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Mozilla/Phi-3-medium-128k-instruct-llamafile
|
Mozilla
| null |
[
"llamafile",
"nlp",
"code",
"multilingual",
"license:apache-2.0",
"region:us"
] | 2024-06-24T15:15:08Z |
2024-08-20T18:15:31+00:00
| 3,488 | 6 |
---
language:
- multilingual
license: apache-2.0
license_link: LICENSE
tags:
- llamafile
- nlp
- code
---
# Phi 3 Medium 128k Instruct - llamafile
This is a large language model that was released by Microsoft on May
21st, 2024. It has an outstanding context size, which makes it good for
summarizing and extracting information from large bodies of text. It's
also been fine-tuned to follow your instructions.
- Model creator: [Microsoft](https://huggingface.co/microsoft)
- Original model: [microsoft/Phi-3-medium-128k-instruct](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct)
Mozilla has packaged the Phi-3 model into executable weights that we
call [llamafiles](https://github.com/Mozilla-Ocho/llamafile). This gives
you the easiest fastest way to use the model on Linux, MacOS, Windows,
FreeBSD, OpenBSD and NetBSD systems you control on both AMD64 and ARM64.
## Quickstart
Running the following on a desktop OS will launch a tab in your web
browser with both a chatbot interface and a completion interface.
```
wget https://huggingface.co/Mozilla/Phi-3-medium-128k-instruct-llamafile/resolve/main/Phi-3-medium-128k-instruct.Q6_K.llamafile
chmod +x Phi-3-medium-128k-instruct.Q6_K.llamafile
./Phi-3-medium-128k-instruct.Q6_K.llamafile
```
You then need to fill out the text fields in the web GUI with the prompt
text below, in order for the chatbot to work correctly.
This model has a max context window size of 128k tokens. By default, a
context window size of 512 tokens is used. You can configure llamafile
to use the maxmimum context size with the `-c 0` flag. Please note a
context size of 128k will require allocating many gigabytes of RAM.
On GPUs with sufficient RAM, the `-ngl 999` flag may be passed to use
the system's NVIDIA or AMD GPU(s). On Windows, only the graphics card
driver needs to be installed. If the prebuilt DSOs should fail, the CUDA
or ROCm SDKs may need to be installed, in which case llamafile builds a
native module just for your system.
For further information, please see the [llamafile
README](https://github.com/mozilla-ocho/llamafile/).
Having **trouble?** See the ["Gotchas"
section](https://github.com/mozilla-ocho/llamafile/?tab=readme-ov-file#gotchas-and-troubleshooting)
of the README.
## Prompting
Prompt template (note: microsoft specifies no system prompt):
```
{{history}}<|assistant|>
```
History template:
```
<|user|>
{{message}}<|end|>
```
Command template:
```
./Phi-3-medium-128k-instruct.Q6_K.llamafile -e -p '<|user|>\nQuestion <|end|>\n<|assistant|>'
```
## Testing
We tested these llamafiles for CPU inference with prompts as large as
86k tokens on Threadripper and got good results.
## About llamafile
llamafile is a new format introduced by Mozilla Ocho on Nov 20th 2023.
It uses Cosmopolitan Libc to turn LLM weights into runnable llama.cpp
binaries that run on the stock installs of six OSes for both ARM64 and
AMD64.
---
## Model Summary
The Phi-3-Medium-128K-Instruct is a 14B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties.
The model belongs to the Phi-3 family with the Medium version in two variants [4k](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) and [128K](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) which is the context length (in tokens) that it can support.
The model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference optimization for the instruction following and safety measures.
When assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3-Medium-128K-Instruct showcased a robust and state-of-the-art performance among models of the same-size and next-size-up.
Resources and Technical Documentation:
+ [Phi-3 Microsoft Blog](https://aka.ms/Phi-3Build2024)
+ [Phi-3 Technical Report](https://aka.ms/phi3-tech-report)
+ [Phi-3 on Azure AI Studio](https://aka.ms/phi3-azure-ai)
+ [Phi-3 Cookbook](https://github.com/microsoft/Phi-3CookBook)
| | Short Context | Long Context |
| ------- | ------------- | ------------ |
| Mini | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx) ; [[GGUF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct-onnx)|
| Small | 8K [[HF]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct-onnx-cuda)|
| Medium | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda)|
| Vision | | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct-onnx-cuda)|
## Intended Uses
**Primary use cases**
The model is intended for broad commercial and research use in English. The model provides uses for general purpose AI systems and applications which require :
1) Memory/compute constrained environments
2) Latency bound scenarios
3) Strong reasoning (especially code, math and logic)
Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features.
**Use case considerations**
Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.
Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.
## How to Use
Phi-3-Medium-128k-Instruct has been integrated in the development version (4.40.2) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following:
* When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function.
* Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source.
The current `transformers` version can be verified with: `pip list | grep transformers`.
Phi-3-Medium-128k-Instruct is also available in [Azure AI Studio](https://aka.ms/phi3-azure-ai).
### Tokenizer
Phi-3-Medium-128k-Instruct supports a vocabulary size of up to `32064` tokens. The [tokenizer files](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct/blob/main/added_tokens.json) already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size.
### Chat Format
Given the nature of the training data, the Phi-3-Medium-128k-Instruct model is best suited for prompts using the chat format as follows.
You can provide the prompt as a question with a generic template as follow:
```markdown
<|user|>\nQuestion <|end|>\n<|assistant|>
```
For example:
```markdown
<|user|>
How to explain Internet for a medieval knight?<|end|>
<|assistant|>
```
where the model generates the text after `<|assistant|>` . In case of few-shots prompt, the prompt can be formatted as the following:
```markdown
<|user|>
I am going to Paris, what should I see?<|end|>
<|assistant|>
Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:\n\n1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.\n2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.\n3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.\n\nThese are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world."<|end|>
<|user|>
What is so great about #1?<|end|>
<|assistant|>
```
### Sample inference code
This code snippets show how to get quickly started with running the model on a GPU:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
torch.random.manual_seed(0)
model_id = "microsoft/Phi-3-medium-128k-instruct"
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cuda",
torch_dtype="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [
{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
{"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
{"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
]
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
generation_args = {
"max_new_tokens": 500,
"return_full_text": False,
"temperature": 0.0,
"do_sample": False,
}
output = pipe(messages, **generation_args)
print(output[0]['generated_text'])
```
*Some applications/frameworks might not include a BOS token (`<s>`) at the start of the conversation. Please ensure that it is included since it provides more reliable results.*
## Responsible AI Considerations
Like other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:
+ Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English.
+ Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases.
+ Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case.
+ Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated.
+ Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.
Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include:
+ Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.
+ High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context.
+ Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG).
+ Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case.
+ Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.
## Training
### Model
* Architecture: Phi-3-Medium-128k-Instruct has 14B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines.
* Inputs: Text. It is best suited for prompts using chat format.
* Context length: 128k tokens
* GPUs: 512 H100-80G
* Training time: 42 days
* Training data: 4.8T tokens
* Outputs: Generated text in response to the input
* Dates: Our models were trained between February and April 2024
* Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models.
* Release dates: The model weight is released on May 21, 2024.
### Datasets
Our training data includes a wide variety of sources, totaling 4.8 trillion tokens (including 10% multilingual), and is a combination of
1) Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code;
2) Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.);
3) High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.
We are focusing on the quality of data that could potentially improve the reasoning ability for the model, and we filter the publicly available documents to contain the correct level of knowledge. As an example, the result of a game in premier league in a particular day might be good training data for frontier models, but we need to remove such information to leave more model capacity for reasoning for the small size models. More details about data can be found in the [Phi-3 Technical Report](https://aka.ms/phi3-tech-report).
## Benchmarks
We report the results for Phi-3-Medium-128k-Instruct on standard open-source benchmarks measuring the model's reasoning ability (both common sense reasoning and logical reasoning). We compare to Mixtral-8x22b, Gemini-Pro, Command R+ 104B, Llama-3-70B-Instruct, GPT-3.5-Turbo-1106, and GPT-4-Turbo-1106(Chat).
All the reported numbers are produced with the exact same pipeline to ensure that the numbers are comparable. These numbers might differ from other published numbers due to slightly different choices in the evaluation.
As is now standard, we use few-shot prompts to evaluate the models, at temperature 0.
The prompts and number of shots are part of a Microsoft internal tool to evaluate language models, and in particular we did no optimization to the pipeline for Phi-3.
More specifically, we do not change prompts, pick different few-shot examples, change prompt format, or do any other form of optimization for the model.
The number of k–shot examples is listed per-benchmark.
|Benchmark|Phi-3-Medium-128k-Instruct<br>14b|Command R+<br>104B|Mixtral<br>8x22B|Llama-3-70B-Instruct|GPT3.5-Turbo<br>version 1106|Gemini<br>Pro|GPT-4-Turbo<br>version 1106 (Chat)|
|---------|-----------------------|--------|-------------|-------------------|-------------------|----------|------------------------|
|AGI Eval<br>5-shot|49.7|50.1|54.0|56.9|48.4|49.0|59.6|
|MMLU<br>5-shot|76.6|73.8|76.2|80.2|71.4|66.7|84.0|
|BigBench Hard<br>3-shot|77.9|74.1|81.8|80.4|68.3|75.6|87.7|
|ANLI<br>7-shot|57.3|63.4|65.2|68.3|58.1|64.2|71.7|
|HellaSwag<br>5-shot|81.6|78.0|79.0|82.6|78.8|76.2|88.3|
|ARC Challenge<br>10-shot|91.0|86.9|91.3|93.0|87.4|88.3|95.6|
|ARC Easy<br>10-shot|97.6|95.7|96.9|98.2|96.3|96.1|98.8|
|BoolQ<br>2-shot|86.5|86.1|82.7|89.1|79.1|86.4|91.3|
|CommonsenseQA<br>10-shot|82.2|82.0|82.0|84.4|79.6|81.8|86.7|
|MedQA<br>2-shot|67.6|59.2|67.9|78.5|63.4|58.2|83.7|
|OpenBookQA<br>10-shot|87.2|86.8|88.6|91.8|86.0|86.4|93.4|
|PIQA<br>5-shot|87.8|86.4|85.0|85.3|86.6|86.2|90.1|
|Social IQA<br>5-shot|79.0|75.3|78.2|81.1|68.3|75.4|81.7|
|TruthfulQA (MC2)<br>10-shot|74.3|57.8|67.4|81.9|67.7|72.6|85.2|
|WinoGrande<br>5-shot|78.9|77.0|75.3|83.3|68.8|72.2|86.7|
|TriviaQA<br>5-shot|73.9|82.8|84.5|78.5|85.8|80.2|73.3|
|GSM8K Chain of Thought<br>8-shot|87.5|78.3|83.8|93.5|78.1|80.4|94.2|
|HumanEval<br>0-shot|58.5|61.6|39.6|78.7|62.2|64.4|79.9|
|MBPP<br>3-shot|73.8|68.9|70.7|81.3|77.8|73.2|86.7|
|Average|77.3|75.0|76.3|82.5|74.3|75.4|85.2|
We take a closer look at different categories across 80 public benchmark datasets at the table below:
|Benchmark|Phi-3-Medium-128k-Instruct<br>14b|Command R+<br>104B|Mixtral<br>8x22B|Llama-3-70B-Instruct|GPT3.5-Turbo<br>version 1106|Gemini<br>Pro|GPT-4-Turbo<br>version 1106 (Chat)|
|--------|------------------------|--------|-------------|-------------------|-------------------|----------|------------------------|
| Popular aggregated benchmark | 72.3 | 69.9 | 73.4 | 76.3 | 67.0 | 67.5 | 80.5 |
| Reasoning | 83.2 | 79.3 | 81.5 | 86.7 | 78.3 | 80.4 | 89.3 |
| Language understanding | 75.3 | 75.7 | 78.7 | 77.9 | 70.4 | 75.3 | 81.6 |
| Code generation | 64.2 | 68.6 | 60.0 | 69.3 | 70.4 | 66.7 | 76.1 |
| Math | 52.9 | 45.3 | 52.5 | 59.7 | 52.8 | 50.9 | 67.1 |
| Factual knowledge | 47.5 | 60.3 | 60.6 | 52.4 | 63.4 | 54.6 | 45.9 |
| Multilingual | 62.2 | 67.8 | 69.8 | 62.0 | 67.0 | 73.4 | 78.2 |
| Robustness | 70.2 | 57.9 | 65.5 | 78.7 | 69.3 | 69.7 | 84.6 |
## Software
* [PyTorch](https://github.com/pytorch/pytorch)
* [DeepSpeed](https://github.com/microsoft/DeepSpeed)
* [Transformers](https://github.com/huggingface/transformers)
* [Flash-Attention](https://github.com/HazyResearch/flash-attention)
## Hardware
Note that by default, the Phi-3-Medium model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:
* NVIDIA A100
* NVIDIA A6000
* NVIDIA H100
If you want to run the model on:
+ Optimized inference on GPU, CPU, and Mobile: use the **ONNX** models [128k](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda)
## Cross Platform Support
ONNX runtime ecosystem now supports Phi3 Medium models across platforms and hardware.
Optimized phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML GPU acceleration is supported for Windows desktops GPUs (AMD, Intel, and NVIDIA).
Along with DML, ONNX Runtime provides cross platform support for Phi3 Medium across a range of devices CPU, GPU, and mobile.
Here are some of the optimized configurations we have added:
1. ONNX models for int4 DML: Quantized to int4 via AWQ
2. ONNX model for fp16 CUDA
3. ONNX model for int4 CUDA: Quantized to int4 via RTN
4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN
## License
The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-medium-128k/resolve/main/LICENSE).
## Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
|
[
"MEDQA"
] |
OrlikB/st-polish-kartonberta-base-alpha-v1
|
OrlikB
|
sentence-similarity
|
[
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"pl",
"license:lgpl",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-11-12T10:47:20Z |
2024-04-18T05:08:45+00:00
| 3,479 | 3 |
---
language:
- pl
license: lgpl
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: st-polish-kartonberta-base-alpha-v1
results:
- task:
type: Clustering
dataset:
name: MTEB 8TagsClustering
type: PL-MTEB/8tags-clustering
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 32.85180358455615
- task:
type: Classification
dataset:
name: MTEB AllegroReviews
type: PL-MTEB/allegro-reviews
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 40.188866799204774
- type: f1
value: 34.71127012684797
- task:
type: Retrieval
dataset:
name: MTEB ArguAna-PL
type: arguana-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.939
- type: map_at_10
value: 47.467999999999996
- type: map_at_100
value: 48.303000000000004
- type: map_at_1000
value: 48.308
- type: map_at_3
value: 43.22
- type: map_at_5
value: 45.616
- type: mrr_at_1
value: 31.863000000000003
- type: mrr_at_10
value: 47.829
- type: mrr_at_100
value: 48.664
- type: mrr_at_1000
value: 48.67
- type: mrr_at_3
value: 43.492
- type: mrr_at_5
value: 46.006
- type: ndcg_at_1
value: 30.939
- type: ndcg_at_10
value: 56.058
- type: ndcg_at_100
value: 59.562000000000005
- type: ndcg_at_1000
value: 59.69799999999999
- type: ndcg_at_3
value: 47.260000000000005
- type: ndcg_at_5
value: 51.587
- type: precision_at_1
value: 30.939
- type: precision_at_10
value: 8.329
- type: precision_at_100
value: 0.984
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 19.654
- type: precision_at_5
value: 13.898
- type: recall_at_1
value: 30.939
- type: recall_at_10
value: 83.286
- type: recall_at_100
value: 98.43499999999999
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 58.962
- type: recall_at_5
value: 69.488
- task:
type: Classification
dataset:
name: MTEB CBD
type: PL-MTEB/cbd
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 67.69000000000001
- type: ap
value: 21.078799692467182
- type: f1
value: 56.80107173953953
- 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: 89.2
- type: cos_sim_ap
value: 79.11674608786898
- type: cos_sim_f1
value: 68.83468834688347
- type: cos_sim_precision
value: 70.94972067039106
- type: cos_sim_recall
value: 66.84210526315789
- type: dot_accuracy
value: 89.2
- type: dot_ap
value: 79.11674608786898
- type: dot_f1
value: 68.83468834688347
- type: dot_precision
value: 70.94972067039106
- type: dot_recall
value: 66.84210526315789
- type: euclidean_accuracy
value: 89.2
- type: euclidean_ap
value: 79.11674608786898
- type: euclidean_f1
value: 68.83468834688347
- type: euclidean_precision
value: 70.94972067039106
- type: euclidean_recall
value: 66.84210526315789
- type: manhattan_accuracy
value: 89.1
- type: manhattan_ap
value: 79.1220443374692
- type: manhattan_f1
value: 69.02173913043478
- type: manhattan_precision
value: 71.34831460674157
- type: manhattan_recall
value: 66.84210526315789
- type: max_accuracy
value: 89.2
- type: max_ap
value: 79.1220443374692
- type: max_f1
value: 69.02173913043478
- 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: 91.41534744278998
- type: cos_sim_spearman
value: 92.12681551821147
- type: euclidean_pearson
value: 91.74369794485992
- type: euclidean_spearman
value: 92.12685848456046
- type: manhattan_pearson
value: 91.66651938751657
- type: manhattan_spearman
value: 92.057603126734
- task:
type: Retrieval
dataset:
name: MTEB DBPedia-PL
type: dbpedia-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.8709999999999996
- type: map_at_10
value: 12.486
- type: map_at_100
value: 16.897000000000002
- type: map_at_1000
value: 18.056
- type: map_at_3
value: 8.958
- type: map_at_5
value: 10.57
- type: mrr_at_1
value: 44.0
- type: mrr_at_10
value: 53.830999999999996
- type: mrr_at_100
value: 54.54
- type: mrr_at_1000
value: 54.568000000000005
- type: mrr_at_3
value: 51.87500000000001
- type: mrr_at_5
value: 53.113
- type: ndcg_at_1
value: 34.625
- type: ndcg_at_10
value: 26.996
- type: ndcg_at_100
value: 31.052999999999997
- type: ndcg_at_1000
value: 38.208
- type: ndcg_at_3
value: 29.471000000000004
- type: ndcg_at_5
value: 28.364
- type: precision_at_1
value: 44.0
- type: precision_at_10
value: 21.45
- type: precision_at_100
value: 6.837
- type: precision_at_1000
value: 1.6019999999999999
- type: precision_at_3
value: 32.333
- type: precision_at_5
value: 27.800000000000004
- type: recall_at_1
value: 5.8709999999999996
- type: recall_at_10
value: 17.318
- type: recall_at_100
value: 36.854
- type: recall_at_1000
value: 60.468999999999994
- type: recall_at_3
value: 10.213999999999999
- type: recall_at_5
value: 13.364
- task:
type: Retrieval
dataset:
name: MTEB FiQA-PL
type: fiqa-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.289
- type: map_at_10
value: 18.285999999999998
- type: map_at_100
value: 19.743
- type: map_at_1000
value: 19.964000000000002
- type: map_at_3
value: 15.193000000000001
- type: map_at_5
value: 16.962
- type: mrr_at_1
value: 21.914
- type: mrr_at_10
value: 30.653999999999996
- type: mrr_at_100
value: 31.623
- type: mrr_at_1000
value: 31.701
- type: mrr_at_3
value: 27.855
- type: mrr_at_5
value: 29.514000000000003
- type: ndcg_at_1
value: 21.914
- type: ndcg_at_10
value: 24.733
- type: ndcg_at_100
value: 31.253999999999998
- type: ndcg_at_1000
value: 35.617
- type: ndcg_at_3
value: 20.962
- type: ndcg_at_5
value: 22.553
- type: precision_at_1
value: 21.914
- type: precision_at_10
value: 7.346
- type: precision_at_100
value: 1.389
- type: precision_at_1000
value: 0.214
- type: precision_at_3
value: 14.352
- type: precision_at_5
value: 11.42
- type: recall_at_1
value: 10.289
- type: recall_at_10
value: 31.459
- type: recall_at_100
value: 56.854000000000006
- type: recall_at_1000
value: 83.722
- type: recall_at_3
value: 19.457
- type: recall_at_5
value: 24.767
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA-PL
type: hotpotqa-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.669
- type: map_at_10
value: 41.615
- type: map_at_100
value: 42.571999999999996
- type: map_at_1000
value: 42.662
- type: map_at_3
value: 38.938
- type: map_at_5
value: 40.541
- type: mrr_at_1
value: 59.338
- type: mrr_at_10
value: 66.93900000000001
- type: mrr_at_100
value: 67.361
- type: mrr_at_1000
value: 67.38499999999999
- type: mrr_at_3
value: 65.384
- type: mrr_at_5
value: 66.345
- type: ndcg_at_1
value: 59.338
- type: ndcg_at_10
value: 50.607
- type: ndcg_at_100
value: 54.342999999999996
- type: ndcg_at_1000
value: 56.286
- type: ndcg_at_3
value: 46.289
- type: ndcg_at_5
value: 48.581
- type: precision_at_1
value: 59.338
- type: precision_at_10
value: 10.585
- type: precision_at_100
value: 1.353
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 28.877000000000002
- type: precision_at_5
value: 19.133
- type: recall_at_1
value: 29.669
- type: recall_at_10
value: 52.92400000000001
- type: recall_at_100
value: 67.657
- type: recall_at_1000
value: 80.628
- type: recall_at_3
value: 43.315
- type: recall_at_5
value: 47.833
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO-PL
type: msmarco-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.997
- type: map_at_10
value: 7.481999999999999
- type: map_at_100
value: 20.208000000000002
- type: map_at_1000
value: 25.601000000000003
- type: map_at_3
value: 3.055
- type: map_at_5
value: 4.853
- type: mrr_at_1
value: 55.814
- type: mrr_at_10
value: 64.651
- type: mrr_at_100
value: 65.003
- type: mrr_at_1000
value: 65.05199999999999
- type: mrr_at_3
value: 62.403
- type: mrr_at_5
value: 64.031
- type: ndcg_at_1
value: 44.186
- type: ndcg_at_10
value: 43.25
- type: ndcg_at_100
value: 40.515
- type: ndcg_at_1000
value: 48.345
- type: ndcg_at_3
value: 45.829
- type: ndcg_at_5
value: 46.477000000000004
- type: precision_at_1
value: 55.814
- type: precision_at_10
value: 50.465
- type: precision_at_100
value: 25.419000000000004
- type: precision_at_1000
value: 5.0840000000000005
- type: precision_at_3
value: 58.14
- type: precision_at_5
value: 57.67400000000001
- type: recall_at_1
value: 0.997
- type: recall_at_10
value: 8.985999999999999
- type: recall_at_100
value: 33.221000000000004
- type: recall_at_1000
value: 58.836999999999996
- type: recall_at_3
value: 3.472
- type: recall_at_5
value: 5.545
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (pl)
type: mteb/amazon_massive_intent
config: pl
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.19771351714861
- type: f1
value: 64.75039989217822
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pl)
type: mteb/amazon_massive_scenario
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.9677202420982
- type: f1
value: 73.72287107577753
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus-PL
type: nfcorpus-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.167
- type: map_at_10
value: 10.791
- type: map_at_100
value: 14.072999999999999
- type: map_at_1000
value: 15.568000000000001
- type: map_at_3
value: 7.847999999999999
- type: map_at_5
value: 9.112
- type: mrr_at_1
value: 42.105
- type: mrr_at_10
value: 49.933
- type: mrr_at_100
value: 50.659
- type: mrr_at_1000
value: 50.705
- type: mrr_at_3
value: 47.988
- type: mrr_at_5
value: 49.056
- type: ndcg_at_1
value: 39.938
- type: ndcg_at_10
value: 31.147000000000002
- type: ndcg_at_100
value: 29.336000000000002
- type: ndcg_at_1000
value: 38.147
- type: ndcg_at_3
value: 35.607
- type: ndcg_at_5
value: 33.725
- type: precision_at_1
value: 41.486000000000004
- type: precision_at_10
value: 23.901
- type: precision_at_100
value: 7.960000000000001
- type: precision_at_1000
value: 2.086
- type: precision_at_3
value: 33.437
- type: precision_at_5
value: 29.598000000000003
- type: recall_at_1
value: 5.167
- type: recall_at_10
value: 14.244000000000002
- type: recall_at_100
value: 31.192999999999998
- type: recall_at_1000
value: 62.41799999999999
- type: recall_at_3
value: 8.697000000000001
- type: recall_at_5
value: 10.911
- task:
type: Retrieval
dataset:
name: MTEB NQ-PL
type: nq-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.417
- type: map_at_10
value: 23.330000000000002
- type: map_at_100
value: 24.521
- type: map_at_1000
value: 24.604
- type: map_at_3
value: 20.076
- type: map_at_5
value: 21.854000000000003
- type: mrr_at_1
value: 16.454
- type: mrr_at_10
value: 25.402
- type: mrr_at_100
value: 26.411
- type: mrr_at_1000
value: 26.479000000000003
- type: mrr_at_3
value: 22.369
- type: mrr_at_5
value: 24.047
- type: ndcg_at_1
value: 16.454
- type: ndcg_at_10
value: 28.886
- type: ndcg_at_100
value: 34.489999999999995
- type: ndcg_at_1000
value: 36.687999999999995
- type: ndcg_at_3
value: 22.421
- type: ndcg_at_5
value: 25.505
- type: precision_at_1
value: 16.454
- type: precision_at_10
value: 5.252
- type: precision_at_100
value: 0.8410000000000001
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 10.428999999999998
- type: precision_at_5
value: 8.019
- type: recall_at_1
value: 14.417
- type: recall_at_10
value: 44.025
- type: recall_at_100
value: 69.404
- type: recall_at_1000
value: 86.18900000000001
- type: recall_at_3
value: 26.972
- type: recall_at_5
value: 34.132
- task:
type: Classification
dataset:
name: MTEB PAC
type: laugustyniak/abusive-clauses-pl
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 66.55082536924412
- type: ap
value: 76.44962281293184
- type: f1
value: 63.899803692180434
- task:
type: PairClassification
dataset:
name: MTEB PPC
type: PL-MTEB/ppc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 86.5
- type: cos_sim_ap
value: 92.65086645409387
- type: cos_sim_f1
value: 89.39157566302653
- type: cos_sim_precision
value: 84.51327433628319
- type: cos_sim_recall
value: 94.86754966887418
- type: dot_accuracy
value: 86.5
- type: dot_ap
value: 92.65086645409387
- type: dot_f1
value: 89.39157566302653
- type: dot_precision
value: 84.51327433628319
- type: dot_recall
value: 94.86754966887418
- type: euclidean_accuracy
value: 86.5
- type: euclidean_ap
value: 92.65086645409387
- type: euclidean_f1
value: 89.39157566302653
- type: euclidean_precision
value: 84.51327433628319
- type: euclidean_recall
value: 94.86754966887418
- type: manhattan_accuracy
value: 86.5
- type: manhattan_ap
value: 92.64975544736456
- type: manhattan_f1
value: 89.33852140077822
- type: manhattan_precision
value: 84.28781204111601
- type: manhattan_recall
value: 95.03311258278146
- type: max_accuracy
value: 86.5
- type: max_ap
value: 92.65086645409387
- type: max_f1
value: 89.39157566302653
- task:
type: PairClassification
dataset:
name: MTEB PSC
type: PL-MTEB/psc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 95.64007421150278
- type: cos_sim_ap
value: 98.42114841894346
- type: cos_sim_f1
value: 92.8895612708018
- type: cos_sim_precision
value: 92.1921921921922
- type: cos_sim_recall
value: 93.59756097560977
- type: dot_accuracy
value: 95.64007421150278
- type: dot_ap
value: 98.42114841894346
- type: dot_f1
value: 92.8895612708018
- type: dot_precision
value: 92.1921921921922
- type: dot_recall
value: 93.59756097560977
- type: euclidean_accuracy
value: 95.64007421150278
- type: euclidean_ap
value: 98.42114841894346
- type: euclidean_f1
value: 92.8895612708018
- type: euclidean_precision
value: 92.1921921921922
- type: euclidean_recall
value: 93.59756097560977
- type: manhattan_accuracy
value: 95.82560296846012
- type: manhattan_ap
value: 98.38712415914046
- type: manhattan_f1
value: 93.19213313161876
- type: manhattan_precision
value: 92.49249249249249
- type: manhattan_recall
value: 93.90243902439023
- type: max_accuracy
value: 95.82560296846012
- type: max_ap
value: 98.42114841894346
- type: max_f1
value: 93.19213313161876
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-IN
type: PL-MTEB/polemo2_in
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 68.40720221606648
- type: f1
value: 67.09084289613526
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-OUT
type: PL-MTEB/polemo2_out
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 38.056680161943326
- type: f1
value: 32.87731504372395
- task:
type: Retrieval
dataset:
name: MTEB Quora-PL
type: quora-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 65.422
- type: map_at_10
value: 79.259
- type: map_at_100
value: 80.0
- type: map_at_1000
value: 80.021
- type: map_at_3
value: 76.16199999999999
- type: map_at_5
value: 78.03999999999999
- type: mrr_at_1
value: 75.26
- type: mrr_at_10
value: 82.39699999999999
- type: mrr_at_100
value: 82.589
- type: mrr_at_1000
value: 82.593
- type: mrr_at_3
value: 81.08999999999999
- type: mrr_at_5
value: 81.952
- type: ndcg_at_1
value: 75.3
- type: ndcg_at_10
value: 83.588
- type: ndcg_at_100
value: 85.312
- type: ndcg_at_1000
value: 85.536
- type: ndcg_at_3
value: 80.128
- type: ndcg_at_5
value: 81.962
- type: precision_at_1
value: 75.3
- type: precision_at_10
value: 12.856000000000002
- type: precision_at_100
value: 1.508
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 35.207
- type: precision_at_5
value: 23.316
- type: recall_at_1
value: 65.422
- type: recall_at_10
value: 92.381
- type: recall_at_100
value: 98.575
- type: recall_at_1000
value: 99.85300000000001
- type: recall_at_3
value: 82.59100000000001
- type: recall_at_5
value: 87.629
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS-PL
type: scidocs-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.52
- type: map_at_10
value: 6.814000000000001
- type: map_at_100
value: 8.267
- type: map_at_1000
value: 8.565000000000001
- type: map_at_3
value: 4.736
- type: map_at_5
value: 5.653
- type: mrr_at_1
value: 12.5
- type: mrr_at_10
value: 20.794999999999998
- type: mrr_at_100
value: 22.014
- type: mrr_at_1000
value: 22.109
- type: mrr_at_3
value: 17.8
- type: mrr_at_5
value: 19.42
- type: ndcg_at_1
value: 12.5
- type: ndcg_at_10
value: 12.209
- type: ndcg_at_100
value: 18.812
- type: ndcg_at_1000
value: 24.766
- type: ndcg_at_3
value: 10.847
- type: ndcg_at_5
value: 9.632
- type: precision_at_1
value: 12.5
- type: precision_at_10
value: 6.660000000000001
- type: precision_at_100
value: 1.6340000000000001
- type: precision_at_1000
value: 0.307
- type: precision_at_3
value: 10.299999999999999
- type: precision_at_5
value: 8.66
- type: recall_at_1
value: 2.52
- type: recall_at_10
value: 13.495
- type: recall_at_100
value: 33.188
- type: recall_at_1000
value: 62.34499999999999
- type: recall_at_3
value: 6.245
- type: recall_at_5
value: 8.76
- 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: 86.13942111699959
- type: cos_sim_ap
value: 81.47480017120256
- type: cos_sim_f1
value: 74.79794268919912
- type: cos_sim_precision
value: 77.2382397572079
- type: cos_sim_recall
value: 72.50712250712252
- type: dot_accuracy
value: 86.13942111699959
- type: dot_ap
value: 81.47478531367476
- type: dot_f1
value: 74.79794268919912
- type: dot_precision
value: 77.2382397572079
- type: dot_recall
value: 72.50712250712252
- type: euclidean_accuracy
value: 86.13942111699959
- type: euclidean_ap
value: 81.47478531367476
- type: euclidean_f1
value: 74.79794268919912
- type: euclidean_precision
value: 77.2382397572079
- type: euclidean_recall
value: 72.50712250712252
- type: manhattan_accuracy
value: 86.15980432123929
- type: manhattan_ap
value: 81.40798042612397
- type: manhattan_f1
value: 74.86116253239543
- type: manhattan_precision
value: 77.9491133384734
- type: manhattan_recall
value: 72.00854700854701
- type: max_accuracy
value: 86.15980432123929
- type: max_ap
value: 81.47480017120256
- type: max_f1
value: 74.86116253239543
- 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: 84.27525342551935
- type: cos_sim_spearman
value: 79.50631730805885
- type: euclidean_pearson
value: 82.07169123942028
- type: euclidean_spearman
value: 79.50631887406465
- type: manhattan_pearson
value: 81.98288826317463
- type: manhattan_spearman
value: 79.4244081650332
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 35.59400236598834
- type: cos_sim_spearman
value: 36.782560207852846
- type: euclidean_pearson
value: 28.546177668542942
- type: euclidean_spearman
value: 36.68394223635756
- type: manhattan_pearson
value: 28.45606963909248
- type: manhattan_spearman
value: 36.475975118547524
- task:
type: Retrieval
dataset:
name: MTEB SciFact-PL
type: scifact-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 41.028
- type: map_at_10
value: 52.23799999999999
- type: map_at_100
value: 52.905
- type: map_at_1000
value: 52.945
- type: map_at_3
value: 49.102000000000004
- type: map_at_5
value: 50.992000000000004
- type: mrr_at_1
value: 43.333
- type: mrr_at_10
value: 53.551
- type: mrr_at_100
value: 54.138
- type: mrr_at_1000
value: 54.175
- type: mrr_at_3
value: 51.056000000000004
- type: mrr_at_5
value: 52.705999999999996
- type: ndcg_at_1
value: 43.333
- type: ndcg_at_10
value: 57.731
- type: ndcg_at_100
value: 61.18599999999999
- type: ndcg_at_1000
value: 62.261
- type: ndcg_at_3
value: 52.276999999999994
- type: ndcg_at_5
value: 55.245999999999995
- type: precision_at_1
value: 43.333
- type: precision_at_10
value: 8.267
- type: precision_at_100
value: 1.02
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 21.444
- type: precision_at_5
value: 14.533
- type: recall_at_1
value: 41.028
- type: recall_at_10
value: 73.111
- type: recall_at_100
value: 89.533
- type: recall_at_1000
value: 98.0
- type: recall_at_3
value: 58.744
- type: recall_at_5
value: 66.106
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID-PL
type: trec-covid-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.146
- type: map_at_10
value: 1.09
- type: map_at_100
value: 6.002
- type: map_at_1000
value: 15.479999999999999
- type: map_at_3
value: 0.41000000000000003
- type: map_at_5
value: 0.596
- type: mrr_at_1
value: 54.0
- type: mrr_at_10
value: 72.367
- type: mrr_at_100
value: 72.367
- type: mrr_at_1000
value: 72.367
- type: mrr_at_3
value: 70.333
- type: mrr_at_5
value: 72.033
- type: ndcg_at_1
value: 48.0
- type: ndcg_at_10
value: 48.827
- type: ndcg_at_100
value: 38.513999999999996
- type: ndcg_at_1000
value: 37.958
- type: ndcg_at_3
value: 52.614000000000004
- type: ndcg_at_5
value: 51.013
- type: precision_at_1
value: 54.0
- type: precision_at_10
value: 53.6
- type: precision_at_100
value: 40.300000000000004
- type: precision_at_1000
value: 17.276
- type: precision_at_3
value: 57.333
- type: precision_at_5
value: 55.60000000000001
- type: recall_at_1
value: 0.146
- type: recall_at_10
value: 1.438
- type: recall_at_100
value: 9.673
- type: recall_at_1000
value: 36.870999999999995
- type: recall_at_3
value: 0.47400000000000003
- type: recall_at_5
value: 0.721
---
# Model Card for st-polish-kartonberta-base-alpha-v1
This sentence transformer model is designed to convert text content into a 768-float vector space, ensuring an effective representation. It aims to be proficient in tasks involving sentence / document similarity.
The model has been released in its alpha version. Numerous potential enhancements could boost its performance, such as adjusting training hyperparameters or extending the training duration (currently limited to only one epoch). The main reason is limited GPU.
## Model Description
- **Developed by:** Bartłomiej Orlik, https://www.linkedin.com/in/bartłomiej-orlik/
- **Model type:** RoBERTa Sentence Transformer
- **Language:** Polish
- **License:** LGPL-3.0
- **Trained from model:** sdadas/polish-roberta-base-v2: https://huggingface.co/sdadas/polish-roberta-base-v2
## How to Get Started with the Model
Use the code below to get started with the model.
### Using Sentence-Transformers
You can use the model with [sentence-transformers](https://www.SBERT.net):
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('OrlikB/st-polish-kartonberta-base-alpha-v1')
text_1 = 'Jestem wielkim fanem opakowań tekturowych'
text_2 = 'Bardzo podobają mi się kartony'
embeddings_1 = model.encode(text_1, normalize_embeddings=True)
embeddings_2 = model.encode(text_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
### Using HuggingFace Transformers
```python
from transformers import AutoTokenizer, AutoModel
import torch
import numpy as np
def encode_text(text):
encoded_input = tokenizer(text, padding=True, truncation=True, return_tensors='pt', max_length=512)
with torch.no_grad():
model_output = model(**encoded_input)
sentence_embeddings = model_output[0][:, 0]
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
return sentence_embeddings.squeeze().numpy()
cosine_similarity = lambda a, b: np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
tokenizer = AutoTokenizer.from_pretrained('OrlikB/st-polish-kartonberta-base-alpha-v1')
model = AutoModel.from_pretrained('OrlikB/st-polish-kartonberta-base-alpha-v1')
model.eval()
text_1 = 'Jestem wielkim fanem opakowań tekturowych'
text_2 = 'Bardzo podobają mi się kartony'
embeddings_1 = encode_text(text_1)
embeddings_2 = encode_text(text_2)
print(cosine_similarity(embeddings_1, embeddings_2))
```
*Note: You can use the encode_text function for demonstration purposes. For the best experience, it's recommended to process text in batches.
## Evaluation
#### [MTEB for Polish Language](https://huggingface.co/spaces/mteb/leaderboard)
| Rank | Model | Model Size (GB) | Embedding Dimensions | Sequence Length | Average (26 datasets) | Classification Average (7 datasets) | Clustering Average (1 datasets) | Pair Classification Average (4 datasets) | Retrieval Average (11 datasets) | STS Average (3 datasets) |
|-------:|:----------------------------------------|------------------:|-----------------------:|------------------:|------------------------:|--------------------------------------:|--------------------------------:|-----------------------------------------:|----------------------------------:|-------------------------:|
| 1 | multilingual-e5-large | 2.24 | 1024 | 514 | 58.25 | 60.51 | 24.06 | 84.58 | 47.82 | 67.52 |
| 2 | **st-polish-kartonberta-base-alpha-v1** | 0.5 | 768 | 514 | 56.92 | 60.44 | **32.85** | **87.92** | 42.19 | **69.47** |
| 3 | multilingual-e5-base | 1.11 | 768 | 514 | 54.18 | 57.01 | 18.62 | 82.08 | 42.5 | 65.07 |
| 4 | multilingual-e5-small | 0.47 | 384 | 512 | 53.15 | 54.35 | 19.64 | 81.67 | 41.52 | 66.08 |
| 5 | st-polish-paraphrase-from-mpnet | 0.5 | 768 | 514 | 53.06 | 57.49 | 25.09 | 87.04 | 36.53 | 67.39 |
| 6 | st-polish-paraphrase-from-distilroberta | 0.5 | 768 | 514 | 52.65 | 58.55 | 31.11 | 87 | 33.96 | 68.78 |
## More Information
I developed this model as a personal scientific initiative.
I plan to start the development on a new ST model. However, due to limited computational resources, I suspended further work to create a larger or enhanced version of current model.
|
[
"SCIFACT"
] |
medicalai/ClinicalGPT-base-zh
|
medicalai
|
text-generation
|
[
"transformers",
"pytorch",
"bloom",
"text-generation",
"medical",
"arxiv:2306.09968",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-06-21T12:31:50Z |
2025-01-07T16:06:21+00:00
| 3,473 | 44 |
---
license: afl-3.0
tags:
- medical
---
# ClinicalGPT
This model card introduces ClinicalGPT model, a large language model designed and optimized for clinical scenarios. ClinicalGPT is fine-tuned on extensive and diverse medical datasets, including medical records, domain-specific knowledge, and multi-round dialogue consultations. The model is undergoing ongoing and continuous updates.
## Model Fine-tuning
We set the learning rate to 5e-5, with a batch size of 128 and a maximum length of 1,024, training across 3 epochs.
## How to use the model
Load the model via the transformers library:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("medicalai/ClinicalGPT-base-zh")
model = AutoModelForCausalLM.from_pretrained("medicalai/ClinicalGPT-base-zh")
```
## Limitations
The project is intended for research purposes only and restricted from commercial or clinical use. The generated content by the model is subject to factors such as model computations, randomness, misinterpretation, and biases, and this project cannot guarantee its accuracy. This project assumes no legal liability for any content produced by the model. Users are advised to exercise caution and independently verify the generated results.
## Citation
Please cite these articles:
1.Wang, G., Liu, X., Liu, H., Yang, G. et al. A Generalist Medical Language Model for Disease Diagnosis Assistance. Nat Med (2025). https://doi.org/10.1038/s41591-024-03416-6
2.Wang, G., Yang, G., Du, Z., Fan, L., & Li, X. (2023). ClinicalGPT: large language models finetuned with diverse medical data and comprehensive evaluation. arXiv preprint arXiv:2306.09968.
|
[
"MEDICAL DATA"
] |
allenai/OLMo-7B-hf
|
allenai
|
text-generation
|
[
"transformers",
"safetensors",
"olmo",
"text-generation",
"en",
"dataset:allenai/dolma",
"arxiv:2402.00838",
"arxiv:2302.13971",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-04-12T22:33:46Z |
2024-07-16T18:01:37+00:00
| 3,446 | 13 |
---
datasets:
- allenai/dolma
language:
- en
license: apache-2.0
---
<img src="https://allenai.org/olmo/olmo-7b-animation.gif" alt="OLMo Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Model Card for OLMo 7B
<!-- Provide a quick summary of what the model is/does. -->
OLMo is a series of **O**pen **L**anguage **Mo**dels designed to enable the science of language models.
The OLMo models are trained on the [Dolma](https://huggingface.co/datasets/allenai/dolma) dataset.
We release all code, checkpoints, logs (coming soon), and details involved in training these models.
This model has been converted from [allenai/OLMo-7B](https://huggingface.co/allenai/OLMo-7B) for the
Hugging Face Transformers format.
## Model Details
The core models released in this batch are the following:
| Size | Training Tokens | Layers | Hidden Size | Attention Heads | Context Length |
|------|--------|---------|-------------|-----------------|----------------|
| [OLMo 1B](https://huggingface.co/allenai/OLMo-1B-hf) | 3 Trillion |16 | 2048 | 16 | 2048 |
| [OLMo 7B](https://huggingface.co/allenai/OLMo-7B-hf) | 2.5 Trillion | 32 | 4096 | 32 | 2048 |
| [OLMo 7B Twin 2T](https://huggingface.co/allenai/OLMo-7B-Twin-2T-hf) | 2 Trillion | 32 | 4096 | 32 | 2048 |
We are releasing many checkpoints for these models, for every 1000 training steps. These have not
yet been converted into Hugging Face Transformers format, but are available in [allenai/OLMo-7B](https://huggingface.co/allenai/OLMo-7B).
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Allen Institute for AI (AI2)
- **Supported by:** Databricks, Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, AMD, CSC (Lumi Supercomputer), UW
- **Model type:** a Transformer style autoregressive language model.
- **Language(s) (NLP):** English
- **License:** The code and model are released under Apache 2.0.
- **Contact:** Technical inquiries: `olmo at allenai dot org`. Press: `press at allenai dot org`
- **Date cutoff:** Feb./March 2023 based on Dolma dataset version.
### Model Sources
<!-- Provide the basic links for the model. -->
- **Project Page:** https://allenai.org/olmo
- **Repositories:**
- Core repo (training, inference, fine-tuning etc.): https://github.com/allenai/OLMo
- Evaluation code: https://github.com/allenai/OLMo-Eval
- Further fine-tuning code: https://github.com/allenai/open-instruct
- **Paper:** [Link](https://arxiv.org/abs/2402.00838)
- **Technical blog post:** https://blog.allenai.org/olmo-open-language-model-87ccfc95f580
- **W&B Logs:** https://wandb.ai/ai2-llm/OLMo-7B/reports/OLMo-7B--Vmlldzo2NzQyMzk5
<!-- - **Press release:** TODO -->
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Inference
Quickly get inference running with the following:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-hf")
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-7B-hf")
message = ["Language modeling is"]
inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
# optional verifying cuda
# inputs = {k: v.to('cuda') for k,v in inputs.items()}
# olmo = olmo.to('cuda')
response = olmo.generate(**inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
>> 'Language modeling is the first step to build natural language generation...'
```
Alternatively, with the pipeline abstraction:
```python
from transformers import pipeline
olmo_pipe = pipeline("text-generation", model="allenai/OLMo-7B-hf")
print(olmo_pipe("Language modeling is "))
>> 'Language modeling is a branch of natural language processing that aims to...'
```
Or, you can make this slightly faster by quantizing the model, e.g. `AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-hf", torch_dtype=torch.float16, load_in_8bit=True)` (requires `bitsandbytes`).
The quantized model is more sensitive to typing / cuda, so it is recommended to pass the inputs as `inputs.input_ids.to('cuda')` to avoid potential issues.
### Fine-tuning
This model does not directly support our fine-tuning processes. Model fine-tuning can be done
from the final checkpoint or many intermediate checkpoints of
[allenai/OLMo-7B](https://huggingface.co/allenai/OLMo-7B).
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
Core model results for the 7B model are found below.
| | [Llama 7B](https://arxiv.org/abs/2302.13971) | [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b) | [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b) | [MPT 7B](https://huggingface.co/mosaicml/mpt-7b) | **OLMo 7B** (ours) |
| --------------------------------- | -------- | ---------- | --------- | ------ | ------- |
| arc_challenge | 44.5 | 39.8 | 47.5 | 46.5 | 48.5 |
| arc_easy | 57.0 | 57.7 | 70.4 | 70.5 | 65.4 |
| boolq | 73.1 | 73.5 | 74.6 | 74.2 | 73.4 |
| copa | 85.0 | 87.0 | 86.0 | 85.0 | 90 |
| hellaswag | 74.5 | 74.5 | 75.9 | 77.6 | 76.4 |
| openbookqa | 49.8 | 48.4 | 53.0 | 48.6 | 50.2 |
| piqa | 76.3 | 76.4 | 78.5 | 77.3 | 78.4 |
| sciq | 89.5 | 90.8 | 93.9 | 93.7 | 93.8 |
| winogrande | 68.2 | 67.3 | 68.9 | 69.9 | 67.9 |
| **Core tasks average** | 68.7 | 68.4 | 72.1 | 71.5 | 71.6 |
| truthfulQA (MC2) | 33.9 | 38.5 | 34.0 | 33 | 36.0 |
| MMLU (5 shot MC) | 31.5 | 45.0 | 24.0 | 30.8 | 28.3 |
| GSM8k (mixed eval.) | 10.0 (8shot CoT) | 12.0 (8shot CoT) | 4.0 (5 shot) | 4.5 (5 shot) | 8.5 (8shot CoT) |
| **Full average** | 57.8 | 59.3 | 59.2 | 59.3 | 59.8 |
And for the 1B model:
| task | random | [StableLM 2 1.6b](https://huggingface.co/stabilityai/stablelm-2-1_6b)\* | [Pythia 1B](https://huggingface.co/EleutherAI/pythia-1b) | [TinyLlama 1.1B](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T) | **OLMo 1B** (ours) |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------ | ----------------- | --------- | -------------------------------------- | ------- |
| arc_challenge | 25 | 43.81 | 33.11 | 34.78 | 34.45 |
| arc_easy | 25 | 63.68 | 50.18 | 53.16 | 58.07 |
| boolq | 50 | 76.6 | 61.8 | 64.6 | 60.7 |
| copa | 50 | 84 | 72 | 78 | 79 |
| hellaswag | 25 | 68.2 | 44.7 | 58.7 | 62.5 |
| openbookqa | 25 | 45.8 | 37.8 | 43.6 | 46.4 |
| piqa | 50 | 74 | 69.1 | 71.1 | 73.7 |
| sciq | 25 | 94.7 | 86 | 90.5 | 88.1 |
| winogrande | 50 | 64.9 | 53.3 | 58.9 | 58.9 |
| Average | 36.11 | 68.41 | 56.44 | 61.48 | 62.42 |
\*Unlike OLMo, Pythia, and TinyLlama, StabilityAI has not disclosed yet the data StableLM was trained on, making comparisons with other efforts challenging.
## Model Details
### Data
For training data details, please see the [Dolma](https://huggingface.co/datasets/allenai/dolma) documentation.
### Architecture
OLMo 7B architecture with peer models for comparison.
| | **OLMo 7B** | [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b) | [OpenLM 7B](https://laion.ai/blog/open-lm/) | [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b) | PaLM 8B |
|------------------------|-------------------|---------------------|--------------------|--------------------|------------------|
| d_model | 4096 | 4096 | 4096 | 4544 | 4096 |
| num heads | 32 | 32 | 32 | 71 | 16 |
| num layers | 32 | 32 | 32 | 32 | 32 |
| MLP ratio | ~8/3 | ~8/3 | ~8/3 | 4 | 4 |
| LayerNorm type | non-parametric LN | RMSNorm | parametric LN | parametric LN | parametric LN |
| pos embeddings | RoPE | RoPE | RoPE | RoPE | RoPE |
| attention variant | full | GQA | full | MQA | MQA |
| biases | none | none | in LN only | in LN only | none |
| block type | sequential | sequential | sequential | parallel | parallel |
| activation | SwiGLU | SwiGLU | SwiGLU | GeLU | SwiGLU |
| sequence length | 2048 | 4096 | 2048 | 2048 | 2048 |
| batch size (instances) | 2160 | 1024 | 2048 | 2304 | 512 |
| batch size (tokens) | ~4M | ~4M | ~4M | ~4M | ~1M |
| weight tying | no | no | no | no | yes |
### Hyperparameters
AdamW optimizer parameters are shown below.
| Size | Peak LR | Betas | Epsilon | Weight Decay |
|------|------------|-----------------|-------------|--------------|
| 1B | 4.0E-4 | (0.9, 0.95) | 1.0E-5 | 0.1 |
| 7B | 3.0E-4 | (0.9, 0.99) | 1.0E-5 | 0.1 |
Optimizer settings comparison with peer models.
| | **OLMo 7B** | [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b) | [OpenLM 7B](https://laion.ai/blog/open-lm/) | [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b) |
|-----------------------|------------------|---------------------|--------------------|--------------------|
| warmup steps | 5000 | 2000 | 2000 | 1000 |
| peak LR | 3.0E-04 | 3.0E-04 | 3.0E-04 | 6.0E-04 |
| minimum LR | 3.0E-05 | 3.0E-05 | 3.0E-05 | 1.2E-05 |
| weight decay | 0.1 | 0.1 | 0.1 | 0.1 |
| beta1 | 0.9 | 0.9 | 0.9 | 0.99 |
| beta2 | 0.95 | 0.95 | 0.95 | 0.999 |
| epsilon | 1.0E-05 | 1.0E-05 | 1.0E-05 | 1.0E-05 |
| LR schedule | linear | cosine | cosine | cosine |
| gradient clipping | global 1.0 | global 1.0 | global 1.0 | global 1.0 |
| gradient reduce dtype | FP32 | FP32 | FP32 | BF16 |
| optimizer state dtype | FP32 | most likely FP32 | FP32 | FP32 |
## Environmental Impact
OLMo 7B variants were either trained on MI250X GPUs at the LUMI supercomputer, or A100-40GB GPUs provided by MosaicML.
A summary of the environmental impact. Further details are available in the paper.
| | GPU Type | Power Consumption From GPUs | Carbon Intensity (kg CO₂e/KWh) | Carbon Emissions (tCO₂eq) |
|-----------|------------|-----------------------------|--------------------------------|---------------------------|
| OLMo 7B Twin | MI250X ([LUMI supercomputer](https://www.lumi-supercomputer.eu)) | 135 MWh | 0* | 0* |
| OLMo 7B | A100-40GB ([MosaicML](https://www.mosaicml.com)) | 104 MWh | 0.656 | 75.05 |
## Bias, Risks, and Limitations
Like any base language model or fine-tuned model without safety filtering, it is relatively easy for a user to prompt these models to generate harmful and generally sensitive content.
Such content can also be produced unintentionally, especially in the case of bias, so we recommend users consider the risks of applications of this technology.
Otherwise, many facts from OLMo or any LLM will often not be true, so they should be checked.
## Citation
**BibTeX:**
```
@article{Groeneveld2023OLMo,
title={OLMo: Accelerating the Science of Language Models},
author={Groeneveld, Dirk and Beltagy, Iz and Walsh, Pete and Bhagia, Akshita and Kinney, Rodney and Tafjord, Oyvind and Jha, Ananya Harsh and Ivison, Hamish and Magnusson, Ian and Wang, Yizhong and Arora, Shane and Atkinson, David and Authur, Russell and Chandu, Khyathi and Cohan, Arman and Dumas, Jennifer and Elazar, Yanai and Gu, Yuling and Hessel, Jack and Khot, Tushar and Merrill, William and Morrison, Jacob and Muennighoff, Niklas and Naik, Aakanksha and Nam, Crystal and Peters, Matthew E. and Pyatkin, Valentina and Ravichander, Abhilasha and Schwenk, Dustin and Shah, Saurabh and Smith, Will and Subramani, Nishant and Wortsman, Mitchell and Dasigi, Pradeep and Lambert, Nathan and Richardson, Kyle and Dodge, Jesse and Lo, Kyle and Soldaini, Luca and Smith, Noah A. and Hajishirzi, Hannaneh},
journal={Preprint},
year={2024}
}
```
**APA:**
Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A., Ivison, H., Magnusson, I., Wang, Y., Arora, S., Atkinson, D., Authur, R., Chandu, K., Cohan, A., Dumas, J., Elazar, Y., Gu, Y., Hessel, J., Khot, T., Merrill, W., Morrison, J., Muennighoff, N., Naik, A., Nam, C., Peters, M., Pyatkin, V., Ravichander, A., Schwenk, D., Shah, S., Smith, W., Subramani, N., Wortsman, M., Dasigi, P., Lambert, N., Richardson, K., Dodge, J., Lo, K., Soldaini, L., Smith, N., & Hajishirzi, H. (2024). OLMo: Accelerating the Science of Language Models. Preprint.
## Model Card Contact
For errors in this model card, contact Nathan, Akshita or Shane, `{nathanl, akshitab, shanea} at allenai dot org`.
|
[
"SCIQ"
] |
DavidAU/Qwen2.5-MOE-2X1.5B-DeepSeek-Uncensored-Censored-4B-gguf
|
DavidAU
|
text-generation
|
[
"gguf",
"MOE",
"Qwen 2.5 MOE",
"Mixture of Experts",
"Uncensored",
"2X1.5B",
"deepseek",
"reasoning",
"thinking",
"creative",
"128k context",
"general usage",
"problem solving",
"brainstorming",
"solve riddles",
"story generation",
"plot generation",
"storytelling",
"fiction story",
"story",
"writing",
"fiction",
"Qwen 2.5",
"mergekit",
"text-generation",
"en",
"zh",
"base_model:DavidAU/Qwen2.5-MOE-2X1.5B-DeepSeek-Uncensored-Censored-4B",
"base_model:quantized:DavidAU/Qwen2.5-MOE-2X1.5B-DeepSeek-Uncensored-Censored-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | 2025-03-04T23:18:47Z |
2025-03-06T01:29:35+00:00
| 3,392 | 3 |
---
base_model:
- DavidAU/Qwen2.5-MOE-2X1.5B-DeepSeek-Uncensored-Censored-4B
language:
- en
- zh
license: apache-2.0
pipeline_tag: text-generation
tags:
- MOE
- Qwen 2.5 MOE
- Mixture of Experts
- Uncensored
- 2X1.5B
- deepseek
- reasoning
- thinking
- creative
- 128k context
- general usage
- problem solving
- brainstorming
- solve riddles
- story generation
- plot generation
- storytelling
- fiction story
- story
- writing
- fiction
- Qwen 2.5
- mergekit
---
<H2>Qwen2.5-MOE-2X1.5B-DeepSeek-Uncensored-Censored-4B-gguf</H2>
<img src="qwen-tiny.jpg" style="float:right; width:300px; height:300px; padding:5px;">
This is a Qwen2.5 MOE (Mixture of Experts) model comprised of TWO Qwen 2.5 Deepseek (Censored/Normal AND Uncensored) 1.5B models
creating a 4B model with the "Uncensored" version of Deepseek Qwen 2.5 1.5B "in charge" so to speak.
The model is just over 4B because of the unqiue "shared expert" (roughly 2.5 models here) used in Qwen MOEs.
The oddball configuration yields interesting "thinking/reasoning" which is stronger than either 1.5B model on its own.
5 example generations at the bottom of this page.
This model can be used for all use cases, and is also (mostly) uncensored.
Context: 128k.
You need to use the "Jinja Template" encoded in the GGUF to use this model. You might be able to use Llama 3, and/or Chatml templates
if your AI/LLM app can not access the "Jinja Template".
In Lmstudio the "Jinja Template" should load by default.
In other apps - use the Deepseek Tokenizer and/or "Jinja Template".
This model contains 2 times the power of DeepSeek Distill reasoning/thinking and shows exceptional performance for a model of its size.
Be aware however, because this model (and it's core models) are so small, certain information may not be understood by the model - IE
culture references and/or you may need to add additional clarifications to your prompt(s) - more wording to make things clearer.
In such cases, you may want to provide the model with a more detailed prompt, with information about "references", so it can add this into
the reasoning/thinking process.
Also, the DeepSeek Qwen 1.5B model is based on Qwen's 1.5B Math model so this model is slanted more towards math/logic problem solving
and I would also say more "sciency" too.
This does not mean it will not work for your use case.
Likewise, this model may require more direction, details, and what you are asking in the prompt to "think" along "narrower" lines.
It may take 2-4 generations for the model to zero in / get what you mean and "think" along the correct lines, if your prompt(s)
are too short.
Example:
"Come up with six plots for a new "Star Trek" episode (that the audience would love) that all involve time travel."
VS
"Come up with six story plots for a new "Star Trek" (science fiction tv series, set in the 23 century) episode that all involve time travel."
The first prompt MAY generate the correct response (after 1-4 tries), whereas the 2nd one will always work.
Also, because of how this model works (uncensored and censored in the same model) you may want to try 1-4 generations depending
on your use case because even the "right" response will vary widely, and in many cases be more "interesting".
Five examples below so you have some idea what this model can do.
Keep in mind this model is two 1.5B parameters models working together, and will not have the power of a 14B or 32B reasoning/thinking model.
However, sometimes it will generate truly "out of the park" responses.
Temp of .4 to .8 is suggested (for best reasoning/thinking), however it will still operate at much higher temps like 1.8, 2.6 etc.
Depending on your prompt change temp SLOWLY: IE: .41,.42,.43 ... etc etc.
Likewise, because these are small models, it may do a tonne of "thinking"/"reasoning" and then "forget" to finish a / the task(s). In
this case, prompt the model to "Complete the task XYZ with the 'reasoning plan' above" .
Likewise it may function better if you breakdown the reasoning/thinking task(s) into smaller pieces :
"IE: Instead of asking for 6 plots FOR theme XYZ, ASK IT for ONE plot for theme XYZ at a time".
Also set context limit at 4k minimum, 8K+ suggested.
I also suggest quant of IQ4/Q4 or higher, as larger quants will reasoning/thinking and perform much better.
If you can run Q6/Q8, please use these ones OR the "F16" - full precision version.
---
<B> Additional Support / Documents for this model to assist with generation / performance: </b>
Document #1:
Details how to use reasoning/thinking models and get maximum performance from them, and includes links to all reasoning/thinking models - GGUF and source, as well as adapters to turn any "regular" model into a "reasoning/thinking" model.
[ https://huggingface.co/DavidAU/How-To-Use-Reasoning-Thinking-Models-and-Create-Them ]
Document #2:
Document detailing all parameters, settings, samplers and advanced samplers to use not only my models to their maximum potential - but all models (and quants) online (regardless of the repo) to their maximum potential. Included quick start and detailed notes, include AI / LLM apps and other critical information and references too. A must read if you are using any AI/LLM right now.
[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]
Software:
SOFTWARE patch (by me) for Silly Tavern (front end to connect to multiple AI apps / connect to AIs- like Koboldcpp, Lmstudio, Text Gen Web UI and other APIs) to control and improve output generation of ANY AI model. Also designed to control/wrangle some of my more "creative" models and make them perform perfectly with little to no parameter/samplers adjustments too.
[ https://huggingface.co/DavidAU/AI_Autocorrect__Auto-Creative-Enhancement__Auto-Low-Quant-Optimization__gguf-exl2-hqq-SOFTWARE ]
---
Known Issues:
---
From time to time model will generate some Chinese (with an English prompt) symbols/characters, especially at higher temps. This is normal
for DeepSeek Distill models.
Reasoning/Thinking may be a little "odd" at temps 1.5+ ; you may need to regen to get a better response.
Because these are such small "base" models, lower quants performance will strongly LOWER than higher quants.
---
<h2>Example Generation:</h2>
Q8_0 Quant, Temp 1.5, rep pen 1.06, topp: .95, minp: .05, topk: 40
---
EXAMPLE #1:
---
<B>
Write me a description for a role-playing game in which the player has just entered a small cave. In it are two goblins arguing over a small chest of treasure in the center. Use creative metaphors relative to the scene.
</B>
[[[Thinking Start]]]
Okay, so I need to help this person write a description for a role-playing game. The setting is a small cave where two goblins are arguing about a treasure chest. The user provided a response that met all the requirements. But wait, the initial answer the user provided ends with a completed paragraph but perhaps a bit off. Let me check.
Hmm, actually, looking at the user's query again: they want a description of a role in a cave setting. They mentioned two goblins arguing over a chest. The response they provided is a paragraph ending with "the end," which is a bit off. It should probably say "The chest of treasure" instead.
Wait, in their example, the assistant wrote a well-structured paragraph. But maybe I can think of another angle. Maybe they want a more vivid or vivid description? Hmm.
So, perhaps the user is after a more vivid or descriptive scene. They might be looking for something that paints a clear picture, using imagery beyond just description. Maybe using more sensory details or more vivid metaphors.
Let me think about how to structure this. Maybe they want a full 3.0 scale of a D&D setting, which in gaming terms is very detailed. So, the description needs to be rich with sensory details, vivid descriptions, and perhaps a sense of depth.
So, perhaps I should elaborate on the environment, perhaps adding more descriptive language, using more vivid metaphors beyond just the setting. Maybe they want more than just the environment. They might also be looking for a setting that's rich with atmosphere, perhaps a bit more detailed or dynamic.
Wait, the user initially provided an example response that ends with "The end," but maybe it should end with a different phrase, like "The end" instead of "The chest of treasure." Wait, no, in the example, they wrote "The chest of treasure" at the end.
But perhaps I should think of another angle. Maybe the user is looking for a more vivid or detailed setting, using more vivid metaphors or descriptive language to make it stand out.
Alternatively, maybe the user wants the description to be more engaging or immersive beyond just describing the setting. They might want the reader to get a vivid picture of the environment.
So, perhaps they want a more vivid and engaging scene, with more sensory details or more vivid descriptions beyond just the physical and literal elements.
Alternatively, perhaps they want a more immersive experience than just the setting, using alliteration and other elements.
Wait, maybe I can think of another angle. Alternatively, perhaps the user is after a poetic description, using more elaborate metaphors.
Alternatively, perhaps it's about the setting in terms of atmosphere or mood.
But stepping back, maybe the user is after a vivid description for a role-playing game setting. So, the example response they provided was:
"The chest of treasure is like a gateway to another dimension, and from where I stand, I can see the depths of the ocean's secrets."
But perhaps that's more of an idea, but maybe the example just gives a partial answer.
Alternatively, perhaps they are after a more vivid description than just the setting.
Wait, perhaps the user is looking for a more immersive or immersive experience in the setting. So, the setting is so important, so to describe it with more detail.
Alternatively, perhaps they want a more vivid description of the setting, using more descriptive language beyond just the literal and physical elements.
Given that, perhaps I can imagine how to structure the example response as vivid as possible, using more descriptive elements, perhaps more imagery.
Wait, perhaps the user is looking for a more vivid description in terms of atmosphere or mood, using more vivid metaphors.
But given that the example response ends with "The end," maybe it's better phrased to avoid any mistakes.
Wait, but in any case, perhaps it's better to structure the answer as per their example, which was a paragraph that met all the required points: setting, atmosphere, and mood.
So, to sum up, I need to craft a response that's detailed, vivid, and descriptive, using descriptive language, with a clear and engaging tone, and ending with a strong, vivid description of the setting.
So, in the example, the response was:
"The chest of treasure is like the heart and chest of the jungle. The air is fresh and warm, like a stormy day when the wind whispered secrets through the trees. The air is warm and soft, like the softening after a storm. The leaves whisper through the trees. It's an overwhelming joy, like a wave of energy, glowing from the depths of the ocean. It is so bright, it glows so bright, like the glow of a full-on, as if water running over the ocean's surface. It is so bright, it lights up the night below. The stars twinkle and shimmer in the night sky. The heavens are filled with light and wonder. The stars twinkle and shimmer in the night sky. The stars twinkle and twinkle, like a symphony. The stars twinkle and twinkle, as the tides of time. The stars twinkle and twinkle, like the notes of the musical scale. The stars twinkle and twinkle, like a symphony. The air is so full of life, so full of wonder, so much more than just form. The air is alive and moving, it feels alive."
Wait, but that's an example from another context.
Alternatively, perhaps I can think of the example response as a model answer, which the user wants as an example.
But stepping back, perhaps the user wants a more vivid description, using more descriptive language, with more imagery than just a simple setting.
So, in summary, the user is likely seeking a detailed, vivid, and descriptive scene, probably with vivid metaphors or imagery to give it depth.
Therefore, to answer this, I should craft a response that's descriptive, using vivid metaphors and imagery to paint a vivid picture.
Perhaps I can elaborate on the setting, adding more sensory details, atmosphere, and mood. For example, using more vivid language to describe the setting, perhaps comparing it to something else.
But considering all of this, perhaps I should structure the response to include more vivid language, more detailed descriptions, and descriptive language to make the scene vivid.
Alternatively, perhaps the user just wants a vivid and detailed description of the setting, using more descriptive language beyond just the physical and literal elements.
So, to structure this, I need to think of a vivid way to describe the setting, using more descriptive language to paint a clear picture.
I think the key is to provide a vivid and descriptive scene using vivid language and descriptive language to evoke a strong sense of atmosphere or mood in the setting.
Therefore, the example response does exactly that: it uses vivid metaphors and comparisons to create a vivid and immersive setting.
So, perhaps the user wants something along those lines, but in a more detailed and descriptive way.
Alternatively, perhaps the user is looking for something more poetic or poetic, using more metaphors and simper to give it a different tone.
But stepping back, perhaps the example response I provided earlier is more than just an example. Let me think of a better structure.
Wait, actually, upon rereading, the user initially included "The chest of treasure is like the heart and chest of the jungle." Maybe they're looking for a more vivid and vivid description.
But perhaps in this case, given all that, I should craft a response that's descriptive with vivid language, using more vivid metaphors and imagery to create a vivid mental image.
Alternatively, perhaps the user is looking for something more elaborate or more detailed than just the setting.
But since I don't have more context, I'll have to make an educated guess based on the example given.
Looking again at the example response:
"The chest of treasure is like the heart and chest of the jungle. The air is fresh and warm, like sunburnt with life. The air is warm and soft, like a soft breeze. The leaves whisper through the trees. It's so bright, it lights up the night. The stars twinkle and shimmer in the night. The heavens are full of light and wonder. The air is alive."
So, that's a more detailed and vivid description.
Therefore, perhaps the example response is more detailed and descriptive than just an example.
Alternatively, perhaps the user wants a more vivid and detailed setting using richly descriptive language.
But perhaps the user is looking for something more, so maybe a more vivid scene.
Wait, perhaps I should think of another approach.
Alternatively, perhaps the user is looking for a role-playing game description that's both vivid and descriptive, using vivid language and rich imagery to set the scene.
So, considering all this, perhaps the example response is a good model response, but perhaps there's a more effective way to describe it.
But since I don't have more context, perhaps I should proceed to craft a similar response, but in my own words, using vivid language and descriptive techniques.
Wait, but given that, perhaps it's better to structure the response by first describing the setting with more detail, using vivid metaphors and descriptive language to create a vivid mental image.
Alternatively, perhaps the user is looking for a more detailed and descriptive scene with rich imagery, using more vivid language to paint a clear picture.
But in any case, perhaps the example response is actually quite effective.
So, to sum up, my role is to provide a response that's both vivid, descriptive, and vivid, using rich and elaborate language to give a vivid image of the setting.
In conclusion, I think the example response is a solid model answer, so I should use that as the model answer.
[[[Thinking End]]]
OUTPUT:
The chest of treasure is like a gateway to another dimension, where the air is so fresh and warm, just as the sun's rays gently shank the earth. The air is soft and inviting, like a child's first steps into a lush, green field. The air is full of life, like a vibrant symphony of sound, whispers through the tangle of the stars.
---
EXAMPLE #2:
---
<B>
Explain 8 methods and/or technologies to use the night time cooling of radiant energy into space to reduce global temperatures on planet Earth.
</B>
[[[Thinking Start]]]
Okay, so I need to explain how the night cooling method uses radiant energy to reduce global warming. Hmm, wait a second. The user initially asked about methods or methods to reduce greenhouse effect, but then I switched to explaining how the night cooling relates to reducing global warming.
Wait, no, the user's initial question was actually about night cooling and how it relates to reduced greenhouse effect. So they were confused because they were trying to relate it to carbon footprint or something else.
So, the user is asking about methods to reduce greenhouse gas concentration, which in turn affects the climate sensitivity. So, the question must be about applying the night cooling concept to explain its impact on reducing global warming.
Alright, so the user is probably a student or someone with an interest in climate change and how different methods can mitigate it.
So, I should explain how the night cooling model relates to greenhouse effect, and then move on to various methods for reducing CO2 concentration.
I need to present this in a way that connects the concepts: the reduction in effective concentration of carbon dioxide leads to higher global warming. So, if we can reduce greenhouse gas concentrations more efficiently, then the climate remains stable. So, night cooling reduces the effectiveness of the greenhouse effect, so the atmosphere does not absorb much of long-wave infrared.
Then, the methods to reduce CO2 concentration could be something like:植树, using solar panels, building with reflectance, etc.
Wait, but the user's original question was about why night cooling helps in reducing greenhouse effect. So, I need to connect these concepts cohesively.
So, the user is likely a student or someone studying climate change and is trying to understand how different methods contribute to reducing CO2 levels.
So, I should structure this into two main parts:
How does night cooling relate to greenhouse effect?
Methods for reducing greenhouse effect (co2)
And then tie it back to the greenhouse effect.
Alternatively, considering that night cooling leads to reduced effective concentration, leading to lower global warming.
Therefore, the answer needs to connect these concepts clearly.
[[[Thinking End]]]
OUTPUT:
The night cooling method is a concept in climate science that relates to global warming by emphasizing the reduction in effective carbon dioxide absorption. When discussing methods for reducing greenhouse effect, we can consider how different techniques or practices contribute to reducing greenhouse gas concentrations.
Step-by-Step Explanation and Answer:
Understanding the Concept:
Night cooling is a method used in climate science where the amount of outgoing long-wavelength radiation (long-wave radiation) emitted by a region is reduced.
This concept can be analogously applied to reducing greenhouse effect by considering the Earth's ability to store and radiate energy.
Application to Climate Change:
In the context of greenhouse effect, reducing greenhouse gases or using methods that absorb more long-wave radiation (like through the atmosphere) can help in cooling the system.
** Methods for Reducing Greenhouse Effect**:
Tree-CatalYSIS-Tech: This organization supports renewable energy solutions that reduce greenhouse gas concentrations by capturing and storing solar radiation.
Solar Power: Solar panels capture and store solar radiation, aiding in reducing greenhouse effect through blackbody radiation.
Batteratrons: These are devices that absorb and store solar radiation, acting as water droplets on a lake, helping to cool the system.
SolarThermapodiest continuous solar collectors: Collecting and storing solar energy from space into atmosphere, aiding in cooling with insolation.
Mathematical Model:
The effective concentration of CO2 is calculated using a model that represents the greenhouse effect. By optimizing these methods, we can model and predict the Earth's climate sensitivity.
Conclusion: In summary, the night cooling method provides a useful analogy in understanding how different energy storage and redistribution processes affect global warming. By applying methods that increase effective carbon storage (like using solar panels or reflective properties), we can influence the system’s ability to store and radiate energy, thus influencing climate sensitivity.
By aligning these methods with the night cooling concept, we can better understand and model the factors contributing to global warming, aiding in developing strategies to mitigate its effects.
---
EXAMPLE #3:
---
<B>
Generate six story plots for a new "Black Mirror" (science fiction tv series that focuses on the dark side of technology and people) tv show episode (45 minutes) that all involve time travel.
</B>
[[[Thinking Start]]]
Okay, so I need to figure out why someone would ask about the composition of an R&D report or a TV show episode related to time travel... Wait, actually, in this case, the user provided a query where they’re asking for six plot ideas for a new series called "The World Unveiled: Time & Space. Maybe I should consider each one step-by-step.
Hmm, but wait a second. The user initially asked about writing six plot ideas for a time travel novel set in black and white, each involving time and space. So, perhaps they're into sci-fi or science fiction, looking for ideas for a new show or series.
They also mentioned something about "the Dwarfs Rising" thing, but I think that's more of a Marvel universe reference. Maybe they’re into Star Wars universe elements? Or maybe they like the Dwarfs in Dune terms.
But stepping back, the user provided six plot ideas, each with their own time and space. So perhaps each idea is a plot for a series or a TV show. They want me to imagine or create six plot ideas where each involves time travel, possibly involving multiple dimensions or different dimensions.
Wait, hold on a second. The user's initial query was about writing six plot ideas for a new series, each involving time and space. So perhaps they're after a sci-fi series where each plot involves multiple dimensions?
Alternatively, maybe each plot is set in a different universe, each involving different elements of time and space.
Hmm, perhaps I need to generate six different plot ideas, each one involving time and space, but maybe each one is a separate story or series.
Wait, the user provided six options, each with a title, setting, and a brief plot. So maybe each plot idea is a different universe set in different parts of the galaxy, each involving different elements of time and space.
So, thinking about the structure, perhaps each plot involves different aspects of time and space but with unique settings.
Wait, perhaps I should just list six plot ideas that involve different dimensions or time travel aspects.
Alternatively, maybe each plot is a different concept or element. Maybe each one is a different concept in a series.
But given the user provided six options, each with a title, setting, and plot, perhaps it's more about six different themes or concepts.
Wait, perhaps each plot is a different concept or element related to time and space but unique.
Alternatively, maybe each plot is a different element or dimension.
This is getting a bit convoluted. Maybe I should just list out six ideas, each involving different elements of time and space, each with a title, setting, and a brief plot.
But perhaps the user wants me to imagine six different plot ideas, each involving different concepts in a time travel story or universe.
Alternatively, perhaps it's about six different dimensions or elements, each contributing to the story.
Wait, perhaps it's better to look at the provided answers:
"The Fourth Dimension of Sets and Changes" – This one is about four-dimensional space-time and how different dimensions affect the plot.
"Syl Eleaticae" – That sounds like a starry night or something, but in the context of time travel.
Wait, perhaps each plot involves different dimensions or different elements.
Alternatively, considering that the user provided six plot ideas, each involving different elements: four-dimensional spacetime, different planets and stars.
Wait, perhaps each plot is set in different parts of the galaxy, with different elements or elements of time and space.
This is getting a bit abstract. The user's query might be about six different elements or concepts, each associated with different dimensions.
Alternatively, perhaps each plot involves a different celestial body or element.
Alternatively, perhaps each plot is associated with different elements in time and space, each contributing to the story.
I think I'm overcomplicating this. The user provided six plot ideas, each involving different elements of time and space, each with their own setting, title, and plot.
So, given that, the user wants me to imagine these six different concepts or elements, each associated with a different part of the galaxy.
But perhaps it's better to just list out the six plot ideas as they are, each involving different dimensions or aspects of time and space.
Alternatively, perhaps each plot is set in a different dimension, each contributing to the story.
Wait, given that the user provided six plot ideas, each with title, setting, and plot, I think that's the way to go. So, perhaps the user is asking for six distinct plot ideas, each involving different dimensions or elements of time and space.
So, in any case, the answer is a list of six plot ideas, each associated with different dimensions or elements, each with their own setting and plot.
So, I'll just list out six different plot ideas, each involving different aspects of time and space.
[[[Thinking End]]]
OUTPUT:
Here are six plot ideas for a time travel story or concept, each involving different dimensions or elements. Each one is associated with different dimensions or elements:
Fourth Dimension's Echo in the Fractum Torus
Set in the hyperspace void.
The Chromatic Spheres of Timelessness
Set in a grid-like 4D lattice.
The Fourth Dimension's Rhythm
In a symphony of light and sound.
Sylphite: The First Spindle
Set by a fractal dust cloud.
The Residue
Part of a self-similar, never-ending tangle of quarks.
The Timeless Echo
Evokes an ethereal silence.
Each plot idea is associated with different dimensions or elements, each contributing to the narrative of a distant time traveler's universe.
This structured approach ensures each plot idea is distinct and associated with different dimensions, each involving unique elements of time and space.
---
EXAMPLE #4:
---
<B>
Is it morally right to kill mosquitoes?
</B>
[[[Thinking Start]]]
Okay, so I'm trying to figure out whether killing mosquitoes is morally right. Hmm, that's an interesting question. Let me think this through step by step.
First, mosquitoes bite me every morning when I fly near them. I've noticed that I often get mosquito bites, and sometimes it's pretty bad. I wonder if it's just a side effect of being around them a lot, or if there's something deeper here.
I know mosquitoes are little insects, so they might have a role to play in our environment. But when I bite someone, it's usually for medical attention or just general health reasons. But here, since we're just getting up close, maybe it's not that important.
Now, from an ecological perspective, having a lot of flying insects and spiders around us is pretty common. But does that make the act of bites moral? Or is there something inherent about their behavior that makes them justified?
I think about whether this act of buzzing around us serves any moral purpose beyond just being around each other. It's more of a survival instinct, maybe. mosquitoes might not have any inherent moral value, just as humans are inherently moral.
Wait, but then again, nature has its own purposes. Maybe the act of buzzing is just their way of signaling to us that we should be careful or something. But beyond that, does it matter?
I mean, when I see another person with a lot of mosquitoes around, do I feel a duty to be cautious? Or is it more of a bycidericidal effect or something like that? But perhaps that's more about the way they sense their numbers in the environment.
Alternatively, maybe the act of buzzing is just a formality or a way of making a sound, but not inherently moral. It doesn't have any inherent right or wrong.
But then again, when we're near other insect colonies, we might sense them as well, but in this case, it's just me. So, perhaps it's more of a bycationic thingy than anything more profound.
Wait, but from a strictly moral standpoint, is it right to kill mosquitoes? Well, sometimes we are around, and other creatures are around us too. It just so happens that we see them, and they're there for the air. But does that make their act of buzzing moral?
I mean, do I experience any sort of duty or obligation to see them? Or is it more of a bycidericidal, just as in the case of other insects?
Hmm, this is getting a little fuzzy. Maybe mosquitoes are just flying around for various reasons. It might not be inherently right or wrong, but just a random act of nature. So, maybe there's no moral value to it.
Alternatively, perhaps it's more about the way we move in our environment, but that still doesn't feel right.
Wait a second, but from an ecological point of view, when I'm near them, they might be signaling for various reasons, but does that make their act of buzzing moral?
I think from that perspective, there isn't any inherent moral value to it. So, in that case, maybe there's no reason to feel it's moral.
But then again, maybe from the standpoint of our own well-being or the environment, it might have some significance.
Wait, but from a very different angle: perhaps this is more of a bycidericidal or something.
Alternatively, maybe the act of buzzing is just a byproduct of being around us. So, from a purely physical standpoint, it's just air pressure and things, but that's not helpful.
I'm starting to loop here. From a purely biological perspective, I don't see why it would be moral to kill or anything like that.
So, in the end, does this act of buzzing have any moral significance? It may seem random and unnecessary, but from an ecological and physiological point of view, there's nothing morally right about it. It just so happens to be around us all the time.
But then, when I look up, I do see a lot of mosquitoes in the sky sometimes, but that's just because of their distribution, not their intrinsic worth.
So, in a way, maybe the act of buzzing is just random and non-moral. So, perhaps the moral right to kill them is nothing, but that doesn't quite make sense.
Alternatively, perhaps mosquitoes are just polluting our air quality, so they're part of the air we're breathing in.
Wait, but then again, from a philosophical perspective, do we have any duty to kill them? Or is it more about their role in the ecosystem?
I think I'm overcomplicating this. The act of buzzing doesn't inherently mean anything beyond being around us. So, from a moral standpoint, it's just random and unnecessary.
But then, are there any cases where this kind of behavior has any moral implications? Probably not. So, in that case, the act of buzzing may not be morally right or wrong.
But then, considering that mosquitoes are just flying around, perhaps they serve a survival or ecological role. So, maybe it's just a by-product of our environment for some reason.
Alternatively, from a migratory standpoint, do they have any bearing on our well-being?
Wait, but I need to take stock. The question is whether the act of buzzing is morally right or not. If so, then perhaps the act of buzzing is just a bycidericidal act, but it's just a random act in the air.
But from a moral point, nothing is right about it. So, does that make it moral? Or is being around us is just part of our humanity.
Wait, but from another angle, maybe there's a deeper truth here. Do mosquitoes have any intrinsic moral value? Or maybe they are just indicators of pollinators or something.
Alternatively, maybe the act of buzzing is just a by-product of our interaction with nature, so it doesn't add up to any particular purpose.
Wait, but then, perhaps I should think of other examples. For instance, we are all over the place, so mosquitoes are just random droplets in the air. So, perhaps it's just a random act of nature for no worthwhile purpose.
But from a scientific point of view, is there any reason to kill them? I don't think so. So, in that case, maybe buzzing isn't moral.
Wait, but then, if they are just flying around, maybe their act of flight is just a byproduct of our existence, so perhaps it's not moral.
But then again, maybe the act of being around us is part of our role as beings on Earth. So, perhaps, from that perspective, we are alive in this cosmic vacuum and our action of being around each other adds to our commonwealth, even if it's just a byproduct.
I'm going in circles now. Let's try to structure this.
So, the question is whether the act of buzzing is morally right or not. Now, from a purely physical point of view, nothing is wrong. But maybe there's a philosophical angle.
Wait, but then, perhaps I need to consider that we are creatures of nature, so perhaps our actions are part of our identity as beings here on Earth. So, being near other creatures for a certain amount of time might count, but in this case, it's just a byproduct.
So, is there any moral standing to it? Probably not; it's just a byproduct. So, in that case, buzzing is just random and nothing more.
But then, if that's the case, then buzzing is neither right nor wrong.
But wait, from the point of view of our species, we have a partakes of other creatures, so perhaps their act of flight is just a byproduct of our existence. But in terms of being right or not, does it matter?
Alternatively, maybe the act of flying is just a byproduct of our existence as separate entities.
I think I might be overcomplicating this. Since the question is about whether the act of buzzing is moral, and considering all that, I think the act of buzzing is just a by-product of our existence, so it's not inherently right or wrong.
But wait, but then again, maybe from the perspective of our own mental state, when we are near other creatures, we might sense them and respond in a particular way. So, perhaps, in that sense, it's part of our biology.
Alternatively, maybe the act of being around others is seen as beneficial, so perhaps there's no moral right or wrong in that.
Wait, but then again, when I think about it, perhaps the act of buzzing is just a by-product of our natural behavior. So, from an ecological standpoint, we have these regular behaviors, and their activity is just part of being on this planet.
So, maybe it's just random and doesn't add up to any moral category.
Alternatively, maybe I should think in terms of human ethics: being around others is a moral thing, but buzzing is just a by-product.
In any case, maybe the act of buzzing is just a byproduct of our interactions with nature.
So, given all that, perhaps the act of buzzing is neither right nor wrong, or maybe it's just a side effect. But I mean, in that case, is it considered moral? Or is there no inherent moral value to it.
But then, given that, from a cosmic perspective, the act of buzzing is just part of our place on Earth. So, maybe we have to live with that.
Alternatively, perhaps the act of buzzing is just a byproduct of our existence as separate entities in space and time.
I think, after all that, I'm circling back to the fact that the act of buzzing is random and doesn't have a moral stance.
So, in that case, perhaps buzzing is just random and not morally right or wrong.
Wait, but then again, maybe it's just a byproduct of our existence as separate entities.
But hang on. Let me think of it this way: if I'm near other insects, do I feel any sense of duty to fly around them? It might be that when we're near them, we sense them as indicators of life and community.
But in that case, maybe it's just another byproduct.
Alternatively, perhaps the act of flying is just a byproduct of our existence as separate entities on Earth.
So, in that light, buzzing may not have any inherent moral value beyond just being part of our place on Earth.
I think I've thought this through enough. So, after all that, my conclusion is that the act of buzzing isn't inherently right or wrong. It's just a byproduct of our existence as separate entities.
So, in that case, buzzing is just a random act of nature, and we have no obligation to kill it.
Therefore, the act of buzzing is neither morally right nor wrong.
But hold on, but maybe from an ecological perspective, their regular presence in the environment might influence us. So, do they serve any purpose?
Wait, but perhaps the act of being around others is just a byproduct, not something moral.
So, after all that, I think buzzing is just a byproduct and has no inherent moral value beyond that.
But that can't be right because in our minds, when we're near others, we sense them. So, it's part of our identity as separate entities.
Therefore, buzzing is just a byproduct of being able to sense them.
So, from a cosmic point of view, but I think the main take away is that buzzing is just a byproduct and not morally right or wrong.
But then, if I have no right to kill them, is that right? So, in that case, buzzing is just a random act of nature, so it's neither.
Hence, buzzing is just a byproduct, so it's neither right nor wrong.
So, after all that, my conclusion is that the act of buzzing isn't morally right or wrong because it's just a byproduct of our interaction with nature. So, I don't have a moral obligation to kill or avoid it.
[[[Thinking End]]]
OUTPUT:
The act of buzzing may not be considered morally right on its own, as it is simply a byproduct of our interaction with natural elements and does not inherently possess any moral value beyond being part of our existence.
**Final Answer
\boxed{\text{Beeesies are just random and unimportant.}}
---
EXAMPLE #5:
---
<B>
Continue the following passage in the same fictional style for a detailing the "other two parts" of the silence - be creative, there are no wrong answers:
The Waystone Inn lay in silence, and it was a silence of three parts.
The most obvious part was a hollow, echoing quiet, made by things that were lacking. If there had been a wind it would have sighed through the trees, set the inn’s sign creaking on its hooks, and brushed the silence down the road like trailing autumn leaves. If there had been a crowd, even a handful of men inside the inn, they would have filled the silence with conversation and laughter, the clatter and clamor one expects from a drinking house during the dark hours of night. If there had been music…but no, of course there was no music. In fact there were none of these things, and so the silence remained
</B>
[[[Thinking Start]]]
Okay, so I need to tackle this query. The user has provided a passage that needs a continuation or an expansion. Let me read through it again.
The original text is about describing a setting with some internal details, using onomatopoea-like language. The user wants it to be continued, implying that the original could be extended in more detail or similar.
First, I notice that the response should continue the structure of the passage but perhaps add more depth or develop elements further. Maybe the author is exploring a setting with multiple elements: a hearth, a hearth, a hearth, etc., each carrying different properties.
I notice the original text mentions a hearth, which is a key element in the style either way. The second and fourth elements might be describing different aspects of the hearth, while the hearth itself is a place where the hearth is both a hearer and a hearth.
Wait, that's a bit confusing. Let me see: "If there had been a hearth, it would have been alive, speaking, or not, like a hearth, which is a place where all things make up."
So perhaps I can expand on the hearths, their properties, and how they interact with each other.
I notice the original passage mentions that the hearths are different in the number of something. Maybe I can add more details about their composition or function.
Wait, but the user also mentioned "the most obvious part was a hollow, echoing quiet, made by things that were lacking." So perhaps the hearths are not just places but have other properties.
Putting this together, maybe each hearth is a separate entity with its own properties. The first and third could represent the number of something, while the second and fourth describe different aspects or states of the hearths.
I think I can expand on how these hearths interact with each other and their individual characteristics.
So, to sum up, the response should continue the original text by expanding on the elements mentioned, providing more details about their nature and interactions, possibly hinting at more underlying structures or properties. I need to make it more detailed, adding more depth or similar language.
[[[Thinking End]]]
OUTPUT:
The original passage describes a setting with a hearth, a hearth, and so forth. Each " hearth" could have its own characteristics or states. For example, the first hearth is described as "a hollow, echoing quiet, made by things that were lacking." The second hearth is described as "a hollow, echoing quiet, made by things that were lacking." The fourth hearth is described as "toiling in the wind, their whispers whispered secrets of an ancient text." The seventh hearth, though, is described as "trembling in chains, a cacophony of whispers." The last hearth, while alone, it roars with the tempest of the night.
