận giúp việc: Ban kiểm tra Phiếu đăng ký dự tuyển, Ban đề thi, Ban coi thi, Ban phách, Ban chấm thi, Ban chấm phúc khảo (nếu có); Ban kiểm tra sát hạch khi tổ chức thực hiện phỏng vấn hoặc thực hành tại vòng 2;\nTrường hợp cần thiết, Chủ tịch Hội đồng tuyển dụng thành lập Tổ Thư ký giúp việc;\nb) Tổ chức thu phí dự tuyển và sử dụng phí dự tuyển theo quy định;\nc) Kiểm tra Phiếu đăng ký dự tuyển, tổ chức thi, chấm thi, chấm phúc khảo theo quy chế;\nd) Báo cáo người đứng đầu cơ quan, đơn vị có thẩm quyền tuyển dụng quyết định công nhận kết quả thi tuyển, xét tuyển;\nđ) Giải quyết khiếu nại, tố cáo trong quá trình tổ chức thi tuyển, xét tuyển;\ne) Hội đồng tuyển dụng tự giải thể sau khi hoàn thành nhiệm vụ.\n4. Không bố trí những người có quan hệ là cha, mẹ, anh, chị, em ruột của người dự tuyển hoặc của bên vợ (chồng) của người dự tuyển; vợ hoặc chồng, con đẻ hoặc con nuôi của người dự tuyển hoặc những người đang trong thời hạn xử lý kỷ luật hoặc đang thi hành quyết định kỷ luật làm thành viên Hội đồng tuyển dụng, thành viên các bộ phận giúp việc của Hội đồng tuyển dụng."', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 21,048 training samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 9 tokens</li><li>mean: 24.27 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 292.38 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> | * Samples: | sentence1 | sentence2 | label | |:----------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | <code>Lao động nữ sinh con có cần phải trực tiếp đi thông báo tìm kiếm việc làm không?</code> | <code>"Điều 10. Thông báo về việc tìm kiếm việc làm theo quy định tại Điều 52 Luật Việc làm<br>1. Trong thời gian hưởng trợ cấp thất nghiệp, hằng tháng người lao động phải trực tiếp thông báo về việc tìm kiếm việc làm với trung tâm dịch vụ việc làm nơi đang hưởng trợ cấp thất nghiệp theo Mẫu số 16 ban hành kèm theo Thông tư này, trừ trường hợp quy định tại Khoản 2 và Khoản 3 Điều này.<br>2. Người lao động đang hưởng trợ cấp thất nghiệp không phải thực hiện thông báo hằng tháng về việc tìm kiếm việc làm nếu thời gian thông báo về việc tìm kiếm việc làm nằm trong khoảng thời gian mà người lao động thuộc một trong các trường hợp sau:<br>a) Nam từ đủ 60 tuổi trở lên, nữ từ đủ 55 tuổi trở lên;<br>b) Người lao động được xác định thuộc danh mục bệnh phải điều trị dài ngày có giấy xác nhận của cơ sở y tế có thẩm quyền;<br>c) Nghỉ hưởng chế độ thai sản có xác nhận của cơ sở y tế có thẩm quyền. Riêng đối với trường hợp nam giới có vợ chết sau khi sinh con mà phải trực tiếp nuôi dưỡng con thì giấy tờ xác nhận là giấy khai sinh của con và giấy chứng tử của mẹ;<br>d) Đang tham gia khóa học nghề theo quyết định của Giám đốc Sở Lao động – Thương binh và Xã hội và có xác nhận của cơ sở dạy nghề;<br>đ) Thực hiện hợp đồng lao động theo mùa vụ hoặc theo một công việc nhất định có thời hạn dưới 03 tháng.<br>Trong thời hạn 03 ngày làm việc kể từ ngày người lao động thuộc một trong các trường hợp tại các Điểm b, c, d, đ của Khoản này thì người lao động phải gửi thư bảo đảm hoặc ủy quyền cho người khác nộp giấy đề nghị không thực hiện thông báo hằng tháng về việc tìm kiếm việc làm theo Mẫu số 17 ban hành kèm theo Thông tư này và kèm theo bản chính hoặc bản sao có chứng thực một trong các giấy tờ theo quy định nêu trên đến trung tâm dịch vụ việc làm nơi người lao động đang hưởng trợ cấp thất nghiệp, trường hợp gửi theo đường bưu điện thì tính theo ngày gửi ghi trên dấu bưu điện. Sau khi hết thời hạn của một trong các trường hợp nêu trên, người lao động phải tiếp tục thực hiện thông báo về việc tìm kiếm việc làm theo quy định.<br>Ví dụ 14: Bà Trần Thị T có quyết định về việc hưởng trợ cấp thất nghiệp 06 tháng kể từ ngày 02/3/2015 đến ngày 01/9/2015 (ngày thông báo về việc tìm kiếm việc làm hằng tháng cụ thể như sau: Tháng hưởng trợ cấp thất nghiệp thứ nhất là ngày nhận trả kết quả, tháng hưởng trợ cấp thất nghiệp thứ hai từ ngày 03 đến ngày 07/4, tháng hưởng trợ cấp thất nghiệp thứ ba từ ngày 04 đến ngày 06/5, tháng hưởng trợ cấp thất nghiệp thứ tư từ ngày 03 đến ngày 05/6, tháng hưởng trợ cấp thất nghiệp thứ năm từ ngày 02 đến ngày 06/7, tháng hưởng trợ cấp thất nghiệp thứ sáu từ ngày 03 đến ngày 07/8). Bà T có quyết định về việc hỗ trợ học nghề của Giám đốc Sở Lao động – Thương binh và Xã hội, thời gian học nghề của bà là 03 tháng tính từ ngày 15/4/2015 đến ngày 15/7/2015. Như vậy, chậm nhất vào ngày 20/4/2015 (sau 03 ngày làm việc kể từ ngày bà T được hỗ trợ học nghề), bà T phải gửi giấy xác nhận của cơ sở dạy nghề về việc đang học nghề cho trung tâm dịch vụ việc làm nơi đang hưởng trợ cấp thất nghiệp. Tháng hưởng trợ cấp thất nghiệp thứ ba, thứ tư, thứ năm bà T không phải thông báo về việc tìm kiếm việc làm. Tuy nhiên, đến tháng hưởng trợ cấp thất nghiệp thứ sáu bà T phải tiếp tục thực hiện thông báo về việc tìm kiếm việc làm theo quy định.<br>3. Người lao động đang hưởng trợ cấp thất nghiệp không phải trực tiếp thông báo hằng tháng về việc tìm kiếm việc làm nếu thời gian thông báo về việc tìm kiếm việc làm nằm trong khoảng thời gian mà người lao động thuộc một trong các trường hợp sau:<br>a) Ốm đau nhưng không thuộc trường hợp quy định tại Điểm b Khoản 2 Điều này có xác nhận của cơ sở y tế có thẩm quyền;<br>b) Bị tai nạn có xác nhận của cảnh sát giao thông hoặc cơ sở y tế có thẩm quyền;<br>c) Bị hỏa hoạn, lũ lụt, động đất, sóng thần, địch họa, dịch bệnh có xác nhận của Chủ tịch Ủy ban nhân dân xã, phường, thị trấn;<br>d) Cha, mẹ, vợ/chồng, con của người lao động chết; người lao động hoặc con của người lao động kết hôn có giấy xác nhận của Ủy ban nhân dân xã, phường, thị trấn.<br>Các trường hợp không trực tiếp đến trung tâm dịch vụ việc làm thì chậm nhất trong thời hạn 03 ngày làm việc kể từ ngày cuối cùng của thời hạn thông báo hằng tháng về việc tìm kiếm việc làm theo quy định, người lao động phải gửi thư bảo đảm hoặc ủy quyền cho người khác nộp bản chính hoặc bản sao có chứng thực một trong các giấy tờ theo quy định nêu trên đến trung tâm dịch vụ việc làm nơi đang hưởng trợ cấp thất nghiệp, trường hợp gửi theo đường bưu điện thì tính theo ngày gửi ghi trên dấu bưu điện.<br>4. Ngày người lao động thông báo hằng tháng về việc tìm kiếm việc làm được ghi cụ thể trong phụ lục quyết định hưởng trợ cấp thất nghiệp của người lao động như sau:<br>a) Ngày của tháng thứ nhất hưởng trợ cấp thất nghiệp là ngày nhận quyết định hưởng trợ cấp thất nghiệp theo phiếu hẹn trả kết quả;<br>b) Từ tháng thứ hai trở đi người lao động thực hiện ngày thông báo hằng tháng về việc tìm kiếm việc làm trong thời hạn 03 ngày làm việc kể từ ngày đầu tiên của tháng hưởng trợ cấp thất nghiệp.<br>5. Trường hợp ngày thông báo hằng tháng về việc tìm kiếm việc làm của người lao động nằm trong khoảng thời gian làm thủ tục chuyển nơi hưởng trợ cấp thất nghiệp theo quy định tại Điều 22 Nghị định số 28/2015/NĐ-CP thì người lao động không phải thực hiện việc thông báo về việc tìm kiếm việc làm với trung tâm dịch vụ việc làm.<br>Ví dụ 15: Bà Nguyễn Lan Y có quyết định hưởng trợ cấp thất nghiệp với thời gian 03 tháng. Tháng hưởng trợ cấp thất nghiệp thứ nhất từ ngày 02/7/2015 đến ngày 01/8/2015, tháng hưởng trợ cấp thất nghiệp thứ hai từ ngày 02/8/2015 đến ngày 01/9/2015, tháng hưởng trợ cấp thất nghiệp thứ ba từ ngày 02/9/2015 đến ngày 01/10/2015. Sau khi hưởng trợ cấp thất nghiệp tháng đầu tiên, ngày 28/7/2015 bà Y làm đề nghị chuyển nơi hưởng trợ cấp thất nghiệp trong khi ngày thông báo về việc tìm kiếm việc làm tháng hưởng trợ cấp thất nghiệp thứ hai của bà Y là ngày 03 đến ngày 05/8/2015. Như vậy, bà Y không phải thực hiện việc thông báo tìm kiếm việc làm hằng tháng với trung tâm dịch vụ việc làm nơi chuyển đi cũng như nơi chuyển đến mà không bị tạm dừng hưởng trợ cấp thất nghiệp.<br>6. Người lao động đang hưởng trợ cấp thất nghiệp được coi là đã thông báo hằng tháng về việc tìm kiếm việc làm khi đã ghi đúng và đầy đủ các nội dung trong thông báo hằng tháng về việc tìm kiếm việc làm và chịu trách nhiệm về nội dung thông báo."</code> | <code>1</code> | | <code>Lao động nữ sinh con có cần phải trực tiếp đi thông báo tìm kiếm việc làm không?</code> | <code>Công bố thông tin trước ngày khai trương hoạt động<br>Doanh nghiệp xếp hạng tín nhiệm phải công bố trên phương tiện thông tin đại chúng và trang thông tin điện tử của doanh nghiệp ít nhất mười (10) ngày làm việc trước ngày dự kiến khai trương hoạt động kinh doanh dịch vụ xếp hạng tín nhiệm các thông tin cơ bản sau đây:<br>1. Tên, địa chỉ trụ sở chính của doanh nghiệp xếp hạng tín nhiệm.<br>2. Địa chỉ trang thông tin điện tử của doanh nghiệp xếp hạng tín nhiệm.<br>3. Giấy chứng nhận đủ điều kiện kinh doanh.<br>4. Vốn điều lệ thực góp.<br>5. Danh sách, tỷ lệ góp vốn tương ứng của từng cổ đông hoặc thành viên góp vốn sở hữu trên 5% vốn điều lệ thực góp của doanh nghiệp xếp hạng tín nhiệm.<br>6. Tên của người đại diện theo pháp luật của doanh nghiệp xếp hạng tín nhiệm.<br>7. Ngày dự kiến khai trương hoạt động xếp hạng tín nhiệm của doanh nghiệp.<br>Theo đó, công ty cổ phần xếp hạng tín nhiệm phải công bố trên phương tiện thông tin đại chúng và trang thông tin điện tử của công ty ít nhất 10 ngày làm việc trước ngày dự kiến khai trương hoạt động kinh doanh dịch vụ xếp hạng tín nhiệm với các thông tin cơ bản sau đây:</code> | <code>0</code> | | <code>Kho bảo quản pháo hoa để kinh doanh được quy định như thế nào?</code> | <code>Kỹ thuật an toàn trong kinh doanh pháo hoa<br>2.2.2. Kho bảo quản pháo hoa để kinh doanh<br>2.2.2.1. Kho bảo quản được xây dựng bảo đảm theo quy định tại QCVN 06:2021/BXD.<br>2.2.2.2. Địa điểm đặt kho bảo đảm khoảng cách an toàn từ 50 m trở lên đối với cửa hàng xăng, dầu, gas, cơ sở kinh doanh có sử dụng gia nhiệt bằng nhiên liệu hóa thạch hoặc có ngọn lửa trần.<br>2.2.2.3. Kho được niêm yết nội quy, quy định, quy trình về bảo đảm an ninh, trật tự, an toàn, phòng cháy, chữa cháy, cứu nạn, cứu hộ; trong kho niêm yết quy trình sắp xếp, bảo quản, xuất, nhập pháo hoa.<br>2.2.2.4. Người quản lý kho được huấn luyện về kỹ thuật an toàn theo quy định;<br>2.2.2.5. Trữ lượng kho không được vượt quá 15 tấn sản phẩm.</code> | <code>1</code> | * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 4 - `learning_rate`: 2e-05 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `prediction_loss_only`: True - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | |:------:|:-----:|:-------------:| | 0.0095 | 50 | 0.334 | | 0.0190 | 100 | 0.5007 | | 0.0285 | 150 | 0.3675 | | 0.0380 | 200 | 0.1756 | | 0.0475 | 250 | 0.0934 | | 0.0570 | 300 | 0.1044 | | 0.0665 | 350 | 0.0976 | | 0.0760 | 400 | 0.0911 | | 0.0855 | 450 | 0.0801 | | 0.0950 | 500 | 0.0668 | | 0.1045 | 550 | 0.0648 | | 0.1140 | 600 | 0.0692 | | 0.1235 | 650 | 0.0834 | | 0.1330 | 700 | 0.0772 | | 0.1425 | 750 | 0.0643 | | 0.1520 | 800 | 0.0725 | | 0.1615 | 850 | 0.0486 | | 0.1710 | 900 | 0.0891 | | 0.1805 | 950 | 0.0638 | | 0.1900 | 1000 | 0.0659 | | 0.1995 | 1050 | 0.0598 | | 0.2090 | 1100 | 0.0571 | | 0.2185 | 1150 | 0.0654 | | 0.2281 | 1200 | 0.0592 | | 0.2376 | 1250 | 0.0628 | | 0.2471 | 1300 | 0.0615 | | 0.2566 | 1350 | 0.0626 | | 0.2661 | 1400 | 0.0781 | | 0.2756 | 1450 | 0.0844 | | 0.2851 | 1500 | 0.0512 | | 0.2946 | 1550 | 0.0609 | | 0.3041 | 1600 | 0.0596 | | 0.3136 | 1650 | 0.0824 | | 0.3231 | 1700 | 0.0794 | | 0.3326 | 1750 | 0.0594 | | 0.3421 | 1800 | 0.0646 | | 0.3516 | 1850 | 0.0802 | | 0.3611 | 1900 | 0.0872 | | 0.3706 | 1950 | 0.0653 | | 0.3801 | 2000 | 0.0784 | | 0.3896 | 2050 | 0.0867 | | 0.3991 | 2100 | 0.0839 | | 0.4086 | 2150 | 0.0637 | | 0.4181 | 2200 | 0.0536 | | 0.4276 | 2250 | 0.0739 | | 0.4371 | 2300 | 0.0596 | | 0.4466 | 2350 | 0.0631 | | 0.4561 | 2400 | 0.0682 | | 0.4656 | 2450 | 0.0955 | | 0.4751 | 2500 | 0.0662 | | 0.4846 | 2550 | 0.0562 | | 0.4941 | 2600 | 0.0649 | | 0.5036 | 2650 | 0.0644 | | 0.5131 | 2700 | 0.0693 | | 0.5226 | 2750 | 0.0594 | | 0.5321 | 2800 | 0.0874 | | 0.5416 | 2850 | 0.0605 | | 0.5511 | 2900 | 0.0775 | | 0.5606 | 2950 | 0.0569 | | 0.5701 | 3000 | 0.0452 | | 0.5796 | 3050 | 0.0417 | | 0.5891 | 3100 | 0.0724 | | 0.5986 | 3150 | 0.061 | | 0.6081 | 3200 | 0.0766 | | 0.6176 | 3250 | 0.0688 | | 0.6271 | 3300 | 0.0777 | | 0.6366 | 3350 | 0.0665 | | 0.6461 | 3400 | 0.0725 | | 0.6556 | 3450 | 0.0816 | | 0.6651 | 3500 | 0.0794 | | 0.6746 | 3550 | 0.0501 | | 0.6842 | 3600 | 0.0663 | | 0.6937 | 3650 | 0.0856 | | 0.7032 | 3700 | 0.093 | | 0.7127 | 3750 | 0.1104 | | 0.7222 | 3800 | 0.1 | | 0.7317 | 3850 | 0.1119 | | 0.7412 | 3900 | 0.0863 | | 0.7507 | 3950 | 0.0871 | | 0.7602 | 4000 | 0.0829 | | 0.7697 | 4050 | 0.093 | | 0.7792 | 4100 | 0.0836 | | 0.7887 | 4150 | 0.0884 | | 0.7982 | 4200 | 0.0819 | | 0.8077 | 4250 | 0.0686 | | 0.8172 | 4300 | 0.0864 | | 0.8267 | 4350 | 0.0659 | | 0.8362 | 4400 | 0.0776 | | 0.8457 | 4450 | 0.0747 | | 0.8552 | 4500 | 0.0645 | | 0.8647 | 4550 | 0.0971 | | 0.8742 | 4600 | 0.0874 | | 0.8837 | 4650 | 0.076 | | 0.8932 | 4700 | 0.0901 | | 0.9027 | 4750 | 0.0832 | | 0.9122 | 4800 | 0.0907 | | 0.9217 | 4850 | 0.094 | | 0.9312 | 4900 | 0.0759 | | 0.9407 | 4950 | 0.0845 | | 0.9502 | 5000 | 0.1135 | | 0.9597 | 5050 | 0.0677 | | 0.9692 | 5100 | 0.0497 | | 0.9787 | 5150 | 0.0508 | | 0.9882 | 5200 | 0.0715 | | 0.9977 | 5250 | 0.0654 | | 1.0072 | 5300 | 0.0752 | | 1.0167 | 5350 | 0.0701 | | 1.0262 | 5400 | 0.0485 | | 1.0357 | 5450 | 0.0752 | | 1.0452 | 5500 | 0.0558 | | 1.0547 | 5550 | 0.0606 | | 1.0642 | 5600 | 0.0741 | | 1.0737 | 5650 | 0.0747 | | 1.0832 | 5700 | 0.0438 | | 1.0927 | 5750 | 0.1096 | | 1.1022 | 5800 | 0.0529 | | 1.1117 | 5850 | 0.0467 | | 1.1212 | 5900 | 0.0735 | | 1.1307 | 5950 | 0.0717 | | 1.1403 | 6000 | 0.069 | | 1.1498 | 6050 | 0.066 | | 1.1593 | 6100 | 0.0642 | | 1.1688 | 6150 | 0.0848 | | 1.1783 | 6200 | 0.0409 | | 1.1878 | 6250 | 0.0557 | | 1.1973 | 6300 | 0.0912 | | 1.2068 | 6350 | 0.0506 | | 1.2163 | 6400 | 0.0559 | | 1.2258 | 6450 | 0.0763 | | 1.2353 | 6500 | 0.0728 | | 1.2448 | 6550 | 0.0548 | | 1.2543 | 6600 | 0.0546 | | 1.2638 | 6650 | 0.0521 | | 1.2733 | 6700 | 0.0549 | | 1.2828 | 6750 | 0.062 | | 1.2923 | 6800 | 0.0578 | | 1.3018 | 6850 | 0.0701 | | 1.3113 | 6900 | 0.0662 | | 1.3208 | 6950 | 0.0601 | | 1.3303 | 7000 | 0.0415 | | 1.3398 | 7050 | 0.0783 | | 1.3493 | 7100 | 0.0613 | | 1.3588 | 7150 | 0.045 | | 1.3683 | 7200 | 0.0468 | | 1.3778 | 7250 | 0.0491 | | 1.3873 | 7300 | 0.0658 | | 1.3968 | 7350 | 0.0663 | | 1.4063 | 7400 | 0.0553 | | 1.4158 | 7450 | 0.0536 | | 1.4253 | 7500 | 0.0413 | | 1.4348 | 7550 | 0.057 | | 1.4443 | 7600 | 0.0647 | | 1.4538 | 7650 | 0.0422 | | 1.4633 | 7700 | 0.0566 | | 1.4728 | 7750 | 0.0674 | | 1.4823 | 7800 | 0.0615 | | 1.4918 | 7850 | 0.0626 | | 1.5013 | 7900 | 0.0673 | | 1.5108 | 7950 | 0.0723 | | 1.5203 | 8000 | 0.0638 | | 1.5298 | 8050 | 0.056 | | 1.5393 | 8100 | 0.0548 | | 1.5488 | 8150 | 0.0615 | | 1.5583 | 8200 | 0.0537 | | 1.5678 | 8250 | 0.0485 | | 1.5773 | 8300 | 0.0434 | | 1.5868 | 8350 | 0.0518 | | 1.5964 | 8400 | 0.0559 | | 1.6059 | 8450 | 0.0533 | | 1.6154 | 8500 | 0.0531 | | 1.6249 | 8550 | 0.0489 | | 1.6344 | 8600 | 0.0567 | | 1.6439 | 8650 | 0.0428 | | 1.6534 | 8700 | 0.0553 | | 1.6629 | 8750 | 0.0595 | | 1.6724 | 8800 | 0.0668 | | 1.6819 | 8850 | 0.0496 | | 1.6914 | 8900 | 0.0408 | | 1.7009 | 8950 | 0.0638 | | 1.7104 | 9000 | 0.0613 | | 1.7199 | 9050 | 0.059 | | 1.7294 | 9100 | 0.0688 | | 1.7389 | 9150 | 0.0604 | | 1.7484 | 9200 | 0.0529 | | 1.7579 | 9250 | 0.0636 | | 1.7674 | 9300 | 0.059 | | 1.7769 | 9350 | 0.0626 | | 1.7864 | 9400 | 0.0432 | | 1.7959 | 9450 | 0.0506 | | 1.8054 | 9500 | 0.0594 | | 1.8149 | 9550 | 0.0438 | | 1.8244 | 9600 | 0.0623 | | 1.8339 | 9650 | 0.053 | | 1.8434 | 9700 | 0.055 | | 1.8529 | 9750 | 0.0574 | | 1.8624 | 9800 | 0.0387 | | 1.8719 | 9850 | 0.0537 | | 1.8814 | 9900 | 0.0363 | | 1.8909 | 9950 | 0.0757 | | 1.9004 | 10000 | 0.0719 | | 1.9099 | 10050 | 0.0484 | | 1.9194 | 10100 | 0.0342 | | 1.9289 | 10150 | 0.0443 | | 1.9384 | 10200 | 0.0291 | | 1.9479 | 10250 | 0.0553 | | 1.9574 | 10300 | 0.0504 | | 1.9669 | 10350 | 0.0472 | | 1.9764 | 10400 | 0.0449 | | 1.9859 | 10450 | 0.0412 | | 1.9954 | 10500 | 0.0398 | | 2.0049 | 10550 | 0.0621 | | 2.0144 | 10600 | 0.0352 | | 2.0239 | 10650 | 0.0304 | | 2.0334 | 10700 | 0.0373 | | 2.0429 | 10750 | 0.0545 | | 2.0525 | 10800 | 0.0244 | | 2.0620 | 10850 | 0.0336 | | 2.0715 | 10900 | 0.0283 | | 2.0810 | 10950 | 0.0469 | | 2.0905 | 11000 | 0.0401 | | 2.1000 | 11050 | 0.0365 | | 2.1095 | 11100 | 0.0377 | | 2.1190 | 11150 | 0.0363 | | 2.1285 | 11200 | 0.0321 | | 2.1380 | 11250 | 0.0397 | | 2.1475 | 11300 | 0.0341 | | 2.1570 | 11350 | 0.0316 | | 2.1665 | 11400 | 0.0464 | | 2.1760 | 11450 | 0.0452 | | 2.1855 | 11500 | 0.0292 | | 2.1950 | 11550 | 0.0395 | | 2.2045 | 11600 | 0.0395 | | 2.2140 | 11650 | 0.0389 | | 2.2235 | 11700 | 0.0261 | | 2.2330 | 11750 | 0.0317 | | 2.2425 | 11800 | 0.0503 | | 2.2520 | 11850 | 0.0473 | | 2.2615 | 11900 | 0.0404 | | 2.2710 | 11950 | 0.0344 | | 2.2805 | 12000 | 0.0458 | | 2.2900 | 12050 | 0.0334 | | 2.2995 | 12100 | 0.0349 | | 2.3090 | 12150 | 0.0581 | | 2.3185 | 12200 | 0.0189 | | 2.3280 | 12250 | 0.0446 | | 2.3375 | 12300 | 0.041 | | 2.3470 | 12350 | 0.0564 | | 2.3565 | 12400 | 0.044 | | 2.3660 | 12450 | 0.0324 | | 2.3755 | 12500 | 0.0428 | | 2.3850 | 12550 | 0.0481 | | 2.3945 | 12600 | 0.044 | | 2.4040 | 12650 | 0.0364 | | 2.4135 | 12700 | 0.0433 | | 2.4230 | 12750 | 0.0376 | | 2.4325 | 12800 | 0.0233 | | 2.4420 | 12850 | 0.0259 | | 2.4515 | 12900 | 0.0345 | | 2.4610 | 12950 | 0.0263 | | 2.4705 | 13000 | 0.0369 | | 2.4800 | 13050 | 0.0423 | | 2.4895 | 13100 | 0.0433 | | 2.4990 | 13150 | 0.0421 | | 2.5086 | 13200 | 0.052 | | 2.5181 | 13250 | 0.0312 | | 2.5276 | 13300 | 0.0324 | | 2.5371 | 13350 | 0.0423 | | 2.5466 | 13400 | 0.0498 | | 2.5561 | 13450 | 0.0327 | | 2.5656 | 13500 | 0.0248 | | 2.5751 | 13550 | 0.0355 | | 2.5846 | 13600 | 0.0424 | | 2.5941 | 13650 | 0.0523 | | 2.6036 | 13700 | 0.0528 | | 2.6131 | 13750 | 0.0361 | | 2.6226 | 13800 | 0.0378 | | 2.6321 | 13850 | 0.0357 | | 2.6416 | 13900 | 0.0477 | | 2.6511 | 13950 | 0.0309 | | 2.6606 | 14000 | 0.0155 | | 2.6701 | 14050 | 0.051 | | 2.6796 | 14100 | 0.04 | | 2.6891 | 14150 | 0.0583 | | 2.6986 | 14200 | 0.0322 | | 2.7081 | 14250 | 0.0351 | | 2.7176 | 14300 | 0.0582 | | 2.7271 | 14350 | 0.0468 | | 2.7366 | 14400 | 0.0422 | | 2.7461 | 14450 | 0.0402 | | 2.7556 | 14500 | 0.0383 | | 2.7651 | 14550 | 0.0557 | | 2.7746 | 14600 | 0.0378 | | 2.7841 | 14650 | 0.0475 | | 2.7936 | 14700 | 0.0418 | | 2.8031 | 14750 | 0.0301 | | 2.8126 | 14800 | 0.0288 | | 2.8221 | 14850 | 0.0291 | | 2.8316 | 14900 | 0.039 | | 2.8411 | 14950 | 0.0394 | | 2.8506 | 15000 | 0.05 | | 2.8601 | 15050 | 0.0216 | | 2.8696 | 15100 | 0.0513 | | 2.8791 | 15150 | 0.0411 | | 2.8886 | 15200 | 0.0434 | | 2.8981 | 15250 | 0.0229 | | 2.9076 | 15300 | 0.0336 | | 2.9171 | 15350 | 0.0485 | | 2.9266 | 15400 | 0.0419 | | 2.9361 | 15450 | 0.0427 | | 2.9456 | 15500 | 0.0387 | | 2.9552 | 15550 | 0.0414 | | 2.9647 | 15600 | 0.0474 | | 2.9742 | 15650 | 0.044 | | 2.9837 | 15700 | 0.0415 | | 2.9932 | 15750 | 0.0342 | | 3.0027 | 15800 | 0.0434 | | 3.0122 | 15850 | 0.0205 | | 3.0217 | 15900 | 0.0255 | | 3.0312 | 15950 | 0.0222 | | 3.0407 | 16000 | 0.0299 | | 3.0502 | 16050 | 0.0305 | | 3.0597 | 16100 | 0.027 | | 3.0692 | 16150 | 0.0239 | | 3.0787 | 16200 | 0.0367 | | 3.0882 | 16250 | 0.025 | | 3.0977 | 16300 | 0.0304 | | 3.1072 | 16350 | 0.0299 | | 3.1167 | 16400 | 0.0288 | | 3.1262 | 16450 | 0.0285 | | 3.1357 | 16500 | 0.0176 | | 3.1452 | 16550 | 0.0274 | | 3.1547 | 16600 | 0.0229 | | 3.1642 | 16650 | 0.0252 | | 3.1737 | 16700 | 0.0273 | | 3.1832 | 16750 | 0.0252 | | 3.1927 | 16800 | 0.0302 | | 3.2022 | 16850 | 0.0283 | | 3.2117 | 16900 | 0.0319 | | 3.2212 | 16950 | 0.0232 | | 3.2307 | 17000 | 0.0306 | | 3.2402 | 17050 | 0.0227 | | 3.2497 | 17100 | 0.0243 | | 3.2592 | 17150 | 0.0277 | | 3.2687 | 17200 | 0.032 | | 3.2782 | 17250 | 0.0279 | | 3.2877 | 17300 | 0.0351 | | 3.2972 | 17350 | 0.0222 | | 3.3067 | 17400 | 0.0293 | | 3.3162 | 17450 | 0.0259 | | 3.3257 | 17500 | 0.027 | | 3.3352 | 17550 | 0.0241 | | 3.3447 | 17600 | 0.0431 | | 3.3542 | 17650 | 0.0177 | | 3.3637 | 17700 | 0.0146 | | 3.3732 | 17750 | 0.0333 | | 3.3827 | 17800 | 0.0289 | | 3.3922 | 17850 | 0.0265 | | 3.4017 | 17900 | 0.0201 | | 3.4113 | 17950 | 0.0244 | | 3.4208 | 18000 | 0.0289 | | 3.4303 | 18050 | 0.0364 | | 3.4398 | 18100 | 0.0276 | | 3.4493 | 18150 | 0.0152 | | 3.4588 | 18200 | 0.0339 | | 3.4683 | 18250 | 0.0232 | | 3.4778 | 18300 | 0.0232 | | 3.4873 | 18350 | 0.0211 | | 3.4968 | 18400 | 0.0265 | | 3.5063 | 18450 | 0.0362 | | 3.5158 | 18500 | 0.0428 | | 3.5253 | 18550 | 0.0306 | | 3.5348 | 18600 | 0.0283 | | 3.5443 | 18650 | 0.0247 | | 3.5538 | 18700 | 0.0347 | | 3.5633 | 18750 | 0.025 | | 3.5728 | 18800 | 0.0206 | | 3.5823 | 18850 | 0.0227 | | 3.5918 | 18900 | 0.0254 | | 3.6013 | 18950 | 0.013 | | 3.6108 | 19000 | 0.0168 | | 3.6203 | 19050 | 0.0222 | | 3.6298 | 19100 | 0.0389 | | 3.6393 | 19150 | 0.032 | | 3.6488 | 19200 | 0.0485 | | 3.6583 | 19250 | 0.0295 | | 3.6678 | 19300 | 0.0302 | | 3.6773 | 19350 | 0.0215 | | 3.6868 | 19400 | 0.0289 | | 3.6963 | 19450 | 0.0324 | | 3.7058 | 19500 | 0.0351 | | 3.7153 | 19550 | 0.0262 | | 3.7248 | 19600 | 0.028 | | 3.7343 | 19650 | 0.0361 | | 3.7438 | 19700 | 0.0268 | | 3.7533 | 19750 | 0.0249 | | 3.7628 | 19800 | 0.0288 | | 3.7723 | 19850 | 0.0252 | | 3.7818 | 19900 | 0.0219 | | 3.7913 | 19950 | 0.0308 | | 3.8008 | 20000 | 0.0317 | | 3.8103 | 20050 | 0.0339 | | 3.8198 | 20100 | 0.0215 | | 3.8293 | 20150 | 0.0249 | | 3.8388 | 20200 | 0.021 | | 3.8483 | 20250 | 0.0288 | | 3.8578 | 20300 | 0.022 | | 3.8674 | 20350 | 0.0278 | | 3.8769 | 20400 | 0.0239 | | 3.8864 | 20450 | 0.0306 | | 3.8959 | 20500 | 0.0223 | | 3.9054 | 20550 | 0.0224 | | 3.9149 | 20600 | 0.0268 | | 3.9244 | 20650 | 0.0286 | | 3.9339 | 20700 | 0.0253 | | 3.9434 | 20750 | 0.0177 | | 3.9529 | 20800 | 0.0224 | | 3.9624 | 20850 | 0.0311 | | 3.9719 | 20900 | 0.024 | | 3.9814 | 20950 | 0.017 | | 3.9909 | 21000 | 0.0252 | | 4.0004 | 21050 | 0.023 | | 4.0099 | 21100 | 0.0206 | | 4.0194 | 21150 | 0.0167 | | 4.0289 | 21200 | 0.0226 | | 4.0384 | 21250 | 0.015 | | 4.0479 | 21300 | 0.0181 | | 4.0574 | 21350 | 0.0258 | | 4.0669 | 21400 | 0.0254 | | 4.0764 | 21450 | 0.0211 | | 4.0859 | 21500 | 0.0156 | | 4.0954 | 21550 | 0.0165 | | 4.1049 | 21600 | 0.007 | | 4.1144 | 21650 | 0.017 | | 4.1239 | 21700 | 0.0278 | | 4.1334 | 21750 | 0.0267 | | 4.1429 | 21800 | 0.0233 | | 4.1524 | 21850 | 0.0213 | | 4.1619 | 21900 | 0.0148 | | 4.1714 | 21950 | 0.0109 | | 4.1809 | 22000 | 0.0238 | | 4.1904 | 22050 | 0.02 | | 4.1999 | 22100 | 0.0168 | | 4.2094 | 22150 | 0.0201 | | 4.2189 | 22200 | 0.0179 | | 4.2284 | 22250 | 0.0235 | | 4.2379 | 22300 | 0.0203 | | 4.2474 | 22350 | 0.0115 | | 4.2569 | 22400 | 0.0144 | | 4.2664 | 22450 | 0.0141 | | 4.2759 | 22500 | 0.0208 | | 4.2854 | 22550 | 0.0157 | | 4.2949 | 22600 | 0.0263 | | 4.3044 | 22650 | 0.0133 | | 4.3139 | 22700 | 0.022 | | 4.3235 | 22750 | 0.0199 | | 4.3330 | 22800 | 0.0224 | | 4.3425 | 22850 | 0.0282 | | 4.3520 | 22900 | 0.0169 | | 4.3615 | 22950 | 0.0092 | | 4.3710 | 23000 | 0.0189 | | 4.3805 | 23050 | 0.0146 | | 4.3900 | 23100 | 0.0142 | | 4.3995 | 23150 | 0.0138 | | 4.4090 | 23200 | 0.0227 | | 4.4185 | 23250 | 0.0209 | | 4.4280 | 23300 | 0.014 | | 4.4375 | 23350 | 0.0119 | | 4.4470 | 23400 | 0.0157 | | 4.4565 | 23450 | 0.0221 | | 4.4660 | 23500 | 0.0165 | | 4.4755 | 23550 | 0.0349 | | 4.4850 | 23600 | 0.0101 | | 4.4945 | 23650 | 0.0162 | | 4.5040 | 23700 | 0.0276 | | 4.5135 | 23750 | 0.0146 | | 4.5230 | 23800 | 0.0165 | | 4.5325 | 23850 | 0.0162 | | 4.5420 | 23900 | 0.0167 | | 4.5515 | 23950 | 0.0227 | | 4.5610 | 24000 | 0.0083 | | 4.5705 | 24050 | 0.029 | | 4.5800 | 24100 | 0.0247 | | 4.5895 | 24150 | 0.0214 | | 4.5990 | 24200 | 0.0308 | | 4.6085 | 24250 | 0.0137 | | 4.6180 | 24300 | 0.0214 | | 4.6275 | 24350 | 0.0215 | | 4.6370 | 24400 | 0.0111 | | 4.6465 | 24450 | 0.0187 | | 4.6560 | 24500 | 0.0156 | | 4.6655 | 24550 | 0.012 | | 4.6750 | 24600 | 0.0127 | | 4.6845 | 24650 | 0.0101 | | 4.6940 | 24700 | 0.0306 | | 4.7035 | 24750 | 0.0296 | | 4.7130 | 24800 | 0.0156 | | 4.7225 | 24850 | 0.0177 | | 4.7320 | 24900 | 0.013 | | 4.7415 | 24950 | 0.0177 | | 4.7510 | 25000 | 0.0186 | | 4.7605 | 25050 | 0.0193 | | 4.7700 | 25100 | 0.0098 | | 4.7796 | 25150 | 0.0344 | | 4.7891 | 25200 | 0.0142 | | 4.7986 | 25250 | 0.0224 | | 4.8081 | 25300 | 0.0121 | | 4.8176 | 25350 | 0.0167 | | 4.8271 | 25400 | 0.0187 | | 4.8366 | 25450 | 0.0179 | | 4.8461 | 25500 | 0.0124 | | 4.8556 | 25550 | 0.0281 | | 4.8651 | 25600 | 0.0191 | | 4.8746 | 25650 | 0.0197 | | 4.8841 | 25700 | 0.023 | | 4.8936 | 25750 | 0.021 | | 4.9031 | 25800 | 0.0154 | | 4.9126 | 25850 | 0.019 | | 4.9221 | 25900 | 0.0165 | | 4.9316 | 25950 | 0.0149 | | 4.9411 | 26000 | 0.0324 | | 4.9506 | 26050 | 0.0146 | | 4.9601 | 26100 | 0.0125 | | 4.9696 | 26150 | 0.0214 | | 4.9791 | 26200 | 0.023 | | 4.9886 | 26250 | 0.0183 | | 4.9981 | 26300 | 0.0252 | </details> ### Framework Versions - Python: 3.10.13 - Sentence Transformers: 3.0.1 - Transformers: 4.39.3 - PyTorch: 2.1.2 - Accelerate: 0.29.3 - Datasets: 2.18.0 - Tokenizers: 0.15.2 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION" ]
[ "CHIA" ]
Non_BioNLP
fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-52686172
fine-tuned
feature-extraction
[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "custom_code", "en", "dataset:fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-52686172", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,716
1,716
8
0
--- datasets: - fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-52686172 - allenai/c4 language: - en - en license: apache-2.0 pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb --- This model is a fine-tuned version of [**jinaai/jina-embeddings-v2-base-en**](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case: None ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-52686172', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
[ "TEXT_CLASSIFICATION" ]
[ "SCIFACT" ]
Non_BioNLP
aajonaa/bge-small-en-v1.5
aajonaa
feature-extraction
[ "sentence-transformers", "pytorch", "onnx", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "mteb", "en", "arxiv:2401.03462", "arxiv:2312.15503", "arxiv:2311.13534", "arxiv:2310.07554", "arxiv:2309.07597", "license:mit", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,738
1,738
12
0
--- language: - en license: mit tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb model-index: - name: bge-small-en-v1.5 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.79104477611939 - type: ap value: 37.21923821573361 - type: f1 value: 68.0914945617093 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.75377499999999 - type: ap value: 89.46766124546022 - type: f1 value: 92.73884001331487 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.986 - type: f1 value: 46.55936786727896 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 35.846000000000004 - type: map_at_10 value: 51.388 - type: map_at_100 value: 52.132999999999996 - type: map_at_1000 value: 52.141000000000005 - type: map_at_3 value: 47.037 - type: map_at_5 value: 49.579 - type: mrr_at_1 value: 36.558 - type: mrr_at_10 value: 51.658 - type: mrr_at_100 value: 52.402 - type: mrr_at_1000 value: 52.410000000000004 - type: mrr_at_3 value: 47.345 - type: mrr_at_5 value: 49.797999999999995 - type: ndcg_at_1 value: 35.846000000000004 - type: ndcg_at_10 value: 59.550000000000004 - type: ndcg_at_100 value: 62.596 - type: ndcg_at_1000 value: 62.759 - type: ndcg_at_3 value: 50.666999999999994 - type: ndcg_at_5 value: 55.228 - type: precision_at_1 value: 35.846000000000004 - type: precision_at_10 value: 8.542 - type: precision_at_100 value: 0.984 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.389 - type: precision_at_5 value: 14.438 - type: recall_at_1 value: 35.846000000000004 - type: recall_at_10 value: 85.42 - type: recall_at_100 value: 98.43499999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 61.166 - type: recall_at_5 value: 72.191 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.402770198163594 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 40.01545436974177 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.586465273207196 - type: mrr value: 74.42169019038825 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 85.1891186537969 - type: cos_sim_spearman value: 83.75492046087288 - type: euclidean_pearson value: 84.11766204805357 - type: euclidean_spearman value: 84.01456493126516 - type: manhattan_pearson value: 84.2132950502772 - type: manhattan_spearman value: 83.89227298813377 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.74025974025975 - type: f1 value: 85.71493566466381 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.467181385006434 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 34.719496037339056 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 29.587000000000003 - type: map_at_10 value: 41.114 - type: map_at_100 value: 42.532 - type: map_at_1000 value: 42.661 - type: map_at_3 value: 37.483 - type: map_at_5 value: 39.652 - type: mrr_at_1 value: 36.338 - type: mrr_at_10 value: 46.763 - type: mrr_at_100 value: 47.393 - type: mrr_at_1000 value: 47.445 - type: mrr_at_3 value: 43.538 - type: mrr_at_5 value: 45.556000000000004 - type: ndcg_at_1 value: 36.338 - type: ndcg_at_10 value: 47.658 - type: ndcg_at_100 value: 52.824000000000005 - type: ndcg_at_1000 value: 54.913999999999994 - type: ndcg_at_3 value: 41.989 - type: ndcg_at_5 value: 44.944 - type: precision_at_1 value: 36.338 - type: precision_at_10 value: 9.156 - type: precision_at_100 value: 1.4789999999999999 - type: precision_at_1000 value: 0.196 - type: precision_at_3 value: 20.076 - type: precision_at_5 value: 14.85 - type: recall_at_1 value: 29.587000000000003 - type: recall_at_10 value: 60.746 - type: recall_at_100 value: 82.157 - type: recall_at_1000 value: 95.645 - type: recall_at_3 value: 44.821 - type: recall_at_5 value: 52.819 - type: map_at_1 value: 30.239 - type: map_at_10 value: 39.989000000000004 - type: map_at_100 value: 41.196 - type: map_at_1000 value: 41.325 - type: map_at_3 value: 37.261 - type: map_at_5 value: 38.833 - type: mrr_at_1 value: 37.516 - type: mrr_at_10 value: 46.177 - type: mrr_at_100 value: 46.806 - type: mrr_at_1000 value: 46.849000000000004 - type: mrr_at_3 value: 44.002 - type: mrr_at_5 value: 45.34 - type: ndcg_at_1 value: 37.516 - type: ndcg_at_10 value: 45.586 - type: ndcg_at_100 value: 49.897000000000006 - type: ndcg_at_1000 value: 51.955 - type: ndcg_at_3 value: 41.684 - type: ndcg_at_5 value: 43.617 - type: precision_at_1 value: 37.516 - type: precision_at_10 value: 8.522 - type: precision_at_100 value: 1.374 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 20.105999999999998 - type: precision_at_5 value: 14.152999999999999 - type: recall_at_1 value: 30.239 - type: recall_at_10 value: 55.03 - type: recall_at_100 value: 73.375 - type: recall_at_1000 value: 86.29599999999999 - type: recall_at_3 value: 43.269000000000005 - type: recall_at_5 value: 48.878 - type: map_at_1 value: 38.338 - type: map_at_10 value: 50.468999999999994 - type: map_at_100 value: 51.553000000000004 - type: map_at_1000 value: 51.608 - type: map_at_3 value: 47.107 - type: map_at_5 value: 49.101 - type: mrr_at_1 value: 44.201 - type: mrr_at_10 value: 54.057 - type: mrr_at_100 value: 54.764 - type: mrr_at_1000 value: 54.791000000000004 - type: mrr_at_3 value: 51.56699999999999 - type: mrr_at_5 value: 53.05 - type: ndcg_at_1 value: 44.201 - type: ndcg_at_10 value: 56.379000000000005 - type: ndcg_at_100 value: 60.645 - type: ndcg_at_1000 value: 61.73499999999999 - type: ndcg_at_3 value: 50.726000000000006 - type: ndcg_at_5 value: 53.58500000000001 - type: precision_at_1 value: 44.201 - type: precision_at_10 value: 9.141 - type: precision_at_100 value: 1.216 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 22.654 - type: precision_at_5 value: 15.723999999999998 - type: recall_at_1 value: 38.338 - type: recall_at_10 value: 70.30499999999999 - type: recall_at_100 value: 88.77199999999999 - type: recall_at_1000 value: 96.49799999999999 - type: recall_at_3 value: 55.218 - type: recall_at_5 value: 62.104000000000006 - type: map_at_1 value: 25.682 - type: map_at_10 value: 33.498 - type: map_at_100 value: 34.461000000000006 - type: map_at_1000 value: 34.544000000000004 - type: map_at_3 value: 30.503999999999998 - type: map_at_5 value: 32.216 - type: mrr_at_1 value: 27.683999999999997 - type: mrr_at_10 value: 35.467999999999996 - type: mrr_at_100 value: 36.32 - type: mrr_at_1000 value: 36.386 - type: mrr_at_3 value: 32.618 - type: mrr_at_5 value: 34.262 - type: ndcg_at_1 value: 27.683999999999997 - type: ndcg_at_10 value: 38.378 - type: ndcg_at_100 value: 43.288 - type: ndcg_at_1000 value: 45.413 - type: ndcg_at_3 value: 32.586 - type: ndcg_at_5 value: 35.499 - type: precision_at_1 value: 27.683999999999997 - type: precision_at_10 value: 5.864 - type: precision_at_100 value: 0.882 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 13.446 - type: precision_at_5 value: 9.718 - type: recall_at_1 value: 25.682 - type: recall_at_10 value: 51.712 - type: recall_at_100 value: 74.446 - type: recall_at_1000 value: 90.472 - type: recall_at_3 value: 36.236000000000004 - type: recall_at_5 value: 43.234 - type: map_at_1 value: 16.073999999999998 - type: map_at_10 value: 24.352999999999998 - type: map_at_100 value: 25.438 - type: map_at_1000 value: 25.545 - type: map_at_3 value: 21.614 - type: map_at_5 value: 23.104 - type: mrr_at_1 value: 19.776 - type: mrr_at_10 value: 28.837000000000003 - type: mrr_at_100 value: 29.755 - type: mrr_at_1000 value: 29.817 - type: mrr_at_3 value: 26.201999999999998 - type: mrr_at_5 value: 27.714 - type: ndcg_at_1 value: 19.776 - type: ndcg_at_10 value: 29.701 - type: ndcg_at_100 value: 35.307 - type: ndcg_at_1000 value: 37.942 - type: ndcg_at_3 value: 24.764 - type: ndcg_at_5 value: 27.025 - type: precision_at_1 value: 19.776 - type: precision_at_10 value: 5.659 - type: precision_at_100 value: 0.971 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 12.065 - type: precision_at_5 value: 8.905000000000001 - type: recall_at_1 value: 16.073999999999998 - type: recall_at_10 value: 41.647 - type: recall_at_100 value: 66.884 - type: recall_at_1000 value: 85.91499999999999 - type: recall_at_3 value: 27.916 - type: recall_at_5 value: 33.729 - type: map_at_1 value: 28.444999999999997 - type: map_at_10 value: 38.218999999999994 - type: map_at_100 value: 39.595 - type: map_at_1000 value: 39.709 - type: map_at_3 value: 35.586 - type: map_at_5 value: 36.895 - type: mrr_at_1 value: 34.841 - type: mrr_at_10 value: 44.106 - type: mrr_at_100 value: 44.98 - type: mrr_at_1000 value: 45.03 - type: mrr_at_3 value: 41.979 - type: mrr_at_5 value: 43.047999999999995 - type: ndcg_at_1 value: 34.841 - type: ndcg_at_10 value: 43.922 - type: ndcg_at_100 value: 49.504999999999995 - type: ndcg_at_1000 value: 51.675000000000004 - type: ndcg_at_3 value: 39.858 - type: ndcg_at_5 value: 41.408 - type: precision_at_1 value: 34.841 - type: precision_at_10 value: 7.872999999999999 - type: precision_at_100 value: 1.2449999999999999 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 18.993 - type: precision_at_5 value: 13.032 - type: recall_at_1 value: 28.444999999999997 - type: recall_at_10 value: 54.984 - type: recall_at_100 value: 78.342 - type: recall_at_1000 value: 92.77 - type: recall_at_3 value: 42.842999999999996 - type: recall_at_5 value: 47.247 - type: map_at_1 value: 23.072 - type: map_at_10 value: 32.354 - type: map_at_100 value: 33.800000000000004 - type: map_at_1000 value: 33.908 - type: map_at_3 value: 29.232000000000003 - type: map_at_5 value: 31.049 - type: mrr_at_1 value: 29.110000000000003 - type: mrr_at_10 value: 38.03 - type: mrr_at_100 value: 39.032 - type: mrr_at_1000 value: 39.086999999999996 - type: mrr_at_3 value: 35.407 - type: mrr_at_5 value: 36.76 - type: ndcg_at_1 value: 29.110000000000003 - type: ndcg_at_10 value: 38.231 - type: ndcg_at_100 value: 44.425 - type: ndcg_at_1000 value: 46.771 - type: ndcg_at_3 value: 33.095 - type: ndcg_at_5 value: 35.459 - type: precision_at_1 value: 29.110000000000003 - type: precision_at_10 value: 7.215000000000001 - type: precision_at_100 value: 1.2109999999999999 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 16.058 - type: precision_at_5 value: 11.644 - type: recall_at_1 value: 23.072 - type: recall_at_10 value: 50.285999999999994 - type: recall_at_100 value: 76.596 - type: recall_at_1000 value: 92.861 - type: recall_at_3 value: 35.702 - type: recall_at_5 value: 42.152 - type: map_at_1 value: 24.937916666666666 - type: map_at_10 value: 33.755250000000004 - type: map_at_100 value: 34.955999999999996 - type: map_at_1000 value: 35.070499999999996 - type: map_at_3 value: 30.98708333333333 - type: map_at_5 value: 32.51491666666666 - type: mrr_at_1 value: 29.48708333333333 - type: mrr_at_10 value: 37.92183333333334 - type: mrr_at_100 value: 38.76583333333333 - type: mrr_at_1000 value: 38.82466666666667 - type: mrr_at_3 value: 35.45125 - type: mrr_at_5 value: 36.827000000000005 - type: ndcg_at_1 value: 29.48708333333333 - type: ndcg_at_10 value: 39.05225 - type: ndcg_at_100 value: 44.25983333333334 - type: ndcg_at_1000 value: 46.568333333333335 - type: ndcg_at_3 value: 34.271583333333325 - type: ndcg_at_5 value: 36.483916666666666 - type: precision_at_1 value: 29.48708333333333 - type: precision_at_10 value: 6.865749999999999 - type: precision_at_100 value: 1.1195833333333332 - type: precision_at_1000 value: 0.15058333333333335 - type: precision_at_3 value: 15.742083333333333 - type: precision_at_5 value: 11.221916666666667 - type: recall_at_1 value: 24.937916666666666 - type: recall_at_10 value: 50.650416666666665 - type: recall_at_100 value: 73.55383333333334 - type: recall_at_1000 value: 89.61691666666667 - type: recall_at_3 value: 37.27808333333334 - type: recall_at_5 value: 42.99475 - type: map_at_1 value: 23.947 - type: map_at_10 value: 30.575000000000003 - type: map_at_100 value: 31.465 - type: map_at_1000 value: 31.558000000000003 - type: map_at_3 value: 28.814 - type: map_at_5 value: 29.738999999999997 - type: mrr_at_1 value: 26.994 - type: mrr_at_10 value: 33.415 - type: mrr_at_100 value: 34.18 - type: mrr_at_1000 value: 34.245 - type: mrr_at_3 value: 31.621 - type: mrr_at_5 value: 32.549 - type: ndcg_at_1 value: 26.994 - type: ndcg_at_10 value: 34.482 - type: ndcg_at_100 value: 38.915 - type: ndcg_at_1000 value: 41.355 - type: ndcg_at_3 value: 31.139 - type: ndcg_at_5 value: 32.589 - type: precision_at_1 value: 26.994 - type: precision_at_10 value: 5.322 - type: precision_at_100 value: 0.8160000000000001 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 13.344000000000001 - type: precision_at_5 value: 8.988 - type: recall_at_1 value: 23.947 - type: recall_at_10 value: 43.647999999999996 - type: recall_at_100 value: 63.851 - type: recall_at_1000 value: 82.0 - type: recall_at_3 value: 34.288000000000004 - type: recall_at_5 value: 38.117000000000004 - type: map_at_1 value: 16.197 - type: map_at_10 value: 22.968 - type: map_at_100 value: 24.095 - type: map_at_1000 value: 24.217 - type: map_at_3 value: 20.771 - type: map_at_5 value: 21.995 - type: mrr_at_1 value: 19.511 - type: mrr_at_10 value: 26.55 - type: mrr_at_100 value: 27.500999999999998 - type: mrr_at_1000 value: 27.578999999999997 - type: mrr_at_3 value: 24.421 - type: mrr_at_5 value: 25.604 - type: ndcg_at_1 value: 19.511 - type: ndcg_at_10 value: 27.386 - type: ndcg_at_100 value: 32.828 - type: ndcg_at_1000 value: 35.739 - type: ndcg_at_3 value: 23.405 - type: ndcg_at_5 value: 25.255 - type: precision_at_1 value: 19.511 - type: precision_at_10 value: 5.017 - type: precision_at_100 value: 0.91 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 11.023 - type: precision_at_5 value: 8.025 - type: recall_at_1 value: 16.197 - type: recall_at_10 value: 37.09 - type: recall_at_100 value: 61.778 - type: recall_at_1000 value: 82.56599999999999 - type: recall_at_3 value: 26.034000000000002 - type: recall_at_5 value: 30.762 - type: map_at_1 value: 25.41 - type: map_at_10 value: 33.655 - type: map_at_100 value: 34.892 - type: map_at_1000 value: 34.995 - type: map_at_3 value: 30.94 - type: map_at_5 value: 32.303 - type: mrr_at_1 value: 29.477999999999998 - type: mrr_at_10 value: 37.443 - type: mrr_at_100 value: 38.383 - type: mrr_at_1000 value: 38.440000000000005 - type: mrr_at_3 value: 34.949999999999996 - type: mrr_at_5 value: 36.228 - type: ndcg_at_1 value: 29.477999999999998 - type: ndcg_at_10 value: 38.769 - type: ndcg_at_100 value: 44.245000000000005 - type: ndcg_at_1000 value: 46.593 - type: ndcg_at_3 value: 33.623 - type: ndcg_at_5 value: 35.766 - type: precision_at_1 value: 29.477999999999998 - type: precision_at_10 value: 6.455 - type: precision_at_100 value: 1.032 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 14.893999999999998 - type: precision_at_5 value: 10.485 - type: recall_at_1 value: 25.41 - type: recall_at_10 value: 50.669 - type: recall_at_100 value: 74.084 - type: recall_at_1000 value: 90.435 - type: recall_at_3 value: 36.679 - type: recall_at_5 value: 41.94 - type: map_at_1 value: 23.339 - type: map_at_10 value: 31.852000000000004 - type: map_at_100 value: 33.411 - type: map_at_1000 value: 33.62 - type: map_at_3 value: 28.929 - type: map_at_5 value: 30.542 - type: mrr_at_1 value: 28.063 - type: mrr_at_10 value: 36.301 - type: mrr_at_100 value: 37.288 - type: mrr_at_1000 value: 37.349 - type: mrr_at_3 value: 33.663 - type: mrr_at_5 value: 35.165 - type: ndcg_at_1 value: 28.063 - type: ndcg_at_10 value: 37.462 - type: ndcg_at_100 value: 43.620999999999995 - type: ndcg_at_1000 value: 46.211 - type: ndcg_at_3 value: 32.68 - type: ndcg_at_5 value: 34.981 - type: precision_at_1 value: 28.063 - type: precision_at_10 value: 7.1739999999999995 - type: precision_at_100 value: 1.486 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_3 value: 15.217 - type: precision_at_5 value: 11.265 - type: recall_at_1 value: 23.339 - type: recall_at_10 value: 48.376999999999995 - type: recall_at_100 value: 76.053 - type: recall_at_1000 value: 92.455 - type: recall_at_3 value: 34.735 - type: recall_at_5 value: 40.71 - type: map_at_1 value: 18.925 - type: map_at_10 value: 26.017000000000003 - type: map_at_100 value: 27.034000000000002 - type: map_at_1000 value: 27.156000000000002 - type: map_at_3 value: 23.604 - type: map_at_5 value: 24.75 - type: mrr_at_1 value: 20.333000000000002 - type: mrr_at_10 value: 27.915 - type: mrr_at_100 value: 28.788000000000004 - type: mrr_at_1000 value: 28.877999999999997 - type: mrr_at_3 value: 25.446999999999996 - type: mrr_at_5 value: 26.648 - type: ndcg_at_1 value: 20.333000000000002 - type: ndcg_at_10 value: 30.673000000000002 - type: ndcg_at_100 value: 35.618 - type: ndcg_at_1000 value: 38.517 - type: ndcg_at_3 value: 25.71 - type: ndcg_at_5 value: 27.679 - type: precision_at_1 value: 20.333000000000002 - type: precision_at_10 value: 4.9910000000000005 - type: precision_at_100 value: 0.8130000000000001 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 11.029 - type: precision_at_5 value: 7.8740000000000006 - type: recall_at_1 value: 18.925 - type: recall_at_10 value: 43.311 - type: recall_at_100 value: 66.308 - type: recall_at_1000 value: 87.49 - type: recall_at_3 value: 29.596 - type: recall_at_5 value: 34.245 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 13.714 - type: map_at_10 value: 23.194 - type: map_at_100 value: 24.976000000000003 - type: map_at_1000 value: 25.166 - type: map_at_3 value: 19.709 - type: map_at_5 value: 21.523999999999997 - type: mrr_at_1 value: 30.619000000000003 - type: mrr_at_10 value: 42.563 - type: mrr_at_100 value: 43.386 - type: mrr_at_1000 value: 43.423 - type: mrr_at_3 value: 39.555 - type: mrr_at_5 value: 41.268 - type: ndcg_at_1 value: 30.619000000000003 - type: ndcg_at_10 value: 31.836 - type: ndcg_at_100 value: 38.652 - type: ndcg_at_1000 value: 42.088 - type: ndcg_at_3 value: 26.733 - type: ndcg_at_5 value: 28.435 - type: precision_at_1 value: 30.619000000000003 - type: precision_at_10 value: 9.751999999999999 - type: precision_at_100 value: 1.71 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_3 value: 19.935 - type: precision_at_5 value: 14.984 - type: recall_at_1 value: 13.714 - type: recall_at_10 value: 37.26 - type: recall_at_100 value: 60.546 - type: recall_at_1000 value: 79.899 - type: recall_at_3 value: 24.325 - type: recall_at_5 value: 29.725 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.462 - type: map_at_10 value: 18.637 - type: map_at_100 value: 26.131999999999998 - type: map_at_1000 value: 27.607 - type: map_at_3 value: 13.333 - type: map_at_5 value: 15.654000000000002 - type: mrr_at_1 value: 66.25 - type: mrr_at_10 value: 74.32600000000001 - type: mrr_at_100 value: 74.60900000000001 - type: mrr_at_1000 value: 74.62 - type: mrr_at_3 value: 72.667 - type: mrr_at_5 value: 73.817 - type: ndcg_at_1 value: 53.87499999999999 - type: ndcg_at_10 value: 40.028999999999996 - type: ndcg_at_100 value: 44.199 - type: ndcg_at_1000 value: 51.629999999999995 - type: ndcg_at_3 value: 44.113 - type: ndcg_at_5 value: 41.731 - type: precision_at_1 value: 66.25 - type: precision_at_10 value: 31.900000000000002 - type: precision_at_100 value: 10.043000000000001 - type: precision_at_1000 value: 1.926 - type: precision_at_3 value: 47.417 - type: precision_at_5 value: 40.65 - type: recall_at_1 value: 8.462 - type: recall_at_10 value: 24.293 - type: recall_at_100 value: 50.146 - type: recall_at_1000 value: 74.034 - type: recall_at_3 value: 14.967 - type: recall_at_5 value: 18.682000000000002 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.84499999999999 - type: f1 value: 42.48106691979349 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 74.034 - type: map_at_10 value: 82.76 - type: map_at_100 value: 82.968 - type: map_at_1000 value: 82.98299999999999 - type: map_at_3 value: 81.768 - type: map_at_5 value: 82.418 - type: mrr_at_1 value: 80.048 - type: mrr_at_10 value: 87.64999999999999 - type: mrr_at_100 value: 87.712 - type: mrr_at_1000 value: 87.713 - type: mrr_at_3 value: 87.01100000000001 - type: mrr_at_5 value: 87.466 - type: ndcg_at_1 value: 80.048 - type: ndcg_at_10 value: 86.643 - type: ndcg_at_100 value: 87.361 - type: ndcg_at_1000 value: 87.606 - type: ndcg_at_3 value: 85.137 - type: ndcg_at_5 value: 86.016 - type: precision_at_1 value: 80.048 - type: precision_at_10 value: 10.372 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 32.638 - type: precision_at_5 value: 20.177 - type: recall_at_1 value: 74.034 - type: recall_at_10 value: 93.769 - type: recall_at_100 value: 96.569 - type: recall_at_1000 value: 98.039 - type: recall_at_3 value: 89.581 - type: recall_at_5 value: 91.906 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 20.5 - type: map_at_10 value: 32.857 - type: map_at_100 value: 34.589 - type: map_at_1000 value: 34.778 - type: map_at_3 value: 29.160999999999998 - type: map_at_5 value: 31.033 - type: mrr_at_1 value: 40.123 - type: mrr_at_10 value: 48.776 - type: mrr_at_100 value: 49.495 - type: mrr_at_1000 value: 49.539 - type: mrr_at_3 value: 46.605000000000004 - type: mrr_at_5 value: 47.654 - type: ndcg_at_1 value: 40.123 - type: ndcg_at_10 value: 40.343 - type: ndcg_at_100 value: 46.56 - type: ndcg_at_1000 value: 49.777 - type: ndcg_at_3 value: 37.322 - type: ndcg_at_5 value: 37.791000000000004 - type: precision_at_1 value: 40.123 - type: precision_at_10 value: 11.08 - type: precision_at_100 value: 1.752 - type: precision_at_1000 value: 0.232 - type: precision_at_3 value: 24.897 - type: precision_at_5 value: 17.809 - type: recall_at_1 value: 20.5 - type: recall_at_10 value: 46.388 - type: recall_at_100 value: 69.552 - type: recall_at_1000 value: 89.011 - type: recall_at_3 value: 33.617999999999995 - type: recall_at_5 value: 38.211 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 39.135999999999996 - type: map_at_10 value: 61.673 - type: map_at_100 value: 62.562 - type: map_at_1000 value: 62.62 - type: map_at_3 value: 58.467999999999996 - type: map_at_5 value: 60.463 - type: mrr_at_1 value: 78.271 - type: mrr_at_10 value: 84.119 - type: mrr_at_100 value: 84.29299999999999 - type: mrr_at_1000 value: 84.299 - type: mrr_at_3 value: 83.18900000000001 - type: mrr_at_5 value: 83.786 - type: ndcg_at_1 value: 78.271 - type: ndcg_at_10 value: 69.935 - type: ndcg_at_100 value: 73.01299999999999 - type: ndcg_at_1000 value: 74.126 - type: ndcg_at_3 value: 65.388 - type: ndcg_at_5 value: 67.906 - type: precision_at_1 value: 78.271 - type: precision_at_10 value: 14.562 - type: precision_at_100 value: 1.6969999999999998 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 41.841 - type: precision_at_5 value: 27.087 - type: recall_at_1 value: 39.135999999999996 - type: recall_at_10 value: 72.809 - type: recall_at_100 value: 84.86200000000001 - type: recall_at_1000 value: 92.208 - type: recall_at_3 value: 62.76199999999999 - type: recall_at_5 value: 67.718 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 90.60600000000001 - type: ap value: 86.6579587804335 - type: f1 value: 90.5938853929307 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 21.852 - type: map_at_10 value: 33.982 - type: map_at_100 value: 35.116 - type: map_at_1000 value: 35.167 - type: map_at_3 value: 30.134 - type: map_at_5 value: 32.340999999999994 - type: mrr_at_1 value: 22.479 - type: mrr_at_10 value: 34.594 - type: mrr_at_100 value: 35.672 - type: mrr_at_1000 value: 35.716 - type: mrr_at_3 value: 30.84 - type: mrr_at_5 value: 32.998 - type: ndcg_at_1 value: 22.493 - type: ndcg_at_10 value: 40.833000000000006 - type: ndcg_at_100 value: 46.357 - type: ndcg_at_1000 value: 47.637 - type: ndcg_at_3 value: 32.995999999999995 - type: ndcg_at_5 value: 36.919000000000004 - type: precision_at_1 value: 22.493 - type: precision_at_10 value: 6.465999999999999 - type: precision_at_100 value: 0.9249999999999999 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.030999999999999 - type: precision_at_5 value: 10.413 - type: recall_at_1 value: 21.852 - type: recall_at_10 value: 61.934999999999995 - type: recall_at_100 value: 87.611 - type: recall_at_1000 value: 97.441 - type: recall_at_3 value: 40.583999999999996 - type: recall_at_5 value: 49.992999999999995 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.36069311445507 - type: f1 value: 93.16456330371453 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 74.74692202462381 - type: f1 value: 58.17903579421599 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 74.80833893745796 - type: f1 value: 72.70786592684664 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.69872225958305 - type: f1 value: 78.61626934504731 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.058658628717694 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.85561739360599 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.290259910144385 - type: mrr value: 32.44223046102856 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.288 - type: map_at_10 value: 12.267999999999999 - type: map_at_100 value: 15.557000000000002 - type: map_at_1000 value: 16.98 - type: map_at_3 value: 8.866 - type: map_at_5 value: 10.418 - type: mrr_at_1 value: 43.653 - type: mrr_at_10 value: 52.681 - type: mrr_at_100 value: 53.315999999999995 - type: mrr_at_1000 value: 53.357 - type: mrr_at_3 value: 51.393 - type: mrr_at_5 value: 51.903999999999996 - type: ndcg_at_1 value: 42.415000000000006 - type: ndcg_at_10 value: 34.305 - type: ndcg_at_100 value: 30.825999999999997 - type: ndcg_at_1000 value: 39.393 - type: ndcg_at_3 value: 39.931 - type: ndcg_at_5 value: 37.519999999999996 - type: precision_at_1 value: 43.653 - type: precision_at_10 value: 25.728 - type: precision_at_100 value: 7.932 - type: precision_at_1000 value: 2.07 - type: precision_at_3 value: 38.184000000000005 - type: precision_at_5 value: 32.879000000000005 - type: recall_at_1 value: 5.288 - type: recall_at_10 value: 16.195 - type: recall_at_100 value: 31.135 - type: recall_at_1000 value: 61.531000000000006 - type: recall_at_3 value: 10.313 - type: recall_at_5 value: 12.754999999999999 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 28.216 - type: map_at_10 value: 42.588 - type: map_at_100 value: 43.702999999999996 - type: map_at_1000 value: 43.739 - type: map_at_3 value: 38.177 - type: map_at_5 value: 40.754000000000005 - type: mrr_at_1 value: 31.866 - type: mrr_at_10 value: 45.189 - type: mrr_at_100 value: 46.056000000000004 - type: mrr_at_1000 value: 46.081 - type: mrr_at_3 value: 41.526999999999994 - type: mrr_at_5 value: 43.704 - type: ndcg_at_1 value: 31.837 - type: ndcg_at_10 value: 50.178 - type: ndcg_at_100 value: 54.98800000000001 - type: ndcg_at_1000 value: 55.812 - type: ndcg_at_3 value: 41.853 - type: ndcg_at_5 value: 46.153 - type: precision_at_1 value: 31.837 - type: precision_at_10 value: 8.43 - type: precision_at_100 value: 1.1119999999999999 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 19.023 - type: precision_at_5 value: 13.911000000000001 - type: recall_at_1 value: 28.216 - type: recall_at_10 value: 70.8 - type: recall_at_100 value: 91.857 - type: recall_at_1000 value: 97.941 - type: recall_at_3 value: 49.196 - type: recall_at_5 value: 59.072 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 71.22800000000001 - type: map_at_10 value: 85.115 - type: map_at_100 value: 85.72 - type: map_at_1000 value: 85.737 - type: map_at_3 value: 82.149 - type: map_at_5 value: 84.029 - type: mrr_at_1 value: 81.96 - type: mrr_at_10 value: 88.00200000000001 - type: mrr_at_100 value: 88.088 - type: mrr_at_1000 value: 88.089 - type: mrr_at_3 value: 87.055 - type: mrr_at_5 value: 87.715 - type: ndcg_at_1 value: 82.01 - type: ndcg_at_10 value: 88.78 - type: ndcg_at_100 value: 89.91 - type: ndcg_at_1000 value: 90.013 - type: ndcg_at_3 value: 85.957 - type: ndcg_at_5 value: 87.56 - type: precision_at_1 value: 82.01 - type: precision_at_10 value: 13.462 - type: precision_at_100 value: 1.528 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.553 - type: precision_at_5 value: 24.732000000000003 - type: recall_at_1 value: 71.22800000000001 - type: recall_at_10 value: 95.69 - type: recall_at_100 value: 99.531 - type: recall_at_1000 value: 99.98 - type: recall_at_3 value: 87.632 - type: recall_at_5 value: 92.117 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 52.31768034366916 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 60.640266772723606 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.7780000000000005 - type: map_at_10 value: 12.299 - type: map_at_100 value: 14.363000000000001 - type: map_at_1000 value: 14.71 - type: map_at_3 value: 8.738999999999999 - type: map_at_5 value: 10.397 - type: mrr_at_1 value: 23.599999999999998 - type: mrr_at_10 value: 34.845 - type: mrr_at_100 value: 35.916 - type: mrr_at_1000 value: 35.973 - type: mrr_at_3 value: 31.7 - type: mrr_at_5 value: 33.535 - type: ndcg_at_1 value: 23.599999999999998 - type: ndcg_at_10 value: 20.522000000000002 - type: ndcg_at_100 value: 28.737000000000002 - type: ndcg_at_1000 value: 34.596 - type: ndcg_at_3 value: 19.542 - type: ndcg_at_5 value: 16.958000000000002 - type: precision_at_1 value: 23.599999999999998 - type: precision_at_10 value: 10.67 - type: precision_at_100 value: 2.259 - type: precision_at_1000 value: 0.367 - type: precision_at_3 value: 18.333 - type: precision_at_5 value: 14.879999999999999 - type: recall_at_1 value: 4.7780000000000005 - type: recall_at_10 value: 21.617 - type: recall_at_100 value: 45.905 - type: recall_at_1000 value: 74.42 - type: recall_at_3 value: 11.148 - type: recall_at_5 value: 15.082999999999998 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.22372750297885 - type: cos_sim_spearman value: 79.40972617119405 - type: euclidean_pearson value: 