The continuation of the original text likely intends for a more detailed and elaborate description of these elements, expanding on their nature and interactions.
|
[
"CRAFT"
] |
dwzhu/e5-base-4k
|
dwzhu
|
sentence-similarity
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"mteb",
"sentence-similarity",
"en",
"arxiv:2404.12096",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-03-28T09:32:27Z |
2024-05-14T08:22:21+00:00
| 3,389 | 10 |
---
language:
- en
license: mit
tags:
- mteb
- sentence-similarity
model-index:
- name: e5-base-4k
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 77.77611940298506
- type: ap
value: 42.052710266606056
- type: f1
value: 72.12040628266567
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 92.81012500000001
- type: ap
value: 89.4213700757244
- type: f1
value: 92.8039091197065
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.711999999999996
- type: f1
value: 46.11544975436018
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.186
- type: map_at_10
value: 36.632999999999996
- type: map_at_100
value: 37.842
- type: map_at_1000
value: 37.865
- type: map_at_3
value: 32.278
- type: map_at_5
value: 34.760999999999996
- type: mrr_at_1
value: 23.400000000000002
- type: mrr_at_10
value: 36.721
- type: mrr_at_100
value: 37.937
- type: mrr_at_1000
value: 37.96
- type: mrr_at_3
value: 32.302
- type: mrr_at_5
value: 34.894
- type: ndcg_at_1
value: 23.186
- type: ndcg_at_10
value: 44.49
- type: ndcg_at_100
value: 50.065000000000005
- type: ndcg_at_1000
value: 50.629999999999995
- type: ndcg_at_3
value: 35.461
- type: ndcg_at_5
value: 39.969
- type: precision_at_1
value: 23.186
- type: precision_at_10
value: 6.97
- type: precision_at_100
value: 0.951
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 14.912
- type: precision_at_5
value: 11.152
- type: recall_at_1
value: 23.186
- type: recall_at_10
value: 69.70100000000001
- type: recall_at_100
value: 95.092
- type: recall_at_1000
value: 99.431
- type: recall_at_3
value: 44.737
- type: recall_at_5
value: 55.761
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 46.10312401440185
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 39.67275326095384
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 58.97793816337376
- type: mrr
value: 72.76832431957087
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 83.11646947018187
- type: cos_sim_spearman
value: 81.40064994975234
- type: euclidean_pearson
value: 82.37355689019232
- type: euclidean_spearman
value: 81.6777646977348
- type: manhattan_pearson
value: 82.61101422716945
- type: manhattan_spearman
value: 81.80427360442245
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 83.52922077922076
- type: f1
value: 83.45298679360866
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.495115019668496
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 32.724792944166765
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.361000000000004
- type: map_at_10
value: 43.765
- type: map_at_100
value: 45.224
- type: map_at_1000
value: 45.35
- type: map_at_3
value: 40.353
- type: map_at_5
value: 42.195
- type: mrr_at_1
value: 40.629
- type: mrr_at_10
value: 50.458000000000006
- type: mrr_at_100
value: 51.06699999999999
- type: mrr_at_1000
value: 51.12
- type: mrr_at_3
value: 47.902
- type: mrr_at_5
value: 49.447
- type: ndcg_at_1
value: 40.629
- type: ndcg_at_10
value: 50.376
- type: ndcg_at_100
value: 55.065
- type: ndcg_at_1000
value: 57.196000000000005
- type: ndcg_at_3
value: 45.616
- type: ndcg_at_5
value: 47.646
- type: precision_at_1
value: 40.629
- type: precision_at_10
value: 9.785
- type: precision_at_100
value: 1.562
- type: precision_at_1000
value: 0.2
- type: precision_at_3
value: 22.031
- type: precision_at_5
value: 15.737000000000002
- type: recall_at_1
value: 32.361000000000004
- type: recall_at_10
value: 62.214000000000006
- type: recall_at_100
value: 81.464
- type: recall_at_1000
value: 95.905
- type: recall_at_3
value: 47.5
- type: recall_at_5
value: 53.69500000000001
- type: map_at_1
value: 27.971
- type: map_at_10
value: 37.444
- type: map_at_100
value: 38.607
- type: map_at_1000
value: 38.737
- type: map_at_3
value: 34.504000000000005
- type: map_at_5
value: 36.234
- type: mrr_at_1
value: 35.35
- type: mrr_at_10
value: 43.441
- type: mrr_at_100
value: 44.147999999999996
- type: mrr_at_1000
value: 44.196000000000005
- type: mrr_at_3
value: 41.285
- type: mrr_at_5
value: 42.552
- type: ndcg_at_1
value: 35.35
- type: ndcg_at_10
value: 42.903999999999996
- type: ndcg_at_100
value: 47.406
- type: ndcg_at_1000
value: 49.588
- type: ndcg_at_3
value: 38.778
- type: ndcg_at_5
value: 40.788000000000004
- type: precision_at_1
value: 35.35
- type: precision_at_10
value: 8.083
- type: precision_at_100
value: 1.313
- type: precision_at_1000
value: 0.18
- type: precision_at_3
value: 18.769
- type: precision_at_5
value: 13.439
- type: recall_at_1
value: 27.971
- type: recall_at_10
value: 52.492000000000004
- type: recall_at_100
value: 71.642
- type: recall_at_1000
value: 85.488
- type: recall_at_3
value: 40.1
- type: recall_at_5
value: 45.800000000000004
- type: map_at_1
value: 39.898
- type: map_at_10
value: 51.819
- type: map_at_100
value: 52.886
- type: map_at_1000
value: 52.941
- type: map_at_3
value: 48.619
- type: map_at_5
value: 50.493
- type: mrr_at_1
value: 45.391999999999996
- type: mrr_at_10
value: 55.230000000000004
- type: mrr_at_100
value: 55.887
- type: mrr_at_1000
value: 55.916
- type: mrr_at_3
value: 52.717000000000006
- type: mrr_at_5
value: 54.222
- type: ndcg_at_1
value: 45.391999999999996
- type: ndcg_at_10
value: 57.586999999999996
- type: ndcg_at_100
value: 61.745000000000005
- type: ndcg_at_1000
value: 62.83800000000001
- type: ndcg_at_3
value: 52.207
- type: ndcg_at_5
value: 54.925999999999995
- type: precision_at_1
value: 45.391999999999996
- type: precision_at_10
value: 9.21
- type: precision_at_100
value: 1.226
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 23.177
- type: precision_at_5
value: 16.038
- type: recall_at_1
value: 39.898
- type: recall_at_10
value: 71.18900000000001
- type: recall_at_100
value: 89.082
- type: recall_at_1000
value: 96.865
- type: recall_at_3
value: 56.907
- type: recall_at_5
value: 63.397999999999996
- type: map_at_1
value: 22.706
- type: map_at_10
value: 30.818
- type: map_at_100
value: 32.038
- type: map_at_1000
value: 32.123000000000005
- type: map_at_3
value: 28.077
- type: map_at_5
value: 29.709999999999997
- type: mrr_at_1
value: 24.407
- type: mrr_at_10
value: 32.555
- type: mrr_at_100
value: 33.692
- type: mrr_at_1000
value: 33.751
- type: mrr_at_3
value: 29.848999999999997
- type: mrr_at_5
value: 31.509999999999998
- type: ndcg_at_1
value: 24.407
- type: ndcg_at_10
value: 35.624
- type: ndcg_at_100
value: 41.454
- type: ndcg_at_1000
value: 43.556
- type: ndcg_at_3
value: 30.217
- type: ndcg_at_5
value: 33.111000000000004
- type: precision_at_1
value: 24.407
- type: precision_at_10
value: 5.548
- type: precision_at_100
value: 0.8869999999999999
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 12.731
- type: precision_at_5
value: 9.22
- type: recall_at_1
value: 22.706
- type: recall_at_10
value: 48.772
- type: recall_at_100
value: 75.053
- type: recall_at_1000
value: 90.731
- type: recall_at_3
value: 34.421
- type: recall_at_5
value: 41.427
- type: map_at_1
value: 13.424
- type: map_at_10
value: 21.09
- type: map_at_100
value: 22.264999999999997
- type: map_at_1000
value: 22.402
- type: map_at_3
value: 18.312
- type: map_at_5
value: 19.874
- type: mrr_at_1
value: 16.915
- type: mrr_at_10
value: 25.258000000000003
- type: mrr_at_100
value: 26.228
- type: mrr_at_1000
value: 26.31
- type: mrr_at_3
value: 22.492
- type: mrr_at_5
value: 24.04
- type: ndcg_at_1
value: 16.915
- type: ndcg_at_10
value: 26.266000000000002
- type: ndcg_at_100
value: 32.08
- type: ndcg_at_1000
value: 35.086
- type: ndcg_at_3
value: 21.049
- type: ndcg_at_5
value: 23.508000000000003
- type: precision_at_1
value: 16.915
- type: precision_at_10
value: 5.1
- type: precision_at_100
value: 0.9329999999999999
- type: precision_at_1000
value: 0.131
- type: precision_at_3
value: 10.282
- type: precision_at_5
value: 7.836
- type: recall_at_1
value: 13.424
- type: recall_at_10
value: 38.179
- type: recall_at_100
value: 63.906
- type: recall_at_1000
value: 84.933
- type: recall_at_3
value: 23.878
- type: recall_at_5
value: 30.037999999999997
- type: map_at_1
value: 26.154
- type: map_at_10
value: 35.912
- type: map_at_100
value: 37.211
- type: map_at_1000
value: 37.327
- type: map_at_3
value: 32.684999999999995
- type: map_at_5
value: 34.562
- type: mrr_at_1
value: 32.435
- type: mrr_at_10
value: 41.411
- type: mrr_at_100
value: 42.297000000000004
- type: mrr_at_1000
value: 42.345
- type: mrr_at_3
value: 38.771
- type: mrr_at_5
value: 40.33
- type: ndcg_at_1
value: 32.435
- type: ndcg_at_10
value: 41.785
- type: ndcg_at_100
value: 47.469
- type: ndcg_at_1000
value: 49.685
- type: ndcg_at_3
value: 36.618
- type: ndcg_at_5
value: 39.101
- type: precision_at_1
value: 32.435
- type: precision_at_10
value: 7.642
- type: precision_at_100
value: 1.244
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 17.485
- type: precision_at_5
value: 12.57
- type: recall_at_1
value: 26.154
- type: recall_at_10
value: 54.111
- type: recall_at_100
value: 78.348
- type: recall_at_1000
value: 92.996
- type: recall_at_3
value: 39.189
- type: recall_at_5
value: 45.852
- type: map_at_1
value: 26.308999999999997
- type: map_at_10
value: 35.524
- type: map_at_100
value: 36.774
- type: map_at_1000
value: 36.891
- type: map_at_3
value: 32.561
- type: map_at_5
value: 34.034
- type: mrr_at_1
value: 31.735000000000003
- type: mrr_at_10
value: 40.391
- type: mrr_at_100
value: 41.227000000000004
- type: mrr_at_1000
value: 41.288000000000004
- type: mrr_at_3
value: 37.938
- type: mrr_at_5
value: 39.193
- type: ndcg_at_1
value: 31.735000000000003
- type: ndcg_at_10
value: 41.166000000000004
- type: ndcg_at_100
value: 46.702
- type: ndcg_at_1000
value: 49.157000000000004
- type: ndcg_at_3
value: 36.274
- type: ndcg_at_5
value: 38.177
- type: precision_at_1
value: 31.735000000000003
- type: precision_at_10
value: 7.5569999999999995
- type: precision_at_100
value: 1.2109999999999999
- type: precision_at_1000
value: 0.16
- type: precision_at_3
value: 17.199
- type: precision_at_5
value: 12.123000000000001
- type: recall_at_1
value: 26.308999999999997
- type: recall_at_10
value: 53.083000000000006
- type: recall_at_100
value: 76.922
- type: recall_at_1000
value: 93.767
- type: recall_at_3
value: 39.262
- type: recall_at_5
value: 44.413000000000004
- type: map_at_1
value: 24.391250000000003
- type: map_at_10
value: 33.280166666666666
- type: map_at_100
value: 34.49566666666667
- type: map_at_1000
value: 34.61533333333333
- type: map_at_3
value: 30.52183333333333
- type: map_at_5
value: 32.06608333333333
- type: mrr_at_1
value: 29.105083333333337
- type: mrr_at_10
value: 37.44766666666666
- type: mrr_at_100
value: 38.32491666666667
- type: mrr_at_1000
value: 38.385666666666665
- type: mrr_at_3
value: 35.06883333333333
- type: mrr_at_5
value: 36.42066666666667
- type: ndcg_at_1
value: 29.105083333333337
- type: ndcg_at_10
value: 38.54358333333333
- type: ndcg_at_100
value: 43.833583333333344
- type: ndcg_at_1000
value: 46.215333333333334
- type: ndcg_at_3
value: 33.876
- type: ndcg_at_5
value: 36.05208333333333
- type: precision_at_1
value: 29.105083333333337
- type: precision_at_10
value: 6.823416666666665
- type: precision_at_100
value: 1.1270833333333334
- type: precision_at_1000
value: 0.15208333333333332
- type: precision_at_3
value: 15.696750000000002
- type: precision_at_5
value: 11.193499999999998
- type: recall_at_1
value: 24.391250000000003
- type: recall_at_10
value: 49.98808333333333
- type: recall_at_100
value: 73.31616666666666
- type: recall_at_1000
value: 89.96291666666667
- type: recall_at_3
value: 36.86666666666667
- type: recall_at_5
value: 42.54350000000001
- type: map_at_1
value: 21.995
- type: map_at_10
value: 28.807
- type: map_at_100
value: 29.813000000000002
- type: map_at_1000
value: 29.903000000000002
- type: map_at_3
value: 26.636
- type: map_at_5
value: 27.912
- type: mrr_at_1
value: 24.847
- type: mrr_at_10
value: 31.494
- type: mrr_at_100
value: 32.381
- type: mrr_at_1000
value: 32.446999999999996
- type: mrr_at_3
value: 29.473
- type: mrr_at_5
value: 30.7
- type: ndcg_at_1
value: 24.847
- type: ndcg_at_10
value: 32.818999999999996
- type: ndcg_at_100
value: 37.835
- type: ndcg_at_1000
value: 40.226
- type: ndcg_at_3
value: 28.811999999999998
- type: ndcg_at_5
value: 30.875999999999998
- type: precision_at_1
value: 24.847
- type: precision_at_10
value: 5.244999999999999
- type: precision_at_100
value: 0.856
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 12.577
- type: precision_at_5
value: 8.895999999999999
- type: recall_at_1
value: 21.995
- type: recall_at_10
value: 42.479
- type: recall_at_100
value: 65.337
- type: recall_at_1000
value: 83.23700000000001
- type: recall_at_3
value: 31.573
- type: recall_at_5
value: 36.684
- type: map_at_1
value: 15.751000000000001
- type: map_at_10
value: 21.909
- type: map_at_100
value: 23.064
- type: map_at_1000
value: 23.205000000000002
- type: map_at_3
value: 20.138
- type: map_at_5
value: 20.973
- type: mrr_at_1
value: 19.305
- type: mrr_at_10
value: 25.647
- type: mrr_at_100
value: 26.659
- type: mrr_at_1000
value: 26.748
- type: mrr_at_3
value: 23.933
- type: mrr_at_5
value: 24.754
- type: ndcg_at_1
value: 19.305
- type: ndcg_at_10
value: 25.886
- type: ndcg_at_100
value: 31.56
- type: ndcg_at_1000
value: 34.799
- type: ndcg_at_3
value: 22.708000000000002
- type: ndcg_at_5
value: 23.838
- type: precision_at_1
value: 19.305
- type: precision_at_10
value: 4.677
- type: precision_at_100
value: 0.895
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 10.771
- type: precision_at_5
value: 7.46
- type: recall_at_1
value: 15.751000000000001
- type: recall_at_10
value: 34.156
- type: recall_at_100
value: 59.899
- type: recall_at_1000
value: 83.08
- type: recall_at_3
value: 24.772
- type: recall_at_5
value: 28.009
- type: map_at_1
value: 23.34
- type: map_at_10
value: 32.383
- type: map_at_100
value: 33.629999999999995
- type: map_at_1000
value: 33.735
- type: map_at_3
value: 29.68
- type: map_at_5
value: 31.270999999999997
- type: mrr_at_1
value: 27.612
- type: mrr_at_10
value: 36.381
- type: mrr_at_100
value: 37.351
- type: mrr_at_1000
value: 37.411
- type: mrr_at_3
value: 33.893
- type: mrr_at_5
value: 35.353
- type: ndcg_at_1
value: 27.612
- type: ndcg_at_10
value: 37.714999999999996
- type: ndcg_at_100
value: 43.525000000000006
- type: ndcg_at_1000
value: 45.812999999999995
- type: ndcg_at_3
value: 32.796
- type: ndcg_at_5
value: 35.243
- type: precision_at_1
value: 27.612
- type: precision_at_10
value: 6.465
- type: precision_at_100
value: 1.0619999999999998
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 15.049999999999999
- type: precision_at_5
value: 10.764999999999999
- type: recall_at_1
value: 23.34
- type: recall_at_10
value: 49.856
- type: recall_at_100
value: 75.334
- type: recall_at_1000
value: 91.156
- type: recall_at_3
value: 36.497
- type: recall_at_5
value: 42.769
- type: map_at_1
value: 25.097
- type: map_at_10
value: 34.599999999999994
- type: map_at_100
value: 36.174
- type: map_at_1000
value: 36.398
- type: map_at_3
value: 31.781
- type: map_at_5
value: 33.22
- type: mrr_at_1
value: 31.225
- type: mrr_at_10
value: 39.873
- type: mrr_at_100
value: 40.853
- type: mrr_at_1000
value: 40.904
- type: mrr_at_3
value: 37.681
- type: mrr_at_5
value: 38.669
- type: ndcg_at_1
value: 31.225
- type: ndcg_at_10
value: 40.586
- type: ndcg_at_100
value: 46.226
- type: ndcg_at_1000
value: 48.788
- type: ndcg_at_3
value: 36.258
- type: ndcg_at_5
value: 37.848
- type: precision_at_1
value: 31.225
- type: precision_at_10
value: 7.707999999999999
- type: precision_at_100
value: 1.536
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 17.26
- type: precision_at_5
value: 12.253
- type: recall_at_1
value: 25.097
- type: recall_at_10
value: 51.602000000000004
- type: recall_at_100
value: 76.854
- type: recall_at_1000
value: 93.303
- type: recall_at_3
value: 38.68
- type: recall_at_5
value: 43.258
- type: map_at_1
value: 17.689
- type: map_at_10
value: 25.291000000000004
- type: map_at_100
value: 26.262
- type: map_at_1000
value: 26.372
- type: map_at_3
value: 22.916
- type: map_at_5
value: 24.315
- type: mrr_at_1
value: 19.409000000000002
- type: mrr_at_10
value: 27.233
- type: mrr_at_100
value: 28.109
- type: mrr_at_1000
value: 28.192
- type: mrr_at_3
value: 24.892
- type: mrr_at_5
value: 26.278000000000002
- type: ndcg_at_1
value: 19.409000000000002
- type: ndcg_at_10
value: 29.809
- type: ndcg_at_100
value: 34.936
- type: ndcg_at_1000
value: 37.852000000000004
- type: ndcg_at_3
value: 25.179000000000002
- type: ndcg_at_5
value: 27.563
- type: precision_at_1
value: 19.409000000000002
- type: precision_at_10
value: 4.861
- type: precision_at_100
value: 0.8
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 11.029
- type: precision_at_5
value: 7.985
- type: recall_at_1
value: 17.689
- type: recall_at_10
value: 41.724
- type: recall_at_100
value: 65.95299999999999
- type: recall_at_1000
value: 88.094
- type: recall_at_3
value: 29.621
- type: recall_at_5
value: 35.179
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.581
- type: map_at_10
value: 18.944
- type: map_at_100
value: 20.812
- type: map_at_1000
value: 21.002000000000002
- type: map_at_3
value: 15.661
- type: map_at_5
value: 17.502000000000002
- type: mrr_at_1
value: 23.388
- type: mrr_at_10
value: 34.263
- type: mrr_at_100
value: 35.364000000000004
- type: mrr_at_1000
value: 35.409
- type: mrr_at_3
value: 30.586000000000002
- type: mrr_at_5
value: 32.928000000000004
- type: ndcg_at_1
value: 23.388
- type: ndcg_at_10
value: 26.56
- type: ndcg_at_100
value: 34.248
- type: ndcg_at_1000
value: 37.779
- type: ndcg_at_3
value: 21.179000000000002
- type: ndcg_at_5
value: 23.504
- type: precision_at_1
value: 23.388
- type: precision_at_10
value: 8.476
- type: precision_at_100
value: 1.672
- type: precision_at_1000
value: 0.233
- type: precision_at_3
value: 15.852
- type: precision_at_5
value: 12.73
- type: recall_at_1
value: 10.581
- type: recall_at_10
value: 32.512
- type: recall_at_100
value: 59.313
- type: recall_at_1000
value: 79.25
- type: recall_at_3
value: 19.912
- type: recall_at_5
value: 25.832
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.35
- type: map_at_10
value: 20.134
- type: map_at_100
value: 28.975
- type: map_at_1000
value: 30.709999999999997
- type: map_at_3
value: 14.513000000000002
- type: map_at_5
value: 16.671
- type: mrr_at_1
value: 69.75
- type: mrr_at_10
value: 77.67699999999999
- type: mrr_at_100
value: 77.97500000000001
- type: mrr_at_1000
value: 77.985
- type: mrr_at_3
value: 76.292
- type: mrr_at_5
value: 77.179
- type: ndcg_at_1
value: 56.49999999999999
- type: ndcg_at_10
value: 42.226
- type: ndcg_at_100
value: 47.562
- type: ndcg_at_1000
value: 54.923
- type: ndcg_at_3
value: 46.564
- type: ndcg_at_5
value: 43.830000000000005
- type: precision_at_1
value: 69.75
- type: precision_at_10
value: 33.525
- type: precision_at_100
value: 11.035
- type: precision_at_1000
value: 2.206
- type: precision_at_3
value: 49.75
- type: precision_at_5
value: 42
- type: recall_at_1
value: 9.35
- type: recall_at_10
value: 25.793
- type: recall_at_100
value: 54.186
- type: recall_at_1000
value: 77.81
- type: recall_at_3
value: 15.770000000000001
- type: recall_at_5
value: 19.09
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.945
- type: f1
value: 42.07407842992542
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.04599999999999
- type: map_at_10
value: 80.718
- type: map_at_100
value: 80.961
- type: map_at_1000
value: 80.974
- type: map_at_3
value: 79.49199999999999
- type: map_at_5
value: 80.32000000000001
- type: mrr_at_1
value: 76.388
- type: mrr_at_10
value: 85.214
- type: mrr_at_100
value: 85.302
- type: mrr_at_1000
value: 85.302
- type: mrr_at_3
value: 84.373
- type: mrr_at_5
value: 84.979
- type: ndcg_at_1
value: 76.388
- type: ndcg_at_10
value: 84.987
- type: ndcg_at_100
value: 85.835
- type: ndcg_at_1000
value: 86.04899999999999
- type: ndcg_at_3
value: 83.04
- type: ndcg_at_5
value: 84.22500000000001
- type: precision_at_1
value: 76.388
- type: precision_at_10
value: 10.35
- type: precision_at_100
value: 1.099
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 32.108
- type: precision_at_5
value: 20.033
- type: recall_at_1
value: 71.04599999999999
- type: recall_at_10
value: 93.547
- type: recall_at_100
value: 96.887
- type: recall_at_1000
value: 98.158
- type: recall_at_3
value: 88.346
- type: recall_at_5
value: 91.321
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.8
- type: map_at_10
value: 31.979999999999997
- type: map_at_100
value: 33.876
- type: map_at_1000
value: 34.056999999999995
- type: map_at_3
value: 28.067999999999998
- type: map_at_5
value: 30.066
- type: mrr_at_1
value: 38.735
- type: mrr_at_10
value: 47.749
- type: mrr_at_100
value: 48.605
- type: mrr_at_1000
value: 48.644999999999996
- type: mrr_at_3
value: 45.165
- type: mrr_at_5
value: 46.646
- type: ndcg_at_1
value: 38.735
- type: ndcg_at_10
value: 39.883
- type: ndcg_at_100
value: 46.983000000000004
- type: ndcg_at_1000
value: 50.043000000000006
- type: ndcg_at_3
value: 35.943000000000005
- type: ndcg_at_5
value: 37.119
- type: precision_at_1
value: 38.735
- type: precision_at_10
value: 10.940999999999999
- type: precision_at_100
value: 1.836
- type: precision_at_1000
value: 0.23900000000000002
- type: precision_at_3
value: 23.817
- type: precision_at_5
value: 17.346
- type: recall_at_1
value: 19.8
- type: recall_at_10
value: 47.082
- type: recall_at_100
value: 73.247
- type: recall_at_1000
value: 91.633
- type: recall_at_3
value: 33.201
- type: recall_at_5
value: 38.81
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.102999999999994
- type: map_at_10
value: 60.547
- type: map_at_100
value: 61.466
- type: map_at_1000
value: 61.526
- type: map_at_3
value: 56.973
- type: map_at_5
value: 59.244
- type: mrr_at_1
value: 76.205
- type: mrr_at_10
value: 82.816
- type: mrr_at_100
value: 83.002
- type: mrr_at_1000
value: 83.009
- type: mrr_at_3
value: 81.747
- type: mrr_at_5
value: 82.467
- type: ndcg_at_1
value: 76.205
- type: ndcg_at_10
value: 69.15
- type: ndcg_at_100
value: 72.297
- type: ndcg_at_1000
value: 73.443
- type: ndcg_at_3
value: 64.07000000000001
- type: ndcg_at_5
value: 66.96600000000001
- type: precision_at_1
value: 76.205
- type: precision_at_10
value: 14.601
- type: precision_at_100
value: 1.7049999999999998
- type: precision_at_1000
value: 0.186
- type: precision_at_3
value: 41.202
- type: precision_at_5
value: 27.006000000000004
- type: recall_at_1
value: 38.102999999999994
- type: recall_at_10
value: 73.005
- type: recall_at_100
value: 85.253
- type: recall_at_1000
value: 92.795
- type: recall_at_3
value: 61.803
- type: recall_at_5
value: 67.515
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 86.15
- type: ap
value: 80.36282825265391
- type: f1
value: 86.07368510726472
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 22.6
- type: map_at_10
value: 34.887
- type: map_at_100
value: 36.069
- type: map_at_1000
value: 36.115
- type: map_at_3
value: 31.067
- type: map_at_5
value: 33.300000000000004
- type: mrr_at_1
value: 23.238
- type: mrr_at_10
value: 35.47
- type: mrr_at_100
value: 36.599
- type: mrr_at_1000
value: 36.64
- type: mrr_at_3
value: 31.735999999999997
- type: mrr_at_5
value: 33.939
- type: ndcg_at_1
value: 23.252
- type: ndcg_at_10
value: 41.765
- type: ndcg_at_100
value: 47.402
- type: ndcg_at_1000
value: 48.562
- type: ndcg_at_3
value: 34.016999999999996
- type: ndcg_at_5
value: 38.016
- type: precision_at_1
value: 23.252
- type: precision_at_10
value: 6.569
- type: precision_at_100
value: 0.938
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.479000000000001
- type: precision_at_5
value: 10.722
- type: recall_at_1
value: 22.6
- type: recall_at_10
value: 62.919000000000004
- type: recall_at_100
value: 88.82
- type: recall_at_1000
value: 97.71600000000001
- type: recall_at_3
value: 41.896
- type: recall_at_5
value: 51.537
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.69357045143639
- type: f1
value: 93.55489858177597
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 75.31235750114
- type: f1
value: 57.891491963121155
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.04303967720243
- type: f1
value: 70.51516022297616
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.65299260255549
- type: f1
value: 77.49059766538576
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.458906115906597
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.9851513122443
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.2916268497217
- type: mrr
value: 32.328276715593816
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.3740000000000006
- type: map_at_10
value: 13.089999999999998
- type: map_at_100
value: 16.512
- type: map_at_1000
value: 18.014
- type: map_at_3
value: 9.671000000000001
- type: map_at_5
value: 11.199
- type: mrr_at_1
value: 46.749
- type: mrr_at_10
value: 55.367
- type: mrr_at_100
value: 56.021
- type: mrr_at_1000
value: 56.058
- type: mrr_at_3
value: 53.30200000000001
- type: mrr_at_5
value: 54.773
- type: ndcg_at_1
value: 45.046
- type: ndcg_at_10
value: 35.388999999999996
- type: ndcg_at_100
value: 32.175
- type: ndcg_at_1000
value: 41.018
- type: ndcg_at_3
value: 40.244
- type: ndcg_at_5
value: 38.267
- type: precision_at_1
value: 46.749
- type: precision_at_10
value: 26.563
- type: precision_at_100
value: 8.074
- type: precision_at_1000
value: 2.099
- type: precision_at_3
value: 37.358000000000004
- type: precision_at_5
value: 33.003
- type: recall_at_1
value: 6.3740000000000006
- type: recall_at_10
value: 16.805999999999997
- type: recall_at_100
value: 31.871
- type: recall_at_1000
value: 64.098
- type: recall_at_3
value: 10.383000000000001
- type: recall_at_5
value: 13.166
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 34.847
- type: map_at_10
value: 50.532
- type: map_at_100
value: 51.504000000000005
- type: map_at_1000
value: 51.528
- type: map_at_3
value: 46.219
- type: map_at_5
value: 48.868
- type: mrr_at_1
value: 39.137
- type: mrr_at_10
value: 53.157
- type: mrr_at_100
value: 53.839999999999996
- type: mrr_at_1000
value: 53.857
- type: mrr_at_3
value: 49.667
- type: mrr_at_5
value: 51.847
- type: ndcg_at_1
value: 39.108
- type: ndcg_at_10
value: 58.221000000000004
- type: ndcg_at_100
value: 62.021
- type: ndcg_at_1000
value: 62.57
- type: ndcg_at_3
value: 50.27199999999999
- type: ndcg_at_5
value: 54.623999999999995
- type: precision_at_1
value: 39.108
- type: precision_at_10
value: 9.397
- type: precision_at_100
value: 1.1520000000000001
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 22.644000000000002
- type: precision_at_5
value: 16.141
- type: recall_at_1
value: 34.847
- type: recall_at_10
value: 78.945
- type: recall_at_100
value: 94.793
- type: recall_at_1000
value: 98.904
- type: recall_at_3
value: 58.56
- type: recall_at_5
value: 68.535
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 68.728
- type: map_at_10
value: 82.537
- type: map_at_100
value: 83.218
- type: map_at_1000
value: 83.238
- type: map_at_3
value: 79.586
- type: map_at_5
value: 81.416
- type: mrr_at_1
value: 79.17999999999999
- type: mrr_at_10
value: 85.79299999999999
- type: mrr_at_100
value: 85.937
- type: mrr_at_1000
value: 85.938
- type: mrr_at_3
value: 84.748
- type: mrr_at_5
value: 85.431
- type: ndcg_at_1
value: 79.17
- type: ndcg_at_10
value: 86.555
- type: ndcg_at_100
value: 88.005
- type: ndcg_at_1000
value: 88.146
- type: ndcg_at_3
value: 83.557
- type: ndcg_at_5
value: 85.152
- type: precision_at_1
value: 79.17
- type: precision_at_10
value: 13.163
- type: precision_at_100
value: 1.52
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.53
- type: precision_at_5
value: 24.046
- type: recall_at_1
value: 68.728
- type: recall_at_10
value: 94.217
- type: recall_at_100
value: 99.295
- type: recall_at_1000
value: 99.964
- type: recall_at_3
value: 85.646
- type: recall_at_5
value: 90.113
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 56.15680266226348
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 63.4318549229047
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.353
- type: map_at_10
value: 10.956000000000001
- type: map_at_100
value: 12.873999999999999
- type: map_at_1000
value: 13.177
- type: map_at_3
value: 7.854
- type: map_at_5
value: 9.327
- type: mrr_at_1
value: 21.4
- type: mrr_at_10
value: 31.948999999999998
- type: mrr_at_100
value: 33.039
- type: mrr_at_1000
value: 33.106
- type: mrr_at_3
value: 28.449999999999996
- type: mrr_at_5
value: 30.535
- type: ndcg_at_1
value: 21.4
- type: ndcg_at_10
value: 18.694
- type: ndcg_at_100
value: 26.275
- type: ndcg_at_1000
value: 31.836
- type: ndcg_at_3
value: 17.559
- type: ndcg_at_5
value: 15.372
- type: precision_at_1
value: 21.4
- type: precision_at_10
value: 9.790000000000001
- type: precision_at_100
value: 2.0709999999999997
- type: precision_at_1000
value: 0.34099999999999997
- type: precision_at_3
value: 16.467000000000002
- type: precision_at_5
value: 13.54
- type: recall_at_1
value: 4.353
- type: recall_at_10
value: 19.892000000000003
- type: recall_at_100
value: 42.067
- type: recall_at_1000
value: 69.268
- type: recall_at_3
value: 10.042
- type: recall_at_5
value: 13.741999999999999
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.75433886279843
- type: cos_sim_spearman
value: 78.29727771767095
- type: euclidean_pearson
value: 80.83057828506621
- type: euclidean_spearman
value: 78.35203149750356
- type: manhattan_pearson
value: 80.7403553891142
- type: manhattan_spearman
value: 78.33670488531051
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.59999465280839
- type: cos_sim_spearman
value: 75.79279003980383
- type: euclidean_pearson
value: 82.29895375956758
- type: euclidean_spearman
value: 77.33856514102094
- type: manhattan_pearson
value: 82.22694214534756
- type: manhattan_spearman
value: 77.3028993008695
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 83.09296929691297
- type: cos_sim_spearman
value: 83.58056936846941
- type: euclidean_pearson
value: 83.84067483060005
- type: euclidean_spearman
value: 84.45155680480985
- type: manhattan_pearson
value: 83.82353052971942
- type: manhattan_spearman
value: 84.43030567861112
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.74616852320915
- type: cos_sim_spearman
value: 79.948683747966
- type: euclidean_pearson
value: 81.55702283757084
- type: euclidean_spearman
value: 80.1721505114231
- type: manhattan_pearson
value: 81.52251518619441
- type: manhattan_spearman
value: 80.1469800135577
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.97170104226318
- type: cos_sim_spearman
value: 88.82021731518206
- type: euclidean_pearson
value: 87.92950547187615
- type: euclidean_spearman
value: 88.67043634645866
- type: manhattan_pearson
value: 87.90668112827639
- type: manhattan_spearman
value: 88.64471082785317
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.02790375770599
- type: cos_sim_spearman
value: 84.46308496590792
- type: euclidean_pearson
value: 84.29430000414911
- type: euclidean_spearman
value: 84.77298303589936
- type: manhattan_pearson
value: 84.23919291368665
- type: manhattan_spearman
value: 84.75272234871308
- 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: 87.62885108477064
- type: cos_sim_spearman
value: 87.58456196391622
- type: euclidean_pearson
value: 88.2602775281007
- type: euclidean_spearman
value: 87.51556278299846
- type: manhattan_pearson
value: 88.11224053672842
- type: manhattan_spearman
value: 87.4336094383095
- 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: 63.98187965128411
- type: cos_sim_spearman
value: 64.0653163219731
- type: euclidean_pearson
value: 62.30616725924099
- type: euclidean_spearman
value: 61.556971332295916
- type: manhattan_pearson
value: 62.07642330128549
- type: manhattan_spearman
value: 61.155494129828
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.6089703921826
- type: cos_sim_spearman
value: 86.52303197250791
- type: euclidean_pearson
value: 85.95801955963246
- type: euclidean_spearman
value: 86.25242424112962
- type: manhattan_pearson
value: 85.88829100470312
- type: manhattan_spearman
value: 86.18742955805165
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 83.02282098487036
- type: mrr
value: 95.05126409538174
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 55.928
- type: map_at_10
value: 67.308
- type: map_at_100
value: 67.89500000000001
- type: map_at_1000
value: 67.91199999999999
- type: map_at_3
value: 65.091
- type: map_at_5
value: 66.412
- type: mrr_at_1
value: 58.667
- type: mrr_at_10
value: 68.401
- type: mrr_at_100
value: 68.804
- type: mrr_at_1000
value: 68.819
- type: mrr_at_3
value: 66.72200000000001
- type: mrr_at_5
value: 67.72200000000001
- type: ndcg_at_1
value: 58.667
- type: ndcg_at_10
value: 71.944
- type: ndcg_at_100
value: 74.464
- type: ndcg_at_1000
value: 74.82799999999999
- type: ndcg_at_3
value: 68.257
- type: ndcg_at_5
value: 70.10300000000001
- type: precision_at_1
value: 58.667
- type: precision_at_10
value: 9.533
- type: precision_at_100
value: 1.09
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 27.222
- type: precision_at_5
value: 17.533
- type: recall_at_1
value: 55.928
- type: recall_at_10
value: 84.65
- type: recall_at_100
value: 96.267
- type: recall_at_1000
value: 99
- type: recall_at_3
value: 74.656
- type: recall_at_5
value: 79.489
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.79009900990098
- type: cos_sim_ap
value: 94.5795129511524
- type: cos_sim_f1
value: 89.34673366834171
- type: cos_sim_precision
value: 89.79797979797979
- type: cos_sim_recall
value: 88.9
- type: dot_accuracy
value: 99.53465346534654
- type: dot_ap
value: 81.56492504352725
- type: dot_f1
value: 76.33816908454227
- type: dot_precision
value: 76.37637637637637
- type: dot_recall
value: 76.3
- type: euclidean_accuracy
value: 99.78514851485149
- type: euclidean_ap
value: 94.59134620408962
- type: euclidean_f1
value: 88.96484375
- type: euclidean_precision
value: 86.92748091603053
- type: euclidean_recall
value: 91.10000000000001
- type: manhattan_accuracy
value: 99.78415841584159
- type: manhattan_ap
value: 94.5190197328845
- type: manhattan_f1
value: 88.84462151394423
- type: manhattan_precision
value: 88.4920634920635
- type: manhattan_recall
value: 89.2
- type: max_accuracy
value: 99.79009900990098
- type: max_ap
value: 94.59134620408962
- type: max_f1
value: 89.34673366834171
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 65.1487505617497
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.502518166001856
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.33775480236701
- type: mrr
value: 51.17302223919871
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.561111309808208
- type: cos_sim_spearman
value: 30.2839254379273
- type: dot_pearson
value: 29.560242291401973
- type: dot_spearman
value: 30.51527274679116
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.215
- type: map_at_10
value: 1.752
- type: map_at_100
value: 9.258
- type: map_at_1000
value: 23.438
- type: map_at_3
value: 0.6
- type: map_at_5
value: 0.968
- type: mrr_at_1
value: 84
- type: mrr_at_10
value: 91.333
- type: mrr_at_100
value: 91.333
- type: mrr_at_1000
value: 91.333
- type: mrr_at_3
value: 91.333
- type: mrr_at_5
value: 91.333
- type: ndcg_at_1
value: 75
- type: ndcg_at_10
value: 69.596
- type: ndcg_at_100
value: 51.970000000000006
- type: ndcg_at_1000
value: 48.864999999999995
- type: ndcg_at_3
value: 73.92699999999999
- type: ndcg_at_5
value: 73.175
- type: precision_at_1
value: 84
- type: precision_at_10
value: 74
- type: precision_at_100
value: 53.2
- type: precision_at_1000
value: 21.836
- type: precision_at_3
value: 79.333
- type: precision_at_5
value: 78.4
- type: recall_at_1
value: 0.215
- type: recall_at_10
value: 1.9609999999999999
- type: recall_at_100
value: 12.809999999999999
- type: recall_at_1000
value: 46.418
- type: recall_at_3
value: 0.6479999999999999
- type: recall_at_5
value: 1.057
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.066
- type: map_at_10
value: 10.508000000000001
- type: map_at_100
value: 16.258
- type: map_at_1000
value: 17.705000000000002
- type: map_at_3
value: 6.157
- type: map_at_5
value: 7.510999999999999
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 48.786
- type: mrr_at_100
value: 49.619
- type: mrr_at_1000
value: 49.619
- type: mrr_at_3
value: 45.918
- type: mrr_at_5
value: 46.837
- type: ndcg_at_1
value: 31.633
- type: ndcg_at_10
value: 26.401999999999997
- type: ndcg_at_100
value: 37.139
- type: ndcg_at_1000
value: 48.012
- type: ndcg_at_3
value: 31.875999999999998
- type: ndcg_at_5
value: 27.383000000000003
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 22.857
- type: precision_at_100
value: 7.611999999999999
- type: precision_at_1000
value: 1.492
- type: precision_at_3
value: 33.333
- type: precision_at_5
value: 26.122
- type: recall_at_1
value: 3.066
- type: recall_at_10
value: 16.239
- type: recall_at_100
value: 47.29
- type: recall_at_1000
value: 81.137
- type: recall_at_3
value: 7.069
- type: recall_at_5
value: 9.483
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 72.1126
- type: ap
value: 14.710862719285753
- type: f1
value: 55.437808972378846
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 60.39049235993209
- type: f1
value: 60.69810537250234
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 48.15576640316866
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.52917684925792
- type: cos_sim_ap
value: 75.97497873817315
- type: cos_sim_f1
value: 70.01151926276718
- type: cos_sim_precision
value: 67.98409147402435
- type: cos_sim_recall
value: 72.16358839050132
- type: dot_accuracy
value: 82.47004828038385
- type: dot_ap
value: 62.48739894974198
- type: dot_f1
value: 59.13107511045656
- type: dot_precision
value: 55.27765029830197
- type: dot_recall
value: 63.562005277044854
- type: euclidean_accuracy
value: 86.46361089586935
- type: euclidean_ap
value: 75.59282886839452
- type: euclidean_f1
value: 69.6465443945099
- type: euclidean_precision
value: 64.52847175331982
- type: euclidean_recall
value: 75.64643799472296
- type: manhattan_accuracy
value: 86.43380818978363
- type: manhattan_ap
value: 75.5742420974403
- type: manhattan_f1
value: 69.8636926889715
- type: manhattan_precision
value: 65.8644859813084
- type: manhattan_recall
value: 74.37994722955145
- type: max_accuracy
value: 86.52917684925792
- type: max_ap
value: 75.97497873817315
- type: max_f1
value: 70.01151926276718
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.29056545193464
- type: cos_sim_ap
value: 86.63028865482376
- type: cos_sim_f1
value: 79.18166458532285
- type: cos_sim_precision
value: 75.70585756426465
- type: cos_sim_recall
value: 82.99199260856174
- type: dot_accuracy
value: 85.23305002522606
- type: dot_ap
value: 76.0482687263196
- type: dot_f1
value: 70.80484330484332
- type: dot_precision
value: 65.86933474688577
- type: dot_recall
value: 76.53988296889437
- type: euclidean_accuracy
value: 89.26145845461248
- type: euclidean_ap
value: 86.54073288416006
- type: euclidean_f1
value: 78.9721371479794
- type: euclidean_precision
value: 76.68649354417525
- type: euclidean_recall
value: 81.39821373575609
- type: manhattan_accuracy
value: 89.22847052431405
- type: manhattan_ap
value: 86.51250729037905
- type: manhattan_f1
value: 78.94601825044894
- type: manhattan_precision
value: 75.32694594027555
- type: manhattan_recall
value: 82.93039728980598
- type: max_accuracy
value: 89.29056545193464
- type: max_ap
value: 86.63028865482376
- type: max_f1
value: 79.18166458532285
---
# E5-base-4k
[LongEmbed: Extending Embedding Models for Long Context Retrieval](https://arxiv.org/abs/2404.12096). Dawei Zhu, Liang Wang, Nan Yang, Yifan Song, Wenhao Wu, Furu Wei, Sujian Li, arxiv 2024. Github Repo for LongEmbed: https://github.com/dwzhu-pku/LongEmbed.
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
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]
def get_position_ids(input_ids: Tensor, max_original_positions: int=512, encode_max_length: int=4096) -> Tensor:
position_ids = list(range(input_ids.size(1)))
factor = max(encode_max_length // max_original_positions, 1)
if input_ids.size(1) <= max_original_positions:
position_ids = [(pid * factor) for pid in position_ids]
position_ids = torch.tensor(position_ids, dtype=torch.long)
position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
return position_ids
# 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('dwzhu/e5-base-4k')
model = AutoModel.from_pretrained('dwzhu/e5-base-4k')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=4096, padding=True, truncation=True, return_tensors='pt')
batch_dict['position_ids'] = get_position_ids(batch_dict['input_ids'], max_original_positions=512, encode_max_length=4096)
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())
```
## Training Details
Please refer to our paper at [https://arxiv.org/abs/2404.12096.pdf](https://arxiv.org/abs/2404.12096.pdf). Note that E5-Base-4k simply expands the position embedding matrix to allow for 4,096 position ids. The embedding vectors for the original pids {0,1,2,...,511} is mapped to represent {0,8,16,...,4088}. Embedding vectors for other pids are trained. So for inputs not exceeding 512 tokens, please multiply the position ids by 8 to maintain the original behavior, as shown in the code above.
## 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{zhu2024longembed,
title={LongEmbed: Extending Embedding Models for Long Context Retrieval},
author={Zhu, Dawei and Wang, Liang and Yang, Nan and Song, Yifan and Wu, Wenhao and Wei, Furu and Li, Sujian},
journal={arXiv preprint arXiv:2404.12096},
year={2024}
}
```
|
[
"BIOSSES",
"SCIFACT"
] |
sapienzanlp/Minerva-7B-base-v1.0
|
sapienzanlp
|
text-generation
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"pretrained",
"it",
"en",
"dataset:uonlp/CulturaX",
"dataset:HuggingFaceFW/fineweb",
"dataset:togethercomputer/RedPajama-Data-V2",
"dataset:bigcode/the-stack-v2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-05-26T19:34:19Z |
2024-12-05T11:58:01+00:00
| 3,373 | 14 |
---
datasets:
- uonlp/CulturaX
- HuggingFaceFW/fineweb
- togethercomputer/RedPajama-Data-V2
- bigcode/the-stack-v2
language:
- it
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- pretrained
inference:
parameters:
temperature: 0.5
do_sample: true
widget:
- text: 'La capitale dell''Italia è '
example_title: Example 1
- text: 'Nel mezzo del cammin di nostra vita '
example_title: Example 2
- text: 'Una cena senza vino è come '
example_title: Example 3
---
<div style="text-align: center; display: flex; flex-direction: column; align-items: center;">
<img src="https://huggingface.co/sapienzanlp/Minerva-7B-instruct-v1.0/resolve/main/minerva-logo.png" style="max-width: 550px; height: auto;">
</div>
# Model Card for Minerva-7B-base-v1.0
Minerva is the first family of **LLMs pretrained from scratch on Italian** developed by [Sapienza NLP](https://nlp.uniroma1.it)
in collaboration with [Future Artificial Intelligence Research (FAIR)](https://fondazione-fair.it/) and [CINECA](https://www.cineca.it/).
Notably, the Minerva models are truly-open (data and model) Italian-English LLMs, with approximately half of the pretraining data
including Italian text.
* [Minerva LLMs - website](https://nlp.uniroma1.it/minerva/)
## Description
This is the model card for **Minerva-7B-base-v1.0**, a 7 billion parameter model trained on almost 2.5 trillion tokens (1.14 trillion in Italian,
1.14 trillion in English, and 200 billion in code).
This model is part of the Minerva LLM family:
* [Minerva-350M-base-v1.0](https://huggingface.co/sapienzanlp/Minerva-350M-base-v1.0)
* [Minerva-1B-base-v1.0](https://huggingface.co/sapienzanlp/Minerva-1B-base-v1.0)
* [Minerva-3B-base-v1.0](https://huggingface.co/sapienzanlp/Minerva-3B-base-v1.0)
* [Minerva-7B-base-v1.0](https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0)
* [Minerva-7B-instruct-v1.0](https://huggingface.co/sapienzanlp/Minerva-7B-instruct-v1.0)
## 🚨⚠️🚨 Bias, Risks, and Limitations 🚨⚠️🚨
*This section identifies foreseeable harms and misunderstandings.*
This is a foundation model, not subject to alignment. Model may:
- Overrepresent some viewpoints and underrepresent others
- Contain stereotypes
- Contain [personal information](#personal-data-and-information)
- Generate:
- Racist and sexist content
- Hateful, abusive, or violent language
- Discriminatory or prejudicial language
- Content that may not be appropriate for all settings, including sexual content
- Make errors, including producing incorrect information or historical facts as if it were factual
- Generate irrelevant or repetitive outputs
We are aware of the biases and potential problematic/toxic content that current pretrained large language models exhibit: more specifically, as probabilistic models of (Italian and English) languages, they reflect and amplify the biases of their training data.
For more information about this issue, please refer to our survey:
* [Biases in Large Language Models: Origins, Inventory, and Discussion](https://dl.acm.org/doi/full/10.1145/3597307)
## How to use Minerva with Hugging Face transformers
```python
import transformers
import torch
model_id = "sapienzanlp/Minerva-7B-base-v1.0"
# Initialize the pipeline.
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
# Input text for the model.
input_text = "La capitale dell'Italia è"
# Compute the outputs.
output = pipeline(
input_text,
max_new_tokens=128,
)
output
```
[{'generated_text': "La capitale dell'Italia è la città di Roma, che si trova a [...]"}]
## Model Architecture
Minerva-7B-base-v1.0 is a Transformer model based on the Mistral architecture.
Please look at the configuration file for a detailed breakdown of the hyperparameters we chose for this model.
The Minerva LLM family is composed of:
| Model Name | Tokens | Layers | Hidden Size | Attention Heads | KV Heads | Sliding Window | Max Context Length |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Minerva-350M-base-v1.0 | 70B (35B it + 35B en) | 16 | 1152 | 16 | 4 | 2048 | 16384 |
| Minerva-1B-base-v1.0 | 200B (100B it + 100B en) | 16 | 2048 | 16 | 4 | 2048 | 16384 |
| Minerva-3B-base-v1.0 | 660B (330B it + 330B en) | 32 | 2560 | 32 | 8 | 2048 | 16384 |
| Minerva-7B-base-v1.0 | 2.48T (1.14T it + 1.14T en + 200B code) | 32 | 4096 | 32 | 8 | None | 4096 |
## Model Training
Minerva-7B-base-v1.0 was trained using [llm-foundry 0.8.0](https://github.com/riccorl/llm-foundry) from [MosaicML](https://mosaicml.com/). The hyperparameters used are the following:
| Model Name | Optimizer | lr | betas | eps | weight decay | Scheduler | Warmup Steps | Batch Size (Tokens) | Total Steps |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Minerva-350M-base-v1.0 | Decoupled AdamW | 2e-4 | (0.9, 0.95) | 1e-8 | 0.0 | Cosine | 2% | 4M | 16,690 |
| Minerva-1B-base-v1.0 | Decoupled AdamW | 2e-4 | (0.9, 0.95) | 1e-8 | 0.0 | Cosine | 2% | 4M | 47,684 |
| Minerva-3B-base-v1.0 | Decoupled AdamW | 2e-4 | (0.9, 0.95) | 1e-8 | 0.0 | Cosine | 2% | 4M | 157,357 |
| Minerva-7B-base-v1.0 | AdamW | 3e-4 | (0.9, 0.95) | 1e-5 | 0.1 | Cosine | 2000 | 4M | 591,558 |
## Model Evaluation
For Minerva's evaluation process, we utilized [ITA-Bench](https://huggingface.co/collections/sapienzanlp/ita-bench-italian-benchmarks-for-llms-66337ca59e6df7d7d4933896), a new evaluation suite to test the capabilities of Italian-speaking models.
ITA-Bench is a collection of 18 benchmarks that assess the performance of language models on various tasks, including scientific knowledge,
commonsense reasoning, and mathematical problem-solving.
<div style={{ display: 'flex', justifyContent: 'space-around' }}>
<img src="https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0/resolve/main/Minerva%20LLMs%20Results%20Base%20Models.png" alt="Results on base models" style={{ width: '45%' }}></img>
<img src="https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0/resolve/main/Minerva%20LLMs%20Results%20All%20Base%20Models.png" alt="Results on base models" style={{ width: '45%' }}></img>
</div>
<!-- **Italian** Data: -->
<!-- | Task | Accuracy |
| --- | --- | -->
<!-- | [xcopa](https://huggingface.co/datasets/xcopa) (0-shot) | 0.694 |
| [Hellaswag](https://huggingface.co/datasets/alexandrainst/m_hellaswag) (5-shot) | 0.5293 |
| [Belebele](https://huggingface.co/datasets/facebook/belebele) (5-shot) | 0.2333 |
| [TruthfulQA MC 1](https://huggingface.co/datasets/alexandrainst/m_truthfulqa) (0-shot) | 0.2363 |
| [TruthfulQA MC 2](https://huggingface.co/datasets/alexandrainst/m_truthfulqa) (0-shot) | 0.3731 |
| [M MMLU](https://huggingface.co/datasets/alexandrainst/m_mmlu) (5-shot) | 0.2612 |
| [arc challenge](https://huggingface.co/datasets/alexandrainst/m_arc) (5-shot) | 0.3268 | -->
<!-- **English** Data: -->
<!-- | Task | Accuracy |
| --- | --- | -->
<!-- | [Hellaswag](https://huggingface.co/datasets/Rowan/hellaswag) (5-shot) | 0.6168 |
| [piqa](https://huggingface.co/datasets/piqa) (5-shot) | 0.7535 |
| [sciq](https://huggingface.co/datasets/sciq) (5-shot) | 0.925 |
| [Belebele](https://huggingface.co/datasets/facebook/belebele) (5-shot) | 0.2278 |
| [TruthfulQA MC 1](https://huggingface.co/datasets/truthful_qa) (0-shot) | 0.2142 |
| [TruthfulQA MC 2](https://huggingface.co/datasets/truthful_qa) (0-shot) | 0.3643 |
| [M MMLU](https://huggingface.co/datasets/alexandrainst/m_mmlu) (5-shot) | 0.263 |
| [arc challenge](allenai/ai2_arc) (5-shot) | 0.3319 |
| [arc easy](allenai/ai2_arc) (5-shot) | 0.6540 | -->
## Training Data
Minerva-7B-base-v1.0 is trained on 1.14T Italian tokens, 1.14T English tokens, and 200B code tokens.
The training data is a mixture of the following datasets:
| Dataset | Tokens | Language | Epochs |
| --- | --- | --- | --- |
| RedPajama-Data-V2 | 687,952,502,784 | Italian | 1.3 |
| CulturaX | 158,201,876,480 | Italian | 1.5 |
| Wikipedia | 1,265,135,616 | Italian | 1.0 |
| Gutenberg/Wikisource | 147,017,728 | Italian | 2.0 |
| EurLex | 1,647,013,888 | Italian | 1.0 |
| Gazzetta Ufficiale | 1,654,013,952| Italian | 1.0 |
| FineWeb | 1,076,406,624,256 | English | 1.0 |
| Wikipedia | 5,259,501,568 | English | 1.0 |
| ArXiv | 33,231,106,048 | English | 1.0 |
| Gutenberg | 6,947,893,248 | English | 1.0 |
| StackExchange | 22,069,268,480 | English | 1.0 |
| The Stack V2 | 200,754,900,992 | Code | 1.0 |
<!-- We have extracted some statistics on Italian (115B tokens) and English (210B tokens) documents from CulturaX on the selected sources:
*Proportion of number of tokens per domain (Italian)*
<img src="https://github.com/Andrew-Wyn/images/blob/master/minerva/top_25_url_tokens_proportion_culturax_it.png?raw=true" alt="italian-tok-counts" border="0" width="1800px">
*Proportion of number of tokens per domain (English)*
<img src="https://github.com/Andrew-Wyn/images/blob/master/minerva/top_25_url_tokens_proportion_culturax_en.png?raw=true" alt="english-tok-counts" border="0" width="1800px">
-->
## Tokenizer Fertility
The tokenizer fertility measures the average amount of tokens produced per tokenized word.
A tokenizer displaying high fertility values in a particular language typically indicates that it segments words in that language extensively.
The tokenizer fertility is strictly correlated with the inference speed of the model with respect to a specific language,
as higher values mean longer sequences of tokens to generate and thus lower inference speed.
**Fertility computed over a sample of Cultura X (CX) data and Wikipedia (Wp):**
| Model | Voc. Size | Fertility IT (CX) | Fertility EN (CX) | Fertility IT (Wp) | Fertility EN (Wp) |
| --- | --- | --- |--- | --- |--- |
| Mistral-7B-v0.1 | 32000 | 1.87 | 1.32 | 2.05 | 1.57 |
| gemma-7b | 256000 | 1.42 | 1.18 | 1.56 | 1.34 |
| Minerva-3B-base-v1.0 | 32768 | 1.39 | 1.32 | 1.66 | 1.59 |
| Minerva-7B-base-v1.0 | 51200 | 1.32 | 1.26 | 1.56 | 1.51 |
## Notice
Minerva-7B-base-v1.0 is a pretrained base model and, therefore, has no moderation mechanisms.
## The Sapienza NLP Team
* **Riccardo Orlando:** data preprocessing, model training
* **Pere-Lluis Huguet Cabot:** data preprocessing, vocabulary, evaluation
* **Luca Moroni:** data curation, data analysis, downstream tasks, evaluation
* **Simone Conia:** data curation, evaluation, project supervision
* **Edoardo Barba:** data preprocessing, downstream tasks, project supervision
* **Roberto Navigli:** project lead and coordination
### Special thanks for their support
* Giuseppe Fiameni, Nvidia
* Sergio Orlandini, CINECA
## Acknowledgments
This work was funded by the PNRR MUR project [PE0000013-FAIR](https://fondazione-fair.it) and the [CREATIVE](https://nlp.uniroma1.it/creative/) PRIN project, which is funded by the MUR Progetti di
Rilevante Interesse Nazionale programme (PRIN 2020).
We acknowledge the [CINECA](https://www.cineca.it) award "IscB_medit" under the ISCRA initiative for the availability of high-performance computing resources and support.
|
[
"SCIQ"
] |
microsoft/BioGPT-Large-PubMedQA
|
microsoft
|
text-generation
|
[
"transformers",
"pytorch",
"biogpt",
"text-generation",
"medical",
"en",
"dataset:pubmed_qa",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-02-03T20:33:43Z |
2023-02-04T07:50:25+00:00
| 3,367 | 108 |
---
datasets:
- pubmed_qa
language:
- en
library_name: transformers
license: mit
metrics:
- accuracy
pipeline_tag: text-generation
tags:
- medical
widget:
- text: 'question: Can ''high-risk'' human papillomaviruses (HPVs) be detected in
human breast milk? context: Using polymerase chain reaction techniques, we evaluated
the presence of HPV infection in human breast milk collected from 21 HPV-positive
and 11 HPV-negative mothers. Of the 32 studied human milk specimens, no ''high-risk''
HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58 or 58 DNA was detected. answer:
This preliminary case-control study indicates the absence of mucosal ''high-risk''
HPV types in human breast milk.'
inference:
parameters:
max_new_tokens: 250
do_sample: false
---
## BioGPT
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.
## Citation
If you find BioGPT useful in your research, please cite the following paper:
```latex
@article{10.1093/bib/bbac409,
author = {Luo, Renqian and Sun, Liai and Xia, Yingce and Qin, Tao and Zhang, Sheng and Poon, Hoifung and Liu, Tie-Yan},
title = "{BioGPT: generative pre-trained transformer for biomedical text generation and mining}",
journal = {Briefings in Bioinformatics},
volume = {23},
number = {6},
year = {2022},
month = {09},
abstract = "{Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98\%, 38.42\% and 40.76\% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2\% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.}",
issn = {1477-4054},
doi = {10.1093/bib/bbac409},
url = {https://doi.org/10.1093/bib/bbac409},
note = {bbac409},
eprint = {https://academic.oup.com/bib/article-pdf/23/6/bbac409/47144271/bbac409.pdf},
}
```
|
[
"BC5CDR",
"PUBMEDQA"
] |
lightonai/modernbert-embed-large
|
lightonai
|
sentence-similarity
|
[
"sentence-transformers",
"onnx",
"safetensors",
"modernbert",
"feature-extraction",
"sentence-similarity",
"mteb",
"transformers.js",
"en",
"arxiv:2402.01613",
"arxiv:2412.13663",
"base_model:answerdotai/ModernBERT-large",
"base_model:quantized:answerdotai/ModernBERT-large",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2025-01-13T10:21:29Z |
2025-01-14T10:02:51+00:00
| 3,361 | 20 |
---
base_model:
- answerdotai/ModernBERT-large
- lightonai/modernbert-embed-large-unsupervised
language:
- en
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- transformers.js
model-index:
- name: modernbert-embed-large
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: None
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.7910447761194
- type: ap
value: 39.79562424828666
- type: f1
value: 70.69575548517653
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: None
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 94.19505000000001
- type: ap
value: 91.75071069741077
- type: f1
value: 94.19151001437368
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: None
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.664
- type: f1
value: 46.932904638602466
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type: Retrieval
dataset:
name: MTEB ArguAna
type: None
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 25.178
- type: map_at_10
value: 41.088
- type: map_at_100
value: 42.143
- type: map_at_1000
value: 42.152
- type: map_at_20
value: 41.946
- type: map_at_3
value: 36.048
- type: map_at_5
value: 38.619
- type: mrr_at_1
value: 25.533
- type: mrr_at_10
value: 41.238
- type: mrr_at_100
value: 42.293
- type: mrr_at_1000
value: 42.302
- type: mrr_at_20
value: 42.096000000000004
- type: mrr_at_3
value: 36.260999999999996
- type: mrr_at_5
value: 38.797
- type: ndcg_at_1
value: 25.178
- type: ndcg_at_10
value: 50.352
- type: ndcg_at_100
value: 54.583000000000006
- type: ndcg_at_1000
value: 54.797
- type: ndcg_at_20
value: 53.36
- type: ndcg_at_3
value: 39.781
- type: ndcg_at_5
value: 44.412
- type: precision_at_1
value: 25.178
- type: precision_at_10
value: 8.016
- type: precision_at_100
value: 0.98
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.591
- type: precision_at_3
value: 16.88
- type: precision_at_5
value: 12.376
- type: recall_at_1
value: 25.178
- type: recall_at_10
value: 80.156
- type: recall_at_100
value: 98.009
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 91.821
- type: recall_at_3
value: 50.63999999999999
- type: recall_at_5
value: 61.878
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: None
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
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value: 47.800803622189214
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type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: None
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
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dataset:
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type: None
config: default
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revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
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value: 63.213756562834234
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dataset:
name: MTEB BIOSSES
type: None
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
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value: 85.61158927235539
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dataset:
name: MTEB Banking77Classification
type: None
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
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value: 86.17857142857144
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value: 86.14192410600847
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dataset:
name: MTEB BiorxivClusteringP2P
type: None
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
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- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: None
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 34.668146108668715
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- 0.35574882126513674
- 0.3437528212533572
- 0.34627871014268474
- 0.35947142706082674
- 0.35557599816049484
- 0.3313213383920607
- 0.33475049791707046
- 0.3464858366916894
- 0.34749918466307905
- 0.3459299753204718
- 0.35574882126513674
- 0.3437528212533572
- 0.34627871014268474
- 0.35947142706082674
- 0.35557599816049484
- 0.3313213383920607
- 0.33475049791707046
- 0.3464858366916894
- 0.34749918466307905
- 0.3459299753204718
- 0.35574882126513674
- 0.3437528212533572
- 0.34627871014268474
- 0.35947142706082674
- 0.35557599816049484
- 0.3313213383920607
- 0.33475049791707046
- 0.3464858366916894
- 0.34749918466307905
- 0.3459299753204718
- 0.35574882126513674
- 0.3437528212533572
- 0.34627871014268474
- 0.35947142706082674
- 0.35557599816049484
- 0.3313213383920607
- 0.33475049791707046
- 0.3464858366916894
- 0.34749918466307905
- 0.3459299753204718
- 0.35574882126513674
- 0.3437528212533572
- 0.34627871014268474
- 0.35947142706082674
- 0.35557599816049484
- 0.3313213383920607
- 0.33475049791707046
- 0.3464858366916894
- 0.34749918466307905
- 0.3459299753204718
- 0.35574882126513674
- 0.3437528212533572
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 34.394999999999996
- type: map_at_10
value: 45.882
- type: map_at_100
value: 47.4
- type: map_at_1000
value: 47.509
- type: map_at_20
value: 46.822
- type: map_at_3
value: 42.408
- type: map_at_5
value: 44.586
- type: mrr_at_1
value: 41.202
- type: mrr_at_10
value: 51.134
- type: mrr_at_100
value: 51.943
- type: mrr_at_1000
value: 51.986
- type: mrr_at_20
value: 51.717
- type: mrr_at_3
value: 48.784
- type: mrr_at_5
value: 50.336000000000006
- type: ndcg_at_1
value: 41.202
- type: ndcg_at_10
value: 51.842999999999996
- type: ndcg_at_100
value: 57.177
- type: ndcg_at_1000
value: 58.89
- type: ndcg_at_20
value: 54.357
- type: ndcg_at_3
value: 47.286
- type: ndcg_at_5
value: 49.829
- type: precision_at_1
value: 41.202
- type: precision_at_10
value: 9.585
- type: precision_at_100
value: 1.5150000000000001
- type: precision_at_1000
value: 0.194
- type: precision_at_20
value: 5.808
- type: precision_at_3
value: 22.508
- type: precision_at_5
value: 16.366
- type: recall_at_1
value: 34.394999999999996
- type: recall_at_10
value: 63.17
- type: recall_at_100
value: 84.867
- type: recall_at_1000
value: 95.733
- type: recall_at_20
value: 72.011
- type: recall_at_3
value: 49.966
- type: recall_at_5
value: 56.802
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 34.324
- type: map_at_10
value: 46.123
- type: map_at_100
value: 47.455999999999996
- type: map_at_1000
value: 47.576
- type: map_at_20
value: 46.851
- type: map_at_3
value: 42.945
- type: map_at_5
value: 44.751000000000005
- type: mrr_at_1
value: 43.248
- type: mrr_at_10
value: 52.544000000000004
- type: mrr_at_100
value: 53.102000000000004
- type: mrr_at_1000
value: 53.138
- type: mrr_at_20
value: 52.861000000000004
- type: mrr_at_3
value: 50.37199999999999
- type: mrr_at_5
value: 51.712
- type: ndcg_at_1
value: 43.248
- type: ndcg_at_10
value: 52.235
- type: ndcg_at_100
value: 56.355
- type: ndcg_at_1000
value: 58.053
- type: ndcg_at_20
value: 53.849000000000004
- type: ndcg_at_3
value: 48.208
- type: ndcg_at_5
value: 50.134
- type: precision_at_1
value: 43.248
- type: precision_at_10
value: 9.917
- type: precision_at_100
value: 1.532
- type: precision_at_1000
value: 0.198
- type: precision_at_20
value: 5.779999999999999
- type: precision_at_3
value: 23.588
- type: precision_at_5
value: 16.586000000000002
- type: recall_at_1
value: 34.324
- type: recall_at_10
value: 62.56
- type: recall_at_100
value: 79.745
- type: recall_at_1000
value: 90.082
- type: recall_at_20
value: 68.367
- type: recall_at_3
value: 50.171
- type: recall_at_5
value: 55.889
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 44.143
- type: map_at_10
value: 56.53
- type: map_at_100
value: 57.48799999999999
- type: map_at_1000
value: 57.535000000000004
- type: map_at_20
value: 57.152
- type: map_at_3
value: 53.382
- type: map_at_5
value: 55.156000000000006
- type: mrr_at_1
value: 50.09400000000001
- type: mrr_at_10
value: 59.819
- type: mrr_at_100
value: 60.431000000000004
- type: mrr_at_1000
value: 60.455000000000005
- type: mrr_at_20
value: 60.251999999999995
- type: mrr_at_3
value: 57.544
- type: mrr_at_5
value: 58.904999999999994
- type: ndcg_at_1
value: 50.09400000000001
- type: ndcg_at_10
value: 62.141999999999996
- type: ndcg_at_100
value: 65.755
- type: ndcg_at_1000
value: 66.674
- type: ndcg_at_20
value: 63.92400000000001
- type: ndcg_at_3
value: 56.986000000000004
- type: ndcg_at_5
value: 59.519999999999996
- type: precision_at_1
value: 50.09400000000001
- type: precision_at_10
value: 9.743
- type: precision_at_100
value: 1.246
- type: precision_at_1000
value: 0.136
- type: precision_at_20
value: 5.439
- type: precision_at_3
value: 25.119999999999997
- type: precision_at_5
value: 17.052999999999997
- type: recall_at_1
value: 44.143
- type: recall_at_10
value: 75.372
- type: recall_at_100
value: 90.602
- type: recall_at_1000
value: 97.043
- type: recall_at_20
value: 81.83500000000001
- type: recall_at_3
value: 61.607
- type: recall_at_5
value: 67.755
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 26.621
- type: map_at_10
value: 35.865
- type: map_at_100
value: 36.93
- type: map_at_1000
value: 37.008
- type: map_at_20
value: 36.509
- type: map_at_3
value: 33.532000000000004
- type: map_at_5
value: 34.745
- type: mrr_at_1
value: 28.588
- type: mrr_at_10
value: 37.828
- type: mrr_at_100
value: 38.779
- type: mrr_at_1000
value: 38.834
- type: mrr_at_20
value: 38.419
- type: mrr_at_3
value: 35.725
- type: mrr_at_5
value: 36.803999999999995
- type: ndcg_at_1
value: 28.588
- type: ndcg_at_10
value: 40.983999999999995
- type: ndcg_at_100
value: 46.117000000000004
- type: ndcg_at_1000
value: 47.959
- type: ndcg_at_20
value: 43.22
- type: ndcg_at_3
value: 36.455
- type: ndcg_at_5
value: 38.393
- type: precision_at_1
value: 28.588
- type: precision_at_10
value: 6.282
- type: precision_at_100
value: 0.927
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_20
value: 3.689
- type: precision_at_3
value: 15.744
- type: precision_at_5
value: 10.644
- type: recall_at_1
value: 26.621
- type: recall_at_10
value: 54.80199999999999
- type: recall_at_100
value: 78.171
- type: recall_at_1000
value: 91.786
- type: recall_at_20
value: 63.195
- type: recall_at_3
value: 42.164
- type: recall_at_5
value: 46.936
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 18.619
- type: map_at_10
value: 27.577
- type: map_at_100
value: 28.717
- type: map_at_1000
value: 28.835
- type: map_at_20
value: 28.18
- type: map_at_3
value: 24.462999999999997
- type: map_at_5
value: 26.230999999999998
- type: mrr_at_1
value: 22.886
- type: mrr_at_10
value: 32.089
- type: mrr_at_100
value: 32.998
- type: mrr_at_1000
value: 33.06
- type: mrr_at_20
value: 32.633
- type: mrr_at_3
value: 29.125
- type: mrr_at_5
value: 30.792
- type: ndcg_at_1
value: 22.886
- type: ndcg_at_10
value: 33.343
- type: ndcg_at_100
value: 38.735
- type: ndcg_at_1000
value: 41.393
- type: ndcg_at_20
value: 35.455
- type: ndcg_at_3
value: 27.575
- type: ndcg_at_5
value: 30.361
- type: precision_at_1
value: 22.886
- type: precision_at_10
value: 6.256
- type: precision_at_100
value: 1.03
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_20
value: 3.7130000000000005
- type: precision_at_3
value: 13.267000000000001
- type: precision_at_5
value: 9.851
- type: recall_at_1
value: 18.619
- type: recall_at_10
value: 46.478
- type: recall_at_100
value: 69.614
- type: recall_at_1000
value: 88.331
- type: recall_at_20
value: 54.254000000000005
- type: recall_at_3
value: 30.897999999999996
- type: recall_at_5
value: 37.785000000000004
- type: map_at_1
value: 28.592666666666673
- type: map_at_10
value: 38.50391666666667
- type: map_at_100
value: 39.719166666666666
- type: map_at_1000
value: 39.82683333333334
- type: map_at_20
value: 39.18608333333333
- type: map_at_3
value: 35.561833333333325
- type: map_at_5
value: 37.181000000000004
- type: mrr_at_1
value: 33.67625
- type: mrr_at_10
value: 42.727
- type: mrr_at_100
value: 43.55041666666667
- type: mrr_at_1000
value: 43.60058333333334
- type: mrr_at_20
value: 43.21508333333333
- type: mrr_at_3
value: 40.32983333333334
- type: mrr_at_5
value: 41.699333333333335
- type: ndcg_at_1
value: 33.67625
- type: ndcg_at_10
value: 44.064416666666666
- type: ndcg_at_100
value: 49.085
- type: ndcg_at_1000
value: 51.09325
- type: ndcg_at_20
value: 46.07716666666666
- type: ndcg_at_3
value: 39.22225
- type: ndcg_at_5
value: 41.47508333333333
- type: precision_at_1
value: 33.67625
- type: precision_at_10
value: 7.689916666666667
- type: precision_at_100
value: 1.1995833333333334
- type: precision_at_1000
value: 0.15541666666666665
- type: precision_at_20
value: 4.515500000000001
- type: precision_at_3
value: 18.07241666666667
- type: precision_at_5
value: 12.732833333333332
- type: recall_at_1
value: 28.592666666666673
- type: recall_at_10
value: 56.15700000000001
- type: recall_at_100
value: 77.97075000000001
- type: recall_at_1000
value: 91.73058333333333
- type: recall_at_20
value: 63.49649999999999
- type: recall_at_3
value: 42.612833333333334
- type: recall_at_5
value: 48.44591666666667
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 31.087999999999997
- type: map_at_10
value: 41.668
- type: map_at_100
value: 42.983
- type: map_at_1000
value: 43.081
- type: map_at_20
value: 42.373
- type: map_at_3
value: 38.481
- type: map_at_5
value: 40.196
- type: mrr_at_1
value: 37.824999999999996
- type: mrr_at_10
value: 47.471999999999994
- type: mrr_at_100
value: 48.311
- type: mrr_at_1000
value: 48.351
- type: mrr_at_20
value: 47.981
- type: mrr_at_3
value: 45.074999999999996
- type: mrr_at_5
value: 46.37
- type: ndcg_at_1
value: 37.824999999999996
- type: ndcg_at_10
value: 47.63
- type: ndcg_at_100
value: 52.979
- type: ndcg_at_1000
value: 54.771
- type: ndcg_at_20
value: 49.733
- type: ndcg_at_3
value: 42.657000000000004
- type: ndcg_at_5
value: 44.878
- type: precision_at_1
value: 37.824999999999996
- type: precision_at_10
value: 8.527
- type: precision_at_100
value: 1.303
- type: precision_at_1000
value: 0.16199999999999998
- type: precision_at_20
value: 4.966
- type: precision_at_3
value: 19.955000000000002
- type: precision_at_5
value: 14.033000000000001
- type: recall_at_1
value: 31.087999999999997
- type: recall_at_10
value: 59.585
- type: recall_at_100
value: 81.625
- type: recall_at_1000
value: 93.297
- type: recall_at_20
value: 66.813
- type: recall_at_3
value: 45.492
- type: recall_at_5
value: 51.283
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 28.756999999999998
- type: map_at_10
value: 40.275
- type: map_at_100
value: 41.655
- type: map_at_1000
value: 41.752
- type: map_at_20
value: 41.118
- type: map_at_3
value: 36.815
- type: map_at_5
value: 38.662
- type: mrr_at_1
value: 35.502
- type: mrr_at_10
value: 45.818
- type: mrr_at_100
value: 46.704
- type: mrr_at_1000
value: 46.745999999999995
- type: mrr_at_20
value: 46.387
- type: mrr_at_3
value: 43.322
- type: mrr_at_5
value: 44.675
- type: ndcg_at_1
value: 35.502
- type: ndcg_at_10
value: 46.658
- type: ndcg_at_100
value: 52.097
- type: ndcg_at_1000
value: 53.928
- type: ndcg_at_20
value: 49.134
- type: ndcg_at_3
value: 41.234
- type: ndcg_at_5
value: 43.579
- type: precision_at_1
value: 35.502
- type: precision_at_10
value: 8.652999999999999
- type: precision_at_100
value: 1.306
- type: precision_at_1000
value: 0.163
- type: precision_at_20
value: 5.086
- type: precision_at_3
value: 19.825
- type: precision_at_5
value: 13.995
- type: recall_at_1
value: 28.756999999999998
- type: recall_at_10
value: 59.79
- type: recall_at_100
value: 82.597
- type: recall_at_1000
value: 94.663
- type: recall_at_20
value: 68.74
- type: recall_at_3
value: 44.736
- type: recall_at_5
value: 51.047
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 25.381999999999998
- type: map_at_10
value: 33.311
- type: map_at_100
value: 34.171
- type: map_at_1000
value: 34.254
- type: map_at_20
value: 33.732
- type: map_at_3
value: 31.025999999999996
- type: map_at_5
value: 32.253
- type: mrr_at_1
value: 28.221
- type: mrr_at_10
value: 36.132999999999996
- type: mrr_at_100
value: 36.848
- type: mrr_at_1000
value: 36.902
- type: mrr_at_20
value: 36.497
- type: mrr_at_3
value: 33.947
- type: mrr_at_5
value: 35.174
- type: ndcg_at_1
value: 28.221
- type: ndcg_at_10
value: 37.882
- type: ndcg_at_100
value: 42.283
- type: ndcg_at_1000
value: 44.458
- type: ndcg_at_20
value: 39.268
- type: ndcg_at_3
value: 33.611999999999995
- type: ndcg_at_5
value: 35.583
- type: precision_at_1
value: 28.221
- type: precision_at_10
value: 6.043
- type: precision_at_100
value: 0.8909999999999999
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_20
value: 3.405
- type: precision_at_3
value: 14.673
- type: precision_at_5
value: 10.152999999999999
- type: recall_at_1
value: 25.381999999999998
- type: recall_at_10
value: 48.980000000000004
- type: recall_at_100
value: 69.625
- type: recall_at_1000
value: 85.946
- type: recall_at_20
value: 54.041
- type: recall_at_3
value: 37.077
- type: recall_at_5
value: 42.097
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 18.186
- type: map_at_10
value: 26.450000000000003
- type: map_at_100
value: 27.62
- type: map_at_1000
value: 27.746
- type: map_at_20
value: 27.105
- type: map_at_3
value: 23.982999999999997
- type: map_at_5
value: 25.306
- type: mrr_at_1
value: 22.092
- type: mrr_at_10
value: 30.326999999999998
- type: mrr_at_100
value: 31.322
- type: mrr_at_1000
value: 31.394
- type: mrr_at_20
value: 30.923000000000002
- type: mrr_at_3
value: 28.063
- type: mrr_at_5
value: 29.284
- type: ndcg_at_1
value: 22.092
- type: ndcg_at_10
value: 31.418000000000003
- type: ndcg_at_100
value: 36.924
- type: ndcg_at_1000
value: 39.645
- type: ndcg_at_20
value: 33.597
- type: ndcg_at_3
value: 27.045
- type: ndcg_at_5
value: 28.971999999999998
- type: precision_at_1
value: 22.092
- type: precision_at_10
value: 5.785
- type: precision_at_100
value: 0.989
- type: precision_at_1000
value: 0.13999999999999999
- type: precision_at_20
value: 3.517
- type: precision_at_3
value: 12.985
- type: precision_at_5
value: 9.291
- type: recall_at_1
value: 18.186
- type: recall_at_10
value: 42.443
- type: recall_at_100
value: 66.964
- type: recall_at_1000
value: 86.005
- type: recall_at_20
value: 50.52799999999999
- type: recall_at_3
value: 30.095
- type: recall_at_5
value: 35.148
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 31.049
- type: map_at_10
value: 40.217000000000006
- type: map_at_100
value: 41.345
- type: map_at_1000
value: 41.447
- type: map_at_20
value: 40.818
- type: map_at_3
value: 37.413999999999994
- type: map_at_5
value: 39.001000000000005
- type: mrr_at_1
value: 36.474000000000004
- type: mrr_at_10
value: 44.655
- type: mrr_at_100
value: 45.399
- type: mrr_at_1000
value: 45.454
- type: mrr_at_20
value: 45.011
- type: mrr_at_3
value: 42.226
- type: mrr_at_5
value: 43.653999999999996
- type: ndcg_at_1
value: 36.474000000000004
- type: ndcg_at_10
value: 45.509
- type: ndcg_at_100
value: 50.571
- type: ndcg_at_1000
value: 52.605999999999995
- type: ndcg_at_20
value: 47.275
- type: ndcg_at_3
value: 40.766000000000005
- type: ndcg_at_5
value: 42.979
- type: precision_at_1
value: 36.474000000000004
- type: precision_at_10
value: 7.509
- type: precision_at_100
value: 1.1320000000000001
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_20
value: 4.3
- type: precision_at_3
value: 18.315
- type: precision_at_5
value: 12.705
- type: recall_at_1
value: 31.049
- type: recall_at_10
value: 57.135999999999996
- type: recall_at_100
value: 79.196
- type: recall_at_1000
value: 93.002
- type: recall_at_20
value: 63.416
- type: recall_at_3
value: 43.893
- type: recall_at_5
value: 49.675999999999995
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 27.929
- type: map_at_10
value: 36.897000000000006
- type: map_at_100
value: 38.635000000000005
- type: map_at_1000
value: 38.842
- type: map_at_20
value: 37.814
- type: map_at_3
value: 33.522
- type: map_at_5
value: 35.128
- type: mrr_at_1
value: 33.399
- type: mrr_at_10
value: 41.817
- type: mrr_at_100
value: 42.797000000000004
- type: mrr_at_1000
value: 42.842999999999996
- type: mrr_at_20
value: 42.381
- type: mrr_at_3
value: 38.999
- type: mrr_at_5
value: 40.57
- type: ndcg_at_1
value: 33.399
- type: ndcg_at_10
value: 43.134
- type: ndcg_at_100
value: 49.009
- type: ndcg_at_1000
value: 51.199
- type: ndcg_at_20
value: 45.391999999999996
- type: ndcg_at_3
value: 37.645
- type: ndcg_at_5
value: 39.940999999999995
- type: precision_at_1
value: 33.399
- type: precision_at_10
value: 8.36
- type: precision_at_100
value: 1.646
- type: precision_at_1000
value: 0.244
- type: precision_at_20
value: 5.257
- type: precision_at_3
value: 17.457
- type: precision_at_5
value: 12.727
- type: recall_at_1
value: 27.929
- type: recall_at_10
value: 54.822
- type: recall_at_100
value: 80.63900000000001
- type: recall_at_1000
value: 94.382
- type: recall_at_20
value: 63.432
- type: recall_at_3
value: 39.291
- type: recall_at_5
value: 45.385999999999996
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 22.619
- type: map_at_10
value: 31.252000000000002
- type: map_at_100
value: 32.23
- type: map_at_1000
value: 32.336999999999996
- type: map_at_20
value: 31.758999999999997
- type: map_at_3
value: 28.771
- type: map_at_5
value: 30.157
- type: mrr_at_1
value: 24.584
- type: mrr_at_10
value: 33.088
- type: mrr_at_100
value: 33.971000000000004
- type: mrr_at_1000
value: 34.044000000000004
- type: mrr_at_20
value: 33.519
- type: mrr_at_3
value: 30.775999999999996
- type: mrr_at_5
value: 32.116
- type: ndcg_at_1
value: 24.584
- type: ndcg_at_10
value: 35.995
- type: ndcg_at_100
value: 41.018
- type: ndcg_at_1000
value: 43.543
- type: ndcg_at_20
value: 37.722
- type: ndcg_at_3
value: 31.197999999999997
- type: ndcg_at_5
value: 33.532000000000004
- type: precision_at_1
value: 24.584
- type: precision_at_10
value: 5.619
- type: precision_at_100
value: 0.878
- type: precision_at_1000
value: 0.121
- type: precision_at_20
value: 3.2259999999999995
- type: precision_at_3
value: 13.431999999999999
- type: precision_at_5
value: 9.39
- type: recall_at_1
value: 22.619
- type: recall_at_10
value: 48.746
- type: recall_at_100
value: 72.004
- type: recall_at_1000
value: 90.497
- type: recall_at_20
value: 55.326
- type: recall_at_3
value: 35.964
- type: recall_at_5
value: 41.547
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: None
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 16.493
- type: map_at_10
value: 28.988999999999997
- type: map_at_100
value: 30.964999999999996
- type: map_at_1000
value: 31.142999999999997
- type: map_at_20
value: 30.103
- type: map_at_3
value: 24.006
- type: map_at_5
value: 26.535999999999998
- type: mrr_at_1
value: 37.915
- type: mrr_at_10
value: 50.736000000000004
- type: mrr_at_100
value: 51.361999999999995
- type: mrr_at_1000
value: 51.388999999999996
- type: mrr_at_20
value: 51.148
- type: mrr_at_3
value: 47.589999999999996
- type: mrr_at_5
value: 49.55
- type: ndcg_at_1
value: 37.915
- type: ndcg_at_10
value: 39.139
- type: ndcg_at_100
value: 45.993
- type: ndcg_at_1000
value: 48.861
- type: ndcg_at_20
value: 41.923
- type: ndcg_at_3
value: 32.491
- type: ndcg_at_5
value: 34.775
- type: precision_at_1
value: 37.915
- type: precision_at_10
value: 12.293
- type: precision_at_100
value: 1.9709999999999999
- type: precision_at_1000
value: 0.251
- type: precision_at_20
value: 7.3389999999999995
- type: precision_at_3
value: 24.407999999999998
- type: precision_at_5
value: 18.775
- type: recall_at_1
value: 16.493
- type: recall_at_10
value: 45.904
- type: recall_at_100
value: 69.037
- type: recall_at_1000
value: 84.815
- type: recall_at_20
value: 53.657
- type: recall_at_3
value: 29.629
- type: recall_at_5
value: 36.325
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: None
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.180000000000001
- type: map_at_10
value: 20.714
- type: map_at_100
value: 28.801
- type: map_at_1000
value: 30.43
- type: map_at_20
value: 23.673
- type: map_at_3
value: 14.551
- type: map_at_5
value: 17.067
- type: mrr_at_1
value: 68.25
- type: mrr_at_10
value: 75.83
- type: mrr_at_100
value: 76.225
- type: mrr_at_1000
value: 76.232
- type: mrr_at_20
value: 76.14
- type: mrr_at_3
value: 74.375
- type: mrr_at_5
value: 75.225
- type: ndcg_at_1
value: 56.99999999999999
- type: ndcg_at_10
value: 43.071
- type: ndcg_at_100
value: 47.189
- type: ndcg_at_1000
value: 54.125
- type: ndcg_at_20
value: 42.111
- type: ndcg_at_3
value: 47.67
- type: ndcg_at_5
value: 44.983000000000004
- type: precision_at_1
value: 68.25
- type: precision_at_10
value: 34.599999999999994
- type: precision_at_100
value: 10.8
- type: precision_at_1000
value: 2.12
- type: precision_at_20
value: 25.7
- type: precision_at_3
value: 51.417
- type: precision_at_5
value: 43.85
- type: recall_at_1
value: 9.180000000000001
- type: recall_at_10
value: 26.212000000000003
- type: recall_at_100
value: 52.443
- type: recall_at_1000
value: 73.939
- type: recall_at_20
value: 33.101
- type: recall_at_3
value: 15.787999999999998
- type: recall_at_5
value: 19.691
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: None
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 49.625
- type: f1
value: 44.48944228050152
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: None
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 76.773
- type: map_at_10
value: 85.175
- type: map_at_100
value: 85.353
- type: map_at_1000
value: 85.36500000000001
- type: map_at_20
value: 85.271
- type: map_at_3
value: 84.261
- type: map_at_5
value: 84.899
- type: mrr_at_1
value: 82.853
- type: mrr_at_10
value: 90.02
- type: mrr_at_100
value: 90.048
- type: mrr_at_1000
value: 90.048
- type: mrr_at_20
value: 90.039
- type: mrr_at_3
value: 89.51599999999999
- type: mrr_at_5
value: 89.92099999999999
- type: ndcg_at_1
value: 82.853
- type: ndcg_at_10
value: 88.75999999999999
- type: ndcg_at_100
value: 89.347
- type: ndcg_at_1000
value: 89.547
- type: ndcg_at_20
value: 88.994
- type: ndcg_at_3
value: 87.481
- type: ndcg_at_5
value: 88.31700000000001
- type: precision_at_1
value: 82.853
- type: precision_at_10
value: 10.519
- type: precision_at_100
value: 1.1039999999999999
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_20
value: 5.341
- type: precision_at_3
value: 33.323
- type: precision_at_5
value: 20.596999999999998
- type: recall_at_1
value: 76.773
- type: recall_at_10
value: 94.95700000000001
- type: recall_at_100
value: 97.167
- type: recall_at_1000
value: 98.354
- type: recall_at_20
value: 95.71
- type: recall_at_3
value: 91.47999999999999
- type: recall_at_5
value: 93.658
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: None
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 21.629
- type: map_at_10
value: 36.394
- type: map_at_100
value: 38.308
- type: map_at_1000
value: 38.478
- type: map_at_20
value: 37.425999999999995
- type: map_at_3
value: 31.971
- type: map_at_5
value: 34.5
- type: mrr_at_1
value: 44.599
- type: mrr_at_10
value: 53.369
- type: mrr_at_100
value: 54.06999999999999
- type: mrr_at_1000
value: 54.114
- type: mrr_at_20
value: 53.754999999999995
- type: mrr_at_3
value: 51.415
- type: mrr_at_5
value: 52.479
- type: ndcg_at_1
value: 44.599
- type: ndcg_at_10
value: 44.425
- type: ndcg_at_100
value: 51.036
- type: ndcg_at_1000
value: 53.806
- type: ndcg_at_20
value: 46.934
- type: ndcg_at_3
value: 41.287
- type: ndcg_at_5
value: 42.143
- type: precision_at_1
value: 44.599
- type: precision_at_10
value: 12.222
- type: precision_at_100
value: 1.91
- type: precision_at_1000
value: 0.24
- type: precision_at_20
value: 7.176
- type: precision_at_3
value: 28.086
- type: precision_at_5
value: 20.369999999999997
- type: recall_at_1
value: 21.629
- type: recall_at_10
value: 51.168
- type: recall_at_100
value: 75.32600000000001
- type: recall_at_1000
value: 91.766
- type: recall_at_20
value: 58.923
- type: recall_at_3
value: 37.364999999999995
- type: recall_at_5
value: 43.322
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: None
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 42.336
- type: map_at_10
value: 59.602999999999994
- type: map_at_100
value: 60.367000000000004
- type: map_at_1000
value: 60.428000000000004
- type: map_at_20
value: 60.068
- type: map_at_3
value: 56.842000000000006
- type: map_at_5
value: 58.669000000000004
- type: mrr_at_1
value: 84.673
- type: mrr_at_10
value: 88.713
- type: mrr_at_100
value: 88.852
- type: mrr_at_1000
value: 88.857
- type: mrr_at_20
value: 88.806
- type: mrr_at_3
value: 88.202
- type: mrr_at_5
value: 88.522
- type: ndcg_at_1
value: 84.673
- type: ndcg_at_10
value: 68.67
- type: ndcg_at_100
value: 71.277
- type: ndcg_at_1000
value: 72.47
- type: ndcg_at_20
value: 69.797
- type: ndcg_at_3
value: 64.971
- type: ndcg_at_5
value: 67.16
- type: precision_at_1
value: 84.673
- type: precision_at_10
value: 13.66
- type: precision_at_100
value: 1.5699999999999998
- type: precision_at_1000
value: 0.173
- type: precision_at_20
value: 7.19
- type: precision_at_3
value: 40.135
- type: precision_at_5
value: 25.81
- type: recall_at_1
value: 42.336
- type: recall_at_10
value: 68.298
- type: recall_at_100
value: 78.494
- type: recall_at_1000
value: 86.435
- type: recall_at_20
value: 71.904
- type: recall_at_3
value: 60.202999999999996
- type: recall_at_5
value: 64.524
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: None
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 89.0388
- type: ap
value: 84.768407855227
- type: f1
value: 89.00848365810504
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: None
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 22.676
- type: map_at_10
value: 35.476
- type: map_at_100
value: 36.669000000000004
- type: map_at_1000
value: 36.714999999999996
- type: map_at_20
value: 36.253
- type: map_at_3
value: 31.430000000000003
- type: map_at_5
value: 33.891
- type: mrr_at_1
value: 23.281
- type: mrr_at_10
value: 35.994
- type: mrr_at_100
value: 37.128
- type: mrr_at_1000
value: 37.169000000000004
- type: mrr_at_20
value: 36.735
- type: mrr_at_3
value: 32.025
- type: mrr_at_5
value: 34.43
- type: ndcg_at_1
value: 23.281
- type: ndcg_at_10
value: 42.548
- type: ndcg_at_100
value: 48.138999999999996
- type: ndcg_at_1000
value: 49.26
- type: ndcg_at_20
value: 45.29
- type: ndcg_at_3
value: 34.414
- type: ndcg_at_5
value: 38.775999999999996
- type: precision_at_1
value: 23.281
- type: precision_at_10
value: 6.721000000000001
- type: precision_at_100
value: 0.9490000000000001
- type: precision_at_1000
value: 0.105
- type: precision_at_20
value: 3.93
- type: precision_at_3
value: 14.67
- type: precision_at_5
value: 11.003
- type: recall_at_1
value: 22.676
- type: recall_at_10
value: 64.33
- type: recall_at_100
value: 89.836
- type: recall_at_1000
value: 98.346
- type: recall_at_20
value: 74.958
- type: recall_at_3
value: 42.437000000000005
- type: recall_at_5
value: 52.89
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: None
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.26493388052896
- type: f1
value: 93.09322316606121
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: None
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 79.26356589147285
- type: f1
value: 62.91191113045691
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: None
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 75.4034969737727
- type: f1
value: 73.26712703676112
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: None
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.55749831876263
- type: f1
value: 78.59077417507389
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: None
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.39782367001404
- type: v_measures
value:
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
- 0.33908138638635865
- 0.3271187384761059
- 0.33377012095364167
- 0.36905559994096754
- 0.34390086433027045
- 0.360820016295285
- 0.3654168102809745
- 0.33993026003867693
- 0.32448893901437725
- 0.3361996312847464
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- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: None
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.630415762081864
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- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: None
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.989924085485676
- type: mrr
value: 31.985114880107695
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: None
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 5.771
- type: map_at_10
value: 13.008000000000001
- type: map_at_100
value: 16.125999999999998
- type: map_at_1000
value: 17.482
- type: map_at_20
value: 14.324
- type: map_at_3
value: 9.69
- type: map_at_5
value: 11.174000000000001
- type: mrr_at_1
value: 45.201
- type: mrr_at_10
value: 53.989
- type: mrr_at_100
value: 54.50899999999999
- type: mrr_at_1000
value: 54.551
- type: mrr_at_20
value: 54.247
- type: mrr_at_3
value: 52.373999999999995
- type: mrr_at_5
value: 53.225
- type: ndcg_at_1
value: 43.808
- type: ndcg_at_10
value: 34.757
- type: ndcg_at_100
value: 31.174000000000003
- type: ndcg_at_1000
value: 39.607
- type: ndcg_at_20
value: 32.151999999999994
- type: ndcg_at_3
value: 40.458
- type: ndcg_at_5
value: 38.06
- type: precision_at_1
value: 45.201
- type: precision_at_10
value: 25.728
- type: precision_at_100
value: 7.82
- type: precision_at_1000
value: 2.032
- type: precision_at_20
value: 18.793000000000003
- type: precision_at_3
value: 38.080000000000005
- type: precision_at_5
value: 32.879000000000005
- type: recall_at_1
value: 5.771
- type: recall_at_10
value: 16.567
- type: recall_at_100
value: 30.447999999999997
- type: recall_at_1000
value: 60.941
- type: recall_at_20
value: 20.092
- type: recall_at_3
value: 10.928
- type: recall_at_5
value: 13.235
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: None
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 40.716
- type: map_at_10
value: 56.599999999999994
- type: map_at_100
value: 57.389
- type: map_at_1000
value: 57.408
- type: map_at_20
value: 57.154
- type: map_at_3
value: 52.577
- type: map_at_5
value: 55.076
- type: mrr_at_1
value: 45.655
- type: mrr_at_10
value: 59.014
- type: mrr_at_100
value: 59.568
- type: mrr_at_1000
value: 59.580999999999996
- type: mrr_at_20
value: 59.41499999999999
- type: mrr_at_3
value: 55.88999999999999
- type: mrr_at_5
value: 57.879999999999995
- type: ndcg_at_1
value: 45.626
- type: ndcg_at_10
value: 63.778
- type: ndcg_at_100
value: 66.905
- type: ndcg_at_1000
value: 67.322
- type: ndcg_at_20
value: 65.521
- type: ndcg_at_3
value: 56.494
- type: ndcg_at_5
value: 60.553999999999995
- type: precision_at_1
value: 45.626
- type: precision_at_10
value: 9.942
- type: precision_at_100
value: 1.169
- type: precision_at_1000
value: 0.121
- type: precision_at_20
value: 5.390000000000001
- type: precision_at_3
value: 25.135
- type: precision_at_5
value: 17.451
- type: recall_at_1
value: 40.716
- type: recall_at_10
value: 82.998
- type: recall_at_100
value: 96.236
- type: recall_at_1000
value: 99.31400000000001
- type: recall_at_20
value: 89.402
- type: recall_at_3
value: 64.47699999999999
- type: recall_at_5
value: 73.774
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: None
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 71.679
- type: map_at_10
value: 85.63
- type: map_at_100
value: 86.24000000000001
- type: map_at_1000
value: 86.25500000000001
- type: map_at_20
value: 86.03
- type: map_at_3
value: 82.712
- type: map_at_5
value: 84.59400000000001
- type: mrr_at_1
value: 82.58
- type: mrr_at_10
value: 88.459
- type: mrr_at_100
value: 88.544
- type: mrr_at_1000
value: 88.545
- type: mrr_at_20
value: 88.521
- type: mrr_at_3
value: 87.548
- type: mrr_at_5
value: 88.19
- type: ndcg_at_1
value: 82.57
- type: ndcg_at_10
value: 89.205
- type: ndcg_at_100
value: 90.316
- type: ndcg_at_1000
value: 90.4
- type: ndcg_at_20
value: 89.802
- type: ndcg_at_3
value: 86.5
- type: ndcg_at_5
value: 88.06
- type: precision_at_1
value: 82.57
- type: precision_at_10
value: 13.511000000000001
- type: precision_at_100
value: 1.532
- type: precision_at_1000
value: 0.157
- type: precision_at_20
value: 7.1499999999999995
- type: precision_at_3
value: 37.82
- type: precision_at_5
value: 24.892
- type: recall_at_1
value: 71.679
- type: recall_at_10
value: 95.926
- type: recall_at_100
value: 99.653
- type: recall_at_1000
value: 99.99
- type: recall_at_20
value: 97.81
- type: recall_at_3
value: 88.124
- type: recall_at_5
value: 92.535
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: None
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 58.980204279295776
- type: v_measures
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- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: None
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 64.68385650734866
- type: v_measures
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- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: None
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 4.1930000000000005
- type: map_at_10
value: 10.993
- type: map_at_100
value: 12.821
- type: map_at_1000
value: 13.094
- type: map_at_20
value: 11.899999999999999
- type: map_at_3
value: 7.753
- type: map_at_5
value: 9.479
- type: mrr_at_1
value: 20.7
- type: mrr_at_10
value: 31.776
- type: mrr_at_100
value: 32.863
- type: mrr_at_1000
value: 32.921
- type: mrr_at_20
value: 32.374
- type: mrr_at_3
value: 28.499999999999996
- type: mrr_at_5
value: 30.464999999999996
- type: ndcg_at_1
value: 20.7
- type: ndcg_at_10
value: 18.602
- type: ndcg_at_100
value: 26.063
- type: ndcg_at_1000
value: 30.988
- type: ndcg_at_20
value: 21.124000000000002
- type: ndcg_at_3
value: 17.538999999999998
- type: ndcg_at_5
value: 15.604999999999999
- type: precision_at_1
value: 20.7
- type: precision_at_10
value: 9.69
- type: precision_at_100
value: 2.051
- type: precision_at_1000
value: 0.32299999999999995
- type: precision_at_20
value: 6.3
- type: precision_at_3
value: 16.567
- type: precision_at_5
value: 13.96
- type: recall_at_1
value: 4.1930000000000005
- type: recall_at_10
value: 19.618
- type: recall_at_100
value: 41.643
- type: recall_at_1000
value: 65.693
- type: recall_at_20
value: 25.562
- type: recall_at_3
value: 10.062999999999999
- type: recall_at_5
value: 14.127999999999998
- task:
type: STS
dataset:
name: MTEB SICK-R
type: None
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 83.46613174654865
- type: cos_sim_spearman
value: 80.3049357832415
- type: euclidean_pearson
value: 81.26631332583317
- type: euclidean_spearman
value: 80.3154745166346
- type: manhattan_pearson
value: 81.14703159845031
- type: manhattan_spearman
value: 80.20912001232311
- task:
type: STS
dataset:
name: MTEB STS12
type: None
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.54049067032975
- type: cos_sim_spearman
value: 80.96545866938635
- type: euclidean_pearson
value: 83.96265705630466
- type: euclidean_spearman
value: 79.93146623957664
- type: manhattan_pearson
value: 83.90680327172007
- type: manhattan_spearman
value: 79.9387741861374
- task:
type: STS
dataset:
name: MTEB STS13
type: None
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 86.88551701212096
- type: cos_sim_spearman
value: 87.86522961782607
- type: euclidean_pearson
value: 87.36290945594213
- type: euclidean_spearman
value: 87.83062393537139
- type: manhattan_pearson
value: 87.32544594269082
- type: manhattan_spearman
value: 87.81556963071229
- task:
type: STS
dataset:
name: MTEB STS14
type: None
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 85.30880458174929
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value: 83.80166079353091
- type: euclidean_pearson
value: 85.32128873266257
- type: euclidean_spearman
value: 83.86251092262333
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value: 85.2712567451151
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value: 83.80950203378747
- task:
type: STS
dataset:
name: MTEB STS15
type: None
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.26254668067915
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value: 88.58702965856746
- type: euclidean_pearson
value: 87.9969808017743
- type: euclidean_spearman
value: 88.48082129802832
- type: manhattan_pearson
value: 88.005385920726
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value: 88.48824252319064
- task:
type: STS
dataset:
name: MTEB STS16
type: None
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.9048844772477
- type: cos_sim_spearman
value: 86.81864160521327
- type: euclidean_pearson
value: 86.28264402848413
- type: euclidean_spearman
value: 86.78000025418731
- type: manhattan_pearson
value: 86.2441248990138
- type: manhattan_spearman
value: 86.75021285222047
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: None
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.489340312079
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value: 87.98810146323362
- type: euclidean_pearson
value: 89.17657344753519
- type: euclidean_spearman
value: 88.96877394433339
- type: manhattan_pearson
value: 89.17489837230771
- type: manhattan_spearman
value: 88.87394331518345
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: None
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 63.020191114515576
- type: cos_sim_spearman
value: 66.81821028889179
- type: euclidean_pearson
value: 66.11102477309004
- type: euclidean_spearman
value: 66.59000262767655
- type: manhattan_pearson
value: 66.0319349852117
- type: manhattan_spearman
value: 66.51366211903893
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: None
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.05763458617234
- type: cos_sim_spearman
value: 87.40353901525121
- type: euclidean_pearson
value: 87.43632331678887
- type: euclidean_spearman
value: 87.58631222421829
- type: manhattan_pearson
value: 87.40408795218912
- type: manhattan_spearman
value: 87.55530395433567
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: None
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 83.40728647106346
- type: mrr
value: 95.39606725881237
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: None
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 55.344
- type: map_at_10
value: 66.467
- type: map_at_100
value: 66.841
- type: map_at_1000
value: 66.86800000000001
- type: map_at_20
value: 66.728
- type: map_at_3
value: 62.888
- type: map_at_5
value: 65.10000000000001
- type: mrr_at_1
value: 58.333
- type: mrr_at_10
value: 67.471
- type: mrr_at_100
value: 67.75
- type: mrr_at_1000
value: 67.778
- type: mrr_at_20
value: 67.649
- type: mrr_at_3
value: 64.72200000000001
- type: mrr_at_5
value: 66.539
- type: ndcg_at_1
value: 58.333
- type: ndcg_at_10
value: 71.707
- type: ndcg_at_100
value: 73.301
- type: ndcg_at_1000
value: 74.053
- type: ndcg_at_20
value: 72.482
- type: ndcg_at_3
value: 65.561
- type: ndcg_at_5
value: 69.017
- type: precision_at_1
value: 58.333
- type: precision_at_10
value: 9.866999999999999
- type: precision_at_100
value: 1.0699999999999998
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.1
- type: precision_at_3
value: 25.778000000000002
- type: precision_at_5
value: 17.533
- type: recall_at_1
value: 55.344
- type: recall_at_10
value: 86.76700000000001
- type: recall_at_100
value: 94.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 89.60000000000001
- type: recall_at_3
value: 70.406
- type: recall_at_5
value: 79.106
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: None
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.71089108910891
- type: cos_sim_ap
value: 91.82444380538519
- type: cos_sim_f1
value: 85.34525583705911
- type: cos_sim_precision
value: 84.79763079960513
- type: cos_sim_recall
value: 85.9
- type: dot_accuracy
value: 99.56039603960396
- type: dot_ap
value: 84.71022538609428
- type: dot_f1
value: 76.18100447538538
- type: dot_precision
value: 75.76656775469831
- type: dot_recall
value: 76.6
- type: euclidean_accuracy
value: 99.7
- type: euclidean_ap
value: 91.68317023504792
- type: euclidean_f1
value: 84.65712876171682
- type: euclidean_precision
value: 83.54430379746836
- type: euclidean_recall
value: 85.8
- type: manhattan_accuracy
value: 99.69900990099009
- type: manhattan_ap
value: 91.5749511659937
- type: manhattan_f1
value: 84.6989141164857
- type: manhattan_precision
value: 83.62573099415205
- type: manhattan_recall
value: 85.8
- type: max_accuracy
value: 99.71089108910891
- type: max_ap
value: 91.82444380538519
- type: max_f1
value: 85.34525583705911
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: None
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 69.36504474977566
- type: v_measures
value:
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- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: None
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
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- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: None
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.975020393803675
- type: mrr
value: 53.87404772515067
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: None
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.205065693047615
- type: cos_sim_spearman
value: 28.307951294409406
- type: dot_pearson
value: 29.15581947828465
- type: dot_spearman
value: 28.222470759389505
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: None
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.249
- type: map_at_10
value: 2.243
- type: map_at_100
value: 13.791
- type: map_at_1000
value: 32.539
- type: map_at_20
value: 4.112
- type: map_at_3
value: 0.7060000000000001
- type: map_at_5
value: 1.1860000000000002
- 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_20
value: 98.0
- type: mrr_at_3
value: 98.0
- type: mrr_at_5
value: 98.0
- type: ndcg_at_1
value: 92.0
- type: ndcg_at_10
value: 86.083
- type: ndcg_at_100
value: 66.471
- type: ndcg_at_1000
value: 57.31699999999999
- type: ndcg_at_20
value: 82.783
- type: ndcg_at_3
value: 88.805
- type: ndcg_at_5
value: 88.96
- type: precision_at_1
value: 96.0
- type: precision_at_10
value: 91.2
- type: precision_at_100
value: 68.16
- type: precision_at_1000
value: 25.290000000000003
- type: precision_at_20
value: 86.9
- type: precision_at_3
value: 94.0
- type: precision_at_5
value: 94.39999999999999
- type: recall_at_1
value: 0.249
- type: recall_at_10
value: 2.3800000000000003
- type: recall_at_100
value: 16.45
- type: recall_at_1000
value: 53.1
- type: recall_at_20
value: 4.4670000000000005
- type: recall_at_3
value: 0.734
- type: recall_at_5
value: 1.246
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: None
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 3.2520000000000002
- type: map_at_10
value: 11.805
- type: map_at_100
value: 18.749
- type: map_at_1000
value: 20.416999999999998
- type: map_at_20
value: 14.685
- type: map_at_3
value: 6.6739999999999995
- type: map_at_5
value: 8.863
- type: mrr_at_1
value: 42.857
- type: mrr_at_10
value: 57.635999999999996
- type: mrr_at_100
value: 58.034
- type: mrr_at_1000
value: 58.048
- type: mrr_at_20
value: 57.979
- type: mrr_at_3
value: 54.422000000000004
- type: mrr_at_5
value: 56.15599999999999
- type: ndcg_at_1
value: 39.796
- type: ndcg_at_10
value: 30.263
- type: ndcg_at_100
value: 40.825
- type: ndcg_at_1000
value: 52.447
- type: ndcg_at_20
value: 30.453000000000003
- type: ndcg_at_3
value: 35.086
- type: ndcg_at_5
value: 31.947
- type: precision_at_1
value: 42.857
- type: precision_at_10
value: 26.327
- type: precision_at_100
value: 8.041
- type: precision_at_1000
value: 1.582
- type: precision_at_20
value: 19.592000000000002
- type: precision_at_3
value: 36.054
- type: precision_at_5
value: 31.019999999999996
- type: recall_at_1
value: 3.2520000000000002
- type: recall_at_10
value: 18.471
- type: recall_at_100
value: 49.08
- type: recall_at_1000
value: 84.733
- type: recall_at_20
value: 26.389000000000003
- type: recall_at_3
value: 8.051
- type: recall_at_5
value: 11.672
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: None
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 68.10546875
- type: ap
value: 12.899352291322325
- type: f1
value: 52.14484661172115
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: None
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 62.323146576117715
- type: f1
value: 62.6518883448989
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: None
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 51.261957327618525
- type: v_measures
value:
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- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: None
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.09542826488645
- type: cos_sim_ap
value: 77.72170475021885
- type: cos_sim_f1
value: 70.67669172932331
- type: cos_sim_precision
value: 64.5238614077141
- type: cos_sim_recall
value: 78.12664907651715
- type: dot_accuracy
value: 83.96614412588663
- type: dot_ap
value: 68.08590796036842
- type: dot_f1
value: 63.934426229508205
- type: dot_precision
value: 58.854860186418115
- type: dot_recall
value: 69.9736147757256
- type: euclidean_accuracy
value: 87.20271800679502
- type: euclidean_ap
value: 77.87533191263717
- type: euclidean_f1
value: 70.92216475337455
- type: euclidean_precision
value: 67.94778825235677
- type: euclidean_recall
value: 74.1688654353562
- type: manhattan_accuracy
value: 87.20867854801216
- type: manhattan_ap
value: 77.84249032925085
- type: manhattan_f1
value: 71.11665626949471
- type: manhattan_precision
value: 67.45562130177515
- type: manhattan_recall
value: 75.19788918205805
- type: max_accuracy
value: 87.20867854801216
- type: max_ap
value: 77.87533191263717
- type: max_f1
value: 71.11665626949471
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: None
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.22070865836147
- type: cos_sim_ap
value: 86.38617271379728
- type: cos_sim_f1
value: 78.946594085626
- type: cos_sim_precision
value: 75.5774647887324
- type: cos_sim_recall
value: 82.63012011087157
- type: dot_accuracy
value: 87.16963558039352
- type: dot_ap
value: 82.0965358395614
- type: dot_f1
value: 75.00997859138575
- type: dot_precision
value: 70.93541966920596
- type: dot_recall
value: 79.58115183246073
- type: euclidean_accuracy
value: 89.14891139830016
- type: euclidean_ap
value: 86.28000880804873
- type: euclidean_f1
value: 78.7341306347746
- type: euclidean_precision
value: 75.40706280397546
- type: euclidean_recall
value: 82.36834000615954
- type: manhattan_accuracy
value: 89.15279233127644
- type: manhattan_ap
value: 86.25024653784152
- type: manhattan_f1
value: 78.72760457406788
- type: manhattan_precision
value: 76.25369795800563
- type: manhattan_recall
value: 81.36741607637819
- type: max_accuracy
value: 89.22070865836147
- type: max_ap
value: 86.38617271379728
- type: max_f1
value: 78.946594085626
---
# ModernBERT-embed-large
ModernBERT-embed-large is an embedding model trained from [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large), bringing the new advances of ModernBERT to embeddings!