80.6101072020434 - type: euclidean_spearman value: 79.53844217225202 - type: manhattan_pearson value: 80.57265975286111 - type: manhattan_spearman value: 79.46335611792958 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.43713315520749 - type: cos_sim_spearman value: 77.44128693329532 - type: euclidean_pearson value: 81.63869928101123 - type: euclidean_spearman value: 77.29512977961515 - type: manhattan_pearson value: 81.63704185566183 - type: manhattan_spearman value: 77.29909412738657 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 81.59451537860527 - type: cos_sim_spearman value: 82.97994638856723 - type: euclidean_pearson value: 82.89478688288412 - type: euclidean_spearman value: 83.58740751053104 - type: manhattan_pearson value: 82.69140840941608 - type: manhattan_spearman value: 83.33665956040555 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.00756527711764 - type: cos_sim_spearman value: 81.83560996841379 - type: euclidean_pearson value: 82.07684151976518 - type: euclidean_spearman value: 82.00913052060511 - type: manhattan_pearson value: 82.05690778488794 - type: manhattan_spearman value: 82.02260252019525 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.13710262895447 - type: cos_sim_spearman value: 87.26412811156248 - type: euclidean_pearson value: 86.94151453230228 - type: euclidean_spearman value: 87.5363796699571 - type: manhattan_pearson value: 86.86989424083748 - type: manhattan_spearman value: 87.47315940781353 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.0230597603627 - type: cos_sim_spearman value: 84.93344499318864 - type: euclidean_pearson value: 84.23754743431141 - type: euclidean_spearman value: 85.09707376597099 - type: manhattan_pearson value: 84.04325160987763 - type: manhattan_spearman value: 84.89353071339909 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 86.75620824563921 - type: cos_sim_spearman value: 87.15065513706398 - type: euclidean_pearson value: 88.26281533633521 - type: euclidean_spearman value: 87.51963738643983 - type: manhattan_pearson value: 88.25599267618065 - type: manhattan_spearman value: 87.58048736047483 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 64.74645319195137 - type: cos_sim_spearman value: 65.29996325037214 - type: euclidean_pearson value: 67.04297794086443 - type: euclidean_spearman value: 65.43841726694343 - type: manhattan_pearson value: 67.39459955690904 - type: manhattan_spearman value: 65.92864704413651 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.31291020270801 - type: cos_sim_spearman value: 85.86473738688068 - type: euclidean_pearson value: 85.65537275064152 - type: euclidean_spearman value: 86.13087454209642 - type: manhattan_pearson value: 85.43946955047609 - type: manhattan_spearman value: 85.91568175344916 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 85.93798118350695 - type: mrr value: 95.93536274908824 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 57.594 - type: map_at_10 value: 66.81899999999999 - type: map_at_100 value: 67.368 - type: map_at_1000 value: 67.4 - type: map_at_3 value: 64.061 - type: map_at_5 value: 65.47 - type: mrr_at_1 value: 60.667 - type: mrr_at_10 value: 68.219 - type: mrr_at_100 value: 68.655 - type: mrr_at_1000 value: 68.684 - type: mrr_at_3 value: 66.22200000000001 - type: mrr_at_5 value: 67.289 - type: ndcg_at_1 value: 60.667 - type: ndcg_at_10 value: 71.275 - type: ndcg_at_100 value: 73.642 - type: ndcg_at_1000 value: 74.373 - type: ndcg_at_3 value: 66.521 - type: ndcg_at_5 value: 68.581 - type: precision_at_1 value: 60.667 - type: precision_at_10 value: 9.433 - type: precision_at_100 value: 1.0699999999999998 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 25.556 - type: precision_at_5 value: 16.8 - type: recall_at_1 value: 57.594 - type: recall_at_10 value: 83.622 - type: recall_at_100 value: 94.167 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 70.64399999999999 - type: recall_at_5 value: 75.983 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.85841584158416 - type: cos_sim_ap value: 96.66996142314342 - type: cos_sim_f1 value: 92.83208020050125 - type: cos_sim_precision value: 93.06532663316584 - type: cos_sim_recall value: 92.60000000000001 - type: dot_accuracy value: 99.85841584158416 - type: dot_ap value: 96.6775307676576 - type: dot_f1 value: 92.69289729177312 - type: dot_precision value: 94.77533960292581 - type: dot_recall value: 90.7 - type: euclidean_accuracy value: 99.86138613861387 - type: euclidean_ap value: 96.6338454403108 - type: euclidean_f1 value: 92.92214357937311 - type: euclidean_precision value: 93.96728016359918 - type: euclidean_recall value: 91.9 - type: manhattan_accuracy value: 99.86237623762376 - type: manhattan_ap value: 96.60370449645053 - type: manhattan_f1 value: 92.91177970423253 - type: manhattan_precision value: 94.7970863683663 - type: manhattan_recall value: 91.10000000000001 - type: max_accuracy value: 99.86237623762376 - type: max_ap value: 96.6775307676576 - type: max_f1 value: 92.92214357937311 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 60.77977058695198 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.2725272535638 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.64052466362125 - type: mrr value: 54.533067014684654 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.677624219206578 - type: cos_sim_spearman value: 30.121368518123447 - type: dot_pearson value: 30.69870088041608 - type: dot_spearman value: 29.61284927093751 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.22 - type: map_at_10 value: 1.855 - type: map_at_100 value: 9.885 - type: map_at_1000 value: 23.416999999999998 - type: map_at_3 value: 0.637 - type: map_at_5 value: 1.024 - type: mrr_at_1 value: 88.0 - type: mrr_at_10 value: 93.067 - type: mrr_at_100 value: 93.067 - type: mrr_at_1000 value: 93.067 - type: mrr_at_3 value: 92.667 - type: mrr_at_5 value: 93.067 - type: ndcg_at_1 value: 82.0 - type: ndcg_at_10 value: 75.899 - type: ndcg_at_100 value: 55.115 - type: ndcg_at_1000 value: 48.368 - type: ndcg_at_3 value: 79.704 - type: ndcg_at_5 value: 78.39699999999999 - type: precision_at_1 value: 88.0 - type: precision_at_10 value: 79.60000000000001 - type: precision_at_100 value: 56.06 - type: precision_at_1000 value: 21.206 - type: precision_at_3 value: 84.667 - type: precision_at_5 value: 83.2 - type: recall_at_1 value: 0.22 - type: recall_at_10 value: 2.078 - type: recall_at_100 value: 13.297 - type: recall_at_1000 value: 44.979 - type: recall_at_3 value: 0.6689999999999999 - type: recall_at_5 value: 1.106 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.258 - type: map_at_10 value: 10.439 - type: map_at_100 value: 16.89 - type: map_at_1000 value: 18.407999999999998 - type: map_at_3 value: 5.668 - type: map_at_5 value: 7.718 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 51.159 - type: mrr_at_100 value: 51.714000000000006 - type: mrr_at_1000 value: 51.714000000000006 - type: mrr_at_3 value: 47.959 - type: mrr_at_5 value: 50.407999999999994 - type: ndcg_at_1 value: 29.592000000000002 - type: ndcg_at_10 value: 26.037 - type: ndcg_at_100 value: 37.924 - type: ndcg_at_1000 value: 49.126999999999995 - type: ndcg_at_3 value: 30.631999999999998 - type: ndcg_at_5 value: 28.571 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 22.857 - type: precision_at_100 value: 7.754999999999999 - type: precision_at_1000 value: 1.529 - type: precision_at_3 value: 34.014 - type: precision_at_5 value: 29.796 - type: recall_at_1 value: 2.258 - type: recall_at_10 value: 16.554 - type: recall_at_100 value: 48.439 - type: recall_at_1000 value: 82.80499999999999 - type: recall_at_3 value: 7.283 - type: recall_at_5 value: 10.732 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 69.8858 - type: ap value: 13.835684144362109 - type: f1 value: 53.803351693244586 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.50650820599886 - type: f1 value: 60.84357825979259 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 48.52131044852134 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.59337187816654 - type: cos_sim_ap value: 73.23925826533437 - type: cos_sim_f1 value: 67.34693877551021 - type: cos_sim_precision value: 62.40432237730752 - type: cos_sim_recall value: 73.13984168865434 - type: dot_accuracy value: 85.31322644096085 - type: dot_ap value: 72.30723963807422 - type: dot_f1 value: 66.47051612112296 - type: dot_precision value: 62.0792305930845 - type: dot_recall value: 71.53034300791556 - type: euclidean_accuracy value: 85.61125350181797 - type: euclidean_ap value: 73.32843720487845 - type: euclidean_f1 value: 67.36549633745895 - type: euclidean_precision value: 64.60755813953489 - type: euclidean_recall value: 70.36939313984169 - type: manhattan_accuracy value: 85.63509566668654 - type: manhattan_ap value: 73.16658488311325 - type: manhattan_f1 value: 67.20597386434349 - type: manhattan_precision value: 63.60424028268551 - type: manhattan_recall value: 71.2401055408971 - type: max_accuracy value: 85.63509566668654 - type: max_ap value: 73.32843720487845 - type: max_f1 value: 67.36549633745895 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.33779640625606 - type: cos_sim_ap value: 84.83868375898157 - type: cos_sim_f1 value: 77.16506154017773 - type: cos_sim_precision value: 74.62064005753327 - type: cos_sim_recall value: 79.88912842623961 - type: dot_accuracy value: 88.02732176815307 - type: dot_ap value: 83.95089283763002 - type: dot_f1 value: 76.29635101196631 - type: dot_precision value: 73.31771720613288 - type: dot_recall value: 79.52725592854944 - type: euclidean_accuracy value: 88.44452206310397 - type: euclidean_ap value: 84.98384576824827 - type: euclidean_f1 value: 77.29311047696697 - type: euclidean_precision value: 74.51232583065381 - type: euclidean_recall value: 80.28949799815214 - type: manhattan_accuracy value: 88.47362906042613 - type: manhattan_ap value: 84.91421462218432 - type: manhattan_f1 value: 77.05107637204792 - type: manhattan_precision value: 74.74484256243214 - type: manhattan_recall value: 79.50415768401602 - type: max_accuracy value: 88.47362906042613 - type: max_ap value: 84.98384576824827 - type: max_f1 value: 77.29311047696697 --- <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <p> <a href=#model-list>Model List</a> | <a href=#frequently-asked-questions>FAQ</a> | <a href=#usage>Usage</a> | <a href="#evaluation">Evaluation</a> | <a href="#train">Train</a> | <a href="#contact">Contact</a> | <a href="#citation">Citation</a> | <a href="#license">License</a> <p> </h4> More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3). [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon) - **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) - **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding) - **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) - **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) ## News - 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. [Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire: - 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire: - 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire: - 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire: - 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf) - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released - 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released - 09/12/2023: New models: - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. <details> <summary>More</summary> <!-- ### More --> - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. </details> ## Model List `bge` is short for `BAAI general embedding`. | Model | Language | | Description | query instruction for retrieval [1] | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| | [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | | | [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | [1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. [2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . ## Frequently asked questions <details> <summary>1. How to fine-tune bge embedding model?</summary> <!-- ### How to fine-tune bge embedding model? --> Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. </details> <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, **what matters is the relative order of the scores, not the absolute value.** If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary> <!-- ### When does the query instruction need to be used --> For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. No instruction only has a slight degradation in retrieval performance compared with using instruction. So you can generate embedding without instruction in all cases for convenience. For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples for using `bge` models with [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation embeddings_1 = model.encode(sentences_1) embeddings_2 = model.encode(sentences_2) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages. ```python from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] instruction = "为这个句子生成表示以用于检索相关文章：" model = SentenceTransformer('BAAI/bge-large-zh-v1.5') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` #### Using Langchain You can use `bge` in langchain like this: ```python from langchain.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章：" ) model.query_instruction = "为这个句子生成表示以用于检索相关文章：" ``` #### Using HuggingFace Transformers With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') model.eval() # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### Using Huggingface transformers ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` #### Usage of the ONNX files ```python from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-small-en-v1.5') model = AutoModel.from_pretrained('BAAI/bge-small-en-v1.5') model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-small-en-v1.5', file_name="onnx/model.onnx") # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') model_output_ort = model_ort(**encoded_input) # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # model_output and model_output_ort are identical ``` #### Usage via infinity Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package. Recommended is `device="cuda", engine="torch"` with flash attention on gpu, and `device="cpu", engine="optimum"` for onnx inference. ```python import asyncio from infinity_emb import AsyncEmbeddingEngine, EngineArgs sentences = ["Embed this is sentence via Infinity.", "Paris is in France."] engine = AsyncEmbeddingEngine.from_args( EngineArgs(model_name_or_path = "BAAI/bge-small-en-v1.5", device="cpu", engine="optimum" # or engine="torch" )) async def main(): async with engine: embeddings, usage = await engine.embed(sentences=sentences) asyncio.run(main()) ``` ## Evaluation `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - **MTEB**: | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | | [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | | [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | | [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | - **C-MTEB**: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | | [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | | [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | - **Reranking**: See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## Contact If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]). ## Citation If you find this repository useful, please consider giving a star :star: and citation ``` @misc{bge_embedding, title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, year={2023}, eprint={2309.07597}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
[ "SEMANTIC_SIMILARITY", "SUMMARIZATION" ]
[ "BEAR", "BIOSSES", "SCIFACT" ]
Non_BioNLP
rjnClarke/BAAI-bge-large-en-v1.5-fine-tuned
rjnClarke
sentence-similarity
[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:10359", "loss:MultipleNegativesRankingLoss", "en", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:BAAI/bge-large-en-v1.5", "base_model:finetune:BAAI/bge-large-en-v1.5", "license:apache-2.0", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,722
1,722
50
0
--- base_model: BAAI/bge-large-en-v1.5 datasets: [] language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@3 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@200 - cosine_map@100 - dot_accuracy@3 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@200 - dot_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10359 - loss:MultipleNegativesRankingLoss widget: - source_sentence: Cleopatra reacts to the news of Antony's death with a mixture of sadness and resignation, contemplating her own mortality and the fickle nature of life. sentences: - "Immortal longings in me. Now no more The juice of Egypt's grape shall moist\ \ this lip. Yare, yare, good Iras; quick. Methinks I hear Antony call. I\ \ see him rouse himself To praise my noble act. I hear him mock The luck\ \ of Caesar, which the gods give men To excuse their after wrath. Husband,\ \ I come. Now to that name my courage prove my title! I am fire and air;\ \ my other elements I give to baser life. So, have you done? Come then,\ \ and take the last warmth of my lips. Farewell, kind Charmian. Iras, long\ \ farewell. [Kisses them. IRAS falls and dies] \ \ Have I the aspic in my lips? Dost fall? If thus thou and nature can so gently\ \ part, The stroke of death is as a lover's pinch, Which hurts and is desir'd.\ \ Dost thou lie still? If thou vanishest, thou tell'st the world It is\ \ not worth leave-taking. CHARMIAN. Dissolve, thick cloud, and rain, that I may\ \ say The gods themselves do weep. CLEOPATRA. This proves me base.\n \ \ If she first meet the curled Antony,\n" - "BURGUNDY. Warlike and martial Talbot, Burgundy\n Enshrines thee in his heart,\ \ and there erects Thy noble deeds as valour's monuments. TALBOT. Thanks,\ \ gentle Duke. But where is Pucelle now? I think her old familiar is asleep.\ \ Now where's the Bastard's braves, and Charles his gleeks? What, all amort?\ \ Rouen hangs her head for grief That such a valiant company are fled. Now\ \ will we take some order in the town, Placing therein some expert officers;\ \ And then depart to Paris to the King, For there young Henry with his nobles\ \ lie. BURGUNDY. What Lord Talbot pleaseth Burgundy. TALBOT. But yet, before\ \ we go, let's not forget The noble Duke of Bedford, late deceas'd, But\ \ see his exequies fulfill'd in Rouen. A braver soldier never couched lance,\ \ A gentler heart did never sway in court; But kings and mightiest potentates\ \ must die, For that's the end of human misery. Exeunt\n" - "Your suffering in this dearth, you may as well\n Strike at the heaven with\ \ your staves as lift them Against the Roman state; whose course will on \ \ The way it takes, cracking ten thousand curbs Of more strong link asunder\ \ than can ever Appear in your impediment. For the dearth, The gods, not\ \ the patricians, make it, and Your knees to them, not arms, must help. Alack,\ \ You are transported by calamity Thither where more attends you; and you\ \ slander The helms o' th' state, who care for you like fathers, When you\ \ curse them as enemies. FIRST CITIZEN. Care for us! True, indeed! They ne'er\ \ car'd for us yet. Suffer us to famish, and their storehouses cramm'd with\ \ grain; make edicts for usury, to support usurers; repeal daily any wholesome\ \ act established against the rich, and provide more piercing statutes daily\ \ to chain up and restrain the poor. If the wars eat us not up, they will;\ \ and there's all the love they bear us. MENENIUS. Either you must Confess\ \ yourselves wondrous malicious, Or be accus'd of folly. I shall tell you \ \ A pretty tale. It may be you have heard it; But, since it serves my purpose,\ \ I will venture To stale't a little more. FIRST CITIZEN. Well, I'll hear\ \ it, sir; yet you must not think to fob off our disgrace with a tale. But,\ \ an't please you, deliver. MENENIUS. There was a time when all the body's members\ \ Rebell'd against the belly; thus accus'd it: That only like a gulf it\ \ did remain I' th' midst o' th' body, idle and unactive, Still cupboarding\ \ the viand, never bearing Like labour with the rest; where th' other instruments\ \ Did see and hear, devise, instruct, walk, feel,\n And, mutually participate,\ \ did minister\n" - source_sentence: How does the excerpt reflect themes of loyalty and sacrifice in the play? sentences: - "me a thousand marks in links and torches, walking with thee in\n the night\ \ betwixt tavern and tavern; but the sack that thou hast drunk me would have\ \ bought me lights as good cheap at the dearest chandler's in Europe. I have\ \ maintained that salamander of yours with fire any time this two-and-thirty\ \ years. God reward me for it! Bard. 'Sblood, I would my face were in your\ \ belly! Fal. God-a-mercy! so should I be sure to be heart-burn'd.\n \ \ Enter Hostess. How now, Dame Partlet the hen? Have you enquir'd\ \ yet who pick'd\n my pocket? Host. Why, Sir John, what do you think, Sir\ \ John? Do you think I keep thieves in my house? I have search'd, I have enquired,\ \ so has my husband, man by man, boy by boy, servant by servant. The tithe\ \ of a hair was never lost in my house before. Fal. Ye lie, hostess. Bardolph\ \ was shav'd and lost many a hair, and I'll be sworn my pocket was pick'd.\ \ Go to, you are a woman, go! Host. Who, I? No; I defy thee! God's light, I was\ \ never call'd so in mine own house before! Fal. Go to, I know you well enough.\ \ Host. No, Sir John; you do not know me, Sir John. I know you, Sir John.\ \ You owe me money, Sir John, and now you pick a quarrel to beguile me of\ \ it. I bought you a dozen of shirts to your back. Fal. Dowlas, filthy dowlas!\ \ I have given them away to bakers' wives; they have made bolters of them.\ \ Host. Now, as I am a true woman, holland of eight shillings an ell. You\ \ owe money here besides, Sir John, for your diet and by-drinkings, and money\ \ lent you, four-and-twenty pound. Fal. He had his part of it; let him pay. \ \ Host. He? Alas, he is poor; he hath nothing. Fal. How? Poor? Look upon his\ \ face. What call you rich? Let them coin his nose, let them coin his cheeks.\ \ I'll not pay a denier.\n What, will you make a younker of me? Shall I not\ \ take mine ease\n" - "EDWARD. I wonder how our princely father scap'd,\n Or whether he be scap'd\ \ away or no From Clifford's and Northumberland's pursuit. Had he been ta'en,\ \ we should have heard the news; Had he been slain, we should have heard the\ \ news; Or had he scap'd, methinks we should have heard The happy tidings\ \ of his good escape. How fares my brother? Why is he so sad? RICHARD. I cannot\ \ joy until I be resolv'd Where our right valiant father is become. I saw\ \ him in the battle range about, And watch'd him how he singled Clifford forth.\ \ Methought he bore him in the thickest troop As doth a lion in a herd of\ \ neat;\n Or as a bear, encompass'd round with dogs,\n Who having pinch'd\ \ a few and made them cry, The rest stand all aloof and bark at him. So\ \ far'd our father with his enemies; So fled his enemies my warlike father.\ \ Methinks 'tis prize enough to be his son. See how the morning opes her\ \ golden gates And takes her farewell of the glorious sun. How well resembles\ \ it the prime of youth, Trimm'd like a younker prancing to his love! EDWARD.\ \ Dazzle mine eyes, or do I see three suns? RICHARD. Three glorious suns, each\ \ one a perfect sun; Not separated with the racking clouds, But sever'd\ \ in a pale clear-shining sky. See, see! they join, embrace, and seem to kiss,\ \ As if they vow'd some league inviolable. Now are they but one lamp, one\ \ light, one sun. In this the heaven figures some event. EDWARD. 'Tis wondrous\ \ strange, the like yet never heard of. I think it cites us, brother, to the\ \ field, That we, the sons of brave Plantagenet, Each one already blazing\ \ by our meeds, Should notwithstanding join our lights together And overshine\ \ the earth, as this the world. Whate'er it bodes, henceforward will I bear\ \ Upon my target three fair shining suns. RICHARD. Nay, bear three daughters-\ \ by your leave I speak it, You love the breeder better than the male.\n" - "Forget that rarest treasure of your cheek,\n Exposing it- but, O, the harder\ \ heart! Alack, no remedy!- to the greedy touch Of common-kissing Titan,\ \ and forget Your laboursome and dainty trims wherein You made great Juno\ \ angry. IMOGEN. Nay, be brief; I see into thy end, and am almost A man\ \ already. PISANIO. First, make yourself but like one. Fore-thinking this,\ \ I have already fit- 'Tis in my cloak-bag- doublet, hat, hose, all That\ \ answer to them. Would you, in their serving, And with what imitation you\ \ can borrow From youth of such a season, fore noble Lucius Present yourself,\ \ desire his service, tell him Wherein you're happy- which will make him know\ \ If that his head have ear in music; doubtless With joy he will embrace\ \ you; for he's honourable, And, doubling that, most holy. Your means abroad-\ \ You have me, rich; and I will never fail Beginning nor supplyment. IMOGEN.\ \ Thou art all the comfort The gods will diet me with. Prithee away! There's\ \ more to be consider'd; but we'll even All that good time will give us. This\ \ attempt I am soldier to, and will abide it with A prince's courage. Away,\ \ I prithee. PISANIO. Well, madam, we must take a short farewell, Lest, being\ \ miss'd, I be suspected of Your carriage from the court. My noble mistress,\ \ Here is a box; I had it from the Queen. What's in't is precious. If you\ \ are sick at sea Or stomach-qualm'd at land, a dram of this\n Will drive\ \ away distemper. To some shade,\n And fit you to your manhood. May the gods\ \ Direct you to the best! IMOGEN. Amen. I thank thee. Exeunt\ \ severally\n" - source_sentence: The excerpt showcases the emotional turmoil and sense of honor that drives Brutus to take his own life in the face of defeat. sentences: - "Thou know'st that we two went to school together;\n Even for that our love\ \ of old, I prithee, Hold thou my sword-hilts, whilst I run on it. VOLUMNIUS.\ \ That's not an office for a friend, my lord. \ \ Alarum still. CLITUS. Fly, fly, my lord, there is no tarrying\ \ here. BRUTUS. Farewell to you, and you, and you, Volumnius. Strato, thou\ \ hast been all this while asleep; Farewell to thee too, Strato. Countrymen,\ \ My heart doth joy that yet in all my life I found no man but he was true\ \ to me. I shall have glory by this losing day, More than Octavius and Mark\ \ Antony By this vile conquest shall attain unto. So, fare you well at once,\ \ for Brutus' tongue Hath almost ended his life's history. Night hangs upon\ \ mine eyes, my bones would rest That have but labor'd to attain this hour.\ \ Alarum. Cry within, \"Fly, fly, fly!\" CLITUS. Fly,\ \ my lord, fly. BRUTUS. Hence! I will follow. Exeunt Clitus,\ \ Dardanius, and Volumnius. I prithee, Strato, stay thou by thy lord. Thou\ \ art a fellow of a good respect; Thy life hath had some smatch of honor in\ \ it. Hold then my sword, and turn away thy face, While I do run upon it.\ \ Wilt thou, Strato? STRATO. Give me your hand first. Fare you well, my lord.\ \ BRUTUS. Farewell, good Strato. Runs on his sword. Caesar,\ \ now be still; I kill'd not thee with half so good a will. Dies.\n\ \ Alarum. Retreat. Enter Octavius, Antony, Messala,\n Lucilius,\ \ and the Army.\n OCTAVIUS. What man is that?\n" - "Elsinore. A room in the Castle.\nEnter King, Queen, Polonius, Ophelia, Rosencrantz,\ \ Guildenstern, and Lords. King. And can you by no drift of circumstance\n \ \ Get from him why he puts on this confusion, Grating so harshly all his days\ \ of quiet With turbulent and dangerous lunacy? Ros. He does confess he feels\ \ himself distracted, But from what cause he will by no means speak. Guil.\ \ Nor do we find him forward to be sounded, But with a crafty madness keeps\ \ aloof When we would bring him on to some confession Of his true state.\ \ Queen. Did he receive you well? Ros. Most like a gentleman. Guil. But with\ \ much forcing of his disposition. Ros. Niggard of question, but of our demands\ \ Most free in his reply. Queen. Did you assay him To any pastime? Ros.\ \ Madam, it so fell out that certain players\n We o'erraught on the way.\ \ Of these we told him,\n" - "VII.\nThe French camp near Agincourt\nEnter the CONSTABLE OF FRANCE, the LORD\ \ RAMBURES, the DUKE OF ORLEANS,\nthe DAUPHIN, with others\n CONSTABLE. Tut!\ \ I have the best armour of the world.\n Would it were day! ORLEANS. You have\ \ an excellent armour; but let my horse have his due. CONSTABLE. It is the\ \ best horse of Europe. ORLEANS. Will it never be morning? DAUPHIN. My Lord\ \ of Orleans and my Lord High Constable, you talk of horse and armour? ORLEANS.\ \ You are as well provided of both as any prince in the world. DAUPHIN. What\ \ a long night is this! I will not change my horse with any that treads but\ \ on four pasterns. Ca, ha! he bounds from the earth as if his entrails were\ \ hairs; le cheval volant, the Pegasus, chez les narines de feu! When I bestride\ \ him I soar, I am a hawk. He trots the air; the earth sings when he touches\ \ it; the basest horn of his hoof is more musical than the pipe of Hermes.\ \ ORLEANS. He's of the colour of the nutmeg. DAUPHIN. And of the heat of the\ \ ginger. It is a beast for Perseus: he is pure air and fire; and the dull\ \ elements of earth and water never appear in him, but only in patient stillness\ \ while his rider mounts him; he is indeed a horse, and all other jades you\ \ may call beasts. CONSTABLE. Indeed, my lord, it is a most absolute and excellent\ \ horse.\n DAUPHIN. It is the prince of palfreys; his neigh is like the\n" - source_sentence: What themes are present in the excerpt from the play? sentences: - "Enter TRAVERS NORTHUMBERLAND. Here comes my servant Travers, whom I sent\n \ \ On Tuesday last to listen after news. LORD BARDOLPH. My lord, I over-rode\ \ him on the way; And he is furnish'd with no certainties More than he haply\ \ may retail from me. NORTHUMBERLAND. Now, Travers, what good tidings comes with\ \ you? TRAVERS. My lord, Sir John Umfrevile turn'd me back With joyful tidings;\ \ and, being better hors'd, Out-rode me. After him came spurring hard A\ \ gentleman, almost forspent with speed, That stopp'd by me to breathe his\ \ bloodied horse. He ask'd the way to Chester; and of him I did demand what\ \ news from Shrewsbury. He told me that rebellion had bad luck, And that\ \ young Harry Percy's spur was cold. With that he gave his able horse the\ \ head And, bending forward, struck his armed heels\n Against the panting\ \ sides of his poor jade\n Up to the rowel-head; and starting so, He seem'd\ \ in running to devour the way, Staying no longer question. NORTHUMBERLAND.\ \ Ha! Again: Said he young Harry Percy's spur was cold? Of Hotspur, Coldspur?\ \ that rebellion Had met ill luck? LORD BARDOLPH. My lord, I'll tell you what:\ \ If my young lord your son have not the day, Upon mine honour, for a silken\ \ point I'll give my barony. Never talk of it. NORTHUMBERLAND. Why should\ \ that gentleman that rode by Travers Give then such instances of loss? LORD\ \ BARDOLPH. Who- he? He was some hilding fellow that had stol'n The horse\ \ he rode on and, upon my life, Spoke at a venture. Look, here comes more news.\ \ \n Enter Morton NORTHUMBERLAND. Yea, this man's brow,\ \ like to a title-leaf,\n" - "ANTONY. Yet they are not join'd. Where yond pine does stand\n I shall discover\ \ all. I'll bring thee word Straight how 'tis like to go. \ \ Exit SCARUS. Swallows have built In Cleopatra's sails their nests.\ \ The augurers Say they know not, they cannot tell; look grimly, And dare\ \ not speak their knowledge. Antony Is valiant and dejected; and by starts\ \ His fretted fortunes give him hope and fear Of what he has and has not.\ \ [Alarum afar off, as at a sea-fight]\n \ \ Re-enter ANTONY ANTONY. All is lost!\n This foul Egyptian hath\ \ betrayed me. My fleet hath yielded to the foe, and yonder They cast\ \ their caps up and carouse together Like friends long lost. Triple-turn'd\ \ whore! 'tis thou\n Hast sold me to this novice; and my heart\n Makes\ \ only wars on thee. Bid them all fly; For when I am reveng'd upon my charm,\ \ I have done all. Bid them all fly; begone. Exit SCARUS O sun, thy\ \ uprise shall I see no more! Fortune and Antony part here; even here Do\ \ we shake hands. All come to this? The hearts That spaniel'd me at heels,\ \ to whom I gave Their wishes, do discandy, melt their sweets On blossoming\ \ Caesar; and this pine is bark'd That overtopp'd them all. Betray'd I am.\ \ O this false soul of Egypt! this grave charm- Whose eye beck'd forth my\ \ wars and call'd them home, Whose bosom was my crownet, my chief end- Like\ \ a right gypsy hath at fast and loose Beguil'd me to the very heart of loss.\ \ What, Eros, Eros! Enter CLEOPATRA\n Ah, thou spell!\ \ Avaunt!\n" - "TALBOT. Saint George and victory! Fight, soldiers, fight.\n The Regent hath\ \ with Talbot broke his word And left us to the rage of France his sword. \ \ Where is John Talbot? Pause and take thy breath; I gave thee life and rescu'd\ \ thee from death. JOHN. O, twice my father, twice am I thy son! The life\ \ thou gav'st me first was lost and done Till with thy warlike sword, despite\ \ of fate, To my determin'd time thou gav'st new date. TALBOT. When from the\ \ Dauphin's crest thy sword struck fire, It warm'd thy father's heart with\ \ proud desire Of bold-fac'd victory. Then leaden age, Quicken'd with youthful\ \ spleen and warlike rage, Beat down Alencon, Orleans, Burgundy, And from\ \ the pride of Gallia rescued thee. The ireful bastard Orleans, that drew blood\ \ From thee, my boy, and had the maidenhood Of thy first fight, I soon encountered\ \ And, interchanging blows, I quickly shed Some of his bastard blood; and\ \ in disgrace\n Bespoke him thus: 'Contaminated, base,\n" - source_sentence: What is the significance of the tennis balls in the excerpt from the play? sentences: - "My fault is past. But, O, what form of prayer\n Can serve my turn? 'Forgive\ \ me my foul murther'? That cannot be; since I am still possess'd Of those\ \ effects for which I did the murther- My crown, mine own ambition, and my\ \ queen. May one be pardon'd and retain th' offence? In the corrupted currents\ \ of this world Offence's gilded hand may shove by justice, And oft 'tis\ \ seen the wicked prize itself Buys out the law; but 'tis not so above. \ \ There is no shuffling; there the action lies In his true nature, and we ourselves\ \ compell'd, Even to the teeth and forehead of our faults, To give in evidence.\ \ What then? What rests? Try what repentance can. What can it not? Yet what\ \ can it when one cannot repent? O wretched state! O bosom black as death!\ \ O limed soul, that, struggling to be free, Art more engag'd! Help, angels!\ \ Make assay. Bow, stubborn knees; and heart with strings of steel, Be\ \ soft as sinews of the new-born babe! All may be well. \ \ He kneels.\n Enter Hamlet. Ham. Now might\ \ I do it pat, now he is praying;\n And now I'll do't. And so he goes to heaven,\ \ And so am I reveng'd. That would be scann'd. A villain kills my father;\ \ and for that, I, his sole son, do this same villain send To heaven. \ \ Why, this is hire and salary, not revenge! He took my father grossly, full\ \ of bread, With all his crimes broad blown, as flush as May; And how his\ \ audit stands, who knows save heaven?\n But in our circumstance and course\ \ of thought,\n" - "YORK. From Ireland thus comes York to claim his right\n And pluck the crown\ \ from feeble Henry's head: Ring bells aloud, burn bonfires clear and bright,\ \ To entertain great England's lawful king. Ah, sancta majestas! who would\ \ not buy thee dear? Let them obey that knows not how to rule; This hand\ \ was made to handle nought but gold. I cannot give due action to my words\ \ Except a sword or sceptre balance it.\n A sceptre shall it have, have\ \ I a soul\n On which I'll toss the flower-de-luce of France.\n \ \ Enter BUCKINGHAM [Aside] Whom have we here? Buckingham, to disturb\ \ me?\n The King hath sent him, sure: I must dissemble. BUCKINGHAM. York,\ \ if thou meanest well I greet thee well. YORK. Humphrey of Buckingham, I accept\ \ thy greeting. Art thou a messenger, or come of pleasure? BUCKINGHAM. A messenger\ \ from Henry, our dread liege, To know the reason of these arms in peace; \ \ Or why thou, being a subject as I am, Against thy oath and true allegiance\ \ sworn, Should raise so great a power without his leave, Or dare to bring\ \ thy force so near the court. YORK. [Aside] Scarce can I speak, my choler is\ \ so great. O, I could hew up rocks and fight with flint, I am so angry\ \ at these abject terms; And now, like Ajax Telamonius, On sheep or oxen\ \ could I spend my fury. I am far better born than is the King, More like\ \ a king, more kingly in my thoughts; But I must make fair weather yet awhile,\ \ Till Henry be more weak and I more strong.- Buckingham, I prithee, pardon\ \ me That I have given no answer all this while; My mind was troubled with\ \ deep melancholy. The cause why I have brought this army hither Is to\ \ remove proud Somerset from the King, Seditious to his Grace and to the state.\ \ BUCKINGHAM. That is too much presumption on thy part; But if thy arms be\ \ to no other end, The King hath yielded unto thy demand:\n The Duke of\ \ Somerset is in the Tower.\n" - "Says that you savour too much of your youth,\n And bids you be advis'd there's\ \ nought in France That can be with a nimble galliard won; You cannot revel\ \ into dukedoms there. He therefore sends you, meeter for your spirit, This\ \ tun of treasure; and, in lieu of this, Desires you let the dukedoms that\ \ you claim Hear no more of you. This the Dauphin speaks. KING HENRY. What\ \ treasure, uncle? EXETER. Tennis-balls, my liege. KING HENRY. We are glad the\ \ Dauphin is so pleasant with us; His present and your pains we thank you for.\ \ When we have match'd our rackets to these balls, We will in France,\ \ by God's grace, play a set Shall strike his father's crown into the hazard.\ \ Tell him he hath made a match with such a wrangler That all the courts\ \ of France will be disturb'd With chaces. And we understand him well, How\ \ he comes o'er us with our wilder days, Not measuring what use we made of\ \ them. We never valu'd this poor seat of England; And therefore, living\ \ hence, did give ourself To barbarous licence; as 'tis ever common That\ \ men are merriest when they are from home. But tell the Dauphin I will keep\ \ my state, Be like a king, and show my sail of greatness, When I do rouse\ \ me in my throne of France; For that I have laid by my majesty And plodded\ \ like a man for working-days; But I will rise there with so full a glory \ \ That I will dazzle all the eyes of France, Yea, strike the Dauphin blind\ \ to look on us. And tell the pleasant Prince this mock of his Hath turn'd\ \ his balls to gun-stones, and his soul Shall stand sore charged for the wasteful\ \ vengeance\n That shall fly with them; for many a thousand widows\n" model-index: - name: RAG_general/rerank/models/BAAI-bge-large-en-v1.5-ft results: - task: type: information-retrieval name: Information Retrieval dataset: name: large dev type: large-dev metrics: - type: cosine_accuracy@3 value: 0.5243266724587315 name: Cosine Accuracy@3 - type: cosine_precision@1 value: 0.4161598609904431 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.17477555748624385 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11268462206776718 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.060729800173761936 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.4161598609904431 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5243266724587315 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.5634231103388357 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.6072980017376195 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5090845268414399 name: Cosine Ndcg@10 - type: cosine_mrr@200 value: 0.483708993138636 name: Cosine Mrr@200 - type: cosine_map@100 value: 0.483416229474969 name: Cosine Map@100 - type: dot_accuracy@3 value: 0.5243266724587315 name: Dot Accuracy@3 - type: dot_precision@1 value: 0.4161598609904431 name: Dot Precision@1 - type: dot_precision@3 value: 0.17477555748624385 name: Dot Precision@3 - type: dot_precision@5 value: 0.11268462206776718 name: Dot Precision@5 - type: dot_precision@10 value: 0.060729800173761936 name: Dot Precision@10 - type: dot_recall@1 value: 0.4161598609904431 name: Dot Recall@1 - type: dot_recall@3 value: 0.5243266724587315 name: Dot Recall@3 - type: dot_recall@5 value: 0.5634231103388357 name: Dot Recall@5 - type: dot_recall@10 value: 0.6072980017376195 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5090845268414399 name: Dot Ndcg@10 - type: dot_mrr@200 value: 0.483708993138636 name: Dot Mrr@200 - type: dot_map@100 value: 0.483416229474969 name: Dot Map@100 --- # RAG_general/rerank/models/BAAI-bge-large-en-v1.5-ft This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity <!-- - **Training Dataset:** Unknown --> - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("rjnClarke/BAAI-bge-large-en-v1.5-fine-tuned") # Run inference sentences = [ 'What is the significance of the tennis balls in the excerpt from the play?', "Says that you savour too much of your youth,\n And bids you be advis'd there's nought in France That can be with a nimble galliard won; You cannot revel into dukedoms there. He therefore sends you, meeter for your spirit, This tun of treasure; and, in lieu of this, Desires you let the dukedoms that you claim Hear no more of you. This the Dauphin speaks. KING HENRY. What treasure, uncle? EXETER. Tennis-balls, my liege. KING HENRY. We are glad the Dauphin is so pleasant with us; His present and your pains we thank you for. When we have match'd our rackets to these balls, We will in France, by God's grace, play a set Shall strike his father's crown into the hazard. Tell him he hath made a match with such a wrangler That all the courts of France will be disturb'd With chaces. And we understand him well, How he comes o'er us with our wilder days, Not measuring what use we made of them. We never valu'd this poor seat of England; And therefore, living hence, did give ourself To barbarous licence; as 'tis ever common That men are merriest when they are from home. But tell the Dauphin I will keep my state, Be like a king, and show my sail of greatness, When I do rouse me in my throne of France; For that I have laid by my majesty And plodded like a man for working-days; But I will rise there with so full a glory That I will dazzle all the eyes of France, Yea, strike the Dauphin blind to look on us. And tell the pleasant Prince this mock of his Hath turn'd his balls to gun-stones, and his soul Shall stand sore charged for the wasteful vengeance\n That shall fly with them; for many a thousand widows\n", "YORK. From Ireland thus comes York to claim his right\n And pluck the crown from feeble Henry's head: Ring bells aloud, burn bonfires clear and bright, To entertain great England's lawful king. Ah, sancta majestas! who would not buy thee dear? Let them obey that knows not how to rule; This hand was made to handle nought but gold. I cannot give due action to my words Except a sword or sceptre balance it.\n A sceptre shall it have, have I a soul\n On which I'll toss the flower-de-luce of France.\n Enter BUCKINGHAM [Aside] Whom have we here? Buckingham, to disturb me?\n The King hath sent him, sure: I must dissemble. BUCKINGHAM. York, if thou meanest well I greet thee well. YORK. Humphrey of Buckingham, I accept thy greeting. Art thou a messenger, or come of pleasure? BUCKINGHAM. A messenger from Henry, our dread liege, To know the reason of these arms in peace; Or why thou, being a subject as I am, Against thy oath and true allegiance sworn, Should raise so great a power without his leave, Or dare to bring thy force so near the court. YORK. [Aside] Scarce can I speak, my choler is so great. O, I could hew up rocks and fight with flint, I am so angry at these abject terms; And now, like Ajax Telamonius, On sheep or oxen could I spend my fury. I am far better born than is the King, More like a king, more kingly in my thoughts; But I must make fair weather yet awhile, Till Henry be more weak and I more strong.- Buckingham, I prithee, pardon me That I have given no answer all this while; My mind was troubled with deep melancholy. The cause why I have brought this army hither Is to remove proud Somerset from the King, Seditious to his Grace and to the state. BUCKINGHAM. That is too much presumption on thy part; But if thy arms be to no other end, The King hath yielded unto thy demand:\n The Duke of Somerset is in the Tower.\n", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> ## Evaluation ### Metrics #### Information Retrieval * Dataset: `large-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@3 | 0.5243 | | cosine_precision@1 | 0.4162 | | cosine_precision@3 | 0.1748 | | cosine_precision@5 | 0.1127 | | cosine_precision@10 | 0.0607 | | cosine_recall@1 | 0.4162 | | cosine_recall@3 | 0.5243 | | cosine_recall@5 | 0.5634 | | cosine_recall@10 | 0.6073 | | cosine_ndcg@10 | 0.5091 | | cosine_mrr@200 | 0.4837 | | **cosine_map@100** | **0.4834** | | dot_accuracy@3 | 0.5243 | | dot_precision@1 | 0.4162 | | dot_precision@3 | 0.1748 | | dot_precision@5 | 0.1127 | | dot_precision@10 | 0.0607 | | dot_recall@1 | 0.4162 | | dot_recall@3 | 0.5243 | | dot_recall@5 | 0.5634 | | dot_recall@10 | 0.6073 | | dot_ndcg@10 | 0.5091 | | dot_mrr@200 | 0.4837 | | dot_map@100 | 0.4834 | <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 10,359 training samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 22.32 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 35 tokens</li><li>mean: 351.19 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | anchor | positive | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Who is the general being described in the excerpt?</code> | <code>PHILO. Nay, but this dotage of our general's<br> O'erflows the measure. Those his goodly eyes, That o'er the files and musters of the war Have glow'd like plated Mars, now bend, now turn, The office and devotion of their view Upon a tawny front. His captain's heart, Which in the scuffles of great fights hath burst<br> The buckles on his breast, reneges all temper,<br> And is become the bellows and the fan To cool a gipsy's lust.<br> Flourish. Enter ANTONY, CLEOPATRA, her LADIES, the train,<br> with eunuchs fanning her<br> Look where they come!<br> Take but good note, and you shall see in him The triple pillar of the world transform'd Into a strumpet's fool. Behold and see. CLEOPATRA. If it be love indeed, tell me how much. ANTONY. There's beggary in the love that can be reckon'd. CLEOPATRA. I'll set a bourn how far to be belov'd. ANTONY. Then must thou needs find out new heaven, new earth.<br> Enter a MESSENGER MESSENGER. News, my good lord, from Rome.<br> ANTONY. Grates me the sum. CLEOPATRA. Nay, hear them, Antony. Fulvia perchance is angry; or who knows If the scarce-bearded Caesar have not sent His pow'rful mandate to you: 'Do this or this; Take in that kingdom and enfranchise that; Perform't, or else we damn thee.' ANTONY. How, my love? CLEOPATRA. Perchance? Nay, and most like, You must not stay here longer; your dismission Is come from Caesar; therefore hear it, Antony. Where's Fulvia's process? Caesar's I would say? Both? Call in the messengers. As I am Egypt's Queen, Thou blushest, Antony, and that blood of thine Is Caesar's homager. Else so thy cheek pays shame<br> When shrill-tongu'd Fulvia scolds. The messengers!<br></code> | | <code>What is the main conflict highlighted in the excerpt?</code> | <code>PHILO. Nay, but this dotage of our general's<br> O'erflows the measure. Those his goodly eyes, That o'er the files and musters of the war Have glow'd like plated Mars, now bend, now turn, The office and devotion of their view Upon a tawny front. His captain's heart, Which in the scuffles of great fights hath burst<br> The buckles on his breast, reneges all temper,<br> And is become the bellows and the fan To cool a gipsy's lust.<br> Flourish. Enter ANTONY, CLEOPATRA, her LADIES, the train,<br> with eunuchs fanning her<br> Look where they come!<br> Take but good note, and you shall see in him The triple pillar of the world transform'd Into a strumpet's fool. Behold and see. CLEOPATRA. If it be love indeed, tell me how much. ANTONY. There's beggary in the love that can be reckon'd. CLEOPATRA. I'll set a bourn how far to be belov'd. ANTONY. Then must thou needs find out new heaven, new earth.<br> Enter a MESSENGER MESSENGER. News, my good lord, from Rome.<br> ANTONY. Grates me the sum. CLEOPATRA. Nay, hear them, Antony. Fulvia perchance is angry; or who knows If the scarce-bearded Caesar have not sent His pow'rful mandate to you: 'Do this or this; Take in that kingdom and enfranchise that; Perform't, or else we damn thee.' ANTONY. How, my love? CLEOPATRA. Perchance? Nay, and most like, You must not stay here longer; your dismission Is come from Caesar; therefore hear it, Antony. Where's Fulvia's process? Caesar's I would say? Both? Call in the messengers. As I am Egypt's Queen, Thou blushest, Antony, and that blood of thine Is Caesar's homager. Else so thy cheek pays shame<br> When shrill-tongu'd Fulvia scolds. The messengers!<br></code> | | <code>The excerpt showcases the tension between Antony's loyalty to Cleopatra and his obligations to Caesar, as well as Cleopatra's influence over him.</code> | <code>PHILO. Nay, but this dotage of our general's<br> O'erflows the measure. Those his goodly eyes, That o'er the files and musters of the war Have glow'd like plated Mars, now bend, now turn, The office and devotion of their view Upon a tawny front. His captain's heart, Which in the scuffles of great fights hath burst<br> The buckles on his breast, reneges all temper,<br> And is become the bellows and the fan To cool a gipsy's lust.<br> Flourish. Enter ANTONY, CLEOPATRA, her LADIES, the train,<br> with eunuchs fanning her<br> Look where they come!<br> Take but good note, and you shall see in him The triple pillar of the world transform'd Into a strumpet's fool. Behold and see. CLEOPATRA. If it be love indeed, tell me how much. ANTONY. There's beggary in the love that can be reckon'd. CLEOPATRA. I'll set a bourn how far to be belov'd. ANTONY. Then must thou needs find out new heaven, new earth.<br> Enter a MESSENGER MESSENGER. News, my good lord, from Rome.<br> ANTONY. Grates me the sum. CLEOPATRA. Nay, hear them, Antony. Fulvia perchance is angry; or who knows If the scarce-bearded Caesar have not sent His pow'rful mandate to you: 'Do this or this; Take in that kingdom and enfranchise that; Perform't, or else we damn thee.' ANTONY. How, my love? CLEOPATRA. Perchance? Nay, and most like, You must not stay here longer; your dismission Is come from Caesar; therefore hear it, Antony. Where's Fulvia's process? Caesar's I would say? Both? Call in the messengers. As I am Egypt's Queen, Thou blushest, Antony, and that blood of thine Is Caesar's homager. Else so thy cheek pays shame<br> When shrill-tongu'd Fulvia scolds. The messengers!<br></code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 2,302 evaluation samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 21.73 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 354.59 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | anchor | positive | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>The excerpt highlights the tension between Antony's loyalty to Cleopatra and his standing in Rome, showcasing the intricate balance of power and love in the play.</code> | <code>When shrill-tongu'd Fulvia scolds. The messengers!<br> ANTONY. Let Rome in Tiber melt, and the wide arch Of the rang'd empire fall! Here is my space. Kingdoms are clay; our dungy earth alike Feeds beast as man. The nobleness of life Is to do thus [emhracing], when such a mutual pair And such a twain can do't, in which I bind, On pain of punishment, the world to weet We stand up peerless. CLEOPATRA. Excellent falsehood! Why did he marry Fulvia, and not love her? I'll seem the fool I am not. Antony Will be himself. ANTONY. But stirr'd by Cleopatra. Now for the love of Love and her soft hours, Let's not confound the time with conference harsh; There's not a minute of our lives should stretch Without some pleasure now. What sport to-night? CLEOPATRA. Hear the ambassadors. ANTONY. Fie, wrangling queen! Whom everything becomes- to chide, to laugh, To weep; whose every passion fully strives To make itself in thee fair and admir'd. No messenger but thine, and all alone To-night we'll wander through the streets and note The qualities of people. Come, my queen; Last night you did desire it. Speak not to us. Exeunt ANTONY and CLEOPATRA, with the train DEMETRIUS. Is Caesar with Antonius priz'd so slight? PHILO. Sir, sometimes when he is not Antony, He comes too short of that great property Which still should go with Antony. DEMETRIUS. I am full sorry That he approves the common liar, who Thus speaks of him at Rome; but I will hope<br> Of better deeds to-morrow. Rest you happy! Exeunt<br></code> | | <code>What is the significance of the soothsayer in the context of the play?</code> | <code>CHARMIAN. Lord Alexas, sweet Alexas, most anything Alexas, almost<br> most absolute Alexas, where's the soothsayer that you prais'd so to th' Queen? O that I knew this husband, which you say must charge his horns with garlands! ALEXAS. Soothsayer! SOOTHSAYER. Your will? CHARMIAN. Is this the man? Is't you, sir, that know things? SOOTHSAYER. In nature's infinite book of secrecy A little I can read. ALEXAS. Show him your hand.<br> Enter ENOBARBUS ENOBARBUS. Bring in the banquet quickly; wine enough<br> Cleopatra's health to drink. CHARMIAN. Good, sir, give me good fortune. SOOTHSAYER. I make not, but foresee. CHARMIAN. Pray, then, foresee me one. SOOTHSAYER. You shall be yet far fairer than you are. CHARMIAN. He means in flesh. IRAS. No, you shall paint when you are old. CHARMIAN. Wrinkles forbid! ALEXAS. Vex not his prescience; be attentive. CHARMIAN. Hush!<br> SOOTHSAYER. You shall be more beloving than beloved.<br></code> | | <code>What is the setting of the scene in which the excerpt takes place?</code> | <code>sweet Isis, I beseech thee! And let her die too, and give him a<br> worse! And let worse follow worse, till the worst of all follow him laughing to his grave, fiftyfold a cuckold! Good Isis, hear me this prayer, though thou deny me a matter of more weight; good Isis, I beseech thee! IRAS. Amen. Dear goddess, hear that prayer of the people! For, as it is a heartbreaking to see a handsome man loose-wiv'd, so it is a deadly sorrow to behold a foul knave uncuckolded. Therefore, dear Isis, keep decorum, and fortune him accordingly! CHARMIAN. Amen. ALEXAS. Lo now, if it lay in their hands to make me a cuckold, they would make themselves whores but they'ld do't!<br> Enter CLEOPATRA ENOBARBUS. Hush! Here comes Antony.<br> CHARMIAN. Not he; the Queen. CLEOPATRA. Saw you my lord? ENOBARBUS. No, lady. CLEOPATRA. Was he not here? CHARMIAN. No, madam. CLEOPATRA. He was dispos'd to mirth; but on the sudden A Roman thought hath struck him. Enobarbus! ENOBARBUS. Madam? CLEOPATRA. Seek him, and bring him hither. Where's Alexas? ALEXAS. Here, at your service. My lord approaches.<br> Enter ANTONY, with a MESSENGER and attendants CLEOPATRA. We will not look upon him. Go with us.<br> Exeunt CLEOPATRA, ENOBARBUS, and the rest MESSENGER. Fulvia thy wife first came into the field. ANTONY. Against my brother Lucius? MESSENGER. Ay.<br> But soon that war had end, and the time's state<br></code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `learning_rate`: 3e-05 - `num_train_epochs`: 4 - `warmup_steps`: 50 - `fp16`: True - `load_best_model_at_end`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 3e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 50 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | loss | large-dev_cosine_map@100 | |:-------:|:--------:|:-------------:|:----------:|:------------------------:| | 1.0 | 324 | - | 1.5357 | 0.4824 | | 1.5432 | 500 | 1.7247 | - | - | | 2.0 | 648 | - | 1.5137 | 0.4806 | | 3.0 | 972 | - | 1.5700 | 0.4732 | | 3.0864 | 1000 | 0.8627 | - | - | | **4.0** | **1296** | **-** | **1.5816** | **0.4834** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.43.4 - PyTorch: 2.3.1+cu121 - Accelerate: 0.32.1 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION" ]