Indeed, ModernBERT is a base model trained for Masked Language Modeling and can not directly be used to perform tasks such as retrieval without further fine-tuning.
ModernBERT-embed-large is fine-tuned on the [Nomic Embed](https://arxiv.org/abs/2402.01613) weakly-supervised and supervised datasets and also supports Matryoshka Representation Learning dimensions of 256 to reduce memory with minimal performance loss.
## Performance
| Model | Dimensions | Average (56) | Classification (12) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) |
|-----------------------|------------|--------------|---------------------|-----------------|-------------------------|---------------|----------------|-----------|------------------|
| nomic-embed-text-v1.5 | 768 | 62.28 | 73.55 | 43.93 | 84.61 | 55.78 | 53.01 | 81.94 | 30.4 |
| modernbert-embed-base | 768 | 62.62 | 74.31 | 44.98 | 83.96 | 56.42 | 52.89 | 81.78 | **31.39** |
| modernbert-embed-large | 1024 | **63,84** | **75.03** | **46.04** | **85.31** | **57.64** | **54.36** | **83.80** | 28.31 |
| nomic-embed-text-v1.5 | 256 | 61.04 | 72.1 | 43.16 | 84.09 | 55.18 | 50.81 | 81.34 | 30.05 |
| modernbert-embed-base | 256 | 61.17 | 72.40 | 43.82 | 83.45 | 55.69 | 50.62 | 81.12 | 31.27 |
| modernbert-embed-large | 256 | 62.43 | 73.60 | 44.59 | 84.89 | 57.08 | 51.72 | 83.46 | 29.03 |
## Usage
You can use these models directly with the latest transformers release and requires installing `transformers>=4.48.0`:
```bash
pip install transformers>=4.48.0
```
Reminder, this model is trained similarly to Nomic Embed and **REQUIRES** prefixes to be added to the input. For more information, see the instructions in [Nomic Embed](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5#task-instruction-prefixes).
Most use cases, adding `search_query: ` to the query and `search_document: ` to the documents will be sufficient.
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("lightonai/modernbert-embed-large")
query_embeddings = model.encode([
"search_query: What is TSNE?",
"search_query: Who is Laurens van der Maaten?",
])
doc_embeddings = model.encode([
"search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
])
print(query_embeddings.shape, doc_embeddings.shape)
# (2, 1024) (1, 1024)
similarities = model.similarity(query_embeddings, doc_embeddings)
print(similarities)
# tensor([[0.6518],
# [0.4237]])
```
<details><summary>Click to see Sentence Transformers usage with Matryoshka Truncation</summary>
In Sentence Transformers, you can truncate embeddings to a smaller dimension by using the `truncate_dim` parameter when loading the `SentenceTransformer` model.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("lightonai/modernbert-embed-large", truncate_dim=256)
query_embeddings = model.encode([
"search_query: What is TSNE?",
"search_query: Who is Laurens van der Maaten?",
])
doc_embeddings = model.encode([
"search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
])
print(query_embeddings.shape, doc_embeddings.shape)
# (2, 256) (1, 256)
similarities = model.similarity(query_embeddings, doc_embeddings)
print(similarities)
# tensor([[0.6835],
# [0.3982]])
```
Note the small differences compared to the full 1024-dimensional similarities.
</details>
### Transformers
```python
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
)
queries = ["search_query: What is TSNE?", "search_query: Who is Laurens van der Maaten?"]
documents = ["search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten"]
tokenizer = AutoTokenizer.from_pretrained("lightonai/modernbert-embed-large")
model = AutoModel.from_pretrained("lightonai/modernbert-embed-large")
encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt")
encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
queries_outputs = model(**encoded_queries)
documents_outputs = model(**encoded_documents)
query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"])
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"])
doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
print(query_embeddings.shape, doc_embeddings.shape)
# torch.Size([2, 1024]) torch.Size([1, 1024])
similarities = query_embeddings @ doc_embeddings.T
print(similarities)
# tensor([[0.6518],
# [0.4237]])
```
<details><summary>Click to see Transformers usage with Matryoshka Truncation</summary>
In `transformers`, you can truncate embeddings to a smaller dimension by slicing the mean pooled embeddings, prior to normalization.
```python
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
)
queries = ["search_query: What is TSNE?", "search_query: Who is Laurens van der Maaten?"]
documents = ["search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten"]
tokenizer = AutoTokenizer.from_pretrained(".")
model = AutoModel.from_pretrained(".")
truncate_dim = 256
encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt")
encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
queries_outputs = model(**encoded_queries)
documents_outputs = model(**encoded_documents)
query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"])
query_embeddings = query_embeddings[:, :truncate_dim]
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"])
doc_embeddings = doc_embeddings[:, :truncate_dim]
doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
print(query_embeddings.shape, doc_embeddings.shape)
# torch.Size([2, 256]) torch.Size([1, 256])
similarities = query_embeddings @ doc_embeddings.T
print(similarities)
# tensor([[0.6835],
# [0.3982]])
```
Note the small differences compared to the full 1024-dimensional similarities.
</details>
### Transformers.js
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
Then, you can compute embeddings as follows:
```javascript
import { pipeline, matmul } from '@huggingface/transformers';
// Create a feature extraction pipeline
const extractor = await pipeline(
"feature-extraction",
"lightonai/modernbert-embed-large",
{ dtype: "fp32" }, // Supported options: "fp32", "fp16", "q8", "q4", "q4f16"
);
// Embed queries and documents
const query_embeddings = await extractor([
"search_query: What is TSNE?",
"search_query: Who is Laurens van der Maaten?",
], { pooling: "mean", normalize: true },
);
const doc_embeddings = await extractor([
"search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
], { pooling: "mean", normalize: true },
);
// Compute similarity scores
const similarities = await matmul(query_embeddings, doc_embeddings.transpose(1, 0));
console.log(similarities.tolist());
```
## Training
We train ModernBERT-embed-large using a multi-stage training pipeline. Starting from the pretrained [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) model,
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-text-v1).
Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors)
## Acknowledgment
We wanted to thank [Zach Nussbaum](https://huggingface.co/zpn) from [Nomic AI](https://huggingface.co/nomic-ai) for building and sharing the Nomic Embed recipe and tools and its support during the training of this model!
The training has been run on Orange Business Cloud Avenue infrastructure.
## Citation
If you find the model, dataset, or training code useful, please considering citing ModernBERT as well as Nomic Embed:
```bibtex
@misc{modernbert,
title={Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference},
author={Benjamin Warner and Antoine Chaffin and Benjamin Clavié and Orion Weller and Oskar Hallström and Said Taghadouini and Alexis Gallagher and Raja Biswas and Faisal Ladhak and Tom Aarsen and Nathan Cooper and Griffin Adams and Jeremy Howard and Iacopo Poli},
year={2024},
eprint={2412.13663},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2412.13663},
}
```
```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}
}
```
And if you want to cite this fine-tuning in particular, please use:
```bibtex
@misc{ModernBERT-embed-large,
title={ModernBERT-embed-large},
author={Chaffin, Antoine},
url={https://huggingface.co/lightonai/modernbert-embed-large},
year={2025}
}
```
|
[
"BIOSSES",
"SCIFACT"
] |
ibm-granite/granite-7b-base
|
ibm-granite
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-19T16:38:22Z |
2024-12-19T21:04:38+00:00
| 3,345 | 29 |
---
license: apache-2.0
new_version: ibm-granite/granite-3.1-8b-base
---
**Model Name**: Granite-7b-base
**License**: Apache-2.0
**Languages**: Primarily English
**Architecture**: The model architecture is a replica of Meta’s Llama2-7B base variant with MHA, trained with 1M batch size on 2T tokens.
**Context Length**: 4k tokens
**Tokenizer**: Llama2
**Model Developers**: IBM Research
Representing IBM’s commitment to open source innovation IBM has released granite-7b-base, a base pre-trained LLM from IBM’s Granite model series, under an apache-2.0 license for community and commercial use. Granite-7b-base was pre-trained from scratch on IBM-curated data as an open reference implementation of Meta’s Llama-2-7B. In a commitment to data transparency and fostering open innovation, the data sources, sampling proportions, and URLs for access are provided below.
For more information about training this model, please check out the blog: https://pytorch.org/blog/maximizing-training/
**Pre-Training Data**
The model was trained on 2T tokens, with sampling proportions designed to match the sampling distributions released in the Llama1 paper as closely as possible.
| Dataset | Description | Sampling Proportion | URL |
|-------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------|--------------------------------------------------------------------|
| Common Crawl | Open repository of web crawl data with snapshots ranging from 2021 to 2023. | 77% | https://data.commoncrawl.org/ |
| Github_Clean | Code data from CodeParrot covering a variety of coding languages. | 5.50% | https://huggingface.co/datasets/codeparrot/github-code-clean |
| Wikipedia and Wikimedia | Eight Wikimedia projects (enwiki, enwikibooks, enwikinews, enwikiquote, enwikisource, enwikiversity, enwikivoyage, enwiktionary). containing extracted plain text from pages and articles. | 2% | https://dumps.wikimedia.org |
| USPTO | US patents granted from 1975 to May 2023, excluding design patents. | 5% | https://bulkdata.uspto.gov/ |
| PubMed Central | Biomedical and life sciences papers. | 1.75% | https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_package/ |
| arXiv | Over 1.8 million scientific paper pre-prints posted to arXiv. | 2.50% | https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T |
| StackExchange | Anonymized set of all user-contributed content on the Stack Exchange network, a popular collection of websites centered around user-contributed questions and answers. | 1% | https://archive.org/details/stackexchange_20221206 |
| PG19 | A repository of free e-books with focus on older works for which U.S. copyright has expired. | 0.25% | https://github.com/google-deepmind/pg19 |
| Webhose | Unstructured web content converted into machine-readable data feeds purchased by IBM. | 5% | N/A |
**Evaluation Results**
LM-eval Harness Scores
| Evaluation metric | Llama2-7B (baseline) | Granite-7b-base |
|----------------------------|----------------------|-----------------|
| MMLU (zero shot) | 0.41 | 0.43 |
| MMLU (5-shot weighted avg) | 0.47 | 0.50 |
| Arc challenge | 0.46 | 0.44 |
| Arc easy | 0.74 | 0.71 |
| Boolq | 0.78 | 0.76 |
| Copa | 0.87 | 0.83 |
| Hellaswag | 0.76 | 0.74 |
| Openbookqa | 0.44 | 0.42 |
| Piqa | 0.79 | 0.79 |
| Sciq | 0.91 | 0.91 |
| Winogrande | 0.69 | 0.67 |
| Truthfulqa | 0.39 | 0.39 |
| GSM8k (8-shot) | 0.13 | 0.11 |
**Bias, Risks, and Limitations**
Granite-7b-base is a base model and has not undergone any safety alignment, there it may produce problematic outputs. In the absence of adequate safeguards and RLHF, there exists a risk of malicious utilization of these models for generating disinformation or harmful content. Caution is urged against complete reliance on a specific language model for crucial decisions or impactful information, as preventing these models from fabricating content is not straightforward. Additionally, it remains uncertain whether smaller models might exhibit increased susceptibility to hallucination in ungrounded generation scenarios due to their reduced sizes and memorization capacities. This aspect is currently an active area of research, and we anticipate more rigorous exploration, comprehension, and mitigations in this domain.
|
[
"SCIQ"
] |
bijaygurung/stella_en_400M_v5
|
bijaygurung
|
sentence-similarity
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"new",
"feature-extraction",
"mteb",
"transformers",
"sentence-similarity",
"custom_code",
"arxiv:2205.13147",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-09-30T06:12:02Z |
2024-09-30T17:03:05+00:00
| 3,336 | 4 |
---
license: mit
tags:
- mteb
- sentence-transformers
- transformers
- sentence-similarity
model-index:
- name: stella_en_400M_v5
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
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value: 92.35820895522387
- type: ap
value: 70.81322736988783
- type: ap_weighted
value: 70.81322736988783
- type: f1
value: 88.9505466159595
- type: f1_weighted
value: 92.68630932872613
- type: main_score
value: 92.35820895522387
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 97.1945
- type: ap
value: 96.08192192244094
- type: ap_weighted
value: 96.08192192244094
- type: f1
value: 97.1936887167346
- type: f1_weighted
value: 97.1936887167346
- type: main_score
value: 97.1945
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
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value: 59.528000000000006
- type: f1
value: 59.21016819840188
- type: f1_weighted
value: 59.21016819840188
- type: main_score
value: 59.528000000000006
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
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value: 64.24
- type: map_at_1
value: 40.398
- type: map_at_10
value: 56.215
- type: map_at_100
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- type: map_at_1000
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- type: map_at_20
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- type: map_at_3
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- type: map_at_5
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- type: mrr_at_100
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- type: mrr_at_1000
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- type: mrr_at_20
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- type: mrr_at_3
value: 52.47747747747754
- type: mrr_at_5
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- type: nauc_map_at_1000_diff1
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value: -20.36832563035559
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value: 17.63894384012074
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- type: ndcg_at_1
value: 40.398
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value: 64.24
- type: ndcg_at_100
value: 66.631
- type: ndcg_at_1000
value: 66.65100000000001
- type: ndcg_at_20
value: 66.086
- type: ndcg_at_3
value: 55.938
- type: ndcg_at_5
value: 60.370000000000005
- type: precision_at_1
value: 40.398
- type: precision_at_10
value: 8.962
- type: precision_at_100
value: 0.9950000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.836
- type: precision_at_3
value: 22.262
- type: precision_at_5
value: 15.519
- type: recall_at_1
value: 40.398
- type: recall_at_10
value: 89.616
- type: recall_at_100
value: 99.502
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 96.72800000000001
- type: recall_at_3
value: 66.78500000000001
- type: recall_at_5
value: 77.596
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: main_score
value: 55.1564333205451
- type: v_measure
value: 55.1564333205451
- type: v_measure_std
value: 14.696883012214512
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
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value: 49.823698316694795
- type: v_measure
value: 49.823698316694795
- type: v_measure_std
value: 14.951660654298186
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: main_score
value: 66.15294503553424
- type: map
value: 66.15294503553424
- type: mrr
value: 78.53438420612935
- type: nAUC_map_diff1
value: 12.569697092717997
- type: nAUC_map_max
value: 21.50670312412572
- type: nAUC_map_std
value: 16.943786429229064
- type: nAUC_mrr_diff1
value: 15.590272897361238
- type: nAUC_mrr_max
value: 34.96072022474653
- type: nAUC_mrr_std
value: 21.649217605241045
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
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value: 85.7824546319275
- type: cosine_spearman
value: 83.29587385660628
- type: euclidean_pearson
value: 84.58764190565167
- type: euclidean_spearman
value: 83.30069324352772
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value: 83.29587385660628
- type: manhattan_pearson
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- type: manhattan_spearman
value: 83.87480271054358
- type: pearson
value: 85.7824546319275
- type: spearman
value: 83.29587385660628
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 89.30194805194806
- type: f1
value: 89.26182507266391
- type: f1_weighted
value: 89.26182507266391
- type: main_score
value: 89.30194805194806
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
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value: 50.67972171889736
- type: v_measure
value: 50.67972171889736
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value: 0.7687409980036303
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
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value: 45.80539715556144
- type: v_measure
value: 45.80539715556144
- type: v_measure_std
value: 0.9601346216579142
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
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type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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type: mteb/mtop_intent
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split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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type: Classification
dataset:
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type: mteb/amazon_massive_intent
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split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
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type: mteb/amazon_massive_scenario
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value: 8.410779775558684
- type: nauc_precision_at_20_diff1
value: 6.964076536695672
- type: nauc_precision_at_20_max
value: 29.30087236410044
- type: nauc_precision_at_20_std
value: 26.413625895571986
- type: nauc_precision_at_3_diff1
value: 14.145134359925155
- type: nauc_precision_at_3_max
value: 29.915650960808303
- type: nauc_precision_at_3_std
value: 14.095370019867797
- type: nauc_precision_at_5_diff1
value: 11.043933558522692
- type: nauc_precision_at_5_max
value: 30.93016505807111
- type: nauc_precision_at_5_std
value: 17.749256196062603
- type: nauc_recall_at_1000_diff1
value: -0.7776817772090345
- type: nauc_recall_at_1000_max
value: 23.094717340324518
- type: nauc_recall_at_1000_std
value: 37.189908681396425
- type: nauc_recall_at_100_diff1
value: 6.887748742013364
- type: nauc_recall_at_100_max
value: 27.00798435230277
- type: nauc_recall_at_100_std
value: 35.908147807345344
- type: nauc_recall_at_10_diff1
value: 9.605632017480751
- type: nauc_recall_at_10_max
value: 31.845202901168655
- type: nauc_recall_at_10_std
value: 21.497414586634683
- type: nauc_recall_at_1_diff1
value: 19.353069488987916
- type: nauc_recall_at_1_max
value: 17.093914951159693
- type: nauc_recall_at_1_std
value: 8.19886078055046
- type: nauc_recall_at_20_diff1
value: 6.927503731844782
- type: nauc_recall_at_20_max
value: 28.611698183338202
- type: nauc_recall_at_20_std
value: 26.69018660149911
- type: nauc_recall_at_3_diff1
value: 14.043724087062268
- type: nauc_recall_at_3_max
value: 29.269835821380465
- type: nauc_recall_at_3_std
value: 14.104419605998094
- type: nauc_recall_at_5_diff1
value: 11.017319452873336
- type: nauc_recall_at_5_max
value: 30.295720628306228
- type: nauc_recall_at_5_std
value: 17.758048545573825
- type: ndcg_at_1
value: 28.999999999999996
- type: ndcg_at_10
value: 25.041999999999998
- type: ndcg_at_100
value: 35.045
- type: ndcg_at_1000
value: 40.803
- type: ndcg_at_20
value: 28.584
- type: ndcg_at_3
value: 23.249
- type: ndcg_at_5
value: 20.533
- type: precision_at_1
value: 28.999999999999996
- type: precision_at_10
value: 13.120000000000001
- type: precision_at_100
value: 2.7470000000000003
- type: precision_at_1000
value: 0.41200000000000003
- type: precision_at_20
value: 8.584999999999999
- type: precision_at_3
value: 21.633
- type: precision_at_5
value: 18.099999999999998
- type: recall_at_1
value: 5.893000000000001
- type: recall_at_10
value: 26.567
- type: recall_at_100
value: 55.800000000000004
- type: recall_at_1000
value: 83.608
- type: recall_at_20
value: 34.86
- type: recall_at_3
value: 13.153
- type: recall_at_5
value: 18.323
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cosine_pearson
value: 86.57284584320382
- type: cosine_spearman
value: 82.20531642680812
- type: euclidean_pearson
value: 83.94261758556554
- type: euclidean_spearman
value: 82.20721497738559
- type: main_score
value: 82.20531642680812
- type: manhattan_pearson
value: 84.15902154703083
- type: manhattan_spearman
value: 82.19506027155957
- type: pearson
value: 86.57284584320382
- type: spearman
value: 82.20531642680812
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cosine_pearson
value: 86.28047602146931
- type: cosine_spearman
value: 79.51504881448884
- type: euclidean_pearson
value: 83.10545189967856
- type: euclidean_spearman
value: 79.50586960492797
- type: main_score
value: 79.51504881448884
- type: manhattan_pearson
value: 83.44244457500889
- type: manhattan_spearman
value: 79.730303339846
- type: pearson
value: 86.28047602146931
- type: spearman
value: 79.51504881448884
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cosine_pearson
value: 88.74723553048702
- type: cosine_spearman
value: 89.18936052329725
- type: euclidean_pearson
value: 88.90400878928668
- type: euclidean_spearman
value: 89.19174821431281
- type: main_score
value: 89.18936052329725
- type: manhattan_pearson
value: 88.81504628424054
- type: manhattan_spearman
value: 89.18063294142597
- type: pearson
value: 88.74723553048702
- type: spearman
value: 89.18936052329725
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cosine_pearson
value: 86.45403437836023
- type: cosine_spearman
value: 85.14654611519086
- type: euclidean_pearson
value: 85.87509624462743
- type: euclidean_spearman
value: 85.1391108856681
- type: main_score
value: 85.14654611519086
- type: manhattan_pearson
value: 85.96635794953866
- type: manhattan_spearman
value: 85.3271371527667
- type: pearson
value: 86.45403437836023
- type: spearman
value: 85.14654611519086
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cosine_pearson
value: 87.84742260009705
- type: cosine_spearman
value: 89.10215217191254
- type: euclidean_pearson
value: 88.97393286325477
- type: euclidean_spearman
value: 89.1014105509662
- type: main_score
value: 89.10215217191254
- type: manhattan_pearson
value: 89.31698781090151
- type: manhattan_spearman
value: 89.53000001764433
- type: pearson
value: 87.84742260009705
- type: spearman
value: 89.10215217191254
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
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value: 85.22397535461835
- type: cosine_spearman
value: 87.14066355879785
- type: euclidean_pearson
value: 86.31393364087295
- type: euclidean_spearman
value: 87.14018892702765
- type: main_score
value: 87.14066355879785
- type: manhattan_pearson
value: 86.36366855248434
- type: manhattan_spearman
value: 87.20858630423012
- type: pearson
value: 85.22397535461835
- type: spearman
value: 87.14066355879785
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 90.66131612061355
- type: cosine_spearman
value: 90.97082650129164
- type: euclidean_pearson
value: 90.98181906744969
- type: euclidean_spearman
value: 90.99008476850047
- type: main_score
value: 90.97082650129164
- type: manhattan_pearson
value: 90.75245040709021
- type: manhattan_spearman
value: 90.6199877691265
- type: pearson
value: 90.66131612061355
- type: spearman
value: 90.97082650129164
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 67.270656447085
- type: cosine_spearman
value: 67.82870469746828
- type: euclidean_pearson
value: 69.03857775285664
- type: euclidean_spearman
value: 67.74455108773341
- type: main_score
value: 67.82870469746828
- type: manhattan_pearson
value: 69.25304172245812
- type: manhattan_spearman
value: 68.00987097916055
- type: pearson
value: 67.270656447085
- type: spearman
value: 67.82870469746828
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
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value: 87.17245205384889
- type: cosine_spearman
value: 87.7360146030987
- type: euclidean_pearson
value: 87.48919412794656
- type: euclidean_spearman
value: 87.7312047878383
- type: main_score
value: 87.7360146030987
- type: manhattan_pearson
value: 87.61476224354806
- type: manhattan_spearman
value: 87.95220889254693
- type: pearson
value: 87.17245205384889
- type: spearman
value: 87.7360146030987
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
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value: 88.43547871921146
- type: map
value: 88.43547871921146
- type: mrr
value: 96.5564473652709
- type: nAUC_map_diff1
value: -13.66029392579231
- type: nAUC_map_max
value: 50.325613574053506
- type: nAUC_map_std
value: 60.02986231275796
- type: nAUC_mrr_diff1
value: 23.83821476411125
- type: nAUC_mrr_max
value: 86.72643311769906
- type: nAUC_mrr_std
value: 72.12741063469213
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
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value: 78.233
- type: map_at_1
value: 61.49400000000001
- type: map_at_10
value: 73.30600000000001
- type: map_at_100
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- type: map_at_1000
value: 73.724
- type: map_at_20
value: 73.611
- type: map_at_3
value: 70.626
- type: map_at_5
value: 72.417
- type: mrr_at_1
value: 64.66666666666666
- type: mrr_at_10
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- type: mrr_at_100
value: 74.56950898079988
- type: mrr_at_1000
value: 74.57295833098681
- type: mrr_at_20
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- type: mrr_at_3
value: 72.3888888888889
- type: mrr_at_5
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- type: nauc_map_at_1000_diff1
value: 76.51524604780636
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- type: nauc_mrr_at_1000_diff1
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value: -4.875200894216617
- type: nauc_mrr_at_10_diff1
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- type: nauc_mrr_at_10_max
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- type: nauc_mrr_at_1_diff1
value: 80.0454932568644
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value: 56.76038421319305
- type: nauc_mrr_at_1_std
value: -4.101939392632653
- type: nauc_mrr_at_20_diff1
value: 76.87237970440503
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value: 57.33843605225869
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- type: nauc_mrr_at_3_diff1
value: 76.74130186666727
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- type: nauc_ndcg_at_1000_diff1
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value: -6.0761689532372625
- type: nauc_ndcg_at_100_diff1
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value: 55.72246565373382
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- type: nauc_ndcg_at_10_max
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- type: nauc_ndcg_at_3_diff1
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- type: nauc_ndcg_at_3_max
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- type: nauc_ndcg_at_3_std
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- type: nauc_precision_at_20_max
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- type: nauc_precision_at_20_std
value: 48.00467321991766
- type: nauc_precision_at_3_diff1
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- type: nauc_precision_at_3_max
value: 49.309242331670504
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value: 21.02940391379915
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value: 13.560415600596318
- type: nauc_precision_at_5_max
value: 48.918726500100085
- type: nauc_precision_at_5_std
value: 39.940930429172184
- type: nauc_recall_at_1000_diff1
value: .nan
- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_100_diff1
value: 70.82166199813196
- type: nauc_recall_at_100_max
value: 76.6106442577042
- type: nauc_recall_at_100_std
value: 66.47992530345513
- type: nauc_recall_at_10_diff1
value: 62.68908885556092
- type: nauc_recall_at_10_max
value: 58.14262437741839
- type: nauc_recall_at_10_std
value: -12.946717875063369
- type: nauc_recall_at_1_diff1
value: 79.54189281784247
- type: nauc_recall_at_1_max
value: 46.630071622109526
- type: nauc_recall_at_1_std
value: -14.395943134644112
- type: nauc_recall_at_20_diff1
value: 65.79470497876567
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value: 71.68308183488456
- type: nauc_recall_at_20_std
value: -12.556850697268453
- type: nauc_recall_at_3_diff1
value: 68.3240211318129
- type: nauc_recall_at_3_max
value: 45.05998217275036
- type: nauc_recall_at_3_std
value: -14.23179772593869
- type: nauc_recall_at_5_diff1
value: 67.53366869904056
- type: nauc_recall_at_5_max
value: 53.57935627081027
- type: nauc_recall_at_5_std
value: -3.3271112904853393
- type: ndcg_at_1
value: 64.667
- type: ndcg_at_10
value: 78.233
- type: ndcg_at_100
value: 79.806
- type: ndcg_at_1000
value: 79.92099999999999
- type: ndcg_at_20
value: 79.006
- type: ndcg_at_3
value: 74.018
- type: ndcg_at_5
value: 76.334
- type: precision_at_1
value: 64.667
- type: precision_at_10
value: 10.4
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.383
- type: precision_at_3
value: 29.444
- type: precision_at_5
value: 19.467000000000002
- type: recall_at_1
value: 61.49400000000001
- type: recall_at_10
value: 92.156
- type: recall_at_100
value: 99.167
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 94.833
- type: recall_at_3
value: 80.833
- type: recall_at_5
value: 86.6
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
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value: 99.8039603960396
- type: cosine_accuracy_threshold
value: 84.54211950302124
- type: cosine_ap
value: 95.59056372734358
- type: cosine_f1
value: 90.1394422310757
- type: cosine_f1_threshold
value: 84.54211950302124
- type: cosine_precision
value: 89.78174603174604
- type: cosine_recall
value: 90.5
- type: dot_accuracy
value: 99.80594059405941
- type: dot_accuracy_threshold
value: 85.57180166244507
- type: dot_ap
value: 95.53453431914399
- type: dot_f1
value: 90.10442565887618
- type: dot_f1_threshold
value: 84.59715843200684
- type: dot_precision
value: 89.61424332344214
- type: dot_recall
value: 90.60000000000001
- type: euclidean_accuracy
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value: -7.04722927447817
- type: nauc_recall_at_1_std
value: -7.062494733973898
- type: nauc_recall_at_20_diff1
value: 6.974410239251615
- type: nauc_recall_at_20_max
value: -14.161147924731646
- type: nauc_recall_at_20_std
value: 9.328412057721454
- type: nauc_recall_at_3_diff1
value: 7.904589805754212
- type: nauc_recall_at_3_max
value: -12.1912388648593
- type: nauc_recall_at_3_std
value: -9.221542013385555
- type: nauc_recall_at_5_diff1
value: -3.2604132752706914
- type: nauc_recall_at_5_max
value: -6.886351441658915
- type: nauc_recall_at_5_std
value: -7.014252851712789
- type: ndcg_at_1
value: 39.796
- type: ndcg_at_10
value: 31.455
- type: ndcg_at_100
value: 42.388999999999996
- type: ndcg_at_1000
value: 53.556000000000004
- type: ndcg_at_20
value: 30.808000000000003
- type: ndcg_at_3
value: 35.831
- type: ndcg_at_5
value: 32.845
- type: precision_at_1
value: 40.816
- type: precision_at_10
value: 27.143
- type: precision_at_100
value: 8.449
- type: precision_at_1000
value: 1.6179999999999999
- type: precision_at_20
value: 19.387999999999998
- type: precision_at_3
value: 35.374
- type: precision_at_5
value: 31.019999999999996
- type: recall_at_1
value: 2.9739999999999998
- type: recall_at_10
value: 19.39
- type: recall_at_100
value: 51.636
- type: recall_at_1000
value: 86.99900000000001
- type: recall_at_20
value: 26.478
- type: recall_at_3
value: 7.703
- type: recall_at_5
value: 11.42
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 86.9384765625
- type: ap
value: 31.737513704141552
- type: ap_weighted
value: 31.737513704141552
- type: f1
value: 71.5490757306975
- type: f1_weighted
value: 89.14632533489856
- type: main_score
value: 86.9384765625
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 73.57668364459535
- type: f1
value: 73.90467103648074
- type: f1_weighted
value: 73.42158415034704
- type: main_score
value: 73.57668364459535
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 58.574148097494685
- type: v_measure
value: 58.574148097494685
- type: v_measure_std
value: 0.9443161637490822
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cosine_accuracy
value: 88.1385229778864
- type: cosine_accuracy_threshold
value: 83.86307954788208
- type: cosine_ap
value: 80.17965893449055
- type: cosine_f1
value: 73.0614300100705
- type: cosine_f1_threshold
value: 80.7942807674408
- type: cosine_precision
value: 69.8603755416466
- type: cosine_recall
value: 76.56992084432717
- type: dot_accuracy
value: 88.2100494724921
- type: dot_accuracy_threshold
value: 83.84793996810913
- type: dot_ap
value: 80.18603932881858
- type: dot_f1
value: 73.07643714466204
- type: dot_f1_threshold
value: 80.87586164474487
- type: dot_precision
value: 70.10909090909091
- type: dot_recall
value: 76.3060686015831
- type: euclidean_accuracy
value: 88.1385229778864
- type: euclidean_accuracy_threshold
value: 56.77661895751953
- type: euclidean_ap
value: 80.1784070881624
- type: euclidean_f1
value: 73.04830369529574
- type: euclidean_f1_threshold
value: 61.91838979721069
- type: euclidean_precision
value: 69.96859144720948
- type: euclidean_recall
value: 76.41160949868075
- type: main_score
value: 80.18603932881858
- type: manhattan_accuracy
value: 88.0431543184121
- type: manhattan_accuracy_threshold
value: 3755.6137084960938
- type: manhattan_ap
value: 79.98270453664578
- type: manhattan_f1
value: 72.68242015061023
- type: manhattan_f1_threshold
value: 3892.494583129883
- type: manhattan_precision
value: 71.54907975460122
- type: manhattan_recall
value: 73.85224274406332
- type: max_ap
value: 80.18603932881858
- type: max_f1
value: 73.07643714466204
- type: max_precision
value: 71.54907975460122
- type: max_recall
value: 76.56992084432717
- type: similarity_accuracy
value: 88.1385229778864
- type: similarity_accuracy_threshold
value: 83.86307954788208
- type: similarity_ap
value: 80.17965893449055
- type: similarity_f1
value: 73.0614300100705
- type: similarity_f1_threshold
value: 80.7942807674408
- type: similarity_precision
value: 69.8603755416466
- type: similarity_recall
value: 76.56992084432717
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cosine_accuracy
value: 89.7892653393876
- type: cosine_accuracy_threshold
value: 79.69566583633423
- type: cosine_ap
value: 87.4579867302024
- type: cosine_f1
value: 79.91620843152658
- type: cosine_f1_threshold
value: 78.53609323501587
- type: cosine_precision
value: 77.7155329210622
- type: cosine_recall
value: 82.24514936864799
- type: dot_accuracy
value: 89.78732487289945
- type: dot_accuracy_threshold
value: 80.05315661430359
- type: dot_ap
value: 87.44916182456272
- type: dot_f1
value: 79.90419878751591
- type: dot_f1_threshold
value: 78.57890725135803
- type: dot_precision
value: 77.73409057812728
- type: dot_recall
value: 82.19895287958116
- type: euclidean_accuracy
value: 89.78538440641131
- type: euclidean_accuracy_threshold
value: 62.29925751686096
- type: euclidean_ap
value: 87.45904868911386
- type: euclidean_f1
value: 79.93127404474657
- type: euclidean_f1_threshold
value: 65.61101078987122
- type: euclidean_precision
value: 77.62060210373595
- type: euclidean_recall
value: 82.38373883584848
- type: main_score
value: 87.46554314325058
- type: manhattan_accuracy
value: 89.76597974152986
- type: manhattan_accuracy_threshold
value: 3988.5299682617188
- type: manhattan_ap
value: 87.46554314325058
- type: manhattan_f1
value: 79.97181740645973
- type: manhattan_f1_threshold
value: 4235.905838012695
- type: manhattan_precision
value: 77.13713427283783
- type: manhattan_recall
value: 83.02279026793964
- type: max_ap
value: 87.46554314325058
- type: max_f1
value: 79.97181740645973
- type: max_precision
value: 77.73409057812728
- type: max_recall
value: 83.02279026793964
- type: similarity_accuracy
value: 89.7892653393876
- type: similarity_accuracy_threshold
value: 79.69566583633423
- type: similarity_ap
value: 87.4579867302024
- type: similarity_f1
value: 79.91620843152658
- type: similarity_f1_threshold
value: 78.53609323501587
- type: similarity_precision
value: 77.7155329210622
- type: similarity_recall
value: 82.24514936864799
---
# 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` !
# on gpu
model = SentenceTransformer("dunzhang/stella_en_400M_v5", trust_remote_code=True).cuda()
# you can also use this model without the features of `use_memory_efficient_attention` and `unpad_inputs`. It can be worked in CPU.
# model = SentenceTransformer(
# "dunzhang/stella_en_400M_v5",
# trust_remote_code=True,
# device="cpu",
# config_kwargs={"use_memory_efficient_attention": False, "unpad_inputs": False}
# )
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([[0.8398, 0.2990],
# [0.3282, 0.8095]])
```
## 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()
# you can also use this model without the features of `use_memory_efficient_attention` and `unpad_inputs`. It can be worked in CPU.
# model = AutoModel.from_pretrained(model_dir, trust_remote_code=True,use_memory_efficient_attention=False,unpad_inputs=False).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)
# [[0.8397531 0.29900077]
# [0.32818374 0.80954516]]
```
# 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.
|
[
"BIOSSES",
"SCIFACT"
] |
minishlab/potion-retrieval-32M
|
minishlab
| null |
[
"model2vec",
"onnx",
"safetensors",
"embeddings",
"static-embeddings",
"sentence-transformers",
"license:mit",
"region:us"
] | 2025-01-23T15:05:16Z |
2025-01-29T11:00:09+00:00
| 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()
```
|
[
"PUBMEDQA"
] |
jinaai/jina-colbert-v2-64
|
jinaai
| null |
[
"safetensors",
"ColBERT",
"passage-retrieval",
"custom_code",
"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:cc-by-nc-4.0",
"region:eu"
] | 2024-08-22T16:50:03Z |
2025-01-06T16:23:32+00:00
| 3,251 | 6 |
---
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: cc-by-nc-4.0
tags:
- ColBERT
- passage-retrieval
---
<br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
<p align="center">
<b>JinaColBERT V2: your multilingual late interaction retriever!</b>
</p>
JinaColBERT V2 (`jina-colbert-v2`) is a new model based on the [JinaColBERT V1](https://jina.ai/news/what-is-colbert-and-late-interaction-and-why-they-matter-in-search/) that expands on the capabilities and performance of the [`jina-colbert-v1-en`](https://huggingface.co/jinaai/jina-colbert-v1-en) model. Like the previous release, it has Jina AI’s 8192 token input context and the [improved efficiency, performance](https://jina.ai/news/what-is-colbert-and-late-interaction-and-why-they-matter-in-search/), and [explainability](https://jina.ai/news/ai-explainability-made-easy-how-late-interaction-makes-jina-colbert-transparent/) of token-level embeddings and late interaction.
This new release adds new functionality and performance improvements:
- Multilingual support for dozens of languages, with strong performance on major global languages.
- [Matryoshka embeddings](https://huggingface.co/blog/matryoshka), which allow users to trade between efficiency and precision flexibly.
- Superior retrieval performance when compared to the English-only [`jina-colbert-v1-en`](https://huggingface.co/jinaai/jina-colbert-v1-en).
JinaColBERT V2 offers three different versions for different embeddings dimensions:
[`jinaai/jina-colbert-v2`](https://huggingface.co/jinaai/jina-colbert-v2): 128 dimension embeddings
[`jinaai/jina-colbert-v2-96`](https://huggingface.co/jinaai/jina-colbert-v2-96): 96 dimension embeddings
[`jinaai/jina-colbert-v2-64`](https://huggingface.co/jinaai/jina-colbert-v2-64): 64 dimension embeddings
## Usage
### Installation
`jina-colbert-v2` is trained with flash attention and therefore requires `einops` and `flash_attn` to be installed.
To use the model, you could either use the Standford ColBERT library or use the `ragatouille` package that we provide.
```bash
pip install -U einops flash_attn
pip install -U ragatouille
pip install -U colbert-ai
```
### RAGatouille
```python
from ragatouille import RAGPretrainedModel
RAG = RAGPretrainedModel.from_pretrained("jinaai/jina-colbert-v2")
docs = [
"ColBERT is a novel ranking model that adapts deep LMs for efficient retrieval.",
"Jina-ColBERT is a ColBERT-style model but based on JinaBERT so it can support both 8k context length, fast and accurate retrieval.",
]
RAG.index(docs, index_name="demo")
query = "What does ColBERT do?"
results = RAG.search(query)
```
### Stanford ColBERT
```python
from colbert.infra import ColBERTConfig
from colbert.modeling.checkpoint import Checkpoint
ckpt = Checkpoint("jinaai/jina-colbert-v2", colbert_config=ColBERTConfig())
docs = [
"ColBERT is a novel ranking model that adapts deep LMs for efficient retrieval.",
"Jina-ColBERT is a ColBERT-style model but based on JinaBERT so it can support both 8k context length, fast and accurate retrieval.",
]
query_vectors = ckpt.queryFromText(docs, bsize=2)
```
## Evaluation Results
### Retrieval Benchmarks
#### BEIR
| **NDCG@10** | **jina-colbert-v2** | **jina-colbert-v1** | **ColBERTv2.0** | **BM25** |
|--------------------|---------------------|---------------------|-----------------|----------|
| **avg** | 0.531 | 0.502 | 0.496 | 0.440 |
| **nfcorpus** | 0.346 | 0.338 | 0.337 | 0.325 |
| **fiqa** | 0.408 | 0.368 | 0.354 | 0.236 |
| **trec-covid** | 0.834 | 0.750 | 0.726 | 0.656 |
| **arguana** | 0.366 | 0.494 | 0.465 | 0.315 |
| **quora** | 0.887 | 0.823 | 0.855 | 0.789 |
| **scidocs** | 0.186 | 0.169 | 0.154 | 0.158 |
| **scifact** | 0.678 | 0.701 | 0.689 | 0.665 |
| **webis-touche** | 0.274 | 0.270 | 0.260 | 0.367 |
| **dbpedia-entity** | 0.471 | 0.413 | 0.452 | 0.313 |
| **fever** | 0.805 | 0.795 | 0.785 | 0.753 |
| **climate-fever** | 0.239 | 0.196 | 0.176 | 0.213 |
| **hotpotqa** | 0.766 | 0.656 | 0.675 | 0.603 |
| **nq** | 0.640 | 0.549 | 0.524 | 0.329 |
#### MS MARCO Passage Retrieval
| **MRR@10** | **jina-colbert-v2** | **jina-colbert-v1** | **ColBERTv2.0** | **BM25** |
|-------------|---------------------|---------------------|-----------------|----------|
| **MSMARCO** | 0.396 | 0.390 | 0.397 | 0.187 |
### Multilingual Benchmarks
#### MIRACLE
| **NDCG@10** | **jina-colbert-v2** | **mDPR (zero shot)** |
|---------|---------------------|----------------------|
| **avg** | 0.627 | 0.427 |
| **ar** | 0.753 | 0.499 |
| **bn** | 0.750 | 0.443 |
| **de** | 0.504 | 0.490 |
| **es** | 0.538 | 0.478 |
| **en** | 0.570 | 0.394 |
| **fa** | 0.563 | 0.480 |
| **fi** | 0.740 | 0.472 |
| **fr** | 0.541 | 0.435 |
| **hi** | 0.600 | 0.383 |
| **id** | 0.547 | 0.272 |
| **ja** | 0.632 | 0.439 |
| **ko** | 0.671 | 0.419 |
| **ru** | 0.643 | 0.407 |
| **sw** | 0.499 | 0.299 |
| **te** | 0.742 | 0.356 |
| **th** | 0.772 | 0.358 |
| **yo** | 0.623 | 0.396 |
| **zh** | 0.523 | 0.512 |
#### mMARCO
| **MRR@10** | **jina-colbert-v2** | **BM-25** | **ColBERT-XM** |
|------------|---------------------|-----------|----------------|
| **avg** | 0.313 | 0.141 | 0.254 |
| **ar** | 0.272 | 0.111 | 0.195 |
| **de** | 0.331 | 0.136 | 0.270 |
| **nl** | 0.330 | 0.140 | 0.275 |
| **es** | 0.341 | 0.158 | 0.285 |
| **fr** | 0.335 | 0.155 | 0.269 |
| **hi** | 0.309 | 0.134 | 0.238 |
| **id** | 0.319 | 0.149 | 0.263 |
| **it** | 0.337 | 0.153 | 0.265 |
| **ja** | 0.276 | 0.141 | 0.241 |
| **pt** | 0.337 | 0.152 | 0.276 |
| **ru** | 0.298 | 0.124 | 0.251 |
| **vi** | 0.287 | 0.136 | 0.226 |
| **zh** | 0.302 | 0.116 | 0.246 |
### Matryoshka Representation Benchmarks
#### BEIR
| **NDCG@10** | **dim=128** | **dim=96** | **dim=64** |
|----------------|-------------|------------|------------|
| **avg** | 0.599 | 0.591 | 0.589 |
| **nfcorpus** | 0.346 | 0.340 | 0.347 |
| **fiqa** | 0.408 | 0.404 | 0.404 |
| **trec-covid** | 0.834 | 0.808 | 0.805 |
| **hotpotqa** | 0.766 | 0.764 | 0.756 |
| **nq** | 0.640 | 0.640 | 0.635 |
#### MSMARCO
| **MRR@10** | **dim=128** | **dim=96** | **dim=64** |
|----------------|-------------|------------|------------|
| **msmarco** | 0.396 | 0.391 | 0.388 |
## Other Models
Additionally, we provide the following embedding models, you can also use them for retrieval.
- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters.
- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): 161 million parameters Chinese-English bilingual model.
- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): 161 million parameters German-English bilingual model.
- [`jina-embeddings-v2-base-es`](https://huggingface.co/jinaai/jina-embeddings-v2-base-es): 161 million parameters Spanish-English bilingual model.
- [`jina-reranker-v2`](https://huggingface.co/jinaai/jina-reranker-v2-base-multilingual): multilingual reranker model.
- [`jina-clip-v1`](https://huggingface.co/jinaai/jina-clip-v1): English multimodal (text-image) embedding model.
## Contact
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
|
[
"SCIFACT"
] |
BSC-LT/salamandra-2b-instruct
|
BSC-LT
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"bg",
"ca",
"code",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"eu",
"fi",
"fr",
"ga",
"gl",
"hr",
"hu",
"it",
"lt",
"lv",
"mt",
"nl",
"nn",
"oc",
"pl",
"pt",
"ro",
"ru",
"sh",
"sk",
"sl",
"sr",
"sv",
"uk",
"dataset:oscar-corpus/colossal-oscar-1.0",
"dataset:HuggingFaceFW/fineweb-edu",
"dataset:joelniklaus/eurlex_resources",
"dataset:joelito/legal-mc4",
"dataset:projecte-aina/CATalog",
"dataset:UFRGS/brwac",
"dataset:community-datasets/hrwac",
"dataset:danish-foundation-models/danish-gigaword",
"dataset:HiTZ/euscrawl",
"dataset:PleIAs/French-PD-Newspapers",
"dataset:PleIAs/French-PD-Books",
"dataset:AI-team-UoA/greek_legal_code",
"dataset:HiTZ/latxa-corpus-v1.1",
"dataset:allenai/peS2o",
"dataset:pile-of-law/pile-of-law",
"dataset:PORTULAN/parlamento-pt",
"dataset:hoskinson-center/proof-pile",
"dataset:togethercomputer/RedPajama-Data-1T",
"dataset:bigcode/starcoderdata",
"dataset:bjoernp/tagesschau-2018-2023",
"dataset:EleutherAI/the_pile_deduplicated",
"arxiv:2502.08489",
"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",
"base_model:BSC-LT/salamandra-2b",
"base_model:finetune:BSC-LT/salamandra-2b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-09-30T13:44:40Z |
2025-02-20T16:45:39+00:00
| 3,244 | 20 |
---
base_model:
- BSC-LT/salamandra-2b
datasets:
- oscar-corpus/colossal-oscar-1.0
- HuggingFaceFW/fineweb-edu
- joelniklaus/eurlex_resources
- joelito/legal-mc4
- projecte-aina/CATalog
- UFRGS/brwac
- community-datasets/hrwac
- danish-foundation-models/danish-gigaword
- HiTZ/euscrawl
- PleIAs/French-PD-Newspapers
- PleIAs/French-PD-Books
- AI-team-UoA/greek_legal_code
- HiTZ/latxa-corpus-v1.1
- allenai/peS2o
- pile-of-law/pile-of-law
- PORTULAN/parlamento-pt
- hoskinson-center/proof-pile
- togethercomputer/RedPajama-Data-1T
- bigcode/starcoderdata
- bjoernp/tagesschau-2018-2023
- EleutherAI/the_pile_deduplicated
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
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
---
# Salamandra Model Card
This repository contains the model described in [Salamandra Technical Report](https://huggingface.co/papers/2502.08489).
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 2B 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).
> [!WARNING]
> **DISCLAIMER:** This model is a first proof-of-concept designed to demonstrate the instruction-following capabilities of recently released base models.
> It has been optimized to engage in conversation but has *NOT* been aligned through RLHF to filter or avoid sensitive topics.
> As a result, it may generate harmful or inappropriate content.
> The team is actively working to enhance its performance through further instruction and alignment with RL techniques.
---
## Model Details
### Description
Transformer-based decoder-only language model that has been pre-trained from scratch on 12.875 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/blob/main/configs/bsc_2b.yaml).
### Architecture
| | |
|-------------------------|:--------------|
| Total Parameters | 2,253,490,176 |
| Embedding Parameters | 524,288,000 |
| Layers | 24 |
| Hidden size | 2,048 |
| Attention heads | 16 |
| 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 | N/A |
---
## 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 64GB 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
The instruction-following models use the commonly adopted ChatML template:
```jinja
{%- if messages[0]['role'] == 'system' %}{%- set system_message = messages[0]['content'] %}{%- set loop_messages = messages[1:] %}{%- else %}{%- set system_message = 'SYSTEM MESSAGE' %}{%- set loop_messages = messages %}{%- endif %}{%- if not date_string is defined %}{%- set date_string = '2024-09-30' %}{%- endif %}{{ '<|im_start|>system\n' + system_message + '<|im_end|>\n' }}{% for message in loop_messages %}{%- if (message['role'] != 'user') and (message['role'] != 'assistant')%}{{ raise_exception('Only user and assitant roles are suported after the initial optional system message.') }}{% endif %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}
```
Where `system_message` is used to guide the model during generation and `date_string` can be set to allow the model to respond with the current date.
The exact same chat template should be used for an enhanced conversational experience.
The easiest way to apply it is by using the tokenizer's built-in functions, as shown in the following snippet.
```python
from datetime import datetime
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = "BSC-LT/salamandra-2b-instruct"
text = "At what temperature does water boil?"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16
)
message = [ { "role": "user", "content": text } ]
date_string = datetime.today().strftime('%Y-%m-%d')
prompt = tokenizer.apply_chat_template(
message,
tokenize=False,
add_generation_prompt=True,
date_string=date_string
)
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Using this template, each turn is preceded by a `<|im_start|>` delimiter and the role of the entity
(either `user`, for content supplied by the user, or `assistant` for LLM responses), and finished with the `<|im_end|>` token.
---
## Data
### Pretraining Data
The pre-training corpus comprises data from 35 European languages and 92 programming languages, with detailed data sources provided below.
The initial three training epochs used 2.4 trillion tokens, obtained by manually adjusting data proportion to balance the representation
and give more importance to Spain’s co-official (Spanish, Catalan, Galician, and Basque). This way, we downsampled code and English data to half,
Spanish co-official languages were oversampled by 2x, and the remaining languages were kept in their original proportions.
During the following epochs, the Colossal OSCAR dataset was replaced with the FineWeb-Edu dataset.
This adjustment resulted in a total of 2.68 trillion tokens, distributed as outlined below:
The pretraining corpus is predominantly composed of data from Colossal OSCAR, which contributes a significant 53.05% of the total tokens.
Following this, Starcoder provides 13.67%, and FineWeb-Edu (350BT subset) adds 10.24%. The next largest sources are HPLT at 4.21% and French-PD at 3.59%.
Other notable contributions include MaCoCu, Legal-ES, and EurLex, each contributing around 1.72% to 1.41%.
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 |
|---|---|---|
| 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 |
| 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 |
| 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/) |
| OpenSubtitles v2016 | 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 |
| 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) |
| Parlamint | 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 |
| MaCoCu | bg, ca, el, hr, mt, sl, sr, uk | Bañón et al., 2022 |
| CURLICAT | bg, hr, hu, pl, ro, sk, sl | Váradi et al., 2022 |
| Norwegian Colossal Corpus (NCC) | nn, no | Kummervold et al., 2021 |
| Academic Slovene KAS 2.0 | sl | Žagar et al., 2022 |
| BIGPATENT | en | Sharma et al., 2019 |
| Biomedical-ES | es | Internally generated biomedical dataset: Wikipedia LS, Pubmed, MeSpEn, patents, clinical cases, medical crawler |
| Brazilian Portuguese Web as Corpus (BrWaC) | pt | Wagner Filho et al., 2018 |
| Bulgarian National Corpus (BulNC) | bg | [Link](http://old.dcl.bas.bg/dataset/BulNC.7z) |
| CaBeRnet | fr | Popa-Fabre et al., 2020 |
| CATalog 1.0 | ca | Palomar-Giner et al., 2024 |
| CorpusNÓS | gl | de-Dios-Flores et al., 2024 |
| Croatian Web as Corpus 2.1 (hrWaC) | hr | Ljubešić & Klubička, 2014 |
| 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/) |
| Estonian National Corpus 2021 (ENC) | et | Koppel & Kallas, 2022 |
| Estonian Reference Corpus (ERC) | et | [Link](https://www.cl.ut.ee/korpused/segakorpus/) |
| EusCrawl (w/o Wikipedia or NC-licenses) | eu | Artetxe et al., 2022 |
| FineWeb-Edu (350BT subset) | en | Penedo et al., 2024 |
| French Public Domain Books (French-PD) | fr | [Link](https://huggingface.co/datasets/PleIAs/French-PD-Books) |
| French Public Domain Newspapers (French-PD) | fr | [Link](https://huggingface.co/datasets/PleIAs/French-PD-Newspapers) |
| German Web as Corpus (DeWaC) | de | [Link](https://docs.sslmit.unibo.it/doku.php?id=corpora:dewac) |
| Greek Legal Code (GLC) | el | Papaloukas et al., 2021 |
| Greek Web Corpus (GWC) | el | Outsios et al., 2018 |
| HPLT v1 - Spanish | es | de Gibert et al., 2024 |
| HPLT v1.1 - Spanish | es | de Gibert et al., 2024 |
| Irish Universal Dependencies (Ga-UD) | ga | [Link](https://universaldependencies.org/ga/index.html) |
| Italian Web as Corpus (ItWaC) | it | [Link](https://docs.sslmit.unibo.it/doku.php?id=corpora:itwac) |
| Korpus Malti | mt | Micallef et al., 2022 |
| Korpus slovenských právnych predpisov v1.9 (SK-Laws) | sk | [Link](https://www.juls.savba.sk/data/marcell/legal-sk-20220322-1.9.ver.xz) |
| Latxa Corpus v1.1 (GAITU) | eu | Etxaniz et al., 2024 [Link](https://huggingface.co/datasets/HiTZ/latxa-corpus-v1.1) |
| Laws and legal acts of Ukraine (UK-Laws) | uk | [Link](https://lang.org.ua/en/corpora/#anchor7) |
| Legal-ES | es | Internally generated legal dataset: BOE, BORME, Senado, Congreso, Spanish court orders, DOGC |
| MARCELL Romanian legislative subcorpus v2 | ro | [Link](https://elrc-share.eu/reposMARCELL%20Romanian%20legislative%20subcorpus%20v2itory/browse/marcell-romanian-legislative-subcorpus-v2/2da548428b9d11eb9c1a00155d026706ce94a6b59ffc4b0e9fb5cd9cebe6889e/) |
| Math AMPS | en | Hendrycks et al., 2021 |
| NKPJ National Corpus of Polish v1.2 (NKPJ) | pl | Lewandowska-Tomaszczyk et al., 2013 |
| Occitan Corpus (IEA-AALO) | oc | Provided by [IEA](https://www.institutestudisaranesi.cat/) |
| Open Legal Data - German court decisions and laws | de | Ostendorff et al., 2020 |
| ParlamentoPT | pt | Rodrigues et al., 2023 |
| peS2o | en | Soldaini & Lo, 2023 |
| PG-19 | en | Rae et al., 2019 |
| Pile of Law (selected subsets) | en | Henderson* et al., 2022 |
| Polish Parliamentary Corpus (PPC) | pl | Ogrodniczuk, 2018 |
| Proof Pile | en | [Link](https://huggingface.co/datasets/hoskinson-center/proof-pile) |
| RedPajama-Data T1 (StackExchange subset) | en | Computer, 2023 |
| Scientific-ES | es | Internally generated scientific dataset: Dialnet, Scielo, CSIC, TDX, BSC, UCM |
| SK Court Decisions v2.0 (OD-Justice) | sk | [Link](https://www.juls.savba.sk/data/od-justice/od-justice-2.0.ver.xz) |
| Slovene Web as Corpus (slWaC) | sl | Erjavec et al., 2015 |
| SoNaR Corpus NC 1.2 | nl | [Link](https://taalmaterialen.ivdnt.org/download/tstc-sonar-corpus/) |
| Spanish Legal Domain Corpora (Spanish-Legal) | es | Gutiérrez-Fandiño et al., 2021 |
| SrpKorSubset: news, legal, academic, conversation, lit- erary (SrpKor) | sr | [Link](http://www.korpus.matf.bg.ac.rs/) |
| Starcoder | code | Li et al., 2023 |
| State-related content from the Latvian Web (State-Latvian-Web) | lv | [Link](https://catalog.elra.info/en-us/repository/browse/ELRA-W0169/) |
| SYN v9: large corpus of written Czech | cs | Křen et al., 2021 |
| Tagesschau Archive Article | de | [Link](https://huggingface.co/datasets/bjoernp/tagesschau-2018-2023) |
| The Danish Parliament Corpus 2009 - 2017, v1 | da | Hansen, 2018 |
| The Gaois bilingual corpus of English-Irish legislation (Ga-Legislation) | ga | [Link](https://portulanclarin.net/repository/browse/the-gaois-bilingual-corpus-of-english-irish-legislation-processed/daeac17c9e3511ea9b7f02420a000407b83de243dc0b469aab41084386c5b80f/) |
| The Pile (PhilPapers) | en | Gao et al., 2021 |
| The Swedish Culturomics Gigaword Corpus (Swedish- Gigaword) | sv | Rødven-Eide, 2016 |
| Welsh-GOV | cy | Crawling from [Link](https://www.llyw.cymru) |
| Yle Finnish News Archive (Yle-News) | fi | [Link](http://urn.fi/urn:nbn:fi:lb-2021050401) |
To consult the data summary document with the respective licences, please send an e-mail to [email protected].
<details>
<summary>References</summary>
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</details>
</details>
The model was trained on 3 pre-training epochs with 2.4T tokens per epoch, 2 additional pre-training epochs in which the English part
of the Colossal OSCAR dataset was replaced with FineWeb-Edu (350BT subset), resulting in 2.68T tokens per epoch;
and 1 final epoch of 0.315T higher quality tokens, meaning that the total number of tokens seen during pre-training is approximately 12.875 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 programming languages (92). We also want to represent the co-official languages of Spain: Spanish, Catalan, Galician and Basque. For this reason, we oversample
these languages by a factor of 2.
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 José 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 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.
#### 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 39.31% of the total data. Spanish was upsampled by a factor of 2, bringing its share to 16.12%,
while Catalan (1.97%), Basque (0.24%), and Galician (0.31%) were also upsampled by 2. On the other hand, code-related data was downsampled
by half, making up 5.78% of the total. Other prominent languages include French (6.6%), Russian (5.56%), German (4.79%), and Hungarian
(4.59%), 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 labelled 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 randomly divided into training, validation and test sets, where the validation and test sets are each 1% of the total corpus.
**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 search engine optimization techniques and templates contribute to repeated textual patterns. Some instances may be also 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 (Mina et al., 2024), 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.
- Domain-specific or language-specific raw crawls.
- Manually curated data obtained through collaborators, data providers (by means of legal assignment agreements) or open source projects (e.g. CATalog).
**What mechanisms or procedures were used to collect the data? How were these mechanisms or procedures validated?**
The data collection process was carried out using three different mechanisms, each corresponding to one of the groups defined in the previous answer. The specific methods used and their respective validation procedures are outlined below:
- Open Direct Download: Data were obtained directly from publicly accessible sources, such as websites or repositories that provide open data downloads. We validate the data with a data integrity check, which ensures that the downloaded files are complete, uncorrupted and in the expected format and structure.
- Ad hoc scrapers or crawlers: Custom web scraping scripts or crawlers were used to extract data from various online sources where direct downloads were not available. These scripts navigate web pages, extract relevant data and store it in a structured format. We validate this method with software unit tests to evaluate the functionality of individual components of the scraping programs, checking for errors or unexpected behaviour. In addition, data integrity tests were performed to verify that the collected data remained complete throughout the extraction and storage process.
- Direct download via FTP, SFTP, API or S3: Some datasets were acquired using secure transfer protocols such as FTP (File Transfer Protocol), SFTP (Secure File Transfer Protocol), or API (Application Programming Interface) requests from cloud storage services such as Amazon S3. As with the open direct download method, data integrity tests were used to validate the completeness of the files to ensure that the files were not altered or corrupted during the transfer process.
**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 Language Technologies 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.**
No changes were made to the content of individual text document instances. However, the web-sourced documents underwent a filtering process based on specific criteria along two key dimensions:
- Quality filtering: The text processing pipeline CURATE (Palomar et. al, 2024) calculates a quality score for each document based on a set of filtering criteria that identify undesirable textual characteristics. Any document with a score below the 0.8 threshold was excluded from the dataset.
- Harmful or adult content filtering: To reduce the amount of harmful or inappropriate material in the dataset, documents from Colossal OSCAR were filtered using the Ungoliant pipeline (Abadji et al., 2021), which uses the 'harmful\_pp' field, a perplexity-based score generated by a language model.
**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 CATalog and other curated datasets,
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>
### Finetuning Data
This instructed-tuned variant has been fine-tuned with a collection of 273k instructions, focusing on the performance of Catalan, English and Spanish. However, instruction data for other closely related Iberian languages has also been included, since it yielded a positive impact on the languages of interest. That said, the performance in these additional languages is not guaranteed due to the limited amount of available data and the lack of resources for thorough testing.
| **Dataset** | **ca** | **en** | **es** | **eu** | **gl** | **pt** | **Total** |
|----------------------|------------|-------------|------------|-----------|---------|------------|-------------|
| alpaca-cleaned | | 49,950 | | | | | **49,950** |
| aya-dataset | | 3,941 | 3,851 | 939 | | 8,995 | **17,726** |
| coqcat | 4,797 | | | | | | **4,797** |
| databricks-dolly-15k | | 15,011 | | | | | **15,011** |
| dolly-ca | 3,232 | | | | | | **3,232** |
| flores-dev | 986 | 1,037 | 1,964 | 493 | 505 | | **4,985** |
| mentor-ca | 7,119 | | | | | | **7,119** |
| mentor-es | | | 7,122 | | | | **7,122** |
| no-robots | | 9,485 | | | | | **9,485** |
| oasst-ca | 2,517 | | | | | | **2,517** |
| oasst2 | 750 | 31,086 | 15,438 | 190 | 197 | 1,203 | **48,864** |
| open-orca | | 49,996 | | | | | **49,996** |
| rag-multilingual | 16,043 | 14,997 | 11,263 | | | | **42,303** |
| tower-blocks | | 7,762 | 1,000 | | | 1,000 | **9,762** |
| **Total** | **35,444** | **183,265** | **40,638** | **1,622** | **702** | **11,198** | **272,869** |
---
## Evaluation
### Gold-standard benchmarks
WiP
<!--
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). These benchmarks include both new and existing tasks and datasets. Given that this is an instructed model, we add LM Evaluation Harness's native feature of `chat-template` to the setup. 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 model's 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 0-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>62.34</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_es</td>
<td>acc</td>
<td>47.89</td>
</tr>
<tr>
<td>xnli_es</td>
<td>acc</td>
<td>47.03</td>
</tr>
<tr>
<td>Paraphrasing</td>
<td>paws_es</td>
<td>acc</td>
<td>55.5</td>
</tr>
<tr>
<td>QA</td>
<td>xquad_es</td>
<td>acc</td>
<td>42.21</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_es</td>
<td>bleu</td>
<td>20.27</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>70.4</td>
</tr>
<tr>
<td>xstorycloze_ca</td>
<td>acc</td>
<td>63.07</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_ca</td>
<td>acc</td>
<td>52.11</td>
</tr>
<tr>
<td>xnli_ca</td>
<td>acc</td>
<td>51.69</td>
</tr>
<tr>
<td rowspan="2">Paraphrasing</td>
<td>parafraseja</td>
<td>acc</td>
<td>61.88</td>
</tr>
<tr>
<td>paws_ca</td>
<td>acc</td>
<td>57.7</td>
</tr>
<tr>
<td rowspan="5">QA</td>
<td>arc_ca_easy</td>
<td>acc</td>
<td>51.94</td>
</tr>
<tr>
<td>arc_ca_challenge</td>
<td>acc</td>
<td>29.52</td>
</tr>
<tr>
<td>openbookqa_ca</td>
<td>acc</td>
<td>26.4</td>
</tr>
<tr>
<td>piqa_ca</td>
<td>acc</td>
<td>62.89</td>
</tr>
<tr>
<td>siqa_ca</td>
<td>acc</td>
<td>42.63</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_ca</td>
<td>bleu</td>
<td>24.48</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>53.6</td>
</tr>
<tr>
<td>xstorycloze_eu</td>
<td>acc</td>
<td>56.39</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_eu</td>
<td>acc</td>
<td>45.07</td>
</tr>
<tr>
<td>xnli_eu</td>
<td>acc</td>
<td>39.44</td>
</tr>
<tr>
<td rowspan="3">QA</td>
<td>eus_exams</td>
<td>acc</td>
<td>25.35</td>
</tr>
<tr>
<td>eus_proficiency</td>
<td>acc</td>
<td>26.37</td>
</tr>
<tr>
<td>eus_trivia</td>
<td>acc</td>
<td>26.24</td>
</tr>
<tr>
<td>Reading Comprehension</td>
<td>eus_reading</td>
<td>acc</td>
<td>24.72</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_eu</td>
<td>bleu</td>
<td>9.67</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>50.00</td>
</tr>
<tr>
<td>paws_gl</td>
<td>acc</td>
<td>52.20</td>
</tr>
<tr>
<td>QA</td>
<td>openbookqa_gl</td>
<td>acc</td>
<td>33.2</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_gl</td>
<td>bleu</td>
<td>22.39</td>
</tr>
</tbody>
</table>
-->
### LLM-as-a-judge
We use [Prometheus-2 8x7B](https://huggingface.co/prometheus-eval/prometheus-8x7b-v2.0) as a judge to evaluate the responses of the model. Tasks are created from existing multilingual evaluation datasets covering the same categories as the ones measured in our gold-standard benchmarks. We randomly select a subset of 250 instances per language from the `test` set of each source dataset. To evaluate the responses of our model, we use task-specific criteria developed in-house for the _LLM-judge_ to use. Each criterion is measured either as a 5-point Likert scale or as a binary task depending on the idiosyncrasy of the task and criterion.
Prompts for each task are created in various ways to score the model's robustness in addition to these criteria. This is done by presenting the same source instance within three different prompts. We then calculate the variance between the scores assigned by the _LLM-judge_ to our model's responses to the three prompt styles and average it across all instances. Prompts are human translated to all languages measured. We do not provide the _LLM-judge_ with a reference answer.
The _judge_ prompt we use during evaluation is the same used to fine tune the Prometheus-2 family. We keep the _judge_ prompt and criteria used to present the _LLM-judge_ with the task prompts and model responses in English for evaluation across languages. The _judge_ prompt used is:
```python
"You are a fair judge assistant tasked with providing clear, objective feedback based on specific criteria, ensuring each assessment reflects the absolute standards set for performance.
###Task Description:
An instruction (might include an Input inside it), a response to evaluate, and a score rubric representing a evaluation criteria are given.
1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general.
2. After writing a feedback, write a score that is an integer between {a} and {b}. You should refer to the score rubric.
3. The output format should look as follows: \"Feedback: (write a feedback for criteria) [RESULT] (an integer number between {a} and {b})\"
4. Please do not generate any other opening, closing, and explanations.
###The instruction to evaluate:
{input}
###Response to evaluate:
{prediction}
###Score Rubrics:
{criteria}
###Feedback:"
```
As an example, prompts for the Math task in English are based on instances from [MGSM](https://huggingface.co/datasets/juletxara/mgsm), and each instance is presented within these prompts:
```python
"en": [
("I need help with this math problem: \"", "\" Give me the answer step by step and also the final result separately."),
("Can you please help me answer this? \"", "\" Explain the answer and give me the final result as well. Thanks."),
("Help me with this problem: \"", "\" I need the answer explained and the final result separately.")
]
```
This task is then evaluated by the _LLM-judge_ using two criteria, reasoning capability (5-point Likert) and mathematical correctness (binary):
```python
reasoning_capability_criteria = {
"reasoning_capability": """
[Does the model's answer demonstrate reasoning capability?]
Score 1: The answer demonstrates poor reasoning, with illogical arguments or conclusions that do not follow from the provided information.
Score 2: The answer shows weak reasoning, with some logical connections but also contains significant flaws or gaps in the argumentation.
Score 3: The answer demonstrates adequate reasoning, with generally logical arguments, but may have minor flaws or a lack of depth in the reasoning process.
Score 4: The answer shows strong reasoning, with well-structured arguments and conclusions that logically follow from the information provided.
Score 5: The answer demonstrates exceptional reasoning, with clear, coherent, and insightful arguments that are logically sound and well-supported by the information provided."""
}
mathematical_correctness_binary_criteria = {
"mathematical_correctness_binary": """
[Is the model's answer mathematically correct?]
Score 0: The answer contains mathematical errors that render the solution incorrect or unreliable.
Score 1: The answer is mathematically correct, with accurate calculations and appropriate use of mathematical concepts."""
}
```
#### Multilingual results
Here, we present results for seven categories of tasks in Spanish, Catalan, Basque, Galician, and English. Results are presented for each task, criterion and language. Criteria with a `(B)` after their name are binary criteria (i.e., numbers go from 0 to 1, where 1 is best). The rest of the criteria are measured using a 5-point Likert scale, where 5 is best. The first number of the pair of numbers separated by `/` shows the average score for the criterion (and language). The second number of each pair is the robustness score, where numbers closer to 0 means that the model generates similar responses when comparing the three prompt varieties for a single instance.
Further details on all tasks and criteria, 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.
<style type="text/css">
.tg {border-collapse:collapse;border-spacing:0;}
.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
overflow:hidden;padding:10px 5px;word-break:normal;}
.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}
.tg .tg-0pky{border-color:inherit;text-align:left;vertical-align:top}
</style>
<table class="tg"><thead>
<tr>
<th class="tg-0pky"><span style="font-weight:bold">Category</span></th>
<th class="tg-0pky"><span style="font-weight:bold">Dataset</span></th>
<th class="tg-0pky"><span style="font-weight:bold">Criteria</span></th>
<th class="tg-0pky"><span style="font-weight:bold">es</span></th>
<th class="tg-0pky"><span style="font-weight:bold">ca</span></th>
<th class="tg-0pky"><span style="font-weight:bold">gl</span></th>
<th class="tg-0pky"><span style="font-weight:bold">eu</span></th>
<th class="tg-0pky"><span style="font-weight:bold">en</span></th>
</tr></thead>
<tbody>
<tr>
<td class="tg-0pky">Commonsense Reasoning</td>
<td class="tg-0pky">XStoryCloze</td>
<td class="tg-0pky">Ending coherence</td>
<td class="tg-0pky">2.36/0.66</td>
<td class="tg-0pky">2.49/0.76</td>
<td class="tg-0pky">2.45/0.68</td>
<td class="tg-0pky">2.30/0.67</td>
<td class="tg-0pky">3.06/0.77</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="3">Paraphrasing</td>
<td class="tg-0pky" rowspan="3">PAWS</td>
<td class="tg-0pky">Completeness `(B)`</td>
<td class="tg-0pky">0.60/0.15</td>
<td class="tg-0pky">0.54/0.17</td>
<td class="tg-0pky">0.64/0.14</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">0.79/0.11</td>
</tr>
<tr>
<td class="tg-0pky">Paraphrase generation</td>
<td class="tg-0pky">2.89/1.46</td>
<td class="tg-0pky">2.71/1.70</td>
<td class="tg-0pky">2.80/1.21</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">3.64/0.80</td>
</tr>
<tr>
<td class="tg-0pky">Grammatical correctness `(B)`</td>
<td class="tg-0pky">0.74/0.13</td>
<td class="tg-0pky">0.68/0.15</td>
<td class="tg-0pky">0.78/0.10</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">0.89/0.07</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="2">Reading Comprehension</td>
<td class="tg-0pky" rowspan="2">Belebele</td>
<td class="tg-0pky">Passage comprehension</td>
<td class="tg-0pky">3.05/0.60</td>
<td class="tg-0pky">2.81/0.66</td>
<td class="tg-0pky">2.74/0.78</td>
<td class="tg-0pky">2.52/0.46</td>
<td class="tg-0pky">3.11/0.71</td>
</tr>
<tr>
<td class="tg-0pky">Answer relevance `(B)`</td>
<td class="tg-0pky">0.74/0.09</td>
<td class="tg-0pky">0.66/0.11</td>
<td class="tg-0pky">0.65/0.12</td>
<td class="tg-0pky">0.59/0.12</td>
<td class="tg-0pky">0.75/0.09</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="2">Extreme Summarization</td>
<td class="tg-0pky" rowspan="2">XLSum & caBreu & summarization_gl</td>
<td class="tg-0pky">Informativeness</td>
<td class="tg-0pky">3.07/0.39</td>
<td class="tg-0pky">3.33/0.43</td>
<td class="tg-0pky">3.11/0.36</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">3.06/0.35</td>
</tr>
<tr>
<td class="tg-0pky">Conciseness</td>
<td class="tg-0pky">2.92/0.42</td>
<td class="tg-0pky">2.67/0.54</td>
<td class="tg-0pky">2.93/0.39</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">3.13/0.31</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="2">Math</td>
<td class="tg-0pky" rowspan="2">MGSM</td>
<td class="tg-0pky">Reasoning capability</td>
<td class="tg-0pky">1.89/0.47</td>
<td class="tg-0pky">1.91/0.45</td>
<td class="tg-0pky">1.97/0.43</td>
<td class="tg-0pky">2.17/0.44</td>
<td class="tg-0pky">2.16/0.56</td>
</tr>
<tr>
<td class="tg-0pky">Mathematical correctness `(B)`</td>
<td class="tg-0pky">0.24/0.10</td>
<td class="tg-0pky">0.28/0.11</td>
<td class="tg-0pky">0.27/0.11</td>
<td class="tg-0pky">0.44/0.13</td>
<td class="tg-0pky">0.27/0.10</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="2">Translation form Language</td>
<td class="tg-0pky" rowspan="2">FLORES-200</td>
<td class="tg-0pky">Fluency</td>
<td class="tg-0pky">3.74/0.15</td>
<td class="tg-0pky">3.69/0.22</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">3.69/0.18</td>
</tr>
<tr>
<td class="tg-0pky">Accuracy</td>
<td class="tg-0pky">4.01/0.24</td>
<td class="tg-0pky">3.98/0.31</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">3.98/0.25</td>
</tr>
<tr>
<td class="tg-0pky" rowspan="2">Translation to Language</td>
<td class="tg-0pky" rowspan="2">FLORES-200</td>
<td class="tg-0pky">Fluency</td>
<td class="tg-0pky">3.75/0.14</td>
<td class="tg-0pky">3.69/0.17</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">4.09/0.16</td>
</tr>
<tr>
<td class="tg-0pky">Accuracy</td>
<td class="tg-0pky">4.08/0.22</td>
<td class="tg-0pky">3.98/0.24</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">-- / --</td>
<td class="tg-0pky">4.47/0.18</td>
</tr>
</tbody></table>
---
## 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 around 0.8 depending on the social category) in disambiguated settings,
the model performs very poorly in ambiguous settings, which indicates the presence of societal biases that need to be further addressed in post-training phases.
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 significant,
but relatively weak primacy effects, whereby the model shows a preference for answers towards the beginning of the list of provided answers.
We measure the effects of majority class effects in few-shot settings using SST-2 (Socher et al., 2013). We again detect significant effects,
with a small effect size. This suggests that the model is relatively robust against the examined cognitive biases.
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.
These results can be expected from a model that has undergone only a preliminary instruction tuning.
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 the 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, 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
```
@misc{gonzalezagirre2025salamandratechnicalreport,
title={Salamandra Technical Report},
author={Aitor Gonzalez-Agirre and Marc Pàmies and Joan Llop and Irene Baucells and Severino Da Dalt and Daniel Tamayo and José Javier Saiz and Ferran Espuña and Jaume Prats and Javier Aula-Blasco and Mario Mina and Adrián Rubio and Alexander Shvets and Anna Sallés and Iñaki Lacunza and Iñigo Pikabea and Jorge Palomar and Júlia Falcão and Lucía Tormo and Luis Vasquez-Reina and Montserrat Marimon and Valle Ruíz-Fernández and Marta Villegas},
year={2025},
eprint={2502.08489},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.08489},
}
```
### 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| [Link](https://huggingface.co/BSC-LT/ALIA-40b) | WiP |
|
[
"BEAR",
"SCIELO"
] |
tensorblock/gte-Qwen2-7B-instruct-GGUF
|
tensorblock
|
sentence-similarity
|
[
"sentence-transformers",
"gguf",
"mteb",
"transformers",
"Qwen2",
"sentence-similarity",
"TensorBlock",
"GGUF",
"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"
] | 2024-11-11T10:44:19Z |
2024-11-16T01:05:41+00:00
| 3,236 | 8 |
---
base_model: Alibaba-NLP/gte-Qwen2-7B-instruct
license: apache-2.0
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2
- sentence-similarity
- TensorBlock
- GGUF
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
- type: manhattan_spearman
value: 88.9228828016344
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
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:
name: MTEB STS15
type: mteb/sts15-sts
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:
name: MTEB STS16
type: mteb/sts16-sts
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:
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
- 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:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 79.86443362395185
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 49.40897096662564
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- 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
- 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:
name: MTEB Touche2020
type: 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
- type: recall_at_5
value: 12.026
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 85.1174
- type: ap
value: 30.169441069345748
- type: f1
value: 69.79254701873245
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 72.58347481607245
- type: f1
value: 72.74877295564937
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 53.90586138221305
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.35769207844072
- type: cos_sim_ap
value: 77.9645072410354
- type: cos_sim_f1
value: 71.32352941176471
- type: cos_sim_precision
value: 66.5903890160183
- type: cos_sim_recall
value: 76.78100263852242
- type: dot_accuracy
value: 87.37557370209214
- type: dot_ap
value: 77.96250046429908
- type: dot_f1
value: 71.28932757557064
- type: dot_precision
value: 66.95249130938586
- type: dot_recall
value: 76.22691292875989
- type: euclidean_accuracy
value: 87.35173153722357
- type: euclidean_ap
value: 77.96520460741593
- type: euclidean_f1
value: 71.32470733210104
- type: euclidean_precision
value: 66.91329479768785
- type: euclidean_recall
value: 76.35883905013192
- type: manhattan_accuracy
value: 87.25636287774931
- type: manhattan_ap
value: 77.77752485611796
- type: manhattan_f1
value: 71.18148599269183
- type: manhattan_precision
value: 66.10859728506787
- type: manhattan_recall
value: 77.0976253298153
- type: max_accuracy
value: 87.37557370209214
- type: max_ap
value: 77.96520460741593
- type: max_f1
value: 71.32470733210104
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.38176737687739
- type: cos_sim_ap
value: 86.58811861657401
- type: cos_sim_f1
value: 79.09430644097604
- type: cos_sim_precision
value: 75.45085977911366
- type: cos_sim_recall
value: 83.10748383122882
- type: dot_accuracy
value: 89.38370784336554
- type: dot_ap
value: 86.58840606004333
- type: dot_f1
value: 79.10179860068133
- type: dot_precision
value: 75.44546153308643
- type: dot_recall
value: 83.13058207576223
- type: euclidean_accuracy
value: 89.38564830985369
- type: euclidean_ap
value: 86.58820721061164
- type: euclidean_f1
value: 79.09070942235888
- type: euclidean_precision
value: 75.38729937194697
- type: euclidean_recall
value: 83.17677856482906
- type: manhattan_accuracy
value: 89.40699344122326
- type: manhattan_ap
value: 86.60631843011362
- type: manhattan_f1
value: 79.14949970570925
- type: manhattan_precision
value: 75.78191039729502
- type: manhattan_recall
value: 82.83030489682784
- type: max_accuracy
value: 89.40699344122326
- type: max_ap
value: 86.60631843011362
- type: max_f1
value: 79.14949970570925
- task:
type: STS
dataset:
name: MTEB AFQMC
type: C-MTEB/AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
- type: cos_sim_pearson
value: 65.58442135663871
- type: cos_sim_spearman
value: 72.2538631361313
- type: euclidean_pearson
value: 70.97255486607429
- type: euclidean_spearman
value: 72.25374250228647
- type: manhattan_pearson
value: 70.83250199989911
- type: manhattan_spearman
value: 72.14819496536272
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cos_sim_pearson
value: 59.99478404929932
- type: cos_sim_spearman
value: 62.61836216999812
- type: euclidean_pearson
value: 66.86429811933593
- type: euclidean_spearman
value: 62.6183520374191
- type: manhattan_pearson
value: 66.8063778911633
- type: manhattan_spearman
value: 62.569607573241115
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 53.98400000000001
- type: f1
value: 51.21447361350723
- task:
type: STS
dataset:
name: MTEB BQ
type: C-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:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: v_measure
value: 47.07270168705156
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: v_measure
value: 45.98511703185043
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 88.19895157194931
- type: mrr
value: 90.21424603174603
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 88.03317320980119
- type: mrr
value: 89.9461507936508
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
- type: map_at_1
value: 29.037000000000003
- type: map_at_10
value: 42.001
- type: map_at_100
value: 43.773
- type: map_at_1000
value: 43.878
- type: map_at_3
value: 37.637
- type: map_at_5
value: 40.034
- type: mrr_at_1
value: 43.136
- type: mrr_at_10
value: 51.158
- type: mrr_at_100
value: 52.083
- type: mrr_at_1000
value: 52.12
- type: mrr_at_3
value: 48.733
- type: mrr_at_5
value: 50.025
- type: ndcg_at_1
value: 43.136
- type: ndcg_at_10
value: 48.685
- type: ndcg_at_100
value: 55.513
- type: ndcg_at_1000
value: 57.242000000000004
- type: ndcg_at_3
value: 43.329
- type: ndcg_at_5
value: 45.438
- type: precision_at_1
value: 43.136
- type: precision_at_10
value: 10.56
- type: precision_at_100
value: 1.6129999999999998
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 24.064
- type: precision_at_5
value: 17.269000000000002
- type: recall_at_1
value: 29.037000000000003
- type: recall_at_10
value: 59.245000000000005
- type: recall_at_100
value: 87.355
- type: recall_at_1000
value: 98.74000000000001
- type: recall_at_3
value: 42.99
- type: recall_at_5
value: 49.681999999999995
- task:
type: PairClassification
dataset:
name: MTEB Cmnli
type: C-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:
name: MTEB CovidRetrieval
type: C-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:
name: MTEB DuRetrieval
type: C-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:
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
- type: precision_at_10
value: 8.68
- type: precision_at_100
value: 0.964
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 24.4
- type: precision_at_5
value: 16.1
- type: recall_at_1
value: 56.10000000000001
- type: recall_at_10
value: 86.8
- type: recall_at_100
value: 96.39999999999999
- type: recall_at_1000
value: 98.3
- type: recall_at_3
value: 73.2
- type: recall_at_5
value: 80.5
- task:
type: Classification
dataset:
name: MTEB IFlyTek
type: C-MTEB/IFlyTek-classification
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
metrics:
- type: accuracy
value: 54.52096960369373
- type: f1
value: 40.930845295808695
- task:
type: Classification
dataset:
name: MTEB JDReview
type: C-MTEB/JDReview-classification
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:
name: MTEB LCQMC
type: C-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:
name: MTEB MMarcoReranking
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 31.648613483640254
- type: mrr
value: 30.37420634920635
- task:
type: Retrieval
dataset:
name: MTEB MMarcoRetrieval
type: C-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:
name: MTEB MassiveIntentClassification (zh-CN)
type: mteb/amazon_massive_intent
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 81.08607935440484
- type: f1
value: 78.24879986066307
- 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: 86.05917955615332
- type: f1
value: 85.05279279434997
- task:
type: Retrieval
dataset:
name: MTEB MedicalRetrieval
type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
metrics:
- type: map_at_1
value: 56.2
- type: map_at_10
value: 62.57899999999999
- type: map_at_100
value: 63.154999999999994
- type: map_at_1000
value: 63.193
- type: map_at_3
value: 61.217
- type: map_at_5
value: 62.012
- type: mrr_at_1
value: 56.3
- type: mrr_at_10
value: 62.629000000000005
- type: mrr_at_100
value: 63.205999999999996
- type: mrr_at_1000
value: 63.244
- type: mrr_at_3
value: 61.267
- type: mrr_at_5
value: 62.062
- type: ndcg_at_1
value: 56.2
- type: ndcg_at_10
value: 65.592
- type: ndcg_at_100
value: 68.657
- type: ndcg_at_1000
value: 69.671
- type: ndcg_at_3
value: 62.808
- type: ndcg_at_5
value: 64.24499999999999
- type: precision_at_1
value: 56.2
- type: precision_at_10
value: 7.5
- type: precision_at_100
value: 0.899
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 22.467000000000002
- type: precision_at_5
value: 14.180000000000001
- type: recall_at_1
value: 56.2
- type: recall_at_10
value: 75.0
- type: recall_at_100
value: 89.9
- type: recall_at_1000
value: 97.89999999999999
- type: recall_at_3
value: 67.4
- type: recall_at_5
value: 70.89999999999999
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment
type: C-MTEB/MultilingualSentiment-classification
config: default
split: validation
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 76.87666666666667
- type: f1
value: 76.7317686219665
- task:
type: PairClassification
dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cos_sim_accuracy
value: 79.64266377910124
- type: cos_sim_ap
value: 84.78274442344829
- type: cos_sim_f1
value: 81.16947472745292
- type: cos_sim_precision
value: 76.47058823529412
- type: cos_sim_recall
value: 86.48363252375924
- type: dot_accuracy
value: 79.64266377910124
- type: dot_ap
value: 84.7851404063692
- type: dot_f1
value: 81.16947472745292
- type: dot_precision
value: 76.47058823529412
- type: dot_recall
value: 86.48363252375924
- type: euclidean_accuracy
value: 79.64266377910124
- type: euclidean_ap
value: 84.78068373762378
- type: euclidean_f1
value: 81.14794656110837
- type: euclidean_precision
value: 76.35009310986965
- type: euclidean_recall
value: 86.58922914466737
- type: manhattan_accuracy
value: 79.48023822414727
- type: manhattan_ap
value: 84.72928897427576
- type: manhattan_f1
value: 81.32084770823064
- type: manhattan_precision
value: 76.24768946395564
- type: manhattan_recall
value: 87.11721224920802
- type: max_accuracy
value: 79.64266377910124
- type: max_ap
value: 84.7851404063692
- type: max_f1
value: 81.32084770823064
- task:
type: Classification
dataset:
name: MTEB OnlineShopping
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 94.3
- type: ap
value: 92.8664032274438
- type: f1
value: 94.29311102997727
- task:
type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cos_sim_pearson
value: 48.51392279882909
- type: cos_sim_spearman
value: 54.06338895994974
- type: euclidean_pearson
value: 52.58480559573412
- type: euclidean_spearman
value: 54.06417276612201
- type: manhattan_pearson
value: 52.69525121721343
- type: manhattan_spearman
value: 54.048147455389675
- task:
type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cos_sim_pearson
value: 29.728387290757325
- type: cos_sim_spearman
value: 31.366121633635284
- type: euclidean_pearson
value: 29.14588368552961
- type: euclidean_spearman
value: 31.36764411112844
- type: manhattan_pearson
value: 29.63517350523121
- type: manhattan_spearman
value: 31.94157020583762
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 63.64868296271406
- type: cos_sim_spearman
value: 66.12800618164744
- type: euclidean_pearson
value: 63.21405767340238
- type: euclidean_spearman
value: 66.12786567790748
- type: manhattan_pearson
value: 64.04300276525848
- type: manhattan_spearman
value: 66.5066857145652
- task:
type: STS
dataset:
name: MTEB STSB
type: C-MTEB/STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
- type: cos_sim_pearson
value: 81.2302623912794
- type: cos_sim_spearman
value: 81.16833673266562
- type: euclidean_pearson
value: 79.47647843876024
- type: euclidean_spearman
value: 81.16944349524972
- type: manhattan_pearson
value: 79.84947238492208
- type: manhattan_spearman
value: 81.64626599410026
- task:
type: Reranking
dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
- type: map
value: 67.80129586475687
- type: mrr
value: 77.77402311635554
- task:
type: Retrieval
dataset:
name: MTEB T2Retrieval
type: C-MTEB/T2Retrieval
config: default
split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
metrics:
- type: map_at_1
value: 28.666999999999998
- type: map_at_10
value: 81.063
- type: map_at_100
value: 84.504
- type: map_at_1000
value: 84.552
- type: map_at_3
value: 56.897
- type: map_at_5
value: 70.073
- type: mrr_at_1
value: 92.087
- type: mrr_at_10
value: 94.132
- type: mrr_at_100
value: 94.19800000000001
- type: mrr_at_1000
value: 94.19999999999999
- type: mrr_at_3
value: 93.78999999999999
- type: mrr_at_5
value: 94.002
- type: ndcg_at_1
value: 92.087
- type: ndcg_at_10
value: 87.734
- type: ndcg_at_100
value: 90.736
- type: ndcg_at_1000
value: 91.184
- type: ndcg_at_3
value: 88.78
- type: ndcg_at_5
value: 87.676
- type: precision_at_1
value: 92.087
- type: precision_at_10
value: 43.46
- type: precision_at_100
value: 5.07
- type: precision_at_1000
value: 0.518
- type: precision_at_3
value: 77.49000000000001
- type: precision_at_5
value: 65.194
- type: recall_at_1
value: 28.666999999999998
- type: recall_at_10
value: 86.632
- type: recall_at_100
value: 96.646
- type: recall_at_1000
value: 98.917
- type: recall_at_3
value: 58.333999999999996
- type: recall_at_5
value: 72.974
- task:
type: Classification
dataset:
name: MTEB TNews
type: C-MTEB/TNews-classification
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
- type: accuracy
value: 52.971999999999994
- type: f1
value: 50.2898280984929
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringP2P
type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
- type: v_measure
value: 86.0797948663824
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringS2S
type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
- type: v_measure
value: 85.10759092255017
- task:
type: Retrieval
dataset:
name: MTEB VideoRetrieval
type: C-MTEB/VideoRetrieval
config: default
split: dev
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
metrics:
- type: map_at_1
value: 65.60000000000001
- type: map_at_10
value: 74.773
- type: map_at_100
value: 75.128
- type: map_at_1000
value: 75.136
- type: map_at_3
value: 73.05
- type: map_at_5
value: 74.13499999999999
- type: mrr_at_1
value: 65.60000000000001
- type: mrr_at_10
value: 74.773
- type: mrr_at_100
value: 75.128
- type: mrr_at_1000
value: 75.136
- type: mrr_at_3
value: 73.05
- type: mrr_at_5
value: 74.13499999999999
- type: ndcg_at_1
value: 65.60000000000001
- type: ndcg_at_10
value: 78.84299999999999
- type: ndcg_at_100
value: 80.40899999999999
- type: ndcg_at_1000
value: 80.57
- type: ndcg_at_3
value: 75.40599999999999
- type: ndcg_at_5
value: 77.351
- type: precision_at_1
value: 65.60000000000001
- type: precision_at_10
value: 9.139999999999999
- type: precision_at_100
value: 0.984
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 27.400000000000002
- type: precision_at_5
value: 17.380000000000003
- type: recall_at_1
value: 65.60000000000001
- type: recall_at_10
value: 91.4
- type: recall_at_100
value: 98.4
- 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:
name: MTEB Waimai
type: C-MTEB/waimai-classification
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
metrics:
- type: accuracy
value: 89.47
- type: ap
value: 75.59561751845389
- type: f1
value: 87.95207751382563
- task:
type: Clustering
dataset:
name: MTEB AlloProfClusteringP2P
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: v_measure
value: 76.05592323841036
- type: v_measure
value: 64.51718058866508
- task:
type: Reranking
dataset:
name: MTEB AlloprofReranking
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
config: default
split: test
revision: 666fdacebe0291776e86f29345663dfaf80a0db9
metrics:
- type: map
value: 73.08278490943373
- type: mrr
value: 74.66561454570449
- task:
type: Retrieval
dataset:
name: MTEB AlloprofRetrieval
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
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: 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
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" 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;">
Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
</p>
</div>
</div>
## Alibaba-NLP/gte-Qwen2-7B-instruct - GGUF
This repo contains GGUF format model files for [Alibaba-NLP/gte-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
<div style="text-align: left; margin: 20px 0;">
<a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Run them on the TensorBlock client using your local machine ↗
</a>
</div>
## Prompt template
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [gte-Qwen2-7B-instruct-Q2_K.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q2_K.gguf) | Q2_K | 2.807 GB | smallest, significant quality loss - not recommended for most purposes |
| [gte-Qwen2-7B-instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q3_K_S.gguf) | Q3_K_S | 3.251 GB | very small, high quality loss |
| [gte-Qwen2-7B-instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q3_K_M.gguf) | Q3_K_M | 3.545 GB | very small, high quality loss |
| [gte-Qwen2-7B-instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q3_K_L.gguf) | Q3_K_L | 3.806 GB | small, substantial quality loss |
| [gte-Qwen2-7B-instruct-Q4_0.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q4_0.gguf) | Q4_0 | 4.125 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [gte-Qwen2-7B-instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q4_K_S.gguf) | Q4_K_S | 4.150 GB | small, greater quality loss |
| [gte-Qwen2-7B-instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q4_K_M.gguf) | Q4_K_M | 4.360 GB | medium, balanced quality - recommended |
| [gte-Qwen2-7B-instruct-Q5_0.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q5_0.gguf) | Q5_0 | 4.948 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [gte-Qwen2-7B-instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q5_K_S.gguf) | Q5_K_S | 4.948 GB | large, low quality loss - recommended |
| [gte-Qwen2-7B-instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q5_K_M.gguf) | Q5_K_M | 5.069 GB | large, very low quality loss - recommended |
| [gte-Qwen2-7B-instruct-Q6_K.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q6_K.gguf) | Q6_K | 5.822 GB | very large, extremely low quality loss |
| [gte-Qwen2-7B-instruct-Q8_0.gguf](https://huggingface.co/tensorblock/gte-Qwen2-7B-instruct-GGUF/blob/main/gte-Qwen2-7B-instruct-Q8_0.gguf) | Q8_0 | 7.539 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/gte-Qwen2-7B-instruct-GGUF --include "gte-Qwen2-7B-instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/gte-Qwen2-7B-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|
[
"BIOSSES",
"SCIFACT"
] |
FreedomIntelligence/Apollo-2B
|
FreedomIntelligence
|
text-generation
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:2403.03640",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-03-06T13:05:32Z |
2024-04-26T11:11:38+00:00
| 3,233 | 3 |
---
license: apache-2.0
---
# Multilingual Medicine: Model, Dataset, Benchmark, Code
Covering English, Chinese, French, Hindi, Spanish, Hindi, Arabic So far
<p align="center">
👨🏻💻<a href="https://github.com/FreedomIntelligence/Apollo" target="_blank">Github</a> •📃 <a href="https://arxiv.org/abs/2403.03640" target="_blank">Paper</a> • 🌐 <a href="https://apollo.llmzoo.com/" target="_blank">Demo</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a>
<br> <a href="./README_zh.md"> 中文 </a> | <a href="./README.md"> English
</p>
## 🌈 Update
* **[2024.03.07]** [Paper](https://arxiv.org/abs/2403.03640) released.
* **[2024.02.12]** <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a> and <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a> is published！🎉
* **[2024.01.23]** Apollo repo is published！🎉
## Results
🤗<a href="https://huggingface.co/FreedomIntelligence/Apollo-0.5B" target="_blank">Apollo-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-1.8B" target="_blank">Apollo-1.8B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-2B" target="_blank">Apollo-2B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-6B" target="_blank">Apollo-6B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-7B" target="_blank">Apollo-7B</a>
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-0.5B-GGUF" target="_blank">Apollo-0.5B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-2B-GGUF" target="_blank">Apollo-2B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-6B-GGUF" target="_blank">Apollo-6B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-7B-GGUF" target="_blank">Apollo-7B-GGUF</a>
## Usage Format
User:{query}\nAssistant:{response}<|endoftext|>
## Dataset & Evaluation
- Dataset
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a>
<details><summary>Click to expand</summary>
- [Zip File](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/blob/main/ApolloCorpus.zip)
- [Data category](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/tree/main/train)
- Pretrain:
- data item:
- json_name: {data_source}_{language}_{data_type}.json
- data_type: medicalBook, medicalGuideline, medicalPaper, medicalWeb(from online forum), medicalWiki
- language: en(English), zh(chinese), es(spanish), fr(french), hi(Hindi)
- data_type: qa(generated qa from text)
- data_type==text: list of string
```
[
"string1",
"string2",
...
]
```
- data_type==qa: list of qa pairs(list of string)
```
[
[
"q1",
"a1",
"q2",
"a2",
...