[ "BEAR" ]
Non_BioNLP
Marqo/multilingual-e5-small
Marqo
sentence-similarity
[ "sentence-transformers", "pytorch", "onnx", "safetensors", "bert", "mteb", "Sentence Transformers", "sentence-similarity", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "arxiv:2402.05672", "arxiv:2108.08787", "arxiv:2104.08663", "arxiv:2210.07316", "license:mit", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
1,725
1,725
79
2
--- language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit tags: - mteb - Sentence Transformers - sentence-similarity - sentence-transformers model-index: - name: intfloat/multilingual-e5-small results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.79104477611939 - type: ap value: 36.9996434842022 - type: f1 value: 67.95453679103099 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (de) type: mteb/amazon_counterfactual config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.64882226980728 - type: ap value: 82.11942130026586 - type: f1 value: 69.87963421606715 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en-ext) type: mteb/amazon_counterfactual config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.8095952023988 - type: ap value: 24.46869495579561 - type: f1 value: 63.00108480037597 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (ja) type: mteb/amazon_counterfactual config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 64.186295503212 - type: ap value: 15.496804690197042 - type: f1 value: 52.07153895475031 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 88.699325 - type: ap value: 85.27039559917269 - type: f1 value: 88.65556295032513 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 44.69799999999999 - type: f1 value: 43.73187348654165 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (de) type: mteb/amazon_reviews_multi config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.245999999999995 - type: f1 value: 39.3863530637684 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (es) type: mteb/amazon_reviews_multi config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.394 - type: f1 value: 39.301223469483446 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.864 - type: f1 value: 37.97974261868003 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (ja) type: mteb/amazon_reviews_multi config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.682 - type: f1 value: 37.07399369768313 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.504 - type: f1 value: 36.62317273874278 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 19.061 - type: map_at_10 value: 31.703 - type: map_at_100 value: 32.967 - type: map_at_1000 value: 33.001000000000005 - type: map_at_3 value: 27.466 - type: map_at_5 value: 29.564 - type: mrr_at_1 value: 19.559 - type: mrr_at_10 value: 31.874999999999996 - type: mrr_at_100 value: 33.146 - type: mrr_at_1000 value: 33.18 - type: mrr_at_3 value: 27.667 - type: mrr_at_5 value: 29.74 - type: ndcg_at_1 value: 19.061 - type: ndcg_at_10 value: 39.062999999999995 - type: ndcg_at_100 value: 45.184000000000005 - type: ndcg_at_1000 value: 46.115 - type: ndcg_at_3 value: 30.203000000000003 - type: ndcg_at_5 value: 33.953 - type: precision_at_1 value: 19.061 - type: precision_at_10 value: 6.279999999999999 - type: precision_at_100 value: 0.9129999999999999 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 12.706999999999999 - type: precision_at_5 value: 9.431000000000001 - type: recall_at_1 value: 19.061 - type: recall_at_10 value: 62.802 - type: recall_at_100 value: 91.323 - type: recall_at_1000 value: 98.72 - type: recall_at_3 value: 38.122 - type: recall_at_5 value: 47.155 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 39.22266660528253 - 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type: precision_at_5 value: 19.781000000000002 - type: recall_at_1 value: 90.802 - type: recall_at_10 value: 99.494 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 99.747 - type: recall_at_3 value: 97.975 - type: recall_at_5 value: 98.90299999999999 --- ## Multilingual-E5-small **Disclaimer**: This model is cloned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). The only difference from the original model is `pad_token_id` in `config.json` which is corrected to `1`. [Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 12 layers and the embedding size is 384. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] # Each input text should start with "query: " or "passage: ", even for non-English texts. # For tasks other than retrieval, you can simply use the "query: " prefix. input_texts = ['query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"] tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-small') model = AutoModel.from_pretrained('intfloat/multilingual-e5-small') # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ## Supported Languages This model is initialized from [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. ## Training Details **Initialization**: [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) **First stage**: contrastive pre-training with weak supervision | Dataset | Weak supervision | # of text pairs | |--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------| | Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B | | [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M | | [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B | | [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M | | Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M | | [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M | | [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M | | [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M | | [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M | **Second stage**: supervised fine-tuning | Dataset | Language | # of text pairs | |----------------------------------------------------------------------------------------|--------------|-----------------| | [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k | | [NQ](https://github.com/facebookresearch/DPR) | English | 70k | | [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k | | [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k | | [ELI5](https://huggingface.co/datasets/eli5) | English | 500k | | [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k | | [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k | | [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k | | [SQuAD](https://huggingface.co/datasets/squad) | English | 87k | | [Quora](https://huggingface.co/datasets/quora) | English | 150k | | [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k | | [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k | For all labeled datasets, we only use its training set for fine-tuning. For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672). ## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787) | Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th | |-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- | | BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 | | mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 | | BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 | | | | | multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 | | multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 | | multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 | ## MTEB Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## Support for Sentence Transformers Below is an example for usage with sentence_transformers. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('intfloat/multilingual-e5-small') input_texts = [ 'query: how much protein should a female eat', 'query: 南瓜的家常做法', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅" ] embeddings = model.encode(input_texts, normalize_embeddings=True) ``` Package requirements `pip install sentence_transformers~=2.2.2` Contributors: [michaelfeil](https://huggingface.co/michaelfeil) ## FAQ **1. Do I need to add the prefix "query: " and "passage: " to input texts?** Yes, this is how the model is trained, otherwise you will see a performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. - Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval. - Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. **2. Why are my reproduced results slightly different from reported in the model card?** Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. **3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue. ## Citation If you find our paper or models helpful, please consider cite as follows: ``` @article{wang2024multilingual, title={Multilingual E5 Text Embeddings: A Technical Report}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2402.05672}, year={2024} } ``` ## Limitations Long texts will be truncated to at most 512 tokens.
[ "SEMANTIC_SIMILARITY", "TRANSLATION", "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
grimjim/llama-3-aaditya-OpenBioLLM-8B
grimjim
text-generation
[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "llama-3", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation", "en", "arxiv:2305.18290", "arxiv:2303.13375", "arxiv:2212.13138", "arxiv:2305.09617", "arxiv:2402.07023", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:finetune:meta-llama/Meta-Llama-3-8B", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,716
1,716
32
0
--- base_model: meta-llama/Meta-Llama-3-8B language: - en license: llama3 tags: - llama-3 - llama - Mixtral - instruct - finetune - chatml - DPO - RLHF - gpt4 - distillation widget: - example_title: OpenBioLLM-8B messages: - role: system content: You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. - role: user content: How long does it take for newborn jaundice to go away? output: text: 'Newborn jaundice, also known as neonatal jaundice, is a common condition in newborns where the yellowing of the skin and eyes occurs due to an elevated level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when red blood cells break down. In most cases, newborn jaundice resolves on its own without any specific treatment. The duration of newborn jaundice can vary depending on several factors such as the underlying cause, gestational age at birth, and individual variations in bilirubin metabolism. Here are some general guidelines: 1. Physiological jaundice: This is the most common type of newborn jaundice and usually appears within 24-72 hours after birth. It tends to peak between the second and fifth day of life and gradually improves over the next week or two. By the time the baby is one week old, the jaundice should have mostly resolved. 2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and may appear later than physiological jaundice, typically between the fifth and fourteenth day of life. It tends to persist for a longer duration but usually resolves within six weeks after birth. 3. Pathological jaundice: This type of jaundice is less common and occurs due to an underlying medical condition that affects bilirubin metabolism or liver function. The duration of pathological jaundice depends on the specific cause and may require treatment. It''s important for parents to monitor their newborn''s jaundice closely and seek medical advice if the jaundice progresses rapidly, becomes severe, or is accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness. In these cases, further evaluation and management may be necessary. Remember that each baby is unique, and the timing of jaundice resolution can vary. If you have concerns about your newborn''s jaundice, it''s always best to consult with a healthcare professional for personalized advice and guidance.' model-index: - name: OpenBioLLM-8B results: [] --- <div align="center"> <img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/2FhDh8NDvMl7iSxbQz9BP.png) <div align="center"> <h1>Advancing Open-source Large Language Models in Medical Domain</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://colab.research.google.com/drive/1F5oV20InEYeAJGmBwYF9NM_QhLmjBkKJ?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> | <a href="https://github.com/openlifescience-ai"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> | <a href="#"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> | <a href="https://discord.gg/A5Fjf5zC69"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/KGmRE5w2sepNtwsEu8t7K.jpeg) Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. 🏥 **Biomedical Specialization**: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. 🎓 **Superior Performance**: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. 🧠 **Advanced Training Techniques**: OpenBioLLM-8B builds upon the powerful foundations of the **Meta-Llama-3-8B** and [Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: <div align="center"> <img width="1200px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png"> </div> - **Policy Optimization**: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290) - **Ranking Dataset**: [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar) - **Fine-tuning dataset**: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated) This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications. ⚙️ **Release Details**: - **Model Size**: 8 billion parameters - **Quantization**: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-Llama3-8B-GGUF) - **Language(s) (NLP):** en - **Developed By**: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs - **License:** Meta-Llama License - **Fine-tuned from models:** [meta-llama/Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) - **Resources for more information:** - Paper: Coming soon The model can be fine-tuned for more specialized tasks and datasets as needed. OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences. We are excited to share OpenBioLLM-8B with researchers and developers around the world. ### Use with transformers **Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.** See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "aaditya/OpenBioLLM-Llama3-8B" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."}, {"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.0, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## **Training procedure** ### **Training hyperparameters** <details> <summary>Click to see details</summary> - learning_rate: 0.0002 - lr_scheduler: cosine - train_batch_size: 12 - eval_batch_size: 8 - GPU: H100 80GB SXM5 - num_devices: 1 - optimizer: adamw_bnb_8bit - lr_scheduler_warmup_steps: 100 - num_epochs: 4 </details> ### **Peft hyperparameters** <details> <summary>Click to see details</summary> - adapter: qlora - lora_r: 128 - lora_alpha: 256 - lora_dropout: 0.05 - lora_target_linear: true -lora_target_modules: - q_proj - v_proj - k_proj - o_proj - gate_proj - down_proj - up_proj </details> ### **Training results** ### **Framework versions** - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1 - Axolotl - Lm harness for evaluation # Benchmark Results 🔥 OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge. 🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy. | | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA 4 opts | PubMedQA | MedMCQA | Avg | |--------------------|-------------|------------------|---------|--------------|-----------------|------------------|--------------|----------|---------|-------| | **OpenBioLLM-70B** | **92.93** | **93.197** | **83.904** | 93.75 | 93.827 | **85.749** | 78.162 | 78.97 | **74.014** | **86.05588** | | Med-PaLM-2 (5-shot) | 88.3 | 90 | 77.8 | **95.2** | 94.4 | 80.9 | **79.7** | **79.2** | 71.3 | 84.08 | | **GPT-4** | 86.04 | 91 | 80 | 93.01 | **95.14** | 76.88 | 78.87 | 75.2 | 69.52 | 82.85 | | Med-PaLM-1 (Flan-PaLM, 5-shot) | 80.4 | 75 | 63.7 | 83.8 | 88.9 | 76.3 | 67.6 | 79 | 57.6 | 74.7 | | **OpenBioLLM-8B** | 76.101 | 86.1 | 69.829 | 78.21 | 84.213 | 68.042 | 58.993 | 74.12 | 56.913 | 72.502 | | Gemini-1.0 | 76.7 | 75.8 | 66.7 | 77.7 | 88 | 69.2 | 58 | 70.7 | 54.3 | 70.79 | | GPT-3.5 Turbo 1106 | 74.71 | 74 | 72.79 | 72.79 | 72.91 | 64.73 | 57.71 | 72.66 | 53.79 | 66 | | Meditron-70B | 66.79 | 69 | 53.33 | 71.69 | 76.38 | 63 | 57.1 | 76.6 | 46.85 | 64.52 | | gemma-7b | 69.81 | 70 | 59.26 | 66.18 | 79.86 | 60.12 | 47.21 | 76.2 | 48.96 | 64.18 | | Mistral-7B-v0.1 | 68.68 | 71 | 55.56 | 68.38 | 68.06 | 59.54 | 50.82 | 75.4 | 48.2 | 62.85 | | Apollo-7B | 62.26 | 72 | 61.48 | 69.12 | 70.83 | 55.49 | 55.22 | 39.8 | 53.77 | 60 | | MedAlpaca-7b | 57.36 | 69 | 57.04 | 67.28 | 65.28 | 54.34 | 41.71 | 72.8 | 37.51 | 58.03 | | BioMistral-7B | 59.9 | 64 | 56.5 | 60.4 | 59 | 54.7 | 50.6 | 77.5 | 48.1 | 57.3 | | AlpaCare-llama2-7b | 49.81 | 49 | 45.92 | 33.82 | 50 | 43.35 | 29.77 | 72.2 | 34.42 | 45.36 | | ClinicalGPT | 30.56 | 27 | 30.37 | 19.48 | 25 | 24.27 | 26.08 | 63.8 | 28.18 | 30.52 | <div align="center"> <img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png"> </div> ## Detailed Medical Subjectwise accuracy ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/UXF-V0col0Z0sS6BGPBkE.png) # Use Cases & Examples 🚨 **Below results are from the quantized version of OpenBioLLM-70B** # Summarize Clinical Notes OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/xdwdBgOxNi_TfML0hKlI8.png) # Answer Medical Questions OpenBioLLM-70B can provide answers to a wide range of medical questions. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/zO95GlwOQEZqCKQF69mE6.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/OKBczKw7gWeW5xsuDpc27.png) <details> <summary>Click to see details</summary> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/eJGHT5khppYvJb8fQ-YW4.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Cnbwrqa_-ORHRuNRC2P6Y.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/J9DhdcvukAc9mnnW9fj2C.png) </details> # Clinical Entity Recognition OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_69BW4k9LVABFwtxixL45.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/DKy5wYCoPhoPPUc1-x8_J.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/7WD9zCCBZT4-4XlfnIQjl.png) # Biomarkers Extraction ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/ZttoM4AiteT7gFYVhjIpN.png) # Classification OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Bf5MW1d75qT-1F_TR_hC0.png) # De-Identification OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/hKX4kzm--Tw5bj6K78msy.png) **Advisory Notice!**  While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments. Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor. Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including: - Thorough testing and evaluation in relevant clinical scenarios - Alignment with evidence-based guidelines and best practices - Mitigation of potential biases and failure modes - Integration with human oversight and interpretation - Compliance with regulatory and ethical standards Always consult a qualified healthcare provider for personal medical needs. # Citation If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows: ``` @misc{OpenBioLLMs, author = {Ankit Pal, Malaikannan Sankarasubbu}, title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}} } ``` The accompanying paper is currently in progress and will be released soon. <div align="center"> <h2> 💌 Contact </h2> </div> We look forward to hearing you and collaborating on this exciting project! **Contributors:** - [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com] - Saama AI Labs - Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. # References We thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models! Result sources - [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375) - [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138) - [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617) - [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023)
[ "QUESTION_ANSWERING" ]
[ "MEDQA", "PUBMEDQA" ]
BioNLP
tomaarsen/glove-mean-pooling-sts
tomaarsen
sentence-similarity
[ "sentence-transformers", "sentence-similarity", "feature-extraction", "loss:CosineSimilarityLoss", "en", "arxiv:1908.10084", "model-index", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,714
1,714
0
0
--- language: - en library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - loss:CosineSimilarityLoss widget: - source_sentence: Women are running. sentences: - Women are running. - The cougar is chasing the bear. - NATO soldier killed in Afghan attack - source_sentence: A woman is reading. sentences: - A woman is writing something. - A person is drawing a picture. - A dog laying in the snow. - source_sentence: A plane in the sky. sentences: - Two airplanes in the sky. - A man is playing an instrument. - Bangladesh executes opposition leader - source_sentence: A man jumping rope sentences: - A man is climbing a rope. - The girl is playing the guitar. - A chef prepared a meal. - source_sentence: A baby is laughing. sentences: - The baby laughed in his car seat. - A person is combing a cat hair. - A man is riding a horse in the desert. co2_eq_emissions: emissions: 0.04787408159843385 energy_consumed: 0.00012316397033828962 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.002 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: SentenceTransformer results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.7683803418925228 name: Pearson Cosine - type: spearman_cosine value: 0.7632727671822109 name: Spearman Cosine - type: pearson_manhattan value: 0.7167343000545916 name: Pearson Manhattan - type: spearman_manhattan value: 0.7284225373129679 name: Spearman Manhattan - type: pearson_euclidean value: 0.7177127625426643 name: Pearson Euclidean - type: spearman_euclidean value: 0.729676171689153 name: Spearman Euclidean - type: pearson_dot value: 0.561565806742925 name: Pearson Dot - type: spearman_dot value: 0.6116263753232491 name: Spearman Dot - type: pearson_max value: 0.7683803418925228 name: Pearson Max - type: spearman_max value: 0.7632727671822109 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.6783055201030597 name: Pearson Cosine - type: spearman_cosine value: 0.6549170846046467 name: Spearman Cosine - type: pearson_manhattan value: 0.6064971288495867 name: Pearson Manhattan - type: spearman_manhattan value: 0.6169187673598634 name: Spearman Manhattan - type: pearson_euclidean value: 0.6073075425801093 name: Pearson Euclidean - type: spearman_euclidean value: 0.6178537671183167 name: Spearman Euclidean - type: pearson_dot value: 0.45009881124802237 name: Pearson Dot - type: spearman_dot value: 0.47227603379856636 name: Spearman Dot - type: pearson_max value: 0.6783055201030597 name: Pearson Max - type: spearman_max value: 0.6549170846046467 name: Spearman Max --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained on the [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) dataset. It maps sentences & paragraphs to a 300-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) --> - **Maximum Sequence Length:** 1000000 tokens - **Output Dimensionality:** 300 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) - **Language:** en <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): WordEmbeddings( (emb_layer): Embedding(400001, 300) ) (1): Pooling({'word_embedding_dimension': 300, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Dense({'in_features': 300, 'out_features': 300, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) (3): Dense({'in_features': 300, 'out_features': 300, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tomaarsen/glove-mean-pooling-sts") # Run inference sentences = [ 'A baby is laughing.', 'The baby laughed in his car seat.', 'A person is combing a cat hair.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 300] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7684 | | **spearman_cosine** | **0.7633** | | pearson_manhattan | 0.7167 | | spearman_manhattan | 0.7284 | | pearson_euclidean | 0.7177 | | spearman_euclidean | 0.7297 | | pearson_dot | 0.5616 | | spearman_dot | 0.6116 | | pearson_max | 0.7684 | | spearman_max | 0.7633 | #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.6783 | | **spearman_cosine** | **0.6549** | | pearson_manhattan | 0.6065 | | spearman_manhattan | 0.6169 | | pearson_euclidean | 0.6073 | | spearman_euclidean | 0.6179 | | pearson_dot | 0.4501 | | spearman_dot | 0.4723 | | pearson_max | 0.6783 | | spearman_max | 0.6549 | <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### sentence-transformers/stsb * Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [d999f12](https://huggingface.co/datasets/sentence-transformers/stsb/tree/d999f12281623b0925506817d9bd85e88289218a) * Size: 5,749 training samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 1 tokens</li><li>mean: 3.38 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 1 tokens</li><li>mean: 3.39 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> | * Samples: | sentence1 | sentence2 | score | |:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------| | <code>A plane is taking off.</code> | <code>An air plane is taking off.</code> | <code>1.0</code> | | <code>A man is playing a large flute.</code> | <code>A man is playing a flute.</code> | <code>0.76</code> | | <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</code> | * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Evaluation Dataset #### sentence-transformers/stsb * Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [d999f12](https://huggingface.co/datasets/sentence-transformers/stsb/tree/d999f12281623b0925506817d9bd85e88289218a) * Size: 1,500 evaluation samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 1 tokens</li><li>mean: 5.17 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 1 tokens</li><li>mean: 5.08 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> | * Samples: | sentence1 | sentence2 | score | |:--------------------------------------------------|:------------------------------------------------------|:------------------| | <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> | | <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> | | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> | * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: False - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: None - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:| | 0.5556 | 100 | 0.0908 | 0.0577 | 0.7633 | - | | 1.0 | 180 | - | - | - | 0.6549 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.000 kWh - **Carbon Emitted**: 0.000 kg of CO2 - **Hours Used**: 0.002 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.0.0.dev0 - Transformers: 4.41.0.dev0 - PyTorch: 2.3.0+cu121 - Accelerate: 0.26.1 - Datasets: 2.18.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