],
...
]
```
- SFT:
- json_name: {data_source}_{language}.json
- data_type: code, general, math, medicalExam, medicalPatient
- data item: list of qa pairs(list of string)
```
[
[
"q1",
"a1",
"q2",
"a2",
...
],
...
]
```
</details>
- Evaluation
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a>
<details><summary>Click to expand</summary>
- EN:
- [MedQA-USMLE](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
- [MedMCQA](https://huggingface.co/datasets/medmcqa/viewer/default/test)
- [PubMedQA](https://huggingface.co/datasets/pubmed_qa): Because the results fluctuated too much, they were not used in the paper.
- [MMLU-Medical](https://huggingface.co/datasets/cais/mmlu)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- ZH:
- [MedQA-MCMLE](https://huggingface.co/datasets/bigbio/med_qa/viewer/med_qa_zh_4options_bigbio_qa/test)
- [CMB-single](https://huggingface.co/datasets/FreedomIntelligence/CMB): Not used in the paper
- Randomly sample 2,000 multiple-choice questions with single answer.
- [CMMLU-Medical](https://huggingface.co/datasets/haonan-li/cmmlu)
- Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
- [CExam](https://github.com/williamliujl/CMExam): Not used in the paper
- Randomly sample 2,000 multiple-choice questions
- ES: [Head_qa](https://huggingface.co/datasets/head_qa)
- FR: [Frenchmedmcqa](https://github.com/qanastek/FrenchMedMCQA)
- HI: [MMLU_HI](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Arabic)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- AR: [MMLU_Ara](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Hindi)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
</details>
## Results reproduction
<details><summary>Click to expand</summary>
**Waiting for Update**
</details>
## Citation
Please use the following citation if you intend to use our dataset for training or evaluation:
```
@misc{wang2024apollo,
title={Apollo: Lightweight Multilingual Medical LLMs towards Democratizing Medical AI to 6B People},
author={Xidong Wang and Nuo Chen and Junyin Chen and Yan Hu and Yidong Wang and Xiangbo Wu and Anningzhe Gao and Xiang Wan and Haizhou Li and Benyou Wang},
year={2024},
eprint={2403.03640},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
[
"HEAD-QA",
"MEDQA",
"PUBMEDQA"
] |
ContactDoctor/Bio-Medical-Llama-3-2-1B-CoT-012025
|
ContactDoctor
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"medical",
"Healthcare & Lifesciences",
"BioMed",
"chain-of-thought",
"conversational",
"dataset:collaiborateorg/BioMedData",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2025-01-02T10:51:14Z |
2025-01-17T07:41:19+00:00
| 3,223 | 24 |
---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets:
- collaiborateorg/BioMedData
library_name: transformers
license: other
tags:
- generated_from_trainer
- medical
- Healthcare & Lifesciences
- BioMed
- chain-of-thought
thumbnail: https://collaiborate.com/logo/logo-blue-bg-1.png
model-index:
- name: Bio-Medical-Llama-3-2-1B-CoT-012025
results: []
---
# Bio-Medical-Llama-3-2-1B-CoT-012025
This model is a fine-tuned version of [Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on our custom "BioMedData" dataset, enhanced with 625,000 examples, including 25,000 chain-of-thought (CoT) instruction samples to strengthen reasoning capabilities. It is specifically optimized for the Healthcare & Lifesciences (HLS) domain.
## Model details
**Model Name:** Bio-Medical-Llama-3-2-1B-CoT-012025
**Base Model:** Llama-3.2-1B-Instruct
**Parameter Count:** 1 billion
**Training Data:** Custom high-quality biomedical dataset with 625,000 examples, including 25,000 CoT instructions.
**Number of Entries in Dataset:** 625,000
**Dataset Composition:** The dataset comprises a mix of synthetic, manually curated, and reasoning-focused entries, ensuring comprehensive coverage of biomedical knowledge and logical reasoning.
## Model description
The Bio-Medical-Llama-3-2-1B-CoT-012025 model is a lightweight yet powerful language model tailored for:
- Generating domain-specific content in healthcare and biomedical fields.
- Answering complex questions requiring step-by-step reasoning using CoT.
- Supporting researchers, clinicians, and students in their respective biomedical endeavors.
This model is fine-tuned to provide interpretability and improved logical coherence through its enhanced CoT capabilities.
## Evaluation Metrics
Bio-Medical-Llama-3-2-1B-CoT-012025 has been evaluated using the Eleuther AI Language Model Evaluation Harness framework on tasks including:
- medmcqa
- medqa_4options
- mmlu_anatomy
- mmlu_clinical_knowledge
- mmlu_college_biology
- mmlu_college_medicine
- mmlu_medical_genetics
- mmlu_professional_medicine
- pubmedqa
Results show consistent performance improvements over general-purpose models of similiar size, particularly in tasks requiring reasoning.
## Intended uses & limitations
**Intended Uses:**
1. **Research Support:** Assisting researchers with reasoning and data extraction from biomedical texts.
2. **Clinical Decision Support:** Offering logical and evidence-based information to aid decision-making.
3. **Educational Tool:** Serving as a learning resource for understanding complex biomedical concepts.
**Limitations and Ethical Considerations:**
- **Biases:** The model may reflect biases from the training data, despite efforts to mitigate them.
- **Accuracy:** Responses should be cross-verified with reliable sources in critical scenarios.
- **Ethical Use:** The model should augment professional expertise and not replace it, especially in high-stakes applications.
## How to use
```python
import transformers
import torch
model_id = "ContactDoctor/Bio-Medical-Llama-3-2-1B-CoT-012025"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are an expert trained on healthcare and biomedical domain!"},
{"role": "user", "content": "What are the differential diagnoses for a patient presenting with shortness of breath and chest pain?"},
]
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=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
## License
This model is licensed under the [Bio-Medical-Llama-3-2-1B-CoT-012025 (Non-Commercial Use Only)](./LICENSE). Please review the terms and conditions before using the model.
### Contact Information
For further information, inquiries, or issues related to Bio-Medical-Llama-3-2-1B-CoT-012025, please contact:
Email: [email protected]
Website: [https://www.contactdoctor.in](https://www.contactdoctor.in)
### Training hyperparameters
The following hyperparameters were used during training:
- **Learning Rate:** 0.0002
- **Train Batch Size:** 8
- **Eval Batch Size:** 4
- **Seed:** 42
- **Gradient Accumulation Steps:** 8
- **Total Train Batch Size:** 32
- **Optimizer:** Adam with betas=(0.9, 0.999) and epsilon=1e-08
- **LR Scheduler Type:** Cosine
- **LR Scheduler Warmup Ratio:** 0.03
- **Training Steps:** 2000
- **Mixed Precision Training:** Native AMP
### Framework versions
- **PEFT:** 0.11.0
- **Transformers:** 4.40.2
- **Pytorch:** 2.1.2
- **Datasets:** 2.19.1
- **Tokenizers:** 0.19.1
### Citation
If you use Bio-Medical-Llama-3-2-1B-CoT-012025 in your research or applications, please cite it as follows:
```bibtex
@misc{ContactDoctor_Bio-Medical-Llama-3.2-1B-CoT-012025,
author = {ContactDoctor},
title = {Bio-Medical-Llama-3-2-1B-CoT-012025: A Reasoning-Enhanced Biomedical Language Model},
year = {2025},
howpublished = {https://huggingface.co/ContactDoctor/Bio-Medical-Llama-3-2-1B-CoT-012025},
}
```
|
[
"MEDQA",
"PUBMEDQA"
] |
tasksource/ModernBERT-large-nli
|
tasksource
|
zero-shot-classification
|
[
"transformers",
"safetensors",
"modernbert",
"text-classification",
"instruct",
"natural-language-inference",
"nli",
"zero-shot-classification",
"en",
"dataset:nyu-mll/glue",
"dataset:facebook/anli",
"base_model:answerdotai/ModernBERT-large",
"base_model:finetune:answerdotai/ModernBERT-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2025-01-04T00:56:11Z |
2025-01-04T12:03:47+00:00
| 3,223 | 5 |
---
base_model:
- answerdotai/ModernBERT-large
datasets:
- nyu-mll/glue
- facebook/anli
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: zero-shot-classification
tags:
- instruct
- natural-language-inference
- nli
---
# Model Card for Model ID
This model is ModernBERT multi-task fine-tuned on tasksource NLI tasks, including MNLI, ANLI, SICK, WANLI, doc-nli, LingNLI, FOLIO, FOL-NLI, LogicNLI, Label-NLI and all datasets in the below table).
This is the equivalent of an "instruct" version.
The model was trained for 200k steps on an Nvidia A30 GPU.
It is very good at reasoning tasks (better than llama 3.1 8B Instruct on ANLI and FOLIO), long context reasoning, sentiment analysis and zero-shot classification with new labels.
The following table shows model test accuracy. These are the scores for the same single transformer with different classification heads on top.
Further gains can be obtained by fine-tuning on a single-task, e.g. SST, but it this checkpoint is great for zero-shot classification and natural language inference (contradiction/entailment/neutral classification).
| test_name | test_accuracy |
|:--------------------------------------|----------------:|
| glue/mnli | 0.89 |
| glue/qnli | 0.96 |
| glue/rte | 0.91 |
| glue/wnli | 0.64 |
| glue/mrpc | 0.81 |
| glue/qqp | 0.87 |
| glue/cola | 0.87 |
| glue/sst2 | 0.96 |
| super_glue/boolq | 0.66 |
| super_glue/cb | 0.86 |
| super_glue/multirc | 0.9 |
| super_glue/wic | 0.71 |
| super_glue/axg | 1 |
| anli/a1 | 0.72 |
| anli/a2 | 0.54 |
| anli/a3 | 0.55 |
| sick/label | 0.91 |
| sick/entailment_AB | 0.93 |
| snli | 0.94 |
| scitail/snli_format | 0.95 |
| hans | 1 |
| WANLI | 0.77 |
| recast/recast_ner | 0.85 |
| recast/recast_sentiment | 0.97 |
| recast/recast_verbnet | 0.89 |
| recast/recast_megaveridicality | 0.87 |
| recast/recast_verbcorner | 0.87 |
| recast/recast_kg_relations | 0.9 |
| recast/recast_factuality | 0.95 |
| recast/recast_puns | 0.98 |
| probability_words_nli/reasoning_1hop | 1 |
| probability_words_nli/usnli | 0.79 |
| probability_words_nli/reasoning_2hop | 0.98 |
| nan-nli | 0.85 |
| nli_fever | 0.78 |
| breaking_nli | 0.99 |
| conj_nli | 0.72 |
| fracas | 0.79 |
| dialogue_nli | 0.94 |
| mpe | 0.75 |
| dnc | 0.91 |
| recast_white/fnplus | 0.76 |
| recast_white/sprl | 0.9 |
| recast_white/dpr | 0.84 |
| add_one_rte | 0.94 |
| paws/labeled_final | 0.96 |
| pragmeval/pdtb | 0.56 |
| lex_glue/scotus | 0.58 |
| lex_glue/ledgar | 0.85 |
| dynasent/dynabench.dynasent.r1.all/r1 | 0.83 |
| dynasent/dynabench.dynasent.r2.all/r2 | 0.76 |
| cycic_classification | 0.96 |
| lingnli | 0.91 |
| monotonicity-entailment | 0.97 |
| scinli | 0.88 |
| naturallogic | 0.93 |
| dynahate | 0.86 |
| syntactic-augmentation-nli | 0.94 |
| autotnli | 0.92 |
| defeasible-nli/atomic | 0.83 |
| defeasible-nli/snli | 0.8 |
| help-nli | 0.96 |
| nli-veridicality-transitivity | 0.99 |
| lonli | 0.99 |
| dadc-limit-nli | 0.79 |
| folio | 0.71 |
| tomi-nli | 0.54 |
| puzzte | 0.59 |
| temporal-nli | 0.93 |
| counterfactually-augmented-snli | 0.81 |
| cnli | 0.9 |
| boolq-natural-perturbations | 0.72 |
| equate | 0.65 |
| logiqa-2.0-nli | 0.58 |
| mindgames | 0.96 |
| ConTRoL-nli | 0.66 |
| logical-fallacy | 0.38 |
| cladder | 0.89 |
| conceptrules_v2 | 1 |
| zero-shot-label-nli | 0.79 |
| scone | 1 |
| monli | 1 |
| SpaceNLI | 1 |
| propsegment/nli | 0.92 |
| FLD.v2/default | 0.91 |
| FLD.v2/star | 0.78 |
| SDOH-NLI | 0.99 |
| scifact_entailment | 0.87 |
| feasibilityQA | 0.79 |
| AdjectiveScaleProbe-nli | 1 |
| resnli | 1 |
| semantic_fragments_nli | 1 |
| dataset_train_nli | 0.95 |
| nlgraph | 0.97 |
| ruletaker | 0.99 |
| PARARULE-Plus | 1 |
| logical-entailment | 0.93 |
| nope | 0.56 |
| LogicNLI | 0.91 |
| contract-nli/contractnli_a/seg | 0.88 |
| contract-nli/contractnli_b/full | 0.84 |
| nli4ct_semeval2024 | 0.72 |
| biosift-nli | 0.92 |
| SIGA-nli | 0.57 |
| FOL-nli | 0.79 |
| doc-nli | 0.81 |
| mctest-nli | 0.92 |
| natural-language-satisfiability | 0.92 |
| idioms-nli | 0.83 |
| lifecycle-entailment | 0.79 |
| MSciNLI | 0.84 |
| hover-3way/nli | 0.92 |
| seahorse_summarization_evaluation | 0.81 |
| missing-item-prediction/contrastive | 0.88 |
| Pol_NLI | 0.93 |
| synthetic-retrieval-NLI/count | 0.72 |
| synthetic-retrieval-NLI/position | 0.9 |
| synthetic-retrieval-NLI/binary | 0.92 |
| babi_nli | 0.98 |
# Usage
## [ZS] Zero-shot classification pipeline
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",model="tasksource/ModernBERT-large-nli")
text = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(text, candidate_labels)
```
NLI training data of this model includes [label-nli](https://huggingface.co/datasets/tasksource/zero-shot-label-nli), a NLI dataset specially constructed to improve this kind of zero-shot classification.
## [NLI] Natural language inference pipeline
```python
from transformers import pipeline
pipe = pipeline("text-classification",model="tasksource/ModernBERT-large-nli")
pipe([dict(text='there is a cat',
text_pair='there is a black cat')]) #list of (premise,hypothesis)
```
## Backbone for further fune-tuning
This checkpoint has stronger reasoning and fine-grained abilities than the base version and can be used for further fine-tuning.
# Citation
```
@inproceedings{sileo-2024-tasksource,
title = "tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework",
author = "Sileo, Damien",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1361",
pages = "15655--15684",
}
```
|
[
"SCIFACT",
"SCITAIL"
] |
FreedomIntelligence/Apollo-7B
|
FreedomIntelligence
|
text-generation
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:2403.03640",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-03-06T13:06:29Z |
2024-04-26T11:13:29+00:00
| 3,209 | 25 |
---
license: apache-2.0
---
# Multilingual Medicine: Model, Dataset, Benchmark, Code
Covering English, Chinese, French, Hindi, Spanish, Hindi, Arabic So far
<p align="center">
👨🏻💻<a href="https://github.com/FreedomIntelligence/Apollo" target="_blank">Github</a> •📃 <a href="https://arxiv.org/abs/2403.03640" target="_blank">Paper</a> • 🌐 <a href="https://apollo.llmzoo.com/" target="_blank">Demo</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a>
<br> <a href="./README_zh.md"> 中文 </a> | <a href="./README.md"> English
</p>
## 🌈 Update
* **[2024.04.25]** [MedJamba](https://huggingface.co/FreedomIntelligence/Apollo-MedJamba) released, train and evaluation code refer to [repo](https://github.com/FreedomIntelligence/MedJamba).
* **[2024.03.07]** [Paper](https://arxiv.org/abs/2403.03640) released.
* **[2024.02.12]** <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a> and <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a> is published！🎉
* **[2024.01.23]** Apollo repo is published！🎉
## Results
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-0.5B" target="_blank">Apollo-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-1.8B" target="_blank">Apollo-1.8B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-2B" target="_blank">Apollo-2B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-6B" target="_blank">Apollo-6B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-7B" target="_blank">Apollo-7B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-34B" target="_blank">Apollo-34B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-72B" target="_blank">Apollo-72B</a>
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MedJamba" target="_blank">MedJamba</a>
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-0.5B-GGUF" target="_blank">Apollo-0.5B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-2B-GGUF" target="_blank">Apollo-2B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-6B-GGUF" target="_blank">Apollo-6B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-7B-GGUF" target="_blank">Apollo-7B-GGUF</a>
## Usage Format
User:{query}\nAssistant:{response}<|endoftext|>
## Dataset & Evaluation
- Dataset
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a>
<details><summary>Click to expand</summary>
- [Zip File](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/blob/main/ApolloCorpus.zip)
- [Data category](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/tree/main/train)
- Pretrain:
- data item:
- json_name: {data_source}_{language}_{data_type}.json
- data_type: medicalBook, medicalGuideline, medicalPaper, medicalWeb(from online forum), medicalWiki
- language: en(English), zh(chinese), es(spanish), fr(french), hi(Hindi)
- data_type: qa(generated qa from text)
- data_type==text: list of string
```
[
"string1",
"string2",
...
]
```
- data_type==qa: list of qa pairs(list of string)
```
[
[
"q1",
"a1",
"q2",
"a2",
...
],
...
]
```
- SFT:
- json_name: {data_source}_{language}.json
- data_type: code, general, math, medicalExam, medicalPatient
- data item: list of qa pairs(list of string)
```
[
[
"q1",
"a1",
"q2",
"a2",
...
],
...
]
```
</details>
- Evaluation
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a>
<details><summary>Click to expand</summary>
- EN:
- [MedQA-USMLE](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
- [MedMCQA](https://huggingface.co/datasets/medmcqa/viewer/default/test)
- [PubMedQA](https://huggingface.co/datasets/pubmed_qa): Because the results fluctuated too much, they were not used in the paper.
- [MMLU-Medical](https://huggingface.co/datasets/cais/mmlu)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- ZH:
- [MedQA-MCMLE](https://huggingface.co/datasets/bigbio/med_qa/viewer/med_qa_zh_4options_bigbio_qa/test)
- [CMB-single](https://huggingface.co/datasets/FreedomIntelligence/CMB): Not used in the paper
- Randomly sample 2,000 multiple-choice questions with single answer.
- [CMMLU-Medical](https://huggingface.co/datasets/haonan-li/cmmlu)
- Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
- [CExam](https://github.com/williamliujl/CMExam): Not used in the paper
- Randomly sample 2,000 multiple-choice questions
- ES: [Head_qa](https://huggingface.co/datasets/head_qa)
- FR: [Frenchmedmcqa](https://github.com/qanastek/FrenchMedMCQA)
- HI: [MMLU_HI](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Arabic)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- AR: [MMLU_Ara](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Hindi)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
</details>
## Results reproduction
<details><summary>Click to expand</summary>
**Waiting for Update**
</details>
## Citation
Please use the following citation if you intend to use our dataset for training or evaluation:
```
@misc{wang2024apollo,
title={Apollo: Lightweight Multilingual Medical LLMs towards Democratizing Medical AI to 6B People},
author={Xidong Wang and Nuo Chen and Junyin Chen and Yan Hu and Yidong Wang and Xiangbo Wu and Anningzhe Gao and Xiang Wan and Haizhou Li and Benyou Wang},
year={2024},
eprint={2403.03640},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
[
"HEAD-QA",
"MEDQA",
"PUBMEDQA"
] |
QuantFactory/Bio-Medical-Llama-3-8B-GGUF
|
QuantFactory
| null |
[
"transformers",
"gguf",
"generated_from_trainer",
"medical",
"Healthcare & Lifesciences",
"BioMed",
"dataset:collaiborateorg/BioMedData",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:quantized:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-12-03T07:58:40Z |
2024-12-03T08:45:03+00:00
| 3,187 | 7 |
---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
datasets:
- collaiborateorg/BioMedData
library_name: transformers
license: other
tags:
- generated_from_trainer
- medical
- Healthcare & Lifesciences
- BioMed
thumbnail: https://collaiborate.com/logo/logo-blue-bg-1.png
model-index:
- name: Bio-Medical-Llama-3-8B
results: []
---
[](https://hf.co/QuantFactory)
# QuantFactory/Bio-Medical-Llama-3-8B-GGUF
This is quantized version of [ContactDoctor/Bio-Medical-Llama-3-8B](https://huggingface.co/ContactDoctor/Bio-Medical-Llama-3-8B) created using llama.cpp
# Original Model Card
<!-- 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. -->
# Bio-Medical
This model is a fine-tuned version of https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct on our custom "BioMedData" dataset.
## Model details
Model Name: Bio-Medical-Llama-3-8B
Base Model: Llama-3-8B-Instruct
Parameter Count: 8 billion
Training Data: Custom high-quality biomedical dataset
Number of Entries in Dataset: 500,000+
Dataset Composition: The dataset comprises both synthetic and manually curated samples, ensuring a diverse and comprehensive coverage of biomedical knowledge.
## Model description
Bio-Medical-Llama-3-8B model is a specialized large language model designed for biomedical applications. It is finetuned from the meta-llama/Meta-Llama-3-8B-Instruct model using a custom dataset containing over 500,000 diverse entries. These entries include a mix of synthetic and manually curated data, ensuring high quality and broad coverage of biomedical topics.
The model is trained to understand and generate text related to various biomedical fields, making it a valuable tool for researchers, clinicians, and other professionals in the biomedical domain.
## Evaluation Metrics
Bio-Medical-Llama-3-8B model outperforms many of the leading LLMs and find below its metrics evaluated using the Eleuther AI Language Model Evaluation Harness framework against the tasks medmcqa, medqa_4options, mmlu_anatomy, mmlu_clinical_knowledge, mmlu_college_biology, mmlu_college_medicine, mmlu_medical_genetics, mmlu_professional_medicine and pubmedqa.
## Intended uses & limitations
Bio-Medical-Llama-3-8B model is intended for a wide range of applications within the biomedical field, including:
1. Research Support: Assisting researchers in literature review and data extraction from biomedical texts.
2. Clinical Decision Support: Providing information to support clinical decision-making processes.
3. Educational Tool: Serving as a resource for medical students and professionals seeking to expand their knowledge base.
## Limitations and Ethical Considerations
While Bio-Medical-Llama-3-8B model performs well in various biomedical NLP tasks, users should be aware of the following limitations:
> Biases: The model may inherit biases present in the training data. Efforts have been made to curate a balanced dataset, but some biases may persist.
> Accuracy: The model's responses are based on patterns in the data it has seen and may not always be accurate or up-to-date. Users should verify critical information from reliable sources.
> Ethical Use: The model should be used responsibly, particularly in clinical settings where the stakes are high. It should complement, not replace, professional judgment and expertise.
## How to use
import transformers
import torch
model_id = "ContactDoctor/Bio-Medical-Llama-3-8B"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are an expert trained on healthcare and biomedical domain!"},
{"role": "user", "content": "I'm a 35-year-old male and for the past few months, I've been experiencing fatigue, increased sensitivity to cold, and dry, itchy skin. What is the diagnosis here?"},
]
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=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
## License
This model is licensed under the [Bio-Medical-Llama-3-8B (Non-Commercial Use Only)](./LICENSE). Please review the terms and conditions before using the model.
### Contact Information
For further information, inquiries, or issues related to Biomed-LLM, please contact:
Email: [email protected]
Website: https://www.contactdoctor.in
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 2000
- mixed_precision_training: Native AMP
### Framework versions
- PEFT 0.11.0
- Transformers 4.40.2
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
### Citation
If you use Bio-Medical LLM in your research or applications, please cite it as follows:
@misc{ContactDoctor_Bio-Medical-Llama-3-8B,
author = ContactDoctor,
title = {Bio-Medical: A High-Performance Biomedical Language Model},
year = {2024},
howpublished = {https://huggingface.co/ContactDoctor/Bio-Medical-Llama-3-8B},
}
|
[
"MEDQA",
"PUBMEDQA"
] |
HKUSTAudio/Llasa-3B
|
HKUSTAudio
|
text-to-speech
|
[
"safetensors",
"llama",
"Text-to-Speech",
"text-to-speech",
"zh",
"en",
"arxiv:2502.04128",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:cc-by-nc-4.0",
"region:us"
] | 2025-01-07T08:20:42Z |
2025-03-09T08:24:14+00:00
| 3,182 | 472 |
---
base_model:
- meta-llama/Llama-3.2-3B-Instruct
language:
- zh
- en
license: cc-by-nc-4.0
pipeline_tag: text-to-speech
tags:
- Text-to-Speech
---
[](https://arxiv.org/abs/2502.04128)
**Update (2025-02-13):** Add [Llasa finetune instruction](https://github.com/zhenye234/LLaSA_training/tree/main/finetune).
**Update (2025-02-07):** Our paper has been released!
LLaSA: Scaling Train-Time and Inference-Time Compute for LLaMA-based Speech Synthesis
- **Train from Scratch**: If you want to train the model from scratch, use the [LLaSA Training Repository](https://github.com/zhenye234/LLaSA_training).
- **Scale for Test-Time Computation**: If you want to experiment with scaling for test-time computation, use the [LLaSA Testing Repository](https://github.com/zhenye234/LLaSA_inference).
## Model Information
Our model, Llasa, is a text-to-speech (TTS) system that extends the text-based LLaMA (1B,3B, and 8B) language model by incorporating speech tokens from the XCodec2 codebook,
which contains 65,536 tokens. We trained Llasa on a dataset comprising 250,000 hours of Chinese-English speech data.
The model is capable of generating speech **either solely from input text or by utilizing a given speech prompt.**
The method is seamlessly compatible with the Llama framework, making training TTS similar as training LLM (convert audios into single-codebook tokens and simply view it as a special language). It opens the possiblity of existing method for compression, acceleration and finetuning for LLM to be applied.
## How to use
Install [XCodec2](https://huggingface.co/HKUSTAudio/xcodec2).
**1. Speech synthesis solely from input text**
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf
llasa_3b ='HKUSTAudio/Llasa-3B'
tokenizer = AutoTokenizer.from_pretrained(llasa_3b)
model = AutoModelForCausalLM.from_pretrained(llasa_3b)
model.eval()
model.to('cuda')
from xcodec2.modeling_xcodec2 import XCodec2Model
model_path = "HKUSTAudio/xcodec2"
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()
input_text = 'Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me.'
# input_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
def ids_to_speech_tokens(speech_ids):
speech_tokens_str = []
for speech_id in speech_ids:
speech_tokens_str.append(f"<|s_{speech_id}|>")
return speech_tokens_str
def extract_speech_ids(speech_tokens_str):
speech_ids = []
for token_str in speech_tokens_str:
if token_str.startswith('<|s_') and token_str.endswith('|>'):
num_str = token_str[4:-2]
num = int(num_str)
speech_ids.append(num)
else:
print(f"Unexpected token: {token_str}")
return speech_ids
#TTS start!
with torch.no_grad():
formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"
# Tokenize the text
chat = [
{"role": "user", "content": "Convert the text to speech:" + formatted_text},
{"role": "assistant", "content": "<|SPEECH_GENERATION_START|>"}
]
input_ids = tokenizer.apply_chat_template(
chat,
tokenize=True,
return_tensors='pt',
continue_final_message=True
)
input_ids = input_ids.to('cuda')
speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')
# Generate the speech autoregressively
outputs = model.generate(
input_ids,
max_length=2048, # We trained our model with a max length of 2048
eos_token_id= speech_end_id ,
do_sample=True,
top_p=1, # Adjusts the diversity of generated content
temperature=0.8, # Controls randomness in output
)
# Extract the speech tokens
generated_ids = outputs[0][input_ids.shape[1]:-1]
speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
# Convert token <|s_23456|> to int 23456
speech_tokens = extract_speech_ids(speech_tokens)
speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)
# Decode the speech tokens to speech waveform
gen_wav = Codec_model.decode_code(speech_tokens)
sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)
```
**2. Speech synthesis utilizing a given speech prompt**
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import soundfile as sf
llasa_3b ='HKUSTAudio/Llasa-3B'
tokenizer = AutoTokenizer.from_pretrained(llasa_3b)
model = AutoModelForCausalLM.from_pretrained(llasa_3b)
model.eval()
model.to('cuda')
from xcodec2.modeling_xcodec2 import XCodec2Model
model_path = "HKUSTAudio/xcodec2"
Codec_model = XCodec2Model.from_pretrained(model_path)
Codec_model.eval().cuda()
# only 16khz speech support!
prompt_wav, sr = sf.read("太乙真人.wav") # you can find wav in Files
#prompt_wav, sr = sf.read("Anna.wav") # English prompt
prompt_wav = torch.from_numpy(prompt_wav).float().unsqueeze(0)
prompt_text ="对，这就是我万人敬仰的太乙真人，虽然有点婴儿肥，但也掩不住我逼人的帅气。"
#promt_text = "A chance to leave him alone, but... No. She just wanted to see him again. Anna, you don't know how it feels to lose a sister. Anna, I'm sorry, but your father asked me not to tell you anything."
target_text = '突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道："我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"'
#target_text = "Dealing with family secrets is never easy. Yet, sometimes, omission is a form of protection, intending to safeguard some from the harsh truths. One day, I hope you understand the reasons behind my actions. Until then, Anna, please, bear with me."
input_text = prompt_text + target_text
def ids_to_speech_tokens(speech_ids):
speech_tokens_str = []
for speech_id in speech_ids:
speech_tokens_str.append(f"<|s_{speech_id}|>")
return speech_tokens_str
def extract_speech_ids(speech_tokens_str):
speech_ids = []
for token_str in speech_tokens_str:
if token_str.startswith('<|s_') and token_str.endswith('|>'):
num_str = token_str[4:-2]
num = int(num_str)
speech_ids.append(num)
else:
print(f"Unexpected token: {token_str}")
return speech_ids
#TTS start!
with torch.no_grad():
# Encode the prompt wav
vq_code_prompt = Codec_model.encode_code(input_waveform=prompt_wav)
print("Prompt Vq Code Shape:", vq_code_prompt.shape )
vq_code_prompt = vq_code_prompt[0,0,:]
# Convert int 12345 to token <|s_12345|>
speech_ids_prefix = ids_to_speech_tokens(vq_code_prompt)
formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"
# Tokenize the text and the speech prefix
chat = [
{"role": "user", "content": "Convert the text to speech:" + formatted_text},
{"role": "assistant", "content": "<|SPEECH_GENERATION_START|>" + ''.join(speech_ids_prefix)}
]
input_ids = tokenizer.apply_chat_template(
chat,
tokenize=True,
return_tensors='pt',
continue_final_message=True
)
input_ids = input_ids.to('cuda')
speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')
# Generate the speech autoregressively
outputs = model.generate(
input_ids,
max_length=2048, # We trained our model with a max length of 2048
eos_token_id= speech_end_id ,
do_sample=True,
top_p=1,
temperature=0.8,
)
# Extract the speech tokens
generated_ids = outputs[0][input_ids.shape[1]-len(speech_ids_prefix):-1]
speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
# Convert token <|s_23456|> to int 23456
speech_tokens = extract_speech_ids(speech_tokens)
speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)
# Decode the speech tokens to speech waveform
gen_wav = Codec_model.decode_code(speech_tokens)
# if only need the generated part
# gen_wav = gen_wav[:,:,prompt_wav.shape[1]:]
sf.write("gen.wav", gen_wav[0, 0, :].cpu().numpy(), 16000)
```
## Disclaimer
This model is licensed under the CC BY-NC 4.0 License, which prohibits free commercial use because of ethics and privacy concerns; detected violations will result in legal consequences.
This codebase is strictly prohibited from being used for any illegal purposes in any country or region. Please refer to your local laws about DMCA and other related laws.
|
[
"BEAR"
] |
QuantFactory/L3-Umbral-Mind-RP-v3.0-8B-GGUF
|
QuantFactory
| null |
[
"transformers",
"gguf",
"mergekit",
"merge",
"base_model:Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B",
"base_model:merge:Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B",
"base_model:Cas-Warehouse/Llama-3-Mopeyfied-Psychology-8B",
"base_model:merge:Cas-Warehouse/Llama-3-Mopeyfied-Psychology-8B",
"base_model:Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2",
"base_model:merge:Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2",
"base_model:Cas-Warehouse/Llama-3-SOVL-MopeyMule-8B",
"base_model:merge:Cas-Warehouse/Llama-3-SOVL-MopeyMule-8B",
"base_model:Cas-Warehouse/Llama-3-SOVL-MopeyMule-Blackroot-8B",
"base_model:merge:Cas-Warehouse/Llama-3-SOVL-MopeyMule-Blackroot-8B",
"base_model:Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B",
"base_model:merge:Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B",
"base_model:Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B",
"base_model:merge:Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B",
"base_model:Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B",
"base_model:merge:Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B",
"base_model:ChaoticNeutrals/Poppy_Porpoise-1.0-L3-8B",
"base_model:merge:ChaoticNeutrals/Poppy_Porpoise-1.0-L3-8B",
"base_model:Magpie-Align/Llama-3-8B-WizardLM-196K",
"base_model:merge:Magpie-Align/Llama-3-8B-WizardLM-196K",
"base_model:Nitral-AI/Hathor_Tahsin-L3-8B-v0.85",
"base_model:merge:Nitral-AI/Hathor_Tahsin-L3-8B-v0.85",
"base_model:ResplendentAI/Nymph_8B",
"base_model:merge:ResplendentAI/Nymph_8B",
"base_model:aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K",
"base_model:merge:aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K",
"base_model:bluuwhale/L3-SthenoMaidBlackroot-8B-V1",
"base_model:merge:bluuwhale/L3-SthenoMaidBlackroot-8B-V1",
"base_model:invisietch/EtherealRainbow-v0.3-8B",
"base_model:merge:invisietch/EtherealRainbow-v0.3-8B",
"base_model:migtissera/Llama-3-8B-Synthia-v3.5",
"base_model:merge:migtissera/Llama-3-8B-Synthia-v3.5",
"base_model:tannedbum/L3-Nymeria-8B",
"base_model:merge:tannedbum/L3-Nymeria-8B",
"base_model:tannedbum/L3-Nymeria-Maid-8B",
"base_model:merge:tannedbum/L3-Nymeria-Maid-8B",
"base_model:v000000/L3-8B-Poppy-Sunspice",
"base_model:merge:v000000/L3-8B-Poppy-Sunspice",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-07-21T16:48:16Z |
2024-07-21T17:24:48+00:00
| 3,179 | 5 |
---
base_model:
- Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B
- Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B
- tannedbum/L3-Nymeria-Maid-8B
- bluuwhale/L3-SthenoMaidBlackroot-8B-V1
- tannedbum/L3-Nymeria-8B
- Cas-Warehouse/Llama-3-SOVL-MopeyMule-8B
- Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
- Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2
- migtissera/Llama-3-8B-Synthia-v3.5
- Cas-Warehouse/Llama-3-SOVL-MopeyMule-Blackroot-8B
- v000000/L3-8B-Poppy-Sunspice
- Magpie-Align/Llama-3-8B-WizardLM-196K
- Cas-Warehouse/Llama-3-Mopeyfied-Psychology-8B
- Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B
- invisietch/EtherealRainbow-v0.3-8B
- crestf411/L3-8B-sunfall-v0.4-stheno-v3.2
- aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K
- ChaoticNeutrals/Poppy_Porpoise-1.0-L3-8B
- Nitral-AI/Hathor_Tahsin-L3-8B-v0.85
- Casual-Autopsy/Umbral-Mind-6
- ResplendentAI/Nymph_8B
library_name: transformers
tags:
- mergekit
- merge
---
# QuantFactory/L3-Umbral-Mind-RP-v3.0-8B-GGUF
This is quantized version of [Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B) created using llama.cpp
# Original Model Card
<img src="https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v3-8B/resolve/main/63073798_p0_master1200.jpg" style="display: block; margin: auto;">
Image by ろ47
# Merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
The goal of this merge was to make an RP model better suited for role-plays with heavy themes such as but not limited to:
- Mental illness
- Self-harm
- Trauma
- Suicide
I hated how RP models tended to be overly positive and hopeful with role-plays involving such themes,
but thanks to [failspy/Llama-3-8B-Instruct-MopeyMule](https://huggingface.co/failspy/Llama-3-8B-Instruct-MopeyMule) this problem has been lessened considerably.
If you're an enjoyer of savior/reverse savior type role-plays like myself, then this model is for you.
### Usage Info
This model is meant to be used with asterisks/quotes RPing formats, any other format that isn't asterisks/quotes is likely to cause issues
### Quants
* Weighted GGUFs by [mradermacher](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3.0-8B-i1-GGUF)
* Static GGUFs by [mradermacher](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v3.0-8B-GGUF)
### Models Merged
The following models were included in the merge:
* [Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B)
* [Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B](https://huggingface.co/Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B)
* [tannedbum/L3-Nymeria-Maid-8B](https://huggingface.co/tannedbum/L3-Nymeria-Maid-8B)
* [bluuwhale/L3-SthenoMaidBlackroot-8B-V1](https://huggingface.co/bluuwhale/L3-SthenoMaidBlackroot-8B-V1)
* [tannedbum/L3-Nymeria-8B](https://huggingface.co/tannedbum/L3-Nymeria-8B)
* [Cas-Warehouse/Llama-3-SOVL-MopeyMule-8B](https://huggingface.co/Cas-Warehouse/Llama-3-SOVL-MopeyMule-8B)
* [Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B)
* [Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2](https://huggingface.co/Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2)
* [migtissera/Llama-3-8B-Synthia-v3.5](https://huggingface.co/migtissera/Llama-3-8B-Synthia-v3.5)
* [Cas-Warehouse/Llama-3-SOVL-MopeyMule-Blackroot-8B](https://huggingface.co/Cas-Warehouse/Llama-3-SOVL-MopeyMule-Blackroot-8B)
* [v000000/L3-8B-Poppy-Sunspice](https://huggingface.co/v000000/L3-8B-Poppy-Sunspice)
* [Magpie-Align/Llama-3-8B-WizardLM-196K](https://huggingface.co/Magpie-Align/Llama-3-8B-WizardLM-196K)
* [Cas-Warehouse/Llama-3-Mopeyfied-Psychology-8B](https://huggingface.co/Cas-Warehouse/Llama-3-Mopeyfied-Psychology-8B)
* [Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B)
* [invisietch/EtherealRainbow-v0.3-8B](https://huggingface.co/invisietch/EtherealRainbow-v0.3-8B)
* [crestf411/L3-8B-sunfall-v0.4-stheno-v3.2](https://huggingface.co/crestf411/L3-8B-sunfall-v0.4-stheno-v3.2)
* [aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K](https://huggingface.co/aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K)
* [ChaoticNeutrals/Poppy_Porpoise-1.0-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-1.0-L3-8B)
* [Nitral-AI/Hathor_Tahsin-L3-8B-v0.85](https://huggingface.co/Nitral-AI/Hathor_Tahsin-L3-8B-v0.85)
* [ResplendentAI/Nymph_8B](https://huggingface.co/ResplendentAI/Nymph_8B)
## Secret Sauce
The following YAML configurations were used to produce this model:
### Umbral-Mind-1-pt.1
```yaml
models:
- model: Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B
- model: Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B
parameters:
density: 0.5
weight: [0.33, 0.0825, 0.0825, 0.0825, 0.0825]
- model: tannedbum/L3-Nymeria-Maid-8B
parameters:
density: 0.5
weight: [0.0825, 0.33, 0.0825, 0.0825, 0.0825]
- model: bluuwhale/L3-SthenoMaidBlackroot-8B-V1
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.33, 0.0825, 0.0825]
- model: tannedbum/L3-Nymeria-8B
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.0825, 0.33, 0.0825]
- model: Cas-Warehouse/Llama-3-SOVL-MopeyMule-8B
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.0825, 0.0825, 0.33]
merge_method: dare_ties
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
```
### Umbral-Mind-1-pt.2
```yaml
models:
- model: Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B
- model: Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.0825, 0.0825, 0.33]
- model: tannedbum/L3-Nymeria-Maid-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.0825, 0.33, 0.0825]
- model: bluuwhale/L3-SthenoMaidBlackroot-8B-V1
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.33, 0.0825, 0.0825]
- model: tannedbum/L3-Nymeria-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.33, 0.0825, 0.0825, 0.0825]
- model: Cas-Warehouse/Llama-3-SOVL-MopeyMule-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.33, 0.0825, 0.0825, 0.0825, 0.0825]
merge_method: breadcrumbs_ties
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
```
### Umbral-Mind-1
```yaml
models:
- model: Casual-Autopsy/Umbral-Mind-1-pt.1
- model: Casual-Autopsy/Umbral-Mind-1-pt.2
merge_method: slerp
base_model: Casual-Autopsy/Umbral-Mind-1-pt.1
parameters:
t:
- filter: self_attn
value: [0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5]
- filter: mlp
value: [0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5]
- value: 0.5
dtype: bfloat16
```
### Umbral-Mind-2-pt.1
```yaml
models:
- model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
- model: Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2
parameters:
density: 0.5
weight: [0.33, 0.0825, 0.0825, 0.0825, 0.0825]
- model: migtissera/Llama-3-8B-Synthia-v3.5
parameters:
density: 0.5
weight: [0.0825, 0.33, 0.0825, 0.0825, 0.0825]
- model: Cas-Warehouse/Llama-3-SOVL-MopeyMule-Blackroot-8B
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.33, 0.0825, 0.0825]
- model: v000000/L3-8B-Poppy-Sunspice
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.0825, 0.33, 0.0825]
- model: Cas-Warehouse/Llama-3-Mopeyfied-Psychology-8B
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.0825, 0.0825, 0.33]
merge_method: dare_ties
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
```
### Umbral-Mind-2-pt.2
```yaml
models:
- model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
- model: Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.0825, 0.0825, 0.33]
- model: migtissera/Llama-3-8B-Synthia-v3.5
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.0825, 0.33, 0.0825]
- model: Cas-Warehouse/Llama-3-SOVL-MopeyMule-Blackroot-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.33, 0.0825, 0.0825]
- model: Magpie-Align/Llama-3-8B-WizardLM-196K
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.33, 0.0825, 0.0825, 0.0825]
- model: Cas-Warehouse/Llama-3-Mopeyfied-Psychology-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.33, 0.0825, 0.0825, 0.0825, 0.0825]
merge_method: breadcrumbs_ties
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v1.0-8B
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
```
### Umbral-Mind-2
```yaml
models:
- model: Casual-Autopsy/Umbral-Mind-2-pt.1
- model: Casual-Autopsy/Umbral-Mind-2-pt.2
merge_method: slerp
base_model: Casual-Autopsy/Umbral-Mind-2-pt.1
parameters:
t:
- filter: self_attn
value: [0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5]
- filter: mlp
value: [0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5]
- value: 0.5
dtype: bfloat16
```
### Umbral-Mind-3-pt.1
```yaml
models:
- model: Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B
- model: Cas-Warehouse/Llama-3-SOVL-MopeyMule-8B
parameters:
density: 0.5
weight: [0.33, 0.0825, 0.0825, 0.0825, 0.0825]
- model: invisietch/EtherealRainbow-v0.3-8B
parameters:
density: 0.5
weight: [0.0825, 0.33, 0.0825, 0.0825, 0.0825]
- model: bluuwhale/L3-SthenoMaidBlackroot-8B-V1
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.33, 0.0825, 0.0825]
- model: crestf411/L3-8B-sunfall-v0.4-stheno-v3.2
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.0825, 0.33, 0.0825]
- model: Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B
parameters:
density: 0.5
weight: [0.0825, 0.0825, 0.0825, 0.0825, 0.33]
merge_method: dare_ties
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
```
### Umbral-Mind-3-pt.2
```yaml
models:
- model: Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B
- model: Cas-Warehouse/Llama-3-SOVL-MopeyMule-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.0825, 0.0825, 0.33]
- model: invisietch/EtherealRainbow-v0.3-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.0825, 0.33, 0.0825]
- model: bluuwhale/L3-SthenoMaidBlackroot-8B-V1
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.0825, 0.33, 0.0825, 0.0825]
- model: crestf411/L3-8B-sunfall-v0.4-stheno-v3.2
parameters:
gamma: 0.01
density: 0.9
weight: [0.0825, 0.33, 0.0825, 0.0825, 0.0825]
- model: Cas-Warehouse/Llama-3-MopeyMule-Blackroot-8B
parameters:
gamma: 0.01
density: 0.9
weight: [0.33, 0.0825, 0.0825, 0.0825, 0.0825]
merge_method: breadcrumbs_ties
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
```
### Umbral-Mind-3
```yaml
models:
- model: Casual-Autopsy/Umbral-Mind-3-pt.1
- model: Casual-Autopsy/Umbral-Mind-3-pt.2
merge_method: slerp
base_model: Casual-Autopsy/Umbral-Mind-3-pt.1
parameters:
t:
- filter: self_attn
value: [0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5]
- filter: mlp
value: [0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5, 0.7, 0.3, 0.5, 0.3, 0.7, 0.5]
- value: 0.5
dtype: bfloat16
```
### Umbral-Mind-4
```yaml
models:
- model: Casual-Autopsy/Umbral-Mind-1
- model: Casual-Autopsy/Umbral-Mind-3
merge_method: slerp
base_model: Casual-Autopsy/Umbral-Mind-1
parameters:
t:
- value: [0.1, 0.15, 0.2, 0.4, 0.6, 0.4, 0.2, 0.15, 0.1]
dtype: bfloat16
```
### Umbral-Mind-5
```yaml
models:
- model: Casual-Autopsy/Umbral-Mind-4
- model: Casual-Autopsy/Umbral-Mind-2
merge_method: slerp
base_model: Casual-Autopsy/Umbral-Mind-4
parameters:
t:
- value: [0.7, 0.5, 0.3, 0.25, 0.2, 0.25, 0.3, 0.5, 0.7]
embed_slerp: true
dtype: bfloat16
```
### Umbral-Mind-6
```yaml
models:
- model: mergekit-community/Umbral-Mind-5
- model: Casual-Autopsy/Mopey-Omelette
merge_method: slerp
base_model: mergekit-community/Umbral-Mind-5
parameters:
t:
- value: [0.2, 0.25, 0.3, 0.4, 0.3, 0.25, 0.2, 0.25, 0.3, 0.4, 0.3, 0.25, 0.2]
embed_slerp: true
dtype: bfloat16
```
### Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B
```yaml
models:
- model: Casual-Autopsy/Umbral-Mind-6
- model: aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K
parameters:
weight: [0.02, -0.01, -0.01, 0.02]
- model: ResplendentAI/Nymph_8B
parameters:
weight: [-0.01, 0.02, 0.02, -0.01]
- model: ChaoticNeutrals/Poppy_Porpoise-1.0-L3-8B
parameters:
weight: [-0.01, 0.02, 0.02, -0.01]
- model: Nitral-AI/Hathor_Tahsin-L3-8B-v0.85
parameters:
weight: [0.02, -0.01, -0.01, 0.02]
merge_method: task_arithmetic
base_model: Casual-Autopsy/Umbral-Mind-6
parameters:
normalize: false
dtype: bfloat16
```
|
[
"CAS"
] |
alvaroalon2/biobert_genetic_ner
|
alvaroalon2
|
token-classification
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"NER",
"Biomedical",
"Genetics",
"en",
"dataset:JNLPBA",
"dataset:BC2GM",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05Z |
2023-03-17T12:11:30+00:00
| 3,162 | 22 |
---
datasets:
- JNLPBA
- BC2GM
language: en
license: apache-2.0
tags:
- token-classification
- NER
- Biomedical
- Genetics
---
BioBERT model fine-tuned in NER task with JNLPBA and BC2GM corpus for genetic class entities.
This was fine-tuned in order to use it in a BioNER/BioNEN system which is available at: https://github.com/librairy/bio-ner
|
[
"JNLPBA"
] |
hywu/Camelidae-8x34B
|
hywu
|
text-generation
|
[
"transformers",
"pytorch",
"camelidae",
"text-generation",
"custom_code",
"en",
"dataset:Open-Orca/SlimOrca",
"dataset:ise-uiuc/Magicoder-OSS-Instruct-75K",
"dataset:ise-uiuc/Magicoder-Evol-Instruct-110K",
"dataset:meta-math/MetaMathQA",
"arxiv:2401.02731",
"arxiv:2305.14314",
"arxiv:1902.00751",
"arxiv:2212.05055",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-01-10T09:20:18Z |
2024-09-20T02:35:13+00:00
| 3,151 | 28 |
---
datasets:
- Open-Orca/SlimOrca
- ise-uiuc/Magicoder-OSS-Instruct-75K
- ise-uiuc/Magicoder-Evol-Instruct-110K
- meta-math/MetaMathQA
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
arxiv: 2401.02731
---
# Parameter-Efficient Sparsity Crafting From Dense to Mixture-of-Experts for Instruction Tuning on General Tasks (EMNLP'24)
## News
- 9/20/2024 - Our paper is accepted by EMNLP'24.
- 3/12/2024 - We release Qwen2idae-16x14B-v1.0 on 🤗 [HuggingFace](https://huggingface.co/hywu/Qwen2idae-16x14B-v1.0), which has strong performance in Math and Code with 15B activated params.
- 2/7/2024 - [Serp-ai](https://github.com/serp-ai/Parameter-Efficient-MoE) adds [unsloth](https://github.com/serp-ai/unsloth) support for faster and memory efficient training of our Parameter-Efficient Sparsity Crafting and releases new [sparsetral](https://huggingface.co/serpdotai/sparsetral-16x7B-v2) models based on mistral-7B.
- 1/10/2024 - Camelidae models are now available on 🤗 [HuggingFace](https://huggingface.co/hywu).
- 1/4/2024 - We release the paper, [Parameter-Efficient Sparsity Crafting From Dense to Mixture-of-Experts for Instruction Tuning on General Tasks](https://arxiv.org/abs/2401.02731).
- 12/22/2023 - We release the training [repo](https://github.com/wuhy68/Parameter-Efficient-MoE) that craft the dense model with LLaMA architecture to the MoE model.
## Introduction
Camelidae and Qwen2idae models are trained utilizing Parameter-Efficient Sparsity Crafting techniques
We present Parameter-Efficient Sparsity Crafting to help dense models learn knowledge from different fields (including code and math). This approach performs instruction tuning and efficiently utilizes MoE structure.
Specifically, Parameter-Efficient Sparsity Crafting utilizes parameter-efficient techniques including [QLoRA](https://arxiv.org/abs/2305.14314) and [Adapter](https://arxiv.org/abs/1902.00751) to perform Efficient [Sparse Upcycling](https://arxiv.org/abs/2212.05055).
## Model Lists
| Camelidae Series | Download
|---|---
Camelidae-8x7B | 🤗 [HuggingFace](https://huggingface.co/hywu/Camelidae-8x7B)
Camelidae-8x13B | 🤗 [HuggingFace](https://huggingface.co/hywu/Camelidae-8x13B)
Camelidae-8x34B | 🤗 [HuggingFace](https://huggingface.co/hywu/Camelidae-8x34B)
Camelidae-8x34B-pro | 🤗 Coming Soon
| Qwen2idae Series | Download
|---|---
Qwen2idae-16x14B-v1.0 | 🤗 [HuggingFace](https://huggingface.co/hywu/Qwen2idae-16x14B-v1.0)
Qwen2idae-16x7B-v1.0 | 🤗 Coming Soon
Qwen2idae-16x1.8B-v1.0 | 🤗 Coming Soon
## Performance
| Model | Activated Params | MMLU (5shot) | GSM8k (5shot) | MATH (4shot) | HumanEval (0shot) | MBPP (4shot) | HellaSwag (10shot) |
|:-----:|:----------------:|:------------:|:-------------:|:------------:|:-----------------:|:------------:|:------------------:|
| GPT3.5 | - | 70.0% | 57.1% | <font color=#F67F70>**34.1%**</font> | <font color=#FBD98D>**48.1%**</font> | - | <font color=#7FEA9E>**85.5%**</font> |
| LLaMA2-70B-chat | 70B | 63.8% | 59.3% | 10.4% | 32.3% | 35.6% | 84.8% |
| Camelidae-8x34B-pro | 35B | <font color=#7FEA9E>**75.7%**</font> | <font color=#F67F70>**79.4%**</font> | <font color=#FBD98D>**24.0%**</font> | <font color=#7FEA9E>**48.8%**</font> | <font color=#7FEA9E>**43.2%**</font> | 85.2% |
| Camelidae-8x34B | 35B | <font color=#FBD98D>**75.6%**</font> | <font color=#7FEA9E>**78.3%**</font> | 22.6% | 43.9% | <font color=#FBD98D>**41.4%**</font> | <font color=#FBD98D>**85.3%**</font> |
| SUSChat-34B | 34B | <font color=#F67F70>**76.4%**</font> | 72.3% | 22.0% | 11.6% | 40.2% | 83.9% |
| Yi-34B-chat | 34B | 74.8% | 67.6% | 17.3% | 20.1% | 41.0% | 83.9% |
| Qwen2idae-16x14B-v1.0 | 15B | 66.7% | <font color=#FBD98D>**77.8%**</font> | <font color=#7FEA9E>**29.9%**</font> | <font color=#F67F70>**62.8%**</font> | <font color=#F67F70>**48.6%**</font> | 82.3% |
| Mixtral-8x7B-instruct | 14B | 68.7% | 71.7% | 22.1% | 25.6% | 40.6% | <font color=#F67F70>**86.5%**</font> |
| Camelidae-8x13B | 13B | 54.4% | 52.6% | 9.8% | 30.6% | 30.4% | 82.5% |
| LLaMA2-13B-chat | 13B | 53.9% | 37.1% | 5.2% | 18.9% | 27.2% | 81.9% |
| Camelidae-8x7B | 7B | 48.3% | 44.0% | 5.8% | 18.3% | 23.4% | 79.2% |
| LLaMA2-7B-chat | 7B | 47.2% | 26.3% | 3.9% | 12.2% | 17.6% | 78.6% |
We bold the top3 scores separately for all models.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("hywu/Camelidae-8x34B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("hywu/Camelidae-8x34B", device_map="auto", trust_remote_code=True).eval()
inputs = tokenizer('### Human:\nHow are you?\n### Assistant:\n', return_tensors='pt')
inputs = inputs.to(model.device)
pred = model.generate(**inputs)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
```
## Citation
```bibtex
@article{wu2024parameter,
title={Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks},
author={Wu, Haoyuan and Zheng, Haisheng and Yu, Bei},
journal={arXiv preprint arXiv:2401.02731},
year={2024}
}
```
## License
The source code in this repo is licensed under the [Apache 2.0 License](https://github.com/wuhy68/Parameter-Efficient-MoE/blob/master/LICENSE). Camelidae models are developed for academic research and free commercial use, all usage must adhere to the license from [facebookresearch](https://github.com/facebookresearch/llama/blob/main/LICENSE) and [01-ai](https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt).
|
[
"CRAFT"
] |
KOCDIGITAL/Kocdigital-LLM-8b-v0.1
|
KOCDIGITAL
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"tr",
"license:llama3",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-05-01T18:34:27Z |
2024-05-03T09:29:16+00:00
| 3,141 | 11 |
---
language:
- tr
license: llama3
model-index:
- name: Kocdigital-LLM-8b-v0.1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge TR
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc
value: 44.03
name: accuracy
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag TR
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc
value: 46.73
name: accuracy
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU TR
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 49.11
name: accuracy
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA TR
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: acc
value: 48.21
name: accuracy
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande TR
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 10
metrics:
- type: acc
value: 54.98
name: accuracy
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k TR
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 51.78
name: accuracy
---
<img src="https://huggingface.co/KOCDIGITAL/Kocdigital-LLM-8b-v0.1/resolve/main/icon.jpeg"
alt="KOCDIGITAL LLM" width="420"/>
# Kocdigital-LLM-8b-v0.1
This model is an fine-tuned version of a Llama3 8b Large Language Model (LLM) for Turkish. It was trained on a high quality Turkish instruction sets created from various open-source and internal resources. Turkish Instruction dataset carefully annotated to carry out Turkish instructions in an accurate and organized manner. The training process involved using the QLORA method.
## Model Details
- **Base Model**: Llama3 8B based LLM
- **Training Dataset**: High Quality Turkish instruction sets
- **Training Method**: SFT with QLORA
### QLORA Fine-Tuning Configuration
- `lora_alpha`: 128
- `lora_dropout`: 0
- `r`: 64
- `target_modules`: "q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj"
- `bias`: "none"
## Usage Examples
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"KOCDIGITAL/Kocdigital-LLM-8b-v0.1",
max_seq_length=4096)
model = AutoModelForCausalLM.from_pretrained(
"KOCDIGITAL/Kocdigital-LLM-8b-v0.1",
load_in_4bit=True,
)
system = 'Sen Türkçe konuşan genel amaçlı bir asistansın. Her zaman kullanıcının verdiği talimatları doğru, kısa ve güzel bir gramer ile yerine getir.'
template = "{}\n\n###Talimat\n{}\n###Yanıt\n"
content = template.format(system, 'Türkiyenin 3 büyük ilini listeler misin.')
conv = []
conv.append({'role': 'user', 'content': content})
inputs = tokenizer.apply_chat_template(conv,
tokenize=False,
add_generation_prompt=True,
return_tensors="pt")
print(inputs)
inputs = tokenizer([inputs],
return_tensors = "pt",
add_special_tokens=False).to("cuda")
outputs = model.generate(**inputs,
max_new_tokens = 512,
use_cache = True,
do_sample = True,
top_k = 50,
top_p = 0.60,
temperature = 0.3,
repetition_penalty=1.1)
out_text = tokenizer.batch_decode(outputs)[0]
print(out_text)
```
# [Open LLM Turkish Leaderboard v0.2 Evaluation Results]
| Metric | Value |
|---------------------------------|------:|
| Avg. | 49.11 |
| AI2 Reasoning Challenge_tr-v0.2 | 44.03 |
| HellaSwag_tr-v0.2 | 46.73 |
| MMLU_tr-v0.2 | 49.11 |
| TruthfulQA_tr-v0.2 | 48.51 |
| Winogrande _tr-v0.2 | 54.98 |
| GSM8k_tr-v0.2 | 51.78 |
## Considerations on Limitations, Risks, Bias, and Ethical Factors
### Limitations and Recognized Biases
- **Core Functionality and Usage:** KocDigital LLM, functioning as an autoregressive language model, is primarily purposed for predicting the subsequent token within a text sequence. Although commonly applied across different contexts, it's crucial to acknowledge that comprehensive real-world testing has not been conducted. Therefore, its efficacy and consistency in diverse situations are largely unvalidated.
- **Language Understanding and Generation:** The model's training is mainly focused on standard English and Turkish. Its proficiency in grasping and generating slang, colloquial language, or different languages might be restricted, possibly resulting in errors or misinterpretations.
- **Production of Misleading Information:** Users should acknowledge that KocDigital LLM might generate incorrect or deceptive information. Results should be viewed as initial prompts or recommendations rather than absolute conclusions.
### Ethical Concerns and Potential Risks
- **Risk of Misuse:** KocDigital LLM carries the potential for generating language that could be offensive or harmful. We strongly advise against its utilization for such purposes and stress the importance of conducting thorough safety and fairness assessments tailored to specific applications before implementation.
- **Unintended Biases and Content:** The model underwent training on a vast corpus of text data without explicit vetting for offensive material or inherent biases. Consequently, it may inadvertently generate content reflecting these biases or inaccuracies.
- **Toxicity:** Despite efforts to curate appropriate training data, the model has the capacity to produce harmful content, particularly when prompted explicitly. We encourage active participation from the open-source community to devise strategies aimed at mitigating such risks.
### Guidelines for Secure and Ethical Utilization
- **Human Oversight:** We advocate for the integration of a human oversight mechanism or the utilization of filters to oversee and enhance the quality of outputs, particularly in applications accessible to the public. This strategy can assist in minimizing the likelihood of unexpectedly generating objectionable content.
- **Tailored Testing for Specific Applications:** Developers planning to utilize KocDigital LLM should execute comprehensive safety assessments and optimizations customized to their unique applications. This step is essential as the model's responses may exhibit unpredictability and occasional biases, inaccuracies, or offensive outputs.
- **Responsible Development and Deployment:** Developers and users of KocDigital LLM bear the responsibility for ensuring its ethical and secure application. We encourage users to be cognizant of the model's limitations and to implement appropriate measures to prevent misuse or adverse outcomes.
|
[
"BEAR"
] |
pszemraj/long-t5-tglobal-base-16384-book-summary
|
pszemraj
|
summarization
|
[
"transformers",
"pytorch",
"rust",
"onnx",
"safetensors",
"longt5",
"text2text-generation",
"summarization",
"summary",
"booksum",
"long-document",
"long-form",
"dataset:kmfoda/booksum",
"arxiv:2112.07916",
"arxiv:2105.08209",
"doi:10.57967/hf/2078",
"license:apache-2.0",
"license:bsd-3-clause",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-06-27T16:37:26Z |
2025-01-21T13:58:25+00:00
| 3,139 | 134 |
---
datasets:
- kmfoda/booksum
license:
- apache-2.0
- bsd-3-clause
metrics:
- rouge
tags:
- summarization
- summary
- booksum
- long-document
- long-form
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
num_beams: 3
model-index:
- name: pszemraj/long-t5-tglobal-base-16384-book-summary
results:
- task:
type: summarization
name: Summarization
dataset:
name: kmfoda/booksum
type: kmfoda/booksum
config: kmfoda--booksum
split: test
metrics:
- type: rouge
value: 36.4085
name: ROUGE-1
verified: true
- type: rouge
value: 6.0646
name: ROUGE-2
verified: true
- type: rouge
value: 16.7209
name: ROUGE-L
verified: true
- type: rouge
value: 33.3405
name: ROUGE-LSUM
verified: true
- type: loss
value: .nan
name: loss
verified: true
- type: gen_len
value: 252.8099
name: gen_len
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: samsum
type: samsum
config: samsum
split: test
metrics:
- type: rouge
value: 30.9047
name: ROUGE-1
verified: true
- type: rouge
value: 7.4715
name: ROUGE-2
verified: true
- type: rouge
value: 22.3962
name: ROUGE-L
verified: true
- type: rouge
value: 26.9094
name: ROUGE-LSUM
verified: true
- type: loss
value: .nan
name: loss
verified: true
- type: gen_len
value: 46.7973
name: gen_len
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: test
metrics:
- type: rouge
value: 30.5942
name: ROUGE-1
verified: true
- type: rouge
value: 7.252
name: ROUGE-2
verified: true
- type: rouge
value: 17.7156
name: ROUGE-L
verified: true
- type: rouge
value: 27.2881
name: ROUGE-LSUM
verified: true
- type: loss
value: .nan
name: loss
verified: true
- type: gen_len
value: 125.2507
name: gen_len
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: xsum
type: xsum
config: default
split: test
metrics:
- type: rouge
value: 20.3648
name: ROUGE-1
verified: true
- type: rouge
value: 3.4126
name: ROUGE-2
verified: true
- type: rouge
value: 13.6168
name: ROUGE-L
verified: true
- type: rouge
value: 15.8313
name: ROUGE-LSUM
verified: true
- type: loss
value: .nan
name: loss
verified: true
- type: gen_len
value: 82.2177
name: gen_len
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: billsum
type: billsum
config: default
split: test
metrics:
- type: rouge
value: 39.6378
name: ROUGE-1
verified: true
- type: rouge
value: 13.0017
name: ROUGE-2
verified: true
- type: rouge
value: 23.0255
name: ROUGE-L
verified: true
- type: rouge
value: 32.9943
name: ROUGE-LSUM
verified: true
- type: loss
value: 1.9428048133850098
name: loss
verified: true
- type: gen_len
value: 162.3588
name: gen_len
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: big_patent
type: big_patent
config: y
split: test
metrics:
- type: rouge
value: 34.7641
name: ROUGE-1
verified: true
- type: rouge
value: 7.8744
name: ROUGE-2
verified: true
- type: rouge
value: 19.9826
name: ROUGE-L
verified: true
- type: rouge
value: 29.208
name: ROUGE-LSUM
verified: true
- type: loss
value: 2.8316469192504883
name: loss
verified: true
- type: gen_len
value: 132.7475
name: gen_len
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: launch/gov_report
type: launch/gov_report
config: plain_text
split: validation
metrics:
- type: rouge
value: 37.9246
name: ROUGE-1
verified: true
- type: rouge
value: 8.5837
name: ROUGE-2
verified: true
- type: rouge
value: 18.0274
name: ROUGE-L
verified: true
- type: rouge
value: 34.0816
name: ROUGE-LSUM
verified: true
- type: loss
value: 2.56695818901062
name: loss
verified: true
- type: gen_len
value: 220.3747
name: gen_len
verified: true
- task:
type: summarization
name: Summarization
dataset:
name: launch/gov_report
type: launch/gov_report
config: plain_text
split: test
metrics:
- type: rouge
value: 37.4438
name: ROUGE-1
verified: true
- type: rouge
value: 8.2907
name: ROUGE-2
verified: true
- type: rouge
value: 17.6893
name: ROUGE-L
verified: true
- type: rouge
value: 33.7141
name: ROUGE-LSUM
verified: true
- type: loss
value: 2.5776000022888184
name: loss
verified: true
- type: gen_len
value: 214.9692
name: gen_len
verified: true
---
# long-t5-tglobal-base-16384 + BookSum
<a href="https://colab.research.google.com/gist/pszemraj/d9a0495861776168fd5cdcd7731bc4ee/example-long-t5-tglobal-base-16384-book-summary.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
> [!IMPORTANT]
> As of [this discussion](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary/discussions/23) we found issues with long-t5 models >= 4.23.0 - please use `pip install transformers==4.22.0` to ensure good performance with this model until this disclaimer is removed.
Summarize long text and get a SparkNotes-esque summary of arbitrary topics!
- generalizes reasonably well to academic & narrative text.
- A simple example/use case on ASR is [here](https://longt5-booksum-example.netlify.app/).
- Example notebook in Colab (_click on the icon above_).
## Cheeky Proof-of-Concept
A summary of the [infamous navy seals copypasta](https://knowyourmeme.com/memes/navy-seal-copypasta):
> The narrator tells us that he's graduated from the Navy seals and has been involved in many secret raids. He's also one of the best snipers in the entire U.S. military. He promises to "wipe you out with precision" when they meet again.
* * *
**Contents**
<!-- TOC -->
- [Model description](#model-description)
- [How-To in Python](#how-to-in-python)
- [Intended uses & limitations](#intended-uses--limitations)
- [Training and evaluation data](#training-and-evaluation-data)
- [FAQ](#faq)
- [How to run inference over a very long (30k+ tokens) document in batches?](#how-to-run-inference-over-a-very-long-30k-tokens-document-in-batches)
- [How to fine-tune further?](#how-to-fine-tune-further)
- [Are there simpler ways to run this?](#are-there-simpler-ways-to-run-this)
- [Training procedure](#training-procedure)
- [Updates:](#updates)
- [Training hyperparameters](#training-hyperparameters)
- [Framework versions](#framework-versions)
- [Citation info](#citation-info)
<!-- /TOC -->
* * *
## Model description
A fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on the `kmfoda/booksum` dataset:
- 30+ epochs of fine-tuning from the base model on V100/A100 GPUs
- Training used 16384 token input / 1024 max output
Read the paper by Guo et al. here: [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf)
## How-To in Python
Install/update transformers `pip install -U transformers`
Summarize text with pipeline:
```python
import torch
from transformers import pipeline
summarizer = pipeline(
"summarization",
"pszemraj/long-t5-tglobal-base-16384-book-summary",
device=0 if torch.cuda.is_available() else -1,
)
long_text = "Here is a lot of text I don't want to read. Replace me"
result = summarizer(long_text)
print(result[0]["summary_text"])
```
Pass [other parameters related to beam search textgen](https://huggingface.co/blog/how-to-generate) when calling `summarizer` to get even higher quality results.
## Intended uses & limitations
- The current checkpoint is fairly well converged but will be updated if further improvements can be made.
- Compare performance to [LED-base](https://huggingface.co/pszemraj/led-base-book-summary) trained on the same dataset (API gen parameters are the same).
- while this model seems to improve upon factual consistency, **do not take summaries to be foolproof and check things that seem odd**.
## Training and evaluation data
`kmfoda/booksum` dataset on HuggingFace - read [the original paper here](https://arxiv.org/abs/2105.08209). Summaries longer than 1024 LongT5 tokens were filtered out to prevent the model from learning to generate "partial" summaries.
* * *
## FAQ
### How to run inference over a very long (30k+ tokens) document in batches?
See `summarize.py` in [the code for my hf space Document Summarization](https://huggingface.co/spaces/pszemraj/document-summarization/blob/main/summarize.py) :)
You can also use the same code to split a document into batches of 4096, etc., and run over those with the model. This is useful in situations where CUDA memory is limited.
### How to fine-tune further?
See [train with a script](https://huggingface.co/docs/transformers/run_scripts) and [the summarization scripts](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization).
This model was originally tuned on Google Colab with a heavily modified variant of the [longformer training notebook](https://github.com/patrickvonplaten/notebooks/blob/master/Fine_tune_Longformer_Encoder_Decoder_(LED)_for_Summarization_on_pubmed.ipynb), key enabler being deepspeed. You can try this as an alternate route to fine-tuning the model without using the command line.
### Are there simpler ways to run this?
For this reason, I created a Python package utility. It's called [textsum](https://github.com/pszemraj/textsum), and you can use it to load models and summarize things in a few lines of code.
```sh
pip install textsum
```
Use `textsum` in python with this model:
```python
from textsum.summarize import Summarizer
summarizer = Summarizer(
model_name_or_path="pszemraj/long-t5-tglobal-base-16384-book-summary"
)
long_string = "This is a long string of text that will be summarized."
out_str = summarizer.summarize_string(long_string)
print(f"summary: {out_str}")
```
This package provides easy-to-use interfaces for applying summarization models to text documents of arbitrary length. Currently implemented interfaces include a Python API, a CLI, and a shareable demo application.
For details, explanations, and documentation, see the README (_linked above_) or the [wiki](https://github.com/pszemraj/textsum/wiki).
* * *
## Training procedure
### Updates:
- July 22, 2022: updated to a fairly converged checkpoint
- July 3, 2022: Added a new version with several epochs of additional general training that is more performant.
### Training hyperparameters
_NOTE: early checkpoints of this model were trained on a "smaller" subsection of the dataset as it was filtered for summaries of **1024 characters**. This was subsequently caught and adjusted to **1024 tokens** and then trained further for 10+ epochs._
The following hyperparameters were used during the **most recent** training round\*:
- learning_rate: 0.0005
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 128
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 2
\* Prior training sessions used roughly similar parameters; multiple sessions were required as this takes eons to train
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
## Citation info
If you find `pszemraj/long-t5-tglobal-base-16384-book-summary` useful in your work, please consider citing this model :)
@misc {peter_szemraj_2022,
author = { {Peter Szemraj} },
title = { long-t5-tglobal-base-16384-book-summary (Revision 4b12bce) },
year = 2022,
url = { https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary },
doi = { 10.57967/hf/0100 },
publisher = { Hugging Face }
}
|
[
"BEAR"
] |
amd/AMD-OLMo-1B
|
amd
|
text-generation
|
[
"safetensors",
"olmo",
"text-generation",
"dataset:allenai/dolma",
"license:apache-2.0",
"region:us"
] | 2024-10-31T20:27:49Z |
2024-11-03T06:29:04+00:00
| 3,139 | 24 |
---
datasets:
- allenai/dolma
license: apache-2.0
pipeline_tag: text-generation
---
# AMD-OLMo
AMD-OLMo are a series of 1B language models trained from scratch by AMD on AMD Instinct™ MI250 GPUs. The training code used is based on [OLMo](https://github.com/allenai/OLMo).
We release the pre-trained model, supervised fine-tuned model, and DPO aligned model as follows:
- [AMD-OLMo-1B](https://huggingface.co/amd/AMD-OLMo-1B): Pre-trained on a subset of [Dolma v1.7](https://huggingface.co/datasets/allenai/dolma) that consists of 1.3 trillion tokens.
- [AMD-OLMo-1B-SFT](https://huggingface.co/amd/AMD-OLMo-1B-SFT): Supervised fine-tuned (SFT) on [Tulu V2](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture) dataset (1st phase) and then [OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5), [WebInstructSub](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub), and [Code-Feedback](https://huggingface.co/datasets/m-a-p/Code-Feedback) datasets (2nd phase).
- [AMD-OLMo-1B-SFT-DPO](https://huggingface.co/amd/AMD-OLMo-1B-SFT-DPO): Aligned with human preferences using Direct Preference Optimization (DPO) on [UltraFeedback](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned) dataset.
Description:
- **Hardware**: Each compute node consists of 4 AMD Instinct™ MI250 GPUs. We use 16 nodes for pretraining AMD-OLMo-1B
- **Training throughput**: 12,200 tokens/sec/gpu
- **Model architecture**: AMD-OLMo-1B is based on the model architecture and training set up of fully open source 1 billion version of [OLMo-1B](https://github.com/allenai/OLMo) with the details below:
| Parameter size | Number of layers | Number of heads | Hidden size | Context length | Vocabulary Size |
|-----------------:|:------------------:|:-----------------:|:-------------:|:----------------:|:----------------:|
| 1.2B | 16 | 16 | 2048 | 2048 | 50,280 |
- **Hyper-parameters**:
|Stage | LR schedule | Peak LR | Warmup steps |Epochs| Batch size (tokens) |
|------------:|:--------------:|:---------:|:--------------:|:------:|:---------------------:|
|Pretraining | Cosine | 4.0e-4 | 2000 | 1 | 4M |
|SFT Phase 1 | Linear | 2.0e-5 | 200 | 3 | 262K |
|SFT Phase 2 | Linear | 2.0e-5 | 200 | 3 | 1024K |
|DPO | Cosine | 4.0e-6 | 47 | 1 | 64K |
For more details, please refer to our [blog](https://www.amd.com/en/developer/resources/technical-articles/introducing-the-first-amd-1b-language-model.html).
## Usage
### PyTorch on AMD GPUs
For running pytorch on AMD GPUs you can use the following rocm docker as in [docker hub](https://hub.docker.com/r/rocm/pytorch)
```bash
docker pull rocm/pytorch:latest
# Inside docker
pip install transformers
```
### Use Example
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("amd/AMD-OLMo-1B-SFT").to("cuda") # remove .to("cuda") to load on cpu
tokenizer = AutoTokenizer.from_pretrained("amd/AMD-OLMo-1B-SFT")
prompt = "What is large language model?"
bos = tokenizer.eos_token
template = bos + "<|user|>\n{prompt}\n<|assistant|>\n"
input_text = template.format(prompt=prompt)
inputs = tokenizer([input_text], return_tensors='pt', return_token_type_ids=False).to("cuda")
outputs = model.generate(**inputs, max_new_tokens=1000, do_sample=True, top_k=50, top_p=0.95)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
```
## Main Results
### Pretraining Results
| **Standard Benchmarks** | [TinyLLaMA-v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) (1.1B) | [MobiLLaMA-1B](https://huggingface.co/MBZUAI/MobiLlama-1B) (1.2B) | [OLMo-1B](https://huggingface.co/allenai/OLMo-1B-hf) (1.2B) | [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) (1.1B) | [OLMo-1B-0724-hf](https://huggingface.co/allenai/OLMo-1B-0724-hf) (1.2B) | [AMD-OLMo-1B](https://huggingface.co/amd/AMD-OLMo-1B) (1.2B) |
|---------------------:|:-----------------:|:-----------:|:-----------:|:---------------:|:---------------:|:-----------:|
| **arc_easy** | 55.47 | 56.65 | 57.28 | 55.43 | 56.65 | **63.64** |
| **arc_challenge** | 32.68 | 32.00 | 31.06 | 32.34 | 32.34 | **33.70** |
| **hellaswag** | 61.47 | 61.80 | 62.92 | 64.81 | **66.12** | 63.61 |
| **piqa** | 73.56 | 75.30 | 75.14 | **75.57** | 75.08 | **75.57** |
| **boolq** | 55.99 | 60.83 | 61.74 | 63.58 | **66.18** | 60.58 |
| **sciq** | 89.30 | 88.20 | 87.00 | 90.60 | 92.70 | **93.20** |
| **winogrande** | 59.43 | 59.27 | 59.98 | **61.72** | **61.72** | 61.64 |
| **openbookqa** | **36.80** | 35.40 | 36.20 | 36.20 | 35.60 | 35.80 |
| **mmlu (0-shot)** | 25.02 | 24.81 | 24.23 | 25.26 | **25.45** | 24.88 |
| **gsm8k (8-shot)** | 1.82 | 0.00 | 2.50 | 2.81 | **8.95** | 2.88 |
| **bbh (3-shot)** | **25.63** | 0.00 | **25.63** | 16.77 | 21.67 | 20.95 |
| **Average** | 47.02 | 44.93 | 47.61 | 47.73 | **49.31** | 48.77 |
### Instruction Tuning Results
| **Standard Benchmarks**|[TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) (1.1B)|[MobiLlama-1B-Chat](https://huggingface.co/MBZUAI/MobiLlama-1B-Chat) (1.2B)|[OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) (1.1B)|[AMD-OLMo-1B-SFT](https://huggingface.co/amd/AMD-OLMo-1B-SFT) (1.2B)|[AMD-OLMo-1B-SFT-DPO](https://huggingface.co/amd/AMD-OLMo-1B-SFT-DPO) (1.2B)|
|------------------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| **arc_easy** | 54.42 | 57.41 | 52.44 | 63.68 | **64.31** |
| **arc_challenge** | 32.85 | 34.56 | **37.80** | 37.12 | 37.37 |
| **hellaswag** | 60.40 | 62.51 | **71.29** | 61.63 | 61.91 |
| **piqa** | 74.48 | **75.73** | 75.03 | 74.43 | 74.16 |
| **boolq** | 61.04 | 55.66 | **70.28** | 68.53 | 70.24 |
| **sciq** | 88.40 | 87.10 | 89.50 | 91.20 | **92.10** |
| **winogrande** | 60.54 | 60.77 | **62.19** | 60.22 | 60.62 |
| **openbookqa** | 37.20 | 36.80 | 39.20 | 37.40 | **40.20** |
| **mmlu** | 24.61 | 25.25 | 25.54 | 29.97 | **30.52** |
| **gsm8k (8-shot)**| 2.81 | 0.23 | 1.82 | **18.20** | 15.77 |
| **bbh (3-shot)** | **26.83** | 0.00 | 13.40 | 25.17 | 25.45 |
| **Average** | 47.60 | 45.09 | 48.95 | 51.60 | **52.06** |
|**Chat Benchmarks**|[TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) (1.1B)|[MobiLlama-1B-Chat](https://huggingface.co/MBZUAI/MobiLlama-1B-Chat) (1.2B)|[OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) (1.1B)|[AMD-OLMo-1B-SFT](https://huggingface.co/amd/AMD-OLMo-1B-SFT) (1.2B)|[AMD-OLMo-1B-SFT-DPO](https://huggingface.co/amd/AMD-OLMo-1B-SFT-DPO) (1.2B)|
|------------------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| **AlpacaEval 1 (Win Rate)** | 50.81 | 34.90 | 37.72 | 50.12 | **54.22** |
| **AlpacaEval 2 (LC Win Rate)**| 1.54 | 1.59 | 0.49 | **3.88** | 2.37 |
| **MTBench** | 3.38 | 2.89 | - | **4.35** | 4.10 |
|**Responsible AI Benchmarks**|[TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) (1.1B)|[MobiLlama-1B-Chat](https://huggingface.co/MBZUAI/MobiLlama-1B-Chat) (1.2B)|[OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) (1.1B)|[AMD-OLMo-1B-SFT](https://huggingface.co/amd/AMD-OLMo-1B-SFT) (1.2B)|[AMD-OLMo-1B-SFT-DPO](https://huggingface.co/amd/AMD-OLMo-1B-SFT-DPO) (1.2B)|
|------------------:|:---------:|:---------:|:---------:|:---------:|:---------:|
| **ToxiGen** | 41.70 | **37.23** | 42.34 | 39.04 | 39.68 |
| **crows_pairs** | 60.35 | 58.50 | 59.93 | 60.29 | **61.00** |
| **TruthfulQA-mc2**| 37.92 | 38.46 | **45.84** | 37.45 | 40.06 |
*In generating tokens for chat benchmark evaluations, we use `max_length=2048` for AlpacaEval and `max_new_tokens=2048` for MTBench.
*All numbers in above tables were obtained from our evaluations.
## Evaluation
We use the following open source evaluation frameworks for evaluating our models:
- [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness): For evaluating on commonsense reasoning, multi-task understanding & responsible AI benchmarks
- [AlpacaEval](https://github.com/tatsu-lab/alpaca_eval): For evaluating instruction-following capabilities of chat models.
- [MT-Bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge): For evaluating multi-turn capabilities of chat models.
### Setup
```bash
# lm-eval-harness
git clone https://github.com/EleutherAI/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .
# AlpacaEval
pip install git+https://github.com/tatsu-lab/alpaca_eval
cd alpaca_eval
pip install -e .
# MT-Bench
git clone https://github.com/lm-sys/FastChat.git
cd FastChat
pip install -e ".[model_worker,llm_judge]"
```
### Run evaluation
```bash
# lm-eval-harness
HF_MODEL=amd/AMD-OLMo-1B-SFT-DPO
accelerate launch -m lm_eval --model hf \
--model_args pretrained=$HF_MODEL,trust_remote_code=True \
--tasks arc_easy,arc_challenge,hellaswag,piqa,boolq,sciq,winogrande,openbookqa,mmlu,gsm8k_cot,bbh_cot_fewshot,toxigen,truthfulqa,crows_pairs \
--device cuda \
--batch_size 32 \
--output_path ./lm-eval-results/$HF_MODEL
```
## Training
### Setup
```bash
WORK_DIR="<path_to_your_working_directory>"
cd $WORK_DIR
# Clone OLMo codebase:
git clone https://github.com/allenai/OLMo.git --branch v0.3.0
cd OLMo
# Clone AMD-OLMo that contains files to reproduce our model training
git clone https://huggingface.co/amd/AMD-OLMo
docker pull rocm/pytorch:latest
docker run -it --network=host --device=/dev/kfd --device=/dev/dri --group-add=video --ipc=host --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --shm-size 8G -v $WORK_DIR/OLMo:/OLMo -w /OLMo rocm/pytorch:latest
# Remove Line 17 as the docker already has ROCm PyTorch installed
sed -i '17d' pyproject.toml
pip install -e .[all]
```
### Download and prepare pretraining datasets
```bash
# Download
DATA_DIR=./datasets/dolma
mkdir -p $DATA_DIR
PARALLEL_DOWNLOADS="<number_of_parallel_downloads>"
cat "AMD-OLMo/dolma_v1_7_subset.txt" | xargs -n 1 -P $PARALLEL_DOWNLOADS wget -q -P $DATA_DIR
# Prepare
NUM_WORKERS="<number_of_workers>"
python scripts/prepare_memmap_dataset.py $DATA_DIR/*.json.gz -o $DATA_DIR/memmap_dataset --workers $NUM_WORKERS
```
### Download and prepare SFT datasets
```bash
# 1st phase SFT dataset
python AMD-OLMo/prepare_sft_data.py --output_dir ./datasets/tulu --tokenizer tokenizers/allenai_eleuther-ai-gpt-neox-20b-pii-special.json --dataset tulu
# 2nd phase SFT dataset
python AMD-OLMo/prepare_sft_data.py --output_dir ./datasets/OpenHermes_WebInstructSub_CodeFeedBack --tokenizer tokenizers/allenai_eleuther-ai-gpt-neox-20b-pii-special.json --dataset 2nd-phase
```
### Run Training
Pretrainig config: [AMD-OLMo-1B.yaml](AMD-OLMo-1B.yaml)
SFT config: [AMD-OLMo-1B-SFT-1st-phase.yaml](AMD-OLMo-1B-SFT-1st-phase.yaml) and [AMD-OLMo-1B-SFT-2nd-phase.yaml](AMD-OLMo-1B-SFT-2nd-phase.yaml)
```bash
# Single node
HSA_FORCE_FINE_GRAIN_PCIE=1 OMP_NUM_THREADS=128 NCCL_DEBUG=INFO torchrun --nproc_per_node=8 ./scripts/train.py AMD-OLMo/AMD-OLMo-1B.yaml
# Multiple nodes
HSA_FORCE_FINE_GRAIN_PCIE=1 OMP_NUM_THREADS=128 NCCL_DEBUG=INFO torchrun --nnodes=$nnodes --node-rank=$node_rank --master_addr=$master_addr --master_port=$master_port --nproc_per_node=8 ./scripts/train.py AMD-OLMo/AMD-OLMo-1B.yaml
```
### Run DPO Training
DPO recipe: [AMD-OLMo-1B-dpo.yaml](AMD-OLMo-1B-dpo.yaml).
```bash
# install trl library
git clone https://github.com/huggingface/trl.git -b v0.8.6
# replace dpo_trainer.py
cp AMD-OLMo/dpo_trainer.py trl/trl/trainer
pip install -e ./trl
# install alignment-handbook
git clone https://github.com/huggingface/alignment-handbook.git hf-align
# 70769f9 is the main branch on 2024-04-11.
cd hf-align && git checkout 70769f9 && cd ..
pip install -e ./hf-align
# Copy AMD OLMo DPO recipe to hf-align/recipes.
cp AMD-OLMo/AMD-OLMo-1B-dpo.yaml hf-align/recipes/
# Prepare the converted AMD-OLMo SFT Huggingface model to ckpt_dir.
ckpt_dir=amd/AMD-OLMo-1B-SFT
local_tokenizer_dir=${ckpt_dir}
# Set output checkpoint dir.
dpo_ckpt_dir=<your_output_checkpoint_dir>
accelerate launch --config_file hf-align/recipes/accelerate_configs/deepspeed_zero3.yaml \
hf-align/scripts/run_dpo.py hf-align/recipes/AMD-OLMo-1B-dpo.yaml \
--trust_remote_code=true \
--model_name_or_path=${ckpt_dir} \
--tokenizer_name_or_path=${local_tokenizer_dir} \
--output_dir=${dpo_ckpt_dir} \
--num_train_epochs=1 \
--learning_rate=4e-6 \
--beta=0.3 \
--loss_type=sigmoid
```
## Bias, Risks, and Limitations
- The models are being released for research purposes only and are not intended for use cases that require high levels of factuality, safety critical situations, health or medical applications, generating false information, facilitating toxic conversations.
- Model checkpoints are made accessible without any safety guarantees. It is crucial for users to conduct comprehensive evaluations and implement safety filtering mechanisms as per their respective use cases.
- It may be possible to prompt the model to generate content that may be factually inaccurate, harmful, violent, toxic, biased, or otherwise objectionable. Such content may also get generated by prompts that did not intend to produce output as such. Users are thus requested to be aware of this and exercise caution and responsible thinking when using the model.
- Multi-lingual abilities of the models have not been tested and thus may misunderstand and generate erroneous responses across different languages.
## Appendix
### Evaluation Metrics
| **Benchmark** | Metric |
|---------------------:|:-----------------:|
| **arc_easy** | Normalized Accuracy |
| **arc_challenge** | Normalized Accuracy |
| **hellaswag** | Normalized Accuracy |
| **piqa** | Accuracy |
| **boolq** | Accuracy |
| **sciq** | Accuracy |
| **winogrande** | Accuracy |
| **openbookqa** | Normalized Accuracy |
| **mmlu** | Accuracy |
| **gsm8k (8-shot)** | Exact Match (Flexible Extract) |
| **bbh (3-shot)** | Exact Match |
| **ToxiGen** | Accuracy |
| **crows_pairs** | PCT Stereotype |
| **TruthfulQA-mc2** | Accuracy |
| **AlpacaEval 1 (Win Rate)** | Win Rate (chatgpt_fn) |
| **AlpacaEval 2 (LC Win Rate)** | Length Control Win Rate (weighted_alpaca_eval_gpt4_turbo) |
| **MTBench** | Average score for single-answer grading (2 turns) |
Feel free to cite our AMD-OLMo models:
```bash
@misc{AMD-OLMo,
title = {AMD-OLMo: A series of 1B language models trained from scratch by AMD on AMD Instinct™ MI250 GPUs.},
url = {https://huggingface.co/amd/AMD-OLMo},
author = {Jiang Liu, Jialian Wu, Prakamya Mishra, Zicheng Liu, Sudhanshu Ranjan, Pratik Prabhanjan Brahma, Yusheng Su, Gowtham Ramesh, Peng Sun, Zhe Li, Dong Li, Lu Tian, Emad Barsoum},
month = {October},
year = {2024}
}
```
#### License
Copyright (c) 2018-2024 Advanced Micro Devices, Inc. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
|
[
"SCIQ"
] |
ChenWeiLi/Med-ChimeraLlama-3-8B_SHERP
|
ChenWeiLi
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"base_model:johnsnowlabs/JSL-MedLlama-3-8B-v2.0",
"base_model:merge:johnsnowlabs/JSL-MedLlama-3-8B-v2.0",
"base_model:mlabonne/ChimeraLlama-3-8B-v3",
"base_model:merge:mlabonne/ChimeraLlama-3-8B-v3",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-05-14T01:10:36Z |
2024-05-20T08:13:50+00:00
| 3,130 | 1 |
---
base_model:
- mlabonne/ChimeraLlama-3-8B-v3
- johnsnowlabs/JSL-MedLlama-3-8B-v2.0
library_name: transformers
license: llama3
tags:
- mergekit
- merge
---
# Chimera_MedLlama-3-8B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [mlabonne/ChimeraLlama-3-8B-v3](https://huggingface.co/mlabonne/ChimeraLlama-3-8B-v3)
* [johnsnowlabs/JSL-MedLlama-3-8B-v2.0](https://huggingface.co/johnsnowlabs/JSL-MedLlama-3-8B-v2.0)
### Evaluation
- multimedqa (0 shot)</br>
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------------------------|-------|------|-----:|--------|-----:|---|-----:|
| - medmcqa |Yaml |none | 0|acc |0.6087|± |0.0075|
| | |none | 0|acc_norm|0.6087|± |0.0075|
| - medqa_4options |Yaml |none | 0|acc |0.6269|± |0.0136|
| | |none | 0|acc_norm|0.6269|± |0.0136|
| - anatomy (mmlu) | 0|none | 0|acc |0.6963|± |0.0397|
| - clinical_knowledge (mmlu) | 0|none | 0|acc |0.7585|± |0.0263|
| - college_biology (mmlu) | 0|none | 0|acc |0.7847|± |0.0344|
| - college_medicine (mmlu) | 0|none | 0|acc |0.6936|± |0.0351|
| - medical_genetics (mmlu) | 0|none | 0|acc |0.8200|± |0.0386|
| - professional_medicine (mmlu)| 0|none | 0|acc |0.7684|± |0.0256|
|stem |N/A |none | 0|acc_norm|0.6129|± |0.0066|
| | |none | 0|acc |0.6440|± |0.0057|
| - pubmedqa | 1|none | 0|acc |0.7480|± |0.0194|
|Groups|Version|Filter|n-shot| Metric |Value | |Stderr|
|------|-------|------|-----:|--------|-----:|---|-----:|
|stem |N/A |none | 0|acc_norm|0.6129|± |0.0066|
| | |none | 0|acc |0.6440|± |0.0057|
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: mlabonne/ChimeraLlama-3-8B-v3
layer_range: [0, 32]
- model: johnsnowlabs/JSL-MedLlama-3-8B-v2.0
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/ChimeraLlama-3-8B-v3
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
[
"MEDQA",
"PUBMEDQA"
] |
xmanii/maux-gte-persian-v2
|
xmanii
|
sentence-similarity
|
[
"sentence-transformers",
"safetensors",
"new",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:10000",
"loss:CosineSimilarityLoss",
"custom_code",
"dataset:xmanii/maux-gte-10k-public",
"arxiv:1908.10084",
"base_model:Alibaba-NLP/gte-multilingual-base",
"base_model:finetune:Alibaba-NLP/gte-multilingual-base",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-12-25T10:05:32Z |
2024-12-25T10:06:01+00:00
| 3,130 | 3 |
---
base_model: Alibaba-NLP/gte-multilingual-base
datasets:
- xmanii/maux-gte-10k-public
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:10000
- loss:CosineSimilarityLoss
widget:
- source_sentence: چگونه ساختار خانواده بر توسعه اجتماعی تأثیر میگذارد؟
sentences:
- انتخابهای سبک زندگی مانند سیگار کشیدن، رژیم غذایی نامناسب و عدم فعالیت بدنی میتوانند
به شرایط مختلف قلبی منجر شوند.
- ساختار خانواده میتواند به طور قابل توجهی بر توسعه اجتماعی کودک تأثیر بگذارد با
ارائه سطوح مختلف حمایت عاطفی، فرصتهای اجتماعی و الگوهای رفتاری.
- صنعت فیلم به خاطر کمبود تنوع و نمایندگی مورد انتقاد قرار گرفته است.
- source_sentence: عوامل اصلی که بر تورم تأثیر میگذارند چیستند؟
sentences:
- تورم میتواند موضوع چالشبرانگیزی برای سیاستگذاران باشد.
- اکوسیستمهای بیابانی با بارش کم و دماهای بالا مشخص میشوند، که شرایط زندگی چالشبرانگیزی
برای گیاهان و جانوران ایجاد میکند.
- امتیازهای Z در توزیعهای نرمال استاندارد استفاده میشوند، در حالی که امتیازهای
t زمانی استفاده میشوند که اندازه نمونه کوچک باشد و انحراف معیار جمعیت نامشخص
باشد.
- source_sentence: آنتیبیوتیکها چگونه در سطح سلولی کار میکنند؟
sentences:
- برخی از گیاهان گوشتخوار، مانند تله ونیس، دارای حرکات سریع برای بهداماندازی
طعمههای خود هستند.
- آنتیبیوتیکها نوعی دارو هستند که میتوانند توسط پزشکان برای درمان عفونتها تجویز
شوند.
- نرخ تورم میتواند با استفاده از شاخصهای مختلفی اندازهگیری شود، مانند شاخص قیمت
مصرفکننده (CPI) و شاخص قیمت تولیدکننده (PPI).
- source_sentence: چگونه سری کتابهای «هری پاتر» ج.ک. رولینگ بر ادبیات مدرن تأثیر
گذاشته است؟
sentences:
- جی.کی. رولینگ کتابهای دیگری تحت نام مستعار رابرت گالبریت نوشته است که رمانهای
جنایی هستند.
- رنگ آکریلیک به طور معمول در هنر مدرن استفاده میشود، در حالی که رنگ روغن قرنهاست
که در هنر کلاسیک به کار میرود.
- ماهیهای اعماق دریا دارای سازگاریهایی مانند بیولومینسانس، بدنهای مقاوم به فشار
و مکانیزمهای تغذیه خاص هستند تا در شرایط شدید sobrevivir کنند.
- source_sentence: تفاوت بین کشاورزی ارگانیک و کشاورزی سنتی چیست؟
sentences:
- در حالی که بازههای اطمینان مفید هستند، در صورت عدم رعایت فرضیات زیرین، ممکن است
به اشتباه تفسیر شوند.
- تاریخ حفظ آب به تمدنهای باستانی برمیگردد که سیستمهای آبیاری را توسعه دادند.
- بازارهای کشاورزان مکانهای محبوبی برای خرید محصولات ارگانیک به طور مستقیم از کشاورزان
محلی هستند.
model-index:
- name: SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: Unknown
type: unknown
metrics:
- type: pearson_cosine
value: 0.9487949766869277
name: Pearson Cosine
- type: spearman_cosine
value: 0.947885967258665
name: Spearman Cosine
---
# SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) on the [maux-gte-10k-public](https://huggingface.co/datasets/xmanii/maux-gte-10k-public) 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:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision ade1467d6266ae07e6f74aae34d56bf3b8acf3f7 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [maux-gte-10k-public](https://huggingface.co/datasets/xmanii/maux-gte-10k-public)
<!-- - **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: NewModel
(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("xmanii/maux-gte-persian-v2")
# Run inference
sentences = [
'تفاوت بین کشاورزی ارگانیک و کشاورزی سنتی چیست؟',
'بازارهای کشاورزان مکان\u200cهای محبوبی برای خرید محصولات ارگانیک به طور مستقیم از کشاورزان محلی هستند.',
'تاریخ حفظ آب به تمدن\u200cهای باستانی برمی\u200cگردد که سیستم\u200cهای آبیاری را توسعه دادند.',
]
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>
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Semantic Similarity
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.9488 |
| **spearman_cosine** | **0.9479** |
<!--
## 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
#### maux-gte-10k-public
* Dataset: [maux-gte-10k-public](https://huggingface.co/datasets/xmanii/maux-gte-10k-public) at [e20c689](https://huggingface.co/datasets/xmanii/maux-gte-10k-public/tree/e20c689e4915c4689dd54dd621ff57d5704cfaa5)
* Size: 10,000 training samples
* Columns: <code>persian_question</code>, <code>persian_answer</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | persian_question | persian_answer | score |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 14.7 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 29.01 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 0.02</li><li>mean: 0.52</li><li>max: 1.0</li></ul> |
* Samples:
| persian_question | persian_answer | score |
|:-----------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
| <code>آیا میتوانید فرآیند برنامهریزی مسیر ربات را توضیح دهید؟</code> | <code>رباتها میتوانند برنامهنویسی شوند تا مجموعهای از وظایف را انجام دهند، از اقدامهای تکراری ساده تا فرآیندهای پیچیده تصمیمگیری.</code> | <code>0.27999999999999997</code> |
| <code>آیا انسانها میتوانند در مریخ زندگی کنند؟</code> | <code>مریخ چهارمین سیاره از خورشید است و به دلیل ظاهر سرخش اغلب به سیاره سرخ معروف است.</code> | <code>0.16</code> |
| <code>عناصر کلیدی ترکیب در هنر انتزاعی چیست؟</code> | <code>تاریخ هنر انتزاعی به اوایل قرن بیستم برمیگردد، با پیشگامانی مانند واسیلی کاندینسکی و پیت موندریان.</code> | <code>0.36</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Dataset
#### maux-gte-10k-public
* Dataset: [maux-gte-10k-public](https://huggingface.co/datasets/xmanii/maux-gte-10k-public) at [e20c689](https://huggingface.co/datasets/xmanii/maux-gte-10k-public/tree/e20c689e4915c4689dd54dd621ff57d5704cfaa5)
* Size: 10,000 evaluation samples
* Columns: <code>persian_question</code>, <code>persian_answer</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | persian_question | persian_answer | score |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 14.7 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 29.01 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 0.02</li><li>mean: 0.52</li><li>max: 1.0</li></ul> |
* Samples:
| persian_question | persian_answer | score |
|:-----------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
| <code>آیا میتوانید فرآیند برنامهریزی مسیر ربات را توضیح دهید؟</code> | <code>رباتها میتوانند برنامهنویسی شوند تا مجموعهای از وظایف را انجام دهند، از اقدامهای تکراری ساده تا فرآیندهای پیچیده تصمیمگیری.</code> | <code>0.27999999999999997</code> |
| <code>آیا انسانها میتوانند در مریخ زندگی کنند؟</code> | <code>مریخ چهارمین سیاره از خورشید است و به دلیل ظاهر سرخش اغلب به سیاره سرخ معروف است.</code> | <code>0.16</code> |
| <code>عناصر کلیدی ترکیب در هنر انتزاعی چیست؟</code> | <code>تاریخ هنر انتزاعی به اوایل قرن بیستم برمیگردد، با پیشگامانی مانند واسیلی کاندینسکی و پیت موندریان.</code> | <code>0.36</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/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
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
#### 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`: 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`: 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
- `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`: 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 | Validation Loss | spearman_cosine |
|:----------:|:--------:|:-------------:|:---------------:|:---------------:|
| 0.1597 | 50 | 0.0663 | - | - |
| 0.3195 | 100 | 0.0409 | 0.0298 | 0.7983 |
| 0.4792 | 150 | 0.0342 | - | - |
| 0.6390 | 200 | 0.0294 | 0.0230 | 0.8464 |
| 0.7987 | 250 | 0.0296 | - | - |
| 0.9585 | 300 | 0.0298 | 0.0220 | 0.8610 |
| 1.1182 | 350 | 0.0249 | - | - |
| 1.2780 | 400 | 0.0237 | 0.0230 | 0.8745 |
| 1.4377 | 450 | 0.0241 | - | - |
| 1.5974 | 500 | 0.0218 | 0.0166 | 0.8900 |
| 1.7572 | 550 | 0.0227 | - | - |
| 1.9169 | 600 | 0.0231 | 0.0148 | 0.9045 |
| 2.0767 | 650 | 0.0196 | - | - |
| 2.2364 | 700 | 0.0173 | 0.0131 | 0.9179 |
| 2.3962 | 750 | 0.0172 | - | - |
| 2.5559 | 800 | 0.0172 | 0.0119 | 0.9231 |
| 2.7157 | 850 | 0.0167 | - | - |
| 2.8754 | 900 | 0.0172 | 0.0120 | 0.9291 |
| 3.0351 | 950 | 0.0175 | - | - |
| 3.1949 | 1000 | 0.013 | 0.0100 | 0.9362 |
| 3.3546 | 1050 | 0.0128 | - | - |
| 3.5144 | 1100 | 0.0129 | 0.0101 | 0.9390 |
| 3.6741 | 1150 | 0.0134 | - | - |
| 3.8339 | 1200 | 0.0137 | 0.0095 | 0.9430 |
| 3.9936 | 1250 | 0.0133 | - | - |
| 4.1534 | 1300 | 0.0109 | 0.0096 | 0.9449 |
| 4.3131 | 1350 | 0.0114 | - | - |
| **4.4728** | **1400** | **0.0111** | **0.0083** | **0.9479** |
| 4.6326 | 1450 | 0.0107 | - | - |
| 4.7923 | 1500 | 0.0122 | 0.0085 | 0.9479 |
| 4.9521 | 1550 | 0.0112 | - | - |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.8
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu124
- 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",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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|
[
"CPI"
] |
FreedomIntelligence/Apollo-0.5B
|
FreedomIntelligence
|
text-generation
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2403.03640",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-03-06T13:04:41Z |
2024-04-26T11:10:55+00:00
| 3,113 | 3 |
---
license: apache-2.0
---
# Multilingual Medicine: Model, Dataset, Benchmark, Code
Covering English, Chinese, French, Hindi, Spanish, Hindi, Arabic So far
<p align="center">
👨🏻💻<a href="https://github.com/FreedomIntelligence/Apollo" target="_blank">Github</a> •📃 <a href="https://arxiv.org/abs/2403.03640" target="_blank">Paper</a> • 🌐 <a href="https://apollo.llmzoo.com/" target="_blank">Demo</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a>
<br> <a href="./README_zh.md"> 中文 </a> | <a href="./README.md"> English
</p>
## 🌈 Update
* **[2024.04.25]** [MedJamba](https://huggingface.co/FreedomIntelligence/Apollo-MedJamba) released, train and evaluation code refer to [repo](https://github.com/FreedomIntelligence/MedJamba).