[ "TEXT_CLASSIFICATION", "SEMANTIC_SIMILARITY" ]
[ "BEAR" ]
Non_BioNLP
TheBloke/UNAversal-8x7B-v1beta-GPTQ
TheBloke
text-generation
[ "transformers", "safetensors", "mixtral", "text-generation", "UNA", "juanako", "MoE", "conversational", "en", "base_model:fblgit/UNAversal-8x7B-v1beta", "base_model:quantized:fblgit/UNAversal-8x7B-v1beta", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]
1,703
1,703
21
1
--- base_model: fblgit/UNAversal-8x7B-v1beta language: - en library_name: transformers license: cc-by-nc-sa-4.0 model_name: UNAversal 8X7B v1Beta tags: - UNA - juanako - mixtral - MoE inference: false model_creator: FBL model_type: mixtral prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # UNAversal 8X7B v1Beta - GPTQ - Model creator: [FBL](https://huggingface.co/fblgit) - Original model: [UNAversal 8X7B v1Beta](https://huggingface.co/fblgit/UNAversal-8x7B-v1beta) <!-- description start --> # Description This repo contains GPTQ model files for [FBL's UNAversal 8X7B v1Beta](https://huggingface.co/fblgit/UNAversal-8x7B-v1beta). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/UNAversal-8x7B-v1beta-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/UNAversal-8x7B-v1beta-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/UNAversal-8x7B-v1beta-GGUF) * [FBL's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/fblgit/UNAversal-8x7B-v1beta) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Unknown ``` {prompt} ``` <!-- prompt-template end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models. These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/UNAversal-8x7B-v1beta-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 23.81 GB | No | 4-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/UNAversal-8x7B-v1beta-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 24.70 GB | No | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/UNAversal-8x7B-v1beta-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 27.42 GB | No | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/UNAversal-8x7B-v1beta-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 18.01 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. | | [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/UNAversal-8x7B-v1beta-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 18.85 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. | | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/UNAversal-8x7B-v1beta-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 47.04 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/UNAversal-8x7B-v1beta-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 48.10 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/UNAversal-8x7B-v1beta-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/UNAversal-8x7B-v1beta-GPTQ:gptq-4bit-128g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `UNAversal-8x7B-v1beta-GPTQ`: ```shell mkdir UNAversal-8x7B-v1beta-GPTQ huggingface-cli download TheBloke/UNAversal-8x7B-v1beta-GPTQ --local-dir UNAversal-8x7B-v1beta-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir UNAversal-8x7B-v1beta-GPTQ huggingface-cli download TheBloke/UNAversal-8x7B-v1beta-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir UNAversal-8x7B-v1beta-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir UNAversal-8x7B-v1beta-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/UNAversal-8x7B-v1beta-GPTQ --local-dir UNAversal-8x7B-v1beta-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (**not** recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/UNAversal-8x7B-v1beta-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/UNAversal-8x7B-v1beta-GPTQ`. - To download from a specific branch, enter for example `TheBloke/UNAversal-8x7B-v1beta-GPTQ:gptq-4bit-128g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `UNAversal-8x7B-v1beta-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/UNAversal-8x7B-v1beta-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' client = InferenceClient(endpoint_url) response = client.text_generation( prompt_template, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## Python code example: inference from this GPTQ model ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install --upgrade transformers optimum # If using PyTorch 2.1 + CUDA 12.x: pip3 install --upgrade auto-gptq # or, if using PyTorch 2.1 + CUDA 11.x: pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ ``` If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.5.1 pip3 install . ``` ### Example Python code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/UNAversal-8x7B-v1beta-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-128g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Write a story about llamas" system_message = "You are a story writing assistant" prompt_template=f'''{prompt} ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: FBL's UNAversal 8X7B v1Beta # UNAversal - Uniform Neural Alignment (MoE) This is just a beta, a first release so people can start working on franksteins and so. It does achieve high GSM/Math and TQA, so ideally you can merge it with other mixtrals and see what coming out of it Based on [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) ## UNA Details For this model we came out with the most obvious, placing UNA on the router_logit. It does work, but we saw a much better performance on SFT by doing so. So this model DOES have UNA-SFT phase, its highly experimental and it was merely using LLaMA-Factory datasets by example alpaca. As the others: - Can be finetuned further, try 2e-5 or **1e-4 (since its MOE)** - Can be merged, here you will have to improvise and please report findings on a discussion thread. **REMINDER**: please.. cite, it does help on the research and the lab itself, seriously. ## NEED YOUR HELP!! I need a multi-turn trainloop for the Mixtral, that can squeeze the juice out of 8xH100's properly. Please feel free to reach @fblgit either discord or twitter. thanks! # Evals Here there are some, but we also submitted it to the HF eval queue.... ## GSM8k 5-Shot ``` |Tasks|Version| Filter |n-shot| Metric |Value | |Stderr| |-----|-------|----------|-----:|-----------|-----:|---|-----:| |gsm8k|Yaml |get-answer| 5|exact_match|0.6603|± | 0.013| ``` ## ARC 25-Shot ``` | Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |-------------|-------|------|-----:|--------|-----:|---|-----:| |arc_challenge|Yaml |none | 25|acc |0.6621|± |0.0138| | | |none | 25|acc_norm|0.6962|± |0.0134| ``` ## TruthfulQA 0-Shot (MC2) ``` | Tasks |Version|Filter|n-shot|Metric|Value | |Stderr| |--------------|-------|------|-----:|------|-----:|---|-----:| |truthfulqa_mc2|Yaml |none | 0|acc |0.7122|± |0.0141| ``` ## 0-Shots Evals ``` | Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |--------------|-------|------|-----:|----------|-----:|---|-----:| |arc_challenge |Yaml |none | 0|acc |0.6101|± |0.0143| | | |none | 0|acc_norm |0.6425|± |0.0140| |arc_easy |Yaml |none | 0|acc |0.8615|± |0.0071| | | |none | 0|acc_norm |0.8375|± |0.0076| |boolq |Yaml |none | 0|acc |0.8624|± |0.0060| |lambada_openai|Yaml |none | 0|perplexity|2.8318|± |0.0507| | | |none | 0|acc |0.7650|± |0.0059| |mathqa |Yaml |none | 0|acc |0.4472|± |0.0091| | | |none | 0|acc_norm |0.4436|± |0.0091| |piqa |Yaml |none | 0|acc |0.8292|± |0.0088| | | |none | 0|acc_norm |0.8422|± |0.0085| |pubmedqa |Yaml |none | 0|acc |0.7920|± |0.0182| |sciq |Yaml |none | 0|acc |0.9630|± |0.0060| | | |none | 0|acc_norm |0.9370|± |0.0077| ``` ## BBH at 0-Shot ``` vllm (pretrained=fblgit/UNAversal-8x7B-v1beta,tensor_parallel_size=2,data_parallel_size=4,gpu_memory_utilization=0.8,dtype=float16), gen_kwargs: (None), limit: None, num_fewshot: 0, batch_size: auto | Tasks |Version| Filter |n-shot| Metric |Value | |Stderr| |----------------------------------------------------------|-------|----------|-----:|-----------|-----:|---|-----:| |bbh |N/A |get-answer| 0|exact_match|0.6752|± |0.1772| | - bbh_cot_fewshot_boolean_expressions |Yaml |get-answer| 0|exact_match|0.8840|± |0.0203| | - bbh_cot_fewshot_causal_judgement |Yaml |get-answer| 0|exact_match|0.6417|± |0.0352| | - bbh_cot_fewshot_date_understanding |Yaml |get-answer| 0|exact_match|0.7600|± |0.0271| | - bbh_cot_fewshot_disambiguation_qa |Yaml |get-answer| 0|exact_match|0.7160|± |0.0286| | - bbh_cot_fewshot_dyck_languages |Yaml |get-answer| 0|exact_match|0.1800|± |0.0243| | - bbh_cot_fewshot_formal_fallacies |Yaml |get-answer| 0|exact_match|0.6520|± |0.0302| | - bbh_cot_fewshot_geometric_shapes |Yaml |get-answer| 0|exact_match|0.3880|± |0.0309| | - bbh_cot_fewshot_hyperbaton |Yaml |get-answer| 0|exact_match|0.9600|± |0.0124| | - bbh_cot_fewshot_logical_deduction_five_objects |Yaml |get-answer| 0|exact_match|0.5360|± |0.0316| | - bbh_cot_fewshot_logical_deduction_seven_objects |Yaml |get-answer| 0|exact_match|0.5040|± |0.0317| | - bbh_cot_fewshot_logical_deduction_three_objects |Yaml |get-answer| 0|exact_match|0.8600|± |0.0220| | - bbh_cot_fewshot_movie_recommendation |Yaml |get-answer| 0|exact_match|0.7840|± |0.0261| | - bbh_cot_fewshot_multistep_arithmetic_two |Yaml |get-answer| 0|exact_match|0.6600|± |0.0300| | - bbh_cot_fewshot_navigate |Yaml |get-answer| 0|exact_match|0.8160|± |0.0246| | - bbh_cot_fewshot_object_counting |Yaml |get-answer| 0|exact_match|0.8360|± |0.0235| | - bbh_cot_fewshot_penguins_in_a_table |Yaml |get-answer| 0|exact_match|0.7329|± |0.0367| | - bbh_cot_fewshot_reasoning_about_colored_objects |Yaml |get-answer| 0|exact_match|0.8120|± |0.0248| | - bbh_cot_fewshot_ruin_names |Yaml |get-answer| 0|exact_match|0.4440|± |0.0315| | - bbh_cot_fewshot_salient_translation_error_detection |Yaml |get-answer| 0|exact_match|0.5200|± |0.0317| | - bbh_cot_fewshot_snarks |Yaml |get-answer| 0|exact_match|0.7135|± |0.0340| | - bbh_cot_fewshot_sports_understanding |Yaml |get-answer| 0|exact_match|0.9400|± |0.0151| | - bbh_cot_fewshot_temporal_sequences |Yaml |get-answer| 0|exact_match|0.7560|± |0.0272| | - bbh_cot_fewshot_tracking_shuffled_objects_five_objects |Yaml |get-answer| 0|exact_match|0.5680|± |0.0314| | - bbh_cot_fewshot_tracking_shuffled_objects_seven_objects|Yaml |get-answer| 0|exact_match|0.6280|± |0.0306| | - bbh_cot_fewshot_tracking_shuffled_objects_three_objects|Yaml |get-answer| 0|exact_match|0.6280|± |0.0306| | - bbh_cot_fewshot_web_of_lies |Yaml |get-answer| 0|exact_match|0.9560|± |0.0130| | - bbh_cot_fewshot_word_sorting |Yaml |get-answer| 0|exact_match|0.3800|± |0.0308| |Groups|Version| Filter |n-shot| Metric |Value | |Stderr| |------|-------|----------|-----:|-----------|-----:|---|-----:| |bbh |N/A |get-answer| 0|exact_match|0.6752|± |0.1772| ```
[ "TRANSLATION" ]
[ "PUBMEDQA", "SCIQ" ]
Non_BioNLP
aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct
aisingapore
text-generation
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "zh", "vi", "id", "th", "fil", "ta", "ms", "km", "lo", "my", "jv", "su", "arxiv:2309.06085", "arxiv:2311.07911", "arxiv:2306.05685", "base_model:aisingapore/llama3.1-8b-cpt-sea-lionv3-base", "base_model:finetune:aisingapore/llama3.1-8b-cpt-sea-lionv3-base", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
1,733
1,734
3,931
4
--- base_model: - aisingapore/llama3.1-8b-cpt-sea-lionv3-base language: - en - zh - vi - id - th - fil - ta - ms - km - lo - my - jv - su library_name: transformers license: llama3.1 pipeline_tag: text-generation --- <div> <img src="llama_3.1_8b_sea-lion_v3_instruct_banner.png"/> </div> # Llama3.1 8B CPT SEA-LIONv3 Instruct SEA-LION is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned for the Southeast Asia (SEA) region. Llama3.1 8B CPT SEA-LIONv3 Instruct is a multilingual model that has been fine-tuned in two stages on approximately **12.3M English instruction-completion pairs** alongside a pool of **4.5M Southeast Asian instruction-completion pairs** from SEA languages such as Indonesian, Javanese, Sundanese, Tamil, Thai and Vietnamese. SEA-LION stands for _Southeast Asian Languages In One Network_. - **Developed by:** Products Pillar, AI Singapore - **Funded by:** Singapore NRF - **Model type:** Decoder - **Languages supported:** Burmese, Chinese, English, Filipino, Indonesia, Javanese, Khmer, Lao, Malay, Sundanese, Tamil, Thai, Vietnamese - **License:** [Llama 3.1 Community License](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct/blob/main/LICENSE) ## Model Details ### Model Description We performed instruction tuning in English and also in SEA languages such as Indonesian, Javanese, Sundanese, Tamil, Thai and Vietnamese on our [continued pre-trained Llama3.1 8B CPT SEA-LIONv3 Base](https://huggingface.co/aisingapore/llama3.1-8b-cpt-sea-lionv3-base), a decoder model using the Llama 3.1 architecture, to create Llama3.1 8B CPT SEA-LIONv3 Instruct. For tokenisation, the model employs the default tokenizer used in Llama 3.1 8B Instruct. The model has a context length of 128k. ### Benchmark Performance We evaluated Llama3.1 8B CPT SEA-LIONv3 Instruct on both general language capabilities and instruction-following capabilities. #### General Language Capabilities For the evaluation of general language capabilities, we employed the [SEA-HELM (also known as BHASA) evaluation benchmark](https://arxiv.org/abs/2309.06085v2) across a variety of tasks. These tasks include Question Answering (QA), Sentiment Analysis (Sentiment), Toxicity Detection (Toxicity), Translation in both directions (Eng>Lang & Lang>Eng), Abstractive Summarisation (Abssum), Causal Reasoning (Causal) and Natural Language Inference (NLI). Note: SEA-HELM is implemented using prompts to elicit answers in a strict format. For all tasks, the model is expected to provide an answer tag from which the answer is automatically extracted. For tasks where options are provided, the answer should comprise one of the pre-defined options. The scores for each task is normalised to account for baseline performance due to random chance. The evaluation was done **zero-shot** with native prompts on a sample of 100-1000 instances for each dataset. #### Instruction-following Capabilities Since Llama3.1 8B CPT SEA-LIONv3 Instruct is an instruction-following model, we also evaluated it on instruction-following capabilities with two datasets, SEA-IFEval (based on [IFEval](https://arxiv.org/abs/2311.07911)) and SEA-MTBench (based on [MT-Bench](https://arxiv.org/abs/2306.05685)). As these two datasets were originally in English, the linguists and native speakers in the team worked together to filter, localise and translate the datasets into the respective target languages to ensure that the examples remained reasonable, meaningful and natural. **SEA-IFEval** SEA-IFEval evaluates a model's ability to adhere to constraints provided in the prompt, for example beginning a response with a specific word/phrase or answering with a certain number of sections. Additionally, accuracy is normalised by the proportion of responses in the correct language (if the model performs the task correctly but responds in the wrong language, it is judged to have failed the task). **SEA-MTBench** SEA-MTBench evaluates a model's ability to engage in multi-turn (2 turns) conversations and respond in ways that align with human needs. We use `gpt-4-1106-preview` as the judge model and compare against `gpt-3.5-turbo-0125` as the baseline model. The metric used is the weighted win rate against the baseline model (i.e. average win rate across each category: Math, Reasoning, STEM, Humanities, Roleplay, Writing, Extraction). A tie is given a score of 0.5. For more details on Llama3.1 8B CPT SEA-LIONv3 Instruct benchmark performance, please refer to the SEA-HELM leaderboard, https://leaderboard.sea-lion.ai/. ### Usage Llama3.1 8B CPT SEA-LIONv3 Instruct can be run using the 🤗 Transformers library ```python import transformers import torch model_id = "aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) messages = [ {"role": "user", "content": "Apa sentimen dari kalimat berikut ini?\nKalimat: Buku ini sangat membosankan.\nJawaban: "}, ] outputs = pipeline( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` ### Caveats It is important for users to be aware that our model exhibits certain limitations that warrant consideration. Like many LLMs, the model can hallucinate and occasionally generates irrelevant content, introducing fictional elements that are not grounded in the provided context. Users should also exercise caution in interpreting and validating the model's responses due to the potential inconsistencies in its reasoning. ## Limitations ### Safety Current SEA-LION models, including this commercially permissive release, have not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights and codes. ## Technical Specifications ### Fine-Tuning Details Llama3.1 8B CPT SEA-LIONv3 Instruct was tuned using a combination of a full parameter fine-tune, on-policy alignment, and model merges of the best performing checkpoints. The training process for fine-tuning was approximately 1024 GPU hours, on a single node of 8x H100-80GB GPUs. ## Data Llama3.1 8B CPT SEA-LIONv3 Instruct was trained on a wide range of synthetic instructions, alongside publicly available instructions hand-curated by the team with the assistance of native speakers. In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data source. ## Call for Contributions We encourage researchers, developers, and language enthusiasts to actively contribute to the enhancement and expansion of SEA-LION. Contributions can involve identifying and reporting bugs, sharing pre-training, instruction, and preference data, improving documentation usability, proposing and implementing new model evaluation tasks and metrics, or training versions of the model in additional Southeast Asian languages. Join us in shaping the future of SEA-LION by sharing your expertise and insights to make these models more accessible, accurate, and versatile. Please check out our GitHub for further information on the call for contributions. ## The Team Chan Adwin, Cheng Nicholas, Choa Esther, Huang Yuli, Hulagadri Adithya Venkatadri, Lau Wayne, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Limkonchotiwat Peerat, Liu Bing Jie Darius, Montalan Jann Railey, Ng Boon Cheong Raymond, Ngui Jian Gang, Nguyen Thanh Ngan, Ong Brandon, Ong Tat-Wee David, Ong Zhi Hao, Rengarajan Hamsawardhini, Siow Bryan, Susanto Yosephine, Tai Ngee Chia, Tan Choon Meng, Teng Walter, Teo Eng Sipp Leslie, Teo Wei Yi, Tjhi William, Yeo Yeow Tong, Yong Xianbin ## Acknowledgements [AI Singapore](​​https://aisingapore.org/) is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the National University of Singapore. ## Contact For more info, please contact us using this [SEA-LION Inquiry Form](https://forms.gle/sLCUVb95wmGf43hi6) [Link to SEA-LION's GitHub repository](https://github.com/aisingapore/sealion) ## Disclaimer This is the repository for the commercial instruction-tuned model. The model has _not_ been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. In no event shall the authors be held liable for any claims, damages, or other liabilities arising from the use of the released weights and codes.
[ "QUESTION_ANSWERING", "TRANSLATION" ]
[ "CHIA" ]
Non_BioNLP
twadada/wl_sw_256
twadada
null
[ "mteb", "model-index", "region:us" ]
1,736
1,736
0
0
--- tags: - mteb model-index: - name: l3_wordllama_256 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 65.97014925373134 - type: ap value: 27.33017285839569 - type: f1 value: 59.04330619047924 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 63.248250000000006 - type: ap value: 58.695642654646576 - type: f1 value: 62.98826255412888 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 31.689999999999998 - type: f1 value: 31.106666192619258 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 19.986 - type: map_at_10 value: 34.634 - type: map_at_100 value: 35.937000000000005 - type: map_at_1000 value: 35.954 - type: map_at_3 value: 29.742 - type: map_at_5 value: 32.444 - type: mrr_at_1 value: 20.341 - type: mrr_at_10 value: 34.763 - type: mrr_at_100 value: 36.065999999999995 - type: mrr_at_1000 value: 36.083 - type: mrr_at_3 value: 29.872 - type: mrr_at_5 value: 32.574999999999996 - type: ndcg_at_1 value: 19.986 - type: ndcg_at_10 value: 43.074 - type: ndcg_at_100 value: 48.819 - type: ndcg_at_1000 value: 49.26 - type: ndcg_at_3 value: 32.934000000000005 - type: ndcg_at_5 value: 37.830999999999996 - type: precision_at_1 value: 19.986 - type: precision_at_10 value: 7.02 - type: precision_at_100 value: 0.958 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.059 - type: precision_at_5 value: 10.825 - type: recall_at_1 value: 19.986 - type: recall_at_10 value: 70.199 - type: recall_at_100 value: 95.804 - type: recall_at_1000 value: 99.21799999999999 - type: recall_at_3 value: 42.176 - type: recall_at_5 value: 54.125 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 39.64176717184799 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 29.06122250673383 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 55.808484614132844 - type: mrr value: 71.09121487930351 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 74.96889982129713 - type: cos_sim_spearman value: 70.34256665852179 - type: euclidean_pearson value: 73.59375229907496 - type: euclidean_spearman value: 70.34256665852179 - type: manhattan_pearson value: 72.38820178677287 - type: manhattan_spearman value: 69.3919425882689 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 73.56818181818181 - type: f1 value: 72.78107232170503 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 33.10380086081637 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 25.238238325966222 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 20.294999999999998 - type: map_at_10 value: 27.535999999999998 - type: map_at_100 value: 28.803 - type: map_at_1000 value: 28.971000000000004 - type: map_at_3 value: 25.029 - type: map_at_5 value: 26.526 - type: mrr_at_1 value: 24.893 - type: mrr_at_10 value: 32.554 - type: mrr_at_100 value: 33.504 - type: mrr_at_1000 value: 33.583 - type: mrr_at_3 value: 30.091 - type: mrr_at_5 value: 31.535999999999998 - type: ndcg_at_1 value: 24.893 - type: ndcg_at_10 value: 32.495000000000005 - type: ndcg_at_100 value: 38.288 - type: ndcg_at_1000 value: 41.559000000000005 - type: ndcg_at_3 value: 28.321 - type: ndcg_at_5 value: 30.401 - type: precision_at_1 value: 24.893 - type: precision_at_10 value: 6.109 - type: precision_at_100 value: 1.142 - type: precision_at_1000 value: 0.179 - type: precision_at_3 value: 13.447999999999999 - type: precision_at_5 value: 9.927999999999999 - type: recall_at_1 value: 20.294999999999998 - type: recall_at_10 value: 42.129 - type: recall_at_100 value: 67.709 - type: recall_at_1000 value: 89.534 - type: recall_at_3 value: 30.148999999999997 - type: recall_at_5 value: 35.804 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 16.426 - type: map_at_10 value: 22.461000000000002 - type: map_at_100 value: 23.424 - type: map_at_1000 value: 23.559 - type: map_at_3 value: 20.643 - type: map_at_5 value: 21.602 - type: mrr_at_1 value: 20.701 - type: mrr_at_10 value: 26.734 - type: mrr_at_100 value: 27.516000000000002 - type: mrr_at_1000 value: 27.594 - type: mrr_at_3 value: 24.936 - type: mrr_at_5 value: 25.901000000000003 - type: ndcg_at_1 value: 20.701 - type: ndcg_at_10 value: 26.381 - type: ndcg_at_100 value: 30.731 - type: ndcg_at_1000 value: 33.603 - type: ndcg_at_3 value: 23.336000000000002 - type: ndcg_at_5 value: 24.644 - type: precision_at_1 value: 20.701 - type: precision_at_10 value: 5.006 - type: precision_at_100 value: 0.9339999999999999 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 11.315999999999999 - type: precision_at_5 value: 8.14 - type: recall_at_1 value: 16.426 - type: recall_at_10 value: 33.593 - type: recall_at_100 value: 52.746 - type: recall_at_1000 value: 72.15899999999999 - type: recall_at_3 value: 24.712 - type: recall_at_5 value: 28.233000000000004 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 24.46 - type: map_at_10 value: 33.292 - type: map_at_100 value: 34.437 - type: map_at_1000 value: 34.534 - type: map_at_3 value: 30.567 - type: map_at_5 value: 32.202 - type: mrr_at_1 value: 28.276 - type: mrr_at_10 value: 36.235 - type: mrr_at_100 value: 37.173 - type: mrr_at_1000 value: 37.234 - type: mrr_at_3 value: 33.783 - type: mrr_at_5 value: 35.237 - type: ndcg_at_1 value: 28.276 - type: ndcg_at_10 value: 38.202000000000005 - type: ndcg_at_100 value: 43.634 - type: ndcg_at_1000 value: 45.894 - type: ndcg_at_3 value: 33.19 - type: ndcg_at_5 value: 35.798 - type: precision_at_1 value: 28.276 - type: precision_at_10 value: 6.332 - type: precision_at_100 value: 1.008 - type: precision_at_1000 value: 0.127 - type: precision_at_3 value: 14.671000000000001 - type: precision_at_5 value: 10.571 - type: recall_at_1 value: 24.46 - type: recall_at_10 value: 50.156 - type: recall_at_100 value: 74.648 - type: recall_at_1000 value: 91.269 - type: recall_at_3 value: 36.937999999999995 - type: recall_at_5 value: 43.15 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 14.052999999999999 - type: map_at_10 value: 18.287 - type: map_at_100 value: 19.137 - type: map_at_1000 value: 19.258 - type: map_at_3 value: 16.79 - type: map_at_5 value: 17.618000000000002 - type: mrr_at_1 value: 15.254000000000001 - type: mrr_at_10 value: 19.88 - type: mrr_at_100 value: 20.71 - type: mrr_at_1000 value: 20.812 - type: mrr_at_3 value: 18.23 - type: mrr_at_5 value: 19.185 - type: ndcg_at_1 value: 15.254000000000001 - type: ndcg_at_10 value: 21.183 - type: ndcg_at_100 value: 25.972 - type: ndcg_at_1000 value: 29.271 - type: ndcg_at_3 value: 18.046 - type: ndcg_at_5 value: 19.570999999999998 - type: precision_at_1 value: 15.254000000000001 - type: precision_at_10 value: 3.288 - type: precision_at_100 value: 0.614 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 7.5329999999999995 - type: precision_at_5 value: 5.379 - type: recall_at_1 value: 14.052999999999999 - type: recall_at_10 value: 28.599999999999998 - type: recall_at_100 value: 51.815 - type: recall_at_1000 value: 77.04299999999999 - type: recall_at_3 value: 20.238999999999997 - type: recall_at_5 value: 23.837 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 8.475000000000001 - type: map_at_10 value: 12.898000000000001 - type: map_at_100 value: 13.950000000000001 - type: map_at_1000 value: 14.063999999999998 - type: map_at_3 value: 10.965 - type: map_at_5 value: 11.905000000000001 - type: mrr_at_1 value: 10.323 - type: mrr_at_10 value: 15.431000000000001 - type: mrr_at_100 value: 16.442 - type: mrr_at_1000 value: 16.526 - type: mrr_at_3 value: 13.288 - type: mrr_at_5 value: 14.382 - type: ndcg_at_1 value: 10.323 - type: ndcg_at_10 value: 16.325 - type: ndcg_at_100 value: 21.831999999999997 - type: ndcg_at_1000 value: 25.079 - type: ndcg_at_3 value: 12.372 - type: ndcg_at_5 value: 14.011999999999999 - type: precision_at_1 value: 10.323 - type: precision_at_10 value: 3.197 - type: precision_at_100 value: 0.6930000000000001 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 5.970000000000001 - type: precision_at_5 value: 4.627 - type: recall_at_1 value: 8.475000000000001 - type: recall_at_10 value: 24.651999999999997 - type: recall_at_100 value: 49.63 - type: recall_at_1000 value: 73.35000000000001 - type: recall_at_3 value: 13.852 - type: recall_at_5 value: 17.813000000000002 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 18.278 - type: map_at_10 value: 24.852 - type: map_at_100 value: 26.308999999999997 - type: map_at_1000 value: 26.450000000000003 - type: map_at_3 value: 22.183 - type: map_at_5 value: 23.493 - type: mrr_at_1 value: 22.522000000000002 - type: mrr_at_10 value: 29.554000000000002 - type: mrr_at_100 value: 30.705 - type: mrr_at_1000 value: 30.774 - type: mrr_at_3 value: 26.821 - type: mrr_at_5 value: 28.288000000000004 - type: ndcg_at_1 value: 22.522000000000002 - type: ndcg_at_10 value: 29.79 - type: ndcg_at_100 value: 36.473 - type: ndcg_at_1000 value: 39.440999999999995 - type: ndcg_at_3 value: 24.915000000000003 - type: ndcg_at_5 value: 26.941 - type: precision_at_1 value: 22.522000000000002 - type: precision_at_10 value: 5.707 - type: precision_at_100 value: 1.076 - type: precision_at_1000 value: 0.153 - type: precision_at_3 value: 11.645999999999999 - type: precision_at_5 value: 8.584999999999999 - type: recall_at_1 value: 18.278 - type: recall_at_10 value: 40.150999999999996 - type: recall_at_100 value: 68.978 - type: recall_at_1000 value: 89.295 - type: recall_at_3 value: 26.548 - type: recall_at_5 value: 31.772 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 14.634 - type: map_at_10 value: 21.377 - type: map_at_100 value: 22.522000000000002 - type: map_at_1000 value: 22.657 - type: map_at_3 value: 19.292 - type: map_at_5 value: 20.278 - type: mrr_at_1 value: 18.151 - type: mrr_at_10 value: 25.263999999999996 - type: mrr_at_100 value: 26.156000000000002 - type: mrr_at_1000 value: 26.247 - type: mrr_at_3 value: 23.154 - type: mrr_at_5 value: 24.188000000000002 - type: ndcg_at_1 value: 18.151 - type: ndcg_at_10 value: 25.773000000000003 - type: ndcg_at_100 value: 31.130999999999997 - type: ndcg_at_1000 value: 34.452 - type: ndcg_at_3 value: 21.975 - type: ndcg_at_5 value: 23.36 - type: precision_at_1 value: 18.151 - type: precision_at_10 value: 4.829 - type: precision_at_100 value: 0.894 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 10.693 - type: precision_at_5 value: 7.648000000000001 - type: recall_at_1 value: 14.634 - type: recall_at_10 value: 35.433 - type: recall_at_100 value: 58.617 - type: recall_at_1000 value: 82.364 - type: recall_at_3 value: 24.59 - type: recall_at_5 value: 28.217 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 14.736583333333334 - type: map_at_10 value: 20.393 - type: map_at_100 value: 21.42775 - type: map_at_1000 value: 21.560666666666666 - type: map_at_3 value: 18.52958333333333 - type: map_at_5 value: 19.509249999999998 - type: mrr_at_1 value: 17.61366666666667 - type: mrr_at_10 value: 23.522250000000003 - type: mrr_at_100 value: 24.424166666666668 - type: mrr_at_1000 value: 24.512166666666666 - type: mrr_at_3 value: 21.64875 - type: mrr_at_5 value: 22.648916666666665 - type: ndcg_at_1 value: 17.61366666666667 - type: ndcg_at_10 value: 24.16458333333333 - type: ndcg_at_100 value: 29.305916666666672 - type: ndcg_at_1000 value: 32.52291666666667 - type: ndcg_at_3 value: 20.732 - type: ndcg_at_5 value: 22.223333333333333 - type: precision_at_1 value: 17.61366666666667 - type: precision_at_10 value: 4.33925 - type: precision_at_100 value: 0.8296666666666666 - type: precision_at_1000 value: 0.12933333333333333 - type: precision_at_3 value: 9.6265 - type: precision_at_5 value: 6.921666666666666 - type: recall_at_1 value: 14.736583333333334 - type: recall_at_10 value: 32.46958333333333 - type: recall_at_100 value: 55.94050000000001 - type: recall_at_1000 value: 79.17466666666667 - type: recall_at_3 value: 22.765749999999997 - type: recall_at_5 value: 26.614583333333336 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 11.152 - type: map_at_10 value: 16.052 - type: map_at_100 value: 16.892 - type: map_at_1000 value: 17.0 - type: map_at_3 value: 14.677999999999999 - type: map_at_5 value: 15.424 - type: mrr_at_1 value: 12.883 - type: mrr_at_10 value: 17.871000000000002 - type: mrr_at_100 value: 18.694 - type: mrr_at_1000 value: 18.793000000000003 - type: mrr_at_3 value: 16.641000000000002 - type: mrr_at_5 value: 17.262 - type: ndcg_at_1 value: 12.883 - type: ndcg_at_10 value: 18.981 - type: ndcg_at_100 value: 23.704 - type: ndcg_at_1000 value: 26.810000000000002 - type: ndcg_at_3 value: 16.361 - type: ndcg_at_5 value: 17.507 - type: precision_at_1 value: 12.883 - type: precision_at_10 value: 3.221 - type: precision_at_100 value: 0.612 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 7.4639999999999995 - type: precision_at_5 value: 5.244999999999999 - type: recall_at_1 value: 11.152 - type: recall_at_10 value: 26.22 - type: recall_at_100 value: 48.870000000000005 - type: recall_at_1000 value: 72.328 - type: recall_at_3 value: 18.838 - type: recall_at_5 value: 21.693 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 8.338 - type: map_at_10 value: 12.315 - type: map_at_100 value: 13.086 - type: map_at_1000 value: 13.214 - type: map_at_3 value: 11.032 - type: map_at_5 value: 11.691 - type: mrr_at_1 value: 10.255 - type: mrr_at_10 value: 14.723 - type: mrr_at_100 value: 15.528 - type: mrr_at_1000 value: 15.626000000000001 - type: mrr_at_3 value: 13.289000000000001 - type: mrr_at_5 value: 14.047 - type: ndcg_at_1 value: 10.255 - type: ndcg_at_10 value: 15.058 - type: ndcg_at_100 value: 19.326 - type: ndcg_at_1000 value: 22.972 - type: ndcg_at_3 value: 12.565999999999999 - type: ndcg_at_5 value: 13.603000000000002 - type: precision_at_1 value: 10.255 - type: precision_at_10 value: 2.815 - type: precision_at_100 value: 0.597 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 6.045 - type: precision_at_5 value: 4.405 - type: recall_at_1 value: 8.338 - type: recall_at_10 value: 21.125 - type: recall_at_100 value: 40.936 - type: recall_at_1000 value: 67.984 - type: recall_at_3 value: 14.018 - type: recall_at_5 value: 16.725 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 13.575000000000001 - type: map_at_10 value: 18.967 - type: map_at_100 value: 19.924 - type: map_at_1000 value: 20.06 - type: map_at_3 value: 17.101 - type: map_at_5 value: 18.142 - type: mrr_at_1 value: 16.418 - type: mrr_at_10 value: 22.131 - type: mrr_at_100 value: 22.993 - type: mrr_at_1000 value: 23.101 - type: mrr_at_3 value: 20.288999999999998 - type: mrr_at_5 value: 21.282999999999998 - type: ndcg_at_1 value: 16.418 - type: ndcg_at_10 value: 22.625 - type: ndcg_at_100 value: 27.676000000000002 - type: ndcg_at_1000 value: 31.41 - type: ndcg_at_3 value: 19.136 - type: ndcg_at_5 value: 20.748 - type: precision_at_1 value: 16.418 - type: precision_at_10 value: 3.9739999999999998 - type: precision_at_100 value: 0.743 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 8.924 - type: precision_at_5 value: 6.381 - type: recall_at_1 value: 13.575000000000001 - type: recall_at_10 value: 30.794 - type: recall_at_100 value: 54.02400000000001 - type: recall_at_1000 value: 81.634 - type: recall_at_3 value: 21.095 - type: recall_at_5 value: 25.25 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 14.915999999999999 - type: map_at_10 value: 20.976 - type: map_at_100 value: 22.127 - type: map_at_1000 value: 22.329 - type: map_at_3 value: 19.62 - type: map_at_5 value: 20.247999999999998 - type: mrr_at_1 value: 18.379 - type: mrr_at_10 value: 24.822 - type: mrr_at_100 value: 25.765 - type: mrr_at_1000 value: 25.852000000000004 - type: mrr_at_3 value: 23.551 - type: mrr_at_5 value: 24.193 - type: ndcg_at_1 value: 18.379 - type: ndcg_at_10 value: 24.956999999999997 - type: ndcg_at_100 value: 30.224 - type: ndcg_at_1000 value: 33.883 - type: ndcg_at_3 value: 23.094 - type: ndcg_at_5 value: 23.659 - type: precision_at_1 value: 18.379 - type: precision_at_10 value: 4.802 - type: precision_at_100 value: 1.105 - type: precision_at_1000 value: 0.2 - type: precision_at_3 value: 11.462 - type: precision_at_5 value: 7.826 - type: recall_at_1 value: 14.915999999999999 - type: recall_at_10 value: 31.902 - type: recall_at_100 value: 57.296 - type: recall_at_1000 value: 82.107 - type: recall_at_3 value: 25.013 - type: recall_at_5 value: 27.281 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 12.237 - type: map_at_10 value: 15.703 - type: map_at_100 value: 16.522000000000002 - type: map_at_1000 value: 16.631999999999998 - type: map_at_3 value: 14.455000000000002 - type: map_at_5 value: 14.982000000000001 - type: mrr_at_1 value: 13.309000000000001 - type: mrr_at_10 value: 17.068 - type: mrr_at_100 value: 17.904 - type: mrr_at_1000 value: 18.004 - type: mrr_at_3 value: 15.712000000000002 - type: mrr_at_5 value: 16.285 - type: ndcg_at_1 value: 13.309000000000001 - type: ndcg_at_10 value: 18.205 - type: ndcg_at_100 value: 22.68 - type: ndcg_at_1000 value: 25.901000000000003 - type: ndcg_at_3 value: 15.472 - type: ndcg_at_5 value: 16.436 - type: precision_at_1 value: 13.309000000000001 - type: precision_at_10 value: 2.791 - type: precision_at_100 value: 0.538 - type: precision_at_1000 value: 0.086 - type: precision_at_3 value: 6.346 - type: precision_at_5 value: 4.324999999999999 - type: recall_at_1 value: 12.237 - type: recall_at_10 value: 24.88 - type: recall_at_100 value: 46.017 - type: recall_at_1000 value: 71.029 - type: recall_at_3 value: 17.197000000000003 - type: recall_at_5 value: 19.6 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 6.732 - type: map_at_10 value: 12.674 - type: map_at_100 value: 14.257 - type: map_at_1000 value: 14.463999999999999 - type: map_at_3 value: 10.355 - type: map_at_5 value: 11.524 - type: mrr_at_1 value: 15.831000000000001 - type: mrr_at_10 value: 25.972 - type: mrr_at_100 value: 27.107999999999997 - type: mrr_at_1000 value: 27.167 - type: mrr_at_3 value: 22.637999999999998 - type: mrr_at_5 value: 24.319 - type: ndcg_at_1 value: 15.831000000000001 - type: ndcg_at_10 value: 19.244 - type: ndcg_at_100 value: 26.329 - type: ndcg_at_1000 value: 30.270999999999997 - type: ndcg_at_3 value: 14.966 - type: ndcg_at_5 value: 16.377 - type: precision_at_1 value: 15.831000000000001 - type: precision_at_10 value: 6.404 - type: precision_at_100 value: 1.403 - type: precision_at_1000 value: 0.212 - type: precision_at_3 value: 11.64 - type: precision_at_5 value: 9.134 - type: recall_at_1 value: 6.732 - type: recall_at_10 value: 24.855 - type: recall_at_100 value: 49.730000000000004 - type: recall_at_1000 value: 72.214 - type: recall_at_3 value: 14.299000000000001 - type: recall_at_5 value: 18.363 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.529 - type: map_at_10 value: 9.075999999999999 - type: map_at_100 value: 12.394 - type: map_at_1000 value: 13.272999999999998 - type: map_at_3 value: 6.688 - type: map_at_5 value: 7.803 - type: mrr_at_1 value: 36.25 - type: mrr_at_10 value: 46.867 - type: mrr_at_100 value: 47.654 - type: mrr_at_1000 value: 47.679 - type: mrr_at_3 value: 43.791999999999994 - type: mrr_at_5 value: 45.742 - type: ndcg_at_1 value: 26.75 - type: ndcg_at_10 value: 21.146 - type: ndcg_at_100 value: 25.113999999999997 - type: ndcg_at_1000 value: 31.873 - type: ndcg_at_3 value: 23.142 - type: ndcg_at_5 value: 22.273 - type: precision_at_1 value: 36.25 - type: precision_at_10 value: 18.25 - type: precision_at_100 value: 6.16 - type: precision_at_1000 value: 1.34 - type: precision_at_3 value: 27.250000000000004 - type: precision_at_5 value: 23.75 - type: recall_at_1 value: 4.529 - type: recall_at_10 value: 13.442000000000002 - type: recall_at_100 value: 32.534 - type: recall_at_1000 value: 55.346 - type: recall_at_3 value: 7.771999999999999 - type: recall_at_5 value: 10.061 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 37.89000000000001 - type: f1 value: 34.12692942265391 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 16.28 - type: map_at_10 value: 24.729 - type: map_at_100 value: 25.785999999999998 - type: map_at_1000 value: 25.855 - type: map_at_3 value: 22.083 - type: map_at_5 value: 23.534 - type: mrr_at_1 value: 17.462 - type: mrr_at_10 value: 26.358999999999998 - type: mrr_at_100 value: 27.412 - type: mrr_at_1000 value: 27.473 - type: mrr_at_3 value: 23.615 - type: mrr_at_5 value: 25.115 - type: ndcg_at_1 value: 17.462 - type: ndcg_at_10 value: 29.885 - type: ndcg_at_100 value: 35.268 - type: ndcg_at_1000 value: 37.203 - type: ndcg_at_3 value: 24.397 - type: ndcg_at_5 value: 26.995 - type: precision_at_1 value: 17.462 - type: precision_at_10 value: 4.851 - type: precision_at_100 value: 0.77 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 10.666 - type: precision_at_5 value: 7.762 - type: recall_at_1 value: 16.28 - type: recall_at_10 value: 44.554 - type: recall_at_100 value: 69.736 - type: recall_at_1000 value: 84.654 - type: recall_at_3 value: 29.529 - type: recall_at_5 value: 35.789 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 7.406 - type: map_at_10 value: 12.162 - type: map_at_100 value: 13.501 - type: map_at_1000 value: 13.700000000000001 - type: map_at_3 value: 10.282 - type: map_at_5 value: 11.182 - type: mrr_at_1 value: 14.969 - type: mrr_at_10 value: 21.453 - type: mrr_at_100 value: 22.579 - type: mrr_at_1000 value: 22.665 - type: mrr_at_3 value: 19.084 - type: mrr_at_5 value: 20.233999999999998 - type: ndcg_at_1 value: 14.969 - type: ndcg_at_10 value: 17.022000000000002 - type: ndcg_at_100 value: 23.415 - type: ndcg_at_1000 value: 27.811000000000003 - type: ndcg_at_3 value: 14.191999999999998 - type: ndcg_at_5 value: 15.026 - type: precision_at_1 value: 14.969 - type: precision_at_10 value: 4.954 - type: precision_at_100 value: 1.133 - type: precision_at_1000 value: 0.191 - type: precision_at_3 value: 9.516 - type: precision_at_5 value: 7.191 - type: recall_at_1 value: 7.406 - type: recall_at_10 value: 22.404 - type: recall_at_100 value: 47.351 - type: recall_at_1000 value: 74.701 - type: recall_at_3 value: 13.108 - type: recall_at_5 value: 16.531000000000002 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 20.662 - type: map_at_10 value: 28.956 - type: map_at_100 value: 29.942999999999998 - type: map_at_1000 value: 30.052 - type: map_at_3 value: 26.767999999999997 - type: map_at_5 value: 28.011000000000003 - type: mrr_at_1 value: 41.323 - type: mrr_at_10 value: 49.242999999999995 - type: mrr_at_100 value: 49.97 - type: mrr_at_1000 value: 50.016000000000005 - type: mrr_at_3 value: 47.207 - type: mrr_at_5 value: 48.364000000000004 - type: ndcg_at_1 value: 41.323 - type: ndcg_at_10 value: 36.756 - type: ndcg_at_100 value: 41.189 - type: ndcg_at_1000 value: 43.667 - type: ndcg_at_3 value: 32.690999999999995 - type: ndcg_at_5 value: 34.703 - type: precision_at_1 value: 41.323 - type: precision_at_10 value: 8.015 - type: precision_at_100 value: 1.155 - type: precision_at_1000 value: 0.148 - type: precision_at_3 value: 20.612 - type: precision_at_5 value: 13.961000000000002 - type: recall_at_1 value: 20.662 - type: recall_at_10 value: 40.074 - type: recall_at_100 value: 57.745000000000005 - type: recall_at_1000 value: 74.24 - type: recall_at_3 value: 30.918 - type: recall_at_5 value: 34.902 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 64.62239999999998 - type: ap value: 59.505106899987936 - type: f1 value: 64.39587267286105 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 6.507000000000001 - type: map_at_10 value: 11.542 - type: map_at_100 value: 12.542 - type: map_at_1000 value: 12.658 - type: map_at_3 value: 9.67 - type: map_at_5 value: 10.631 - type: mrr_at_1 value: 6.705 - type: mrr_at_10 value: 11.857 - type: mrr_at_100 value: 12.863 - type: mrr_at_1000 value: 12.974 - type: mrr_at_3 value: 9.957 - type: mrr_at_5 value: 10.933 - type: ndcg_at_1 value: 6.705 - type: ndcg_at_10 value: 14.764 - type: ndcg_at_100 value: 20.258000000000003 - type: ndcg_at_1000 value: 23.685000000000002 - type: ndcg_at_3 value: 10.809000000000001 - type: ndcg_at_5 value: 12.543000000000001 - type: precision_at_1 value: 6.705 - type: precision_at_10 value: 2.579 - type: precision_at_100 value: 0.543 - type: precision_at_1000 value: 0.084 - type: precision_at_3 value: 4.771 - type: precision_at_5 value: 3.734 - type: recall_at_1 value: 6.507000000000001 - type: recall_at_10 value: 24.842 - type: recall_at_100 value: 51.697 - type: recall_at_1000 value: 79.081 - type: recall_at_3 value: 13.828 - type: recall_at_5 value: 18.009 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 84.40264477884178 - type: f1 value: 83.43871348215795 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 54.90196078431372 - type: f1 value: 35.66115135754105 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.371889710827176 - type: f1 value: 58.91304009131599 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 67.52185608607937 - type: f1 value: 66.27921261407421 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 30.40912967319626 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 26.77476593032722 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.522211560565317 - type: mrr value: 31.540554976019745 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 2.871 - type: map_at_10 value: 6.643000000000001 - type: map_at_100 value: 8.801 - type: map_at_1000 value: 9.961 - type: map_at_3 value: 4.862 - type: map_at_5 value: 5.704 - type: mrr_at_1 value: 29.102 - type: mrr_at_10 value: 38.79 - type: mrr_at_100 value: 39.616 - type: mrr_at_1000 value: 39.659 - type: mrr_at_3 value: 35.913000000000004 - type: mrr_at_5 value: 37.74 - type: ndcg_at_1 value: 27.554000000000002 - type: ndcg_at_10 value: 22.215 - type: ndcg_at_100 value: 21.386 - type: ndcg_at_1000 value: 30.615 - type: ndcg_at_3 value: 25.546000000000003 - type: ndcg_at_5 value: 24.425 - type: precision_at_1 value: 29.102 - type: precision_at_10 value: 17.121 - type: precision_at_100 value: 6.146 - type: precision_at_1000 value: 1.9029999999999998 - type: precision_at_3 value: 24.871 - type: precision_at_5 value: 22.291 - type: recall_at_1 value: 2.871 - type: recall_at_10 value: 10.184999999999999 - type: recall_at_100 value: 24.057000000000002 - type: recall_at_1000 value: 56.788000000000004 - type: recall_at_3 value: 5.606 - type: recall_at_5 value: 7.353 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 10.455 - type: map_at_10 value: 17.904999999999998 - type: map_at_100 value: 19.215 - type: map_at_1000 value: 19.314 - type: map_at_3 value: 15.133 - type: map_at_5 value: 16.624 - type: mrr_at_1 value: 11.906 - type: mrr_at_10 value: 19.595000000000002 - type: mrr_at_100 value: 20.765 - type: mrr_at_1000 value: 20.845 - type: mrr_at_3 value: 16.7 - type: mrr_at_5 value: 18.314 - type: ndcg_at_1 value: 11.906 - type: ndcg_at_10 value: 22.733999999999998 - type: ndcg_at_100 value: 29.179 - type: ndcg_at_1000 value: 31.848 - type: ndcg_at_3 value: 16.98 - type: ndcg_at_5 value: 19.695 - type: precision_at_1 value: 11.906 - type: precision_at_10 value: 4.234999999999999 - type: precision_at_100 value: 0.79 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 7.976 - type: precision_at_5 value: 6.286 - type: recall_at_1 value: 10.455 - type: recall_at_10 value: 36.114000000000004 - type: recall_at_100 value: 65.742 - type: recall_at_1000 value: 86.22800000000001 - type: recall_at_3 value: 20.826 - type: recall_at_5 value: 27.165 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 63.336000000000006 - type: map_at_10 value: 76.859 - type: map_at_100 value: 77.679 - type: map_at_1000 value: 77.705 - type: map_at_3 value: 73.681 - type: map_at_5 value: 75.558 - type: mrr_at_1 value: 73.13 - type: mrr_at_10 value: 80.757 - type: mrr_at_100 value: 80.99300000000001 - type: mrr_at_1000 value: 80.99499999999999 - type: mrr_at_3 value: 79.267 - type: mrr_at_5 value: 80.209 - type: ndcg_at_1 value: 73.15 - type: ndcg_at_10 value: 81.693 - type: ndcg_at_100 value: 83.733 - type: ndcg_at_1000 value: 83.943 - type: ndcg_at_3 value: 77.866 - type: ndcg_at_5 value: 79.779 - type: precision_at_1 value: 73.15 - type: precision_at_10 value: 12.603 - type: precision_at_100 value: 1.51 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 34.123 - type: precision_at_5 value: 22.636 - type: recall_at_1 value: 63.336000000000006 - type: recall_at_10 value: 91.36999999999999 - type: recall_at_100 value: 98.831 - type: recall_at_1000 value: 99.901 - type: recall_at_3 value: 80.495 - type: recall_at_5 value: 85.799 - type: map_at_1 value: 3.5479999999999996 - type: map_at_10 value: 8.923 - type: map_at_100 value: 11.038 - type: map_at_1000 value: 11.384 - type: map_at_3 value: 6.387 - type: map_at_5 value: 7.646999999999999 - type: mrr_at_1 value: 17.5 - type: mrr_at_10 value: 27.71 - type: mrr_at_100 value: 28.898000000000003 - type: mrr_at_1000 value: 28.96 - type: mrr_at_3 value: 24.282999999999998 - type: mrr_at_5 value: 26.123 - type: ndcg_at_1 value: 17.5 - type: ndcg_at_10 value: 15.831999999999999 - type: ndcg_at_100 value: 24.478 - type: ndcg_at_1000 value: 30.548 - type: ndcg_at_3 value: 14.66 - type: ndcg_at_5 value: 12.969 - type: precision_at_1 value: 17.5 - type: precision_at_10 value: 8.38 - type: precision_at_100 value: 2.103 - type: precision_at_1000 value: 0.356 - type: precision_at_3 value: 13.866999999999999 - type: precision_at_5 value: 11.58 - type: recall_at_1 value: 3.5479999999999996 - type: recall_at_10 value: 16.958000000000002 - type: recall_at_100 value: 42.687999999999995 - type: recall_at_1000 value: 72.173 - type: recall_at_3 value: 8.437999999999999 - type: recall_at_5 value: 11.738 - type: map_at_1 value: 0.186 - type: map_at_10 value: 1.2149999999999999 - type: map_at_100 value: 6.516 - type: map_at_1000 value: 14.704999999999998 - type: map_at_3 value: 0.469 - type: map_at_5 value: 0.701 - type: mrr_at_1 value: 72.0 - type: mrr_at_10 value: 80.238 - type: mrr_at_100 value: 80.622 - type: mrr_at_1000 value: 80.622 - type: mrr_at_3 value: 79.667 - type: mrr_at_5 value: 79.667 - type: ndcg_at_1 value: 64.0 - type: ndcg_at_10 value: 57.147000000000006 - type: ndcg_at_100 value: 40.5 - type: ndcg_at_1000 value: 33.954 - type: ndcg_at_3 value: 62.754 - type: ndcg_at_5 value: 59.933 - type: precision_at_1 value: 72.0 - type: precision_at_10 value: 60.6 - type: precision_at_100 value: 42.1 - type: precision_at_1000 value: 15.512 - type: precision_at_3 value: 67.333 - type: precision_at_5 value: 64.0 - type: recall_at_1 value: 0.186 - type: recall_at_10 value: 1.385 - type: recall_at_100 value: 9.332 - type: recall_at_1000 value: 31.922 - type: recall_at_3 value: 0.503 - type: recall_at_5 value: 0.759 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 43.4964655583453 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 48.31404856068323 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 77.88215495721286 - type: cos_sim_spearman value: 66.95635868609415 - type: euclidean_pearson value: 71.95058611790435 - type: euclidean_spearman value: 66.95635868609415 - type: manhattan_pearson value: 71.73499967722593 - type: manhattan_spearman value: 66.76136105777387 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 72.56521014258115 - type: cos_sim_spearman value: 64.21841908004934 - type: euclidean_pearson value: 68.51846331737438 - type: euclidean_spearman value: 64.21841908004934 - type: manhattan_pearson value: 68.27567108498233 - type: manhattan_spearman value: 64.09725470920785 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 72.71775862893193 - type: cos_sim_spearman value: 73.28911820172492 - type: euclidean_pearson value: 72.83254599010056 - type: euclidean_spearman value: 73.28922176679981 - type: manhattan_pearson value: 72.56589783996398 - type: manhattan_spearman value: 72.99829341365574 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 73.89757752366668 - type: cos_sim_spearman value: 68.93443322328304 - type: euclidean_pearson value: 71.74950262447223 - type: euclidean_spearman value: 68.93447340804855 - type: manhattan_pearson value: 71.53131355539159 - type: manhattan_spearman value: 68.75571712820332 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 80.97565977782956 - type: cos_sim_spearman value: 81.43311223145955 - type: euclidean_pearson value: 80.99231321031297 - type: euclidean_spearman value: 81.43311223145955 - type: manhattan_pearson value: 80.85980250491755 - type: manhattan_spearman value: 81.28760623160176 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 75.52199164461821 - type: cos_sim_spearman value: 76.00370946904079 - type: euclidean_pearson value: 75.52316904078243 - type: euclidean_spearman value: 76.00370946904079 - type: manhattan_pearson value: 75.3120467704852 - type: manhattan_spearman value: 75.73102913980114 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 84.71078769268394 - type: cos_sim_spearman value: 84.92569102013795 - type: euclidean_pearson value: 84.42768434149738 - type: euclidean_spearman value: 84.92569102013795 - type: manhattan_pearson value: 84.36599569720875 - type: manhattan_spearman value: 84.97627760625926 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 60.75551853889779 - type: cos_sim_spearman value: 59.56097878013177 - type: euclidean_pearson value: 62.25756001900302 - type: euclidean_spearman value: 59.56097878013177 - type: manhattan_pearson value: 61.56622096305194 - type: manhattan_spearman value: 58.794887940253346 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 78.57502299404004 - type: cos_sim_spearman value: 76.84123747775618 - type: euclidean_pearson value: 78.18263544350317 - type: euclidean_spearman value: 76.84123747775618 - type: manhattan_pearson value: 78.06611402413624 - type: manhattan_spearman value: 76.79100666899737 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 82.80038681665185 - type: mrr value: 94.90057418978986 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 39.056000000000004 - type: map_at_10 value: 48.714 - type: map_at_100 value: 49.653999999999996 - type: map_at_1000 value: 49.706 - type: map_at_3 value: 45.806000000000004 - type: map_at_5 value: 47.5 - type: mrr_at_1 value: 41.0 - type: mrr_at_10 value: 50.104000000000006 - type: mrr_at_100 value: 50.859 - type: mrr_at_1000 value: 50.903 - type: mrr_at_3 value: 47.556 - type: mrr_at_5 value: 48.972 - type: ndcg_at_1 value: 41.0 - type: ndcg_at_10 value: 54.144999999999996 - type: ndcg_at_100 value: 58.269999999999996 - type: ndcg_at_1000 value: 59.648 - type: ndcg_at_3 value: 48.451 - type: ndcg_at_5 value: 51.319 - type: precision_at_1 value: 41.0 - type: precision_at_10 value: 7.7 - type: precision_at_100 value: 0.997 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 19.444 - type: precision_at_5 value: 13.333 - type: recall_at_1 value: 39.056000000000004 - type: recall_at_10 value: 69.61699999999999 - type: recall_at_100 value: 87.922 - type: recall_at_1000 value: 98.667 - type: recall_at_3 value: 54.193999999999996 - type: recall_at_5 value: 61.138999999999996 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.73762376237623 - type: cos_sim_ap value: 91.61413659372461 - type: cos_sim_f1 value: 86.34046890927624 - type: cos_sim_precision value: 88.04573804573805 - type: cos_sim_recall value: 84.7 - type: dot_accuracy value: 99.73762376237623 - type: dot_ap value: 91.61413659372461 - type: dot_f1 value: 86.34046890927624 - type: dot_precision value: 88.04573804573805 - type: dot_recall value: 84.7 - type: euclidean_accuracy value: 99.73762376237623 - type: euclidean_ap value: 91.61413659372461 - type: euclidean_f1 value: 86.34046890927624 - type: euclidean_precision value: 88.04573804573805 - type: euclidean_recall value: 84.7 - type: manhattan_accuracy value: 99.74059405940594 - type: manhattan_ap value: 91.56213824792806 - type: manhattan_f1 value: 86.22502628811776 - type: manhattan_precision value: 90.9090909090909 - type: manhattan_recall value: 82.0 - type: max_accuracy value: 99.74059405940594 - type: max_ap value: 91.61413659372461 - type: max_f1 value: 86.34046890927624 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 53.09338784502622 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 32.57087655180163 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 41.59188785875835 - type: mrr value: 41.92390024191495 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.69015090602311 - type: cos_sim_spearman value: 30.124791626004075 - type: dot_pearson value: 29.69015070868056 - type: dot_spearman value: 30.09621990241238 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.0660000000000003 - type: map_at_10 value: 9.783999999999999 - type: map_at_100 value: 16.005 - type: map_at_1000 value: 17.694 - type: map_at_3 value: 4.524 - type: map_at_5 value: 6.651 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 49.26 - type: mrr_at_100 value: 49.791000000000004 - type: mrr_at_1000 value: 49.791000000000004 - type: mrr_at_3 value: 45.238 - type: mrr_at_5 value: 47.177 - type: ndcg_at_1 value: 29.592000000000002 - type: ndcg_at_10 value: 26.35 - type: ndcg_at_100 value: 38.078 - type: ndcg_at_1000 value: 49.222 - type: ndcg_at_3 value: 28.749000000000002 - type: ndcg_at_5 value: 28.156 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 25.306 - type: precision_at_100 value: 8.449 - type: precision_at_1000 value: 1.559 - type: precision_at_3 value: 31.293 - type: precision_at_5 value: 30.203999999999997 - type: recall_at_1 value: 2.0660000000000003 - type: recall_at_10 value: 17.009 - type: recall_at_100 value: 50.065000000000005 - type: recall_at_1000 value: 84.247 - type: recall_at_3 value: 6.223 - type: recall_at_5 value: 10.062 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 65.9572 - type: ap value: 11.472412091038306 - type: f1 value: 50.25348253932964 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 49.60384833050367 - type: f1 value: 49.6458985672963 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 32.85259172670649 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 79.30500089408118 - type: cos_sim_ap value: 48.463983264840934 - type: cos_sim_f1 value: 49.28199791883455 - type: cos_sim_precision value: 40.687285223367695 - type: cos_sim_recall value: 62.48021108179419 - type: dot_accuracy value: 79.30500089408118 - type: dot_ap value: 48.463988663433994 - type: dot_f1 value: 49.28199791883455 - type: dot_precision value: 40.687285223367695 - type: dot_recall value: 62.48021108179419 - type: euclidean_accuracy value: 79.30500089408118 - type: euclidean_ap value: 48.463983264840934 - type: euclidean_f1 value: 49.28199791883455 - type: euclidean_precision value: 40.687285223367695 - type: euclidean_recall value: 62.48021108179419 - type: manhattan_accuracy value: 79.2811587292126 - type: manhattan_ap value: 48.38522593516497 - type: manhattan_f1 value: 49.11896465903435 - type: manhattan_precision value: 39.440447641886486 - type: manhattan_recall value: 65.09234828496042 - type: max_accuracy value: 79.30500089408118 - type: max_ap value: 48.463988663433994 - type: max_f1 value: 49.28199791883455 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 86.58167423448597 - type: cos_sim_ap value: 80.70276946703169 - type: cos_sim_f1 value: 73.6376389338513 - type: cos_sim_precision value: 69.10146492945385 - type: cos_sim_recall value: 78.81121034801355 - type: dot_accuracy value: 86.58167423448597 - type: dot_ap value: 80.70276237270826 - type: dot_f1 value: 73.6376389338513 - type: dot_precision value: 69.10146492945385 - type: dot_recall value: 78.81121034801355 - type: euclidean_accuracy value: 86.58167423448597 - type: euclidean_ap value: 80.70277058558774 - type: euclidean_f1 value: 73.6376389338513 - type: euclidean_precision value: 69.10146492945385 - type: euclidean_recall value: 78.81121034801355 - type: manhattan_accuracy value: 86.47882951061435 - type: manhattan_ap value: 80.56146544234434 - type: manhattan_f1 value: 73.43608995415659 - type: manhattan_precision value: 69.1267414203194 - type: manhattan_recall value: 78.31844779796735 - type: max_accuracy value: 86.58167423448597 - type: max_ap value: 80.70277058558774 - type: max_f1 value: 73.6376389338513 ---