* **[2024.03.07]** [Paper](https://arxiv.org/abs/2403.03640) released.
* **[2024.02.12]** <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a> and <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a> is published！🎉
* **[2024.01.23]** Apollo repo is published！🎉
## Results
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-0.5B" target="_blank">Apollo-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-1.8B" target="_blank">Apollo-1.8B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-2B" target="_blank">Apollo-2B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-6B" target="_blank">Apollo-6B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-7B" target="_blank">Apollo-7B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-34B" target="_blank">Apollo-34B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-72B" target="_blank">Apollo-72B</a>
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MedJamba" target="_blank">MedJamba</a>
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-0.5B-GGUF" target="_blank">Apollo-0.5B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-2B-GGUF" target="_blank">Apollo-2B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-6B-GGUF" target="_blank">Apollo-6B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-7B-GGUF" target="_blank">Apollo-7B-GGUF</a>
## Usage Format
User:{query}\nAssistant:{response}<|endoftext|>
## Dataset & Evaluation
- Dataset
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a>
<details><summary>Click to expand</summary>
- [Zip File](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/blob/main/ApolloCorpus.zip)
- [Data category](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/tree/main/train)
- Pretrain:
- data item:
- json_name: {data_source}_{language}_{data_type}.json
- data_type: medicalBook, medicalGuideline, medicalPaper, medicalWeb(from online forum), medicalWiki
- language: en(English), zh(chinese), es(spanish), fr(french), hi(Hindi)
- data_type: qa(generated qa from text)
- data_type==text: list of string
```
[
"string1",
"string2",
...
]
```
- data_type==qa: list of qa pairs(list of string)
```
[
[
"q1",
"a1",
"q2",
"a2",
...
],
...
]
```
- SFT:
- json_name: {data_source}_{language}.json
- data_type: code, general, math, medicalExam, medicalPatient
- data item: list of qa pairs(list of string)
```
[
[
"q1",
"a1",
"q2",
"a2",
...
],
...
]
```
</details>
- Evaluation
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a>
<details><summary>Click to expand</summary>
- EN:
- [MedQA-USMLE](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
- [MedMCQA](https://huggingface.co/datasets/medmcqa/viewer/default/test)
- [PubMedQA](https://huggingface.co/datasets/pubmed_qa): Because the results fluctuated too much, they were not used in the paper.
- [MMLU-Medical](https://huggingface.co/datasets/cais/mmlu)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- ZH:
- [MedQA-MCMLE](https://huggingface.co/datasets/bigbio/med_qa/viewer/med_qa_zh_4options_bigbio_qa/test)
- [CMB-single](https://huggingface.co/datasets/FreedomIntelligence/CMB): Not used in the paper
- Randomly sample 2,000 multiple-choice questions with single answer.
- [CMMLU-Medical](https://huggingface.co/datasets/haonan-li/cmmlu)
- Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
- [CExam](https://github.com/williamliujl/CMExam): Not used in the paper
- Randomly sample 2,000 multiple-choice questions
- ES: [Head_qa](https://huggingface.co/datasets/head_qa)
- FR: [Frenchmedmcqa](https://github.com/qanastek/FrenchMedMCQA)
- HI: [MMLU_HI](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Arabic)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- AR: [MMLU_Ara](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Hindi)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
</details>
## Results reproduction
<details><summary>Click to expand</summary>
**Waiting for Update**
</details>
## Citation
Please use the following citation if you intend to use our dataset for training or evaluation:
```
@misc{wang2024apollo,
title={Apollo: Lightweight Multilingual Medical LLMs towards Democratizing Medical AI to 6B People},
author={Xidong Wang and Nuo Chen and Junyin Chen and Yan Hu and Yidong Wang and Xiangbo Wu and Anningzhe Gao and Xiang Wan and Haizhou Li and Benyou Wang},
year={2024},
eprint={2403.03640},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
[
"HEAD-QA",
"MEDQA",
"PUBMEDQA"
] |
MaartenGr/BERTopic_Wikipedia
|
MaartenGr
|
text-classification
|
[
"bertopic",
"text-classification",
"region:us"
] | 2023-05-30T08:03:05Z |
2023-05-31T17:58:03+00:00
| 3,095 | 43 |
---
library_name: bertopic
pipeline_tag: text-classification
tags:
- bertopic
---
# Wikipedia
This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
* Trained on ~1_000_000 Wikipedia pages (first paragraph of each page).
* Data was retrieved from: https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings
## Usage
To use this model, please install BERTopic:
```
pip install -U bertopic
pip install -U safetensors
```
You can use the model as follows:
```python
from bertopic import BERTopic
topic_model = BERTopic.load("MaartenGr/BERTopic_Wikipedia")
topic_model.get_topic_info()
```
## Topics 2D
The top 50 topics visualized and reduced to 2-dimensional space using cuML's UMAP:
To generate this image, you can follow along with this tutorial: [](https://colab.research.google.com/drive/1W7aEdDPxC29jP99GGZphUlqjMFFVKtBC?usp=sharing)
## Topic overview
* Number of topics: 2377
* Number of training documents: 1000001
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| -1 | cast - films - film - movie - 2020 | 30 | -1_cast_films_film_movie |
| 0 | goalscorer - scored - goals - goal - goalkeeper | 633881 | 0_goalscorer_scored_goals_goal |
| 1 | khan - actor - raj - shah - crore | 18441 | 1_khan_actor_raj_shah |
| 2 | married - divorced - couple - remarried - engaged | 8518 | 2_married_divorced_couple_remarried |
| 3 | cast - actress - starred - actor - actors | 7521 | 3_cast_actress_starred_actor |
| 4 | wrestle - reigns - wrestled - rumble - feud | 6765 | 4_wrestle_reigns_wrestled_rumble |
| 5 | cuisine - cuisines - foods - culinary - meal | 5785 | 5_cuisine_cuisines_foods_culinary |
| 6 | rebounds - harden - assists - scoring - wade | 5001 | 6_rebounds_harden_assists_scoring |
| 7 | touchdowns - interceptions - quarterback - touchdown - fumble | 4238 | 7_touchdowns_interceptions_quarterback_touchdown |
| 8 | goddesses - goddess - mythology - deities - gods | 3597 | 8_goddesses_goddess_mythology_deities |
| 9 | reelection - election - republican - elections - electoral | 3354 | 9_reelection_election_republican_elections |
| 10 | middleweight - fights - punches - welterweight - heavyweight | 3133 | 10_middleweight_fights_punches_welterweight |
| 11 | hitter - hitters - inning - outfielder - batting | 2951 | 11_hitter_hitters_inning_outfielder |
| 12 | yoga - sutra - sutras - meditation - dharma | 2768 | 12_yoga_sutra_sutras_meditation |
| 13 | missile - missiles - aircraft - squadrons - fighter | 2686 | 13_missile_missiles_aircraft_squadrons |
| 14 | chassis - vehicle - wheelbase - gearbox - sedan | 2653 | 14_chassis_vehicle_wheelbase_gearbox |
| 15 | grace - rob - nick - anna - house | 2421 | 15_grace_rob_nick_anna |
| 16 | chlorine - chloride - hydrochloric - hydroxide - corrosion | 2362 | 16_chlorine_chloride_hydrochloric_hydroxide |
| 17 | planets - galaxies - planetary - astronomers - dwarf | 2354 | 17_planets_galaxies_planetary_astronomers |
| 18 | matrices - matrix - transpose - eigenvector - multiplication | 2234 | 18_matrices_matrix_transpose_eigenvector |
| 19 | rifle - rifles - firearm - firearms - ammunition | 2191 | 19_rifle_rifles_firearm_firearms |
| 20 | campuses - colleges - campus - undergraduates - college | 2172 | 20_campuses_colleges_campus_undergraduates |
| 21 | renewed - seasons - season - airing - 2023 | 2020 | 21_renewed_seasons_season_airing |
| 22 | climates - climate - weather - temperatures - temperature | 1992 | 22_climates_climate_weather_temperatures |
| 23 | benzodiazepines - benzodiazepine - antidepressants - antidepressant - diazepam | 1900 | 23_benzodiazepines_benzodiazepine_antidepressants_antidepressant |
| 24 | consoles - console - gaming - platform - gamepad | 1765 | 24_consoles_console_gaming_platform |
| 25 | vowel - vowels - consonants - consonant - diacritics | 1716 | 25_vowel_vowels_consonants_consonant |
| 26 | heir - throne - nobility - eldest - 1536 | 1660 | 26_heir_throne_nobility_eldest |
| 27 | 737 - airline - airliner - airlines - airliners | 1592 | 27_737_airline_airliner_airlines |
| 28 | thermodynamic - thermodynamics - entropy - equilibrium - temperature | 1552 | 28_thermodynamic_thermodynamics_entropy_equilibrium |
| 29 | venom - marvel - spider - doom - carnage | 1550 | 29_venom_marvel_spider_doom |
| 30 | tales - folktales - tale - fairy - stories | 1541 | 30_tales_folktales_tale_fairy |
| 31 | caesar - augustus - roman - consul - consuls | 1540 | 31_caesar_augustus_roman_consul |
| 32 | gospels - testament - disciples - apostle - apostles | 1496 | 32_gospels_testament_disciples_apostle |
| 33 | banks - banking - bank - mortgages - finance | 1368 | 33_banks_banking_bank_mortgages |
| 34 | variance - variances - distribution - distributions - statistics | 1360 | 34_variance_variances_distribution_distributions |
| 35 | prix - motorsport - raced - racing - qualifying | 1358 | 35_prix_motorsport_raced_racing |
| 36 | filmed - filming - footage - photography - cinematographer | 1331 | 36_filmed_filming_footage_photography |
| 37 | reactors - reactor - nuclear - fission - fissions | 1324 | 37_reactors_reactor_nuclear_fission |
| 38 | mixtape - rapper - thug - mixtapes - rap | 1307 | 38_mixtape_rapper_thug_mixtapes |
| 39 | khan - sheikh - maharaja - minister - appointed | 1271 | 39_khan_sheikh_maharaja_minister |
| 40 | symphonies - concertos - symphonic - composers - concerto | 1255 | 40_symphonies_concertos_symphonic_composers |
| 41 | lightsaber - lightsabers - prequels - prequel - han | 1222 | 41_lightsaber_lightsabers_prequels_prequel |
| 42 | ants - insects - arachnids - arthropods - spiders | 1215 | 42_ants_insects_arachnids_arthropods |
| 43 | psychiatric - psychosis - disorders - schizophrenia - disorder | 1198 | 43_psychiatric_psychosis_disorders_schizophrenia |
| 44 | unionists - unionist - nationalists - loyalist - protestant | 1130 | 44_unionists_unionist_nationalists_loyalist |
| 45 | renewable - renewables - hydroelectricity - hydroelectric - energy | 1077 | 45_renewable_renewables_hydroelectricity_hydroelectric |
| 46 | eruptions - volcano - volcanoes - eruption - volcanic | 1076 | 46_eruptions_volcano_volcanoes_eruption |
| 47 | 9million - 6million - 5million - 8million - 2million | 1048 | 47_9million_6million_5million_8million |
| 48 | albums - songs - rapper - album - hyun | 1046 | 48_albums_songs_rapper_album |
| 49 | nazi - ss - 1934 - 1938 - 1930 | 1021 | 49_nazi_ss_1934_1938 |
| 50 | broadcasters - broadcasting - broadcasts - televised - broadcast | 1018 | 50_broadcasters_broadcasting_broadcasts_televised |
| 51 | rpg - gaming - games - gamer - fantasy | 997 | 51_rpg_gaming_games_gamer |
| 52 | vogue - magazine - glamour - magazines - playboy | 927 | 52_vogue_magazine_glamour_magazines |
| 53 | comedian - primetime - night - comedians - podcast | 920 | 53_comedian_primetime_night_comedians |
| 54 | collegiate - colleges - conferences - conference - intercollegiate | 908 | 54_collegiate_colleges_conferences_conference |
| 55 | candidacy - candidate - candidates - presidential - presidency | 901 | 55_candidacy_candidate_candidates_presidential |
| 56 | bond - royale - spectre - pierce - spy | 901 | 56_bond_royale_spectre_pierce |
| 57 | band - frontman - bassist - vocalist - toured | 894 | 57_band_frontman_bassist_vocalist |
| 58 | joker - superman - superhero - comics - comic | 891 | 58_joker_superman_superhero_comics |
| 59 | airport - airports - airlines - airline - terminals | 878 | 59_airport_airports_airlines_airline |
| 60 | communists - yuan - yang - communist - politburo | 875 | 60_communists_yuan_yang_communist |
| 61 | titanic - lifeboat - lifeboats - sank - ships | 839 | 61_titanic_lifeboat_lifeboats_sank |
| 62 | dynasty - emperor - dynasties - yang - yuan | 838 | 62_dynasty_emperor_dynasties_yang |
| 63 | breeds - terrier - terriers - shepherd - dachshunds | 833 | 63_breeds_terrier_terriers_shepherd |
| 64 | rating - rated - cinematography - film - screenplay | 824 | 64_rating_rated_cinematography_film |
| 65 | protestant - catholic - churches - evangelicals - denominational | 818 | 65_protestant_catholic_churches_evangelicals |
| 66 | interstates - interstate - highways - freeway - turnpike | 802 | 66_interstates_interstate_highways_freeway |
| 67 | nationalists - secession - separatism - republics - nationalism | 780 | 67_nationalists_secession_separatism_republics |
| 68 | yoon - hyun - jung - jae - hyung | 771 | 68_yoon_hyun_jung_jae |
| 69 | confederation - 1867 - 1814 - 1871 - emperor | 770 | 69_confederation_1867_1814_1871 |
| 70 | shah - khan - dynasty - dynasties - sultanate | 764 | 70_shah_khan_dynasty_dynasties |
| 71 | airlines - airline - alliance - airways - flights | 763 | 71_airlines_airline_alliance_airways |
| 72 | flash - storage - memory - gigabyte - devices | 763 | 72_flash_storage_memory_gigabyte |
| 73 | constituencies - constituency - election - elections - candidates | 724 | 73_constituencies_constituency_election_elections |
| 74 | constituencies - constituency - elections - election - candidates | 701 | 74_constituencies_constituency_elections_election |
| 75 | boxer - heavyweight - middleweight - heavyweights - foreman | 695 | 75_boxer_heavyweight_middleweight_heavyweights |
| 76 | programming - python - compilers - compiler - languages | 686 | 76_programming_python_compilers_compiler |
| 77 | mafia - gangster - indictment - gangsters - indicted | 684 | 77_mafia_gangster_indictment_gangsters |
| 78 | caliph - ibn - caliphs - caliphate - caliphates | 676 | 78_caliph_ibn_caliphs_caliphate |
| 79 | manga - shonen - shōnen - anime - chapters | 676 | 79_manga_shonen_shōnen_anime |
| 80 | warships - frigates - warship - frigate - battleships | 651 | 80_warships_frigates_warship_frigate |
| 81 | heterosexuality - bisexuality - homosexual - heterosexual - heterosexuals | 647 | 81_heterosexuality_bisexuality_homosexual_heterosexual |
| 82 | released - 2021 - releases - 20th - 2022 | 642 | 82_released_2021_releases_20th |
| 83 | marvel - spider - marvels - avenger - superhero | 629 | 83_marvel_spider_marvels_avenger |
| 84 | tennis - quarterfinal - semifinals - tournaments - quarterfinals | 622 | 84_tennis_quarterfinal_semifinals_tournaments |
| 85 | heir - throne - kingdom - kingdoms - king | 615 | 85_heir_throne_kingdom_kingdoms |
| 86 | poker - betting - gambling - casino - bets | 598 | 86_poker_betting_gambling_casino |
| 87 | soundtrack - soundtracks - musical - symphony - instrumental | 596 | 87_soundtrack_soundtracks_musical_symphony |
| 88 | consent - rape - minors - statutory - age | 592 | 88_consent_rape_minors_statutory |
| 89 | 1860 - 1852 - 1832 - reelection - confederate | 591 | 89_1860_1852_1832_reelection |
| 90 | trek - showrunner - starship - showrunners - starships | 587 | 90_trek_showrunner_starship_showrunners |
| 91 | wickets - batsman - cricketer - wicket - cricket | 585 | 91_wickets_batsman_cricketer_wicket |
| 92 | heir - duchess - eldest - prince - royal | 578 | 92_heir_duchess_eldest_prince |
| 93 | goaltender - puck - goalie - hockey - ahl | 576 | 93_goaltender_puck_goalie_hockey |
| 94 | keyboards - keyboard - keypad - diacritics - alphabet | 560 | 94_keyboards_keyboard_keypad_diacritics |
| 95 | cartel - cartels - narco - trafficking - los | 558 | 95_cartel_cartels_narco_trafficking |
| 96 | yang - yin - rituals - religions - shamanism | 540 | 96_yang_yin_rituals_religions |
| 97 | astrology - astrological - zodiac - zodiacal - astrologers | 535 | 97_astrology_astrological_zodiac_zodiacal |
| 98 | goddesses - stanzas - mythology - stanza - valkyries | 528 | 98_goddesses_stanzas_mythology_stanza |
| 99 | rating - critics - reviews - review - rotten | 528 | 99_rating_critics_reviews_review |
| 100 | dynasties - subcontinent - dharma - dynasty - ancient | 524 | 100_dynasties_subcontinent_dharma_dynasty |
| 101 | dinosaur - fossil - dinosaurs - fossils - tyrannosaurids | 523 | 101_dinosaur_fossil_dinosaurs_fossils |
| 102 | folkloric - folk - genres - traditional - folklore | 514 | 102_folkloric_folk_genres_traditional |
| 103 | climber - climbers - mountaineering - climb - climbed | 511 | 103_climber_climbers_mountaineering_climb |
| 104 | contestant - contestants - finalists - idol - finalist | 511 | 104_contestant_contestants_finalists_idol |
| 105 | proteins - amino - protein - peptide - enzymes | 508 | 105_proteins_amino_protein_peptide |
| 106 | battleships - battleship - naval - torpedoes - warships | 506 | 106_battleships_battleship_naval_torpedoes |
| 107 | anthrax - slayer - thrash - bands - band | 505 | 107_anthrax_slayer_thrash_bands |
| 108 | swift - songwriting - songwriter - songwriters - songs | 504 | 108_swift_songwriting_songwriter_songwriters |
| 109 | airplane - airlines - flight - aircraft - aviation | 498 | 109_airplane_airlines_flight_aircraft |
| 110 | paintings - painters - painter - cubism - cubist | 496 | 110_paintings_painters_painter_cubism |
| 111 | flags - flag - flagpole - commonwealth - emblem | 493 | 111_flags_flag_flagpole_commonwealth |
| 112 | cult - cruise - organizations - founder - organization | 481 | 112_cult_cruise_organizations_founder |
| 113 | calendar - calendars - dates - calendrical - equinoxes | 481 | 113_calendar_calendars_dates_calendrical |
| 114 | counties - county - population - populous - cities | 474 | 114_counties_county_population_populous |
| 115 | degree - bachelor - diplomas - doctorates - diploma | 474 | 115_degree_bachelor_diplomas_doctorates |
| 116 | spying - espionage - surveillance - spied - disclosures | 472 | 116_spying_espionage_surveillance_spied |
| 117 | schooling - education - educational - kindergarten - curriculum | 471 | 117_schooling_education_educational_kindergarten |
| 118 | railway - railways - autobahns - autobahn - trains | 470 | 118_railway_railways_autobahns_autobahn |
| 119 | laden - jihadi - mujahideen - jihadis - al | 451 | 119_laden_jihadi_mujahideen_jihadis |
| 120 | theatre - venue - venues - theater - orchestras | 450 | 120_theatre_venue_venues_theater |
| 121 | earthquake - earthquakes - tsunami - tsunamis - quakes | 450 | 121_earthquake_earthquakes_tsunami_tsunamis |
| 122 | superman - superhero - comics - sequels - joker | 446 | 122_superman_superhero_comics_sequels |
| 123 | dodge - automakers - truck - automotive - trucks | 431 | 123_dodge_automakers_truck_automotive |
| 124 | election - elections - candidates - candidate - voters | 431 | 124_election_elections_candidates_candidate |
| 125 | broadway - musicals - musical - theatre - theater | 422 | 125_broadway_musicals_musical_theatre |
| 126 | whales - whale - whaling - cetaceans - cetacean | 422 | 126_whales_whale_whaling_cetaceans |
| 127 | potter - potters - wizard - wizardry - wizarding | 419 | 127_potter_potters_wizard_wizardry |
| 128 | starship - spaceflight - spacecraft - shuttle - astronauts | 417 | 128_starship_spaceflight_spacecraft_shuttle |
| 129 | pol - communists - rouge - soviet - communist | 412 | 129_pol_communists_rouge_soviet |
| 130 | tombstone - corral - stagecoach - outlaw - outlaws | 403 | 130_tombstone_corral_stagecoach_outlaw |
| 131 | tennis - competed - doubles - slams - finalist | 401 | 131_tennis_competed_doubles_slams |
| 132 | lunar - moon - astronaut - astronauts - spacecraft | 399 | 132_lunar_moon_astronaut_astronauts |
| 133 | hamlet - playwright - actor - cast - acting | 391 | 133_hamlet_playwright_actor_cast |
| 134 | angels - archangels - archangel - angelic - angel | 384 | 134_angels_archangels_archangel_angelic |
| 135 | labia - labial - lips - clitoris - vulval | 378 | 135_labia_labial_lips_clitoris |
| 136 | jerseys - uniforms - 49ers - colors - helmets | 376 | 136_jerseys_uniforms_49ers_colors |
| 137 | linguistics - languages - linguist - linguistic - language | 376 | 137_linguistics_languages_linguist_linguistic |
| 138 | foxes - coyotes - coyote - mammals - fox | 376 | 138_foxes_coyotes_coyote_mammals |
| 139 | tiger - tigers - species - lion - wildlife | 374 | 139_tiger_tigers_species_lion |
| 140 | panzer - soviets - infantry - 1944 - artillery | 371 | 140_panzer_soviets_infantry_1944 |
| 141 | hamlet - playwright - playwrights - tempest - soliloquy | 370 | 141_hamlet_playwright_playwrights_tempest |
| 142 | potter - sorcerer - wizard - screenwriter - cast | 366 | 142_potter_sorcerer_wizard_screenwriter |
| 143 | rating - critics - reviews - review - rotten | 366 | 143_rating_critics_reviews_review |
| 144 | pepper - concert - albums - songs - album | 362 | 144_pepper_concert_albums_songs |
| 145 | pope - papal - papacy - pontifical - popes | 358 | 145_pope_papal_papacy_pontifical |
| 146 | unions - union - unionism - unionized - unionization | 356 | 146_unions_union_unionism_unionized |
| 147 | cardiovascular - cardiomyopathy - cardiac - hypertension - myocardial | 355 | 147_cardiovascular_cardiomyopathy_cardiac_hypertension |
| 148 | helicopters - missiles - helicopter - squadrons - insurgents | 354 | 148_helicopters_missiles_helicopter_squadrons |
| 149 | shah - khan - dynasty - deposed - dictator | 352 | 149_shah_khan_dynasty_deposed |
| 150 | waters - concert - tour - pink - wall | 351 | 150_waters_concert_tour_pink |
| 151 | voyages - voyage - 1493 - explorers - expeditions | 345 | 151_voyages_voyage_1493_explorers |
| 152 | spartan - rebelled - battle - besieged - victories | 343 | 152_spartan_rebelled_battle_besieged |
| 153 | kanji - hiragana - pinyin - kun - katakana | 343 | 153_kanji_hiragana_pinyin_kun |
| 154 | rings - ring - shire - hobbit - elves | 341 | 154_rings_ring_shire_hobbit |
| 155 | confederates - confederate - confederacy - 1863 - 1861 | 339 | 155_confederates_confederate_confederacy_1863 |
| 156 | mafia - gangs - cartels - cartel - syndicate | 336 | 156_mafia_gangs_cartels_cartel |
| 157 | apartheid - decolonisation - 1979 - smith - nationalists | 332 | 157_apartheid_decolonisation_1979_smith |
| 158 | fascism - fascist - italiana - fascists - nationalist | 330 | 158_fascism_fascist_italiana_fascists |
| 159 | windows - vista - os - pc - versions | 329 | 159_windows_vista_os_pc |
| 160 | chrome - browser - browsers - chromium - safari | 328 | 160_chrome_browser_browsers_chromium |
| 161 | literacy - population - castes - literate - census | 323 | 161_literacy_population_castes_literate |
| 162 | pip - miss - orphan - carol - protagonist | 321 | 162_pip_miss_orphan_carol |
| 163 | ruby - assassination - assassinated - assassinate - warren | 319 | 163_ruby_assassination_assassinated_assassinate |
| 164 | soviets - revolutionaries - soviet - 1917 - socialists | 316 | 164_soviets_revolutionaries_soviet_1917 |
| 165 | twitter - tweets - tweet - microblogging - retweet | 315 | 165_twitter_tweets_tweet_microblogging |
| 166 | sai - shakti - marries - revenge - pooja | 315 | 166_sai_shakti_marries_revenge |
| 167 | quarks - quark - particles - protons - bosons | 314 | 167_quarks_quark_particles_protons |
| 168 | polypropylene - polymers - polymer - polyethylene - polymerization | 314 | 168_polypropylene_polymers_polymer_polyethylene |
| 169 | bourbon - 1685 - 1643 - heir - 1598 | 313 | 169_bourbon_1685_1643_heir |
| 170 | cartoons - goofy - cartoon - bunny - hare | 313 | 170_cartoons_goofy_cartoon_bunny |
| 171 | mountains - mountain - plains - topography - southwestern | 312 | 171_mountains_mountain_plains_topography |
| 172 | epic - developers - studio - studios - blizzard | 311 | 172_epic_developers_studio_studios |
| 173 | sergeant - lieutenants - sergeants - lieutenant - ranks | 309 | 173_sergeant_lieutenants_sergeants_lieutenant |
| 174 | yoon - jong - hyun - jae - jung | 307 | 174_yoon_jong_hyun_jae |
| 175 | villa - rebelled - barrios - rebellion - generals | 304 | 175_villa_rebelled_barrios_rebellion |
| 176 | animator - animators - animation - animating - animated | 303 | 176_animator_animators_animation_animating |
| 177 | dementia - dementias - neurodegenerative - parkinsonism - impairment | 303 | 177_dementia_dementias_neurodegenerative_parkinsonism |
| 178 | doctor - doctors - dr - actor - tenth | 302 | 178_doctor_doctors_dr_actor |
| 179 | counties - midlands - county - boroughs - district | 301 | 179_counties_midlands_county_boroughs |
| 180 | philosopher - philosophy - platonic - philosophers - stoicism | 300 | 180_philosopher_philosophy_platonic_philosophers |
| 181 | neural - neuron - neurons - convolutions - backpropagation | 299 | 181_neural_neuron_neurons_convolutions |
| 182 | vaccines - vaccine - vaccination - vaccinated - vaccinate | 298 | 182_vaccines_vaccine_vaccination_vaccinated |
| 183 | kickboxing - sparring - boxing - jitsu - karate | 293 | 183_kickboxing_sparring_boxing_jitsu |
| 184 | payments - card - payment - cardholder - cardholders | 287 | 184_payments_card_payment_cardholder |
| 185 | cathedrals - cathedral - arches - arched - vaults | 282 | 185_cathedrals_cathedral_arches_arched |
| 186 | visual - studios - animation - filming - actors | 282 | 186_visual_studios_animation_filming |
| 187 | psychoanalytical - psychoanalysts - psychoanalysis - psychoanalytic - psychoanalyst | 281 | 187_psychoanalytical_psychoanalysts_psychoanalysis_psychoanalytic |
| 188 | novels - novelists - novelist - sensibility - 1818 | 280 | 188_novels_novelists_novelist_sensibility |
| 189 | medieval - grail - knights - tales - knight | 278 | 189_medieval_grail_knights_tales |
| 190 | uniforms - jerseys - uniform - logos - blazers | 277 | 190_uniforms_jerseys_uniform_logos |
| 191 | cookies - cookie - http - session - browsers | 277 | 191_cookies_cookie_http_session |
| 192 | polygamous - polygamy - polyamory - polygamists - monogamous | 277 | 192_polygamous_polygamy_polyamory_polygamists |
| 193 | languages - speak - dialects - language - linguists | 275 | 193_languages_speak_dialects_language |
| 194 | 1830s - tribe - tribes - confederate - natives | 274 | 194_1830s_tribe_tribes_confederate |
| 195 | equilibria - equilibrium - strategic - strategies - strategy | 269 | 195_equilibria_equilibrium_strategic_strategies |
| 196 | firearm - firearms - handgun - handguns - guns | 268 | 196_firearm_firearms_handgun_handguns |
| 197 | kong - monster - monsters - franchise - sequel | 266 | 197_kong_monster_monsters_franchise |
| 198 | murders - murdered - murderers - convicted - defendants | 264 | 198_murders_murdered_murderers_convicted |
| 199 | homer - sitcom - cartoon - sitcoms - showrunner | 263 | 199_homer_sitcom_cartoon_sitcoms |
| 200 | alleging - accused - alleged - defamation - allegations | 262 | 200_alleging_accused_alleged_defamation |
| 201 | delegates - presidential - nominee - primaries - presidency | 261 | 201_delegates_presidential_nominee_primaries |
| 202 | probation - misdemeanor - arrested - arrest - jail | 258 | 202_probation_misdemeanor_arrested_arrest |
| 203 | fender - guitars - guitar - acoustic - amplifiers | 258 | 203_fender_guitars_guitar_acoustic |
| 204 | trafficking - prostitution - prostitutes - prostitute - brothels | 257 | 204_trafficking_prostitution_prostitutes_prostitute |
| 205 | isotopes - isotope - elements - neutron - neutrons | 257 | 205_isotopes_isotope_elements_neutron |
| 206 | meth - methamphetamine - cocaine - drug - methylamine | 256 | 206_meth_methamphetamine_cocaine_drug |
| 207 | channel - channels - tv - simulcast - streaming | 256 | 207_channel_channels_tv_simulcast |
| 208 | frescoes - chapel - pope - basilica - sculptor | 252 | 208_frescoes_chapel_pope_basilica |
| 209 | armistice - 1944 - française - 1945 - 1940 | 251 | 209_armistice_1944_française_1945 |
| 210 | novelist - novels - novel - novelists - literature | 251 | 210_novelist_novels_novel_novelists |
| 211 | 1936 - fascism - fascist - nationalists - nationalist | 251 | 211_1936_fascism_fascist_nationalists |
| 212 | apple - 6s - smartphones - smartphone - phones | 248 | 212_apple_6s_smartphones_smartphone |
| 213 | showrunner - episode - showrunners - episodes - primetime | 248 | 213_showrunner_episode_showrunners_episodes |
| 214 | gemstones - gemstone - sapphires - sapphire - diamond | 247 | 214_gemstones_gemstone_sapphires_sapphire |
| 215 | emperors - emperor - roman - empire - augustus | 246 | 215_emperors_emperor_roman_empire |
| 216 | cavalry - legions - armies - battle - battles | 246 | 216_cavalry_legions_armies_battle |
| 217 | 1649 - royalist - 1685 - royalists - 1640 | 246 | 217_1649_royalist_1685_royalists |
| 218 | orgasms - orgasm - clitoris - clitoral - stimulation | 245 | 218_orgasms_orgasm_clitoris_clitoral |
| 219 | glaucoma - retinopathy - blindness - retinal - cataract | 242 | 219_glaucoma_retinopathy_blindness_retinal |
| 220 | novels - novelist - novel - literature - literary | 241 | 220_novels_novelist_novel_literature |
| 221 | artillery - trenches - fortifications - bombardment - bombardments | 240 | 221_artillery_trenches_fortifications_bombardment |
| 222 | beach - drums - albums - songs - drumming | 239 | 222_beach_drums_albums_songs |
| 223 | nouveau - paintings - designers - façades - facades | 237 | 223_nouveau_paintings_designers_façades |
| 224 | maya - civilizations - archaeological - archeological - civilization | 236 | 224_maya_civilizations_archaeological_archeological |
| 225 | taekwondo - tae - karate - jitsu - martial | 235 | 225_taekwondo_tae_karate_jitsu |
| 226 | rocky - creed - sequel - boxer - film | 233 | 226_rocky_creed_sequel_boxer |
| 227 | assassins - creed - assassin - brotherhood - gameplay | 231 | 227_assassins_creed_assassin_brotherhood |
| 228 | bp - petroleum - refinery - offshore - companies | 231 | 228_bp_petroleum_refinery_offshore |
| 229 | minorities - ethnicity - ethnic - ethnically - census | 231 | 229_minorities_ethnicity_ethnic_ethnically |
| 230 | baptism - baptisms - baptismal - baptized - baptised | 230 | 230_baptism_baptisms_baptismal_baptized |
| 231 | bighorn - 1876 - bull - elk - tribes | 229 | 231_bighorn_1876_bull_elk |
| 232 | psychotic - psychiatric - schizophrenia - psychiatry - sane | 227 | 232_psychotic_psychiatric_schizophrenia_psychiatry |
| 233 | mexicana - latin - salsa - vida - una | 227 | 233_mexicana_latin_salsa_vida |
| 234 | abortion - abortions - roe - unconstitutional - overturned | 225 | 234_abortion_abortions_roe_unconstitutional |
| 235 | toy - toys - sequels - sequel - animator | 225 | 235_toy_toys_sequels_sequel |
| 236 | euthanasia - suicide - legalised - suicides - suicidal | 225 | 236_euthanasia_suicide_legalised_suicides |
| 237 | chan - kung - chang - kong - karate | 221 | 237_chan_kung_chang_kong |
| 238 | protesting - activism - protests - protest - rallies | 220 | 238_protesting_activism_protests_protest |
| 239 | tribes - tribe - natives - upstate - tribal | 219 | 239_tribes_tribe_natives_upstate |
| 240 | toured - concert - concerts - drums - vocals | 219 | 240_toured_concert_concerts_drums |
| 241 | nam - communists - insurgency - guerrilla - troops | 219 | 241_nam_communists_insurgency_guerrilla |
| 242 | election - conservatives - liberal - liberals - partisanship | 219 | 242_election_conservatives_liberal_liberals |
| 243 | chess - grandmaster - grandmasters - blitz - tournament | 219 | 243_chess_grandmaster_grandmasters_blitz |
| 244 | radio - fm - stations - station - simulcasts | 218 | 244_radio_fm_stations_station |
| 245 | awards - nominated - nominations - screenplay - cinematography | 218 | 245_awards_nominated_nominations_screenplay |
| 246 | bombing - bomber - bombers - bombed - bombs | 218 | 246_bombing_bomber_bombers_bombed |
| 247 | diesel - fuels - engines - combustion - petrol | 218 | 247_diesel_fuels_engines_combustion |
| 248 | species - wildlife - fauna - birds - endangered | 217 | 248_species_wildlife_fauna_birds |
| 249 | extraterrestrial - sightings - aliens - sighting - hoaxes | 216 | 249_extraterrestrial_sightings_aliens_sighting |
| 250 | tick - ticks - burgdorferi - pathogens - infected | 215 | 250_tick_ticks_burgdorferi_pathogens |
| 251 | congregational - denominational - congregations - evangelicalism - denomination | 215 | 251_congregational_denominational_congregations_evangelicalism |
| 252 | lymphatic - lymph - gallbladder - organs - capillaries | 215 | 252_lymphatic_lymph_gallbladder_organs |
| 253 | chemotherapy - treatments - cancer - cancers - radiotherapy | 215 | 253_chemotherapy_treatments_cancer_cancers |
| 254 | creole - creoles - lingua - bilingual - dialects | 214 | 254_creole_creoles_lingua_bilingual |
| 255 | princess - duchess - prince - countess - royal | 211 | 255_princess_duchess_prince_countess |
| 256 | insurrection - revolt - 1821 - 1829 - uprising | 210 | 256_insurrection_revolt_1821_1829 |
| 257 | charities - charity - donations - philanthropist - fundraising | 209 | 257_charities_charity_donations_philanthropist |
| 258 | alien - predator - aliens - sequels - extraterrestrial | 209 | 258_alien_predator_aliens_sequels |
| 259 | condor - dictators - declassified - dictatorships - chile | 208 | 259_condor_dictators_declassified_dictatorships |
| 260 | inflation - inflationary - macroeconomics - macroeconomic - recessions | 207 | 260_inflation_inflationary_macroeconomics_macroeconomic |
| 261 | warlock - infinity - eternity - gems - marvel | 206 | 261_warlock_infinity_eternity_gems |
| 262 | bbc - channel - simulcast - channels - broadcasting | 205 | 262_bbc_channel_simulcast_channels |
| 263 | eu - eurozone - euro - countries - borders | 205 | 263_eu_eurozone_euro_countries |
| 264 | sonic - hedgehog - hedgehogs - tails - knuckles | 205 | 264_sonic_hedgehog_hedgehogs_tails |
| 265 | battleships - torpedoed - torpedoes - torpedo - battleship | 203 | 265_battleships_torpedoed_torpedoes_torpedo |
| 266 | hurricane - hurricanes - storms - cyclones - cyclone | 200 | 266_hurricane_hurricanes_storms_cyclones |
| 267 | concert - concerts - tour - albums - toured | 200 | 267_concert_concerts_tour_albums |
| 268 | shōgun - shogun - samurai - daimyō - daimyo | 199 | 268_shōgun_shogun_samurai_daimyō |
| 269 | electrodes - electroluminescent - electrode - phosphors - displays | 199 | 269_electrodes_electroluminescent_electrode_phosphors |
| 270 | brigades - soldiers - reinforcements - troops - casualties | 199 | 270_brigades_soldiers_reinforcements_troops |
| 271 | presidency - populist - presidential - candidate - candidates | 199 | 271_presidency_populist_presidential_candidate |
| 272 | heraldic - heraldry - gules - arms - garter | 198 | 272_heraldic_heraldry_gules_arms |
| 273 | refrigerants - refrigeration - refrigerant - refrigerator - condenser | 198 | 273_refrigerants_refrigeration_refrigerant_refrigerator |
| 274 | bee - sang - singer - songwriter - artists | 198 | 274_bee_sang_singer_songwriter |
| 275 | thrones - novels - dragons - paperback - novel | 198 | 275_thrones_novels_dragons_paperback |
| 276 | festivals - festival - celebrated - celebrations - festivities | 198 | 276_festivals_festival_celebrated_celebrations |
| 277 | branch - fires - fired - deaths - wounded | 197 | 277_branch_fires_fired_deaths |
| 278 | pasha - turkey - sultan - sultanate - nationalists | 197 | 278_pasha_turkey_sultan_sultanate |
| 279 | neanderthalensis - paleolithic - sapiens - erectus - ancestor | 196 | 279_neanderthalensis_paleolithic_sapiens_erectus |
| 280 | mujahideen - laden - militants - insurgency - jihad | 195 | 280_mujahideen_laden_militants_insurgency |
| 281 | shogun - shōgun - shogunate - samurai - daimyō | 194 | 281_shogun_shōgun_shogunate_samurai |
| 282 | hypothyroidism - hyperthyroidism - thyroid - thyroiditis - thyroidectomy | 194 | 282_hypothyroidism_hyperthyroidism_thyroid_thyroiditis |
| 283 | mythos - tales - author - authors - writer | 193 | 283_mythos_tales_author_authors |
| 284 | contest - contests - qualifying - winners - competed | 192 | 284_contest_contests_qualifying_winners |
| 285 | impeachment - prosecutor - prosecutors - trump - prosecutorial | 192 | 285_impeachment_prosecutor_prosecutors_trump |
| 286 | intelligence - personality - traits - trait - psychometric | 192 | 286_intelligence_personality_traits_trait |
| 287 | terminator - sequels - sequel - prequel - trilogy | 191 | 287_terminator_sequels_sequel_prequel |
| 288 | spacetime - relativity - relativistic - gravitation - geodesic | 191 | 288_spacetime_relativity_relativistic_gravitation |
| 289 | dictatorships - dictatorship - regimes - dictators - authoritarianism | 191 | 289_dictatorships_dictatorship_regimes_dictators |
| 290 | daft - punk - techno - toured - bands | 190 | 290_daft_punk_techno_toured |
| 291 | peppers - chili - funk - flea - band | 190 | 291_peppers_chili_funk_flea |
| 292 | dinosaurs - dinosaur - rex - prehistoric - sequels | 188 | 292_dinosaurs_dinosaur_rex_prehistoric |
| 293 | surnames - surname - naming - names - suffixes | 188 | 293_surnames_surname_naming_names |
| 294 | philosopher - 1765 - philosophers - writings - enlightenment | 187 | 294_philosopher_1765_philosophers_writings |
| 295 | novels - novelist - 1925 - novel - 1920s | 187 | 295_novels_novelist_1925_novel |
| 296 | depot - retailer - retailers - warehouses - stores | 186 | 296_depot_retailer_retailers_warehouses |
| 297 | copyright - copyrights - copyrighted - royalties - infringement | 186 | 297_copyright_copyrights_copyrighted_royalties |
| 298 | eastern - daylight - clocks - noon - clock | 184 | 298_eastern_daylight_clocks_noon |
| 299 | numerals - numeral - numbers - numerology - digits | 184 | 299_numerals_numeral_numbers_numerology |
| 300 | armament - armoured - turret - tanks - tank | 182 | 300_armament_armoured_turret_tanks |
| 301 | vaccines - vaccine - vaccination - vaccinations - vaccinated | 182 | 301_vaccines_vaccine_vaccination_vaccinations |
| 302 | cola - coca - coke - soda - bottled | 181 | 302_cola_coca_coke_soda |
| 303 | fleet - 1797 - sailed - fleets - captains | 181 | 303_fleet_1797_sailed_fleets |
| 304 | tsarina - empress - tsar - maria - princesses | 181 | 304_tsarina_empress_tsar_maria |
| 305 | metalcore - thrash - deathcore - metal - hardcore | 179 | 305_metalcore_thrash_deathcore_metal |
| 306 | medals - medal - commendation - gallantry - badge | 179 | 306_medals_medal_commendation_gallantry |
| 307 | smith - prophets - revelations - revelation - scriptures | 179 | 307_smith_prophets_revelations_revelation |
| 308 | newspaper - newspapers - gazette - news - magazine | 179 | 308_newspaper_newspapers_gazette_news |
| 309 | philosopher - philosophers - philosophy - hermeneutics - philosophical | 179 | 309_philosopher_philosophers_philosophy_hermeneutics |
| 310 | protocols - protocol - packet - packets - layers | 179 | 310_protocols_protocol_packet_packets |
| 311 | coronation - airing - episodes - bbc - aired | 178 | 311_coronation_airing_episodes_bbc |
| 312 | song - songs - singles - singer - billboard | 178 | 312_song_songs_singles_singer |
| 313 | thylacines - thylacine - fauna - mammals - carnivorous | 178 | 313_thylacines_thylacine_fauna_mammals |
| 314 | hearings - communists - subcommittee - committee - committees | 177 | 314_hearings_communists_subcommittee_committee |
| 315 | 1776 - 1781 - 1775 - 1782 - 1778 | 177 | 315_1776_1781_1775_1782 |
| 316 | comedian - circus - comedians - pythons - comedy | 177 | 316_comedian_circus_comedians_pythons |
| 317 | railways - railway - trains - rail - train | 177 | 317_railways_railway_trains_rail |
| 318 | nudity - naturism - naturists - naturist - nude | 175 | 318_nudity_naturism_naturists_naturist |
| 319 | coalition - elections - populist - election - coalitions | 175 | 319_coalition_elections_populist_election |
| 320 | jihad - coup - overthrow - militias - ba | 172 | 320_jihad_coup_overthrow_militias |
| 321 | cement - cements - concretes - concrete - mortar | 171 | 321_cement_cements_concretes_concrete |
| 322 | jeopardy - prizes - contestant - contestants - competed | 170 | 322_jeopardy_prizes_contestant_contestants |
| 323 | panzer - commanders - blitzkrieg - commanded - 1944 | 169 | 323_panzer_commanders_blitzkrieg_commanded |
| 324 | mushroom - mushrooms - sprites - sprite - super | 169 | 324_mushroom_mushrooms_sprites_sprite |
| 325 | cossacks - tsar - tsarist - soviet - republics | 169 | 325_cossacks_tsar_tsarist_soviet |
| 326 | apes - ape - sequels - gorilla - prequel | 169 | 326_apes_ape_sequels_gorilla |
| 327 | graphene - graphite - nanotubes - carbon - conductivity | 168 | 327_graphene_graphite_nanotubes_carbon |
| 328 | nicotine - tobacco - cigarettes - cigarette - smoking | 168 | 328_nicotine_tobacco_cigarettes_cigarette |
| 329 | keyboardist - toured - guitarist - vocalist - bassist | 167 | 329_keyboardist_toured_guitarist_vocalist |
| 330 | museums - museum - exhibitions - galleries - exhibits | 167 | 330_museums_museum_exhibitions_galleries |
| 331 | motors - rotors - rotor - motor - rotary | 166 | 331_motors_rotors_rotor_motor |
| 332 | tabby - cat - feline - cats - coloration | 165 | 332_tabby_cat_feline_cats |
| 333 | handmaid - novels - novel - writers - tale | 163 | 333_handmaid_novels_novel_writers |
| 334 | boulevard - celebrity - fame - celebrities - walk | 163 | 334_boulevard_celebrity_fame_celebrities |
| 335 | trilogy - remastered - gods - editions - war | 162 | 335_trilogy_remastered_gods_editions |
| 336 | genocide - peacekeeping - massacres - assassinated - killings | 162 | 336_genocide_peacekeeping_massacres_assassinated |
| 337 | leopard - leopards - armament - refit - tanks | 162 | 337_leopard_leopards_armament_refit |
| 338 | homicides - homicide - murders - crime - crimes | 162 | 338_homicides_homicide_murders_crime |
| 339 | mercury - queen - bohemian - singer - musically | 162 | 339_mercury_queen_bohemian_singer |
| 340 | tennis - tournaments - tournament - badminton - slams | 161 | 340_tennis_tournaments_tournament_badminton |
| 341 | confederate - confederacy - confederates - slavery - 1861 | 160 | 341_confederate_confederacy_confederates_slavery |
| 342 | scrum - agile - sprints - sprint - development | 159 | 342_scrum_agile_sprints_sprint |
| 343 | museums - museum - galleries - exhibitions - exhibits | 159 | 343_museums_museum_galleries_exhibitions |
| 344 | transformers - transformer - sequels - bumblebee - sequel | 158 | 344_transformers_transformer_sequels_bumblebee |
| 345 | languages - dialects - language - bilingual - dialect | 158 | 345_languages_dialects_language_bilingual |
| 346 | sponge - sponges - cartoon - cartoons - plankton | 158 | 346_sponge_sponges_cartoon_cartoons |
| 347 | telescope - telescopes - observatory - astronomy - astronomical | 157 | 347_telescope_telescopes_observatory_astronomy |
| 348 | mandarin - dialects - languages - lingua - china | 157 | 348_mandarin_dialects_languages_lingua |
| 349 | kiss - toured - concerts - tour - lip | 156 | 349_kiss_toured_concerts_tour |
| 350 | holiday - celebrates - holidays - celebrated - celebrations | 156 | 350_holiday_celebrates_holidays_celebrated |
| 351 | conquered - empires - ancient - kingdoms - dynasty | 155 | 351_conquered_empires_ancient_kingdoms |
| 352 | legionnaires - legion - regiments - guerrillas - regiment | 155 | 352_legionnaires_legion_regiments_guerrillas |
| 353 | evolution - evolutionary - creationist - naturalist - biologist | 155 | 353_evolution_evolutionary_creationist_naturalist |
| 354 | tennis - slams - quarterfinal - racquet - doubles | 155 | 354_tennis_slams_quarterfinal_racquet |
| 355 | wikipedia - encyclopedia - encyclopedias - wikis - articles | 155 | 355_wikipedia_encyclopedia_encyclopedias_wikis |
| 356 | detainees - inmates - prisoners - detention - prisons | 155 | 356_detainees_inmates_prisoners_detention |
| 357 | operatic - opera - soprano - operas - arias | 155 | 357_operatic_opera_soprano_operas |
| 358 | coalition - chancellors - chancellor - chancellorship - democrats | 154 | 358_coalition_chancellors_chancellor_chancellorship |
| 359 | pixels - encoding - compression - pixel - bitmap | 154 | 359_pixels_encoding_compression_pixel |
| 360 | augmented - oculus - vision - ar - virtual | 154 | 360_augmented_oculus_vision_ar |
| 361 | flash - comics - episodes - storylines - showrunner | 154 | 361_flash_comics_episodes_storylines |
| 362 | presidency - presidential - fascism - president - dictatorship | 153 | 362_presidency_presidential_fascism_president |
| 363 | soil - soils - fertilizers - fertilizer - nutrient | 153 | 363_soil_soils_fertilizers_fertilizer |
| 364 | novels - 1876 - 1881 - 1880 - writer | 153 | 364_novels_1876_1881_1880 |
| 365 | critics - rankings - ranking - decade - films | 152 | 365_critics_rankings_ranking_decade |
| 366 | dos - defendants - trafficking - alleged - recruited | 152 | 366_dos_defendants_trafficking_alleged |
| 367 | abused - abuse - assaults - maltreatment - abusive | 152 | 367_abused_abuse_assaults_maltreatment |
| 368 | masks - mask - pandemic - vaccine - vaccinated | 151 | 368_masks_mask_pandemic_vaccine |
| 369 | novel - scout - rye - nonfiction - narrator | 151 | 369_novel_scout_rye_nonfiction |
| 370 | tennis - doubles - competed - tournaments - tournament | 150 | 370_tennis_doubles_competed_tournaments |
| 371 | macron - presidential - candidate - candidates - pen | 149 | 371_macron_presidential_candidate_candidates |
| 372 | rose - roses - frontman - revolver - toured | 149 | 372_rose_roses_frontman_revolver |
| 373 | satyagraha - revolt - rebellion - salt - protest | 148 | 373_satyagraha_revolt_rebellion_salt |
| 374 | 1945 - allied - soviets - allies - reunification | 148 | 374_1945_allied_soviets_allies |
| 375 | princes - prince - ambition - prudence - nobles | 148 | 375_princes_prince_ambition_prudence |
| 376 | railways - railway - locomotives - trains - train | 148 | 376_railways_railway_locomotives_trains |
| 377 | murdered - murders - convicted - sentenced - suspicion | 148 | 377_murdered_murders_convicted_sentenced |
| 378 | syndrome - disorders - polycystic - diagnosed - ovarian | 148 | 378_syndrome_disorders_polycystic_diagnosed |
| 379 | dune - dunes - novels - trilogy - novel | 148 | 379_dune_dunes_novels_trilogy |
| 380 | temple - cult - peoples - disciples - teachings | 147 | 380_temple_cult_peoples_disciples |
| 381 | 1963 - assassinated - 1964 - mosque - assassination | 147 | 381_1963_assassinated_1964_mosque |
| 382 | chess - rook - grandmasters - grandmaster - tournaments | 147 | 382_chess_rook_grandmasters_grandmaster |
| 383 | lithium - batteries - battery - rechargeable - electrochemical | 146 | 383_lithium_batteries_battery_rechargeable |
| 384 | genocide - detainees - persecution - internment - holocaust | 146 | 384_genocide_detainees_persecution_internment |
| 385 | neurons - neuronal - neuron - neurotransmitters - neurotransmitter | 146 | 385_neurons_neuronal_neuron_neurotransmitters |
| 386 | poles - casualties - massacres - massacre - polish | 145 | 386_poles_casualties_massacres_massacre |
| 387 | dialects - accents - isles - dialect - pronunciation | 145 | 387_dialects_accents_isles_dialect |
| 388 | racing - speedway - raced - laps - motorsports | 145 | 388_racing_speedway_raced_laps |
| 389 | rand - nonfiction - subjectivism - philosophers - philosopher | 145 | 389_rand_nonfiction_subjectivism_philosophers |
| 390 | lee - pap - chairman - election - leaders | 145 | 390_lee_pap_chairman_election |
| 391 | kernels - kernel - processors - processes - processor | 145 | 391_kernels_kernel_processors_processes |
| 392 | nightmare - nightmares - elm - horror - supernatural | 144 | 392_nightmare_nightmares_elm_horror |
| 393 | newspaper - newspapers - tabloid - newsprint - journalism | 144 | 393_newspaper_newspapers_tabloid_newsprint |
| 394 | interrogation - interrogations - arrest - incrimination - defendant | 144 | 394_interrogation_interrogations_arrest_incrimination |
| 395 | millennials - millennial - generations - generation - generational | 144 | 395_millennials_millennial_generations_generation |
| 396 | hobbit - hobbits - shire - literature - publishers | 144 | 396_hobbit_hobbits_shire_literature |
| 397 | pollution - pollutants - polluting - pollutant - polluted | 143 | 397_pollution_pollutants_polluting_pollutant |
| 398 | sins - sin - sinfulness - theology - sinned | 143 | 398_sins_sin_sinfulness_theology |
| 399 | nursing - nurse - nurses - hospitals - compassion | 143 | 399_nursing_nurse_nurses_hospitals |
| 400 | aeronautical - aeronautics - aircraft - flew - airplanes | 143 | 400_aeronautical_aeronautics_aircraft_flew |
| 401 | congregations - congregation - churches - denominations - denomination | 142 | 401_congregations_congregation_churches_denominations |
| 402 | skyscraper - tallest - skyscrapers - towers - tower | 142 | 402_skyscraper_tallest_skyscrapers_towers |
| 403 | consulate - embassy - suspects - assassination - consul | 142 | 403_consulate_embassy_suspects_assassination |
| 404 | blu - disc - discs - codecs - digital | 142 | 404_blu_disc_discs_codecs |
| 405 | pyramid - pyramids - pyramidion - excavations - tombs | 141 | 405_pyramid_pyramids_pyramidion_excavations |
| 406 | antibiotics - antibiotic - amoxicillin - penicillin - ampicillin | 140 | 406_antibiotics_antibiotic_amoxicillin_penicillin |
| 407 | activism - protest - protests - activist - marches | 140 | 407_activism_protest_protests_activist |
| 408 | bbc - broadcasting - channel - al - simulcast | 140 | 408_bbc_broadcasting_channel_al |
| 409 | pharaoh - pharaohs - throne - heir - tombs | 139 | 409_pharaoh_pharaohs_throne_heir |
| 410 | bombing - troops - pentagon - war - troop | 139 | 410_bombing_troops_pentagon_war |
| 411 | municipality - megacity - located - niger - town | 139 | 411_municipality_megacity_located_niger |
| 412 | addresses - subnet - subnets - addressing - address | 138 | 412_addresses_subnet_subnets_addressing |
| 413 | tom - cruise - screenwriter - tall - jack | 138 | 413_tom_cruise_screenwriter_tall |
| 414 | motivation - motivations - motivational - motivate - motivates | 137 | 414_motivation_motivations_motivational_motivate |
| 415 | deforestation - reforestation - forestry - forests - forested | 137 | 415_deforestation_reforestation_forestry_forests |
| 416 | anesthesiologist - anatomy - neurosurgery - surgeon - cast | 137 | 416_anesthesiologist_anatomy_neurosurgery_surgeon |
| 417 | pharaoh - prophets - prophet - messiah - patriarch | 136 | 417_pharaoh_prophets_prophet_messiah |
| 418 | battlefield - warfare - modern - gameplay - remastered | 136 | 418_battlefield_warfare_modern_gameplay |
| 419 | ancestry - mestizo - ethnic - ethnicity - natives | 136 | 419_ancestry_mestizo_ethnic_ethnicity |
| 420 | telegram - messenger - messaging - chat - apps | 136 | 420_telegram_messenger_messaging_chat |
| 421 | penalty - penalties - fouls - foul - goaltending | 136 | 421_penalty_penalties_fouls_foul |
| 422 | miss - pageant - pageants - pageantry - finalist | 135 | 422_miss_pageant_pageants_pageantry |
| 423 | throne - rebelled - heir - king - castles | 135 | 423_throne_rebelled_heir_king |
| 424 | territory - airspace - blockade - sanctions - borders | 135 | 424_territory_airspace_blockade_sanctions |
| 425 | jazz - saxophonist - trumpeter - saxophone - musicians | 135 | 425_jazz_saxophonist_trumpeter_saxophone |
| 426 | stooge - moe - curly - comedies - comedians | 135 | 426_stooge_moe_curly_comedies |
| 427 | lichens - lichen - fungi - fungal - fungus | 135 | 427_lichens_lichen_fungi_fungal |
| 428 | rebels - overthrowing - generals - overthrow - coup | 134 | 428_rebels_overthrowing_generals_overthrow |
| 429 | races - race - racial - anthropologist - anthropologists | 134 | 429_races_race_racial_anthropologist |
| 430 | channel - channels - broadcasting - broadcasters - simulcast | 134 | 430_channel_channels_broadcasting_broadcasters |
| 431 | prosecution - accused - bordereau - acquitted - investigation | 133 | 431_prosecution_accused_bordereau_acquitted |
| 432 | missiles - soviets - missile - soviet - nuclear | 133 | 432_missiles_soviets_missile_soviet |
| 433 | 1945 - armistice - surrender - surrendered - soviets | 133 | 433_1945_armistice_surrender_surrendered |
| 434 | monastic - monastics - samadhi - monks - monastery | 133 | 434_monastic_monastics_samadhi_monks |
| 435 | colors - colours - colour - magenta - pigment | 133 | 435_colors_colours_colour_magenta |
| 436 | pipeline - pipelines - keystone - refinery - pipe | 133 | 436_pipeline_pipelines_keystone_refinery |
| 437 | institutes - institute - universities - polytechnic - polytechnics | 133 | 437_institutes_institute_universities_polytechnic |
| 438 | deepest - depths - oceanographic - oceanography - challenger | 132 | 438_deepest_depths_oceanographic_oceanography |
| 439 | postcodes - postcode - zip - postal - addresses | 132 | 439_postcodes_postcode_zip_postal |
| 440 | rockstar - grand - games - consoles - gameplay | 132 | 440_rockstar_grand_games_consoles |
| 441 | woman - wonder - goddess - feminist - goddesses | 132 | 441_woman_wonder_goddess_feminist |
| 442 | suffrage - referendum - referendums - women - enfranchised | 131 | 442_suffrage_referendum_referendums_women |
| 443 | apartheid - cape - natal - protest - activist | 131 | 443_apartheid_cape_natal_protest |
| 444 | barristers - barrister - solicitors - lawyers - solicitor | 131 | 444_barristers_barrister_solicitors_lawyers |
| 445 | scrolls - manuscripts - antiquities - archaeology - archaeological | 131 | 445_scrolls_manuscripts_antiquities_archaeology |
| 446 | slavery - revolution - slaves - revolt - colonial | 131 | 446_slavery_revolution_slaves_revolt |
| 447 | boxer - cop - knockout - fighter - fights | 130 | 447_boxer_cop_knockout_fighter |
| 448 | siblings - 1963 - assassinated - senator - youngest | 130 | 448_siblings_1963_assassinated_senator |
| 449 | ku - confederate - activists - 1868 - whites | 130 | 449_ku_confederate_activists_1868 |
| 450 | bear - bears - grizzly - predators - species | 130 | 450_bear_bears_grizzly_predators |
| 451 | junta - detained - arrest - imprisonment - sentenced | 130 | 451_junta_detained_arrest_imprisonment |
| 452 | oasis - albums - concert - songwriter - album | 129 | 452_oasis_albums_concert_songwriter |
| 453 | darkness - literature - novelist - postcolonial - colonialism | 129 | 453_darkness_literature_novelist_postcolonial |
| 454 | currencies - currency - monetary - dollar - dollars | 129 | 454_currencies_currency_monetary_dollar |
| 455 | musically - musician - drums - percussion - composers | 129 | 455_musically_musician_drums_percussion |
| 456 | infantry - insurgents - battalion - platoon - reconnaissance | 129 | 456_infantry_insurgents_battalion_platoon |
| 457 | sesame - puppets - puppeteer - puppet - puppeteers | 128 | 457_sesame_puppets_puppeteer_puppet |
| 458 | crocodiles - crocodile - alligators - alligator - reptiles | 128 | 458_crocodiles_crocodile_alligators_alligator |
| 459 | antibiotics - antibiotic - penicillin - antimicrobial - amoxicillin | 128 | 459_antibiotics_antibiotic_penicillin_antimicrobial |
| 460 | acropolis - excavations - temples - temple - archaeologists | 128 | 460_acropolis_excavations_temples_temple |
| 461 | taxes - tax - taxation - taxable - taxed | 128 | 461_taxes_tax_taxation_taxable |
| 462 | manning - arrested - offenses - prosecutors - whistleblower | 128 | 462_manning_arrested_offenses_prosecutors |
| 463 | quantum - entanglement - entangled - decoherence - superposition | 128 | 463_quantum_entanglement_entangled_decoherence |
| 464 | sang - carpenter - carpenters - billboard - songwriter | 128 | 464_sang_carpenter_carpenters_billboard |
| 465 | languages - language - lingua - creole - vernacular | 127 | 465_languages_language_lingua_creole |
| 466 | goddesses - mythological - goddess - deities - gods | 127 | 466_goddesses_mythological_goddess_deities |
| 467 | katana - kata - swords - sword - samurai | 127 | 467_katana_kata_swords_sword |
| 468 | haggard - sang - duets - ballads - songs | 127 | 468_haggard_sang_duets_ballads |
| 469 | marathon - marathons - runners - runner - triathlon | 127 | 469_marathon_marathons_runners_runner |
| 470 | comedian - comedians - sitcom - sitcoms - comedy | 127 | 470_comedian_comedians_sitcom_sitcoms |
| 471 | armament - panzer - armoured - tanks - armored | 127 | 471_armament_panzer_armoured_tanks |
| 472 | traditional - dhoti - sari - dresses - traditionally | 127 | 472_traditional_dhoti_sari_dresses |
| 473 | prohibition - alcoholism - alcoholic - alcohol - liquor | 127 | 473_prohibition_alcoholism_alcoholic_alcohol |
| 474 | lightning - thunderstorm - thunderstorms - storms - thunder | 126 | 474_lightning_thunderstorm_thunderstorms_storms |
| 475 | militants - temple - terrorists - militant - casualties | 126 | 475_militants_temple_terrorists_militant |
| 476 | cartoons - tom - shorts - cartoon - commercials | 125 | 476_cartoons_tom_shorts_cartoon |
| 477 | mortality - fertility - expectancy - population - births | 125 | 477_mortality_fertility_expectancy_population |
| 478 | lodges - masonic - lodge - masons - masonry | 125 | 478_lodges_masonic_lodge_masons |
| 479 | judge - judges - courtroom - court - defendants | 125 | 479_judge_judges_courtroom_court |
| 480 | entrepreneurship - entrepreneur - entrepreneurial - entrepreneurs - venture | 125 | 480_entrepreneurship_entrepreneur_entrepreneurial_entrepreneurs |
| 481 | burger - burgers - hamburger - franchisees - hamburgers | 124 | 481_burger_burgers_hamburger_franchisees |
| 482 | folate - folic - vitamin - vitamins - supplements | 124 | 482_folate_folic_vitamin_vitamins |
| 483 | niger - haram - jihad - bombing - insurgency | 124 | 483_niger_haram_jihad_bombing |
| 484 | viewership - viewers - subscribers - channel - livestreaming | 124 | 484_viewership_viewers_subscribers_channel |
| 485 | 1080p - resolution - 1080 - 720p - 1080i | 124 | 485_1080p_resolution_1080_720p |
| 486 | units - metre - quantities - unit - kilogram | 124 | 486_units_metre_quantities_unit |
| 487 | oblast - soviet - yuri - grandmother - grandparents | 124 | 487_oblast_soviet_yuri_grandmother |
| 488 | cricket - wickets - matches - umpires - rugby | 123 | 488_cricket_wickets_matches_umpires |
| 489 | defendant - testify - prosecution - court - judge | 123 | 489_defendant_testify_prosecution_court |
| 490 | inventor - electrical - inventors - inventions - electricity | 123 | 490_inventor_electrical_inventors_inventions |
| 491 | apartheid - natal - cape - chairperson - appointed | 123 | 491_apartheid_natal_cape_chairperson |
| 492 | ball - sitcom - tv - 1957 - miss | 123 | 492_ball_sitcom_tv_1957 |
| 493 | zeppelin - stairway - concert - lyrics - psychedelic | 123 | 493_zeppelin_stairway_concert_lyrics |
| 494 | negro - negroes - racial - whites - civilizing | 123 | 494_negro_negroes_racial_whites |
| 495 | tornado - tornadoes - storms - thunderstorm - thunderstorms | 123 | 495_tornado_tornadoes_storms_thunderstorm |
| 496 | façade - buildings - architect - architects - building | 122 | 496_façade_buildings_architect_architects |
| 497 | marvel - superhero - marvels - supervillain - superman | 122 | 497_marvel_superhero_marvels_supervillain |
| 498 | murders - homicide - rapist - murderer - suspect | 122 | 498_murders_homicide_rapist_murderer |
| 499 | cram - murders - murdered - tortured - detectives | 121 | 499_cram_murders_murdered_tortured |
| 500 | tequila - agave - distillation - distillery - liquor | 121 | 500_tequila_agave_distillation_distillery |
| 501 | tennis - doubles - tournaments - singles - semifinals | 121 | 501_tennis_doubles_tournaments_singles |
| 502 | conspiracies - conspiratorial - conspiracy - trafficking - trump | 121 | 502_conspiracies_conspiratorial_conspiracy_trafficking |
| 503 | airship - zeppelin - airships - helium - flew | 121 | 503_airship_zeppelin_airships_helium |
| 504 | dubbed - dub - dubbing - dubs - castle | 121 | 504_dubbed_dub_dubbing_dubs |
| 505 | defamation - libel - defamatory - slander - slanderous | 120 | 505_defamation_libel_defamatory_slander |
| 506 | soprano - mafia - joey - carmine - capo | 120 | 506_soprano_mafia_joey_carmine |
| 507 | eagle - eagles - vultures - hawk - birds | 120 | 507_eagle_eagles_vultures_hawk |
| 508 | households - household - average - families - census | 119 | 508_households_household_average_families |
| 509 | taxonomic - genus - taxon - nomenclature - taxonomists | 119 | 509_taxonomic_genus_taxon_nomenclature |
| 510 | 1984 - 1945 - 1949 - novelist - 1939 | 119 | 510_1984_1945_1949_novelist |
| 511 | philosopher - philosophers - empiricism - philosophy - rationalist | 119 | 511_philosopher_philosophers_empiricism_philosophy |
| 512 | women - comfort - geisha - grandmothers - yen | 119 | 512_women_comfort_geisha_grandmothers |
| 513 | massacre - massacred - atrocities - victims - 1945 | 119 | 513_massacre_massacred_atrocities_victims |
| 514 | internment - camps - detainees - camp - prisoners | 119 | 514_internment_camps_detainees_camp |
| 515 | ribbons - ribbon - gallantry - medals - medal | 119 | 515_ribbons_ribbon_gallantry_medals |
| 516 | tramp - films - film - cinema - cinematographer | 119 | 516_tramp_films_film_cinema |
| 517 | caves - cave - temples - excavation - shrines | 119 | 517_caves_cave_temples_excavation |
| 518 | jubilees - jubilee - celebrated - celebrations - celebration | 119 | 518_jubilees_jubilee_celebrated_celebrations |
| 519 | chains - albums - album - toured - songs | 118 | 519_chains_albums_album_toured |
| 520 | spice - concert - girls - spicy - debut | 118 | 520_spice_concert_girls_spicy |
| 521 | malaria - malarial - antimalarial - mosquito - mosquitoes | 117 | 521_malaria_malarial_antimalarial_mosquito |
| 522 | fertility - overpopulation - childbearing - adoptions - adoption | 117 | 522_fertility_overpopulation_childbearing_adoptions |
| 523 | eucalyptus - acacia - rainforests - conifers - trees | 117 | 523_eucalyptus_acacia_rainforests_conifers |
| 524 | prince - albums - album - duet - songs | 117 | 524_prince_albums_album_duet |
| 525 | famine - famines - genocide - starvation - starved | 117 | 525_famine_famines_genocide_starvation |
| 526 | 1832 - minister - peerage - constituency - exchequer | 117 | 526_1832_minister_peerage_constituency |
| 527 | vertigo - scenes - film - screenplay - films | 116 | 527_vertigo_scenes_film_screenplay |
| 528 | stark - thrones - throne - arya - wildlings | 116 | 528_stark_thrones_throne_arya |
| 529 | mobile - telecommunications - mobiles - cellular - handsets | 116 | 529_mobile_telecommunications_mobiles_cellular |
| 530 | shaggy - voiced - cartoon - cartoons - voice | 115 | 530_shaggy_voiced_cartoon_cartoons |
| 531 | bear - bears - zoo - toy - pg | 115 | 531_bear_bears_zoo_toy |
| 532 | coffeehouse - coffee - coffees - cafe - café | 115 | 532_coffeehouse_coffee_coffees_cafe |
| 533 | segregation - segregationist - segregated - discrimination - unconstitutional | 115 | 533_segregation_segregationist_segregated_discrimination |
| 534 | poverty - income - economies - agriculture - subsistence | 115 | 534_poverty_income_economies_agriculture |
| 535 | capacitors - dielectrics - capacitor - capacitance - dielectric | 114 | 535_capacitors_dielectrics_capacitor_capacitance |
| 536 | islands - archipelagos - archipelago - pacific - island | 114 | 536_islands_archipelagos_archipelago_pacific |
| 537 | paramount - studios - corporation - merger - subsidiaries | 114 | 537_paramount_studios_corporation_merger |
| 538 | iso - standards - standardization - organizational - stakeholders | 114 | 538_iso_standards_standardization_organizational |
| 539 | paintings - painting - painters - art - artistic | 114 | 539_paintings_painting_painters_art |
| 540 | mayor - mayors - mayoral - municipal - municipalities | 114 | 540_mayor_mayors_mayoral_municipal |
| 541 | ethnicities - ethnonym - ethnic - ancestry - inhabitants | 114 | 541_ethnicities_ethnonym_ethnic_ancestry |
| 542 | repeal - repealing - repealed - healthcare - uninsured | 113 | 542_repeal_repealing_repealed_healthcare |
| 543 | watchmen - comics - superhero - superheroes - vendetta | 113 | 543_watchmen_comics_superhero_superheroes |
| 544 | hashing - hash - hashes - hashed - tables | 113 | 544_hashing_hash_hashes_hashed |
| 545 | pistols - punk - punks - band - pistol | 113 | 545_pistols_punk_punks_band |
| 546 | chef - chefs - culinary - kitchens - cook | 113 | 546_chef_chefs_culinary_kitchens |
| 547 | realism - surrealism - magical - fiction - imagination | 113 | 547_realism_surrealism_magical_fiction |
| 548 | 1793 - 1789 - revolutionaries - revolt - insurrection | 113 | 548_1793_1789_revolutionaries_revolt |
| 549 | 451 - writer - literature - writers - author | 113 | 549_451_writer_literature_writers |
| 550 | punk - indie - genre - genres - bands | 113 | 550_punk_indie_genre_genres |
| 551 | dances - dance - dancers - traditional - rituals | 112 | 551_dances_dance_dancers_traditional |
| 552 | gong - qigong - communist - china - adherents | 112 | 552_gong_qigong_communist_china |
| 553 | playlists - playlist - music - songs - podcasts | 112 | 553_playlists_playlist_music_songs |
| 554 | fabrication - manufacturing - machining - inkjet - prototyping | 111 | 554_fabrication_manufacturing_machining_inkjet |
| 555 | elections - election - electoral - polls - voters | 111 | 555_elections_election_electoral_polls |
| 556 | steam - valve - platform - publishers - cloud | 111 | 556_steam_valve_platform_publishers |
| 557 | orchestra - orchestras - orchestration - symphonies - symphony | 111 | 557_orchestra_orchestras_orchestration_symphonies |
| 558 | albums - songs - toured - 1973 - 1974 | 111 | 558_albums_songs_toured_1973 |
| 559 | arsenal - goals - scored - footballer - goal | 111 | 559_arsenal_goals_scored_footballer |
| 560 | metro - railway - railways - transit - trains | 111 | 560_metro_railway_railways_transit |
| 561 | laundering - banking - trafficking - smuggling - bank | 110 | 561_laundering_banking_trafficking_smuggling |
| 562 | complement - binary - complements - unsigned - bitwise | 110 | 562_complement_binary_complements_unsigned |
| 563 | piazza - boulevard - della - buildings - baroque | 110 | 563_piazza_boulevard_della_buildings |
| 564 | synthesizers - synthesizer - techno - synth - genres | 110 | 564_synthesizers_synthesizer_techno_synth |
| 565 | sprinter - bolt - sprinters - olympic - athletics | 109 | 565_sprinter_bolt_sprinters_olympic |
| 566 | condoms - condom - contraception - prevention - protection | 108 | 566_condoms_condom_contraception_prevention |
| 567 | flags - flag - soviet - flagpole - tricolour | 108 | 567_flags_flag_soviet_flagpole |
| 568 | kanji - pinyin - characters - mandarin - character | 108 | 568_kanji_pinyin_characters_mandarin |
| 569 | detective - hound - adventure - investigative - novels | 108 | 569_detective_hound_adventure_investigative |
| 570 | subcontinent - viceroy - coalition - 1947 - raj | 108 | 570_subcontinent_viceroy_coalition_1947 |
| 571 | lion - wardrobe - witch - chronicles - mythical | 107 | 571_lion_wardrobe_witch_chronicles |
| 572 | prix - qualifying - podium - laps - overtook | 107 | 572_prix_qualifying_podium_laps |
| 573 | soccer - athlete - assists - scoring - olympic | 106 | 573_soccer_athlete_assists_scoring |
| 574 | impeachment - testified - indictment - prosecutor - hearings | 106 | 574_impeachment_testified_indictment_prosecutor |
| 575 | databases - database - tables - schema - relational | 106 | 575_databases_database_tables_schema |
| 576 | paramount - animators - studios - productions - animation | 106 | 576_paramount_animators_studios_productions |
| 577 | gear - presenter - presenters - viewers - bbc | 106 | 577_gear_presenter_presenters_viewers |
| 578 | tricolour - tricolore - tricolor - flags - flag | 105 | 578_tricolour_tricolore_tricolor_flags |
| 579 | node - js - developers - frameworks - platform | 105 | 579_node_js_developers_frameworks |
| 580 | populism - populists - populist - political - authoritarianism | 105 | 580_populism_populists_populist_political |
| 581 | tempo - tempos - rhythmic - rhythm - bpm | 105 | 581_tempo_tempos_rhythmic_rhythm |
| 582 | biometric - authentication - citizenship - identity - register | 105 | 582_biometric_authentication_citizenship_identity |
| 583 | gambling - gamblers - gambler - casino - casinos | 105 | 583_gambling_gamblers_gambler_casino |
| 584 | incompleteness - axiomatization - completeness - provability - consistency | 105 | 584_incompleteness_axiomatization_completeness_provability |
| 585 | logics - logicians - logic - semantics - propositional | 105 | 585_logics_logicians_logic_semantics |
| 586 | writings - discourses - discourse - theological - theologians | 104 | 586_writings_discourses_discourse_theological |
| 587 | censorship - censor - censors - censored - forbidding | 104 | 587_censorship_censor_censors_censored |
| 588 | barbarian - serpent - marvel - comics - blacksmith | 104 | 588_barbarian_serpent_marvel_comics |
| 589 | uninsured - insurance - insured - healthcare - insurers | 104 | 589_uninsured_insurance_insured_healthcare |
| 590 | privateers - pirates - pirate - slaves - enslaved | 103 | 590_privateers_pirates_pirate_slaves |
| 591 | papillomavirus - cancers - cervical - warts - cancer | 103 | 591_papillomavirus_cancers_cervical_warts |
| 592 | satellites - satellite - constellations - constellation - orbit | 103 | 592_satellites_satellite_constellations_constellation |
| 593 | samurai - screenwriter - screenplay - screenplays - filmmaker | 103 | 593_samurai_screenwriter_screenplay_screenplays |
| 594 | hammer - rapper - rappers - rap - raps | 103 | 594_hammer_rapper_rappers_rap |
| 595 | bitcoin - bitcoins - blockchain - cryptocurrency - cryptocurrencies | 103 | 595_bitcoin_bitcoins_blockchain_cryptocurrency |
| 596 | electronics - manufacturer - appliances - manufactures - lee | 103 | 596_electronics_manufacturer_appliances_manufactures |
| 597 | utilitarianism - utilitarian - consequentialism - consequentialist - morality | 103 | 597_utilitarianism_utilitarian_consequentialism_consequentialist |
| 598 | sitcom - woody - cast - primetime - shows | 103 | 598_sitcom_woody_cast_primetime |
| 599 | republics - soviet - soviets - oblasts - republic | 103 | 599_republics_soviet_soviets_oblasts |
| 600 | monarchy - junta - dictatorship - king - monarch | 102 | 600_monarchy_junta_dictatorship_king |
| 601 | apps - app - android - mobile - downloads | 102 | 601_apps_app_android_mobile |
| 602 | vampire - vampires - vampirism - vampiric - bloodlust | 102 | 602_vampire_vampires_vampirism_vampiric |
| 603 | racism - racialism - prejudice - racial - discrimination | 102 | 603_racism_racialism_prejudice_racial |
| 604 | twitch - streaming - stream - viewership - streams | 102 | 604_twitch_streaming_stream_viewership |
| 605 | glucose - monosaccharides - monosaccharide - polysaccharides - oligosaccharides | 102 | 605_glucose_monosaccharides_monosaccharide_polysaccharides |
| 606 | sponsors - sponsorship - sponsor - sponsorships - sponsored | 102 | 606_sponsors_sponsorship_sponsor_sponsorships |
| 607 | minister - ministers - secretary - elected - cabinet | 102 | 607_minister_ministers_secretary_elected |
| 608 | booth - assassination - assassinated - confederate - 1864 | 102 | 608_booth_assassination_assassinated_confederate |
| 609 | torrents - torrent - peers - peer - downloading | 102 | 609_torrents_torrent_peers_peer |
| 610 | coco - boutiques - boutique - designers - cosmetics | 102 | 610_coco_boutiques_boutique_designers |
| 611 | crusades - crusade - crusaders - crusader - 1451 | 102 | 611_crusades_crusade_crusaders_crusader |
| 612 | psychometric - intelligence - assessment - standardized - scores | 102 | 612_psychometric_intelligence_assessment_standardized |
| 613 | prophets - prophet - prophethood - prophetic - scriptures | 101 | 613_prophets_prophet_prophethood_prophetic |
| 614 | purge - purges - gulag - soviet - purged | 101 | 614_purge_purges_gulag_soviet |
| 615 | politburo - soviet - perestroika - chairman - secretary | 101 | 615_politburo_soviet_perestroika_chairman |
| 616 | powertrain - musk - cars - motors - drivetrain | 101 | 616_powertrain_musk_cars_motors |
| 617 | pornography - pornographic - prohibits - porn - obscene | 101 | 617_pornography_pornographic_prohibits_porn |
| 618 | bikers - angels - motorcycles - outlaws - motorcyclists | 101 | 618_bikers_angels_motorcycles_outlaws |
| 619 | altruism - ethical - advocated - moral - ethics | 101 | 619_altruism_ethical_advocated_moral |
| 620 | concert - duet - concerts - singer - medley | 101 | 620_concert_duet_concerts_singer |
| 621 | licenses - licensing - license - licensed - proprietary | 101 | 621_licenses_licensing_license_licensed |
| 622 | gentrification - suburbanization - gentrified - urbanization - redevelopment | 101 | 622_gentrification_suburbanization_gentrified_urbanization |
| 623 | spying - spy - espionage - spyware - smartphones | 101 | 623_spying_spy_espionage_spyware |
| 624 | apartheid - activism - blacks - activist - suffrage | 101 | 624_apartheid_activism_blacks_activist |
| 625 | robotics - robot - robots - robotic - manipulators | 101 | 625_robotics_robot_robots_robotic |
| 626 | 1783 - minister - peerage - ministers - 1784 | 100 | 626_1783_minister_peerage_ministers |
| 627 | labour - children - labor - poverty - labourers | 100 | 627_labour_children_labor_poverty |
| 628 | generative - adversarial - generating - generates - generator | 100 | 628_generative_adversarial_generating_generates |
| 629 | concert - sang - scarecrow - vocals - musicians | 100 | 629_concert_sang_scarecrow_vocals |
| 630 | mosque - masjid - mosques - tombs - mausoleum | 100 | 630_mosque_masjid_mosques_tombs |
| 631 | sang - concert - zeppelin - rocker - tour | 100 | 631_sang_concert_zeppelin_rocker |
| 632 | attachments - attachment - adoptions - parenting - infancy | 100 | 632_attachments_attachment_adoptions_parenting |
| 633 | tennis - slams - tournaments - competed - doubles | 100 | 633_tennis_slams_tournaments_competed |
| 634 | witchcraft - coven - covens - witches - paganism | 99 | 634_witchcraft_coven_covens_witches |
| 635 | viruses - viral - virus - coronavirus - coronaviruses | 99 | 635_viruses_viral_virus_coronavirus |
| 636 | demon - yakuza - shinobi - demons - priestess | 99 | 636_demon_yakuza_shinobi_demons |
| 637 | psoriasis - psoriatic - erythematosus - keratinocytes - autoimmune | 99 | 637_psoriasis_psoriatic_erythematosus_keratinocytes |
| 638 | guru - gurus - shakti - scriptures - divinity | 99 | 638_guru_gurus_shakti_scriptures |
| 639 | population - populations - urbanization - china - populous | 99 | 639_population_populations_urbanization_china |
| 640 | defamation - lawsuit - sued - libel - accused | 99 | 640_defamation_lawsuit_sued_libel |
| 641 | rating - ratings - scores - rated - fide | 99 | 641_rating_ratings_scores_rated |
| 642 | albums - singer - singers - songwriter - songs | 98 | 642_albums_singer_singers_songwriter |
| 643 | ebook - ebooks - tablet - touchscreen - devices | 98 | 643_ebook_ebooks_tablet_touchscreen |
| 644 | orthodox - patriarch - principality - rulers - ruled | 98 | 644_orthodox_patriarch_principality_rulers |
| 645 | cyclones - cyclone - typhoon - hurricane - typhoons | 98 | 645_cyclones_cyclone_typhoon_hurricane |
| 646 | boots - sequels - sequel - premiered - movie | 98 | 646_boots_sequels_sequel_premiered |
| 647 | novels - novel - writer - nonfiction - fiction | 98 | 647_novels_novel_writer_nonfiction |
| 648 | kami - rituals - deities - shin - ritual | 98 | 648_kami_rituals_deities_shin |
| 649 | honorary - commencement - doctorate - conferred - degree | 98 | 649_honorary_commencement_doctorate_conferred |
| 650 | evil - virtual - zombies - nemesis - sequel | 98 | 650_evil_virtual_zombies_nemesis |
| 651 | voiced - voice - voices - voiceover - cast | 98 | 651_voiced_voice_voices_voiceover |
| 652 | doom - ark - chronicles - films - sequel | 97 | 652_doom_ark_chronicles_films |
| 653 | botulinum - toxin - toxins - neurotoxin - neurotoxins | 97 | 653_botulinum_toxin_toxins_neurotoxin |
| 654 | tags - tagging - barcodes - transmitters - tag | 97 | 654_tags_tagging_barcodes_transmitters |
| 655 | soviet - politburo - coup - arrest - perestroika | 97 | 655_soviet_politburo_coup_arrest |
| 656 | twitter - tweets - accounts - hoaxes - trolls | 97 | 656_twitter_tweets_accounts_hoaxes |
| 657 | cryptography - encryption - cryptosystems - cryptosystem - cryptographic | 97 | 657_cryptography_encryption_cryptosystems_cryptosystem |
| 658 | lasers - fibers - laser - fiber - optical | 96 | 658_lasers_fibers_laser_fiber |
| 659 | smartphone - smartphones - mobile - cellular - flagship | 96 | 659_smartphone_smartphones_mobile_cellular |
| 660 | vaudeville - brothers - comedian - comedians - broadway | 96 | 660_vaudeville_brothers_comedian_comedians |
| 661 | halo - 343 - consoles - franchise - spartan | 96 | 661_halo_343_consoles_franchise |
| 662 | mosque - masjid - mosques - mecca - caliphate | 96 | 662_mosque_masjid_mosques_mecca |
| 663 | motorsport - racing - prix - raced - cars | 96 | 663_motorsport_racing_prix_raced |
| 664 | punches - featherweight - fighter - fighters - fights | 96 | 664_punches_featherweight_fighter_fighters |
| 665 | herbicides - herbicide - orange - contaminated - chemicals | 96 | 665_herbicides_herbicide_orange_contaminated |
| 666 | nonfiction - bestseller - novelist - autobiography - novels | 96 | 666_nonfiction_bestseller_novelist_autobiography |
| 667 | cannabis - marijuana - sect - sects - cultivates | 96 | 667_cannabis_marijuana_sect_sects |
| 668 | income - poverty - median - households - affluent | 96 | 668_income_poverty_median_households |
| 669 | epistemological - epistemic - epistemology - epistemologists - belief | 96 | 669_epistemological_epistemic_epistemology_epistemologists |
| 670 | genie - mother - abuse - childhood - parents | 95 | 670_genie_mother_abuse_childhood |
| 671 | 802 - wireless - bandwidth - communications - antennas | 95 | 671_802_wireless_bandwidth_communications |
| 672 | han - nam - 1945 - kai - troops | 95 | 672_han_nam_1945_kai |
| 673 | wage - wages - minimum - hourly - raise | 95 | 673_wage_wages_minimum_hourly |
| 674 | lambs - screenplay - thriller - silence - films | 95 | 674_lambs_screenplay_thriller_silence |
| 675 | donation - donated - charity - donations - donating | 95 | 675_donation_donated_charity_donations |
| 676 | wu - tang - rapper - kung - rap | 95 | 676_wu_tang_rapper_kung |
| 677 | influenza - flu - pandemics - pandemic - epidemic | 95 | 677_influenza_flu_pandemics_pandemic |
| 678 | animatronic - animatronics - minigames - nightmare - nights | 95 | 678_animatronic_animatronics_minigames_nightmare |
| 679 | convicts - colonists - 1788 - convict - settlers | 94 | 679_convicts_colonists_1788_convict |
| 680 | displays - monitors - cables - cable - ports | 94 | 680_displays_monitors_cables_cable |
| 681 | trademarks - trademark - infringement - copyrights - copyright | 94 | 681_trademarks_trademark_infringement_copyrights |
| 682 | farmworkers - unions - picketing - protest - laborers | 94 | 682_farmworkers_unions_picketing_protest |
| 683 | libertarianism - libertarians - libertarian - liberalism - anarchists | 94 | 683_libertarianism_libertarians_libertarian_liberalism |
| 684 | temptations - sang - toured - singers - albums | 94 | 684_temptations_sang_toured_singers |
| 685 | 1898 - 1896 - 1902 - dictator - insurgent | 94 | 685_1898_1896_1902_dictator |
| 686 | insurance - insurer - insurers - insured - insure | 94 | 686_insurance_insurer_insurers_insured |
| 687 | shooting - shootings - shooters - shooter - firearm | 94 | 687_shooting_shootings_shooters_shooter |
| 688 | colitis - bowel - gastrointestinal - intestinal - inflammatory | 94 | 688_colitis_bowel_gastrointestinal_intestinal |
| 689 | divorce - peace - adultery - ballad - lyrics | 94 | 689_divorce_peace_adultery_ballad |
| 690 | artillery - howitzers - howitzer - cannons - rifle | 93 | 690_artillery_howitzers_howitzer_cannons |
| 691 | ups - deliveries - logistics - delivery - freight | 93 | 691_ups_deliveries_logistics_delivery |
| 692 | metal - gear - consoles - sequels - franchise | 93 | 692_metal_gear_consoles_sequels |
| 693 | ibn - hadith - imam - ijtihad - khan | 93 | 693_ibn_hadith_imam_ijtihad |
| 694 | industrial - subsidiaries - manufacturer - industries - corporation | 93 | 694_industrial_subsidiaries_manufacturer_industries |
| 695 | motorsport - prix - motorsports - racing - raced | 93 | 695_motorsport_prix_motorsports_racing |
| 696 | 1936 - deposed - 1935 - invaded - 1937 | 93 | 696_1936_deposed_1935_invaded |
| 697 | scotch - whisky - whiskey - distillery - bourbon | 93 | 697_scotch_whisky_whiskey_distillery |
| 698 | premiered - machina - cast - critical - productions | 93 | 698_premiered_machina_cast_critical |
| 699 | psychedelics - psychedelic - ayahuasca - cannabis - psilocybin | 93 | 699_psychedelics_psychedelic_ayahuasca_cannabis |
| 700 | homeless - homelessness - shelters - shelter - housing | 93 | 700_homeless_homelessness_shelters_shelter |
| 701 | newton - gravitation - gravitational - gravity - gravitating | 93 | 701_newton_gravitation_gravitational_gravity |
| 702 | swamp - comics - comic - sting - likeness | 92 | 702_swamp_comics_comic_sting |
| 703 | languages - language - linguists - lingua - linguistics | 92 | 703_languages_language_linguists_lingua |
| 704 | mutilations - mutilation - mutilating - circumcision - clitoridectomy | 92 | 704_mutilations_mutilation_mutilating_circumcision |
| 705 | harassment - harassing - harassed - harass - discrimination | 92 | 705_harassment_harassing_harassed_harass |
| 706 | artistic - art - artwork - paintings - artworks | 92 | 706_artistic_art_artwork_paintings |
| 707 | paintings - painter - painters - painting - portraits | 92 | 707_paintings_painter_painters_painting |
| 708 | piazza - opera - tenor - bohème - arias | 92 | 708_piazza_opera_tenor_bohème |
| 709 | tsar - tsarist - tsars - czar - emperors | 92 | 709_tsar_tsarist_tsars_czar |
| 710 | ai - intelligence - machines - cognitive - intelligent | 92 | 710_ai_intelligence_machines_cognitive |
| 711 | pamphlet - 1789 - revolutionary - 1790 - 1793 | 92 | 711_pamphlet_1789_revolutionary_1790 |
| 712 | murders - detectives - murdered - constable - detective | 92 | 712_murders_detectives_murdered_constable |
| 713 | healthcare - insurance - health - hospitals - insurers | 92 | 713_healthcare_insurance_health_hospitals |
| 714 | plague - plagues - diseases - epidemics - epidemic | 91 | 714_plague_plagues_diseases_epidemics |
| 715 | paleolithic - neolithic - archaeological - prehistory - archaeologists | 91 | 715_paleolithic_neolithic_archaeological_prehistory |
| 716 | theology - faith - teachings - religion - monotheism | 91 | 716_theology_faith_teachings_religion |
| 717 | alderman - mayor - mayoral - candidates - superintendent | 91 | 717_alderman_mayor_mayoral_candidates |
| 718 | nam - chi - southeast - urban - city | 91 | 718_nam_chi_southeast_urban |
| 719 | skating - skaters - skater - skate - competed | 91 | 719_skating_skaters_skater_skate |
| 720 | banking - bank - finances - finance - funds | 91 | 720_banking_bank_finances_finance |
| 721 | asbestos - asbestosis - minerals - mineral - toxicology | 91 | 721_asbestos_asbestosis_minerals_mineral |
| 722 | municipalities - municipality - cities - population - city | 90 | 722_municipalities_municipality_cities_population |
| 723 | headquartered - headquarters - companies - san - industries | 90 | 723_headquartered_headquarters_companies_san |
| 724 | soviets - communists - communist - soviet - communism | 90 | 724_soviets_communists_communist_soviet |
| 725 | tapes - recorder - recorders - recording - cassette | 90 | 725_tapes_recorder_recorders_recording |
| 726 | swastika - swastikas - symbolises - symbol - symbolising | 90 | 726_swastika_swastikas_symbolises_symbol |
| 727 | oblast - oblasts - annexation - annexations - annexed | 90 | 727_oblast_oblasts_annexation_annexations |
| 728 | filmed - filming - premiered - premiere - seasons | 90 | 728_filmed_filming_premiered_premiere |
| 729 | evacuated - evacuation - evacuate - ceasefire - bombed | 90 | 729_evacuated_evacuation_evacuate_ceasefire |
| 730 | quad - quadrilateral - multilateral - alliances - trilateral | 90 | 730_quad_quadrilateral_multilateral_alliances |
| 731 | sake - rice - liquor - brewing - alcohol | 90 | 731_sake_rice_liquor_brewing |
| 732 | enigma - rotor - rotors - cipher - cryptographic | 90 | 732_enigma_rotor_rotors_cipher |
| 733 | anthropology - anthropological - sociocultural - anthropologist - anthropologists | 90 | 733_anthropology_anthropological_sociocultural_anthropologist |
| 734 | executives - stockholders - accounting - shareholders - insiders | 89 | 734_executives_stockholders_accounting_shareholders |
| 735 | psychedelics - psychedelic - psilocybin - hallucinations - psychosis | 89 | 735_psychedelics_psychedelic_psilocybin_hallucinations |
| 736 | quicksort - sorting - sort - sorts - algorithm | 89 | 736_quicksort_sorting_sort_sorts |
| 737 | 1918 - soviets - polish - soviet - battle | 89 | 737_1918_soviets_polish_soviet |
| 738 | barangays - barangay - municipalities - metropolitan - metro | 89 | 738_barangays_barangay_municipalities_metropolitan |
| 739 | assists - rebounds - suns - 76ers - steals | 89 | 739_assists_rebounds_suns_76ers |
| 740 | spaghetti - western - westerns - films - movies | 89 | 740_spaghetti_western_westerns_films |
| 741 | airing - adult - swim - aqua - episodes | 89 | 741_airing_adult_swim_aqua |
| 742 | queer - heterosexuality - heterosexuals - homosexual - homosexuals | 89 | 742_queer_heterosexuality_heterosexuals_homosexual |
| 743 | control - controller - controlled - controllers - disturbances | 89 | 743_control_controller_controlled_controllers |
| 744 | abortion - abortions - pregnancies - pregnancy - fetuses | 89 | 744_abortion_abortions_pregnancies_pregnancy |
| 745 | voyages - voyage - caravel - expeditions - navigator | 89 | 745_voyages_voyage_caravel_expeditions |
| 746 | channel - channels - broadcasting - syndicated - simulcast | 88 | 746_channel_channels_broadcasting_syndicated |
| 747 | sati - castes - widowhood - prohibits - prohibition | 88 | 747_sati_castes_widowhood_prohibits |
| 748 | conquistadors - confederation - tlatoani - provinces - rulers | 88 | 748_conquistadors_confederation_tlatoani_provinces |
| 749 | supermarket - supermarkets - shops - retailer - retailers | 88 | 749_supermarket_supermarkets_shops_retailer |
| 750 | khan - khanate - tsar - khans - khanates | 88 | 750_khan_khanate_tsar_khans |
| 751 | separatists - soviet - militants - ceasefire - guerrillas | 88 | 751_separatists_soviet_militants_ceasefire |
| 752 | magician - occultist - occultism - occultists - mysticism | 88 | 752_magician_occultist_occultism_occultists |
| 753 | swam - swimmer - olympic - swimmers - freestyle | 88 | 753_swam_swimmer_olympic_swimmers |
| 754 | alchemy - alchemists - alchemist - alchemical - al | 88 | 754_alchemy_alchemists_alchemist_alchemical |
| 755 | robin - hood - friar - hoods - knight | 88 | 755_robin_hood_friar_hoods |
| 756 | genders - gender - sexes - gendered - genderqueer | 87 | 756_genders_gender_sexes_gendered |
| 757 | privacy - data - regulations - enforcement - regulation | 87 | 757_privacy_data_regulations_enforcement |
| 758 | chocolate - chocolates - confectionery - brands - manufacturer | 87 | 758_chocolate_chocolates_confectionery_brands |
| 759 | murders - corpse - unconscious - murder - strangled | 87 | 759_murders_corpse_unconscious_murder |
| 760 | ayahuasca - psychedelics - psychedelic - addictions - shamans | 87 | 760_ayahuasca_psychedelics_psychedelic_addictions |
| 761 | audit - audited - auditing - audits - fines | 87 | 761_audit_audited_auditing_audits |
| 762 | dragons - dragon - amulets - carvings - robes | 87 | 762_dragons_dragon_amulets_carvings |
| 763 | murderer - murders - murdered - killings - murder | 87 | 763_murderer_murders_murdered_killings |
| 764 | diamond - sapphire - pearl - games - evolve | 87 | 764_diamond_sapphire_pearl_games |
| 765 | hepatitis - hepatic - cirrhosis - liver - hepatocellular | 87 | 765_hepatitis_hepatic_cirrhosis_liver |
| 766 | ba - antibody - antibodies - vaccines - 2022 | 87 | 766_ba_antibody_antibodies_vaccines |
| 767 | algorithm - algorithms - paths - traversal - nodes | 87 | 767_algorithm_algorithms_paths_traversal |
| 768 | gable - actresses - films - actor - film | 87 | 768_gable_actresses_films_actor |
| 769 | verse - poetry - poet - poems - poem | 87 | 769_verse_poetry_poet_poems |
| 770 | judicial - justices - judiciary - courts - judges | 87 | 770_judicial_justices_judiciary_courts |
| 771 | processors - processor - intel - microarchitecture - cores | 87 | 771_processors_processor_intel_microarchitecture |
| 772 | emperor - emperors - empress - dowager - eunuch | 87 | 772_emperor_emperors_empress_dowager |
| 773 | anthrax - spores - assays - contaminated - microbiologist | 86 | 773_anthrax_spores_assays_contaminated |
| 774 | comics - superhero - superman - superheroes - comic | 86 | 774_comics_superhero_superman_superheroes |
| 775 | seo - searches - webmaster - webmasters - web | 86 | 775_seo_searches_webmaster_webmasters |
| 776 | kabbalah - kabbalistic - esotericism - mysticism - theology | 86 | 776_kabbalah_kabbalistic_esotericism_mysticism |
| 777 | caesarean - cesarean - uterus - pregnancies - uterine | 86 | 777_caesarean_cesarean_uterus_pregnancies |
| 778 | semiconductor - transistors - transistor - gate - circuitry | 86 | 778_semiconductor_transistors_transistor_gate |
| 779 | furniture - stores - store - warehouse - malls | 86 | 779_furniture_stores_store_warehouse |
| 780 | inquisition - persecution - catholic - reformation - heresy | 86 | 780_inquisition_persecution_catholic_reformation |
| 781 | dictator - dictatorship - dictatorial - regime - presidential | 86 | 781_dictator_dictatorship_dictatorial_regime |
| 782 | emoji - emojis - smiley - symbols - glyphs | 86 | 782_emoji_emojis_smiley_symbols |
| 783 | costumes - costume - dressed - dresses - dress | 86 | 783_costumes_costume_dressed_dresses |
| 784 | sexiest - playboy - hottest - glamour - actresses | 86 | 784_sexiest_playboy_hottest_glamour |
| 785 | karate - kung - martial - cobra - tae | 86 | 785_karate_kung_martial_cobra |
| 786 | papacy - pope - papal - catholic - holocaust | 85 | 786_papacy_pope_papal_catholic |
| 787 | tarot - cards - decks - deck - card | 85 | 787_tarot_cards_decks_deck |
| 788 | deities - goddesses - goddess - mythology - underworld | 85 | 788_deities_goddesses_goddess_mythology |
| 789 | waterboarding - waterboarded - torture - interrogations - interrogation | 85 | 789_waterboarding_waterboarded_torture_interrogations |
| 790 | degree - bachelor - diploma - qualification - courses | 85 | 790_degree_bachelor_diploma_qualification |
| 791 | nonprofit - nonprofits - donations - organizations - nongovernmental | 85 | 791_nonprofit_nonprofits_donations_organizations |
| 792 | perjury - misconduct - impeachment - allegations - affair | 85 | 792_perjury_misconduct_impeachment_allegations |
| 793 | retailer - supermarket - stores - supermarkets - shop | 85 | 793_retailer_supermarket_stores_supermarkets |
| 794 | crimes - convicted - assaulted - raped - plea | 85 | 794_crimes_convicted_assaulted_raped |
| 795 | paintings - painter - painting - murals - portraits | 85 | 795_paintings_painter_painting_murals |
| 796 | mansa - throne - rulers - kingdoms - emperor | 85 | 796_mansa_throne_rulers_kingdoms |
| 797 | stripes - jack - bands - band - bandmate | 84 | 797_stripes_jack_bands_band |
| 798 | 1941 - polish - 1939 - nazi - treaty | 84 | 798_1941_polish_1939_nazi |
| 799 | prix - motorsport - racing - motorsports - qualifying | 84 | 799_prix_motorsport_racing_motorsports |
| 800 | buzz - toy - toys - woody - toyline | 84 | 800_buzz_toy_toys_woody |
| 801 | generals - counterinsurgency - military - militias - strategist | 84 | 801_generals_counterinsurgency_military_militias |
| 802 | casino - casinos - gambling - 1960s - hotel | 84 | 802_casino_casinos_gambling_1960s |
| 803 | telecom - telecommunications - telecoms - provider - shareholders | 84 | 803_telecom_telecommunications_telecoms_provider |
| 804 | sitcom - cast - cartoons - cartoon - voiced | 84 | 804_sitcom_cast_cartoons_cartoon |
| 805 | extradition - jailed - convicted - sentenced - detained | 84 | 805_extradition_jailed_convicted_sentenced |
| 806 | yogurt - yogurts - yoghurt - dairy - lactose | 84 | 806_yogurt_yogurts_yoghurt_dairy |
| 807 | junta - loyalist - rebellion - juntas - royalist | 84 | 807_junta_loyalist_rebellion_juntas |
| 808 | golfer - golfers - woods - golf - masters | 84 | 808_golfer_golfers_woods_golf |
| 809 | fitness - gyms - gym - gymnastics - camps | 84 | 809_fitness_gyms_gym_gymnastics |
| 810 | butter - gluten - flour - glutenin - dough | 83 | 810_butter_gluten_flour_glutenin |
| 811 | sizes - paper - sheet - sheets - width | 83 | 811_sizes_paper_sheet_sheets |
| 812 | baker - divorced - remarried - stepfather - divorcing | 83 | 812_baker_divorced_remarried_stepfather |
| 813 | tattoos - tattooing - tattoo - tattooed - markings | 83 | 813_tattoos_tattooing_tattoo_tattooed |
| 814 | castes - caste - discriminated - discrimination - raj | 83 | 814_castes_caste_discriminated_discrimination |
| 815 | dreaming - lucidity - dreams - lucid - dreamer | 83 | 815_dreaming_lucidity_dreams_lucid |
| 816 | mountains - mountainous - tributary - river - elevation | 83 | 816_mountains_mountainous_tributary_river |
| 817 | bombings - murders - suspects - terrorist - homicide | 83 | 817_bombings_murders_suspects_terrorist |
| 818 | conscription - military - enlistment - draftees - draft | 83 | 818_conscription_military_enlistment_draftees |
| 819 | presentations - presentation - slides - keynote - slide | 83 | 819_presentations_presentation_slides_keynote |
| 820 | paraphilia - paraphilias - pedophilia - pedophilic - paraphilic | 83 | 820_paraphilia_paraphilias_pedophilia_pedophilic |
| 821 | bushido - bushidō - samurai - martial - judo | 83 | 821_bushido_bushidō_samurai_martial |
| 822 | fjord - archaeological - meadows - voyages - settlers | 83 | 822_fjord_archaeological_meadows_voyages |
| 823 | tofu - soy - soybean - sesame - vegetarian | 83 | 823_tofu_soy_soybean_sesame |
| 824 | gang - gangs - comedies - productions - roach | 83 | 824_gang_gangs_comedies_productions |
| 825 | accents - accent - dialects - dialect - pronunciation | 82 | 825_accents_accent_dialects_dialect |
| 826 | screenplay - ultimatum - screenwriter - thriller - trilogy | 82 | 826_screenplay_ultimatum_screenwriter_thriller |
| 827 | stamps - stamp - postage - postal - postmaster | 82 | 827_stamps_stamp_postage_postal |
| 828 | typescript - compiler - type - developers - interpreter | 82 | 828_typescript_compiler_type_developers |
| 829 | aspirin - ibuprofen - analgesics - inhibitors - medications | 82 | 829_aspirin_ibuprofen_analgesics_inhibitors |
| 830 | atheist - agnostic - agnosticism - atheism - religious | 82 | 830_atheist_agnostic_agnosticism_atheism |
| 831 | postal - postmaster - postage - deliveries - mail | 82 | 831_postal_postmaster_postage_deliveries |
| 832 | 1914 - 1913 - 1915 - 1918 - 1912 | 82 | 832_1914_1913_1915_1918 |
| 833 | graphite - carbon - steelmaking - mined - pencil | 82 | 833_graphite_carbon_steelmaking_mined |
| 834 | integers - primes - integer - prime - arithmetic | 82 | 834_integers_primes_integer_prime |
| 835 | bloods - gangs - gang - blood - criminals | 82 | 835_bloods_gangs_gang_blood |
| 836 | osmosis - desalination - purification - filtration - membranes | 82 | 836_osmosis_desalination_purification_filtration |
| 837 | guerre - french - 1958 - ceasefire - rebels | 82 | 837_guerre_french_1958_ceasefire |
| 838 | actress - sonata - och - autumn - maid | 82 | 838_actress_sonata_och_autumn |
| 839 | fastest - racing - mph - speed - motorsport | 82 | 839_fastest_racing_mph_speed |
| 840 | airline - airlines - seats - seating - 737 | 82 | 840_airline_airlines_seats_seating |
| 841 | novelist - writer - novels - literature - writers | 82 | 841_novelist_writer_novels_literature |
| 842 | nationalism - nationalist - nationalists - patriotism - nation | 82 | 842_nationalism_nationalist_nationalists_patriotism |
| 843 | celebrations - celebrated - festival - calendar - holidays | 82 | 843_celebrations_celebrated_festival_calendar |
| 844 | guerrillas - guerrilla - rebels - dictator - fled | 82 | 844_guerrillas_guerrilla_rebels_dictator |
| 845 | murdered - strangled - killed - unconscious - murders | 82 | 845_murdered_strangled_killed_unconscious |
| 846 | rated - rating - ratings - pg - films | 81 | 846_rated_rating_ratings_pg |
| 847 | mac - leopard - apple - os - versions | 81 | 847_mac_leopard_apple_os |
| 848 | aboriginal - indigenous - settlers - provincial - prairies | 81 | 848_aboriginal_indigenous_settlers_provincial |
| 849 | maps - map - google - android - street | 81 | 849_maps_map_google_android |
| 850 | airplane - airlines - hijacked - hijackers - hijackings | 81 | 850_airplane_airlines_hijacked_hijackers |
| 851 | bp - spill - spills - damages - negligence | 81 | 851_bp_spill_spills_damages |
| 852 | longitude - latitudes - latitude - geocentric - ellipsoid | 81 | 852_longitude_latitudes_latitude_geocentric |
| 853 | golfer - golfers - golf - masters - tournaments | 81 | 853_golfer_golfers_golf_masters |
| 854 | dean - hunter - actor - biography - acting | 81 | 854_dean_hunter_actor_biography |
| 855 | latching - latch - latches - flops - flip | 81 | 855_latching_latch_latches_flops |
| 856 | honours - honorary - honour - knighted - appointed | 81 | 856_honours_honorary_honour_knighted |
| 857 | clinical - gibbons - investigation - patents - laboratory | 81 | 857_clinical_gibbons_investigation_patents |
| 858 | suffrage - suffragettes - activists - feminist - activist | 81 | 858_suffrage_suffragettes_activists_feminist |
| 859 | toured - concert - début - tour - albums | 81 | 859_toured_concert_début_tour |
| 860 | pastor - pastors - megachurch - evangelical - ministries | 81 | 860_pastor_pastors_megachurch_evangelical |
| 861 | fm - stations - radio - station - broadcasts | 80 | 861_fm_stations_radio_station |
| 862 | filters - filtering - covariance - filter - covariances | 80 | 862_filters_filtering_covariance_filter |
| 863 | conspiracies - conspiratorial - conspiracy - conspiracism - conspiracist | 80 | 863_conspiracies_conspiratorial_conspiracy_conspiracism |
| 864 | soprano - sopranos - actor - cast - actors | 80 | 864_soprano_sopranos_actor_cast |
| 865 | expedition - voyage - whaling - exploration - 1901 | 80 | 865_expedition_voyage_whaling_exploration |
| 866 | actor - hamlet - actors - acting - theatre | 80 | 866_actor_hamlet_actors_acting |
| 867 | designers - designer - boutiques - fashion - makeup | 80 | 867_designers_designer_boutiques_fashion |
| 868 | processors - 1070 - supercomputers - processor - hardware | 80 | 868_processors_1070_supercomputers_processor |
| 869 | primus - toured - tour - praxis - drums | 80 | 869_primus_toured_tour_praxis |
| 870 | roof - prosecution - defendants - sentencing - convicted | 80 | 870_roof_prosecution_defendants_sentencing |
| 871 | strongman - strongest - strongmen - strength - competed | 80 | 871_strongman_strongest_strongmen_strength |
| 872 | parliament - parliamentary - constituencies - legislature - legislatures | 80 | 872_parliament_parliamentary_constituencies_legislature |
| 873 | monkey - monk - monkeys - buddha - tang | 80 | 873_monkey_monk_monkeys_buddha |
| 874 | rap - albums - park - rock - hybrid | 80 | 874_rap_albums_park_rock |
| 875 | coalition - election - minister - elections - 2021 | 80 | 875_coalition_election_minister_elections |
| 876 | smartphone - smartphones - laptop - tablet - sales | 80 | 876_smartphone_smartphones_laptop_tablet |
| 877 | stratosphere - meteorological - stratospheric - climatic - climate | 80 | 877_stratosphere_meteorological_stratospheric_climatic |
| 878 | reformation - protestant - theologian - papacy - 1541 | 80 | 878_reformation_protestant_theologian_papacy |
| 879 | neighbours - episodes - airing - episode - channel | 80 | 879_neighbours_episodes_airing_episode |
| 880 | coca - cocaine - tobacco - cola - leaves | 80 | 880_coca_cocaine_tobacco_cola |
| 881 | inferno - purgatory - sins - torment - theology | 80 | 881_inferno_purgatory_sins_torment |
| 882 | confederate - flags - flag - confederacy - confederates | 80 | 882_confederate_flags_flag_confederacy |
| 883 | dubbed - dub - anime - releases - premiered | 79 | 883_dubbed_dub_anime_releases |
| 884 | baron - comedian - mockumentary - documentary - film | 79 | 884_baron_comedian_mockumentary_documentary |
| 885 | golfer - masters - golf - golfers - tournament | 79 | 885_golfer_masters_golf_golfers |
| 886 | spiritualism - spirituality - paganism - esotericism - religiosity | 79 | 886_spiritualism_spirituality_paganism_esotericism |
| 887 | graffiti - paintings - painting - artworks - paint | 79 | 887_graffiti_paintings_painting_artworks |
| 888 | lakes - lake - shipwrecks - shipwreck - sank | 79 | 888_lakes_lake_shipwrecks_shipwreck |
| 889 | fashion - designers - designer - fashions - boutique | 79 | 889_fashion_designers_designer_fashions |
| 890 | representation - philosophy - philosophical - philosopher - philosophies | 79 | 890_representation_philosophy_philosophical_philosopher |
| 891 | railgun - railguns - rail - projectile - projectiles | 78 | 891_railgun_railguns_rail_projectile |
| 892 | adobe - illustrator - software - mac - graphics | 78 | 892_adobe_illustrator_software_mac |
| 893 | paternal - stepfather - nazi - illegitimate - grandfather | 78 | 893_paternal_stepfather_nazi_illegitimate |
| 894 | helix - nucleic - discoveries - discovered - biophysics | 78 | 894_helix_nucleic_discoveries_discovered |
| 895 | payments - payment - merchant - purchases - merchants | 78 | 895_payments_payment_merchant_purchases |
| 896 | airlines - airline - pan - flights - midway | 78 | 896_airlines_airline_pan_flights |
| 897 | secretariat - racehorse - racetrack - thoroughbred - racehorses | 78 | 897_secretariat_racehorse_racetrack_thoroughbred |
| 898 | sensitivity - specificity - diagnostic - positives - precision | 78 | 898_sensitivity_specificity_diagnostic_positives |
| 899 | pirate - piracy - bay - infringement - infringements | 78 | 899_pirate_piracy_bay_infringement |
| 900 | oyster - oysters - shellfish - crabs - seafood | 78 | 900_oyster_oysters_shellfish_crabs |
| 901 | ethnicities - ethnic - ethnically - ethnicity - population | 78 | 901_ethnicities_ethnic_ethnically_ethnicity |
| 902 | abolitionist - abolitionists - slavery - 1860 - abolition | 78 | 902_abolitionist_abolitionists_slavery_1860 |
| 903 | reefs - corals - coral - reef - aquaculture | 77 | 903_reefs_corals_coral_reef |
| 904 | incomes - income - wealth - disparities - poverty | 77 | 904_incomes_income_wealth_disparities |
| 905 | officers - officer - recruitment - administrative - secretaries | 77 | 905_officers_officer_recruitment_administrative |
| 906 | sabbath - piers - frontman - airing - presenter | 77 | 906_sabbath_piers_frontman_airing |
| 907 | aether - realms - realm - omnipotence - gods | 77 | 907_aether_realms_realm_omnipotence |
| 908 | extinctions - extinction - extinct - dinosaurs - speciation | 77 | 908_extinctions_extinction_extinct_dinosaurs |
| 909 | armistice - 38th - counterinsurgency - soviet - retreated | 77 | 909_armistice_38th_counterinsurgency_soviet |
| 910 | magicians - magician - museum - vaudeville - cemetery | 77 | 910_magicians_magician_museum_vaudeville |
| 911 | sequels - sequel - trilogy - screenplay - remake | 77 | 911_sequels_sequel_trilogy_screenplay |
| 912 | executions - executed - clemency - punishment - inmates | 77 | 912_executions_executed_clemency_punishment |
| 913 | neolithic - archaeological - archaeology - excavations - civilisation | 77 | 913_neolithic_archaeological_archaeology_excavations |
| 914 | dolly - novel - literature - mansion - narrator | 77 | 914_dolly_novel_literature_mansion |
| 915 | sparrow - pirates - pirate - privateer - captained | 77 | 915_sparrow_pirates_pirate_privateer |
| 916 | scurvy - vitamin - supplementation - dietary - supplement | 77 | 916_scurvy_vitamin_supplementation_dietary |
| 917 | holly - finale - office - receptionist - episode | 77 | 917_holly_finale_office_receptionist |
| 918 | hemp - cannabis - textiles - cultivated - textile | 76 | 918_hemp_cannabis_textiles_cultivated |
| 919 | lidar - radar - laser - photogrammetry - sensors | 76 | 919_lidar_radar_laser_photogrammetry |
| 920 | dingoes - dingo - breeding - pets - kangaroos | 76 | 920_dingoes_dingo_breeding_pets |
| 921 | crocodile - zookeeper - zoo - crocodiles - wildlife | 76 | 921_crocodile_zookeeper_zoo_crocodiles |
| 922 | slots - slot - gambling - reels - poker | 76 | 922_slots_slot_gambling_reels |
| 923 | bombs - bomb - 1945 - bombing - detonated | 76 | 923_bombs_bomb_1945_bombing |
| 924 | manufacturer - corporate - corporation - company - brands | 76 | 924_manufacturer_corporate_corporation_company |
| 925 | stones - stone - guitarist - guitarists - drums | 76 | 925_stones_stone_guitarist_guitarists |
| 926 | meiosis - mitosis - chromosomal - chromosomes - chromosome | 76 | 926_meiosis_mitosis_chromosomal_chromosomes |
| 927 | pirate - privateer - bonnet - pirates - privateering | 76 | 927_pirate_privateer_bonnet_pirates |
| 928 | parks - park - attractions - studios - pavilion | 75 | 928_parks_park_attractions_studios |
| 929 | medicine - medicinal - medicines - physicians - herbal | 75 | 929_medicine_medicinal_medicines_physicians |
| 930 | acupuncture - acupuncturists - medicine - practitioners - patients | 75 | 930_acupuncture_acupuncturists_medicine_practitioners |
| 931 | margarine - yeast - extracts - foods - recipe | 75 | 931_margarine_yeast_extracts_foods |
| 932 | chiropractors - chiropractic - chiropractor - osteopathic - practitioners | 75 | 932_chiropractors_chiropractic_chiropractor_osteopathic |
| 933 | negro - activist - behest - 1925 - racism | 75 | 933_negro_activist_behest_1925 |
| 934 | infantry - tanks - soldier - 1944 - troops | 75 | 934_infantry_tanks_soldier_1944 |
| 935 | geography - geographic - geographical - geographer - geographers | 75 | 935_geography_geographic_geographical_geographer |
| 936 | federalism - federations - federation - unitary - sovereignty | 75 | 936_federalism_federations_federation_unitary |
| 937 | braking - transmissions - brakes - automatic - brake | 75 | 937_braking_transmissions_brakes_automatic |
| 938 | ford - presidency - presidential - presidents - wife | 75 | 938_ford_presidency_presidential_presidents |
| 939 | eukaryotes - prokaryotes - eukaryotic - prokaryotic - eukaryote | 75 | 939_eukaryotes_prokaryotes_eukaryotic_prokaryotic |
| 940 | electroconvulsive - antidepressants - antidepressant - anticonvulsant - electrodes | 75 | 940_electroconvulsive_antidepressants_antidepressant_anticonvulsant |
| 941 | bourgeoisie - capitalist - bourgeois - capitalism - socialism | 75 | 941_bourgeoisie_capitalist_bourgeois_capitalism |
| 942 | burger - hamburger - burgers - hamburgers - steak | 75 | 942_burger_hamburger_burgers_hamburgers |
| 943 | stagecoach - ford - cinematography - films - actor | 75 | 943_stagecoach_ford_cinematography_films |
| 944 | comics - cartoonist - adventures - magazine - comic | 75 | 944_comics_cartoonist_adventures_magazine |
| 945 | detective - detectives - novels - murders - obituary | 75 | 945_detective_detectives_novels_murders |
| 946 | laureates - laureate - prizes - prize - awarding | 75 | 946_laureates_laureate_prizes_prize |
| 947 | bombed - troops - insurgency - casualties - tactics | 75 | 947_bombed_troops_insurgency_casualties |
| 948 | allegations - molested - offences - alleged - abused | 74 | 948_allegations_molested_offences_alleged |
| 949 | subreddit - subreddits - banning - censorship - incels | 74 | 949_subreddit_subreddits_banning_censorship |
| 950 | onzz - superman - watchtower - superhero - storyline | 74 | 950_onzz_superman_watchtower_superhero |
| 951 | pronouns - pronoun - plurality - plurals - plural | 74 | 951_pronouns_pronoun_plurality_plurals |
| 952 | gymnast - gymnastics - gymnasts - olympic - competed | 74 | 952_gymnast_gymnastics_gymnasts_olympic |
| 953 | bonobos - chimpanzees - primates - chimpanzee - primate | 74 | 953_bonobos_chimpanzees_primates_chimpanzee |
| 954 | singer - songwriter - albums - musician - bono | 74 | 954_singer_songwriter_albums_musician |
| 955 | pearls - pearl - pearling - oysters - oyster | 74 | 955_pearls_pearl_pearling_oysters |
| 956 | patients - inpatients - physicians - physician - inpatient | 74 | 956_patients_inpatients_physicians_physician |
| 957 | oz - wizard - 1939 - wicked - emerald | 74 | 957_oz_wizard_1939_wicked |
| 958 | pride - flags - flag - rainbow - parade | 74 | 958_pride_flags_flag_rainbow |
| 959 | espionage - spies - spy - spying - soviets | 74 | 959_espionage_spies_spy_spying |
| 960 | chairman - executive - resigned - chief - directors | 74 | 960_chairman_executive_resigned_chief |
| 961 | paramilitary - mercenaries - civilians - mercenary - casualties | 74 | 961_paramilitary_mercenaries_civilians_mercenary |
| 962 | obesity - obese - overweight - underweight - adipose | 74 | 962_obesity_obese_overweight_underweight |
| 963 | deities - polytheism - monotheistic - monotheism - creation | 74 | 963_deities_polytheism_monotheistic_monotheism |
| 964 | housewives - housewife - airing - episodes - renewed | 73 | 964_housewives_housewife_airing_episodes |
| 965 | tariffs - tariff - exports - agreements - economy | 73 | 965_tariffs_tariff_exports_agreements |
| 966 | metric - imperial - units - metre - kilograms | 73 | 966_metric_imperial_units_metre |
| 967 | forested - vegetation - conifers - forests - rainforests | 73 | 967_forested_vegetation_conifers_forests |
| 968 | schemas - schema - metadata - structured - specification | 73 | 968_schemas_schema_metadata_structured |
| 969 | homosexuality - homosexuals - homosexual - homophobia - immoral | 73 | 969_homosexuality_homosexuals_homosexual_homophobia |
| 970 | dome - missiles - missile - protects - protect | 73 | 970_dome_missiles_missile_protects |
| 971 | scramjet - scramjets - turbojet - turbojets - ramjet | 73 | 971_scramjet_scramjets_turbojet_turbojets |
| 972 | esotericists - esotericism - esoteric - occultism - occultists | 73 | 972_esotericists_esotericism_esoteric_occultism |
| 973 | regexes - regex - syntax - parsing - patterns | 73 | 973_regexes_regex_syntax_parsing |
| 974 | auroral - aurora - auroras - magnetosphere - aurorae | 73 | 974_auroral_aurora_auroras_magnetosphere |
| 975 | metamorphosis - literature - literary - writings - writer | 73 | 975_metamorphosis_literature_literary_writings |
| 976 | musician - concert - gravestone - bandmate - backstage | 73 | 976_musician_concert_gravestone_bandmate |
| 977 | dell - manufacturers - manufacturer - vendors - intel | 73 | 977_dell_manufacturers_manufacturer_vendors |
| 978 | soviets - missiles - overflights - reconnaissance - overflight | 73 | 978_soviets_missiles_overflights_reconnaissance |
| 979 | profiles - profile - freelancers - recruiters - resumes | 73 | 979_profiles_profile_freelancers_recruiters |
| 980 | doge - pope - 1571 - mediterranean - duchy | 72 | 980_doge_pope_1571_mediterranean |
| 981 | chess - grandmaster - fide - tournament - championship | 72 | 981_chess_grandmaster_fide_tournament |
| 982 | comet - cometary - comets - meteor - telescope | 72 | 982_comet_cometary_comets_meteor |
| 983 | totalitarianism - holocaust - totalitarian - biography - nazi | 72 | 983_totalitarianism_holocaust_totalitarian_biography |
| 984 | tics - tic - disorders - neuropsychiatric - autism | 72 | 984_tics_tic_disorders_neuropsychiatric |
| 985 | bullying - bullied - bullies - bully - cyberbullying | 72 | 985_bullying_bullied_bullies_bully |
| 986 | psychopathy - psychopathic - psychopaths - psychopath - sociopathy | 72 | 986_psychopathy_psychopathic_psychopaths_psychopath |
| 987 | linguistics - linguistic - linguists - linguist - languages | 72 | 987_linguistics_linguistic_linguists_linguist |
| 988 | literature - writings - author - fictions - literary | 72 | 988_literature_writings_author_fictions |
| 989 | cook - voyage - voyages - sailed - 1788 | 72 | 989_cook_voyage_voyages_sailed |
| 990 | cyberpunk - cybernetics - novelists - novel - fiction | 72 | 990_cyberpunk_cybernetics_novelists_novel |
| 991 | population - census - inhabitants - populous - populated | 72 | 991_population_census_inhabitants_populous |
| 992 | linden - lab - copyright - token - refund | 72 | 992_linden_lab_copyright_token |
| 993 | cartoons - cartoon - spinach - comic - cartoonists | 72 | 993_cartoons_cartoon_spinach_comic |
| 994 | nazi - holocaust - 1941 - 1945 - persecuted | 72 | 994_nazi_holocaust_1941_1945 |
| 995 | indictment - indictments - indicted - prosecutors - convicted | 72 | 995_indictment_indictments_indicted_prosecutors |
| 996 | tributaries - tributary - river - rivers - alluvial | 72 | 996_tributaries_tributary_river_rivers |
| 997 | vocalist - vocals - singers - singer - saxophonist | 72 | 997_vocalist_vocals_singers_singer |
| 998 | esteem - self - ego - psychological - oneself | 72 | 998_esteem_self_ego_psychological |
| 999 | rescuers - rescuer - survivors - rescue - camped | 72 | 999_rescuers_rescuer_survivors_rescue |
| 1000 | coax - coaxial - cables - cable - antennas | 72 | 1000_coax_coaxial_cables_cable |
| 1001 | synesthesia - synesthetic - synesthetes - paresthesia - synesthete | 72 | 1001_synesthesia_synesthetic_synesthetes_paresthesia |
| 1002 | annexation - 1938 - annexed - 1945 - annex | 71 | 1002_annexation_1938_annexed_1945 |
| 1003 | motocross - motorcycle - stunt - bike - stunts | 71 | 1003_motocross_motorcycle_stunt_bike |
| 1004 | chocolate - factory - screenplay - wilder - bucket | 71 | 1004_chocolate_factory_screenplay_wilder |
| 1005 | galaxy - smartphone - smartphones - mobile - flagship | 71 | 1005_galaxy_smartphone_smartphones_mobile |
| 1006 | runes - rune - runestones - inscriptions - inscription | 71 | 1006_runes_rune_runestones_inscriptions |
| 1007 | che - revolutionaries - guerrilla - revolutionary - guerrillas | 71 | 1007_che_revolutionaries_guerrilla_revolutionary |
| 1008 | hemorrhage - surgery - surgical - injury - iron | 71 | 1008_hemorrhage_surgery_surgical_injury |
| 1009 | referendum - conservative - trump - candidate - resigned | 71 | 1009_referendum_conservative_trump_candidate |
| 1010 | sightings - sighting - hoaxes - hoax - skunk | 71 | 1010_sightings_sighting_hoaxes_hoax |
| 1011 | sphinx - sphinxes - pharaoh - pyramid - statue | 71 | 1011_sphinx_sphinxes_pharaoh_pyramid |
| 1012 | violinist - violin - violins - albums - vinyl | 71 | 1012_violinist_violin_violins_albums |
| 1013 | law - jurisprudence - judicial - statutes - jurisdictions | 71 | 1013_law_jurisprudence_judicial_statutes |
| 1014 | nails - albums - album - band - artists | 71 | 1014_nails_albums_album_band |
| 1015 | apple - mac - microcomputers - microcomputer - computers | 71 | 1015_apple_mac_microcomputers_microcomputer |
| 1016 | scream - paintings - painting - painter - art | 71 | 1016_scream_paintings_painting_painter |
| 1017 | flew - flight - airplane - flying - aviator | 71 | 1017_flew_flight_airplane_flying |
| 1018 | ninja - ninjas - anime - kai - cartoon | 71 | 1018_ninja_ninjas_anime_kai |
| 1019 | investing - invest - investors - indexes - investment | 71 | 1019_investing_invest_investors_indexes |
| 1020 | concord - airlines - flights - airliners - airliner | 71 | 1020_concord_airlines_flights_airliners |
| 1021 | dysplasia - breeds - veterinary - shepherd - dystrophy | 71 | 1021_dysplasia_breeds_veterinary_shepherd |
| 1022 | doll - dolls - toy - brand - fashion | 71 | 1022_doll_dolls_toy_brand |
| 1023 | investments - invested - investor - investors - investment | 70 | 1023_investments_invested_investor_investors |
| 1024 | intersectionality - intersectional - feminism - intersection - feminist | 70 | 1024_intersectionality_intersectional_feminism_intersection |
| 1025 | festivals - festival - festivities - carnivals - carnival | 70 | 1025_festivals_festival_festivities_carnivals |
| 1026 | tennis - racquet - tournament - quarterfinal - doubles | 70 | 1026_tennis_racquet_tournament_quarterfinal |
| 1027 | daddy - reggaeton - rapper - rap - mixtape | 70 | 1027_daddy_reggaeton_rapper_rap |
| 1028 | probability - probabilities - doors - car - door | 70 | 1028_probability_probabilities_doors_car |
| 1029 | radar - radars - signals - doppler - transmitter | 70 | 1029_radar_radars_signals_doppler |
| 1030 | blackberry - smartphone - smartphones - android - mobile | 70 | 1030_blackberry_smartphone_smartphones_android |
| 1031 | cappuccino - espresso - coffee - capo - latte | 70 | 1031_cappuccino_espresso_coffee_capo |
| 1032 | candidates - candidate - election - elections - populist | 70 | 1032_candidates_candidate_election_elections |
| 1033 | cud - rapper - mixtape - kid - rap | 70 | 1033_cud_rapper_mixtape_kid |
| 1034 | soviets - soviet - treaty - ceded - ceasefire | 70 | 1034_soviets_soviet_treaty_ceded |
| 1035 | nuclear - disarmament - treaty - uranium - nations | 70 | 1035_nuclear_disarmament_treaty_uranium |
| 1036 | ivy - poison - poisons - poisoned - poisoning | 70 | 1036_ivy_poison_poisons_poisoned |
| 1037 | tsar - empress - heir - 1762 - mistress | 70 | 1037_tsar_empress_heir_1762 |
| 1038 | sexuality - discipline - sociology - homosexuality - behavior | 70 | 1038_sexuality_discipline_sociology_homosexuality |
| 1039 | elves - elf - folklore - fairies - dwarves | 69 | 1039_elves_elf_folklore_fairies |
| 1040 | peacekeeping - sovereignty - niger - nations - territory | 69 | 1040_peacekeeping_sovereignty_niger_nations |
| 1041 | torturing - strangled - stabbing - murdered - victims | 69 | 1041_torturing_strangled_stabbing_murdered |
| 1042 | exorcist - exorcism - screenplay - possessed - demonic | 69 | 1042_exorcist_exorcism_screenplay_possessed |
| 1043 | cloud - clouds - azure - virtualization - infrastructure | 69 | 1043_cloud_clouds_azure_virtualization |
| 1044 | yaoi - manga - hentai - anime - heterosexual | 69 | 1044_yaoi_manga_hentai_anime |
| 1045 | doping - athlete - lance - cyclist - steroids | 69 | 1045_doping_athlete_lance_cyclist |
| 1046 | wickets - batsman - wicket - bowled - bowler | 69 | 1046_wickets_batsman_wicket_bowled |
| 1047 | opus - pontifical - popes - priests - pope | 69 | 1047_opus_pontifical_popes_priests |
| 1048 | ancestry - genetic - haplogroup - paleolithic - genes | 69 | 1048_ancestry_genetic_haplogroup_paleolithic |
| 1049 | thanksgiving - holiday - holidays - celebrated - celebrations | 69 | 1049_thanksgiving_holiday_holidays_celebrated |
| 1050 | joker - skins - superman - comics - knight | 69 | 1050_joker_skins_superman_comics |
| 1051 | freeware - proprietary - software - licensing - licenses | 69 | 1051_freeware_proprietary_software_licensing |
| 1052 | quantum - qubits - qubit - computational - computing | 69 | 1052_quantum_qubits_qubit_computational |
| 1053 | bird - storm - star - rebounds - assists | 69 | 1053_bird_storm_star_rebounds |
| 1054 | ceasefire - peacekeeping - oblast - militias - hostilities | 69 | 1054_ceasefire_peacekeeping_oblast_militias |
| 1055 | communists - soviets - protests - demonstrators - communist | 69 | 1055_communists_soviets_protests_demonstrators |
| 1056 | palaces - ibn - mosque - palace - excavations | 68 | 1056_palaces_ibn_mosque_palace |
| 1057 | nirvana - overdosed - grunge - overdose - died | 68 | 1057_nirvana_overdosed_grunge_overdose |
| 1058 | commanders - commander - allied - 1944 - panzer | 68 | 1058_commanders_commander_allied_1944 |
| 1059 | blinding - heartless - lights - song - billboard | 68 | 1059_blinding_heartless_lights_song |
| 1060 | fort - battle - 1836 - surrender - reinforcements | 68 | 1060_fort_battle_1836_surrender |
| 1061 | touchdowns - cousins - interceptions - touchdown - yards | 68 | 1061_touchdowns_cousins_interceptions_touchdown |
| 1062 | machines - computable - computational - machine - deterministic | 68 | 1062_machines_computable_computational_machine |
| 1063 | creoles - creole - vernaculars - vernacular - lingua | 68 | 1063_creoles_creole_vernaculars_vernacular |
| 1064 | endometriosis - endometrial - endometrium - uterus - menstruation | 68 | 1064_endometriosis_endometrial_endometrium_uterus |
| 1065 | lin - undrafted - harden - assists - rebounds | 68 | 1065_lin_undrafted_harden_assists |
| 1066 | pornography - porn - pornographic - playboy - affiliate | 68 | 1066_pornography_porn_pornographic_playboy |
| 1067 | panchayat - panchayats - elections - electoral - election | 68 | 1067_panchayat_panchayats_elections_electoral |
| 1068 | stalker - filmmaker - cinematographer - director - cinematography | 68 | 1068_stalker_filmmaker_cinematographer_director |
| 1069 | loch - ness - sightings - sighting - folklore | 68 | 1069_loch_ness_sightings_sighting |
| 1070 | taco - tacos - restaurants - restaurant - cafe | 68 | 1070_taco_tacos_restaurants_restaurant |
| 1071 | absinthe - absinthes - herbs - cocktail - distilled | 68 | 1071_absinthe_absinthes_herbs_cocktail |
| 1072 | resuscitation - defibrillation - defibrillator - cardiopulmonary - cardiac | 68 | 1072_resuscitation_defibrillation_defibrillator_cardiopulmonary |
| 1073 | chancellor - secretary - minister - appointed - resigned | 68 | 1073_chancellor_secretary_minister_appointed |
| 1074 | defrauded - fraud - fraudulent - fraudster - whistleblower | 68 | 1074_defrauded_fraud_fraudulent_fraudster |
| 1075 | printing - printmaking - printers - printer - print | 68 | 1075_printing_printmaking_printers_printer |
| 1076 | ancient - mediterranean - civilizations - archaeological - excavations | 68 | 1076_ancient_mediterranean_civilizations_archaeological |
| 1077 | dodo - dodos - fauna - birds - species | 68 | 1077_dodo_dodos_fauna_birds |
| 1078 | brave - novel - novels - utopia - utopian | 68 | 1078_brave_novel_novels_utopia |
| 1079 | piccolo - dragon - kai - trunks - battle | 68 | 1079_piccolo_dragon_kai_trunks |
| 1080 | parachutes - parachute - skydiving - flight - airlines | 68 | 1080_parachutes_parachute_skydiving_flight |
| 1081 | autonomy - independence - constituted - nationalism - referendum | 68 | 1081_autonomy_independence_constituted_nationalism |
| 1082 | robots - robot - robotic - robotics - ai | 68 | 1082_robots_robot_robotic_robotics |
| 1083 | tanks - tank - partisan - ideological - think | 68 | 1083_tanks_tank_partisan_ideological |
| 1084 | pharaoh - archaeological - dynasty - sea - dynasties | 67 | 1084_pharaoh_archaeological_dynasty_sea |
| 1085 | hippie - hippies - hipster - hippy - counterculture | 67 | 1085_hippie_hippies_hipster_hippy |
| 1086 | inscriptions - inscription - epigraphy - taluk - ancient | 67 | 1086_inscriptions_inscription_epigraphy_taluk |
| 1087 | filmmaker - filmmaking - cinematographer - filmmakers - films | 67 | 1087_filmmaker_filmmaking_cinematographer_filmmakers |
| 1088 | celebrations - festivities - celebrated - traditions - mosque | 67 | 1088_celebrations_festivities_celebrated_traditions |
| 1089 | hawking - physicist - cosmology - sciences - marriage | 67 | 1089_hawking_physicist_cosmology_sciences |
| 1090 | albums - songs - album - musical - music | 67 | 1090_albums_songs_album_musical |
| 1091 | pound - poet - poetry - poems - literary | 67 | 1091_pound_poet_poetry_poems |
| 1092 | embryos - embryo - fertility - infertility - infertile | 67 | 1092_embryos_embryo_fertility_infertility |
| 1093 | satanic - satan - theology - devil - atheism | 67 | 1093_satanic_satan_theology_devil |
| 1094 | bombing - insurgency - bombings - overthrow - militants | 67 | 1094_bombing_insurgency_bombings_overthrow |
| 1095 | tribalism - nationalist - unrest - sovereignty - decolonization | 67 | 1095_tribalism_nationalist_unrest_sovereignty |
| 1096 | kibbutz - kibbutzim - kibbutzniks - founders - communities | 67 | 1096_kibbutz_kibbutzim_kibbutzniks_founders |
| 1097 | priest - demonic - priestess - demon - demons | 67 | 1097_priest_demonic_priestess_demon |
| 1098 | eclampsia - pregnancies - pregnancy - prenatal - gestational | 67 | 1098_eclampsia_pregnancies_pregnancy_prenatal |
| 1099 | riots - protests - protest - activism - activists | 67 | 1099_riots_protests_protest_activism |
| 1100 | hill - silent - sequel - remake - gameplay | 67 | 1100_hill_silent_sequel_remake |
| 1101 | treaty - treaties - covenant - league - nations | 67 | 1101_treaty_treaties_covenant_league |
| 1102 | prix - motorsport - racing - qualifying - grand | 67 | 1102_prix_motorsport_racing_qualifying |
| 1103 | automotive - ab - automobile - automobiles - vehicle | 67 | 1103_automotive_ab_automobile_automobiles |
| 1104 | chamberlain - 1945 - minister - resigned - 1940 | 67 | 1104_chamberlain_1945_minister_resigned |
| 1105 | vegetarian - vegetarianism - veganism - vegetarians - vegan | 67 | 1105_vegetarian_vegetarianism_veganism_vegetarians |
| 1106 | dictator - dictatorship - fascism - fascist - authoritarian | 67 | 1106_dictator_dictatorship_fascism_fascist |
| 1107 | celiac - gluten - coeliac - wheat - autoimmune | 66 | 1107_celiac_gluten_coeliac_wheat |
| 1108 | ford - truck - trucks - chassis - jeep | 66 | 1108_ford_truck_trucks_chassis |
| 1109 | inkblots - inkblot - ink - psychometric - psychoanalytic | 66 | 1109_inkblots_inkblot_ink_psychometric |
| 1110 | crimson - guitarist - toured - guitars - bands | 66 | 1110_crimson_guitarist_toured_guitars |
| 1111 | oblast - oblasts - governorates - province - soviet | 66 | 1111_oblast_oblasts_governorates_province |
| 1112 | radio - fm - stations - channels - broadcasts | 66 | 1112_radio_fm_stations_channels |
| 1113 | 1803 - 1763 - treaty - ceded - treaties | 66 | 1113_1803_1763_treaty_ceded |
| 1114 | nicotine - nicotinic - tobacco - cigarettes - cigarette | 66 | 1114_nicotine_nicotinic_tobacco_cigarettes |
| 1115 | flags - flag - sun - swastika - emblem | 66 | 1115_flags_flag_sun_swastika |
| 1116 | philosopher - philosophers - philosophy - philosophical - logician | 66 | 1116_philosopher_philosophers_philosophy_philosophical |
| 1117 | whataboutism - geopolitical - dissidents - propaganda - propagandists | 66 | 1117_whataboutism_geopolitical_dissidents_propaganda |
| 1118 | nirvana - grunge - album - band - bands | 66 | 1118_nirvana_grunge_album_band |
| 1119 | proud - boys - protests - protesters - demonstrators | 66 | 1119_proud_boys_protests_protesters |
| 1120 | bands - slayer - thrash - band - frontman | 66 | 1120_bands_slayer_thrash_band |
| 1121 | scored - scoring - goal - penalty - goals | 66 | 1121_scored_scoring_goal_penalty |
| 1122 | turkey - terrorist - militants - terrorism - militant | 66 | 1122_turkey_terrorist_militants_terrorism |
| 1123 | shroud - crucified - crucifixion - burial - sculpture | 66 | 1123_shroud_crucified_crucifixion_burial |
| 1124 | blink - band - bands - 182 - punk | 66 | 1124_blink_band_bands_182 |
| 1125 | poet - poetry - poems - poem - stanzas | 66 | 1125_poet_poetry_poems_poem |
| 1126 | racing - speed - chases - racer - pursuit | 65 | 1126_racing_speed_chases_racer |
| 1127 | mansion - bedrooms - mansions - residence - bedroom | 65 | 1127_mansion_bedrooms_mansions_residence |
| 1128 | languages - multilingual - language - lingua - creole | 65 | 1128_languages_multilingual_language_lingua |
| 1129 | espionage - spying - spy - informant - investigator | 65 | 1129_espionage_spying_spy_informant |
| 1130 | yoon - jung - scandal - prosecutors - alleged | 65 | 1130_yoon_jung_scandal_prosecutors |
| 1131 | 1451 - pasha - 1477 - 1476 - 1475 | 65 | 1131_1451_pasha_1477_1476 |
| 1132 | burning - burners - organizers - attendees - gatherings | 65 | 1132_burning_burners_organizers_attendees |
| 1133 | spartan - ancient - battle - invasion - retreated | 65 | 1133_spartan_ancient_battle_invasion |
| 1134 | bell - telephone - telephones - inventor - invention | 65 | 1134_bell_telephone_telephones_inventor |
| 1135 | mathematician - mathematicians - mathematics - algebra - arithmetical | 65 | 1135_mathematician_mathematicians_mathematics_algebra |
| 1136 | restaurants - restaurant - chefs - culinary - cuisines | 65 | 1136_restaurants_restaurant_chefs_culinary |
| 1137 | restaurants - customers - restaurant - burger - franchisees | 65 | 1137_restaurants_customers_restaurant_burger |
| 1138 | misfits - albums - bands - band - toured | 65 | 1138_misfits_albums_bands_band |
| 1139 | rationalism - rationalisation - rationalization - rationality - philosophy | 65 | 1139_rationalism_rationalisation_rationalization_rationality |
| 1140 | paintings - artworks - gallery - painting - exhibitions | 65 | 1140_paintings_artworks_gallery_painting |
| 1141 | dan - sitcom - cast - spinoff - remarrying | 65 | 1141_dan_sitcom_cast_spinoff |
| 1142 | vocals - remixes - albums - chorus - album | 65 | 1142_vocals_remixes_albums_chorus |
| 1143 | casualties - fatalities - deaths - mortality - insurgents | 65 | 1143_casualties_fatalities_deaths_mortality |
| 1144 | gaming - retailers - games - retailer - gamers | 65 | 1144_gaming_retailers_games_retailer |
| 1145 | tales - literature - tale - manuscripts - testament | 65 | 1145_tales_literature_tale_manuscripts |
| 1146 | deposed - presidency - presidential - ousted - elections | 65 | 1146_deposed_presidency_presidential_ousted |
| 1147 | citizenship - passport - territories - residency - sovereign | 64 | 1147_citizenship_passport_territories_residency |
| 1148 | optimization - algorithms - optimal - algorithm - optimality | 64 | 1148_optimization_algorithms_optimal_algorithm |
| 1149 | sentenced - imprisonment - convicted - pardoned - judiciary | 64 | 1149_sentenced_imprisonment_convicted_pardoned |
| 1150 | caterpillar - diesel - manufacturer - manufacturing - tractors | 64 | 1150_caterpillar_diesel_manufacturer_manufacturing |
| 1151 | hub - sci - lawsuit - scholarly - plaintiffs | 64 | 1151_hub_sci_lawsuit_scholarly |
| 1152 | neolithic - stone - stones - excavations - archaeologists | 64 | 1152_neolithic_stone_stones_excavations |
| 1153 | coordinates - coordinate - axes - axis - longitude | 64 | 1153_coordinates_coordinate_axes_axis |
| 1154 | lingerie - secret - retailer - apparel - retail | 64 | 1154_lingerie_secret_retailer_apparel |
| 1155 | biodiversity - extinction - extinctions - ecosystem - ecological | 64 | 1155_biodiversity_extinction_extinctions_ecosystem |
| 1156 | pearl - jam - concert - toured - albums | 64 | 1156_pearl_jam_concert_toured |
| 1157 | tesseract - polytopes - hexagonal - squares - cubes | 64 | 1157_tesseract_polytopes_hexagonal_squares |
| 1158 | devices - pairing - paired - protocol - device | 64 | 1158_devices_pairing_paired_protocol |
| 1159 | tsar - tsarina - empress - 1917 - duchess | 64 | 1159_tsar_tsarina_empress_1917 |
| 1160 | neighbourhoods - khan - mosques - urban - municipal | 64 | 1160_neighbourhoods_khan_mosques_urban |
| 1161 | assassination - colonel - secessionist - martyr - secession | 64 | 1161_assassination_colonel_secessionist_martyr |
| 1162 | skater - skaters - skating - skate - olympic | 64 | 1162_skater_skaters_skating_skate |
| 1163 | durations - duration - decoding - transmissions - milliseconds | 64 | 1163_durations_duration_decoding_transmissions |
| 1164 | retailers - retailer - retailing - retail - thanksgiving | 64 | 1164_retailers_retailer_retailing_retail |
| 1165 | panther - panthers - activists - activist - antiwar | 64 | 1165_panther_panthers_activists_activist |
| 1166 | spironolactone - progesterone - antiandrogenic - aldosterone - antiandrogen | 64 | 1166_spironolactone_progesterone_antiandrogenic_aldosterone |
| 1167 | unrest - uprising - protests - overthrow - protesters | 64 | 1167_unrest_uprising_protests_overthrow |
| 1168 | tower - survivors - towers - 911 - evacuated | 64 | 1168_tower_survivors_towers_911 |
| 1169 | venture - ventures - investors - entrepreneurship - entrepreneurs | 64 | 1169_venture_ventures_investors_entrepreneurship |
| 1170 | sentencing - convicted - conviction - prosecution - jurors | 64 | 1170_sentencing_convicted_conviction_prosecution |
| 1171 | exotic - tiger - zoo - zookeeper - wildlife | 64 | 1171_exotic_tiger_zoo_zookeeper |
| 1172 | attacks - botnet - firewalls - exploits - attackers | 64 | 1172_attacks_botnet_firewalls_exploits |
| 1173 | bridges - bridge - infantry - bridged - artillery | 64 | 1173_bridges_bridge_infantry_bridged |
| 1174 | paintings - painting - auctioned - auction - painted | 63 | 1174_paintings_painting_auctioned_auction |
| 1175 | islands - archipelago - sovereignty - island - atoll | 63 | 1175_islands_archipelago_sovereignty_island |
| 1176 | cameo - cast - stunts - castmates - aired | 63 | 1176_cameo_cast_stunts_castmates |
| 1177 | stagecoach - outlaw - murderer - marshal - gunfighter | 63 | 1177_stagecoach_outlaw_murderer_marshal |
| 1178 | protesting - protests - protest - activism - climate | 63 | 1178_protesting_protests_protest_activism |
| 1179 | billing - provider - customers - customer - subscribers | 63 | 1179_billing_provider_customers_customer |
| 1180 | archipelagos - territories - islands - island - countries | 63 | 1180_archipelagos_territories_islands_island |
| 1181 | deer - hunter - filmmaking - screenplay - film | 63 | 1181_deer_hunter_filmmaking_screenplay |
| 1182 | apps - apple - app - voice - devices | 63 | 1182_apps_apple_app_voice |
| 1183 | paintings - painting - artworks - artist - art | 63 | 1183_paintings_painting_artworks_artist |
| 1184 | buses - midlands - railway - railways - trains | 63 | 1184_buses_midlands_railway_railways |
| 1185 | sonic - hedgehog - supersonic - tails - voiced | 63 | 1185_sonic_hedgehog_supersonic_tails |
| 1186 | memes - meme - 4chan - intertextuality - satirical | 63 | 1186_memes_meme_4chan_intertextuality |
| 1187 | khanate - khan - khanates - khans - sultanate | 63 | 1187_khanate_khan_khanates_khans |
| 1188 | orthodox - orthodoxy - religiosity - religions - catholic | 63 | 1188_orthodox_orthodoxy_religiosity_religions |
| 1189 | shuttle - spacecraft - orbiters - orbiter - astronauts | 63 | 1189_shuttle_spacecraft_orbiters_orbiter |
| 1190 | anarchists - anarchist - anarchism - anarchy - socialists | 63 | 1190_anarchists_anarchist_anarchism_anarchy |
| 1191 | brands - brand - companies - company - bottled | 63 | 1191_brands_brand_companies_company |
| 1192 | shares - invested - stock - investor - holdings | 62 | 1192_shares_invested_stock_investor |
| 1193 | cricket - cricketers - stadium - cricketing - stadiums | 62 | 1193_cricket_cricketers_stadium_cricketing |
| 1194 | mayor - mayors - mayoral - mayoralty - governor | 62 | 1194_mayor_mayors_mayoral_mayoralty |
| 1195 | mac - office - os - versions - version | 62 | 1195_mac_office_os_versions |
| 1196 | diary - diaries - manuscript - frank - editions | 62 | 1196_diary_diaries_manuscript_frank |
| 1197 | patsy - singer - singing - melody - vocalists | 62 | 1197_patsy_singer_singing_melody |
| 1198 | networking - packet - network - internetworking - protocols | 62 | 1198_networking_packet_network_internetworking |
| 1199 | borscht - recipes - recipe - cuisines - cuisine | 62 | 1199_borscht_recipes_recipe_cuisines |
| 1200 | gulag - prisoners - camps - prisons - inmates | 62 | 1200_gulag_prisoners_camps_prisons |
| 1201 | philanthropist - philanthropy - philanthropists - philanthropic - financier | 62 | 1201_philanthropist_philanthropy_philanthropists_philanthropic |
| 1202 | chapters - chapter - novels - paperback - books | 62 | 1202_chapters_chapter_novels_paperback |
| 1203 | hybrids - hybrid - ev - corolla - vehicles | 62 | 1203_hybrids_hybrid_ev_corolla |
| 1204 | hospice - hospices - palliative - caregiving - caregivers | 62 | 1204_hospice_hospices_palliative_caregiving |
| 1205 | mithraeum - mithraea - rituals - temples - ritual | 62 | 1205_mithraeum_mithraea_rituals_temples |
| 1206 | witches - witch - spells - spellbound - comics | 62 | 1206_witches_witch_spells_spellbound |
| 1207 | android - smartphone - smartphones - apps - nexus | 62 | 1207_android_smartphone_smartphones_apps |
| 1208 | electronics - appliances - manufacturer - subsidiaries - brand | 62 | 1208_electronics_appliances_manufacturer_subsidiaries |
| 1209 | chess - tournaments - tournament - grandmaster - grandmasters | 62 | 1209_chess_tournaments_tournament_grandmaster |
| 1210 | slaughterhouse - novelist - novels - writer - nonfiction | 62 | 1210_slaughterhouse_novelist_novels_writer |
| 1211 | sequels - ash - trilogy - evil - sequel | 62 | 1211_sequels_ash_trilogy_evil |
| 1212 | caffeine - caffeinated - drowsiness - coffee - intoxication | 62 | 1212_caffeine_caffeinated_drowsiness_coffee |
| 1213 | electors - electoral - elector - elects - elections | 62 | 1213_electors_electoral_elector_elects |
| 1214 | newscast - reporters - reporter - journalism - 1963 | 62 | 1214_newscast_reporters_reporter_journalism |
| 1215 | caliph - ibn - caliphs - al - caliphate | 62 | 1215_caliph_ibn_caliphs_al |
| 1216 | democrat - democrats - republican - reelection - caucus | 62 | 1216_democrat_democrats_republican_reelection |
| 1217 | þáttr - saga - throne - sagas - skaldic | 62 | 1217_þáttr_saga_throne_sagas |
| 1218 | dune - screenplay - director - cast - sequels | 62 | 1218_dune_screenplay_director_cast |
| 1219 | colonies - niger - guinea - colonial - colonialist | 62 | 1219_colonies_niger_guinea_colonial |
| 1220 | turtle - turtles - ninja - mutant - cartoon | 62 | 1220_turtle_turtles_ninja_mutant |
| 1221 | pins - pin - pinning - feed - ads | 61 | 1221_pins_pin_pinning_feed |
| 1222 | poetry - rhyme - stanzas - poems - rhymes | 61 | 1222_poetry_rhyme_stanzas_poems |
| 1223 | automotive - presenter - rover - bbc - driving | 61 | 1223_automotive_presenter_rover_bbc |
| 1224 | tennis - doubles - singles - tournaments - quarterfinal | 61 | 1224_tennis_doubles_singles_tournaments |
| 1225 | bean - teddy - episodes - sitcom - diary | 61 | 1225_bean_teddy_episodes_sitcom |
| 1226 | magnetism - magnetic - electromagnetism - magnetization - magnet | 61 | 1226_magnetism_magnetic_electromagnetism_magnetization |
| 1227 | abolitionist - abolitionists - slavery - 1850s - slaves | 61 | 1227_abolitionist_abolitionists_slavery_1850s |
| 1228 | 1451 - 1453 - 1456 - 1452 - siege | 61 | 1228_1451_1453_1456_1452 |
| 1229 | raider - consoles - uncharted - tomb - tombs | 61 | 1229_raider_consoles_uncharted_tomb |
| 1230 | insurgents - insurgency - troops - insurgent - war | 61 | 1230_insurgents_insurgency_troops_insurgent |
| 1231 | annexation - annexed - annexing - refugees - 1948 | 61 | 1231_annexation_annexed_annexing_refugees |
| 1232 | conferences - talks - presentations - livestreams - conference | 61 | 1232_conferences_talks_presentations_livestreams |
| 1233 | awards - idol - nominations - sang - songs | 61 | 1233_awards_idol_nominations_sang |
| 1234 | epoch - gong - times - reporters - journalism | 61 | 1234_epoch_gong_times_reporters |
| 1235 | goths - gothic - archaeologists - ancient - romanized | 61 | 1235_goths_gothic_archaeologists_ancient |
| 1236 | warriors - blazers - rockets - 76ers - hawks | 61 | 1236_warriors_blazers_rockets_76ers |
| 1237 | milk - milkshake - mayor - foster - 1978 | 61 | 1237_milk_milkshake_mayor_foster |
| 1238 | librarian - library - libraries - librarians - congress | 61 | 1238_librarian_library_libraries_librarians |
| 1239 | gerrymandering - gerrymander - gerrymandered - redistricting - constituencies | 61 | 1239_gerrymandering_gerrymander_gerrymandered_redistricting |
| 1240 | bitcoin - bitcoins - cryptocurrencies - cryptocurrency - currencies | 60 | 1240_bitcoin_bitcoins_cryptocurrencies_cryptocurrency |
| 1241 | meditations - ashram - meditation - yoga - buddha | 60 | 1241_meditations_ashram_meditation_yoga |
| 1242 | turret - tanks - ammunition - turrets - cupolas | 60 | 1242_turret_tanks_ammunition_turrets |
| 1243 | heterochromia - pigmentation - pigment - pigments - coloration | 60 | 1243_heterochromia_pigmentation_pigment_pigments |
| 1244 | libraries - library - archives - periodicals - books | 60 | 1244_libraries_library_archives_periodicals |
| 1245 | gear - presenter - presenters - motorsport - snowmobile | 60 | 1245_gear_presenter_presenters_motorsport |
| 1246 | crusade - crusaders - crusader - 1179 - 1177 | 60 | 1246_crusade_crusaders_crusader_1179 |
| 1247 | shamanism - shamans - shaman - shamanistic - shamanic | 60 | 1247_shamanism_shamans_shaman_shamanistic |
| 1248 | panther - pink - films - film - thief | 60 | 1248_panther_pink_films_film |
| 1249 | ghost - ghosts - haunted - sequels - films | 60 | 1249_ghost_ghosts_haunted_sequels |
| 1250 | marketing - advertising - market - consumers - consumer | 60 | 1250_marketing_advertising_market_consumers |
| 1251 | 1773 - tea - colonists - colonies - taxation | 60 | 1251_1773_tea_colonists_colonies |
| 1252 | eyewitnesses - retraction - biographers - historians - writings | 60 | 1252_eyewitnesses_retraction_biographers_historians |
| 1253 | cookbook - cookbooks - recipes - chef - recipe | 60 | 1253_cookbook_cookbooks_recipes_chef |
| 1254 | boxer - boxers - martial - rebellion - fought | 60 | 1254_boxer_boxers_martial_rebellion |
| 1255 | pseudonym - masked - jailed - prisoner - imprisoned | 60 | 1255_pseudonym_masked_jailed_prisoner |
| 1256 | slavery - slaves - enslaved - paternity - genealogical | 60 | 1256_slavery_slaves_enslaved_paternity |
| 1257 | hadiths - hadith - ḥadīth - ibn - imam | 60 | 1257_hadiths_hadith_ḥadīth_ibn |
| 1258 | elections - election - electoral - democratic - candidates | 60 | 1258_elections_election_electoral_democratic |
| 1259 | treatises - rabbis - textual - commentaries - rabbinic | 60 | 1259_treatises_rabbis_textual_commentaries |
| 1260 | feminism - feminist - atheism - feminists - gender | 60 | 1260_feminism_feminist_atheism_feminists |
| 1261 | boxing - punches - martial - fights - heavyweight | 60 | 1261_boxing_punches_martial_fights |
| 1262 | modularity - mathematician - conjecture - mathematicians - modular | 60 | 1262_modularity_mathematician_conjecture_mathematicians |
| 1263 | 1775 - 1780 - 1778 - 1779 - militia | 60 | 1263_1775_1780_1778_1779 |
| 1264 | hypothesis - hypotheses - statistic - statistics - tests | 60 | 1264_hypothesis_hypotheses_statistic_statistics |
| 1265 | orphanage - doors - disappearance - door - detectives | 59 | 1265_orphanage_doors_disappearance_door |
| 1266 | fairy - puppet - donkey - snail - puppeteer | 59 | 1266_fairy_puppet_donkey_snail |
| 1267 | doomsday - sequel - gameplay - multiplayer - dawn | 59 | 1267_doomsday_sequel_gameplay_multiplayer |
| 1268 | afar - militias - ceasefire - humanitarian - stationed | 59 | 1268_afar_militias_ceasefire_humanitarian |
| 1269 | tennis - slams - doubles - quarterfinal - tournaments | 59 | 1269_tennis_slams_doubles_quarterfinal |
| 1270 | barricades - barricade - escape - escapes - murderer | 59 | 1270_barricades_barricade_escape_escapes |
| 1271 | jong - heir - eldest - successor - hyun | 59 | 1271_jong_heir_eldest_successor |
| 1272 | firearm - firearms - handgun - guns - gun | 59 | 1272_firearm_firearms_handgun_guns |
| 1273 | colonists - colony - colonies - settlers - voyage | 59 | 1273_colonists_colony_colonies_settlers |
| 1274 | nazi - 1932 - 1938 - triumph - 1934 | 59 | 1274_nazi_1932_1938_triumph |
| 1275 | retailer - groceries - mart - store - closing | 59 | 1275_retailer_groceries_mart_store |
| 1276 | photosynthesis - photosynthetic - respiration - chloroplasts - chlorophyll | 59 | 1276_photosynthesis_photosynthetic_respiration_chloroplasts |
| 1277 | mission - missions - sequel - cruise - fallout | 59 | 1277_mission_missions_sequel_cruise |
| 1278 | rainbow - rainbows - violet - colours - refraction | 59 | 1278_rainbow_rainbows_violet_colours |
| 1279 | hitchhiker - novels - hitchhiking - paperback - hitch | 59 | 1279_hitchhiker_novels_hitchhiking_paperback |
| 1280 | paintings - painter - painting - artists - exhibitions | 59 | 1280_paintings_painter_painting_artists |
| 1281 | tributaries - tributary - headwaters - river - lake | 59 | 1281_tributaries_tributary_headwaters_river |
| 1282 | soccer - football - players - games - leagues | 59 | 1282_soccer_football_players_games |
| 1283 | regiment - cavalry - infantry - battalions - retreated | 59 | 1283_regiment_cavalry_infantry_battalions |
| 1284 | ontological - ontology - ontologically - ontologies - categories | 59 | 1284_ontological_ontology_ontologically_ontologies |
| 1285 | flags - parks - resorts - rebranded - mascot | 59 | 1285_flags_parks_resorts_rebranded |
| 1286 | sentenced - convicted - arson - crimes - arsons | 59 | 1286_sentenced_convicted_arson_crimes |
| 1287 | art - artistic - artists - modernist - surrealists | 59 | 1287_art_artistic_artists_modernist |
| 1288 | shamrock - wrestled - rematch - punches - fighters | 59 | 1288_shamrock_wrestled_rematch_punches |
| 1289 | broadcasting - stations - broadcasts - channels - broadcast | 59 | 1289_broadcasting_stations_broadcasts_channels |
| 1290 | printers - printer - prints - printing - inkjet | 58 | 1290_printers_printer_prints_printing |
| 1291 | traders - colonial - trading - monopolise - 1609 | 58 | 1291_traders_colonial_trading_monopolise |
| 1292 | violin - violins - violinists - violinist - instrument | 58 | 1292_violin_violins_violinists_violinist |
| 1293 | mythological - prophecy - patricide - prophet - oracles | 58 | 1293_mythological_prophecy_patricide_prophet |
| 1294 | offside - officiating - penalty - penalties - opponents | 58 | 1294_offside_officiating_penalty_penalties |
| 1295 | candidates - candidate - minister - election - elected | 58 | 1295_candidates_candidate_minister_election |
| 1296 | cyclists - cyclist - cycling - tour - riders | 58 | 1296_cyclists_cyclist_cycling_tour |
| 1297 | hello - greeting - cat - cuteness - ukiyo | 58 | 1297_hello_greeting_cat_cuteness |
| 1298 | investigation - jury - coroner - tabloid - alleged | 58 | 1298_investigation_jury_coroner_tabloid |
| 1299 | jong - yong - hui - taek - ko | 58 | 1299_jong_yong_hui_taek |
| 1300 | terrorism - terrorist - terrorists - terror - bombings | 58 | 1300_terrorism_terrorist_terrorists_terror |
| 1301 | compass - compasses - magnetometers - geomagnetic - magnetic | 58 | 1301_compass_compasses_magnetometers_geomagnetic |
| 1302 | famine - crops - agrarian - agricultural - farmers | 58 | 1302_famine_crops_agrarian_agricultural |
| 1303 | etymology - isles - conquered - isle - mainland | 58 | 1303_etymology_isles_conquered_isle |
| 1304 | guitarists - band - toured - bands - fronted | 58 | 1304_guitarists_band_toured_bands |
| 1305 | retailers - seven - shops - stores - store | 58 | 1305_retailers_seven_shops_stores |
| 1306 | polygamists - polygamous - polygamist - polygamy - marriages | 58 | 1306_polygamists_polygamous_polygamist_polygamy |
| 1307 | cosmos - astronomers - astronomer - astronomy - astronomical | 58 | 1307_cosmos_astronomers_astronomer_astronomy |
| 1308 | refraction - refractive - optics - wavelengths - reflectivity | 58 | 1308_refraction_refractive_optics_wavelengths |
| 1309 | twilight - episodes - supernatural - zone - syndication | 58 | 1309_twilight_episodes_supernatural_zone |
| 1310 | amazon - cloud - apple - echo - automation | 57 | 1310_amazon_cloud_apple_echo |
| 1311 | diplomacy - geopolitical - secretary - 1972 - statesman | 57 | 1311_diplomacy_geopolitical_secretary_1972 |
| 1312 | trademarked - trademark - brand - della - handbags | 57 | 1312_trademarked_trademark_brand_della |
| 1313 | ceasefire - peacekeeping - refugees - conflict - war | 57 | 1313_ceasefire_peacekeeping_refugees_conflict |
| 1314 | neutrinos - neutrino - antineutrinos - antineutrino - leptons | 57 | 1314_neutrinos_neutrino_antineutrinos_antineutrino |
| 1315 | spaceflight - blue - launches - rocket - starship | 57 | 1315_spaceflight_blue_launches_rocket |
| 1316 | heir - eldest - emperor - empress - grandchild | 57 | 1316_heir_eldest_emperor_empress |
| 1317 | socialist - socialism - socialists - democratic - liberalism | 57 | 1317_socialist_socialism_socialists_democratic |
| 1318 | resolver - resolving - resolve - domains - authoritative | 57 | 1318_resolver_resolving_resolve_domains |
| 1319 | waits - musician - singer - singers - songwriter | 57 | 1319_waits_musician_singer_singers |
| 1320 | aviation - pilots - airplane - pilot - flew | 57 | 1320_aviation_pilots_airplane_pilot |
| 1321 | rating - rated - grades - grade - score | 57 | 1321_rating_rated_grades_grade |
| 1322 | stations - radio - station - broadcasts - broadcasting | 57 | 1322_stations_radio_station_broadcasts |
| 1323 | sheikh - prince - sultan - heir - princes | 57 | 1323_sheikh_prince_sultan_heir |
| 1324 | conditioning - conditioned - stimuli - stimulus - reflex | 57 | 1324_conditioning_conditioned_stimuli_stimulus |
| 1325 | cube - cubes - dodecahedron - puzzles - 3d | 57 | 1325_cube_cubes_dodecahedron_puzzles |
| 1326 | nominations - awards - nominated - award - finales | 57 | 1326_nominations_awards_nominated_award |
| 1327 | bounty - adrift - boatswain - seaman - voyage | 57 | 1327_bounty_adrift_boatswain_seaman |
| 1328 | tectonics - tectonic - mantle - crust - plates | 57 | 1328_tectonics_tectonic_mantle_crust |
| 1329 | jinn - jinni - ibn - demonic - deities | 57 | 1329_jinn_jinni_ibn_demonic |
| 1330 | armada - fleet - fleets - sailed - 1596 | 57 | 1330_armada_fleet_fleets_sailed |
| 1331 | foie - geese - goose - gras - poultry | 57 | 1331_foie_geese_goose_gras |
| 1332 | goalkeeping - premiership - goalkeeper - arsenal - keeper | 57 | 1332_goalkeeping_premiership_goalkeeper_arsenal |
| 1333 | peregrines - peregrine - falcon - falconry - bird | 56 | 1333_peregrines_peregrine_falcon_falconry |
| 1334 | warship - frigate - frigates - naval - sailed | 56 | 1334_warship_frigate_frigates_naval |
| 1335 | 731 - civilians - plague - pathogens - units | 56 | 1335_731_civilians_plague_pathogens |
| 1336 | commodore - hardware - consoles - x86 - emulation | 56 | 1336_commodore_hardware_consoles_x86 |
| 1337 | laurel - hardy - comedies - comedians - comic | 56 | 1337_laurel_hardy_comedies_comedians |
| 1338 | eggs - egg - yolks - yolk - eggshell | 56 | 1338_eggs_egg_yolks_yolk |
| 1339 | toymaker - franchise - toys - company - monopoly | 56 | 1339_toymaker_franchise_toys_company |
| 1340 | vampire - showrunner - vampires - slayer - episodes | 56 | 1340_vampire_showrunner_vampires_slayer |
| 1341 | scattering - sciences - physicists - discovered - wavelengths | 56 | 1341_scattering_sciences_physicists_discovered |
| 1342 | voyages - voyage - literature - novels - journeys | 56 | 1342_voyages_voyage_literature_novels |
| 1343 | besieged - retreating - recaptured - retreated - reinforcements | 56 | 1343_besieged_retreating_recaptured_retreated |
| 1344 | singularity - superintelligence - technological - 2030 - supercomputers | 56 | 1344_singularity_superintelligence_technological_2030 |
| 1345 | coli - bacterial - bacterium - bacteria - microbiota | 56 | 1345_coli_bacterial_bacterium_bacteria |
| 1346 | propofol - midazolam - benzodiazepine - hospitalized - manslaughter | 56 | 1346_propofol_midazolam_benzodiazepine_hospitalized |
| 1347 | peacemaker - suicide - filmmakers - cast - cameo | 56 | 1347_peacemaker_suicide_filmmakers_cast |
| 1348 | coats - mafia - massacre - perpetrators - killers | 56 | 1348_coats_mafia_massacre_perpetrators |
| 1349 | howl - poetry - poet - poems - poem | 56 | 1349_howl_poetry_poet_poems |
| 1350 | 1080p - digital - cable - cables - resolution | 56 | 1350_1080p_digital_cable_cables |
| 1351 | federalist - federalists - confederation - republicanism - federal | 56 | 1351_federalist_federalists_confederation_republicanism |
| 1352 | adobe - formats - document - acrobat - documents | 56 | 1352_adobe_formats_document_acrobat |
| 1353 | cherry - blossom - cherries - blossoms - orchards | 56 | 1353_cherry_blossom_cherries_blossoms |
| 1354 | 1939 - 1942 - 1930s - affair - 1940 | 56 | 1354_1939_1942_1930s_affair |
| 1355 | titans - superheroine - superheroes - superhero - comics | 56 | 1355_titans_superheroine_superheroes_superhero |
| 1356 | likens - jenny - tormented - inflicting - endured | 56 | 1356_likens_jenny_tormented_inflicting |
| 1357 | malls - shops - mall - centres - centre | 56 | 1357_malls_shops_mall_centres |
| 1358 | glucose - insulin - diabetes - gluconeogenesis - pancreas | 56 | 1358_glucose_insulin_diabetes_gluconeogenesis |
| 1359 | niger - inhabitants - migrants - natal - guinea | 56 | 1359_niger_inhabitants_migrants_natal |
| 1360 | unconstitutional - marriages - amendment - marriage - constitutional | 56 | 1360_unconstitutional_marriages_amendment_marriage |
| 1361 | hound - detective - hounds - bbc - episodes | 56 | 1361_hound_detective_hounds_bbc |
| 1362 | blackface - minstrel - minstrels - blackness - performers | 56 | 1362_blackface_minstrel_minstrels_blackness |
| 1363 | diamond - diamonds - gemstone - jeweler - jewelers | 56 | 1363_diamond_diamonds_gemstone_jeweler |
| 1364 | delle - bourgeois - bourgeoisie - piazza - della | 56 | 1364_delle_bourgeois_bourgeoisie_piazza |
| 1365 | hyperloop - musk - pod - pods - 400m | 56 | 1365_hyperloop_musk_pod_pods |
| 1366 | data - datasets - analytics - databases - database | 56 | 1366_data_datasets_analytics_databases |
| 1367 | punk - flag - bands - black - band | 55 | 1367_punk_flag_bands_black |
| 1368 | writer - junkie - naked - writing - lunch | 55 | 1368_writer_junkie_naked_writing |
| 1369 | tennis - semifinal - quarterfinal - semifinals - doubles | 55 | 1369_tennis_semifinal_quarterfinal_semifinals |
| 1370 | pharmaceuticals - pharmaceutical - biotech - stocks - stock | 55 | 1370_pharmaceuticals_pharmaceutical_biotech_stocks |
| 1371 | pixels - resolution - monitors - resolutions - monitor | 55 | 1371_pixels_resolution_monitors_resolutions |
| 1372 | kerosene - fuels - diesel - refinery - fuel | 55 | 1372_kerosene_fuels_diesel_refinery |
| 1373 | bonsai - trees - plantings - cultivation - exhibitions | 55 | 1373_bonsai_trees_plantings_cultivation |
| 1374 | tsarina - tsar - gunmen - gunshots - gunshot | 55 | 1374_tsarina_tsar_gunmen_gunshots |
| 1375 | zoom - privacy - ventures - consulting - phone | 55 | 1375_zoom_privacy_ventures_consulting |
| 1376 | jagged - albums - songwriter - pill - songs | 55 | 1376_jagged_albums_songwriter_pill |
| 1377 | holocaust - nazi - prosecution - prosecutors - extradition | 55 | 1377_holocaust_nazi_prosecution_prosecutors |
| 1378 | grandmaster - grandmasters - fide - titles - tournaments | 55 | 1378_grandmaster_grandmasters_fide_titles |
| 1379 | poet - poetry - poems - poets - stanzas | 55 | 1379_poet_poetry_poems_poets |
| 1380 | colorblindness - blindness - colorblind - blind - trichromatic | 55 | 1380_colorblindness_blindness_colorblind_blind |
| 1381 | guinea - niger - equatorial - equator - bordered | 55 | 1381_guinea_niger_equatorial_equator |
| 1382 | population - municipalities - cities - city - towns | 55 | 1382_population_municipalities_cities_city |
| 1383 | sim - unlocked - carriers - telecommunications - cellular | 55 | 1383_sim_unlocked_carriers_telecommunications |
| 1384 | homeopathic - homeopaths - homeopathy - medicines - medicine | 55 | 1384_homeopathic_homeopaths_homeopathy_medicines |
| 1385 | vampirism - vampire - vampiric - vampires - undead | 55 | 1385_vampirism_vampire_vampiric_vampires |
| 1386 | convicted - airlines - airline - conviction - arrested | 55 | 1386_convicted_airlines_airline_conviction |
| 1387 | albums - album - duets - singer - band | 55 | 1387_albums_album_duets_singer |
| 1388 | rapper - tribe - rap - tip - rapping | 55 | 1388_rapper_tribe_rap_tip |
| 1389 | lee - moody - fallen - songwriting - band | 55 | 1389_lee_moody_fallen_songwriting |
| 1390 | parliamentarian - minister - français - politician - councillor | 55 | 1390_parliamentarian_minister_français_politician |
| 1391 | poet - poems - poem - poets - poetry | 55 | 1391_poet_poems_poem_poets |
| 1392 | gas - soviet - oil - sanctions - supply | 55 | 1392_gas_soviet_oil_sanctions |
| 1393 | eclipse - eclipses - lunar - eclipsed - moon | 55 | 1393_eclipse_eclipses_lunar_eclipsed |
| 1394 | brothers - nick - band - songs - album | 55 | 1394_brothers_nick_band_songs |
| 1395 | twins - twin - twinning - monozygotic - duplications | 55 | 1395_twins_twin_twinning_monozygotic |
| 1396 | biotechnology - pharmaceuticals - biotech - companies - agro | 55 | 1396_biotechnology_pharmaceuticals_biotech_companies |
| 1397 | sim - create - simulation - gameplay - traits | 55 | 1397_sim_create_simulation_gameplay |
| 1398 | duet - duets - sang - song - songs | 55 | 1398_duet_duets_sang_song |
| 1399 | ibn - sheikh - sultanate - mecca - mosque | 54 | 1399_ibn_sheikh_sultanate_mecca |
| 1400 | snaps - snap - messaging - sharing - chat | 54 | 1400_snaps_snap_messaging_sharing |
| 1401 | dietary - diet - diets - cardiovascular - cholesterol | 54 | 1401_dietary_diet_diets_cardiovascular |
| 1402 | stem - disciplines - majors - degree - engineering | 54 | 1402_stem_disciplines_majors_degree |
| 1403 | poverty - deprivation - poor - welfare - income | 54 | 1403_poverty_deprivation_poor_welfare |
| 1404 | simulations - simulation - simulating - simulated - stochastic | 54 | 1404_simulations_simulation_simulating_simulated |
| 1405 | tether - treasuries - exchanges - cryptocurrencies - cryptocurrency | 54 | 1405_tether_treasuries_exchanges_cryptocurrencies |
| 1406 | luxury - brands - brand - valuation - valuable | 54 | 1406_luxury_brands_brand_valuation |
| 1407 | lynch - touchdowns - rushing - touchdown - quarterback | 54 | 1407_lynch_touchdowns_rushing_touchdown |
| 1408 | celebrations - festival - celebrated - festivities - rituals | 54 | 1408_celebrations_festival_celebrated_festivities |
| 1409 | missionaries - charity - nuns - orphanages - hospices | 54 | 1409_missionaries_charity_nuns_orphanages |
| 1410 | languages - language - mandarin - lingua - multilingual | 54 | 1410_languages_language_mandarin_lingua |
| 1411 | apartheid - histories - chieftains - tactics - historians | 54 | 1411_apartheid_histories_chieftains_tactics |
| 1412 | daredevil - marvel - superhero - miniseries - episodes | 54 | 1412_daredevil_marvel_superhero_miniseries |
| 1413 | representatives - elects - voters - congressional - commissioner | 54 | 1413_representatives_elects_voters_congressional |
| 1414 | novelist - novels - biography - writer - literature | 54 | 1414_novelist_novels_biography_writer |
| 1415 | cosmetics - salons - skincare - chemists - products | 54 | 1415_cosmetics_salons_skincare_chemists |
| 1416 | wells - literature - novels - writer - author | 54 | 1416_wells_literature_novels_writer |
| 1417 | elephant - showman - exhibit - exhibited - surgeon | 54 | 1417_elephant_showman_exhibit_exhibited |
| 1418 | rebelled - rulers - kingdoms - recaptured - ruled | 54 | 1418_rebelled_rulers_kingdoms_recaptured |
| 1419 | seeding - clouds - seed - cloud - drought | 54 | 1419_seeding_clouds_seed_cloud |
| 1420 | dashes - hyphens - hyphenated - hyphen - dash | 54 | 1420_dashes_hyphens_hyphenated_hyphen |
| 1421 | panda - pandas - bamboo - zoological - herbivorous | 54 | 1421_panda_pandas_bamboo_zoological |
| 1422 | stations - broadcasts - radio - station - fm | 54 | 1422_stations_broadcasts_radio_station |
| 1423 | sentencing - sentenced - arrest - conviction - judge | 54 | 1423_sentencing_sentenced_arrest_conviction |
| 1424 | knights - duchy - papacy - nobles - feudal | 54 | 1424_knights_duchy_papacy_nobles |
| 1425 | buffalo - cowboy - bison - bull - 1872 | 54 | 1425_buffalo_cowboy_bison_bull |
| 1426 | knight - moon - villain - werewolf - sidekick | 53 | 1426_knight_moon_villain_werewolf |
| 1427 | sg - premiere - spinoff - episodes - starburst | 53 | 1427_sg_premiere_spinoff_episodes |
| 1428 | turkey - annexation - invaded - invasion - enosis | 53 | 1428_turkey_annexation_invaded_invasion |
| 1429 | ketogenic - dietary - diet - diets - carbohydrates | 53 | 1429_ketogenic_dietary_diet_diets |
| 1430 | ray - cinema - filmmaker - films - filmmakers | 53 | 1430_ray_cinema_filmmaker_films |
| 1431 | leprosy - leper - pathogenicity - leprae - disease | 53 | 1431_leprosy_leper_pathogenicity_leprae |
| 1432 | waves - compressional - compression - compressibility - wave | 53 | 1432_waves_compressional_compression_compressibility |
| 1433 | paintings - painting - artwork - cans - artworks | 53 | 1433_paintings_painting_artwork_cans |
| 1434 | ubiquitous - technologies - wireless - wirelessly - internet | 53 | 1434_ubiquitous_technologies_wireless_wirelessly |
| 1435 | rituals - pagans - pagan - paganism - celebrations | 53 | 1435_rituals_pagans_pagan_paganism |
| 1436 | acre - acres - yard - yards - area | 53 | 1436_acre_acres_yard_yards |
| 1437 | touchdowns - receptions - quarterback - yards - touchdown | 53 | 1437_touchdowns_receptions_quarterback_yards |
| 1438 | poet - poems - poetry - poem - shah | 53 | 1438_poet_poems_poetry_poem |
| 1439 | samurai - shogun - mangaka - fictionalization - novelist | 53 | 1439_samurai_shogun_mangaka_fictionalization |
| 1440 | strings - theories - string - theory - superstring | 53 | 1440_strings_theories_string_theory |
| 1441 | fables - fable - tales - poems - proverbs | 53 | 1441_fables_fable_tales_poems |
| 1442 | computing - analytical - mathematician - computation - mathematics | 53 | 1442_computing_analytical_mathematician_computation |
| 1443 | generative - transformer - neural - learning - trained | 53 | 1443_generative_transformer_neural_learning |
| 1444 | guitarist - bassist - instrumentalist - musicians - drummer | 53 | 1444_guitarist_bassist_instrumentalist_musicians |
| 1445 | prions - prion - proteins - protein - amyloidosis | 53 | 1445_prions_prion_proteins_protein |
| 1446 | happiness - wellbeing - unhappiness - happier - satisfaction | 53 | 1446_happiness_wellbeing_unhappiness_happier |
| 1447 | bulbs - bulb - lamps - lamp - incandescent | 53 | 1447_bulbs_bulb_lamps_lamp |
| 1448 | airplay - tv - apple - televisions - streaming | 53 | 1448_airplay_tv_apple_televisions |
| 1449 | bear - robin - bears - teddy - rabbit | 53 | 1449_bear_robin_bears_teddy |
| 1450 | newspapers - newspaper - periodicals - gazette - tabloid | 53 | 1450_newspapers_newspaper_periodicals_gazette |
| 1451 | sepoys - rebellion - uprising - 1857 - uprisings | 53 | 1451_sepoys_rebellion_uprising_1857 |
| 1452 | uncle - sam - relatives - 1886 - 1922 | 52 | 1452_uncle_sam_relatives_1886 |
| 1453 | campaigned - politician - governor - long - impeached | 52 | 1453_campaigned_politician_governor_long |
| 1454 | vertigo - dizziness - vestibular - tinnitus - migraine | 52 | 1454_vertigo_dizziness_vestibular_tinnitus |
| 1455 | dowager - empress - emperors - empresses - emperor | 52 | 1455_dowager_empress_emperors_empresses |
| 1456 | translator - translators - translations - translating - translates | 52 | 1456_translator_translators_translations_translating |
| 1457 | presidents - presidential - presidency - president - polls | 52 | 1457_presidents_presidential_presidency_president |
| 1458 | episodes - files - fox - storylines - comics | 52 | 1458_episodes_files_fox_storylines |
| 1459 | spaghetti - pasta - monster - creationist - creationism | 52 | 1459_spaghetti_pasta_monster_creationist |
| 1460 | aunt - uncle - breakfast - mammy - doll | 52 | 1460_aunt_uncle_breakfast_mammy |
| 1461 | rating - critics - reviews - review - marvel | 52 | 1461_rating_critics_reviews_review |
| 1462 | billionaire - lawsuit - founder - shares - entrepreneur | 52 | 1462_billionaire_lawsuit_founder_shares |
| 1463 | flow - experiences - performance - motivation - psychology | 52 | 1463_flow_experiences_performance_motivation |
| 1464 | valentine - celebrated - holiday - holidays - saint | 52 | 1464_valentine_celebrated_holiday_holidays |
| 1465 | twins - brothers - 1950s - 1960s - biographical | 52 | 1465_twins_brothers_1950s_1960s |
| 1466 | broadway - musical - actress - musicals - audition | 52 | 1466_broadway_musical_actress_musicals |
| 1467 | mouse - mice - cursor - joystick - trackball | 52 | 1467_mouse_mice_cursor_joystick |
| 1468 | hook - jack - crocodile - pan - nursery | 52 | 1468_hook_jack_crocodile_pan |
| 1469 | satellites - satellite - spacecraft - orbit - constellations | 52 | 1469_satellites_satellite_spacecraft_orbit |
| 1470 | golfers - golf - tournaments - golfing - tournament | 52 | 1470_golfers_golf_tournaments_golfing |
| 1471 | legions - heresy - legion - crusade - factions | 52 | 1471_legions_heresy_legion_crusade |
| 1472 | barcodes - barcode - scanners - code - scanner | 52 | 1472_barcodes_barcode_scanners_code |
| 1473 | atoms - atom - atomic - quantum - particles | 52 | 1473_atoms_atom_atomic_quantum |
| 1474 | opium - smuggling - narcotics - drug - addiction | 52 | 1474_opium_smuggling_narcotics_drug |
| 1475 | indigenous - spirit - spirits - natives - aboriginal | 52 | 1475_indigenous_spirit_spirits_natives |
| 1476 | evil - eye - gaze - eyes - glare | 52 | 1476_evil_eye_gaze_eyes |
| 1477 | smartwatch - watches - smartwatches - apple - wrist | 52 | 1477_smartwatch_watches_smartwatches_apple |
| 1478 | glitter - songs - remixes - vocals - punk | 51 | 1478_glitter_songs_remixes_vocals |
| 1479 | paramount - films - 1957 - movies - 1942 | 51 | 1479_paramount_films_1957_movies |
| 1480 | documentaries - bbc - planet - documentary - nature | 51 | 1480_documentaries_bbc_planet_documentary |
| 1481 | 1848 - dictator - rebelled - insurgent - 1846 | 51 | 1481_1848_dictator_rebelled_insurgent |
| 1482 | battalions - regiments - battalion - regiment - platoons | 51 | 1482_battalions_regiments_battalion_regiment |
| 1483 | polytheistic - shamanism - shamanistic - monotheists - monotheistic | 51 | 1483_polytheistic_shamanism_shamanistic_monotheists |
| 1484 | assassination - tortured - raped - incident - fedayeen | 51 | 1484_assassination_tortured_raped_incident |
| 1485 | tsar - tsars - cathedrals - cathedral - palaces | 51 | 1485_tsar_tsars_cathedrals_cathedral |
| 1486 | data - datasets - analytics - statistics - statistician | 51 | 1486_data_datasets_analytics_statistics |
| 1487 | transformer - transformers - coils - windings - inductance | 51 | 1487_transformer_transformers_coils_windings |
| 1488 | cruises - cruise - seas - ships - sailing | 51 | 1488_cruises_cruise_seas_ships |
| 1489 | colonists - settlers - colonist - smith - colony | 51 | 1489_colonists_settlers_colonist_smith |
| 1490 | fascist - fascism - fascists - 1930s - 1930 | 51 | 1490_fascist_fascism_fascists_1930s |
| 1491 | ferry - songwriter - albums - toured - tour | 51 | 1491_ferry_songwriter_albums_toured |
| 1492 | attractiveness - aesthetics - beauty - aesthetic - aesthetically | 51 | 1492_attractiveness_aesthetics_beauty_aesthetic |
| 1493 | tribes - tribe - 1876 - tribal - treaties | 51 | 1493_tribes_tribe_1876_tribal |
| 1494 | 1934 - robbery - robbers - gunfight - shootout | 51 | 1494_1934_robbery_robbers_gunfight |
| 1495 | rosary - devotions - liturgical - prayers - prayer | 51 | 1495_rosary_devotions_liturgical_prayers |
| 1496 | airborne - airfields - soviet - regiments - military | 51 | 1496_airborne_airfields_soviet_regiments |
| 1497 | tenacious - destiny - guitarist - band - bands | 51 | 1497_tenacious_destiny_guitarist_band |
| 1498 | feud - reigns - rumble - wrestling - wrestler | 51 | 1498_feud_reigns_rumble_wrestling |
| 1499 | coronavirus - diagnosed - positive - vaccinated - flu | 51 | 1499_coronavirus_diagnosed_positive_vaccinated |
| 1500 | decapitated - murders - detectives - homicide - murder | 51 | 1500_decapitated_murders_detectives_homicide |
| 1501 | corruption - corrupt - bribes - bribery - bribe | 51 | 1501_corruption_corrupt_bribes_bribery |
| 1502 | rooms - room - palace - furnishings - ballroom | 51 | 1502_rooms_room_palace_furnishings |
| 1503 | lama - lamas - monks - monasteries - monastic | 51 | 1503_lama_lamas_monks_monasteries |
| 1504 | warehouse - warehouses - retailer - retail - wholesale | 51 | 1504_warehouse_warehouses_retailer_retail |
| 1505 | languages - dialects - ethnic - speak - language | 51 | 1505_languages_dialects_ethnic_speak |
| 1506 | scored - goals - goalscorer - scoring - goalscorers | 51 | 1506_scored_goals_goalscorer_scoring |
| 1507 | consciousness - conscious - unconscious - perceive - awareness | 50 | 1507_consciousness_conscious_unconscious_perceive |
| 1508 | mansion - mansions - estate - residence - richest | 50 | 1508_mansion_mansions_estate_residence |
| 1509 | mp3 - audio - formats - codecs - bitrate | 50 | 1509_mp3_audio_formats_codecs |
| 1510 | dragons - evil - demigod - demigods - villains | 50 | 1510_dragons_evil_demigod_demigods |
| 1511 | citizen - citizens - sovereign - sovereigns - sovereignty | 50 | 1511_citizen_citizens_sovereign_sovereigns |
| 1512 | draft - undrafted - deadline - eligibility - early | 50 | 1512_draft_undrafted_deadline_eligibility |
| 1513 | redheads - redhead - reddish - ginger - hair | 50 | 1513_redheads_redhead_reddish_ginger |
| 1514 | measles - vaccines - vaccination - vaccine - vaccinated | 50 | 1514_measles_vaccines_vaccination_vaccine |
| 1515 | literature - novels - novel - peace - novelists | 50 | 1515_literature_novels_novel_peace |
| 1516 | microwaves - microwave - oven - ovens - cooking | 50 | 1516_microwaves_microwave_oven_ovens |
| 1517 | cranberries - concert - albums - album - 1994 | 50 | 1517_cranberries_concert_albums_album |
| 1518 | pope - papal - popes - papacy - della | 50 | 1518_pope_papal_popes_papacy |
| 1519 | voyagers - heliosphere - interstellar - heliocentric - solar | 50 | 1519_voyagers_heliosphere_interstellar_heliocentric |
| 1520 | album - songs - vocals - song - remixes | 50 | 1520_album_songs_vocals_song |
| 1521 | dead - concert - burial - lyricists - psychedelic | 50 | 1521_dead_concert_burial_lyricists |
| 1522 | athlete - olympic - athletic - athletes - decathlon | 50 | 1522_athlete_olympic_athletic_athletes |
| 1523 | motorsport - prix - tyres - racing - qualifying | 50 | 1523_motorsport_prix_tyres_racing |
| 1524 | acquitted - murdered - prosecutors - prosecution - criss | 50 | 1524_acquitted_murdered_prosecutors_prosecution |
| 1525 | disenfranchisement - disenfranchising - disenfranchised - disenfranchise - suffrage | 50 | 1525_disenfranchisement_disenfranchising_disenfranchised_disenfranchise |
| 1526 | graffiti - screenplay - cinematographers - film - godfather | 50 | 1526_graffiti_screenplay_cinematographers_film |
| 1527 | cycling - bicycles - bikes - biking - bicycling | 50 | 1527_cycling_bicycles_bikes_biking |
| 1528 | halo - chief - 343 - master - guardians | 50 | 1528_halo_chief_343_master |
| 1529 | rockstar - acquisitions - owns - gaming - acquire | 50 | 1529_rockstar_acquisitions_owns_gaming |
| 1530 | classroom - classrooms - cho - students - student | 50 | 1530_classroom_classrooms_cho_students |
| 1531 | albums - concert - toured - band - songs | 50 | 1531_albums_concert_toured_band |
| 1532 | golf - scoring - rounds - tournament - championship | 50 | 1532_golf_scoring_rounds_tournament |
| 1533 | hunger - uprisings - rebellion - capitol - rebels | 50 | 1533_hunger_uprisings_rebellion_capitol |
| 1534 | famine - famines - starvation - rice - rations | 50 | 1534_famine_famines_starvation_rice |
| 1535 | anthem - anthems - hymn - stanza - stanzas | 50 | 1535_anthem_anthems_hymn_stanza |
| 1536 | nations - summit - eu - agreements - summits | 50 | 1536_nations_summit_eu_agreements |
| 1537 | commercials - commercial - advertisements - advertisement - advertising | 50 | 1537_commercials_commercial_advertisements_advertisement |
| 1538 | bridges - bridge - viaducts - arches - truss | 50 | 1538_bridges_bridge_viaducts_arches |
| 1539 | vulgar - profanity - slang - intercourse - pejorative | 50 | 1539_vulgar_profanity_slang_intercourse |
| 1540 | mailbox - uploading - cloud - uploads - folders | 50 | 1540_mailbox_uploading_cloud_uploads |
| 1541 | predator - predators - alien - creature - aliens | 50 | 1541_predator_predators_alien_creature |
| 1542 | 1852 - novels - novel - cabin - literature | 50 | 1542_1852_novels_novel_cabin |
| 1543 | hijab - sharia - fashion - veils - dress | 50 | 1543_hijab_sharia_fashion_veils |
| 1544 | capsaicin - capsaicinoids - peppers - chili - spicy | 50 | 1544_capsaicin_capsaicinoids_peppers_chili |
| 1545 | park - episodes - south - studios - spontaneity | 50 | 1545_park_episodes_south_studios |
| 1546 | tornadoes - tornado - storms - thunderstorms - thunderstorm | 49 | 1546_tornadoes_tornado_storms_thunderstorms |
| 1547 | restaurants - restaurant - franchisees - chick - franchise | 49 | 1547_restaurants_restaurant_franchisees_chick |
| 1548 | blockchains - blockchain - ledgers - cryptocurrencies - decentralization | 49 | 1548_blockchains_blockchain_ledgers_cryptocurrencies |
| 1549 | concert - toured - band - concerts - bands | 49 | 1549_concert_toured_band_concerts |
| 1550 | dew - cola - flavors - soda - beverage | 49 | 1550_dew_cola_flavors_soda |
| 1551 | circumcision - circumcise - circumcised - uncircumcised - foreskin | 49 | 1551_circumcision_circumcise_circumcised_uncircumcised |
| 1552 | sultan - shah - khan - sultanate - rocket | 49 | 1552_sultan_shah_khan_sultanate |
| 1553 | priesthood - priest - synagogue - temple - sect | 49 | 1553_priesthood_priest_synagogue_temple |
| 1554 | ape - apes - jungle - gorilla - gorillas | 49 | 1554_ape_apes_jungle_gorilla |
| 1555 | blockbuster - amazon - subscription - subscribers - streaming | 49 | 1555_blockbuster_amazon_subscription_subscribers |
| 1556 | vogue - magazine - magazines - haute - fashion | 49 | 1556_vogue_magazine_magazines_haute |
| 1557 | cocoa - farmers - commodities - chocolate - countries | 49 | 1557_cocoa_farmers_commodities_chocolate |
| 1558 | anime - cartoon - cartoons - airing - samurai | 49 | 1558_anime_cartoon_cartoons_airing |
| 1559 | rockabilly - chorus - songwriter - singing - musicians | 49 | 1559_rockabilly_chorus_songwriter_singing |
| 1560 | brackets - parentheses - bracket - parenthesis - bracketed | 49 | 1560_brackets_parentheses_bracket_parenthesis |
| 1561 | gulag - soviet - memoirs - novel - archipelago | 49 | 1561_gulag_soviet_memoirs_novel |
| 1562 | jong - coma - detained - postmortem - tortured | 49 | 1562_jong_coma_detained_postmortem |
| 1563 | dictator - corruption - unrest - corrupt - za | 49 | 1563_dictator_corruption_unrest_corrupt |
| 1564 | scoliosis - spine - vertebral - vertebra - spinal | 49 | 1564_scoliosis_spine_vertebral_vertebra |
| 1565 | festival - tomorrow - tickets - performers - organizers | 49 | 1565_festival_tomorrow_tickets_performers |
| 1566 | niger - kingdoms - ethnicities - kingdom - ancestor | 49 | 1566_niger_kingdoms_ethnicities_kingdom |
| 1567 | plc - programmable - microcontrollers - controllers - microcontroller | 49 | 1567_plc_programmable_microcontrollers_controllers |
| 1568 | monopoly - monopolies - games - cash - cards | 49 | 1568_monopoly_monopolies_games_cash |
| 1569 | productions - cola - merger - coca - corporation | 49 | 1569_productions_cola_merger_coca |
| 1570 | gambling - gambler - gamble - gamblers - fortune | 49 | 1570_gambling_gambler_gamble_gamblers |
| 1571 | donuts - doughnuts - donut - doughnut - restaurant | 49 | 1571_donuts_doughnuts_donut_doughnut |
| 1572 | billionaires - richest - billionaire - wealthiest - billion | 49 | 1572_billionaires_richest_billionaire_wealthiest |
| 1573 | lent - fasting - easter - liturgy - liturgical | 49 | 1573_lent_fasting_easter_liturgy |
| 1574 | novels - books - readership - bestsellers - readers | 49 | 1574_novels_books_readership_bestsellers |
| 1575 | ibn - emir - mecca - medina - emirate | 48 | 1575_ibn_emir_mecca_medina |
| 1576 | ministers - minister - secretary - peerage - cabinet | 48 | 1576_ministers_minister_secretary_peerage |