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP
bigscience/sgpt-bloom-7b1-msmarco
bigscience
sentence-similarity
[ "sentence-transformers", "pytorch", "bloom", "feature-extraction", "sentence-similarity", "mteb", "arxiv:2202.08904", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
1,661
1,712
58
43
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb model-index: - name: sgpt-bloom-7b1-msmarco results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 68.05970149253731 - type: ap value: 31.640363460776193 - type: f1 value: 62.50025574145796 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (de) type: mteb/amazon_counterfactual config: de split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 61.34903640256959 - type: ap value: 75.18797161500426 - type: f1 value: 59.04772570730417 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en-ext) type: mteb/amazon_counterfactual config: en-ext split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 67.78110944527737 - type: ap value: 19.218916023322706 - type: f1 value: 56.24477391445512 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (ja) type: mteb/amazon_counterfactual config: ja split: test revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996 metrics: - type: accuracy value: 58.23340471092078 - type: ap value: 13.20222967424681 - type: f1 value: 47.511718095460296 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: 80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1 metrics: - type: accuracy value: 68.97232499999998 - type: ap value: 63.53632885535693 - type: f1 value: 68.62038513152868 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 33.855999999999995 - type: f1 value: 33.43468222830134 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (de) type: mteb/amazon_reviews_multi config: de split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 29.697999999999997 - type: f1 value: 29.39935388885501 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (es) type: mteb/amazon_reviews_multi config: es split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 35.974000000000004 - type: f1 value: 35.25910820714383 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 35.922 - type: f1 value: 35.38637028933444 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (ja) type: mteb/amazon_reviews_multi config: ja split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 27.636 - type: f1 value: 27.178349955978266 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: c379a6705fec24a2493fa68e011692605f44e119 metrics: - type: accuracy value: 32.632 - type: f1 value: 32.08014766494587 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: 5b3e3697907184a9b77a3c99ee9ea1a9cbb1e4e3 metrics: - type: map_at_1 value: 23.684 - type: map_at_10 value: 38.507999999999996 - type: map_at_100 value: 39.677 - type: map_at_1000 value: 39.690999999999995 - type: map_at_3 value: 33.369 - type: map_at_5 value: 36.15 - type: mrr_at_1 value: 24.04 - type: mrr_at_10 value: 38.664 - type: mrr_at_100 value: 39.833 - type: mrr_at_1000 value: 39.847 - type: mrr_at_3 value: 33.476 - type: mrr_at_5 value: 36.306 - type: ndcg_at_1 value: 23.684 - type: ndcg_at_10 value: 47.282000000000004 - type: ndcg_at_100 value: 52.215 - type: ndcg_at_1000 value: 52.551 - type: ndcg_at_3 value: 36.628 - type: ndcg_at_5 value: 41.653 - type: precision_at_1 value: 23.684 - type: precision_at_10 value: 7.553 - type: precision_at_100 value: 0.97 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 15.363 - type: precision_at_5 value: 11.664 - type: recall_at_1 value: 23.684 - type: recall_at_10 value: 75.533 - type: recall_at_100 value: 97.013 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 46.088 - type: recall_at_5 value: 58.321 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8 metrics: - type: v_measure value: 44.59375023881131 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3 metrics: - type: v_measure value: 38.02921907752556 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c metrics: - type: map value: 59.97321570342109 - type: mrr value: 73.18284746955106 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: 9ee918f184421b6bd48b78f6c714d86546106103 metrics: - type: cos_sim_pearson value: 89.09091435741429 - type: cos_sim_spearman value: 85.31459455332202 - type: euclidean_pearson value: 79.3587681410798 - type: euclidean_spearman value: 76.8174129874685 - type: manhattan_pearson value: 79.57051762121769 - type: manhattan_spearman value: 76.75837549768094 - task: type: BitextMining dataset: name: MTEB BUCC (de-en) type: mteb/bucc-bitext-mining config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 54.27974947807933 - type: f1 value: 54.00144411132214 - type: precision value: 53.87119374071357 - type: recall value: 54.27974947807933 - task: type: BitextMining dataset: name: MTEB BUCC (fr-en) type: mteb/bucc-bitext-mining config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 97.3365617433414 - type: f1 value: 97.06141316310809 - type: precision value: 96.92567319685965 - type: recall value: 97.3365617433414 - task: type: BitextMining dataset: name: MTEB BUCC (ru-en) type: mteb/bucc-bitext-mining config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 46.05472809144441 - type: f1 value: 45.30319274690595 - type: precision value: 45.00015469655234 - type: recall value: 46.05472809144441 - task: type: BitextMining dataset: name: MTEB BUCC (zh-en) type: mteb/bucc-bitext-mining config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.10426540284361 - type: f1 value: 97.96384061786905 - type: precision value: 97.89362822538178 - type: recall value: 98.10426540284361 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 44fa15921b4c889113cc5df03dd4901b49161ab7 metrics: - type: accuracy value: 84.33441558441558 - type: f1 value: 84.31653077470322 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 11d0121201d1f1f280e8cc8f3d98fb9c4d9f9c55 metrics: - type: v_measure value: 36.025318694698086 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: c0fab014e1bcb8d3a5e31b2088972a1e01547dc1 metrics: - type: v_measure value: 32.484889034590346 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db metrics: - type: map_at_1 value: 30.203999999999997 - type: map_at_10 value: 41.314 - type: map_at_100 value: 42.66 - type: map_at_1000 value: 42.775999999999996 - type: map_at_3 value: 37.614999999999995 - type: map_at_5 value: 39.643 - type: mrr_at_1 value: 37.482 - type: mrr_at_10 value: 47.075 - 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type: map_at_5 value: 39.782000000000004 - type: mrr_at_1 value: 38.79 - type: mrr_at_10 value: 46.944 - type: mrr_at_100 value: 47.61 - type: mrr_at_1000 value: 47.650999999999996 - type: mrr_at_3 value: 45.053 - type: mrr_at_5 value: 46.101 - type: ndcg_at_1 value: 38.79 - type: ndcg_at_10 value: 46.286 - type: ndcg_at_100 value: 50.637 - type: ndcg_at_1000 value: 52.649 - type: ndcg_at_3 value: 42.851 - type: ndcg_at_5 value: 44.311 - type: precision_at_1 value: 38.79 - type: precision_at_10 value: 8.516 - type: precision_at_100 value: 1.3679999999999999 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 20.637 - type: precision_at_5 value: 14.318 - type: recall_at_1 value: 30.889 - type: recall_at_10 value: 55.327000000000005 - type: recall_at_100 value: 74.091 - type: recall_at_1000 value: 86.75500000000001 - type: recall_at_3 value: 44.557 - type: recall_at_5 value: 49.064 - type: map_at_1 value: 39.105000000000004 - type: map_at_10 value: 50.928 - type: map_at_100 value: 51.958000000000006 - type: map_at_1000 value: 52.017 - type: map_at_3 value: 47.638999999999996 - type: map_at_5 value: 49.624 - type: mrr_at_1 value: 44.639 - type: mrr_at_10 value: 54.261 - type: mrr_at_100 value: 54.913999999999994 - type: mrr_at_1000 value: 54.945 - type: mrr_at_3 value: 51.681999999999995 - type: mrr_at_5 value: 53.290000000000006 - type: ndcg_at_1 value: 44.639 - type: ndcg_at_10 value: 56.678 - type: ndcg_at_100 value: 60.649 - type: ndcg_at_1000 value: 61.855000000000004 - type: ndcg_at_3 value: 51.092999999999996 - type: ndcg_at_5 value: 54.096999999999994 - type: precision_at_1 value: 44.639 - type: precision_at_10 value: 9.028 - type: precision_at_100 value: 1.194 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 22.508 - type: precision_at_5 value: 15.661 - type: recall_at_1 value: 39.105000000000004 - type: recall_at_10 value: 70.367 - type: recall_at_100 value: 87.359 - type: recall_at_1000 value: 95.88 - type: recall_at_3 value: 55.581 - type: recall_at_5 value: 62.821000000000005 - type: map_at_1 value: 23.777 - type: map_at_10 value: 32.297 - type: map_at_100 value: 33.516 - type: map_at_1000 value: 33.592 - type: map_at_3 value: 30.001 - type: map_at_5 value: 31.209999999999997 - type: mrr_at_1 value: 25.989 - type: mrr_at_10 value: 34.472 - type: mrr_at_100 value: 35.518 - type: mrr_at_1000 value: 35.577 - type: mrr_at_3 value: 32.185 - type: mrr_at_5 value: 33.399 - type: ndcg_at_1 value: 25.989 - type: ndcg_at_10 value: 37.037 - type: ndcg_at_100 value: 42.699 - type: ndcg_at_1000 value: 44.725 - type: ndcg_at_3 value: 32.485 - type: ndcg_at_5 value: 34.549 - type: precision_at_1 value: 25.989 - type: precision_at_10 value: 5.718 - type: precision_at_100 value: 0.89 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 14.049 - type: precision_at_5 value: 9.672 - type: recall_at_1 value: 23.777 - type: recall_at_10 value: 49.472 - type: recall_at_100 value: 74.857 - type: recall_at_1000 value: 90.289 - type: recall_at_3 value: 37.086000000000006 - type: recall_at_5 value: 42.065999999999995 - type: map_at_1 value: 13.377 - type: map_at_10 value: 21.444 - type: map_at_100 value: 22.663 - type: map_at_1000 value: 22.8 - type: map_at_3 value: 18.857 - type: map_at_5 value: 20.426 - type: mrr_at_1 value: 16.542 - type: mrr_at_10 value: 25.326999999999998 - type: mrr_at_100 value: 26.323 - type: mrr_at_1000 value: 26.406000000000002 - type: mrr_at_3 value: 22.823 - type: mrr_at_5 value: 24.340999999999998 - type: ndcg_at_1 value: 16.542 - type: ndcg_at_10 value: 26.479000000000003 - type: ndcg_at_100 value: 32.29 - type: ndcg_at_1000 value: 35.504999999999995 - type: ndcg_at_3 value: 21.619 - type: ndcg_at_5 value: 24.19 - type: precision_at_1 value: 16.542 - type: precision_at_10 value: 5.075 - type: precision_at_100 value: 0.9339999999999999 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 10.697 - type: precision_at_5 value: 8.134 - type: recall_at_1 value: 13.377 - type: recall_at_10 value: 38.027 - type: recall_at_100 value: 63.439 - type: recall_at_1000 value: 86.354 - type: recall_at_3 value: 25.0 - type: recall_at_5 value: 31.306 - type: map_at_1 value: 28.368 - type: map_at_10 value: 39.305 - type: map_at_100 value: 40.637 - type: map_at_1000 value: 40.753 - type: map_at_3 value: 36.077999999999996 - type: map_at_5 value: 37.829 - type: mrr_at_1 value: 34.937000000000005 - type: mrr_at_10 value: 45.03 - type: mrr_at_100 value: 45.78 - type: mrr_at_1000 value: 45.827 - type: mrr_at_3 value: 42.348 - type: mrr_at_5 value: 43.807 - type: ndcg_at_1 value: 34.937000000000005 - type: ndcg_at_10 value: 45.605000000000004 - type: ndcg_at_100 value: 50.941 - type: ndcg_at_1000 value: 52.983000000000004 - type: ndcg_at_3 value: 40.366 - type: ndcg_at_5 value: 42.759 - type: precision_at_1 value: 34.937000000000005 - type: precision_at_10 value: 8.402 - type: precision_at_100 value: 1.2959999999999998 - type: precision_at_1000 value: 0.164 - type: precision_at_3 value: 19.217000000000002 - type: precision_at_5 value: 13.725000000000001 - type: recall_at_1 value: 28.368 - type: recall_at_10 value: 58.5 - type: recall_at_100 value: 80.67999999999999 - type: recall_at_1000 value: 93.925 - type: recall_at_3 value: 43.956 - type: recall_at_5 value: 50.065000000000005 - type: map_at_1 value: 24.851 - type: map_at_10 value: 34.758 - type: map_at_100 value: 36.081 - type: map_at_1000 value: 36.205999999999996 - type: map_at_3 value: 31.678 - type: map_at_5 value: 33.398 - type: mrr_at_1 value: 31.279 - type: mrr_at_10 value: 40.138 - type: mrr_at_100 value: 41.005 - type: mrr_at_1000 value: 41.065000000000005 - type: mrr_at_3 value: 37.519000000000005 - type: mrr_at_5 value: 38.986 - type: ndcg_at_1 value: 31.279 - type: ndcg_at_10 value: 40.534 - type: ndcg_at_100 value: 46.093 - type: ndcg_at_1000 value: 48.59 - type: ndcg_at_3 value: 35.473 - type: ndcg_at_5 value: 37.801 - type: precision_at_1 value: 31.279 - type: precision_at_10 value: 7.477 - type: precision_at_100 value: 1.2 - type: precision_at_1000 value: 0.159 - type: precision_at_3 value: 17.047 - type: precision_at_5 value: 12.306000000000001 - type: recall_at_1 value: 24.851 - type: recall_at_10 value: 52.528 - type: recall_at_100 value: 76.198 - type: recall_at_1000 value: 93.12 - type: recall_at_3 value: 38.257999999999996 - type: recall_at_5 value: 44.440000000000005 - type: map_at_1 value: 25.289833333333334 - type: map_at_10 value: 34.379333333333335 - type: map_at_100 value: 35.56916666666666 - type: map_at_1000 value: 35.68633333333333 - type: map_at_3 value: 31.63916666666666 - type: map_at_5 value: 33.18383333333334 - type: mrr_at_1 value: 30.081749999999996 - type: mrr_at_10 value: 38.53658333333333 - type: mrr_at_100 value: 39.37825 - type: mrr_at_1000 value: 39.43866666666666 - type: mrr_at_3 value: 36.19025 - type: mrr_at_5 value: 37.519749999999995 - type: ndcg_at_1 value: 30.081749999999996 - type: ndcg_at_10 value: 39.62041666666667 - type: ndcg_at_100 value: 44.74825 - type: ndcg_at_1000 value: 47.11366666666667 - type: ndcg_at_3 value: 35.000499999999995 - type: ndcg_at_5 value: 37.19283333333333 - type: precision_at_1 value: 30.081749999999996 - type: precision_at_10 value: 6.940249999999999 - type: precision_at_100 value: 1.1164166666666668 - type: precision_at_1000 value: 0.15025000000000002 - type: precision_at_3 value: 16.110416666666666 - type: precision_at_5 value: 11.474416666666668 - type: recall_at_1 value: 25.289833333333334 - type: recall_at_10 value: 51.01591666666667 - type: recall_at_100 value: 73.55275000000002 - type: recall_at_1000 value: 90.02666666666667 - type: recall_at_3 value: 38.15208333333334 - type: recall_at_5 value: 43.78458333333334 - type: map_at_1 value: 23.479 - type: map_at_10 value: 31.2 - type: map_at_100 value: 32.11 - type: map_at_1000 value: 32.214 - type: map_at_3 value: 29.093999999999998 - type: map_at_5 value: 30.415 - type: mrr_at_1 value: 26.840000000000003 - type: mrr_at_10 value: 34.153 - type: mrr_at_100 value: 34.971000000000004 - type: mrr_at_1000 value: 35.047 - type: mrr_at_3 value: 32.285000000000004 - type: mrr_at_5 value: 33.443 - type: ndcg_at_1 value: 26.840000000000003 - type: ndcg_at_10 value: 35.441 - type: ndcg_at_100 value: 40.150000000000006 - type: ndcg_at_1000 value: 42.74 - type: ndcg_at_3 value: 31.723000000000003 - type: ndcg_at_5 value: 33.71 - type: precision_at_1 value: 26.840000000000003 - type: precision_at_10 value: 5.552 - type: precision_at_100 value: 0.859 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 13.804 - type: precision_at_5 value: 9.600999999999999 - type: recall_at_1 value: 23.479 - type: recall_at_10 value: 45.442 - type: recall_at_100 value: 67.465 - type: recall_at_1000 value: 86.53 - type: recall_at_3 value: 35.315999999999995 - type: recall_at_5 value: 40.253 - type: map_at_1 value: 16.887 - type: map_at_10 value: 23.805 - type: map_at_100 value: 24.804000000000002 - type: map_at_1000 value: 24.932000000000002 - type: map_at_3 value: 21.632 - type: map_at_5 value: 22.845 - type: mrr_at_1 value: 20.75 - type: mrr_at_10 value: 27.686 - type: mrr_at_100 value: 28.522 - type: mrr_at_1000 value: 28.605000000000004 - type: mrr_at_3 value: 25.618999999999996 - type: mrr_at_5 value: 26.723999999999997 - type: ndcg_at_1 value: 20.75 - type: ndcg_at_10 value: 28.233000000000004 - type: ndcg_at_100 value: 33.065 - type: ndcg_at_1000 value: 36.138999999999996 - type: ndcg_at_3 value: 24.361 - type: ndcg_at_5 value: 26.111 - type: precision_at_1 value: 20.75 - type: precision_at_10 value: 5.124 - type: precision_at_100 value: 0.8750000000000001 - type: precision_at_1000 value: 0.131 - type: precision_at_3 value: 11.539000000000001 - type: precision_at_5 value: 8.273 - type: recall_at_1 value: 16.887 - type: recall_at_10 value: 37.774 - type: recall_at_100 value: 59.587 - type: recall_at_1000 value: 81.523 - type: recall_at_3 value: 26.837 - type: recall_at_5 value: 31.456 - type: map_at_1 value: 25.534000000000002 - type: map_at_10 value: 33.495999999999995 - type: map_at_100 value: 34.697 - type: map_at_1000 value: 34.805 - type: map_at_3 value: 31.22 - type: map_at_5 value: 32.277 - type: mrr_at_1 value: 29.944 - type: mrr_at_10 value: 37.723 - type: mrr_at_100 value: 38.645 - type: mrr_at_1000 value: 38.712999999999994 - type: mrr_at_3 value: 35.665 - type: mrr_at_5 value: 36.681999999999995 - type: ndcg_at_1 value: 29.944 - type: ndcg_at_10 value: 38.407000000000004 - type: ndcg_at_100 value: 43.877 - type: ndcg_at_1000 value: 46.312 - type: ndcg_at_3 value: 34.211000000000006 - type: ndcg_at_5 value: 35.760999999999996 - type: precision_at_1 value: 29.944 - type: precision_at_10 value: 6.343 - type: precision_at_100 value: 1.023 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 15.360999999999999 - type: precision_at_5 value: 10.428999999999998 - type: recall_at_1 value: 25.534000000000002 - type: recall_at_10 value: 49.204 - type: recall_at_100 value: 72.878 - type: recall_at_1000 value: 89.95 - type: recall_at_3 value: 37.533 - type: recall_at_5 value: 41.611 - type: map_at_1 value: 26.291999999999998 - type: map_at_10 value: 35.245 - type: map_at_100 value: 36.762 - type: map_at_1000 value: 36.983 - type: map_at_3 value: 32.439 - type: map_at_5 value: 33.964 - type: mrr_at_1 value: 31.423000000000002 - type: mrr_at_10 value: 39.98 - type: mrr_at_100 value: 40.791 - type: mrr_at_1000 value: 40.854 - type: mrr_at_3 value: 37.451 - type: mrr_at_5 value: 38.854 - type: ndcg_at_1 value: 31.423000000000002 - type: ndcg_at_10 value: 40.848 - type: ndcg_at_100 value: 46.35 - type: ndcg_at_1000 value: 49.166 - type: ndcg_at_3 value: 36.344 - type: ndcg_at_5 value: 38.36 - type: precision_at_1 value: 31.423000000000002 - type: precision_at_10 value: 7.767 - type: precision_at_100 value: 1.498 - type: precision_at_1000 value: 0.23700000000000002 - type: precision_at_3 value: 16.733 - type: precision_at_5 value: 12.213000000000001 - type: recall_at_1 value: 26.291999999999998 - type: recall_at_10 value: 51.184 - type: recall_at_100 value: 76.041 - type: recall_at_1000 value: 94.11500000000001 - type: recall_at_3 value: 38.257000000000005 - type: recall_at_5 value: 43.68 - type: map_at_1 value: 20.715 - type: map_at_10 value: 27.810000000000002 - type: map_at_100 value: 28.810999999999996 - type: map_at_1000 value: 28.904999999999998 - type: map_at_3 value: 25.069999999999997 - type: map_at_5 value: 26.793 - type: mrr_at_1 value: 22.366 - type: mrr_at_10 value: 29.65 - type: mrr_at_100 value: 30.615 - type: mrr_at_1000 value: 30.686999999999998 - type: mrr_at_3 value: 27.017999999999997 - type: mrr_at_5 value: 28.644 - type: ndcg_at_1 value: 22.366 - type: ndcg_at_10 value: 32.221 - type: ndcg_at_100 value: 37.313 - type: ndcg_at_1000 value: 39.871 - type: ndcg_at_3 value: 26.918 - type: ndcg_at_5 value: 29.813000000000002 - type: precision_at_1 value: 22.366 - type: precision_at_10 value: 5.139 - type: precision_at_100 value: 0.8240000000000001 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 11.275 - type: precision_at_5 value: 8.540000000000001 - type: recall_at_1 value: 20.715 - type: recall_at_10 value: 44.023 - type: recall_at_100 value: 67.458 - type: recall_at_1000 value: 87.066 - type: recall_at_3 value: 30.055 - type: recall_at_5 value: 36.852000000000004 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: 392b78eb68c07badcd7c2cd8f39af108375dfcce metrics: - type: map_at_1 value: 11.859 - type: map_at_10 value: 20.625 - type: map_at_100 value: 22.5 - type: map_at_1000 value: 22.689 - type: map_at_3 value: 16.991 - type: map_at_5 value: 18.781 - type: mrr_at_1 value: 26.906000000000002 - type: mrr_at_10 value: 39.083 - type: mrr_at_100 value: 39.978 - type: mrr_at_1000 value: 40.014 - type: mrr_at_3 value: 35.44 - type: mrr_at_5 value: 37.619 - type: ndcg_at_1 value: 26.906000000000002 - type: ndcg_at_10 value: 29.386000000000003 - type: ndcg_at_100 value: 36.510999999999996 - type: ndcg_at_1000 value: 39.814 - type: ndcg_at_3 value: 23.558 - type: ndcg_at_5 value: 25.557999999999996 - type: precision_at_1 value: 26.906000000000002 - type: precision_at_10 value: 9.342 - 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type: euclidean_ap value: 60.361102792772535 - type: euclidean_f1 value: 57.50518791791561 - type: euclidean_precision value: 51.06470106470107 - type: euclidean_recall value: 65.8047493403694 - type: manhattan_accuracy value: 82.14221851344102 - type: manhattan_ap value: 60.341937223793366 - type: manhattan_f1 value: 57.53803596127247 - type: manhattan_precision value: 51.08473188702415 - type: manhattan_recall value: 65.85751978891821 - type: max_accuracy value: 83.53102461703523 - type: max_ap value: 65.30753664579191 - type: max_f1 value: 61.739943872778305 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.75305623471883 - type: cos_sim_ap value: 85.46387153880272 - type: cos_sim_f1 value: 77.91527673159008 - type: cos_sim_precision value: 72.93667315828353 - type: cos_sim_recall value: 83.62334462580844 - type: dot_accuracy value: 85.08169363915086 - type: dot_ap value: 74.96808060965559 - type: dot_f1 value: 71.39685033990366 - type: dot_precision value: 64.16948111759288 - type: dot_recall value: 80.45888512473051 - type: euclidean_accuracy value: 85.84235650250321 - type: euclidean_ap value: 78.42045145247211 - type: euclidean_f1 value: 70.32669630775179 - type: euclidean_precision value: 70.6298050788227 - type: euclidean_recall value: 70.02617801047121 - type: manhattan_accuracy value: 85.86176116738464 - type: manhattan_ap value: 78.54012451558276 - type: manhattan_f1 value: 70.56508080693389 - type: manhattan_precision value: 69.39626293456413 - type: manhattan_recall value: 71.77394518016631 - type: max_accuracy value: 88.75305623471883 - type: max_ap value: 85.46387153880272 - type: max_f1 value: 77.91527673159008 --- ## Usage For usage instructions, refer to: https://github.com/Muennighoff/sgpt#asymmetric-semantic-search-be The model was trained with the command ```bash CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 accelerate launch examples/training/ms_marco/train_bi-encoder_mnrl.py --model_name bigscience/bloom-7b1 --train_batch_size 32 --eval_batch_size 16 --freezenonbias --specb --lr 4e-4 --wandb --wandbwatchlog gradients --pooling weightedmean --gradcache --chunksize 8 ``` ## Evaluation Results `{"ndcgs": {"sgpt-bloom-7b1-msmarco": {"scifact": {"NDCG@10": 0.71824}, "nfcorpus": {"NDCG@10": 0.35748}, "arguana": {"NDCG@10": 0.47281}, "scidocs": {"NDCG@10": 0.18435}, "fiqa": {"NDCG@10": 0.35736}, "cqadupstack": {"NDCG@10": 0.3708525}, "quora": {"NDCG@10": 0.74655}, "trec-covid": {"NDCG@10": 0.82731}, "webis-touche2020": {"NDCG@10": 0.2365}}}` See the evaluation folder or [MTEB](https://huggingface.co/spaces/mteb/leaderboard) for more results. ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 15600 with parameters: ``` {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` The model uses BitFit, weighted-mean pooling & GradCache, for details see: https://arxiv.org/abs/2202.08904 **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MNRLGradCache` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 0.0004 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: BloomModel (1): Pooling({'word_embedding_dimension': 4096, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors ```bibtex @article{muennighoff2022sgpt, title={SGPT: GPT Sentence Embeddings for Semantic Search}, author={Muennighoff, Niklas}, journal={arXiv preprint arXiv:2202.08904}, year={2022} } ```
[ "SUMMARIZATION" ]
[ "BIOSSES", "SCIFACT" ]
Non_BioNLP