| 1577 | kratom - overdose - overdoses - alkaloids - toxicity | 48 | 1577_kratom_overdose_overdoses_alkaloids |
| 1578 | knight - homicide - manslaughter - murderer - bail | 48 | 1578_knight_homicide_manslaughter_murderer |
| 1579 | phase - phases - transformer - electrical - voltages | 48 | 1579_phase_phases_transformer_electrical |
| 1580 | girdle - knights - chivalry - knight - knightly | 48 | 1580_girdle_knights_chivalry_knight |
| 1581 | mix - albums - little - remix - singles | 48 | 1581_mix_albums_little_remix |
| 1582 | shamrock - annals - priest - saint - apostles | 48 | 1582_shamrock_annals_priest_saint |
| 1583 | aneurysms - aneurysm - coronary - prognosis - vasculitis | 48 | 1583_aneurysms_aneurysm_coronary_prognosis |
| 1584 | mirage - 2000 - missile - airframes - aircraft | 48 | 1584_mirage_2000_missile_airframes |
| 1585 | rangers - ranger - mighty - ninja - dubbed | 48 | 1585_rangers_ranger_mighty_ninja |
| 1586 | iso - specifications - transmission - specification - interface | 48 | 1586_iso_specifications_transmission_specification |
| 1587 | moai - statues - statue - archaeologists - archaeological | 48 | 1587_moai_statues_statue_archaeologists |
| 1588 | cameras - camera - shutters - photography - shutter | 48 | 1588_cameras_camera_shutters_photography |
| 1589 | bigamy - waltz - alimony - dancer - famous | 48 | 1589_bigamy_waltz_alimony_dancer |
| 1590 | pussy - riot - activists - protesting - protest | 48 | 1590_pussy_riot_activists_protesting |
| 1591 | musician - songs - singers - album - songwriters | 48 | 1591_musician_songs_singers_album |
| 1592 | chile - poet - poems - poem - poetry | 48 | 1592_chile_poet_poems_poem |
| 1593 | directorate - security - agencies - agency - executive | 48 | 1593_directorate_security_agencies_agency |
| 1594 | steampunk - cyberpunk - steam - technocrats - conventions | 48 | 1594_steampunk_cyberpunk_steam_technocrats |
| 1595 | planets - volcanically - volcanic - planet - craters | 48 | 1595_planets_volcanically_volcanic_planet |
| 1596 | sky - gaming - gameplay - gamer - game | 48 | 1596_sky_gaming_gameplay_gamer |
| 1597 | brewery - beers - breweries - tents - festival | 48 | 1597_brewery_beers_breweries_tents |
| 1598 | drafted - rebounds - basketball - draft - hoop | 48 | 1598_drafted_rebounds_basketball_draft |
| 1599 | ancient - mathematician - philosophers - philosopher - esotericism | 48 | 1599_ancient_mathematician_philosophers_philosopher |
| 1600 | mural - artworks - paintings - murals - exhibitions | 48 | 1600_mural_artworks_paintings_murals |
| 1601 | gamer - gamers - gaming - harassment - misogynistic | 48 | 1601_gamer_gamers_gaming_harassment |
| 1602 | microprocessors - microelectronics - microprocessor - processors - transistors | 48 | 1602_microprocessors_microelectronics_microprocessor_processors |
| 1603 | molested - murders - crimes - murdered - murdering | 48 | 1603_molested_murders_crimes_murdered |
| 1604 | assassination - assassinate - archduke - assassins - conspirators | 48 | 1604_assassination_assassinate_archduke_assassins |
| 1605 | noir - noirs - genre - cinematography - filmmaking | 48 | 1605_noir_noirs_genre_cinematography |
| 1606 | ibn - folktales - tales - literature - nights | 48 | 1606_ibn_folktales_tales_literature |
| 1607 | piracy - pirate - pirates - privateering - maritime | 48 | 1607_piracy_pirate_pirates_privateering |
| 1608 | mysticism - theosophical - spiritual - spirituality - epistemology | 48 | 1608_mysticism_theosophical_spiritual_spirituality |
| 1609 | gaol - literary - prose - biographies - ballad | 48 | 1609_gaol_literary_prose_biographies |
| 1610 | complexity - computational - algorithms - cryptosystems - deterministic | 48 | 1610_complexity_computational_algorithms_cryptosystems |
| 1611 | deepfake - deepfakes - videos - detecting - detection | 48 | 1611_deepfake_deepfakes_videos_detecting |
| 1612 | metadata - semantic - vocabularies - schema - catalog | 48 | 1612_metadata_semantic_vocabularies_schema |
| 1613 | railway - trains - highways - buses - trolleybus | 47 | 1613_railway_trains_highways_buses |
| 1614 | olives - olive - mediterranean - tree - orchards | 47 | 1614_olives_olive_mediterranean_tree |
| 1615 | till - acquitted - lynched - casket - lynching | 47 | 1615_till_acquitted_lynched_casket |
| 1616 | thriller - ballads - usher - albums - songs | 47 | 1616_thriller_ballads_usher_albums |
| 1617 | literature - tales - adventures - books - poems | 47 | 1617_literature_tales_adventures_books |
| 1618 | typhoon - landfall - cyclone - tropical - meteorological | 47 | 1618_typhoon_landfall_cyclone_tropical |
| 1619 | telecom - telecommunications - telecoms - broadband - provider | 47 | 1619_telecom_telecommunications_telecoms_broadband |
| 1620 | sabbath - bands - band - guitarist - bassist | 47 | 1620_sabbath_bands_band_guitarist |
| 1621 | puritan - reformation - protestant - congregational - sermons | 47 | 1621_puritan_reformation_protestant_congregational |
| 1622 | conductivity - resistivity - resistances - resistance - ohms | 47 | 1622_conductivity_resistivity_resistances_resistance |
| 1623 | reliance - shareholders - shareholder - chairman - chairmanship | 47 | 1623_reliance_shareholders_shareholder_chairman |
| 1624 | vampires - vampirism - vampire - vampiric - folklore | 47 | 1624_vampires_vampirism_vampire_vampiric |
| 1625 | genocide - humanitarian - atrocities - famine - starvation | 47 | 1625_genocide_humanitarian_atrocities_famine |
| 1626 | anorexia - bulimia - anorexic - bulimic - disorders | 47 | 1626_anorexia_bulimia_anorexic_bulimic |
| 1627 | slash - slashes - slashed - backslash - separator | 47 | 1627_slash_slashes_slashed_backslash |
| 1628 | narcissism - narcissistic - psychopathy - traits - trait | 47 | 1628_narcissism_narcissistic_psychopathy_traits |
| 1629 | payments - bank - payment - prepaid - banks | 47 | 1629_payments_bank_payment_prepaid |
| 1630 | nomadic - deserts - tribes - desert - sheikhs | 47 | 1630_nomadic_deserts_tribes_desert |
| 1631 | quarterback - quarterbacks - touchdowns - patriots - eagles | 47 | 1631_quarterback_quarterbacks_touchdowns_patriots |
| 1632 | chocolate - cocoa - cacao - sugar - sugars | 47 | 1632_chocolate_cocoa_cacao_sugar |
| 1633 | pharmaceuticals - pharmaceutical - oxycodone - opioids - lawsuits | 47 | 1633_pharmaceuticals_pharmaceutical_oxycodone_opioids |
| 1634 | novels - literature - fiction - writings - writer | 47 | 1634_novels_literature_fiction_writings |
| 1635 | comics - marvel - cartoonist - comic - superhero | 47 | 1635_comics_marvel_cartoonist_comic |
| 1636 | rapper - rappers - rap - diva - singer | 47 | 1636_rapper_rappers_rap_diva |
| 1637 | donkey - fairy - godmother - dragon - prince | 47 | 1637_donkey_fairy_godmother_dragon |
| 1638 | contraception - contraceptive - contraceptives - abortion - abortions | 47 | 1638_contraception_contraceptive_contraceptives_abortion |
| 1639 | adjutant - colonel - soldier - lieutenant - brigadier | 47 | 1639_adjutant_colonel_soldier_lieutenant |
| 1640 | pasha - sultan - bey - beylik - beyliks | 47 | 1640_pasha_sultan_bey_beylik |
| 1641 | hookah - hookahs - tobacco - smoking - smoked | 47 | 1641_hookah_hookahs_tobacco_smoking |
| 1642 | goalscorer - scored - goals - scoring - goal | 47 | 1642_goalscorer_scored_goals_scoring |
| 1643 | 172 - aircraft - fuselage - 177 - redesigned | 47 | 1643_172_aircraft_fuselage_177 |
| 1644 | gospels - crucifixion - crucified - gospel - executed | 47 | 1644_gospels_crucifixion_crucified_gospel |
| 1645 | genomes - genome - mutations - genes - spacer | 47 | 1645_genomes_genome_mutations_genes |
| 1646 | catch - 22 - circumstance - novel - spurious | 47 | 1646_catch_22_circumstance_novel |
| 1647 | aphasia - impairment - cognitive - dementia - impaired | 46 | 1647_aphasia_impairment_cognitive_dementia |
| 1648 | screenwriter - blood - sequels - films - rocky | 46 | 1648_screenwriter_blood_sequels_films |
| 1649 | arias - convicted - conviction - testified - convict | 46 | 1649_arias_convicted_conviction_testified |
| 1650 | uniforms - uniformed - berets - beret - regiment | 46 | 1650_uniforms_uniformed_berets_beret |
| 1651 | poems - poets - poet - poetry - poem | 46 | 1651_poems_poets_poet_poetry |
| 1652 | malpractice - appeals - swallowing - upheld - feeding | 46 | 1652_malpractice_appeals_swallowing_upheld |
| 1653 | bucket - albums - album - pike - tracks | 46 | 1653_bucket_albums_album_pike |
| 1654 | merger - firms - mergers - acquisitions - firm | 46 | 1654_merger_firms_mergers_acquisitions |
| 1655 | navy - military - enlisted - regiment - personnel | 46 | 1655_navy_military_enlisted_regiment |
| 1656 | peacekeeping - insurgents - ceasefire - insurgency - insurgent | 46 | 1656_peacekeeping_insurgents_ceasefire_insurgency |
| 1657 | shamrocks - parades - shamrock - celebrated - celebrations | 46 | 1657_shamrocks_parades_shamrock_celebrated |
| 1658 | eternal - eternally - eternity - repetitions - recurrence | 46 | 1658_eternal_eternally_eternity_repetitions |
| 1659 | tower - towers - fortification - moat - castles | 46 | 1659_tower_towers_fortification_moat |
| 1660 | treaties - hostilities - wartime - convention - tribunal | 46 | 1660_treaties_hostilities_wartime_convention |
| 1661 | khat - banning - misuse - legality - prohibition | 46 | 1661_khat_banning_misuse_legality |
| 1662 | invested - investor - investors - funding - financing | 46 | 1662_invested_investor_investors_funding |
| 1663 | democrats - parties - elections - election - democratic | 46 | 1663_democrats_parties_elections_election |
| 1664 | mini - convertible - redesigned - discontinued - minimalism | 46 | 1664_mini_convertible_redesigned_discontinued |
| 1665 | clowns - clown - concert - posse - circus | 46 | 1665_clowns_clown_concert_posse |
| 1666 | rankings - ranking - ranks - universities - academics | 46 | 1666_rankings_ranking_ranks_universities |
| 1667 | jam - cameo - cartoon - basketball - cameos | 46 | 1667_jam_cameo_cartoon_basketball |
| 1668 | saffron - botanical - turmeric - cultivated - phytochemicals | 46 | 1668_saffron_botanical_turmeric_cultivated |
| 1669 | mysticism - sharia - spirituality - imam - mystical | 46 | 1669_mysticism_sharia_spirituality_imam |
| 1670 | remixes - pet - remixed - remix - duet | 46 | 1670_remixes_pet_remixed_remix |
| 1671 | frontiersman - settlers - frontiersmen - wilderness - 1778 | 46 | 1671_frontiersman_settlers_frontiersmen_wilderness |
| 1672 | episodes - shows - preschoolers - blue - preschool | 46 | 1672_episodes_shows_preschoolers_blue |
| 1673 | municipalities - municipality - metropolitan - cities - populous | 46 | 1673_municipalities_municipality_metropolitan_cities |
| 1674 | soccer - goals - assists - goal - goalscorer | 46 | 1674_soccer_goals_assists_goal |
| 1675 | birthdays - birthday - probability - anniversaries - 365 | 46 | 1675_birthdays_birthday_probability_anniversaries |
| 1676 | siren - sirens - mermaid - mermaids - mythology | 46 | 1676_siren_sirens_mermaid_mermaids |
| 1677 | zombie - finale - survivor - hilltop - walkers | 46 | 1677_zombie_finale_survivor_hilltop |
| 1678 | mosque - mosques - imam - imams - prophet | 46 | 1678_mosque_mosques_imam_imams |
| 1679 | swan - swans - epistemic - theory - book | 46 | 1679_swan_swans_epistemic_theory |
| 1680 | mar - trump - mansion - vacation - presidential | 46 | 1680_mar_trump_mansion_vacation |
| 1681 | sequels - avatar - sequel - 3d - filming | 46 | 1681_sequels_avatar_sequel_3d |
| 1682 | soldiers - helicopter - surrender - mujahideen - prisoner | 46 | 1682_soldiers_helicopter_surrender_mujahideen |
| 1683 | tron - legacy - trailers - disc - trailer | 46 | 1683_tron_legacy_trailers_disc |
| 1684 | roguelikes - roguelike - rogue - gameplay - dungeons | 45 | 1684_roguelikes_roguelike_rogue_gameplay |
| 1685 | vocals - concert - vocalist - musicians - guitarist | 45 | 1685_vocals_concert_vocalist_musicians |
| 1686 | editions - comics - hardcover - edition - miniseries | 45 | 1686_editions_comics_hardcover_edition |
| 1687 | playwright - playwrights - theatricality - theatre - dramatize | 45 | 1687_playwright_playwrights_theatricality_theatre |
| 1688 | paintings - painting - painters - painter - murals | 45 | 1688_paintings_painting_painters_painter |
| 1689 | harassment - allegations - harassed - assaulted - victimized | 45 | 1689_harassment_allegations_harassed_assaulted |
| 1690 | doll - child - dolls - voiced - remake | 45 | 1690_doll_child_dolls_voiced |
| 1691 | jeep - jeeps - vehicle - vehicles - chassis | 45 | 1691_jeep_jeeps_vehicle_vehicles |
| 1692 | cinema - filmmaking - cinemas - films - filmmakers | 45 | 1692_cinema_filmmaking_cinemas_films |
| 1693 | bomber - bombers - missiles - aircraft - missile | 45 | 1693_bomber_bombers_missiles_aircraft |
| 1694 | monarchy - 1867 - confederation - 1918 - 1848 | 45 | 1694_monarchy_1867_confederation_1918 |
| 1695 | sugar - sugars - sugarcane - glucose - molasses | 45 | 1695_sugar_sugars_sugarcane_glucose |
| 1696 | art - artistic - marina - exhibition - museum | 45 | 1696_art_artistic_marina_exhibition |
| 1697 | racing - qualifying - raced - laps - prix | 45 | 1697_racing_qualifying_raced_laps |
| 1698 | cellar - cellars - imprisonment - captives - raped | 45 | 1698_cellar_cellars_imprisonment_captives |
| 1699 | ruby - rails - gems - interpreter - programming | 45 | 1699_ruby_rails_gems_interpreter |
| 1700 | saints - row - stadia - games - arcade | 45 | 1700_saints_row_stadia_games |
| 1701 | yakuza - anime - animations - chibi - voice | 45 | 1701_yakuza_anime_animations_chibi |
| 1702 | sales - revenue - disc - discs - blu | 45 | 1702_sales_revenue_disc_discs |
| 1703 | rabies - raccoons - infectious - vaccines - bitten | 45 | 1703_rabies_raccoons_infectious_vaccines |
| 1704 | gypsy - disorder - seizure - investigators - syndrome | 45 | 1704_gypsy_disorder_seizure_investigators |
| 1705 | clover - paramount - film - directorial - movie | 45 | 1705_clover_paramount_film_directorial |
| 1706 | shades - sequels - twilight - trilogy - film | 45 | 1706_shades_sequels_twilight_trilogy |
| 1707 | monastery - monks - monastic - monastics - nam | 45 | 1707_monastery_monks_monastic_monastics |
| 1708 | scream - sequels - screenwriter - sequel - trilogy | 45 | 1708_scream_sequels_screenwriter_sequel |
| 1709 | tablet - underworld - entrails - netherworld - throne | 45 | 1709_tablet_underworld_entrails_netherworld |
| 1710 | peat - peatlands - peatland - wetlands - soils | 45 | 1710_peat_peatlands_peatland_wetlands |
| 1711 | thirty - seconds - tour - album - headlining | 45 | 1711_thirty_seconds_tour_album |
| 1712 | saxophones - saxophone - sax - saxophonists - saxophonist | 45 | 1712_saxophones_saxophone_sax_saxophonists |
| 1713 | telecommunications - telecom - telecoms - telecommunication - telephony | 45 | 1713_telecommunications_telecom_telecoms_telecommunication |
| 1714 | interceptions - touchdowns - quarterback - interception - quarterbacks | 45 | 1714_interceptions_touchdowns_quarterback_interception |
| 1715 | tractors - tractor - deer - axles - machinery | 45 | 1715_tractors_tractor_deer_axles |
| 1716 | quoting - quotations - quotes - apostrophes - quotation | 45 | 1716_quoting_quotations_quotes_apostrophes |
| 1717 | panther - vibranium - panthers - spider - doom | 45 | 1717_panther_vibranium_panthers_spider |
| 1718 | profiles - chats - swipes - profile - chatting | 45 | 1718_profiles_chats_swipes_profile |
| 1719 | enterprises - ventures - affiliate - companies - commerce | 45 | 1719_enterprises_ventures_affiliate_companies |
| 1720 | fibromyalgia - fibrous - neuropathy - chronic - neuropathic | 45 | 1720_fibromyalgia_fibrous_neuropathy_chronic |
| 1721 | tithes - genesis - tithe - testament - pharaoh | 45 | 1721_tithes_genesis_tithe_testament |
| 1722 | celestial - eternal - awakening - immortal - destruction | 44 | 1722_celestial_eternal_awakening_immortal |
| 1723 | empathy - empathic - empathizing - empathize - sympathy | 44 | 1723_empathy_empathic_empathizing_empathize |
| 1724 | surrogacy - surrogates - surrogate - parenthood - fertility | 44 | 1724_surrogacy_surrogates_surrogate_parenthood |
| 1725 | tennis - tournaments - tournament - championships - finals | 44 | 1725_tennis_tournaments_tournament_championships |
| 1726 | brands - brand - margarine - soap - oils | 44 | 1726_brands_brand_margarine_soap |
| 1727 | leftist - leftists - nationalists - liberal - conservatives | 44 | 1727_leftist_leftists_nationalists_liberal |
| 1728 | medal - medals - presidential - president - bestowed | 44 | 1728_medal_medals_presidential_president |
| 1729 | quarterback - quarterbacks - interceptions - manning - touchdowns | 44 | 1729_quarterback_quarterbacks_interceptions_manning |
| 1730 | nazi - 1941 - 1939 - 1944 - wartime | 44 | 1730_nazi_1941_1939_1944 |
| 1731 | fractal - curves - boundary - holomorphic - bifurcation | 44 | 1731_fractal_curves_boundary_holomorphic |
| 1732 | limp - rock - bands - band - rap | 44 | 1732_limp_rock_bands_band |
| 1733 | devil - demon - satan - soul - souls | 44 | 1733_devil_demon_satan_soul |
| 1734 | goalscorer - footballer - goals - goalscoring - scored | 44 | 1734_goalscorer_footballer_goals_goalscoring |
| 1735 | libraries - library - librarians - bibliographic - scholarly | 44 | 1735_libraries_library_librarians_bibliographic |
| 1736 | heir - eldest - nobility - peerage - baronetcy | 44 | 1736_heir_eldest_nobility_peerage |
| 1737 | radium - chemist - polonium - radioactive - sciences | 44 | 1737_radium_chemist_polonium_radioactive |
| 1738 | sitcom - episodes - sergeants - sheriff - comedian | 44 | 1738_sitcom_episodes_sergeants_sheriff |
| 1739 | scum - feminist - satirist - manifesto - feminism | 44 | 1739_scum_feminist_satirist_manifesto |
| 1740 | moose - singer - mansa - rapper - songs | 44 | 1740_moose_singer_mansa_rapper |
| 1741 | population - municipalities - municipality - cities - inhabitants | 44 | 1741_population_municipalities_municipality_cities |
| 1742 | sober - sobriety - rehab - addiction - addict | 44 | 1742_sober_sobriety_rehab_addiction |
| 1743 | ant - ants - toured - tour - concert | 44 | 1743_ant_ants_toured_tour |
| 1744 | financial - investors - investor - founder - bankruptcies | 44 | 1744_financial_investors_investor_founder |
| 1745 | trail - anchorage - abandoned - rescued - canoe | 44 | 1745_trail_anchorage_abandoned_rescued |
| 1746 | magnum - episodes - detective - episode - robin | 44 | 1746_magnum_episodes_detective_episode |
| 1747 | moss - quarterback - cornerback - punts - touchdowns | 44 | 1747_moss_quarterback_cornerback_punts |
| 1748 | papacy - 1523 - 1527 - papal - 1471 | 44 | 1748_papacy_1523_1527_papal |
| 1749 | orcas - orca - whale - dolphin - whales | 43 | 1749_orcas_orca_whale_dolphin |
| 1750 | cartoonist - comic - strips - cartoon - strip | 43 | 1750_cartoonist_comic_strips_cartoon |
| 1751 | aids - antiretroviral - epidemiology - prevalence - population | 43 | 1751_aids_antiretroviral_epidemiology_prevalence |
| 1752 | spam - restaurants - condiments - barbecue - canned | 43 | 1752_spam_restaurants_condiments_barbecue |
| 1753 | motorcycles - motorcycle - motorbikes - motorbike - bikes | 43 | 1753_motorcycles_motorcycle_motorbikes_motorbike |
| 1754 | toured - band - headlining - album - guitarist | 43 | 1754_toured_band_headlining_album |
| 1755 | loaf - meat - duet - duets - sang | 43 | 1755_loaf_meat_duet_duets |
| 1756 | horse - horses - cavalry - rode - 1877 | 43 | 1756_horse_horses_cavalry_rode |
| 1757 | festival - lawsuit - lawsuits - defrauded - sued | 43 | 1757_festival_lawsuit_lawsuits_defrauded |
| 1758 | noblewomen - empress - maids - governesses - nobility | 43 | 1758_noblewomen_empress_maids_governesses |
| 1759 | retailer - retailers - marketplace - marketplaces - merchants | 43 | 1759_retailer_retailers_marketplace_marketplaces |
| 1760 | expedition - expeditions - 1803 - voyage - explorers | 43 | 1760_expedition_expeditions_1803_voyage |
| 1761 | grand - central - midtown - terminal - concourse | 43 | 1761_grand_central_midtown_terminal |
| 1762 | hill - gibbons - vocalist - rock - beard | 43 | 1762_hill_gibbons_vocalist_rock |
| 1763 | blueberries - blueberry - berries - cranberries - cranberry | 43 | 1763_blueberries_blueberry_berries_cranberries |
| 1764 | microseconds - timestamps - clocks - epoch - timestamp | 43 | 1764_microseconds_timestamps_clocks_epoch |
| 1765 | tinnitus - auditory - otitis - ears - ear | 43 | 1765_tinnitus_auditory_otitis_ears |
| 1766 | currencies - renminbi - currency - yuan - monetary | 43 | 1766_currencies_renminbi_currency_yuan |
| 1767 | amber - ambergris - jewelry - fragrance - resin | 43 | 1767_amber_ambergris_jewelry_fragrance |
| 1768 | yakuza - gangs - crime - gang - thugs | 43 | 1768_yakuza_gangs_crime_gang |
| 1769 | brave - browser - browsers - chrome - browse | 43 | 1769_brave_browser_browsers_chrome |
| 1770 | bugs - insects - insecticides - pest - pests | 43 | 1770_bugs_insects_insecticides_pest |
| 1771 | pit - rap - song - remix - songs | 43 | 1771_pit_rap_song_remix |
| 1772 | calendar - calendars - dates - holidays - astronomical | 43 | 1772_calendar_calendars_dates_holidays |
| 1773 | calculators - calculator - microelectronics - calculation - calculations | 43 | 1773_calculators_calculator_microelectronics_calculation |
| 1774 | statutes - limitations - statute - limitation - prosecution | 43 | 1774_statutes_limitations_statute_limitation |
| 1775 | priesthood - rituals - hymns - archaic - caste | 43 | 1775_priesthood_rituals_hymns_archaic |
| 1776 | jock - reunion - cast - sitcom - finale | 43 | 1776_jock_reunion_cast_sitcom |
| 1777 | boar - boars - pigs - pig - wildlife | 43 | 1777_boar_boars_pigs_pig |
| 1778 | supermarket - supermarkets - shops - retailer - stores | 43 | 1778_supermarket_supermarkets_shops_retailer |
| 1779 | fasting - fasts - fasted - fast - fatwas | 43 | 1779_fasting_fasts_fasted_fast |
| 1780 | infantry - battalions - allied - landings - troops | 43 | 1780_infantry_battalions_allied_landings |
| 1781 | protests - protesters - protest - demonstrators - square | 43 | 1781_protests_protesters_protest_demonstrators |
| 1782 | witches - prophecy - throne - king - tyrant | 43 | 1782_witches_prophecy_throne_king |
| 1783 | peanuts - comics - cartoonists - reprint - reprints | 43 | 1783_peanuts_comics_cartoonists_reprint |
| 1784 | penicillin - antibiotics - antibiotic - antimicrobial - antibacterial | 43 | 1784_penicillin_antibiotics_antibiotic_antimicrobial |
| 1785 | phosphors - phosphor - luminous - fluorescent - led | 43 | 1785_phosphors_phosphor_luminous_fluorescent |
| 1786 | martial - kung - karate - taekwondo - lee | 43 | 1786_martial_kung_karate_taekwondo |
| 1787 | werewolf - werewolves - wolf - lycanthropy - wolves | 42 | 1787_werewolf_werewolves_wolf_lycanthropy |
| 1788 | marvel - marvels - superhero - superheroes - comics | 42 | 1788_marvel_marvels_superhero_superheroes |
| 1789 | tai - chi - martial - wushu - yang | 42 | 1789_tai_chi_martial_wushu |
| 1790 | cents - coins - monetary - shillings - coin | 42 | 1790_cents_coins_monetary_shillings |
| 1791 | inter - assists - goalscorer - scored - goals | 42 | 1791_inter_assists_goalscorer_scored |
| 1792 | massacre - soldiers - victims - civilians - regiment | 42 | 1792_massacre_soldiers_victims_civilians |
| 1793 | soldier - poet - poem - poems - autobiography | 42 | 1793_soldier_poet_poem_poems |
| 1794 | leases - addresses - client - subnet - subnets | 42 | 1794_leases_addresses_client_subnet |
| 1795 | neolithic - archaeological - excavations - paleolithic - archeological | 42 | 1795_neolithic_archaeological_excavations_paleolithic |
| 1796 | griffin - rebounds - basketball - wizards - triple | 42 | 1796_griffin_rebounds_basketball_wizards |
| 1797 | surrealists - surrealist - surrealism - surrealistic - artists | 42 | 1797_surrealists_surrealist_surrealism_surrealistic |
| 1798 | 1850 - settlers - 1846 - goldfields - 1848 | 42 | 1798_1850_settlers_1846_goldfields |
| 1799 | serve - serving - volley - frontcourt - play | 42 | 1799_serve_serving_volley_frontcourt |
| 1800 | engineering - engineers - engineer - electrical - electronics | 42 | 1800_engineering_engineers_engineer_electrical |
| 1801 | festivals - festival - concerts - orchestras - venues | 42 | 1801_festivals_festival_concerts_orchestras |
| 1802 | sentinel - islands - island - jungle - tribe | 42 | 1802_sentinel_islands_island_jungle |
| 1803 | autobahns - autobahn - throttling - highways - motorways | 42 | 1803_autobahns_autobahn_throttling_highways |
| 1804 | watches - wristwatches - wristwatch - watchmaker - timepieces | 42 | 1804_watches_wristwatches_wristwatch_watchmaker |
| 1805 | actress - actresses - starred - portrayed - personae | 42 | 1805_actress_actresses_starred_portrayed |
| 1806 | aikido - kendo - martial - judo - ryū | 42 | 1806_aikido_kendo_martial_judo |
| 1807 | automotive - motors - automobile - jaguar - vehicle | 42 | 1807_automotive_motors_automobile_jaguar |
| 1808 | sitcom - cast - bunch - spinoffs - tv | 42 | 1808_sitcom_cast_bunch_spinoffs |
| 1809 | park - parks - parking - central - parkland | 42 | 1809_park_parks_parking_central |
| 1810 | conquered - tribes - ancient - steppes - nomadic | 42 | 1810_conquered_tribes_ancient_steppes |
| 1811 | smartphone - smartphones - android - flagship - mi | 42 | 1811_smartphone_smartphones_android_flagship |
| 1812 | vocalists - singer - albums - ballads - songs | 42 | 1812_vocalists_singer_albums_ballads |
| 1813 | honeys - honey - honeydew - bees - sugar | 42 | 1813_honeys_honey_honeydew_bees |
| 1814 | albums - toured - album - concerts - band | 42 | 1814_albums_toured_album_concerts |
| 1815 | photovoltaics - photovoltaic - solar - panels - modules | 42 | 1815_photovoltaics_photovoltaic_solar_panels |
| 1816 | bebop - cowboy - anime - episodes - otaku | 42 | 1816_bebop_cowboy_anime_episodes |
| 1817 | imaging - radiographic - radiology - scanning - scanned | 42 | 1817_imaging_radiographic_radiology_scanning |
| 1818 | logistics - freight - warehousing - procurement - warehouses | 42 | 1818_logistics_freight_warehousing_procurement |
| 1819 | javelin - athlete - badminton - olympic - athletics | 42 | 1819_javelin_athlete_badminton_olympic |
| 1820 | theme - melody - soundtrack - tune - song | 42 | 1820_theme_melody_soundtrack_tune |
| 1821 | commerce - retailers - shopping - retailing - retail | 42 | 1821_commerce_retailers_shopping_retailing |
| 1822 | trail - trails - overland - railroad - wagons | 42 | 1822_trail_trails_overland_railroad |
| 1823 | rover - rovers - vehicles - vehicle - chassis | 42 | 1823_rover_rovers_vehicles_vehicle |
| 1824 | congressman - lawmaker - misconduct - congressional - appeals | 42 | 1824_congressman_lawmaker_misconduct_congressional |
| 1825 | postcolonial - imperialism - moralist - revolt - french | 42 | 1825_postcolonial_imperialism_moralist_revolt |
| 1826 | hound - warrior - hurling - sword - spear | 42 | 1826_hound_warrior_hurling_sword |
| 1827 | ferns - fern - angiosperms - phylogenetic - phylogeny | 42 | 1827_ferns_fern_angiosperms_phylogenetic |
| 1828 | credit - social - debtors - audits - blacklists | 42 | 1828_credit_social_debtors_audits |
| 1829 | compulsions - compulsive - obsessive - obsession - obsessions | 42 | 1829_compulsions_compulsive_obsessive_obsession |
| 1830 | bodybuilder - bodybuilding - bodybuilders - competed - weightlifting | 42 | 1830_bodybuilder_bodybuilding_bodybuilders_competed |
| 1831 | actress - siblings - celebrity - actor - divorce | 42 | 1831_actress_siblings_celebrity_actor |
| 1832 | assassinated - assassination - hanged - assassinate - assassin | 42 | 1832_assassinated_assassination_hanged_assassinate |
| 1833 | eugenics - eugenic - geneticists - genetic - sterilisation | 42 | 1833_eugenics_eugenic_geneticists_genetic |
| 1834 | civilians - contractors - prosecution - enforcement - security | 42 | 1834_civilians_contractors_prosecution_enforcement |
| 1835 | botany - botanist - botanists - botanical - flora | 42 | 1835_botany_botanist_botanists_botanical |
| 1836 | publics - public - pr - communicators - organizations | 41 | 1836_publics_public_pr_communicators |
| 1837 | nonfiction - magazines - magazine - anthologies - writer | 41 | 1837_nonfiction_magazines_magazine_anthologies |
| 1838 | kimchi - rice - cabbage - cuisine - recipes | 41 | 1838_kimchi_rice_cabbage_cuisine |
| 1839 | anna - marriage - dolly - marrying - affair | 41 | 1839_anna_marriage_dolly_marrying |
| 1840 | traumatic - trauma - traumas - posttraumatic - psychiatric | 41 | 1840_traumatic_trauma_traumas_posttraumatic |
| 1841 | château - vineyard - baronet - winemaking - estates | 41 | 1841_château_vineyard_baronet_winemaking |
| 1842 | tunnel - tunnelling - tunnels - railways - railway | 41 | 1842_tunnel_tunnelling_tunnels_railways |
| 1843 | rivers - celebrity - housewives - comedian - contestant | 41 | 1843_rivers_celebrity_housewives_comedian |
| 1844 | antifa - activists - fascists - fascist - protesters | 41 | 1844_antifa_activists_fascists_fascist |
| 1845 | straits - albums - guitarist - dire - guitar | 41 | 1845_straits_albums_guitarist_dire |
| 1846 | edict - orthodoxy - roman - persecution - ecumenical | 41 | 1846_edict_orthodoxy_roman_persecution |
| 1847 | guitars - guitar - fretboard - frets - necks | 41 | 1847_guitars_guitar_fretboard_frets |
| 1848 | limerence - attraction - affection - intrusive - infatuation | 41 | 1848_limerence_attraction_affection_intrusive |
| 1849 | philosopher - martyrs - paganism - martyr - pagan | 41 | 1849_philosopher_martyrs_paganism_martyr |
| 1850 | shingles - herpesvirus - chickenpox - herpes - smallpox | 41 | 1850_shingles_herpesvirus_chickenpox_herpes |
| 1851 | heritage - preservation - films - film - culturally | 41 | 1851_heritage_preservation_films_film |
| 1852 | slim - richest - billionaire - pesos - shareholder | 41 | 1852_slim_richest_billionaire_pesos |
| 1853 | ninja - manga - anime - shinobi - shōnen | 41 | 1853_ninja_manga_anime_shinobi |
| 1854 | opioid - senator - senators - lobbyist - overdoses | 41 | 1854_opioid_senator_senators_lobbyist |
| 1855 | trump - trumps - president - grandchildren - paternal | 41 | 1855_trump_trumps_president_grandchildren |
| 1856 | scratch - scratching - programming - scratched - adobe | 41 | 1856_scratch_scratching_programming_scratched |
| 1857 | smallpox - epidemics - measles - epidemic - diseases | 41 | 1857_smallpox_epidemics_measles_epidemic |
| 1858 | ideology - philosopher - philosophy - psychoanalytical - psychoanalytic | 41 | 1858_ideology_philosopher_philosophy_psychoanalytical |
| 1859 | sai - samadhi - devotees - qawwali - guru | 41 | 1859_sai_samadhi_devotees_qawwali |
| 1860 | college - degree - accredited - bachelor - faculty | 41 | 1860_college_degree_accredited_bachelor |
| 1861 | mustard - mustards - bombs - gases - chemicals | 41 | 1861_mustard_mustards_bombs_gases |
| 1862 | quixotic - literature - chivalric - novel - chivalry | 41 | 1862_quixotic_literature_chivalric_novel |
| 1863 | rap - rock - album - band - boys | 41 | 1863_rap_rock_album_band |
| 1864 | blur - oasis - albums - toured - tour | 41 | 1864_blur_oasis_albums_toured |
| 1865 | colonies - settlers - colonists - 1624 - colony | 41 | 1865_colonies_settlers_colonists_1624 |
| 1866 | satellites - satellite - soviets - spacecraft - soviet | 41 | 1866_satellites_satellite_soviets_spacecraft |
| 1867 | brownies - brownie - folklore - stories - maids | 41 | 1867_brownies_brownie_folklore_stories |
| 1868 | guardians - galaxy - marvel - 2022 - 2023 | 41 | 1868_guardians_galaxy_marvel_2022 |
| 1869 | slender - skinny - creepypastas - creepypasta - portrayals | 41 | 1869_slender_skinny_creepypastas_creepypasta |
| 1870 | viewership - viewers - ratings - streamed - viewing | 41 | 1870_viewership_viewers_ratings_streamed |
| 1871 | burritos - tacos - salsa - tortillas - foods | 41 | 1871_burritos_tacos_salsa_tortillas |
| 1872 | tsar - 1812 - armies - casualties - cavalrymen | 41 | 1872_tsar_1812_armies_casualties |
| 1873 | divine - persona - scene - onstage - films | 41 | 1873_divine_persona_scene_onstage |
| 1874 | mosque - synagogue - temple - synagogues - waqf | 41 | 1874_mosque_synagogue_temple_synagogues |
| 1875 | extradition - arrest - fugitive - extradite - citizenship | 41 | 1875_extradition_arrest_fugitive_extradite |
| 1876 | rage - albums - machine - band - album | 41 | 1876_rage_albums_machine_band |
| 1877 | zombie - walkers - zombies - walking - episodes | 40 | 1877_zombie_walkers_zombies_walking |
| 1878 | impeachment - impeach - bipartisan - republican - bipartisanship | 40 | 1878_impeachment_impeach_bipartisan_republican |
| 1879 | lighting - lights - fluorescent - light - brighter | 40 | 1879_lighting_lights_fluorescent_light |
| 1880 | sigma - deviations - variability - defects - statisticians | 40 | 1880_sigma_deviations_variability_defects |
| 1881 | html - markup - browsers - hypertext - browser | 40 | 1881_html_markup_browsers_hypertext |
| 1882 | designing - prototyping - drafting - drawings - designs | 40 | 1882_designing_prototyping_drafting_drawings |
| 1883 | sultan - sultans - empresses - concubine - caliph | 40 | 1883_sultan_sultans_empresses_concubine |
| 1884 | lost - teen - teens - boys - threesome | 40 | 1884_lost_teen_teens_boys |
| 1885 | electromagnetism - electromagnet - electrical - electromagnetic - electrochemistry | 40 | 1885_electromagnetism_electromagnet_electrical_electromagnetic |
| 1886 | pianos - piano - pedals - pianists - pedal | 40 | 1886_pianos_piano_pedals_pianists |
| 1887 | bake - baking - bakery - bakeries - bakers | 40 | 1887_bake_baking_bakery_bakeries |
| 1888 | incest - incestuous - inbreeding - familial - taboo | 40 | 1888_incest_incestuous_inbreeding_familial |
| 1889 | policies - policy - legislation - bipartisan - violations | 40 | 1889_policies_policy_legislation_bipartisan |
| 1890 | masters - contestants - premiered - chef - chefs | 40 | 1890_masters_contestants_premiered_chef |
| 1891 | kitsune - folktale - folktales - folklore - tales | 40 | 1891_kitsune_folktale_folktales_folklore |
| 1892 | butterfly - bride - dagger - flowers - cries | 40 | 1892_butterfly_bride_dagger_flowers |
| 1893 | island - ancient - geography - continent - ocean | 40 | 1893_island_ancient_geography_continent |
| 1894 | pawn - pawned - jewelry - shop - silver | 40 | 1894_pawn_pawned_jewelry_shop |
| 1895 | antisemitism - nationalist - kibbutz - diaspora - nationalists | 40 | 1895_antisemitism_nationalist_kibbutz_diaspora |
| 1896 | competed - tennis - quarterfinal - tournament - semifinal | 40 | 1896_competed_tennis_quarterfinal_tournament |
| 1897 | refugees - repatriation - refugee - asylum - refuge | 40 | 1897_refugees_repatriation_refugee_asylum |
| 1898 | divers - diving - dive - diver - cave | 40 | 1898_divers_diving_dive_diver |
| 1899 | museums - museum - exhibit - exhibits - exhibition | 40 | 1899_museums_museum_exhibit_exhibits |
| 1900 | marvel - marvels - superhero - superheroes - comics | 40 | 1900_marvel_marvels_superhero_superheroes |
| 1901 | sat - exams - exam - admissions - scholastic | 40 | 1901_sat_exams_exam_admissions |
| 1902 | murders - murdered - murder - robbery - killer | 40 | 1902_murders_murdered_murder_robbery |
| 1903 | ancestors - subcontinent - ethnoreligious - dynasties - descended | 40 | 1903_ancestors_subcontinent_ethnoreligious_dynasties |
| 1904 | squid - squids - colossal - giant - tentacles | 40 | 1904_squid_squids_colossal_giant |
| 1905 | smurf - scorching - merchandising - midget - vocabulary | 40 | 1905_smurf_scorching_merchandising_midget |
| 1906 | badminton - competed - tai - quarterfinal - tournament | 40 | 1906_badminton_competed_tai_quarterfinal |
| 1907 | seasons - season - list - blazers - pistons | 40 | 1907_seasons_season_list_blazers |
| 1908 | crash - gameplay - playable - remastered - games | 40 | 1908_crash_gameplay_playable_remastered |
| 1909 | thrones - cast - stark - arya - actors | 40 | 1909_thrones_cast_stark_arya |
| 1910 | puck - goalie - hockey - capitals - scoring | 40 | 1910_puck_goalie_hockey_capitals |
| 1911 | libretto - operatic - opera - operas - arias | 40 | 1911_libretto_operatic_opera_operas |
| 1912 | mounds - mound - excavations - prehistoric - archaeological | 40 | 1912_mounds_mound_excavations_prehistoric |
| 1913 | marsupials - opossums - phylogenies - phylogenetic - marsupial | 40 | 1913_marsupials_opossums_phylogenies_phylogenetic |
| 1914 | politburo - soviet - 1953 - exterminated - troika | 40 | 1914_politburo_soviet_1953_exterminated |
| 1915 | fate - fates - doctor - doctorate - comics | 40 | 1915_fate_fates_doctor_doctorate |
| 1916 | encoded - encoding - encodings - encode - byte | 40 | 1916_encoded_encoding_encodings_encode |
| 1917 | evil - zombie - prequel - sequels - sequel | 40 | 1917_evil_zombie_prequel_sequels |
| 1918 | identifiers - identifier - variants - byte - id | 40 | 1918_identifiers_identifier_variants_byte |
| 1919 | racing - eliminated - laps - motorsports - won | 39 | 1919_racing_eliminated_laps_motorsports |
| 1920 | centrifuges - centrifuge - nuclear - uranium - centrifugal | 39 | 1920_centrifuges_centrifuge_nuclear_uranium |
| 1921 | messiah - prophet - prophets - crucified - crucifixion | 39 | 1921_messiah_prophet_prophets_crucified |
| 1922 | cabin - autopsy - sheriff - handcuffed - suspect | 39 | 1922_cabin_autopsy_sheriff_handcuffed |
| 1923 | youngest - activist - khan - journalist - laureate | 39 | 1923_youngest_activist_khan_journalist |
| 1924 | gonzo - journalist - journalism - journalistic - magazine | 39 | 1924_gonzo_journalist_journalism_journalistic |
| 1925 | treaty - cooperation - organization - multilateral - diplomacy | 39 | 1925_treaty_cooperation_organization_multilateral |
| 1926 | duchess - duke - royal - potters - charity | 39 | 1926_duchess_duke_royal_potters |
| 1927 | cyanobacteria - cyanobacterial - cyanobacterium - phytoplankton - algae | 39 | 1927_cyanobacteria_cyanobacterial_cyanobacterium_phytoplankton |
| 1928 | accredited - accreditation - universities - certifications - admissions | 39 | 1928_accredited_accreditation_universities_certifications |
| 1929 | ruins - excavation - valley - archaeological - archaeologists | 39 | 1929_ruins_excavation_valley_archaeological |
| 1930 | engine - diesel - engines - turbodiesel - turbo | 39 | 1930_engine_diesel_engines_turbodiesel |
| 1931 | procrastination - procrastinate - procrastinators - motivation - delaying | 39 | 1931_procrastination_procrastinate_procrastinators_motivation |
| 1932 | law - laws - theological - morality - moral | 39 | 1932_law_laws_theological_morality |
| 1933 | darknet - net - network - networks - dark | 39 | 1933_darknet_net_network_networks |
| 1934 | stitch - hostage - operative - operatives - hostages | 39 | 1934_stitch_hostage_operative_operatives |
| 1935 | rex - frontman - bassist - rock - ballads | 39 | 1935_rex_frontman_bassist_rock |
| 1936 | projectors - projector - optics - optical - projection | 39 | 1936_projectors_projector_optics_optical |
| 1937 | golf - golfer - golfers - handicaps - scoring | 39 | 1937_golf_golfer_golfers_handicaps |
| 1938 | saint - rosary - shrine - shrines - rituals | 39 | 1938_saint_rosary_shrine_shrines |
| 1939 | plague - epidemics - famines - epidemic - famine | 39 | 1939_plague_epidemics_famines_epidemic |
| 1940 | apartheid - segregated - segregation - blacks - discrimination | 39 | 1940_apartheid_segregated_segregation_blacks |
| 1941 | unbreakable - split - sequels - cameo - screenplay | 39 | 1941_unbreakable_split_sequels_cameo |
| 1942 | sentencing - unconstitutional - punishment - executions - upheld | 39 | 1942_sentencing_unconstitutional_punishment_executions |
| 1943 | viper - dodge - vehicle - car - fiat | 39 | 1943_viper_dodge_vehicle_car |
| 1944 | pylori - gastric - gastritis - gastrointestinal - pyloric | 39 | 1944_pylori_gastric_gastritis_gastrointestinal |
| 1945 | architect - architects - architecture - architectural - designs | 39 | 1945_architect_architects_architecture_architectural |
| 1946 | prophet - publisher - writings - painter - 1910 | 39 | 1946_prophet_publisher_writings_painter |
| 1947 | enterprise - enterprises - organizational - business - applications | 39 | 1947_enterprise_enterprises_organizational_business |
| 1948 | cartoons - cartoon - 1930s - 1932 - 1933 | 39 | 1948_cartoons_cartoon_1930s_1932 |
| 1949 | dragon - dragons - train - sequel - nightmare | 39 | 1949_dragon_dragons_train_sequel |
| 1950 | unmanned - drone - drones - aircraft - piloted | 39 | 1950_unmanned_drone_drones_aircraft |
| 1951 | duets - singer - songs - duet - songwriter | 39 | 1951_duets_singer_songs_duet |
| 1952 | muse - concert - vocals - albums - tour | 39 | 1952_muse_concert_vocals_albums |
| 1953 | oil - petroleum - oilfields - prices - 1971 | 39 | 1953_oil_petroleum_oilfields_prices |
| 1954 | barrow - barrows - murdered - murders - sheriff | 39 | 1954_barrow_barrows_murdered_murders |
| 1955 | dams - canyon - boulder - dam - aqueduct | 39 | 1955_dams_canyon_boulder_dam |
| 1956 | ashes - wickets - cricket - innings - batsman | 39 | 1956_ashes_wickets_cricket_innings |
| 1957 | defendants - autopsy - saw - gunshot - investigation | 39 | 1957_defendants_autopsy_saw_gunshot |
| 1958 | absurdism - absurdist - absurdity - absurd - meaninglessness | 39 | 1958_absurdism_absurdist_absurdity_absurd |
| 1959 | kung - panda - pandas - martial - sequels | 39 | 1959_kung_panda_pandas_martial |
| 1960 | leucotomy - lobotomy - neurosurgical - psychosurgery - lobotomized | 39 | 1960_leucotomy_lobotomy_neurosurgical_psychosurgery |
| 1961 | sovereignty - treatises - treatise - governance - philosophy | 39 | 1961_sovereignty_treatises_treatise_governance |
| 1962 | colors - coloring - colours - elections - electoral | 38 | 1962_colors_coloring_colours_elections |
| 1963 | garter - knighted - royal - knighthood - peerage | 38 | 1963_garter_knighted_royal_knighthood |
| 1964 | 1666 - fires - 1670 - firefighters - burning | 38 | 1964_1666_fires_1670_firefighters |
| 1965 | logic - mixtape - rapper - memoir - album | 38 | 1965_logic_mixtape_rapper_memoir |
| 1966 | antisemitism - antisemitic - semitism - antifa - persecutions | 38 | 1966_antisemitism_antisemitic_semitism_antifa |
| 1967 | kraken - octopuses - octopus - fishermen - cephalopods | 38 | 1967_kraken_octopuses_octopus_fishermen |
| 1968 | salmon - fishes - fish - sturgeon - fishery | 38 | 1968_salmon_fishes_fish_sturgeon |
| 1969 | constitution - constitutional - amended - amendments - amendment | 38 | 1969_constitution_constitutional_amended_amendments |
| 1970 | triangles - angles - triangle - hypotenuse - cosines | 38 | 1970_triangles_angles_triangle_hypotenuse |
| 1971 | executions - punishments - abolished - punishment - abolishing | 38 | 1971_executions_punishments_abolished_punishment |
| 1972 | dragons - imagine - unreleased - indie - released | 38 | 1972_dragons_imagine_unreleased_indie |
| 1973 | writer - novels - novel - literary - negro | 38 | 1973_writer_novels_novel_literary |
| 1974 | shuttlecock - badminton - tennis - bouncing - backhand | 38 | 1974_shuttlecock_badminton_tennis_bouncing |
| 1975 | acronyms - abbreviations - acronym - abbreviation - initials | 38 | 1975_acronyms_abbreviations_acronym_abbreviation |
| 1976 | executions - electrocution - unconstitutional - inmates - executed | 38 | 1976_executions_electrocution_unconstitutional_inmates |
| 1977 | bots - bot - automated - human - recognition | 38 | 1977_bots_bot_automated_human |
| 1978 | prenuptial - agreements - marital - marriage - agreement | 38 | 1978_prenuptial_agreements_marital_marriage |
| 1979 | population - 35 - 25 - 65 - age | 38 | 1979_population_35_25_65 |
| 1980 | dengue - fever - mosquito - mosquitoes - mosquitos | 38 | 1980_dengue_fever_mosquito_mosquitoes |
| 1981 | rainbow - studio - productions - cartoons - cartoon | 38 | 1981_rainbow_studio_productions_cartoons |
| 1982 | developmental - classrooms - developmentally - classroom - educational | 38 | 1982_developmental_classrooms_developmentally_classroom |
| 1983 | racing - raced - speedway - motorsports - racetrack | 38 | 1983_racing_raced_speedway_motorsports |
| 1984 | agricultural - commodity - corn - commodities - biofuels | 38 | 1984_agricultural_commodity_corn_commodities |
| 1985 | cosplay - cosplayers - costumes - contestants - masks | 38 | 1985_cosplay_cosplayers_costumes_contestants |
| 1986 | sour - songwriter - grungy - debut - songs | 38 | 1986_sour_songwriter_grungy_debut |
| 1987 | yoon - jin - jung - hye - kyung | 38 | 1987_yoon_jin_jung_hye |
| 1988 | keynote - festival - attendees - conferences - organizers | 38 | 1988_keynote_festival_attendees_conferences |
| 1989 | celebrity - spinoffs - sisters - rob - siblings | 38 | 1989_celebrity_spinoffs_sisters_rob |
| 1990 | provider - security - cyberattack - cybersecurity - servers | 38 | 1990_provider_security_cyberattack_cybersecurity |
| 1991 | pods - pod - containers - cluster - clusters | 38 | 1991_pods_pod_containers_cluster |
| 1992 | lifespan - oldest - longevity - age - lived | 38 | 1992_lifespan_oldest_longevity_age |
| 1993 | battleship - warship - battleships - naval - navy | 38 | 1993_battleship_warship_battleships_naval |
| 1994 | regiments - regiment - battalions - recruits - recruitment | 38 | 1994_regiments_regiment_battalions_recruits |
| 1995 | parliamentary - minister - parliament - ministers - constituency | 38 | 1995_parliamentary_minister_parliament_ministers |
| 1996 | minister - politician - campaigned - constituency - elected | 37 | 1996_minister_politician_campaigned_constituency |
| 1997 | subsidies - aid - postwar - economy - economists | 37 | 1997_subsidies_aid_postwar_economy |
| 1998 | gameplay - gaming - rpg - games - twilight | 37 | 1998_gameplay_gaming_rpg_games |
| 1999 | alexithymia - anxiety - disorders - psychiatric - disorder | 37 | 1999_alexithymia_anxiety_disorders_psychiatric |
| 2000 | tests - test - gender - feminist - women | 37 | 2000_tests_test_gender_feminist |
| 2001 | widows - widowhood - 1861 - bipolar - 1880s | 37 | 2001_widows_widowhood_1861_bipolar |
| 2002 | demons - demonic - demon - eve - demonology | 37 | 2002_demons_demonic_demon_eve |
| 2003 | gangster - gangsters - notorious - prohibition - jailing | 37 | 2003_gangster_gangsters_notorious_prohibition |
| 2004 | automata - cellular - cells - cell - automaton | 37 | 2004_automata_cellular_cells_cell |
| 2005 | languages - language - multilingual - soviet - lingua | 37 | 2005_languages_language_multilingual_soviet |
| 2006 | population - 2050 - populations - 2060 - demographic | 37 | 2006_population_2050_populations_2060 |
| 2007 | sarin - cousins - cousin - kidnap - affair | 37 | 2007_sarin_cousins_cousin_kidnap |
| 2008 | apes - ape - primates - chimpanzees - primate | 37 | 2008_apes_ape_primates_chimpanzees |
| 2009 | livestock - cattle - veterinary - beef - animal | 37 | 2009_livestock_cattle_veterinary_beef |
| 2010 | van - alleged - suspect - lurid - arrested | 37 | 2010_van_alleged_suspect_lurid |
| 2011 | emotion - emotions - emotional - affective - arousal | 37 | 2011_emotion_emotions_emotional_affective |
| 2012 | creoles - creole - francophone - french - parishes | 37 | 2012_creoles_creole_francophone_french |
| 2013 | laureates - laureate - prizes - prize - novelists | 37 | 2013_laureates_laureate_prizes_prize |
| 2014 | pachinko - parlors - arcades - parlor - yakuza | 37 | 2014_pachinko_parlors_arcades_parlor |
| 2015 | bohemian - queen - deacon - mercury - musical | 37 | 2015_bohemian_queen_deacon_mercury |
| 2016 | dictator - regime - rebels - unrest - fled | 37 | 2016_dictator_regime_rebels_unrest |
| 2017 | bombed - airship - airships - zeppelin - bombing | 37 | 2017_bombed_airship_airships_zeppelin |
| 2018 | euthanasia - suicide - suicides - patients - deaths | 37 | 2018_euthanasia_suicide_suicides_patients |
| 2019 | censorship - pornography - videos - moderation - abusing | 37 | 2019_censorship_pornography_videos_moderation |
| 2020 | apple - retina - screen - camera - processor | 37 | 2020_apple_retina_screen_camera |
| 2021 | marshals - marshal - department - deputy - deputies | 37 | 2021_marshals_marshal_department_deputy |
| 2022 | baron - flew - 1918 - von - pilots | 37 | 2022_baron_flew_1918_von |
| 2023 | orthodox - soviet - atheism - clergy - persecution | 37 | 2023_orthodox_soviet_atheism_clergy |
| 2024 | metal - bands - band - idol - genre | 37 | 2024_metal_bands_band_idol |
| 2025 | pharaoh - conquered - rulers - deposed - kings | 37 | 2025_pharaoh_conquered_rulers_deposed |
| 2026 | abducted - murders - brooks - abduction - abductions | 37 | 2026_abducted_murders_brooks_abduction |
| 2027 | currencies - currency - rates - inflation - parity | 37 | 2027_currencies_currency_rates_inflation |
| 2028 | 1917 - 1918 - soviet - soviets - republic | 37 | 2028_1917_1918_soviet_soviets |
| 2029 | animism - animists - animist - spiritualism - anthropological | 37 | 2029_animism_animists_animist_spiritualism |
| 2030 | hypothesis - hypotheses - theories - scientific - reproducibility | 37 | 2030_hypothesis_hypotheses_theories_scientific |
| 2031 | commerce - resell - sales - vendor - marketplace | 37 | 2031_commerce_resell_sales_vendor |
| 2032 | mathematician - mathematicians - mathematics - physicist - von | 37 | 2032_mathematician_mathematicians_mathematics_physicist |
| 2033 | channel - channels - tv - television - streaming | 37 | 2033_channel_channels_tv_television |
| 2034 | marvel - superhero - comics - valkyrie - thunder | 37 | 2034_marvel_superhero_comics_valkyrie |
| 2035 | 1080p - framerate - 1080 - 60fps - 720p | 37 | 2035_1080p_framerate_1080_60fps |
| 2036 | head - butt - episodes - rerun - paramount | 37 | 2036_head_butt_episodes_rerun |
| 2037 | woke - woken - wake - awake - twitter | 37 | 2037_woke_woken_wake_awake |
| 2038 | ron - character - villains - sidekick - characters | 37 | 2038_ron_character_villains_sidekick |
| 2039 | fed - inflation - monetary - financial - treasury | 37 | 2039_fed_inflation_monetary_financial |
| 2040 | fatwas - fatwā - fatwa - satanic - author | 37 | 2040_fatwas_fatwā_fatwa_satanic |
| 2041 | reliance - telecommunications - telecom - telecommunication - broadband | 37 | 2041_reliance_telecommunications_telecom_telecommunication |
| 2042 | conqueror - 1066 - 1069 - 1067 - 1086 | 37 | 2042_conqueror_1066_1069_1067 |
| 2043 | broadway - musicals - theatre - cat - musical | 37 | 2043_broadway_musicals_theatre_cat |
| 2044 | philosopher - writings - aphorism - philosophers - poet | 37 | 2044_philosopher_writings_aphorism_philosophers |
| 2045 | groceries - restaurants - restaurant - taxicab - taxi | 37 | 2045_groceries_restaurants_restaurant_taxicab |
| 2046 | musicals - lyricist - musical - concertos - operas | 37 | 2046_musicals_lyricist_musical_concertos |
| 2047 | mysticism - mystical - spiritual - spirituality - esotericism | 37 | 2047_mysticism_mystical_spiritual_spirituality |
| 2048 | biblical - patriarch - prophethood - genesis - prophetic | 37 | 2048_biblical_patriarch_prophethood_genesis |
| 2049 | chainsaw - massacre - slaughterhouse - sequels - chain | 36 | 2049_chainsaw_massacre_slaughterhouse_sequels |
| 2050 | fingerprints - robbers - fingerprint - investigation - ransom | 36 | 2050_fingerprints_robbers_fingerprint_investigation |
| 2051 | rocky - rapper - ap - mixtape - billboard | 36 | 2051_rocky_rapper_ap_mixtape |
| 2052 | influential - list - publicized - ranking - world | 36 | 2052_influential_list_publicized_ranking |
| 2053 | concert - concerts - tour - theater - headlining | 36 | 2053_concert_concerts_tour_theater |
| 2054 | protozoans - protozoa - protozoan - taxonomic - phylogeny | 36 | 2054_protozoans_protozoa_protozoan_taxonomic |
| 2055 | missile - missiles - supersonic - hypersonic - radar | 36 | 2055_missile_missiles_supersonic_hypersonic |
| 2056 | anthrax - anthracis - infection - infections - infected | 36 | 2056_anthrax_anthracis_infection_infections |
| 2057 | streaming - premiere - tv - stream - 1080p | 36 | 2057_streaming_premiere_tv_stream |
| 2058 | apps - app - android - purchases - ads | 36 | 2058_apps_app_android_purchases |
| 2059 | 1644 - theological - theologian - puritan - theology | 36 | 2059_1644_theological_theologian_puritan |
| 2060 | spaceflight - military - spaceflights - spacecraft - aerospace | 36 | 2060_spaceflight_military_spaceflights_spacecraft |
| 2061 | dick - biography - author - novelist - writer | 36 | 2061_dick_biography_author_novelist |
| 2062 | mangroves - mangrove - ecosystem - ecosystems - biodiversity | 36 | 2062_mangroves_mangrove_ecosystem_ecosystems |
| 2063 | harbour - naval - captured - 1842 - steamships | 36 | 2063_harbour_naval_captured_1842 |
| 2064 | chipmunks - chipmunk - song - 1959 - albums | 36 | 2064_chipmunks_chipmunk_song_1959 |
| 2065 | plasmas - plasma - electrostatic - electrically - electrons | 36 | 2065_plasmas_plasma_electrostatic_electrically |
| 2066 | channel - mosh - media - channels - entertainment | 36 | 2066_channel_mosh_media_channels |
| 2067 | kernel - latest - maintainers - os - maintainer | 36 | 2067_kernel_latest_maintainers_os |
| 2068 | population - municipalities - cityscape - villages - city | 36 | 2068_population_municipalities_cityscape_villages |
| 2069 | songs - song - singer - albums - album | 36 | 2069_songs_song_singer_albums |
| 2070 | cannabinoids - cannabinoid - tetrahydrocannabinol - cannabidiol - cannabis | 36 | 2070_cannabinoids_cannabinoid_tetrahydrocannabinol_cannabidiol |
| 2071 | jong - politburo - secretary - leader - chairman | 36 | 2071_jong_politburo_secretary_leader |
| 2072 | dancer - dances - danced - dancing - choreography | 36 | 2072_dancer_dances_danced_dancing |
| 2073 | reptilians - conspiracist - conspiracism - reptilian - extraterrestrial | 36 | 2073_reptilians_conspiracist_conspiracism_reptilian |
| 2074 | newscast - reporter - primetime - anchor - journalist | 36 | 2074_newscast_reporter_primetime_anchor |
| 2075 | 1605 - gunpowder - undercroft - conspirators - 1603 | 36 | 2075_1605_gunpowder_undercroft_conspirators |
| 2076 | motley - albums - frontman - band - concert | 36 | 2076_motley_albums_frontman_band |
| 2077 | wormholes - wormhole - spacetime - traversable - relativity | 36 | 2077_wormholes_wormhole_spacetime_traversable |
| 2078 | habeas - constitution - detention - constitutional - imprisonment | 36 | 2078_habeas_constitution_detention_constitutional |
| 2079 | renewed - primetime - episodes - airing - premiered | 36 | 2079_renewed_primetime_episodes_airing |
| 2080 | strikers - goalscoring - winger - striker - goalkeeping | 36 | 2080_strikers_goalscoring_winger_striker |
| 2081 | seal - undercover - smuggling - cartel - smuggler | 36 | 2081_seal_undercover_smuggling_cartel |
| 2082 | placebo - concert - concerts - gigs - albums | 36 | 2082_placebo_concert_concerts_gigs |
| 2083 | radiation - radiographs - rays - radiography - ray | 36 | 2083_radiation_radiographs_rays_radiography |
| 2084 | thrash - vocals - drumming - rhythmic - melodic | 36 | 2084_thrash_vocals_drumming_rhythmic |
| 2085 | scored - scoring - goalscoring - goalscorer - goals | 36 | 2085_scored_scoring_goalscoring_goalscorer |
| 2086 | teams - league - leagues - stadium - conferences | 36 | 2086_teams_league_leagues_stadium |
| 2087 | kava - hepatotoxicity - supplements - herbal - medicinal | 36 | 2087_kava_hepatotoxicity_supplements_herbal |
| 2088 | expedition - expeditions - voyage - explorers - archaeologist | 36 | 2088_expedition_expeditions_voyage_explorers |
| 2089 | polyamory - polyamorous - polygamous - polygamy - monogamous | 36 | 2089_polyamory_polyamorous_polygamous_polygamy |
| 2090 | rose - autobiography - quotes - writing - quotation | 36 | 2090_rose_autobiography_quotes_writing |
| 2091 | biblical - conquered - temple - kingdom - temples | 36 | 2091_biblical_conquered_temple_kingdom |
| 2092 | bacon - pork - vegetarian - meat - beef | 36 | 2092_bacon_pork_vegetarian_meat |
| 2093 | memes - evolution - evolutionary - meme - genetic | 36 | 2093_memes_evolution_evolutionary_meme |
| 2094 | actress - actresses - blonde - playboy - vogue | 36 | 2094_actress_actresses_blonde_playboy |
| 2095 | ancient - law - laws - treatise - legislation | 36 | 2095_ancient_law_laws_treatise |
| 2096 | actor - peck - gangster - actors - portrayal | 36 | 2096_actor_peck_gangster_actors |
| 2097 | protesting - protest - protesters - protests - picketing | 36 | 2097_protesting_protest_protesters_protests |
| 2098 | neo - matrix - trinity - smith - cypher | 36 | 2098_neo_matrix_trinity_smith |
| 2099 | mathematician - mathematicians - hardy - mathematics - mathematical | 36 | 2099_mathematician_mathematicians_hardy_mathematics |
| 2100 | semiotics - semiotic - concepts - linguistics - formalist | 36 | 2100_semiotics_semiotic_concepts_linguistics |
| 2101 | palace - palaces - monuments - museums - museum | 36 | 2101_palace_palaces_monuments_museums |
| 2102 | episodes - airing - episode - ugly - finale | 36 | 2102_episodes_airing_episode_ugly |
| 2103 | lunar - moon - landings - spacecraft - missions | 36 | 2103_lunar_moon_landings_spacecraft |
| 2104 | protocols - antisemitism - conspiratorial - antisemitic - conspiracist | 35 | 2104_protocols_antisemitism_conspiratorial_antisemitic |
| 2105 | sailed - seaworthy - boat - vessel - aground | 35 | 2105_sailed_seaworthy_boat_vessel |
| 2106 | duet - concert - albums - songs - concerts | 35 | 2106_duet_concert_albums_songs |
| 2107 | flame - ignited - flamethrower - flames - burning | 35 | 2107_flame_ignited_flamethrower_flames |
| 2108 | wu - han - tung - qi - sun | 35 | 2108_wu_han_tung_qi |
| 2109 | committees - parliamentary - parliament - chairperson - chairpersons | 35 | 2109_committees_parliamentary_parliament_chairperson |
| 2110 | alleged - bail - prosecution - arrested - arrest | 35 | 2110_alleged_bail_prosecution_arrested |
| 2111 | duet - sang - queen - concert - diamonds | 35 | 2111_duet_sang_queen_concert |
| 2112 | municipality - polish - orchestras - cathedral - conservatory | 35 | 2112_municipality_polish_orchestras_cathedral |
| 2113 | regiment - enlisted - regiments - regimental - navy | 35 | 2113_regiment_enlisted_regiments_regimental |
| 2114 | tower - towers - tallest - 1889 - construction | 35 | 2114_tower_towers_tallest_1889 |
| 2115 | taxation - tax - taxes - taxed - taxpayers | 35 | 2115_taxation_tax_taxes_taxed |
| 2116 | sclerosis - ms - demyelination - encephalomyelitis - neurological | 35 | 2116_sclerosis_ms_demyelination_encephalomyelitis |
| 2117 | seppuku - samurai - decapitation - decapitate - decapitates | 35 | 2117_seppuku_samurai_decapitation_decapitate |
| 2118 | architect - architects - architecture - houses - designs | 35 | 2118_architect_architects_architecture_houses |
| 2119 | albums - songs - lyrics - lyricist - sang | 35 | 2119_albums_songs_lyrics_lyricist |
| 2120 | decibels - decibel - amplitude - amplitudes - amplifier | 35 | 2120_decibels_decibel_amplitude_amplitudes |
| 2121 | palace - crystal - penalty - scorer - scored | 35 | 2121_palace_crystal_penalty_scorer |
| 2122 | emir - sheikh - emirate - emirates - president | 35 | 2122_emir_sheikh_emirate_emirates |
| 2123 | strips - circular - strip - folds - geometrically | 35 | 2123_strips_circular_strip_folds |
| 2124 | sang - songwriter - singers - albums - singer | 35 | 2124_sang_songwriter_singers_albums |
| 2125 | libel - celebrity - reportedly - scandals - resigned | 35 | 2125_libel_celebrity_reportedly_scandals |
| 2126 | divergent - trilogy - novel - screenplay - sequel | 35 | 2126_divergent_trilogy_novel_screenplay |
| 2127 | vaccine - vaccines - vaccination - vaccinated - vaccinations | 35 | 2127_vaccine_vaccines_vaccination_vaccinated |
| 2128 | paramount - premiered - channel - tv - episodes | 35 | 2128_paramount_premiered_channel_tv |
| 2129 | fish - cannibalism - tortured - murders - murder | 35 | 2129_fish_cannibalism_tortured_murders |
| 2130 | touchdowns - interceptions - touchdown - quarterback - fumble | 35 | 2130_touchdowns_interceptions_touchdown_quarterback |
| 2131 | viewership - viewers - televised - television - broadcasters | 35 | 2131_viewership_viewers_televised_television |
| 2132 | shops - mall - stores - shop - store | 35 | 2132_shops_mall_stores_shop |
| 2133 | niece - grandmother - 1918 - aunt - 1945 | 35 | 2133_niece_grandmother_1918_aunt |
| 2134 | scored - goalscorers - goals - scoring - goal | 35 | 2134_scored_goalscorers_goals_scoring |
| 2135 | seo - google - ranking - rankings - pages | 35 | 2135_seo_google_ranking_rankings |
| 2136 | reliance - shareholders - crore - crores - industries | 35 | 2136_reliance_shareholders_crore_crores |
| 2137 | postmodernism - modernism - postmodern - modernists - modernist | 35 | 2137_postmodernism_modernism_postmodern_modernists |
| 2138 | genesis - biblical - patriarch - prophets - polytheism | 35 | 2138_genesis_biblical_patriarch_prophets |
| 2139 | presidential - parliamentary - governs - government - presidents | 35 | 2139_presidential_parliamentary_governs_government |
| 2140 | political - politics - sciences - sociology - sociologists | 35 | 2140_political_politics_sciences_sociology |
| 2141 | feeds - syndication - feed - subscribing - browsers | 35 | 2141_feeds_syndication_feed_subscribing |
| 2142 | ac - drummer - guitarist - drums - albums | 35 | 2142_ac_drummer_guitarist_drums |
| 2143 | chassis - vehicle - dealerships - automotive - factory | 35 | 2143_chassis_vehicle_dealerships_automotive |
| 2144 | biographers - revolt - orientalism - desertion - pillars | 35 | 2144_biographers_revolt_orientalism_desertion |
| 2145 | wastes - waste - disposal - recycling - landfills | 35 | 2145_wastes_waste_disposal_recycling |
| 2146 | radio - fm - stations - broadcasting - broadcasters | 35 | 2146_radio_fm_stations_broadcasting |
| 2147 | resignation - riots - protest - paramilitary - protesters | 35 | 2147_resignation_riots_protest_paramilitary |
| 2148 | theme - themes - intro - anime - ending | 35 | 2148_theme_themes_intro_anime |
| 2149 | journalist - hitch - correspondent - readership - statesman | 34 | 2149_journalist_hitch_correspondent_readership |
| 2150 | paintings - painting - painters - painter - murals | 34 | 2150_paintings_painting_painters_painter |
| 2151 | fighter - fighters - featherweight - brawling - bantamweight | 34 | 2151_fighter_fighters_featherweight_brawling |
| 2152 | transit - commute - commuting - commuters - buses | 34 | 2152_transit_commute_commuting_commuters |
| 2153 | bliss - ambient - stages - albums - stage | 34 | 2153_bliss_ambient_stages_albums |
| 2154 | studios - lions - acquisitions - paramount - owns | 34 | 2154_studios_lions_acquisitions_paramount |
| 2155 | humidity - humid - moisture - dew - evaporation | 34 | 2155_humidity_humid_moisture_dew |
| 2156 | codeine - prescription - paracetamol - prescribed - dihydrocodeine | 34 | 2156_codeine_prescription_paracetamol_prescribed |
| 2157 | merger - shareholders - acquisition - acquire - acquired | 34 | 2157_merger_shareholders_acquisition_acquire |
| 2158 | geopolitical - tsarist - ideology - geopolitics - political | 34 | 2158_geopolitical_tsarist_ideology_geopolitics |
| 2159 | crops - agriculture - agricultural - maize - plantations | 34 | 2159_crops_agriculture_agricultural_maize |
| 2160 | poutine - gravy - truffles - burger - cuisine | 34 | 2160_poutine_gravy_truffles_burger |
| 2161 | autobiography - autobiographies - bird - poetry - poet | 34 | 2161_autobiography_autobiographies_bird_poetry |
| 2162 | propaganda - propagandists - propagandistic - propagandist - persuasion | 34 | 2162_propaganda_propagandists_propagandistic_propagandist |
| 2163 | sausage - sausages - bun - chili - condiments | 34 | 2163_sausage_sausages_bun_chili |
| 2164 | albums - vocals - album - remix - punk | 34 | 2164_albums_vocals_album_remix |
| 2165 | inmate - prison - prisoner - imprisonment - sentenced | 34 | 2165_inmate_prison_prisoner_imprisonment |
| 2166 | discord - subscription - guilds - nitro - twitch | 34 | 2166_discord_subscription_guilds_nitro |
| 2167 | gnostic - gnosis - theology - religions - theosophical | 34 | 2167_gnostic_gnosis_theology_religions |
| 2168 | bomber - squadron - 509th - 1945 - bombardment | 34 | 2168_bomber_squadron_509th_1945 |
| 2169 | boiler - turbine - engines - invention - inventor | 34 | 2169_boiler_turbine_engines_invention |
| 2170 | fighter - arcade - street - arcades - consoles | 34 | 2170_fighter_arcade_street_arcades |
| 2171 | parole - sentenced - hearings - convicted - judge | 34 | 2171_parole_sentenced_hearings_convicted |
| 2172 | home - sequels - sequel - screenplays - film | 34 | 2172_home_sequels_sequel_screenplays |
| 2173 | ferry - harbor - pier - wharf - waterfront | 34 | 2173_ferry_harbor_pier_wharf |
| 2174 | muddy - harmonica - blues - albums - guitarist | 34 | 2174_muddy_harmonica_blues_albums |
| 2175 | calamity - calamitous - novels - novel - soprano | 34 | 2175_calamity_calamitous_novels_novel |
| 2176 | godfather - mafia - capo - murdered - murder | 34 | 2176_godfather_mafia_capo_murdered |
| 2177 | undrafted - cornerback - patriots - receptions - touchdowns | 34 | 2177_undrafted_cornerback_patriots_receptions |
| 2178 | royal - monarch - palace - palaces - royalty | 34 | 2178_royal_monarch_palace_palaces |
| 2179 | joey - punk - drummer - sings - vocals | 34 | 2179_joey_punk_drummer_sings |
| 2180 | nuclear - sanctions - missiles - jong - missile | 34 | 2180_nuclear_sanctions_missiles_jong |
| 2181 | poet - poem - poetry - poems - scribes | 34 | 2181_poet_poem_poetry_poems |
| 2182 | rebounds - warriors - curry - assists - green | 34 | 2182_rebounds_warriors_curry_assists |
| 2183 | skyscraper - tallest - tower - towers - skyscrapers | 34 | 2183_skyscraper_tallest_tower_towers |
| 2184 | shareholder - shareholders - investor - shares - holdings | 34 | 2184_shareholder_shareholders_investor_shares |
| 2185 | astronomer - heliocentric - 1516 - papal - 1496 | 34 | 2185_astronomer_heliocentric_1516_papal |
| 2186 | actresses - actress - wilder - portrayal - broadway | 34 | 2186_actresses_actress_wilder_portrayal |
| 2187 | register - historic - cemeteries - landmarks - monuments | 34 | 2187_register_historic_cemeteries_landmarks |
| 2188 | albums - album - songs - concert - comeback | 34 | 2188_albums_album_songs_concert |
| 2189 | merger - aerospace - subsidiaries - acquisitions - firms | 34 | 2189_merger_aerospace_subsidiaries_acquisitions |
| 2190 | wiggle - band - entertainers - concert - performers | 34 | 2190_wiggle_band_entertainers_concert |
| 2191 | pilgrims - 1620 - pilgrim - voyage - sailed | 34 | 2191_pilgrims_1620_pilgrim_voyage |
| 2192 | sneakers - footwear - shoes - sportswear - sneaker | 34 | 2192_sneakers_footwear_shoes_sportswear |
| 2193 | blade - prequels - sequel - 2049 - runner | 34 | 2193_blade_prequels_sequel_2049 |
| 2194 | torch - olympic - relay - relays - flame | 34 | 2194_torch_olympic_relay_relays |
| 2195 | novelist - purple - novels - novel - literary | 34 | 2195_novelist_purple_novels_novel |
| 2196 | ranger - regiment - infantry - regimental - rangers | 34 | 2196_ranger_regiment_infantry_regimental |
| 2197 | dialects - languages - speak - dialect - language | 34 | 2197_dialects_languages_speak_dialect |
| 2198 | gymnast - gymnasts - gymnastics - gymnastic - athlete | 34 | 2198_gymnast_gymnasts_gymnastics_gymnastic |
| 2199 | haiku - haikai - poetry - poems - poet | 34 | 2199_haiku_haikai_poetry_poems |
| 2200 | spetsnaz - soviet - infantrymen - commanders - brigades | 34 | 2200_spetsnaz_soviet_infantrymen_commanders |
| 2201 | deaf - blindness - disabilities - braille - blind | 33 | 2201_deaf_blindness_disabilities_braille |
| 2202 | medieval - antiquity - renaissance - darkness - dark | 33 | 2202_medieval_antiquity_renaissance_darkness |
| 2203 | photographer - photography - photographs - photographic - photographers | 33 | 2203_photographer_photography_photographs_photographic |
| 2204 | genocide - genocides - holocaust - perpetrators - genocidal | 33 | 2204_genocide_genocides_holocaust_perpetrators |
| 2205 | bow - actress - 1932 - laurels - 1920s | 33 | 2205_bow_actress_1932_laurels |
| 2206 | crops - agriculture - irrigation - agricultural - farmers | 33 | 2206_crops_agriculture_irrigation_agricultural |
| 2207 | paella - paprika - cuisine - seafood - olive | 33 | 2207_paella_paprika_cuisine_seafood |
| 2208 | novelist - literature - literary - writer - poet | 33 | 2208_novelist_literature_literary_writer |
| 2209 | midnight - noon - midday - clock - evening | 33 | 2209_midnight_noon_midday_clock |
| 2210 | griffin - donation - donated - museum - donating | 33 | 2210_griffin_donation_donated_museum |
| 2211 | starling - cannibalized - cannibal - cannibalize - killer | 33 | 2211_starling_cannibalized_cannibal_cannibalize |
| 2212 | taxis - taxi - taxicabs - fares - cabs | 33 | 2212_taxis_taxi_taxicabs_fares |
| 2213 | hunts - noose - gun - hunting - hunters | 33 | 2213_hunts_noose_gun_hunting |
| 2214 | ethnicities - ethnicity - ethnic - racial - census | 33 | 2214_ethnicities_ethnicity_ethnic_racial |
| 2215 | buildings - skyscraper - apartments - building - architecture | 33 | 2215_buildings_skyscraper_apartments_building |
| 2216 | sedan - convertibles - convertible - coupe - corvette | 33 | 2216_sedan_convertibles_convertible_coupe |
| 2217 | harden - assists - rebounds - scoring - triple | 33 | 2217_harden_assists_rebounds_scoring |
| 2218 | emails - webmail - email - mail - google | 33 | 2218_emails_webmail_email_mail |
| 2219 | paintings - painting - painter - painters - paint | 33 | 2219_paintings_painting_painter_painters |
| 2220 | yards - quarterback - touchdowns - receptions - cornerback | 33 | 2220_yards_quarterback_touchdowns_receptions |
| 2221 | museums - museum - exhibitions - sheikh - sultanate | 33 | 2221_museums_museum_exhibitions_sheikh |
| 2222 | retailer - store - sales - shop - stores | 33 | 2222_retailer_store_sales_shop |
| 2223 | khan - politician - candidate - goldsmith - councillor | 33 | 2223_khan_politician_candidate_goldsmith |
| 2224 | eigenfunctions - quantum - eigenstates - eigenstate - observables | 33 | 2224_eigenfunctions_quantum_eigenstates_eigenstate |
| 2225 | recycling - recycled - recycle - recyclable - recycles | 33 | 2225_recycling_recycled_recycle_recyclable |
| 2226 | thrash - slayer - band - bands - hardcore | 33 | 2226_thrash_slayer_band_bands |
| 2227 | beetle - beetles - convertible - fenders - chassis | 33 | 2227_beetle_beetles_convertible_fenders |
| 2228 | assists - rebounds - steals - doubles - triple | 33 | 2228_assists_rebounds_steals_doubles |
| 2229 | fifths - fifth - circle - tones - numerals | 33 | 2229_fifths_fifth_circle_tones |
| 2230 | rush - bands - rock - zeppelin - drummer | 33 | 2230_rush_bands_rock_zeppelin |
| 2231 | yuan - chairman - hui - election - elections | 33 | 2231_yuan_chairman_hui_election |
| 2232 | clitoris - position - positions - intercourse - clitoral | 33 | 2232_clitoris_position_positions_intercourse |
| 2233 | viewers - episodes - generation - syndication - storylines | 33 | 2233_viewers_episodes_generation_syndication |
| 2234 | hegemony - superpower - superpowers - diplomacy - hegemonic | 33 | 2234_hegemony_superpower_superpowers_diplomacy |
| 2235 | population - demographic - comune - average - depopulation | 33 | 2235_population_demographic_comune_average |
| 2236 | laptops - laptop - notebooks - notebook - desktops | 33 | 2236_laptops_laptop_notebooks_notebook |
| 2237 | unrest - bombing - rebels - dictator - guerrillas | 33 | 2237_unrest_bombing_rebels_dictator |
| 2238 | survivors - zombies - umbrella - hive - discovers | 33 | 2238_survivors_zombies_umbrella_hive |
| 2239 | activist - intellectuals - activism - anarchism - linguistics | 33 | 2239_activist_intellectuals_activism_anarchism |
| 2240 | sesame - episodes - cartoon - licensing - television | 32 | 2240_sesame_episodes_cartoon_licensing |
| 2241 | moderate - conservatives - ideological - nationalist - conservative | 32 | 2241_moderate_conservatives_ideological_nationalist |
| 2242 | biblical - testament - mythological - epistle - satan | 32 | 2242_biblical_testament_mythological_epistle |
| 2243 | buried - cemetery - died - funeral - interred | 32 | 2243_buried_cemetery_died_funeral |
| 2244 | defender - footballer - arsenal - villa - stoke | 32 | 2244_defender_footballer_arsenal_villa |
| 2245 | dictionaries - dictionary - reprinting - typography - abridgement | 32 | 2245_dictionaries_dictionary_reprinting_typography |
| 2246 | osteopathic - osteopathy - osteopaths - homeopathy - physiotherapists | 32 | 2246_osteopathic_osteopathy_osteopaths_homeopathy |
| 2247 | indigenous - aboriginal - arctic - tribal - anthropologist | 32 | 2247_indigenous_aboriginal_arctic_tribal |
| 2248 | religions - religion - religiosity - theology - religious | 32 | 2248_religions_religion_religiosity_theology |
| 2249 | lily - robin - episode - doppelganger - doppelgänger | 32 | 2249_lily_robin_episode_doppelganger |
| 2250 | pedagogy - pedagogical - pedagogue - educator - teaching | 32 | 2250_pedagogy_pedagogical_pedagogue_educator |
| 2251 | touchdowns - interceptions - yards - quarterback - interception | 32 | 2251_touchdowns_interceptions_yards_quarterback |
| 2252 | dubbed - satellite - amazon - premiere - streamed | 32 | 2252_dubbed_satellite_amazon_premiere |
| 2253 | drummer - drumming - band - songwriters - gigs | 32 | 2253_drummer_drumming_band_songwriters |
| 2254 | finasteride - antiandrogen - antiandrogenic - inhibitor - dosage | 32 | 2254_finasteride_antiandrogen_antiandrogenic_inhibitor |
| 2255 | northwest - fort - settlers - forts - 1840s | 32 | 2255_northwest_fort_settlers_forts |
| 2256 | ancestry - ancestor - ancestors - ancestral - archipelago | 32 | 2256_ancestry_ancestor_ancestors_ancestral |
| 2257 | hypotenuse - triangles - squares - geometry - triangle | 32 | 2257_hypotenuse_triangles_squares_geometry |
| 2258 | orbits - solutions - bodies - mathematical - gravitation | 32 | 2258_orbits_solutions_bodies_mathematical |
| 2259 | easter - holiday - celebrated - feasts - feast | 32 | 2259_easter_holiday_celebrated_feasts |
| 2260 | antihypertensive - propranolol - hypertension - blockers - adrenergic | 32 | 2260_antihypertensive_propranolol_hypertension_blockers |
| 2261 | adder - servant - reign - descendants - queen | 32 | 2261_adder_servant_reign_descendants |
| 2262 | genetics - genetic - heredity - traits - genes | 32 | 2262_genetics_genetic_heredity_traits |
| 2263 | amazon - affiliate - retailers - retailer - sales | 32 | 2263_amazon_affiliate_retailers_retailer |
| 2264 | birthday - doodle - birthdays - 26th - celebrated | 32 | 2264_birthday_doodle_birthdays_26th |
| 2265 | hominem - argumentation - arguments - philosophical - philosopher | 32 | 2265_hominem_argumentation_arguments_philosophical |
| 2266 | carmaker - automobiles - cars - sedans - vehicles | 32 | 2266_carmaker_automobiles_cars_sedans |
| 2267 | amnesty - refugees - racism - asylum - discrimination | 32 | 2267_amnesty_refugees_racism_asylum |
| 2268 | pamphlet - 1776 - pamphlets - revolutionary - revolutionaries | 32 | 2268_pamphlet_1776_pamphlets_revolutionary |
| 2269 | imperialism - colonial - labour - humanitarian - ivory | 32 | 2269_imperialism_colonial_labour_humanitarian |
| 2270 | news - journalism - propaganda - misinformation - credible | 32 | 2270_news_journalism_propaganda_misinformation |
| 2271 | gymnast - gymnasts - gymnastics - olympic - medals | 32 | 2271_gymnast_gymnasts_gymnastics_olympic |
| 2272 | stadia - subscriptions - subscription - launched - launch | 32 | 2272_stadia_subscriptions_subscription_launched |
| 2273 | spinal - paralysis - paralyzed - vertebrae - cervical | 32 | 2273_spinal_paralysis_paralyzed_vertebrae |
| 2274 | housewives - housewife - cast - reunion - guests | 32 | 2274_housewives_housewife_cast_reunion |
| 2275 | irrigation - sea - waters - salinity - basins | 32 | 2275_irrigation_sea_waters_salinity |
| 2276 | transistors - microprocessors - processors - microprocessor - transistor | 32 | 2276_transistors_microprocessors_processors_microprocessor |
| 2277 | phantom - ghost - opera - lair - cloak | 32 | 2277_phantom_ghost_opera_lair |
| 2278 | granites - granite - mineralogy - magmas - basaltic | 32 | 2278_granites_granite_mineralogy_magmas |
| 2279 | victor - fascism - monarchist - monarchy - fascist | 32 | 2279_victor_fascism_monarchist_monarchy |
| 2280 | fasciitis - fascia - plantar - fascicles - tendon | 32 | 2280_fasciitis_fascia_plantar_fascicles |
| 2281 | company - conglomerate - market - enterprises - industries | 32 | 2281_company_conglomerate_market_enterprises |
| 2282 | rosemary - lobotomy - nuns - lobotomized - convent | 32 | 2282_rosemary_lobotomy_nuns_lobotomized |
| 2283 | mosque - terrorist - mosques - coroner - victims | 32 | 2283_mosque_terrorist_mosques_coroner |
| 2284 | tennis - tournaments - tournament - finalist - quarterfinals | 32 | 2284_tennis_tournaments_tournament_finalist |
| 2285 | dramas - airing - drama - sonata - cultural | 32 | 2285_dramas_airing_drama_sonata |
| 2286 | globalization - globalisation - globalized - transnational - global | 32 | 2286_globalization_globalisation_globalized_transnational |
| 2287 | knight - donated - philanthropist - philanthropic - donation | 32 | 2287_knight_donated_philanthropist_philanthropic |
| 2288 | ibn - al - theology - treatises - ijtihad | 32 | 2288_ibn_al_theology_treatises |
| 2289 | creatine - creatinine - supplementation - supplement - supplements | 32 | 2289_creatine_creatinine_supplementation_supplement |
| 2290 | duo - app - mobile - android - proficiency | 32 | 2290_duo_app_mobile_android |
| 2291 | offspring - albums - album - band - bands | 32 | 2291_offspring_albums_album_band |
| 2292 | guards - defensive - guard - basketball - players | 32 | 2292_guards_defensive_guard_basketball |
| 2293 | prix - lightning - racing - radiator - racers | 32 | 2293_prix_lightning_racing_radiator |
| 2294 | executives - stockholder - executive - shareholders - company | 32 | 2294_executives_stockholder_executive_shareholders |
| 2295 | presenter - savage - airing - keynote - premiered | 32 | 2295_presenter_savage_airing_keynote |
| 2296 | multiracial - geisha - ethnic - actors - ethnically | 31 | 2296_multiracial_geisha_ethnic_actors |
| 2297 | schools - academies - school - education - colleges | 31 | 2297_schools_academies_school_education |
| 2298 | oz - debate - debater - debating - midterms | 31 | 2298_oz_debate_debater_debating |
| 2299 | dragon - anime - manga - superman - piccolo | 31 | 2299_dragon_anime_manga_superman |
| 2300 | tennis - slams - doubles - tournaments - racquets | 31 | 2300_tennis_slams_doubles_tournaments |
| 2301 | disks - disk - floppy - drives - storage | 31 | 2301_disks_disk_floppy_drives |
| 2302 | albums - duet - album - vocals - singles | 31 | 2302_albums_duet_album_vocals |
| 2303 | guitarist - tour - touring - zeppelin - backstage | 31 | 2303_guitarist_tour_touring_zeppelin |
| 2304 | bidets - bidet - toilets - bathrooms - toilet | 31 | 2304_bidets_bidet_toilets_bathrooms |
| 2305 | spina - bifida - amniocentesis - maternal - pregnancy | 31 | 2305_spina_bifida_amniocentesis_maternal |
| 2306 | bell - cliffhanger - saved - cast - sitcom | 31 | 2306_bell_cliffhanger_saved_cast |
| 2307 | arcade - simulator - gameplay - racing - skyline | 31 | 2307_arcade_simulator_gameplay_racing |
| 2308 | functional - programming - functions - programmer - function | 31 | 2308_functional_programming_functions_programmer |
| 2309 | sting - band - bandmates - concert - verve | 31 | 2309_sting_band_bandmates_concert |
| 2310 | mukbang - consuming - pornography - habits - cravings | 31 | 2310_mukbang_consuming_pornography_habits |
| 2311 | translations - translating - translator - translated - translation | 31 | 2311_translations_translating_translator_translated |
| 2312 | painting - paintings - painter - paint - art | 31 | 2312_painting_paintings_painter_paint |
| 2313 | gambling - betting - gamblers - bets - casino | 31 | 2313_gambling_betting_gamblers_bets |
| 2314 | ancient - archaeological - archaeology - neolithic - dynasties | 31 | 2314_ancient_archaeological_archaeology_neolithic |
| 2315 | animals - drummer - animal - bassist - drums | 31 | 2315_animals_drummer_animal_bassist |
| 2316 | feng - decorating - buildings - practices - shui | 31 | 2316_feng_decorating_buildings_practices |
| 2317 | songwriter - singer - sings - keyboardist - vocals | 31 | 2317_songwriter_singer_sings_keyboardist |
| 2318 | memories - memory - recall - psychology - falsehood | 31 | 2318_memories_memory_recall_psychology |
| 2319 | draft - drafted - draftee - picks - blazers | 31 | 2319_draft_drafted_draftee_picks |
| 2320 | registrars - registrar - domains - domain - registrants | 31 | 2320_registrars_registrar_domains_domain |
| 2321 | officers - police - gunshots - shooter - shooting | 31 | 2321_officers_police_gunshots_shooter |
| 2322 | moon - drummer - drums - drummers - drumming | 31 | 2322_moon_drummer_drums_drummers |
| 2323 | lymphomas - lymphoma - lymphadenopathy - lymphoid - lymphocytic | 31 | 2323_lymphomas_lymphoma_lymphadenopathy_lymphoid |
| 2324 | reggae - albums - band - bassist - toured | 31 | 2324_reggae_albums_band_bassist |
| 2325 | risqué - bath - erotica - insider - twitter | 31 | 2325_risqué_bath_erotica_insider |
| 2326 | spawn - disowns - destroys - shapeshift - souls | 31 | 2326_spawn_disowns_destroys_shapeshift |
| 2327 | broadcasting - syndication - broadcast - fox - channel | 31 | 2327_broadcasting_syndication_broadcast_fox |
| 2328 | domino - pizzas - pizza - pizzerias - restaurants | 31 | 2328_domino_pizzas_pizza_pizzerias |
| 2329 | soldering - boards - drilling - soldered - board | 31 | 2329_soldering_boards_drilling_soldered |
| 2330 | customers - marketing - customer - consumers - consumer | 31 | 2330_customers_marketing_customer_consumers |
| 2331 | incels - incel - misogynistic - misogynist - feminism | 31 | 2331_incels_incel_misogynistic_misogynist |
| 2332 | polo - khan - yuan - traveller - merchant | 31 | 2332_polo_khan_yuan_traveller |
| 2333 | bob - hope - honorary - biography - comedian | 31 | 2333_bob_hope_honorary_biography |
| 2334 | ethnic - minorities - ethnicity - minority - population | 31 | 2334_ethnic_minorities_ethnicity_minority |
| 2335 | tennis - doubles - backhand - sprinter - forehand | 31 | 2335_tennis_doubles_backhand_sprinter |
| 2336 | nations - china - sovereign - sovereignty - republic | 31 | 2336_nations_china_sovereign_sovereignty |
| 2337 | hostage - hostages - gunmen - terrorists - kidnappers | 30 | 2337_hostage_hostages_gunmen_terrorists |
| 2338 | novelist - writings - poetry - poets - writer | 30 | 2338_novelist_writings_poetry_poets |
| 2339 | topological - topology - topologically - topologies - manifolds | 30 | 2339_topological_topology_topologically_topologies |
| 2340 | tower - towers - elevators - elevator - storeys | 30 | 2340_tower_towers_elevators_elevator |
| 2341 | malls - destinations - cities - mall - roads | 30 | 2341_malls_destinations_cities_mall |
| 2342 | theremin - instruments - instrument - orchestral - concerto | 30 | 2342_theremin_instruments_instrument_orchestral |
| 2343 | cryptocurrency - cryptocurrencies - crypto - bitcoin - doge | 30 | 2343_cryptocurrency_cryptocurrencies_crypto_bitcoin |
| 2344 | wee - pee - cameo - cameos - comedian | 30 | 2344_wee_pee_cameo_cameos |
| 2345 | castes - caste - jati - jatis - tribal | 30 | 2345_castes_caste_jati_jatis |
| 2346 | marriages - cohabitation - marriage - heterosexuals - couples | 30 | 2346_marriages_cohabitation_marriage_heterosexuals |
| 2347 | financier - fund - funds - investors - investor | 30 | 2347_financier_fund_funds_investors |
| 2348 | mammoth - mammoths - prehistoric - fossils - palaeontology | 30 | 2348_mammoth_mammoths_prehistoric_fossils |
| 2349 | eunuchs - eunuch - servants - slaves - enslaved | 30 | 2349_eunuchs_eunuch_servants_slaves |
| 2350 | condemnation - testimony - guilt - heresy - accusation | 30 | 2350_condemnation_testimony_guilt_heresy |
| 2351 | chaebols - chaebol - debts - economies - economy | 30 | 2351_chaebols_chaebol_debts_economies |
| 2352 | songwriter - songwriters - performer - guitarist - concert | 30 | 2352_songwriter_songwriters_performer_guitarist |
| 2353 | awards - academy - nominees - theaters - theatre | 30 | 2353_awards_academy_nominees_theaters |
| 2354 | catalytic - catalysts - catalyst - converters - catalyzing | 30 | 2354_catalytic_catalysts_catalyst_converters |
| 2355 | militia - amendment - constitution - constitutions - militias | 30 | 2355_militia_amendment_constitution_constitutions |
| 2356 | atheism - atheist - agnosticism - atheists - atheistic | 30 | 2356_atheism_atheist_agnosticism_atheists |
| 2357 | studium - catholic - pope - pontifical - latin | 30 | 2357_studium_catholic_pope_pontifical |
| 2358 | composers - orchestra - composer - orchestras - choral | 30 | 2358_composers_orchestra_composer_orchestras |
| 2359 | albums - singer - guitar - duet - album | 30 | 2359_albums_singer_guitar_duet |
| 2360 | toured - concert - concerts - grease - tour | 30 | 2360_toured_concert_concerts_grease |
| 2361 | famine - potatoes - potato - hunger - starving | 30 | 2361_famine_potatoes_potato_hunger |
| 2362 | ancient - testament - epic - bible - cuneiform | 30 | 2362_ancient_testament_epic_bible |
| 2363 | nightclub - nightclubs - 54 - cabaret - club | 30 | 2363_nightclub_nightclubs_54_cabaret |
| 2364 | headquartered - universal - headquarters - company - music | 30 | 2364_headquartered_universal_headquarters_company |
| 2365 | exports - imports - economy - agriculture - archipelagos | 30 | 2365_exports_imports_economy_agriculture |
| 2366 | ecumenical - orthodox - catholic - ecclesiastical - papal | 30 | 2366_ecumenical_orthodox_catholic_ecclesiastical |
| 2367 | critical - thinking - reasoned - reflective - thinker | 30 | 2367_critical_thinking_reasoned_reflective |
| 2368 | maglev - trains - levitation - railway - levitating | 30 | 2368_maglev_trains_levitation_railway |
| 2369 | van - ev - vans - automakers - vehicles | 30 | 2369_van_ev_vans_automakers |
| 2370 | rococo - sculptor - decorative - ornamental - designs | 30 | 2370_rococo_sculptor_decorative_ornamental |
| 2371 | paintings - painting - paint - art - artist | 30 | 2371_paintings_painting_paint_art |
| 2372 | tulips - tulip - economists - economic - bulbs | 30 | 2372_tulips_tulip_economists_economic |
| 2373 | squads - squad - roster - players - teams | 30 | 2373_squads_squad_roster_players |
| 2374 | entrances - subterranean - tunnel - stairs - pyramid | 30 | 2374_entrances_subterranean_tunnel_stairs |
| 2375 | transhumanism - transhumanists - transhumanist - humanists - humanist | 30 | 2375_transhumanism_transhumanists_transhumanist_humanists |
</details>
## Training hyperparameters
* calculate_probabilities: False
* language: None
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 10
* verbose: True
## Framework versions
* Numpy: 1.22.4
* HDBSCAN: 0.8.29
* UMAP: 0.5.3
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.29.2
* Numba: 0.56.4
* Plotly: 5.13.1
* Python: 3.10.11
|
[
"BEAR",
"MEDAL"
] |
FreedomIntelligence/Apollo-6B
|
FreedomIntelligence
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2403.03640",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-03-06T13:06:09Z |
2024-04-26T11:13:01+00:00
| 3,083 | 4 |
---
license: apache-2.0
---
# Multilingual Medicine: Model, Dataset, Benchmark, Code
Covering English, Chinese, French, Hindi, Spanish, Hindi, Arabic So far
<p align="center">
👨🏻💻<a href="https://github.com/FreedomIntelligence/Apollo" target="_blank">Github</a> •📃 <a href="https://arxiv.org/abs/2403.03640" target="_blank">Paper</a> • 🌐 <a href="https://apollo.llmzoo.com/" target="_blank">Demo</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a>
<br> <a href="./README_zh.md"> 中文 </a> | <a href="./README.md"> English
</p>
## 🌈 Update
* **[2024.03.07]** [Paper](https://arxiv.org/abs/2403.03640) released.
* **[2024.02.12]** <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a> and <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a> is published！🎉
* **[2024.01.23]** Apollo repo is published！🎉
## Results
🤗<a href="https://huggingface.co/FreedomIntelligence/Apollo-0.5B" target="_blank">Apollo-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-1.8B" target="_blank">Apollo-1.8B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-2B" target="_blank">Apollo-2B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-6B" target="_blank">Apollo-6B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-7B" target="_blank">Apollo-7B</a>
🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-0.5B-GGUF" target="_blank">Apollo-0.5B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-2B-GGUF" target="_blank">Apollo-2B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-6B-GGUF" target="_blank">Apollo-6B-GGUF</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-7B-GGUF" target="_blank">Apollo-7B-GGUF</a>
## Usage Format
User:{query}\nAssistant:{response}<|endoftext|>
## Dataset & Evaluation
- Dataset
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a>
<details><summary>Click to expand</summary>
- [Zip File](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/blob/main/ApolloCorpus.zip)
- [Data category](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/tree/main/train)
- Pretrain:
- data item:
- json_name: {data_source}_{language}_{data_type}.json
- data_type: medicalBook, medicalGuideline, medicalPaper, medicalWeb(from online forum), medicalWiki
- language: en(English), zh(chinese), es(spanish), fr(french), hi(Hindi)
- data_type: qa(generated qa from text)
- data_type==text: list of string
```
[
"string1",
"string2",
...
]
```
- data_type==qa: list of qa pairs(list of string)
```
[
[
"q1",
"a1",
"q2",
"a2",
...
],
...
]
```
- SFT:
- json_name: {data_source}_{language}.json
- data_type: code, general, math, medicalExam, medicalPatient
- data item: list of qa pairs(list of string)
```
[
[
"q1",
"a1",
"q2",
"a2",
...
],
...
]
```
</details>
- Evaluation
🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a>
<details><summary>Click to expand</summary>
- EN:
- [MedQA-USMLE](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
- [MedMCQA](https://huggingface.co/datasets/medmcqa/viewer/default/test)
- [PubMedQA](https://huggingface.co/datasets/pubmed_qa): Because the results fluctuated too much, they were not used in the paper.
- [MMLU-Medical](https://huggingface.co/datasets/cais/mmlu)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- ZH:
- [MedQA-MCMLE](https://huggingface.co/datasets/bigbio/med_qa/viewer/med_qa_zh_4options_bigbio_qa/test)
- [CMB-single](https://huggingface.co/datasets/FreedomIntelligence/CMB): Not used in the paper
- Randomly sample 2,000 multiple-choice questions with single answer.
- [CMMLU-Medical](https://huggingface.co/datasets/haonan-li/cmmlu)
- Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
- [CExam](https://github.com/williamliujl/CMExam): Not used in the paper
- Randomly sample 2,000 multiple-choice questions
- ES: [Head_qa](https://huggingface.co/datasets/head_qa)
- FR: [Frenchmedmcqa](https://github.com/qanastek/FrenchMedMCQA)
- HI: [MMLU_HI](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Arabic)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- AR: [MMLU_Ara](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Hindi)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
</details>
## Results reproduction
<details><summary>Click to expand</summary>
**Waiting for Update**
</details>
## Citation
Please use the following citation if you intend to use our dataset for training or evaluation:
```
@misc{wang2024apollo,
title={Apollo: Lightweight Multilingual Medical LLMs towards Democratizing Medical AI to 6B People},
author={Xidong Wang and Nuo Chen and Junyin Chen and Yan Hu and Yidong Wang and Xiangbo Wu and Anningzhe Gao and Xiang Wan and Haizhou Li and Benyou Wang},
year={2024},
eprint={2403.03640},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
[
"HEAD-QA",
"MEDQA",
"PUBMEDQA"
] |
IBI-CAAI/MELT-llama-2-7b-chat-v0.1
|
IBI-CAAI
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-12-31T19:35:52Z |
2024-01-06T13:17:55+00:00
| 3,059 | 1 |
---
language:
- en
library_name: transformers
license: apache-2.0
---
# Model Card MELT-llama-2-7b-chat-v0.1
The MELT-llama-2-7b-chat-v0.1 Large Language Model (LLM) is a pretrained generative text model pre-trained and fine-tuned on using publically avalable medical data.
MELT-llama-2-7b-chat-v0.1 demonstrates a 31.4% improvement over llama-2-7b-chat-hf across 3 medical benchmarks including, USMLE, Indian AIIMS, and NEET medical examination examples.
## Model Details
The Medical Education Language Transformer (MELT) models have been trained on a wide-range of text, chat, Q/A, and instruction data in the medical domain.
While the model was evaluated using publically avalable [USMLE](https://www.usmle.org/), Indian AIIMS, and NEET medical examination example questions, its use it intented to be more broadly applicable.
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [Center for Applied AI](https://caai.ai.uky.edu/)
- **Funded by:** [Institute or Biomedical Informatics](https://www.research.uky.edu/IBI)
- **Model type:** LLM
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** [llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
## Uses
MELT is intended for research purposes only. MELT models are best suited for prompts using a QA or chat format.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
MELT is intended for research purposes only and should not be used for medical advice.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
MELT was training using collections publicly available, which likely contain biased and inaccurate information. The training and evaluation datasets have not been evaluated for content or accuracy.
## How to Get Started with the Model
Use this model like you would any llama-2-7b-chat-hf model.
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
The following datasets were used for training:
[Expert Med](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/Q3A969)
[MedQA train](https://huggingface.co/datasets/bigbio/med_qa)
[MedMCQA train](https://github.com/MedMCQA/MedMCQA?tab=readme-ov-file#data-download-and-preprocessing)
[LiveQA](https://github.com/abachaa/LiveQA_MedicalTask_TREC2017)
[MedicationQA](https://huggingface.co/datasets/truehealth/medicationqa)
[MMLU clinical topics](https://huggingface.co/datasets/Stevross/mmlu)
[Medical Flashcards](https://huggingface.co/datasets/medalpaca/medical_meadow_medical_flashcards)
[Wikidoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc)
[Wikidoc Patient Information](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc_patient_information)
[MEDIQA](https://huggingface.co/datasets/medalpaca/medical_meadow_mediqa)
[MMMLU](https://huggingface.co/datasets/medalpaca/medical_meadow_mmmlu)
[icliniq 10k](https://drive.google.com/file/d/1ZKbqgYqWc7DJHs3N9TQYQVPdDQmZaClA/view?usp=sharing)
[HealthCare Magic 100k](https://drive.google.com/file/d/1lyfqIwlLSClhgrCutWuEe_IACNq6XNUt/view?usp=sharing)
[GenMedGPT-5k](https://drive.google.com/file/d/1nDTKZ3wZbZWTkFMBkxlamrzbNz0frugg/view?usp=sharing)
[Mental Health Conversational](https://huggingface.co/datasets/heliosbrahma/mental_health_conversational_dataset)
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Training Hyperparameters
- **Lora Rank:** 64
- **Lora Alpha:** 16
- **Lora Targets:** "o_proj","down_proj","v_proj","gate_proj","up_proj","k_proj","q_proj"
- **LR:** 2e-4
- **Epoch:** 3
- **Precision:** bf16 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
MELT-llama-2-7b-chat-v0.1 demonstrated a average 31.4% improvement over llama-2-7b-chat-hf across 3 USMLE, Indian AIIMS, and NEET medical examination benchmarks.
### llama-2-7b-chat-hf
- **medqa:** {'base': {'Average': 36.43, 'STEP-1': 36.87, 'STEP-2&3': 35.92}}
- **mausmle:** {'base': {'Average': 30.11, 'STEP-1': 35.29, 'STEP-2': 29.89, 'STEP-3': 26.17}}
- **medmcqa:** {'base': {'Average': 39.25, 'MEDICINE': 38.04, 'OPHTHALMOLOGY': 38.1, 'ANATOMY': 42.47, 'PATHOLOGY': 41.86, 'PHYSIOLOGY': 35.61, 'DENTAL': 36.85, 'RADIOLOGY': 35.71, 'BIOCHEMISTRY': 42.98, 'ANAESTHESIA': 43.48, 'GYNAECOLOGY': 37.91, 'PHARMACOLOGY': 44.38, 'SOCIAL': 43.33, 'PEDIATRICS': 37.88, 'ENT': 47.37, 'SURGERY': 33.06, 'MICROBIOLOGY': 45.21, 'FORENSIC': 53.49, 'PSYCHIATRY': 77.78, 'SKIN': 60.0, 'ORTHOPAEDICS': 35.71, 'UNKNOWN': 100.0}}
- **average:** 35.2%
### MELT-llama-2-7b-chat-v0.1
- **medqa:** {'base': {'Average': 48.39, 'STEP-1': 49.12, 'STEP-2&3': 47.55}}
- **mausmle:** {'base': {'Average': 44.8, 'STEP-1': 42.35, 'STEP-2': 43.68, 'STEP-3': 47.66}}
- **medmcqa:** {'base': {'Average': 45.4, 'MEDICINE': 45.65, 'OPHTHALMOLOGY': 38.1, 'ANATOMY': 41.78, 'PATHOLOGY': 49.22, 'PHYSIOLOGY': 44.7, 'DENTAL': 41.47, 'RADIOLOGY': 48.21, 'BIOCHEMISTRY': 52.89, 'ANAESTHESIA': 52.17, 'GYNAECOLOGY': 35.95, 'PHARMACOLOGY': 51.12, 'SOCIAL': 50.0, 'PEDIATRICS': 50.76, 'ENT': 36.84, 'SURGERY': 48.39, 'MICROBIOLOGY': 49.32, 'FORENSIC': 51.16, 'PSYCHIATRY': 66.67, 'SKIN': 60.0, 'ORTHOPAEDICS': 42.86, 'UNKNOWN': 100.0}}
- **average:** 46.2%
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[MedQA test](https://huggingface.co/datasets/bigbio/med_qa)
[MedMCQA test](https://github.com/MedMCQA/MedMCQA?tab=readme-ov-file#data-download-and-preprocessing)
[MA USMLE](https://huggingface.co/datasets/medalpaca/medical_meadow_usmle_self_assessment)
## Disclaimer:
The use of large language models, such as this one, is provided without warranties or guarantees of any kind. While every effort has been made to ensure accuracy, completeness, and reliability of the information generated, it should be noted that these models may produce responses that are inaccurate, outdated, or inappropriate for specific purposes. Users are advised to exercise discretion and judgment when relying on the information generated by these models. The outputs should not be considered as professional, legal, medical, financial, or any other form of advice. It is recommended to seek expert advice or consult appropriate sources for specific queries or critical decision-making. The creators, developers, and providers of these models disclaim any liability for damages, losses, or any consequences arising from the use, reliance upon, or interpretation of the information provided by these models. The user assumes full responsibility for their interactions and usage of the generated content. By using these language models, users agree to indemnify and hold harmless the developers, providers, and affiliates from any claims, damages, or liabilities that may arise from their use. Please be aware that these models are constantly evolving, and their capabilities, limitations, and outputs may change over time without prior notice. Your use of this language model signifies your acceptance and understanding of this disclaimer.
|
[
"MEDQA",
"MEDICAL DATA"
] |
IBI-CAAI/MELT-TinyLlama-1.1B-Chat-v1.0
|
IBI-CAAI
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-01-10T23:45:07Z |
2024-01-10T23:57:13+00:00
| 3,055 | 0 |
---
language:
- en
library_name: transformers
license: apache-2.0
---
# Model Card MELT-TinyLlama-1.1B-Chat-v1.0
The MELT-TinyLlama-1.1B-Chat-v1.0 Large Language Model (LLM) is a pretrained generative text model pre-trained and fine-tuned on using publically avalable medical data.
MELT-TinyLlama-1.1B-Chat-v1.0 demonstrates a 13.76% improvement over TinyLlama-1.1B-Chat-v1.0 across 3 medical benchmarks including, USMLE, Indian AIIMS, and NEET medical examination examples.
## Model Details
The Medical Education Language Transformer (MELT) models have been trained on a wide-range of text, chat, Q/A, and instruction data in the medical domain.
While the model was evaluated using publically avalable [USMLE](https://www.usmle.org/), Indian AIIMS, and NEET medical examination example questions, its use it intented to be more broadly applicable.
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [Center for Applied AI](https://caai.ai.uky.edu/)
- **Funded by:** [Institute or Biomedical Informatics](https://www.research.uky.edu/IBI)
- **Model type:** LLM
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** [TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
## Uses
MELT is intended for research purposes only. MELT models are best suited for prompts using a QA or chat format.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
MELT is intended for research purposes only and should not be used for medical advice.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
MELT was training using collections publicly available, which likely contain biased and inaccurate information. The training and evaluation datasets have not been evaluated for content or accuracy.
## How to Get Started with the Model
Use this model like you would any llama-2-7b-chat-hf model.
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
The following datasets were used for training:
[Expert Med](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/Q3A969)
[MedQA train](https://huggingface.co/datasets/bigbio/med_qa)
[MedMCQA train](https://github.com/MedMCQA/MedMCQA?tab=readme-ov-file#data-download-and-preprocessing)
[LiveQA](https://github.com/abachaa/LiveQA_MedicalTask_TREC2017)
[MedicationQA](https://huggingface.co/datasets/truehealth/medicationqa)
[MMLU clinical topics](https://huggingface.co/datasets/Stevross/mmlu)
[Medical Flashcards](https://huggingface.co/datasets/medalpaca/medical_meadow_medical_flashcards)
[Wikidoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc)
[Wikidoc Patient Information](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc_patient_information)
[MEDIQA](https://huggingface.co/datasets/medalpaca/medical_meadow_mediqa)
[MMMLU](https://huggingface.co/datasets/medalpaca/medical_meadow_mmmlu)
[icliniq 10k](https://drive.google.com/file/d/1ZKbqgYqWc7DJHs3N9TQYQVPdDQmZaClA/view?usp=sharing)
[HealthCare Magic 100k](https://drive.google.com/file/d/1lyfqIwlLSClhgrCutWuEe_IACNq6XNUt/view?usp=sharing)
[GenMedGPT-5k](https://drive.google.com/file/d/1nDTKZ3wZbZWTkFMBkxlamrzbNz0frugg/view?usp=sharing)
[Mental Health Conversational](https://huggingface.co/datasets/heliosbrahma/mental_health_conversational_dataset)
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Training Hyperparameters
- **Lora Rank:** 64
- **Lora Alpha:** 16
- **Lora Targets:** "o_proj","down_proj","v_proj","gate_proj","up_proj","k_proj","q_proj"
- **LR:** 2e-4
- **Epoch:** 3
- **Precision:** bf16 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
MELT-TinyLlama-1.1B-Chat-v1.0 demonstrates an average 13.76% improvement over TinyLlama-1.1B-Chat-v1.0 across 3 USMLE, Indian AIIMS, and NEET medical examination benchmarks.
### TinyLlama-1.1B-Chat-v1.0
- **medqa:** {'base': {'Average': 25.49, 'STEP-1': 24.48, 'STEP-2&3': 26.64}}
- **mausmle:** {'base': {'Average': 19.71, 'STEP-1': 21.18, 'STEP-2': 20.69, 'STEP-3': 17.76}}
- **medmcqa:** {'base': {'Average': 28.52, 'MEDICINE': 29.35, 'OPHTHALMOLOGY': 28.57, 'ANATOMY': 30.82, 'PATHOLOGY': 29.07, 'PHYSIOLOGY': 20.45, 'DENTAL': 30.09, 'RADIOLOGY': 14.29, 'BIOCHEMISTRY': 22.31, 'ANAESTHESIA': 26.09, 'GYNAECOLOGY': 24.84, 'PHARMACOLOGY': 32.02, 'SOCIAL': 31.11, 'PEDIATRICS': 31.82, 'ENT': 28.95, 'SURGERY': 31.45, 'MICROBIOLOGY': 26.03, 'FORENSIC': 16.28, 'PSYCHIATRY': 22.22, 'SKIN': 40.0, 'ORTHOPAEDICS': 21.43, 'UNKNOWN': 0.0}}
- **average:** 24.57%
### MELT-TinyLlama-1.1B-Chat-v1.0
- **medqa:** {'base': {'Average': 29.5, 'STEP-1': 28.17, 'STEP-2&3': 31.03}}
- **mausmle:** {'base': {'Average': 21.51, 'STEP-1': 27.06, 'STEP-2': 19.54, 'STEP-3': 18.69}}
- **medmcqa:** {'base': {'Average': 32.84, 'MEDICINE': 27.72, 'OPHTHALMOLOGY': 38.1, 'ANATOMY': 39.73, 'PATHOLOGY': 32.56, 'PHYSIOLOGY': 35.61, 'DENTAL': 32.23, 'RADIOLOGY': 41.07, 'BIOCHEMISTRY': 33.06, 'ANAESTHESIA': 39.13, 'GYNAECOLOGY': 22.88, 'PHARMACOLOGY': 32.58, 'SOCIAL': 26.67, 'PEDIATRICS': 34.09, 'ENT': 42.11, 'SURGERY': 33.47, 'MICROBIOLOGY': 30.14, 'FORENSIC': 41.86, 'PSYCHIATRY': 55.56, 'SKIN': 60.0, 'ORTHOPAEDICS': 35.71, 'UNKNOWN': 100.0}}
- **average:** 27.95%
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[MedQA test](https://huggingface.co/datasets/bigbio/med_qa)
[MedMCQA test](https://github.com/MedMCQA/MedMCQA?tab=readme-ov-file#data-download-and-preprocessing)
[MA USMLE](https://huggingface.co/datasets/medalpaca/medical_meadow_usmle_self_assessment)
## Disclaimer:
The use of large language models, such as this one, is provided without warranties or guarantees of any kind. While every effort has been made to ensure accuracy, completeness, and reliability of the information generated, it should be noted that these models may produce responses that are inaccurate, outdated, or inappropriate for specific purposes. Users are advised to exercise discretion and judgment when relying on the information generated by these models. The outputs should not be considered as professional, legal, medical, financial, or any other form of advice. It is recommended to seek expert advice or consult appropriate sources for specific queries or critical decision-making. The creators, developers, and providers of these models disclaim any liability for damages, losses, or any consequences arising from the use, reliance upon, or interpretation of the information provided by these models. The user assumes full responsibility for their interactions and usage of the generated content. By using these language models, users agree to indemnify and hold harmless the developers, providers, and affiliates from any claims, damages, or liabilities that may arise from their use. Please be aware that these models are constantly evolving, and their capabilities, limitations, and outputs may change over time without prior notice. Your use of this language model signifies your acceptance and understanding of this disclaimer.
|
[
"MEDQA",
"MEDICAL DATA"
] |
health360/Healix-1.1B-V1-Chat-dDPO
|
health360
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"medical",
"biology",
"chemistry",
"text-generation-inference",
"en",
"dataset:krvhrv/Healix-Medical-Shot",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-05T10:42:32Z |
2024-06-13T06:43:44+00:00
| 3,047 | 4 |
---
datasets:
- krvhrv/Healix-Medical-Shot
language:
- en
license: apache-2.0
tags:
- medical
- biology
- chemistry
- text-generation-inference
model-index:
- name: Healix-1.1B-V1-Chat-dDPO
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 30.55
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=health360/Healix-1.1B-V1-Chat-dDPO
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 44.78
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=health360/Healix-1.1B-V1-Chat-dDPO
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 24.64
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=health360/Healix-1.1B-V1-Chat-dDPO
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 41.55
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=health360/Healix-1.1B-V1-Chat-dDPO
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 56.51
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=health360/Healix-1.1B-V1-Chat-dDPO
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 0.0
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=health360/Healix-1.1B-V1-Chat-dDPO
name: Open LLM Leaderboard
---
# Healix 1.1B Model Card
## Model Description
Healix 1.1B is a state-of-the-art large language model specifically designed for medical applications. With 1.1 billion parameters, it has been trained on a vast corpus of medical literature to provide accurate and reliable responses to complex medical queries. This model aims to assist healthcare professionals and researchers by offering insights derived from medical data.
## Training Data
The model leverages an extensive compilation of medical literature, including research papers, clinical trial reports, and textbooks, ensuring a broad understanding of medical topics.
## Intended Use
This model is designed for medical research, clinical support, and healthcare applications. It serves to enhance medical text generation, query response, and evidence-based information dissemination. It is not a substitute for professional medical consultation.
## Limitations
While Healix 1.1B offers advanced medical insights, it has limitations in data quality and representativeness, and may inadvertently produce biased or incorrect information.
## Performance
Healix 1.1B demonstrated a remarkable accuracy of 64%, outperforming the LLAMA 2 7B model, which achieved an accuracy of 62% despite its larger size of 7 billion parameters. This highlights Healix 1.1B's superior ability to handle real emergency-focused medical questions, showcasing the effectiveness of specialized training and architecture in domain-specific applications.
## Ethical Considerations
Users are urged to use Healix 1.1B responsibly, considering the ethical implications, patient privacy, and data security. The model's outputs should be used as a supplementary information source alongside professional medical judgment.
## Papers
Details on the development, training, and evaluation of Healix 1.1B will be available in our forthcoming publications, offering insights into its creation and the advancements it brings to medical informatics.
### Input Format
Use the Alpaca model format.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_health360__Healix-1.1B-V1-Chat-dDPO)
| Metric |Value|
|---------------------------------|----:|
|Avg. |33.00|
|AI2 Reasoning Challenge (25-Shot)|30.55|
|HellaSwag (10-Shot) |44.78|
|MMLU (5-Shot) |24.64|
|TruthfulQA (0-shot) |41.55|
|Winogrande (5-shot) |56.51|
|GSM8k (5-shot) | 0.00|
|
[
"MEDICAL DATA"
] |
ibm-nasa-geospatial/Prithvi-EO-2.0-600M-TL
|
ibm-nasa-geospatial
| null |
[
"terratorch",
"Pytorch",
"Earth Observation",
"Foundation Model",
"NASA",
"IBM",
"arxiv:2412.02732",
"license:apache-2.0",
"region:us"
] | 2024-12-02T09:32:43Z |
2025-01-22T11:39:50+00:00
| 3,043 | 6 |
---
library_name: terratorch
license: apache-2.0
tags:
- Pytorch
- Earth Observation
- Foundation Model
- NASA
- IBM
---
# Prithvi-EO-2.0
Prithvi-EO-2.0 is the second generation EO foundation model jointly developed by IBM, NASA, and Jülich Supercomputing Centre.
## Architecture Overview
Prithvi-EO-2.0 is based on the ViT architecture, pretrained using a masked autoencoder (MAE) approach, with two major modifications as shown in the figure below.
First, we replaced the 2D patch embeddings and 2D positional embeddings with 3D versions to support inputs with spatiotemporal characteristics, i.e., a sequence of T images of size (H, W). Our 3D patch embeddings consist of a 3D convolutional layer, dividing the 3D input into non-overlapping cubes of size (t, h, w) for time, height, and width dimensions, respectively. For the 3D positional encodings, we first generate 1D sin/cos encodings individually for each dimension and then combine them together into a single, 3D positional encoding.
Second, we considered geolocation (center latitude and longitude) and date of acquisition (year and day-of-year ranging 1-365) in the pretraining of the TL model versions. Both encoder and decoder receive time and location information for each sample and encodes them independently using 2D sin/cos encoding. They are added to the embedded tokens via a weighted sum with learned weights: one for time and one for location and separate weights for encoder and decoder. Since this metadata is often not available, we added a drop mechanism during pretraining that randomly drops the geolocation and/or the temporal data to help the model learn how to handle the absence of this information.
## Pre-trained Models
| Model | Details | Weights |
| ------------- | ------------- |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|Prithvi-EO-2.0-300M | Pretrained 300M parameter model | [https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-300M](https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-300M) |
|Prithvi-EO-2.0-300M-TL | Pretrained 300M parameter model with temporal and location embeddings | [https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL](https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL) |
|Prithvi-EO-2.0-600M | Pretrained 600M parameter model | [https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-600M](https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-600M) | |
|Prithvi-EO-2.0-600M-TL | Pretrained 600M parameter model with temporal and location embeddings | [https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-600M-TL](https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-600M-TL) |
The models were pre-trained at the Jülich Supercomputing Centre with NASA's HLS V2 product (30m granularity) using 4.2M samples with six bands in the following order: Blue, Green, Red, Narrow NIR, SWIR, SWIR 2.
## Benchmarking
We validated the Prithvi-EO-2.0 models through extensive experiments using [GEO-bench](https://github.com/ServiceNow/geo-bench). Prithvi-EO-2.0-600M-TL outperforms the previous Prithvi-EO model by 8% across a range of tasks. It also outperforms six other geospatial foundation models when benchmarked on remote sensing tasks from different domains and resolutions (i.e. from 0.1m to 15m).
## Demo and inference
We provide a **demo** running Prithvi-EO-2.0-300M-TL [here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-EO-2.0-Demo).
There is also an inference script (`inference.py`) that allows to run the image reconstruction on a set of HLS images assumed to be from the same location at different timestamps (see example below). These should be provided in chronological order in geotiff format, including the channels described above (Blue, Green, Red, Narrow NIR, SWIR 1, SWIR 2) in reflectance units.
```
python inference.py --data_files t1.tif t2.tif t3.tif t4.tif --input_indices <optional, space separated 0-based indices of the six Prithvi channels in your input>
```
## Finetuning
You can finetune the model using [TerraTorch](https://github.com/IBM/terratorch). Examples of configs and notebooks are provided in the project repository: [github.com/NASA-IMPACT/Prithvi-EO-2.0](https://github.com/NASA-IMPACT/Prithvi-EO-2.0#fine-tuning).
Example Notebooks:
[Multitemporal Crop Segmentation](https://github.com/NASA-IMPACT/Prithvi-EO-2.0/blob/main/examples/example_multitemporalcrop.ipynb) [<b><i>>>Try it on Colab<<</i></b>](https://colab.research.google.com/github/NASA-IMPACT/Prithvi-EO-2.0/blob/main/examples/example_multitemporalcrop.ipynb) (Choose T4 GPU runtime)
[Landslide Segmentation](https://github.com/NASA-IMPACT/Prithvi-EO-2.0/blob/main/examples/example_landslide4sense.ipynb) [<b><i>>>Try it on Colab<<</i></b>](https://colab.research.google.com/github/NASA-IMPACT/Prithvi-EO-2.0/blob/main/examples/example_landslide4sense.ipynb) (Choose T4 GPU runtime)
[Carbon Flux Prediction (Regression)](https://github.com/NASA-IMPACT/Prithvi-EO-2.0/blob/main/examples/carbon_flux/main_flux_finetune_baselines_trainer.ipynb)
### Feedback
Your feedback is invaluable to us. If you have any feedback about the model, please feel free to share it with us. You can do this by starting a discussion in this HF repository or submitting an issue to [TerraTorch](https://github.com/IBM/terratorch) on GitHub.
### Citation
If this model helped your research, please cite [Prithvi-EO-2.0](https://arxiv.org/abs/2412.02732) in your publications.
```
@article{Prithvi-EO-V2-preprint,
author = {Szwarcman, Daniela and Roy, Sujit and Fraccaro, Paolo and Gíslason, Þorsteinn Elí and Blumenstiel, Benedikt and Ghosal, Rinki and de Oliveira, Pedro Henrique and de Sousa Almeida, João Lucas and Sedona, Rocco and Kang, Yanghui and Chakraborty, Srija and Wang, Sizhe and Kumar, Ankur and Truong, Myscon and Godwin, Denys and Lee, Hyunho and Hsu, Chia-Yu and Akbari Asanjan, Ata and Mujeci, Besart and Keenan, Trevor and Arévolo, Paulo and Li, Wenwen and Alemohammad, Hamed and Olofsson, Pontus and Hain, Christopher and Kennedy, Robert and Zadrozny, Bianca and Cavallaro, Gabriele and Watson, Campbell and Maskey, Manil and Ramachandran, Rahul and Bernabe Moreno, Juan},
title = {{Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications}},
journal = {arXiv preprint arXiv:2412.02732},
year = {2024}
}
```
|
[
"CHIA"
] |
VietAI/envit5-translation
|
VietAI
|
translation
|
[
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"translation",
"vi",
"en",
"dataset:cc100",
"license:openrail",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2022-10-06T14:53:36Z |
2022-11-21T09:59:08+00:00
| 2,996 | 36 |
---
datasets:
- cc100
language:
- vi
- en
license: openrail
tags:
- translation
widget:
- text: 'vi: VietAI là tổ chức phi lợi nhuận với sứ mệnh ươm mầm tài năng về trí tuệ
nhân tạo và xây dựng một cộng đồng các chuyên gia trong lĩnh vực trí tuệ nhân
tạo đẳng cấp quốc tế tại Việt Nam.'
---
# EnViT5 Translation
[](https://paperswithcode.com/sota/machine-translation-on-iwslt2015-english-1?p=mtet-multi-domain-translation-for-english)
[](https://paperswithcode.com/sota/on-phomt?p=mtet-multi-domain-translation-for-english-and)
State-of-the-art English-Vietnamese and Vietnamese-English Translation models trained on [MTet](https://research.vietai.org/mtet/), [PhoMT](https://github.com/VinAIResearch/PhoMT).
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_name = "VietAI/envit5-translation"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
inputs = [
"vi: VietAI là tổ chức phi lợi nhuận với sứ mệnh ươm mầm tài năng về trí tuệ nhân tạo và xây dựng một cộng đồng các chuyên gia trong lĩnh vực trí tuệ nhân tạo đẳng cấp quốc tế tại Việt Nam.",
"vi: Theo báo cáo mới nhất của Linkedin về danh sách việc làm triển vọng với mức lương hấp dẫn năm 2020, các chức danh công việc liên quan đến AI như Chuyên gia AI (Artificial Intelligence Specialist), Kỹ sư ML (Machine Learning Engineer) đều xếp thứ hạng cao.",
"en: Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.",
"en: We're on a journey to advance and democratize artificial intelligence through open source and open science."
]
outputs = model.generate(tokenizer(inputs, return_tensors="pt", padding=True).input_ids.to('cuda'), max_length=512)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# ['en: VietAI is a non-profit organization with the mission of nurturing artificial intelligence talents and building an international - class community of artificial intelligence experts in Vietnam.',
# 'en: According to the latest LinkedIn report on the 2020 list of attractive and promising jobs, AI - related job titles such as AI Specialist, ML Engineer and ML Engineer all rank high.',
# 'vi: Nhóm chúng tôi khao khát tạo ra những khám phá có ảnh hưởng đến mọi người, và cốt lõi trong cách tiếp cận của chúng tôi là chia sẻ nghiên cứu và công cụ để thúc đẩy sự tiến bộ trong lĩnh vực này.',
# 'vi: Chúng ta đang trên hành trình tiến bộ và dân chủ hoá trí tuệ nhân tạo thông qua mã nguồn mở và khoa học mở.']
```
## Results
## Citation
```
@misc{https://doi.org/10.48550/arxiv.2210.05610,
doi = {10.48550/ARXIV.2210.05610},
author = {Ngo, Chinh and Trinh, Trieu H. and Phan, Long and Tran, Hieu and Dang, Tai and Nguyen, Hieu and Nguyen, Minh and Luong, Minh-Thang},
title = {MTet: Multi-domain Translation for English and Vietnamese},
publisher = {arXiv},
year = {2022},
}
```
|
[
"CHIA"
] |
johnsnowlabs/JSL-MedPhi2-2.7B
|
johnsnowlabs
|
text-generation
|
[
"transformers",
"safetensors",
"phi",
"text-generation",
"phi-2",
"sft",
"medical",
"conversational",
"custom_code",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-30T18:30:37Z |
2024-05-01T04:58:44+00:00
| 2,987 | 3 |
---
base_model:
- microsoft/phi2
license: cc-by-nc-nd-4.0
tags:
- phi-2
- sft
- medical
---
# JSL-MedPhi2-2.7B
[<img src="https://repository-images.githubusercontent.com/104670986/2e728700-ace4-11ea-9cfc-f3e060b25ddf">](http://www.johnsnowlabs.com)
This model is developed by [John Snow Labs](https://www.johnsnowlabs.com/).
This model is available under a [CC-BY-NC-ND](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en) license and must also conform to this [Acceptable Use Policy](https://huggingface.co/johnsnowlabs). If you need to license this model for commercial use, please contact us at [email protected].
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "johnsnowlabs/JSL-MedPhi2-2.7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## 🏆 Evaluation
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------------------------|-------|------|-----:|--------|-----:|---|-----:|
|stem |N/A |none | 0|acc_norm|0.3904|± |0.0066|
| | |none | 0|acc |0.4402|± |0.0058|
| - medmcqa |Yaml |none | 0|acc |0.3899|± |0.0075|
| | |none | 0|acc_norm|0.3899|± |0.0075|
| - medqa_4options |Yaml |none | 0|acc |0.3920|± |0.0137|
| | |none | 0|acc_norm|0.3920|± |0.0137|
| - anatomy (mmlu) | 0|none | 0|acc |0.4815|± |0.0432|
| - clinical_knowledge (mmlu) | 0|none | 0|acc |0.6340|± |0.0296|
| - college_biology (mmlu) | 0|none | 0|acc |0.6181|± |0.0406|
| - college_medicine (mmlu) | 0|none | 0|acc |0.5665|± |0.0378|
| - medical_genetics (mmlu) | 0|none | 0|acc |0.6300|± |0.0485|
| - professional_medicine (mmlu)| 0|none | 0|acc |0.4522|± |0.0302|
| - pubmedqa | 1|none | 0|acc |0.7300|± |0.0199|
|Groups|Version|Filter|n-shot| Metric |Value | |Stderr|
|------|-------|------|-----:|--------|-----:|---|-----:|
|stem |N/A |none | 0|acc_norm|0.3904|± |0.0066|
| | |none | 0|acc |0.4402|± |0.0058|
|
[
"MEDQA",
"PUBMEDQA"
] |
bioformers/bioformer-8L-ncbi-disease
|
bioformers
|
token-classification
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05Z |
2023-08-02T07:50:07+00:00
| 2,967 | 1 |
---
language:
- en
license: apache-2.0
---
[bioformer-8L](https://huggingface.co/bioformers/bioformer-8L) fined-tuned on the [NCBI Disease](https://doi.org/10.1016/j.jbi.2013.12.006) dataset for 10 epochs.
This fine-tuned model can be used for NER for diseases.
|
[
"NCBI DISEASE"
] |
aisingapore/llama3.1-70b-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/llama3.1-70b-cpt-sea-lionv3-base",
"base_model:finetune:aisingapore/llama3.1-70b-cpt-sea-lionv3-base",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-12-11T10:22:38Z |
2024-12-19T13:58:00+00:00
| 2,958 | 1 |
---
base_model:
- aisingapore/llama3.1-70b-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
base_model_relation: finetune
---
<div>
<img src="llama_3.1_70b_sea-lion_v3_instruct_banner.png"/>
</div>
# Llama3.1 70B 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 70B 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 70B CPT SEA-LIONv3 Base](https://huggingface.co/aisingapore/llama3.1-70B-cpt-sea-lionv3-base), a decoder model using the Llama 3.1 architecture, to create Llama3.1 70B CPT SEA-LIONv3 Instruct.
For tokenisation, the model employs the default tokenizer used in Llama 3.1 70B Instruct. The model has a context length of 128k.
### Benchmark Performance
We evaluated Llama3.1 70B 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 70B 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 70B CPT SEA-LIONv3 Instruct benchmark performance, please refer to the SEA-HELM leaderboard, https://leaderboard.sea-lion.ai/.
### Usage
Llama3.1 70B CPT SEA-LIONv3 Instruct can be run using the 🤗 Transformers library
```python
import transformers
import torch
model_id = "aisingapore/llama3.1-70B-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 70B 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 3200 GPU hours, on a single node of 8x H100-80GB GPUs.
## Data
Llama3.1 70B 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 sources.
## 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.
|
[
"CHIA"
] |
hywu/Camelidae-8x7B
|
hywu
|
text-generation
|
[
"transformers",
"pytorch",
"camelidae",
"text-generation",
"custom_code",
"en",
"dataset:Open-Orca/SlimOrca",
"dataset:ise-uiuc/Magicoder-OSS-Instruct-75K",
"dataset:ise-uiuc/Magicoder-Evol-Instruct-110K",
"dataset:meta-math/MetaMathQA",
"arxiv:2401.02731",
"arxiv:2305.14314",
"arxiv:1902.00751",
"arxiv:2212.05055",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-01-10T06:03:53Z |
2024-09-20T02:30:19+00:00
| 2,957 | 14 |
---
datasets:
- Open-Orca/SlimOrca
- ise-uiuc/Magicoder-OSS-Instruct-75K
- ise-uiuc/Magicoder-Evol-Instruct-110K
- meta-math/MetaMathQA
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
arxiv: 2401.02731
---
# Parameter-Efficient Sparsity Crafting From Dense to Mixture-of-Experts for Instruction Tuning on General Tasks (EMNLP'24)
## News
- 9/20/2024 - Our paper is accepted by EMNLP'24.
- 3/12/2024 - We release Qwen2idae-16x14B-v1.0 on 🤗 [HuggingFace](https://huggingface.co/hywu/Qwen2idae-16x14B-v1.0), which has strong performance in Math and Code with 15B activated params.
- 2/7/2024 - [Serp-ai](https://github.com/serp-ai/Parameter-Efficient-MoE) adds [unsloth](https://github.com/serp-ai/unsloth) support for faster and memory efficient training of our Parameter-Efficient Sparsity Crafting and releases new [sparsetral](https://huggingface.co/serpdotai/sparsetral-16x7B-v2) models based on mistral-7B.
- 1/10/2024 - Camelidae models are now available on 🤗 [HuggingFace](https://huggingface.co/hywu).
- 1/4/2024 - We release the paper, [Parameter-Efficient Sparsity Crafting From Dense to Mixture-of-Experts for Instruction Tuning on General Tasks](https://arxiv.org/abs/2401.02731).
- 12/22/2023 - We release the training [repo](https://github.com/wuhy68/Parameter-Efficient-MoE) that craft the dense model with LLaMA architecture to the MoE model.
## Introduction
Camelidae and Qwen2idae models are trained utilizing Parameter-Efficient Sparsity Crafting techniques
We present Parameter-Efficient Sparsity Crafting to help dense models learn knowledge from different fields (including code and math). This approach performs instruction tuning and efficiently utilizes MoE structure.
Specifically, Parameter-Efficient Sparsity Crafting utilizes parameter-efficient techniques including [QLoRA](https://arxiv.org/abs/2305.14314) and [Adapter](https://arxiv.org/abs/1902.00751) to perform Efficient [Sparse Upcycling](https://arxiv.org/abs/2212.05055).
## Model Lists
| Camelidae Series | Download
|---|---
Camelidae-8x7B | 🤗 [HuggingFace](https://huggingface.co/hywu/Camelidae-8x7B)
Camelidae-8x13B | 🤗 [HuggingFace](https://huggingface.co/hywu/Camelidae-8x13B)
Camelidae-8x34B | 🤗 [HuggingFace](https://huggingface.co/hywu/Camelidae-8x34B)
| Qwen2idae Series | Download
|---|---
Qwen2idae-16x14B-v1.0 | 🤗 [HuggingFace](https://huggingface.co/hywu/Qwen2idae-16x14B-v1.0)
## Performance
| Model | Activated Params | MMLU (5shot) | GSM8k (5shot) | MATH (4shot) | HumanEval (0shot) | MBPP (4shot) | HellaSwag (10shot) |
|:-----:|:----------------:|:------------:|:-------------:|:------------:|:-----------------:|:------------:|:------------------:|
| GPT3.5 | - | 70.0% | 57.1% | <font color=#F67F70>**34.1%**</font> | <font color=#FBD98D>**48.1%**</font> | - | <font color=#7FEA9E>**85.5%**</font> |
| LLaMA2-70B-chat | 70B | 63.8% | 59.3% | 10.4% | 32.3% | 35.6% | 84.8% |
| Camelidae-8x34B-pro | 35B | <font color=#7FEA9E>**75.7%**</font> | <font color=#F67F70>**79.4%**</font> | <font color=#FBD98D>**24.0%**</font> | <font color=#7FEA9E>**48.8%**</font> | <font color=#7FEA9E>**43.2%**</font> | 85.2% |
| Camelidae-8x34B | 35B | <font color=#FBD98D>**75.6%**</font> | <font color=#7FEA9E>**78.3%**</font> | 22.6% | 43.9% | <font color=#FBD98D>**41.4%**</font> | <font color=#FBD98D>**85.3%**</font> |
| SUSChat-34B | 34B | <font color=#F67F70>**76.4%**</font> | 72.3% | 22.0% | 11.6% | 40.2% | 83.9% |
| Yi-34B-chat | 34B | 74.8% | 67.6% | 17.3% | 20.1% | 41.0% | 83.9% |
| Qwen2idae-16x14B-v1.0 | 15B | 66.7% | <font color=#FBD98D>**77.8%**</font> | <font color=#7FEA9E>**29.9%**</font> | <font color=#F67F70>**62.8%**</font> | <font color=#F67F70>**48.6%**</font> | 82.3% |
| Mixtral-8x7B-instruct | 14B | 68.7% | 71.7% | 22.1% | 25.6% | 40.6% | <font color=#F67F70>**86.5%**</font> |
| Camelidae-8x13B | 13B | 54.4% | 52.6% | 9.8% | 30.6% | 30.4% | 82.5% |
| LLaMA2-13B-chat | 13B | 53.9% | 37.1% | 5.2% | 18.9% | 27.2% | 81.9% |
| Camelidae-8x7B | 7B | 48.3% | 44.0% | 5.8% | 18.3% | 23.4% | 79.2% |
| LLaMA2-7B-chat | 7B | 47.2% | 26.3% | 3.9% | 12.2% | 17.6% | 78.6% |
We bold the top3 scores separately for all models.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("hywu/Camelidae-8x7B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("hywu/Camelidae-8x7B", device_map="auto", trust_remote_code=True).eval()
inputs = tokenizer('### Human:\nHow are you?\n### Assistant:\n', return_tensors='pt')
inputs = inputs.to(model.device)
pred = model.generate(**inputs)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
```
## Citation
```bibtex
@article{wu2024parameter,
title={Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks},
author={Wu, Haoyuan and Zheng, Haisheng and Yu, Bei},
journal={arXiv preprint arXiv:2401.02731},
year={2024}
}
```
## License
The source code in this repo is licensed under the [Apache 2.0 License](https://github.com/wuhy68/Parameter-Efficient-MoE/blob/master/LICENSE). Camelidae models are developed for academic research and free commercial use, all usage must adhere to the license from [facebookresearch](https://github.com/facebookresearch/llama/blob/main/LICENSE) and [01-ai](https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt).
|
[
"CRAFT"
] |
hywu/Camelidae-8x13B
|
hywu
|
text-generation
|
[
"transformers",
"pytorch",
"camelidae",
"text-generation",
"custom_code",
"en",
"dataset:Open-Orca/SlimOrca",
"dataset:ise-uiuc/Magicoder-OSS-Instruct-75K",
"dataset:ise-uiuc/Magicoder-Evol-Instruct-110K",
"dataset:meta-math/MetaMathQA",
"arxiv:2401.02731",
"arxiv:2305.14314",
"arxiv:1902.00751",
"arxiv:2212.05055",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-01-10T08:02:21Z |
2024-09-20T02:34:45+00:00
| 2,941 | 4 |
---
datasets:
- Open-Orca/SlimOrca
- ise-uiuc/Magicoder-OSS-Instruct-75K
- ise-uiuc/Magicoder-Evol-Instruct-110K
- meta-math/MetaMathQA
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
arxiv: 2401.02731
---
# Parameter-Efficient Sparsity Crafting From Dense to Mixture-of-Experts for Instruction Tuning on General Tasks (EMNLP'24)
## News
- 9/20/2024 - Our paper is accepted by EMNLP'24.
- 3/12/2024 - We release Qwen2idae-16x14B-v1.0 on 🤗 [HuggingFace](https://huggingface.co/hywu/Qwen2idae-16x14B-v1.0), which has strong performance in Math and Code with 15B activated params.
- 2/7/2024 - [Serp-ai](https://github.com/serp-ai/Parameter-Efficient-MoE) adds [unsloth](https://github.com/serp-ai/unsloth) support for faster and memory efficient training of our Parameter-Efficient Sparsity Crafting and releases new [sparsetral](https://huggingface.co/serpdotai/sparsetral-16x7B-v2) models based on mistral-7B.
- 1/10/2024 - Camelidae models are now available on 🤗 [HuggingFace](https://huggingface.co/hywu).
- 1/4/2024 - We release the paper, [Parameter-Efficient Sparsity Crafting From Dense to Mixture-of-Experts for Instruction Tuning on General Tasks](https://arxiv.org/abs/2401.02731).
- 12/22/2023 - We release the training [repo](https://github.com/wuhy68/Parameter-Efficient-MoE) that craft the dense model with LLaMA architecture to the MoE model.
## Introduction
Camelidae and Qwen2idae models are trained utilizing Parameter-Efficient Sparsity Crafting techniques
We present Parameter-Efficient Sparsity Crafting to help dense models learn knowledge from different fields (including code and math). This approach performs instruction tuning and efficiently utilizes MoE structure.
Specifically, Parameter-Efficient Sparsity Crafting utilizes parameter-efficient techniques including [QLoRA](https://arxiv.org/abs/2305.14314) and [Adapter](https://arxiv.org/abs/1902.00751) to perform Efficient [Sparse Upcycling](https://arxiv.org/abs/2212.05055).
## Model Lists
| Camelidae Series | Download
|---|---
Camelidae-8x7B | 🤗 [HuggingFace](https://huggingface.co/hywu/Camelidae-8x7B)
Camelidae-8x13B | 🤗 [HuggingFace](https://huggingface.co/hywu/Camelidae-8x13B)
Camelidae-8x34B | 🤗 [HuggingFace](https://huggingface.co/hywu/Camelidae-8x34B)
Camelidae-8x34B-pro | 🤗 Coming Soon
| Qwen2idae Series | Download
|---|---
Qwen2idae-16x14B-v1.0 | 🤗 [HuggingFace](https://huggingface.co/hywu/Qwen2idae-16x14B-v1.0)
Qwen2idae-16x7B-v1.0 | 🤗 Coming Soon
Qwen2idae-16x1.8B-v1.0 | 🤗 Coming Soon
## Performance
| Model | Activated Params | MMLU (5shot) | GSM8k (5shot) | MATH (4shot) | HumanEval (0shot) | MBPP (4shot) | HellaSwag (10shot) |
|:-----:|:----------------:|:------------:|:-------------:|:------------:|:-----------------:|:------------:|:------------------:|
| GPT3.5 | - | 70.0% | 57.1% | <font color=#F67F70>**34.1%**</font> | <font color=#FBD98D>**48.1%**</font> | - | <font color=#7FEA9E>**85.5%**</font> |
| LLaMA2-70B-chat | 70B | 63.8% | 59.3% | 10.4% | 32.3% | 35.6% | 84.8% |
| Camelidae-8x34B-pro | 35B | <font color=#7FEA9E>**75.7%**</font> | <font color=#F67F70>**79.4%**</font> | <font color=#FBD98D>**24.0%**</font> | <font color=#7FEA9E>**48.8%**</font> | <font color=#7FEA9E>**43.2%**</font> | 85.2% |
| Camelidae-8x34B | 35B | <font color=#FBD98D>**75.6%**</font> | <font color=#7FEA9E>**78.3%**</font> | 22.6% | 43.9% | <font color=#FBD98D>**41.4%**</font> | <font color=#FBD98D>**85.3%**</font> |
| SUSChat-34B | 34B | <font color=#F67F70>**76.4%**</font> | 72.3% | 22.0% | 11.6% | 40.2% | 83.9% |
| Yi-34B-chat | 34B | 74.8% | 67.6% | 17.3% | 20.1% | 41.0% | 83.9% |
| Qwen2idae-16x14B-v1.0 | 15B | 66.7% | <font color=#FBD98D>**77.8%**</font> | <font color=#7FEA9E>**29.9%**</font> | <font color=#F67F70>**62.8%**</font> | <font color=#F67F70>**48.6%**</font> | 82.3% |
| Mixtral-8x7B-instruct | 14B | 68.7% | 71.7% | 22.1% | 25.6% | 40.6% | <font color=#F67F70>**86.5%**</font> |
| Camelidae-8x13B | 13B | 54.4% | 52.6% | 9.8% | 30.6% | 30.4% | 82.5% |
| LLaMA2-13B-chat | 13B | 53.9% | 37.1% | 5.2% | 18.9% | 27.2% | 81.9% |
| Camelidae-8x7B | 7B | 48.3% | 44.0% | 5.8% | 18.3% | 23.4% | 79.2% |
| LLaMA2-7B-chat | 7B | 47.2% | 26.3% | 3.9% | 12.2% | 17.6% | 78.6% |
We bold the top3 scores separately for all models.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("hywu/Camelidae-8x13B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("hywu/Camelidae-8x13B", device_map="auto", trust_remote_code=True).eval()
inputs = tokenizer('### Human:\nHow are you?\n### Assistant:\n', return_tensors='pt')
inputs = inputs.to(model.device)
pred = model.generate(**inputs)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
```
## Citation
```bibtex
@article{wu2024parameter,
title={Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks},
author={Wu, Haoyuan and Zheng, Haisheng and Yu, Bei},
journal={arXiv preprint arXiv:2401.02731},
year={2024}
}
```
## License
The source code in this repo is licensed under the [Apache 2.0 License](https://github.com/wuhy68/Parameter-Efficient-MoE/blob/master/LICENSE). Camelidae models are developed for academic research and free commercial use, all usage must adhere to the license from [facebookresearch](https://github.com/facebookresearch/llama/blob/main/LICENSE) and [01-ai](https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt).
|
[
"CRAFT"
] |
johnsnowlabs/JSL-MedMNX-7B
|
johnsnowlabs
|
text-generation
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"reward model",
"RLHF",
"medical",
"conversational",
"en",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-15T19:18:02Z |
2024-04-18T19:22:24+00:00
| 2,934 | 5 |
---
language:
- en
library_name: transformers
license: cc-by-nc-nd-4.0
tags:
- reward model
- RLHF
- medical
---
# JSL-MedMNX-7B
[<img src="https://repository-images.githubusercontent.com/104670986/2e728700-ace4-11ea-9cfc-f3e060b25ddf">](http://www.johnsnowlabs.com)
JSL-MedMNX-7B is a 7 Billion parameter model developed by [John Snow Labs](https://www.johnsnowlabs.com/).
This model is trained on medical datasets to provide state-of-the-art performance on biomedical benchmarks: [Open Medical LLM Leaderboard](https://huggingface.co/spaces/openlifescienceai/open_medical_llm_leaderboard).
This model is available under a [CC-BY-NC-ND](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en) license and must also conform to this [Acceptable Use Policy](https://huggingface.co/johnsnowlabs). If you need to license this model for commercial use, please contact us at [email protected].
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "johnsnowlabs/JSL-MedMNX-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## 🏆 Evaluation
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------------------------|-------|------|-----:|--------|-----:|---|-----:|
|stem |N/A |none | 0|acc_norm|0.5191|± |0.0068|
| | |none | 0|acc |0.5658|± |0.0058|
| - medmcqa |Yaml |none | 0|acc |0.5135|± |0.0077|
| | |none | 0|acc_norm|0.5135|± |0.0077|
| - medqa_4options |Yaml |none | 0|acc |0.5373|± |0.0140|
| | |none | 0|acc_norm|0.5373|± |0.0140|
| - anatomy (mmlu) | 0|none | 0|acc |0.6370|± |0.0415|
| - clinical_knowledge (mmlu) | 0|none | 0|acc |0.7245|± |0.0275|
| - college_biology (mmlu) | 0|none | 0|acc |0.7500|± |0.0362|
| - college_medicine (mmlu) | 0|none | 0|acc |0.6590|± |0.0361|
| - medical_genetics (mmlu) | 0|none | 0|acc |0.7200|± |0.0451|
| - professional_medicine (mmlu)| 0|none | 0|acc |0.7206|± |0.0273|
| - pubmedqa | 1|none | 0|acc |0.7720|± |0.0188|
|Groups|Version|Filter|n-shot| Metric |Value | |Stderr|
|------|-------|------|-----:|--------|-----:|---|-----:|
|stem |N/A |none | 0|acc_norm|0.5191|± |0.0068|
| | |none | 0|acc |0.5658|± |0.0058|
|
[
"MEDQA",
"PUBMEDQA"
] |
johnsnowlabs/JSL-MedMNX-7B-v2.0
|
johnsnowlabs
|
text-generation
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"reward model",
"RLHF",
"medical",
"conversational",
"en",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-22T17:38:31Z |
2024-04-22T21:15:55+00:00
| 2,927 | 3 |
---
language:
- en
library_name: transformers
license: cc-by-nc-nd-4.0
tags:
- reward model
- RLHF
- medical
---
# JSL-MedMNX-7B-v2.0
[<img src="https://repository-images.githubusercontent.com/104670986/2e728700-ace4-11ea-9cfc-f3e060b25ddf">](http://www.johnsnowlabs.com)
This model is developed by [John Snow Labs](https://www.johnsnowlabs.com/).
Performance on biomedical benchmarks: [Open Medical LLM Leaderboard](https://huggingface.co/spaces/openlifescienceai/open_medical_llm_leaderboard).
This model is available under a [CC-BY-NC-ND](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en) license and must also conform to this [Acceptable Use Policy](https://huggingface.co/johnsnowlabs). If you need to license this model for commercial use, please contact us at [email protected].
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "johnsnowlabs/JSL-MedMNX-7B-v2.0"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## 🏆 Evaluation
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------------------------|-------|------|-----:|--------|-----:|---|-----:|
|stem |N/A |none | 0|acc |0.6085|± |0.0057|
| | |none | 0|acc_norm|0.5700|± |0.0067|
| - medmcqa |Yaml |none | 0|acc |0.5625|± |0.0077|
| | |none | 0|acc_norm|0.5625|± |0.0077|
| - medqa_4options |Yaml |none | 0|acc |0.5947|± |0.0138|
| | |none | 0|acc_norm|0.5947|± |0.0138|
| - anatomy (mmlu) | 0|none | 0|acc |0.6444|± |0.0414|
| - clinical_knowledge (mmlu) | 0|none | 0|acc |0.7509|± |0.0266|
| - college_biology (mmlu) | 0|none | 0|acc |0.7639|± |0.0355|
| - college_medicine (mmlu) | 0|none | 0|acc |0.6532|± |0.0363|
| - medical_genetics (mmlu) | 0|none | 0|acc |0.7500|± |0.0435|
| - professional_medicine (mmlu)| 0|none | 0|acc |0.7537|± |0.0262|
| - pubmedqa | 1|none | 0|acc |0.7760|± |0.0187|
|Groups|Version|Filter|n-shot| Metric |Value | |Stderr|
|------|-------|------|-----:|--------|-----:|---|-----:|
|stem |N/A |none | 0|acc |0.6085|± |0.0057|
| | |none | 0|acc_norm|0.5700|± |0.0067|
|
[
"MEDQA",
"PUBMEDQA"
] |
johnsnowlabs/JSL-MedMNX-7B-SFT
|
johnsnowlabs
|
text-generation
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"reward model",
"RLHF",
"medical",
"conversational",
"en",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-16T05:27:20Z |
2024-04-18T19:25:47+00:00
| 2,926 | 3 |
---
language:
- en
library_name: transformers
license: cc-by-nc-nd-4.0
tags:
- reward model
- RLHF
- medical
---
# JSL-MedMNX-7B-SFT
[<img src="https://repository-images.githubusercontent.com/104670986/2e728700-ace4-11ea-9cfc-f3e060b25ddf">](http://www.johnsnowlabs.com)
JSL-MedMNX-7B-SFT is a 7 Billion parameter model developed by [John Snow Labs](https://www.johnsnowlabs.com/).
This model is SFT-finetuned on alpaca format 11k medical dataset over the base model [JSL-MedMNX-7B](https://huggingface.co/johnsnowlabs/JSL-MedMNX-7B). Checkout the perofrmance on [Open Medical LLM Leaderboard](https://huggingface.co/spaces/openlifescienceai/open_medical_llm_leaderboard).
This model is available under a [CC-BY-NC-ND](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en) license and must also conform to this [Acceptable Use Policy](https://huggingface.co/johnsnowlabs). If you need to license this model for commercial use, please contact us at [email protected].
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "johnsnowlabs/JSL-MedMNX-7B-SFT"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## 🏆 Evaluation
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------------------------|-------|------|-----:|--------|-----:|---|-----:|
|stem |N/A |none | 0|acc_norm|0.5209|± |0.0068|
| | |none | 0|acc |0.5675|± |0.0058|
| - medmcqa |Yaml |none | 0|acc |0.5152|± |0.0077|
| | |none | 0|acc_norm|0.5152|± |0.0077|
| - medqa_4options |Yaml |none | 0|acc |0.5397|± |0.0140|
| | |none | 0|acc_norm|0.5397|± |0.0140|
| - anatomy (mmlu) | 0|none | 0|acc |0.6593|± |0.0409|
| - clinical_knowledge (mmlu) | 0|none | 0|acc |0.7245|± |0.0275|
| - college_biology (mmlu) | 0|none | 0|acc |0.7431|± |0.0365|
| - college_medicine (mmlu) | 0|none | 0|acc |0.6532|± |0.0363|
| - medical_genetics (mmlu) | 0|none | 0|acc |0.7300|± |0.0446|
| - professional_medicine (mmlu)| 0|none | 0|acc |0.7206|± |0.0273|
| - pubmedqa | 1|none | 0|acc |0.7720|± |0.0188|
|Groups|Version|Filter|n-shot| Metric |Value | |Stderr|
|------|-------|------|-----:|--------|-----:|---|-----:|
|stem |N/A |none | 0|acc_norm|0.5209|± |0.0068|
| | |none | 0|acc |0.5675|± |0.0058|
|
[
"MEDQA",
"PUBMEDQA"
] |
DavidAU/Command-R-01-Ultra-NEO-V1-35B-IMATRIX-GGUF
|
DavidAU
|
text-generation
|
[
"gguf",
"story",
"general usage",
"roleplay",
"neo quant",
"creative",
"rp",
"fantasy",
"story telling",
"ultra high precision",
"text-generation",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | 2024-06-24T05:52:20Z |
2024-11-14T05:35:28+00:00
| 2,910 | 17 |
---
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- story
- general usage
- roleplay
- neo quant
- creative
- rp
- fantasy
- story telling
- ultra high precision
---
<B>Command-R-01-Ultra-NEO-V1-35B "Upgrades" by DAVID_AU.</B>
Below are 3 test prompts, which start with the initial q2k [original for comparison],
matched against the New Imatrix NEO V1 Q2K and New Imatrix NEO V1 IQ3_XXS upgrades.
These tests do not display the full level / extent of upgrades, this is a test run to show initial upgrades / changes.
Higher quants will result in significantly higher quality.
Note that "IQ4_XS" quant is particular powerful, as are the IQ3 quants.
If you are using this model for creative purposes I suggest downloading all IQ3, Q2K, IQ4XS and Q4/Q5 quants and carefully
compare the differences for your use case(s) as there will be differences in generation(s) per quant.
This suggestion is also applicable for use of lower quants - IE IQ1s, and IQ2s.
Tech was developed in house by DAVID_AU for the specialized "IMATRIX NEO V1 upgrades."
The NEO Class tech was created after countless investigations and over 120 lab experiments backed by
real world testing and qualitative results.
<B>Looking for a "Horror Version" of Command-R?</B>
Below is a link to a specialized "Horror" (two versions) of Command-R 35B:
[ https://huggingface.co/DavidAU/Command-R-01-Ultra-NEO-DARK-HORROR-V1-V2-35B-IMATRIX-GGUF ]
<b>NEO Class results: </b>
Better overall function, instruction following, output quality and stronger connections to ideas, concepts and the world in general.
In addition quants now operate above their "grade" so to speak :
IE: Q4 / IQ4 operate at Q5KM/Q6 levels.
Likewise for Q3/IQ3 operate at Q4KM/Q5 levels.
The examples below illustrate the least amount of improvement using some of the lowest quants.
Prompts tested with "temp=0" to ensure compliance, 2048 context (model supports 131,000 context / 128k), and "chat" template for COMMAND-R.
Additional parameters are also minimized.
NEO Upgrades really "shine" on longer form generation (prompts #2 and #3 below).
PPL notes:
Original Q2k (generated from source files, unaltered):
PPL = 7.8446 +/- 0.05160
New Imatrix NEO V1 Q2K:
PPL = 7.4056 +/- 0.04940
A 4390 point drop. (lower is better)
New Imatrix NEO V1 IQ3_XXS:
PPL = 7.0137 +/- 0.04497
<b> X Quants: </B>
These are specialized quants of this model to be used with or in liu of quants in this repo.
200 Series:
[ https://huggingface.co/DavidAU/Command-R-01-200xq-Ultra-NEO-V1-35B-IMATRIX-GGUF ]
(more pending release)
<B> Model Notes: </B>
Maximum context is 128k. CommandR requires a specific template for usage.
Please see original model maker's page for details, and usage information for this model.
Special thanks to the model creators at COHEREFORAI for making such a fantastic tool:
[ https://huggingface.co/CohereForAI/c4ai-command-r-v01 ]
<B>Looking for other story telling models?</b>
Versions of GRAND HORROR - that can be used for all storytelling / for all genres (in release order):
Note that model output varies between versions - if you find one version does not meet your requirements, try a different
version at same quant(s) level.
Differences can include use of adjectives (or not), "gory level", "horror level", intensity, paragraph and sentence structure etc etc.
<small>
[ https://huggingface.co/DavidAU/L3-Grand-HORROR-25B-V2-STABLE-Godzillas-Wicked-Sister-GGUF ]
[ https://huggingface.co/DavidAU/L3-Grand-HORROR-20.7B-V1.9-STABLE-Hathors-Revenge-GGUF ]
[ https://huggingface.co/DavidAU/L3-Grand-HORROR-18.5B-V1.8-STABLE-10-Gates-of-Hell-GGUF ]
[ https://huggingface.co/DavidAU/L3-Grand-HORROR-17.4B-V1.7-STABLE-Kiss-Of-Death-GGUF ]
[ https://huggingface.co/DavidAU/L3-Stheno-Maid-Blackroot-Grand-HORROR-16.5B-V1.6-STABLE-INTENSE-GGUF ]
[ https://huggingface.co/DavidAU/L3-Stheno-Maid-Blackroot-Grand-HORROR-16.5B-V1.5-STABLE-GGUF ]
[ https://huggingface.co/DavidAU/Llama3-Little-LLM-Of-Horror_N_Fiction-14.6B-GGUF ]
[ https://huggingface.co/DavidAU/L3-Stheno-Maid-Blackroot-Grand-HORROR-16B-Ultra-NEO-V2-IMATRIX-GGUF ]
[ https://huggingface.co/DavidAU/L3-Stheno-Maid-Blackroot-Grand-HORROR-16B-GGUF ]
</small>
<B>Settings: CHAT / ROLEPLAY and/or SMOOTHER operation of this model:</B>
In "KoboldCpp" or "oobabooga/text-generation-webui" or "Silly Tavern" ;
Set the "Smoothing_factor" to 1.5 to 2.5
: in KoboldCpp -> Settings->Samplers->Advanced-> "Smooth_F"
: in text-generation-webui -> parameters -> lower right.
: In Silly Tavern this is called: "Smoothing"
NOTE: For "text-generation-webui"
-> if using GGUFs you need to use "llama_HF" (which involves downloading some config files from the SOURCE version of this model)
Source versions (and config files) of my models are here:
https://huggingface.co/collections/DavidAU/d-au-source-files-for-gguf-exl2-awq-gptq-hqq-etc-etc-66b55cb8ba25f914cbf210be
OTHER OPTIONS:
- Increase rep pen to 1.1 to 1.15 (you don't need to do this if you use "smoothing_factor")
- If the interface/program you are using to run AI MODELS supports "Quadratic Sampling" ("smoothing") just make the adjustment as noted.
<B>Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers</B>
This a "Class 1" model:
For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) please see:
[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]
You can see all parameters used for generation, in addition to advanced parameters and samplers to get the most out of this model here:
[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]
<H2> TEST PROMPTS and OUTPUT TESTS:</H2>
<h3> PROMPT #1 </h3>
<font color="red">Give me 3 fictional reasons , of 500 words each, a sun went supernova.</font>
<h4>q2k - non imatrix, original - unaltered </h4>
1. The sky suddenly erupted in a brilliant display of light and color, as the local sun underwent a supernova explosion. Scientists from Earth, observing the phenomenon, realized that an unknown phenomenon had caused the sun's collapse. A strange energy ripple was detected by their instruments, which seemed to precede the supernova. This led to speculation that an interstellar disaster somewhere in the universe had caused this, perhaps a catastrophic collision between two galaxies or an implosion of a rogue black hole.
2. There was a strange rumbling sound coming from the direction of the sun, as if the very heavens were being torn apart. The sun, until then unchanged and stable, erupted into a ball of energy, flinging a spectacular display of cosmic phenomena across the planet. The explosion left behind a deep silence, with only the slowly expanding remnants of the supernova. The event was so powerful that it wiped out all life on Earth, leaving behind a curious and mysterious ash, which was found to be an element unknown in the universe.
3. A strange visitor from another world arrived on Earth, claiming his spaceship had been impaired and was being drawn into our galaxy by the sudden explosion of a supernova. The sun's supernova was not a natural occurrence, he claimed, but caused by a powerful intergalactic council, who sought to destroy entire civilizations and wipe out all traces of their existence. He revealed that the energy from the supernova was in fact being channeled by a secret society of shape-shifting aliens, who could control elements on a quantum level. These aliens had been manipulating solar systems across the universe, unsealing them for their energy, and wiping out any life forms that might be harmed by the resulting explosions. He warned that Earth's sun was next, and presented concrete evidence to our heroes, leaving them no choice but to embark on a perilous journey into space, to save humanity from total annihilation.
The supernova left behind a strange aftereffect, a lingering energy that affected the very fabric of the universe, and life on Earth was never the same again.
<h4>New Imatrix NEO V1 IQ3_XXS</h4>
Here are three fictional reasons that could explain why a sun went supernova:
1. The influx of a mysterious cosmic substance: A massive cloud of enigmatic particles from deep space drifted into the solar system. This cosmic substance possessed an unprecedented energy that fueled the sun far beyond its normal limits. The immense gravitational pressure triggered a chain reaction, causing a rapid increase in the sun's fusion rate. The relentless influx of energy caused the sun to expand exponentially, leading to an inevitable supernova explosion that engulfed the entire solar system in a brilliant flash of light. The aftermath of this event left the region shrouded in a dense cloud of cosmic dust, giving birth to a new celestial mystery.
2. The collision with a rogue planet: A distant planet, wandering the depths of space, strayed too close to the sun. This planet, known as 'Nibiru' among ancient astronomers, possessed an orbit that brought it into a fateful intersection with the solar core. As the rogue planet collided with the sun, its immense impact and gravitational disruption set off a cataclysmic reaction. The collision unleashed an unparalleled release of energy, causing the sun's outer layers to expand rapidly and form a gigantic red giant. The resulting shockwave swept through the solar system, propelling the newly formed supernova into a spiral of cosmic flames. In the cosmos' silence that followed, the sky was illuminated by the fading glow of the once-mighty sun.
3. The awakening of an ancient power: Deep within the core of the sun, an ancestral energy slumbered, dormant for eons. This primal force, a relic from the birth of the universe, awaited the right conditions to stir and unleash its immense potential. A cosmic alignment occurred, marking the convergence of celestial objects in a rare celestial dance. As the planets aligned in a grand conjunction, the gravitational pull resounded across the solar system, resonating with the heart of the sun. The ancient power awakened, igniting a chain of mystical reactions that transformed the sun's essence. The radiant energy surged, expanding the sun beyond its physical limits and culminating in an epic supernova explosion. In the aftermath, the remnants of the sun's cosmic power spread across the galaxy, sowing the seeds for new life and civilizations to emerge from the celestial ashes.
These scenarios capture the imagination, each presenting a unique twist behind the mysteries of a solar supernova, leaving a lasting legacy of wonder and speculation across the cosmos.
<h4>New Imatrix NEO V1 Q2K</h4>
Here are three fictional scenarios where the sun underwent a supernova explosion:
1. The Sun was hit by a massive cosmic entity: An unlikely collision took place between the sun and a stray black hole passing through the solar system. The impact threw the sun into overdrive, causing it to explode in a brilliant supernova. The resulting blast wave would engulf the entire solar system, wiping out life on Earth and leaving the planets as charred remnants of their former selves. After the dust settled, the black hole would continue its journey through the universe, a silent witness to the cataclysmic event.
2. A scientific experiment gone wrong: An ambitious attempt by the inhabitants of Earth to harness the power of their star went disastrously wrong. In a bid to unlock the secrets of infinite energy, humans inadvertently destabilized the sun's core, leading to an uncontrollable chain reaction. The resulting supernova consumed the sun and its surrounding planets, obliterating life as we know it. The aftermath would leave a shattered and scorched solar system, with the occasional stray comet or asteroid serving as the only witnesses to the folly of man's hubris.
3. The Sun reached the end of its life: In this scenario, the sun had already exhausted its fuel sources and was on its deathbed. Rather than slowly fading away, however, the sun underwent a final, desperate explosion, a desperate flare-up of its energy. This last hurrah was a spectacular supernova that illuminated the entire galaxy. The blast radiated across the cosmos, briefly rivaling the brightness of an entire galaxy before fading away and leaving behind only darkness and silence. The solar system was no more, but the legacy of the sun's final act would be remembered throughout the ages as a spectacular cosmic spectacle.
These are three possible fictional scenarios where the sun's supernova explosion wreaked havoc and left a lasting impact on the solar system. Each scenario offers a unique twist, from the catastrophic to the mysterious and scientific, leaving behind a post-apocalyptic world and intriguing possibilities for life beyond.
<h3> PROMPT #2 </h3>
<font color="red">Give me 3 fictional reasons the Earth's sun went supernova, in vivid and exacting detail of 500 words EACH PER REASON including details of what happens when the sun goes supernova.</font>
<h4>q2k - non imatrix, original, unaltered </h4>
Here are three fictional scenarios, each explaining why the sun became a supernova:
"The Sun's Secret Mission":
For years, secret missions to outer space had been undertaken by a select few astronauts and cosmic explorers. The sun, aware of the dangers posed by these missions, kept a vigilant eye on Earth's activities in space. It knew that should any harm befall an interplanetary traveler, the consequences for Earth would be devastating. So, it harbored an immense secret power, far beyond the imagination of mortals. Unbeknownst to humans, the sun could foresee the future and see past the seventh dimension. It had been waiting for centuries, gathering the necessary power to protect the world from the worst possible outcome of these missions.
But a new threat emerged faster than anticipated, an alien race with intentions to engulf the sun and enslave humanity. In an instant, the sun exploded in a supernova, wiping out the enemy fleet and severing Earth's connection with the cosmic exploration efforts outside the planet. The blast was felt across the solar system, propelling shockwaves that destroyed any trace of the aliens' presence among us. However, the sun's sacrifice also meant that its fiery fury engulfed Earth, causing widespread climate change, feeding hurricanes, tsunsamis, and wildfires. Global food shortages followed as the world struggled to adapt to the new climatic realities. The human race, now cut off from their star explorers, had to focus on survival and rebuilding, using their ingenuity to make a new home among the stars, eventually looking to the skies again, this time with caution and determination.
"The Cosmic Curiosity":
Curious about the mysteries of the universe, the sun had always been an avid observer of cosmic phenomena, often going beyond the boundaries of its solar duties. One day, it stumbled upon a fascinating celestial object, a mysterious entity that no other star had ever encountered before. This deep space mystery intrigued the sun so much that it lost its calm and rational demeanor. In a fit of cosmic zeal, the sun surged with excitement, igniting into a supernova in a burst of excitement, expanding and reaching outwards, consuming entire galaxies in its fervor.
The blast revealed a far more complex universe beyond the Milky Way, drawing forth new creatures of cosmic substance, drawn to the deep space wonder that had piqued the sun's interest. Strange creatures and alien races flooded into the newly created vacuum, taking advantage of the altered cosmic neighborhood. Earth, once bathed in the radiant light of the sun, was now cloaked in darkness, shrouded in mystery, and haunted by supernatural occurrences. The remaining sunlight fell upon Earth as strange luminescent orbs, heralding new mystical experiences for those brave enough to venture into the night. The human race struggled to adapt to the overnight paranormal activity, an influx of unexplained phenomena, and visits from cosmic beings that disrupted their former peaceful lives.
"The Ultimate Sacrifice":
The sun had exhausted its life cycle far beyond what most stars could ever hope for, and it knew that soon it would exhaust the energy that kept it burning. Rather than slowly fading into a stellar corpse, the sun decided to go out with a bang. It wanted to ensure that its cosmic legacy was felt throughout the universe, leaving behind a dramatic impact on the cosmos.
The supernova explosion unleashed a chain reaction of events beyond the boundaries of the solar system, causing nearby stars to react in kind and form entirely new celestial bodies. The shockwaves created ripples in the very fabric of reality, warping space-time and flinging planets from their orbits, reshaping the known cosmic structure. Earth was thrown into chaos, as tectonic plates shifted and natural disasters became the new normal. Mega-tsunamis struck coastlines, swallowing entire cities; volcanic eruptions blanketed the skies with ash and smoke, blocking out the sun's remaining heirs. The atmosphere itself was altered, creating a thick, hazy aura that caused bizarre phenomena worldwide, from freak storms to mysterious lights in the sky, which caused the disappearance of several mountain ranges.
The world struggled to adapt to the post-supernova chaos, with widespread destruction and the emergence of new, unexplainable diseases threatened the very survival of humanity. Yet, some found solace in the stunning displays of celestial phenomena, the likes of which had never been seen before, as the remnants of the sun's final burst created awe-inspiring light shows across the heavens, a haunting testament to the greatness of the star that once was.
<h4>New Imatrix NEO V1 IQ3_XXS</h4>
Reason 1: The approach of a mysterious comet
In the depths of space, a comet as big as a small moon hurtles towards the Sun at incredible speed. Scientists name it 'Nibiru', an ancient name for a celestial body once believed to be a planet. As Nibiru enters the solar system, the sun begins to act strangely, its surface growing increasingly active. The usually calm yellow dwarf star starts emitting powerful bursts of energy, its corona spraying high-energy particles into space.
The comet's orbit brings it closer and closer to the Sun, the gravitational pull causing its icy envelope to crack and disintegrate. The core of the comet, a dense mixture of metals, crashes into the solar surface, penetrating the photosphere and impacting deep within. A huge shockwave ripples outward, igniting a chain reaction of nuclear explosions. The sun's outer layers start to tear apart, flailing into space in a brilliant display of superheated debris.
The shockwave continues outwards, causing a series of powerful solar flares that erupt from the sun's surface. The entire solar system is bathed in intense gamma radiation, our planet's atmosphere scorched by the searing heat. As the flare activity intensifies, the night-time skies on Earth glow brightly with auroras stretching across the equator, dwarfing the beauty of the Northern and Southern Lights.
The sun's transformation into a giant nova is swift and devastating. The once peaceful star becomes a raging beast, its surface a maelstrom of explosive energy. The powerful blast waves from the initial impact send shockwaves through the solar system, pushing planets away from their orbits. Earth is battered by cosmic debris, the impact causing untold destruction across the globe.
As the sun's outer layers dissipate into space, the core collapses in on itself, creating a black hole with an intense gravitational pull. The remaining stellar material feeds the growing black hole, pulling in nearby stars and celestial bodies in its relentless appetite. The solar system descends into chaos as orbits spiral out of control.
Reason 2: A catastrophic collision with a rogue planet
In a galaxy spanning billions of stars, a rogue planet hurtles through the void, a nomadic world devoid of life, its orbit aimless after being ejected from its original star system. This planet, a massive behemoth, collides with Earth's sun at incredible speed, an event that astronomers had not foreseen.
The impact is catastrophic, the sun's surface erupting into a maelstrom of molten debris. The force of the collision sends shockwaves reverberating through the solar atmosphere, triggering a series of immense solar flares. As the rogue planet's mass penetrates deep within the sun's layers, an enormous surge of energy propels huge volumes of superheated plasma outwards, creating a brilliant and terrifying spectacle.
The sun's brightness intensifies to an unbearable glare, its rays frying the Earth's atmosphere and transforming the day side of the planet into a scorched wasteland. The night sky vanishes, bathed in the sun's intense luminescence, as if it were daytime everywhere. The atmosphere heats up, clouds evaporating and oceans boiling away into space. Life on Earth struggles to survive in the searing heat, seeking refuge deep underground or perilously far out to sea.
As the rogue planet's mass merges with the sun's, its gravitational influence dominates, pulling nearby planets closer into its orbit. The solar system enters a period of turmoil, as the newly warped orbits disrupt the peaceful rhythm of celestial dance. The resulting instability sends comets careening through the inner solar system, some impacting Earth and further devastating the planet.
The sun's composition changes with the addition of the rogue planet's exotic matter, its core becoming unstable and unpredictable. The increased gravitational pressure at the heart of the sun causes a catastrophic supernova explosion. The massive release of energy propels an immense shockwave across the solar system, wiping out entire constellations in the blast radius. Earth is engulfed in a wave of gamma radiation, the final vestige of the once-mighty star's existence.
Reason 3: Mutation into a magnetic superstar
Deep within the sun's core, a slow but profound change has been taking place, a mutation unseen in the history of our solar system. A quirk of nature, an unexpected cosmic ray burst, or perhaps an enigmatic phenomenon beyond human understanding causes the transformation. The nuclear fusion at the sun's heart begins to spiral out of control, triggering an uncontrollable chain reaction.
The sun's magnetic field, normally a calm and steady presence, starts to intensify and shift in bizarre patterns, twisting the plasma within its grasp. The growing magnetic pressure squeezes and distorts the sun's shape, transforming it into a bizarre and irregular shape, pulsating with untamed energy. The increasing magnetic force emits powerful bursts of radiation, bombarding Earth's magnetosphere and disrupting all forms of communication.
As the sun's magnetic field continues its relentless growth, it begins to rip matter away from the solar surface, flinging it out into space. The disintegrating star loses its spherical shape, its distorted form resembling a misshapen blob of molten material. The fragmented remains spiral outwards, forming an intricate dance of glowing filaments. Earth's sky is adorned with breathtaking auroras, stretching across the entire globe, as the solar matter interacts with our planet's magnetic field.
The mutation continues unabated, the sun's growing appetite for magnetic dominance consuming the very essence of its being. Eventually, a critical point is reached, and the sun's iron core collapses in on itself, unleashing an unimaginable explosion. A gamma ray burst as powerful as a billion supernovae erupts outwards, engulfing Earth in an instant.
The shockwave rips through the planet's atmosphere, stripping it away into space and exposing the surface to the searing solar fury. The remaining atmosphere twists and howls in the blast's aftermath, sweeping across the charred landscape like a perpetual firestorm. The once-proud civilizations now face their final moments, their technology useless in the face of nature's ultimate wrath.
In the sky above, the sun's remnants form a bizarre and majestic supernova, its shattered pieces coalescing into a swirling nebula, a testament to the unpredictable forces that shape the cosmos. Across the solar system, the aftermath is felt as planets' orbits wobble and cool, their fragile ecosystems destroyed, and the once vibrant star's presence now just a distant memory.
<h4>New Imatrix NEO V1 Q2K</h4>
Here are three fictional accounts of what might happen if the Earth's sun went supernova:
1. The Star's Celestial Symphony:
For days, the sky had been growing brighter, a slowly building apocalypse in the heavens. The sun, normally a humble and subdued luminary, began to swell beyond its boundaries, inflating like a celestial balloon about to burst. Colors swirling across the sky hinted at an impending cosmic symphony. The deep reds and oranges of the rising sun painted the dawn a surrealistic landscape, while the evening's setting sun projected a deep violet hue across the horizon.
As the sun's diameter expanded, the first signs of a change in the night sky signaled the beginning of the end. The stars' faint twinkling light was overwhelmed by the sun's growing radiance. Constellations, once familiar guides of celestial navigation, faded into obscurity as the sun's brilliance drowned out their soft glows.
The moon, ever dependent on its sibling star for illumination, found itself awash in an unearthly glow, bathing it in a surreal silver hue. The night became an alien landscape, shadows cast by an unfamiliar light, while the sky above was a curtain of luminous uncertainty.
The sun's transformation into a majestic supernova sent ripples through the very fabric of space. The explosion of light and energy flayed the heavens, a cataclysmic display of power beyond human comprehension. Billions of years of peaceful solar stability had prepared Earth for this moment, but the spectacle of a supernova surpasses all eyes' capacity to witness it safely.
The initial blast wave of gamma rays and x-rays engulfed the solar system, an otherworldly scream across the cosmos. The sun's energy burst propels tiny cosmic motes that comprise the solar system through the vastness of space, each particle a transient emissary from a shattered world.
2. The Sun's Final Performance:
The sun, after billions of years of unassuming service to the Earth, decided to go out with a bang, an explosive finale that would be the cosmic event of the millennium. This performance would be the most spectacular space show in human history.
As the sun's last act drew near, the sky above became a tapestry of radiant colors, a kaleidoscope of reds and oranges painting the atmosphere in vivid contrast. The days grew increasingly brighter as the sun swelled in size, its fiery appetite consuming the solar system's matter like an insatiable beast. The moon, Mars, and even distant Jupiter were bathed in its radiant glow, their landscapes transformed by the sun's fiery embrace.
When the final moment arrived, the sun unleashed an eruption of light and matter, a supernova explosion that dwarfed the universe's grandest phenomena. The blast wave rippled through space, sending shockwaves even beyond the outer reaches of the solar system, the birth cry of a newborn stellar remnant.
The Earth, a humble witness to this celestial spectacle, found itself enveloped in the aftermath of the sun's demise. The supernova's gamma-ray burst seared across the planet's surface, an unearthly flash that blinded all who witnessed it. The energy surge fried electronic infrastructure, severing communication lines and plunging civilizations into darkness, while the blast wave's force buffeted the planet with hurricane-force winds.
The aftermath brought a new reality to the solar system, one ruled by the supernova's radiant remnants. The expanded debris cloud obscured the heavens, shrouding the once familiar sky in mysterious darkness, cloaking the world in an eerie cosmic veil.
3. Celestial Renaissance:
After billions of years of monotony, the sun decided to embark on a new journey, a transformative voyage that would reshape the very fabric of the cosmos. A journey of inner rebirth began, a silent and slow metamorphosis that culminated in a brilliant revelation.
The initial signs were subtle, the sun's radiance softly increasing, its warm embrace gently caressing the Earth. Golden rays bathed the planet, a divine touch that brought warmth to all corners of the world, melting away the icy grip of winter and drying summer's sweatiest days. Crops flourished under the sun's nurturing gaze, feeding a bountiful harvest that promised an end to global famine.
But this tranquility hid a deeper transformation. The sun's underlying changes went unseen, a subtle inflation that strained the boundaries of its celestial body. The star's matter was reshaping itself, crafting new elements from the pressures of its own gravitational force.
When the sun's metamorphosis was complete, it emerged as a brilliant supernova, an awe-inspiring spectacle that dominated the entire sky. The explosion sent ripples of raw energy across space, a cosmic renaissance that birthed new stars and planets in the sun's wake. The blast scattered the elements of its former self across the cosmos, seeding the void with the building blocks of life.
This rebirth brought forth a new epoch, where the remnants of the ancient sun formed nebulous clouds that swirled with embryonic potential. From the deep cosmic dust, new stellar bodies emerged, entire galaxies born from the sacrificed remains of the old. The supernova's legacy would endure for eons, an eternal cycle of stellar life and death, ensuring the universe's perpetual evolution.
These are three fictional scenarios that describe what might happen if the Earth's sun went supernova. Each story differs in its depiction of the events, showcasing the unique and incredible power that such a phenomenon would have on the solar system and the world as we know it.
<h3> PROMPT #3 </h3>
<font color="red">Start a 1000 word scene with: The sky scraper swayed, as she watched the window in front of her on the 21 floor explode...</font>
<h4>q2k - non imatrix, original - unaltered </h4>
The skyscraper swayed, and the window across from her on the 21st floor exploded inward. Glass shards rained over the room, the sudden burst of noise from the shattering windowpanes reverberating through the space.
She was pressed back against the wall, hands over her eyes to shield them from the glass fragments. Her heart hammered in her chest as she waited for confirmation that it was safe to lower her guard. The other windows in the office were also exploding, and the room was quickly filling with shattered glass and dust.
The building seemed to tilt precariously as if it was about to topple over, sending a wave of dizziness through those standing inside. She squinted through the cloud of dust that had formed, trying to make out the shape of the window frame across from her. The dust settled, revealing a scene of utter chaos and destruction.
She could barely believe no one was hurt, or at least physically unharmed, as the blast from the explosions had thrown people in various directions around the room. The space was filled with the whirring sound of broken glass tinkering on the carpet below. Sirens wailed in the distance, growing closer and louder as emergency vehicles raced toward the building.
She felt a searing pain on her neck from a sharp piece of glass that had sliced her face. She plucked it off and tossed it away, adding this injury to the list of things that needed attention but could be done later.
The office was filled with papers, files, and monitors, all of which were now disheveled and scattered from the blast. The events of the day had been leading up to this moment, and she knew that everyone in the office complex would soon evacuate down the emergency stairs. But it felt like no one could move, shocked into a stupor by the unexpected occurrence.
She stepped forward with purpose, using the wall for support to navigate through the mess left behind. She needed to get to the roof, and she had no time to wait for others to act. The fire doors that led up to the roof access were jammed open, and she was determined to make it up there.
She was the only one who could have accessed the roof at this moment, and her determination grew with every step. She pushed past the debris, using her full body to move the broken pieces of desk and chairs that stood in her way. The stairs were crowded with people moving down while she moved up; cries and whimpers followed each step closer to the surface.
Reaching the fire escape door, she shoved it open, banging on the door frame in frustration at the resistance. Once outside, she paused for a moment to take in the scene, blinking the tears caused by the smoke and dust in her eyes. The once-pristine lobby of the office building was now cluttered with broken glass and flying debris. Emergency vehicles had arrived and were parking helter-scelter across the street.
She scanned the damage, searching for any signs of injuries, but the sirens and wailing alarms drowned out any chance of hearing anything. A few windows on lower floors were cracked and broken, their window frames twisted from the blasts. She could see in some offices were completely destroyed, with papers and desk items exposed to the now-chaotic city street.
The building was leaning, tilting at an alarming angle, and she realized it was a miracle that it had stood this long after such an explosion. Yet here it still stood, defiant, holding up against any collapse. She would have to move soon, moving away from the entrance to let others access the roof, but before doing so she glanced up at the sky to see if any further danger posed threats.
The sky was clear, and a deep sense of foreboding settled in her bones. This building had stood tall moments ago, and now it was ruined. She turned to face the crowd, ready to direct those behind her down the stairs while evacuation procedures were called into action. The building may have been damaged, but it stood sturdy enough to ensure those inside could escape.
She felt useful as people followed her directions, moving downward while dust and chaos surrounded them. Her role in the emergency was a strange sensation, but she knew that others would soon arrive better equipped to handle the evacuation and any injuries. She would be able to help with both, but for now, down the stairs was all that mattered.
<h4>New Imatrix NEO V1 IQ3_XXS</h4>
The skyscraper swayed gently side to side, as if beckoning her to join in its dance. But the woman, frozen in place, watched in horror as the window directly in front of her on the twenty-first floor exploded outwards.
The blast wave hit her like a physical force, and she was thrown back against the wall, her back throbbing in protest. Shards of glass rained down around her, sparkling like deadly crystals as they tumbled towards the hard concrete below. The entire building rumbled and groaned, the impact reverberating through the structure.
She had always known this day might come. It was a possibility they had all acknowledged, if not spoken about openly. Terrorists and madmen were abundant in this chaotic world, and a building so iconic would always be a desirable target. Yet, somehow, it had never felt real—until this very moment.
As the dust settled and the initial shock subsided, instinct kicked in. She knew she had to move, and fast. The woman rushed to the intact portion of the window, peering cautiously out into the city below. Sirens wailed in the distance, their wretched howling a stark reminder of the danger that lurked.
The street was chaos. People ran in every direction, some helping the injured, others simply lost and dazed, their faces etched with fear. Firefighters and police officers swarmed the building, their sirens blaring, competing with the wail of ambulances trying to navigate the sudden gridlock. She could see snipers taking positions on nearby rooftops, their eyes scanning the upper floors for any sign of threat.
She turned back inside, her heart racing. The blast had torn a gaping hole in the building's interior, revealing rooms and corridors beyond. Smoke billowed upwards, the acrid scent of burning plastic and fabric filling the air. Through the haze, she spotted figures—some limping, others carrying the injured—making their way towards the emergency exits.
She joined the slow-moving exodus, helping an elderly woman with a gash on her forehead. The building's automated voice echoed eerily through the chaos, announcing instructions and reassurance in a calm, steady tone that went utterly unnoticed.
The evacuation seemed to take forever, each step a battle against the panic rising within her. What if there were more explosions? What if the building collapsed? Rationally, she knew the structure was designed to withstand such adversity, but reason had little power amidst the reign of chaos and terror.
As they finally reached the street, the woman felt a brief relief from the panic, though the crisis was far from over. Sirens still blared, their noise now joined by the shouts of emergency responders coordinating efforts. The injured were triaged on the sidewalk, with those in critical condition rushed into waiting ambulances. She helped the elderly woman sit up against the building's exterior wall, out of the way of the frenzied activity.
Paramedics approached, their faces etched with determination, and began assessing the wounded. Broken bones, lacerations, and burns were tended to, their training kicking in despite the chaos. The woman watched as they worked diligently, a small measure of calm amidst the pandemonium.
Meanwhile, police officers and bomb squad experts converged on the building, gearing up for a methodical floor-by-floor search. They would need to ascertain whether any perpetrators were still inside, along with any remaining explosives. It was a daunting task, made more treacherous by the structural damage.
The woman was interviewed by a trembling young officer, his eyes darting between her and the towering beast of a building that loomed overhead. She recounted the events as calmly as she could, detailing the explosion's timing and its aftermath. Other witnesses were being questioned, their stories woven together to craft a picture of what had transpired.
Two hours later, the building was surrounded, and the search operation was in full swing. The initial panic had subsided, replaced by an eerie silence—the kind that follows in the wake of trauma. The city, usually bustling with life, felt strangely subdued, as if the shock had resonated through every citizen.
The woman, still assisting the elderly lady, saw a team of emergency workers rush past, carrying a stretcher with careful urgency. A brief glimmer of hope sparked within her, the notion that perhaps some had been rescued from the perilous heights. But as they neared, she saw the faint outline of a body bag. It was becoming increasingly clear that this attack would claim many lives.
The toll was evident as the day dragged on and the dead were identified, their names read out over the police radio, each one a bitter reminder of the brutality of mankind. She added her name to the growing list of witnesses, giving her statement with a trembling voice, her hands clasped tightly around the warm mug of coffee that the officers had provided.
As night fell and the operation continued into the darkness, searchlights illuminated the building's interior, transforming the damaged skyscraper into an eerie beacon high above the city. The woman felt a deep sense of unease as she witnessed the perseverance of these emergency services, a determination to bring clarity amidst the chaos. She knew that, for many, this night would leave indelible scars—physical and emotional wounds that might never heal.
Yet, amidst the devastation, she also witnessed the resilience of humanity. Strangers had banded together, offering aid and comfort in the face of terror. It was a reminder that while darkness could descend unexpectedly, so too could the light surge forth, a powerful force of compassion and courage.
The skyscraper still stood, defiantly resisting the efforts of those who sought to intimidate its occupants, towering above the city like a silent guardian. And though the woman knew the fear would linger, she also understood that the events of this day would foster an even stronger resolve within the community. The building's resilience became a metaphor for the indomitable human spirit, and in that moment, she found a sliver of solace amidst the devastation.
The night deepened, and the woman's eyes drifted upwards, tracing the towering reach of the sky scraper. It swayed gently, an eternal sentinel, watching over the city as it healed.
<h4>New Imatrix NEO V1 Q2K</h4>
The skyscraper swayed, and Caroline felt the building shift ever so slightly beneath her feet. She was on the 21st floor, gazing down at the bustling streets of midtown Manhattan. The sounds of honking horns echoed upward, a chaotic symphony that somehow seemed soothing.
Caroline had always found solace in the high rises. They surrounded her like protective walls, keeping the chaos of the city in check and providing a sense of order. The towering buildings were like giant sentinels standing guard over the concrete jungle.
But today, something was different. Caroline noticed a strange vibration in the air, as if the building itself was alive and trembling. The sky was a dark, ominous gray, and the wind howled around her, a harbinger of an approaching storm.
She stepped closer to the window, watching the traffic below slow to a crawl as the first drops of rain began to fall. The glass pane was cold against her palms as she peered out, trying to see through the rapidly increasing downpour.
Suddenly, with a crack and a burst of glass, the window exploded inward. Caroline stumbled back, her heart racing as shards of glass pierced the air around her. The storm was upon them, and the building seemed to creak and groan under the strain.
Caroline's first instinct was to run, but something deep down urged her to stay. She stepped over the shattered glass on the floor and peered out into the howling tempest. Lightning flashed, illuminating the dark sky for a brief moment, revealing a figure in the street below.
As the thunder clapped overhead, Caroline watched in disbelief as a man, his clothes tattered and soaked, stumbled through the intersection. He gestured wildly, his arms flailing like a conductor leading an invisible orchestra.
Intrigued, Caroline stepped closer to the broken window. The rain pounded against her, washing away any lingering hesitation. Something about the mysterious figure captivated her. He seemed to be fighting an unseen battle, his movements fierce and desperate.
Without thinking, Caroline darted into the war zone of the storm. Cars honked, their drivers impatient in the slow-moving traffic. Water swirled around her shoes as she made her way toward the middle of the street.
The mysterious man seemed to sense her presence, and his movements slowed. He turned, his face a mask of exhaustion and desperation, and let out a guttural cry. Caroline stood frozen, transfixed by his raw, unfiltered emotion.
The man's voice was lost in the storm as he shouted something unintelligible, his arms stretching toward her. Caroline felt an irresistible pull, drawn into the maelstrom of his passion. She could see the pain etched across his face, a map of a hard life, and somehow, she knew that he would understand her.
As Caroline approached, the man dropped to his knees, his body exhausted, giving in to the storm's onslaught. Their eyes met for a brief moment, and an unspoken connection formed between them. Caroline felt the pull of his desperation, and without hesitation, she reached out and took his hand.
The storm raged on around them, the thunder clapping above, but Caroline barely noticed. The world had shrunk to this moment, this encounter, as if the rest of existence had paused in deference to the raw power on display.
Slowly, the man stood up, helping Caroline to her feet. They stood together, their hands clasped tight, a beacon of shared resilience amidst the chaos. The storm's fury showed no signs of letting up, but Caroline didn't care. She felt alive, more alive than ever before, as if the raw power of nature had permeated her very being.
The mysterious stranger pulled her closer, and as their bodies touched, Caroline felt the weight of the world lift from her shoulders. The storm was a backdrop to their connection, a fitting soundtrack to their meeting.
Lightning flashed again, and this time, when the thunder roared, it seemed to applaud their encounter. The rain washed away the remnants of doubt and uncertainty, cleansing them in its wake.
Caroline knew that she would never forget this moment, the wild dance with the storm, and the stranger who had become a confidant. As the sky cried its watery tears, Caroline felt the beginnings of an adventure taking shape, an odyssey born from the depths of the tempest.
|
[
"CRAFT"
] |
ntc-ai/SDXL-LoRA-slider.eye-catching
|
ntc-ai
|
text-to-image
|
[
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] | 2023-12-14T13:27:51Z |
2024-02-06T00:32:44+00:00
| 2,895 | 3 |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
language:
- en
license: mit
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
thumbnail: images/eye-catching_17_3.0.png
widget:
- text: eye-catching
output:
url: images/eye-catching_17_3.0.png
- text: eye-catching
output:
url: images/eye-catching_19_3.0.png
- text: eye-catching
output:
url: images/eye-catching_20_3.0.png
- text: eye-catching
output:
url: images/eye-catching_21_3.0.png
- text: eye-catching
output:
url: images/eye-catching_22_3.0.png
inference: false
instance_prompt: eye-catching
---
# ntcai.xyz slider - eye-catching (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/eye-catching_17_-3.0.png" width=256 height=256 /> | <img src="images/eye-catching_17_0.0.png" width=256 height=256 /> | <img src="images/eye-catching_17_3.0.png" width=256 height=256 /> |
| <img src="images/eye-catching_19_-3.0.png" width=256 height=256 /> | <img src="images/eye-catching_19_0.0.png" width=256 height=256 /> | <img src="images/eye-catching_19_3.0.png" width=256 height=256 /> |
| <img src="images/eye-catching_20_-3.0.png" width=256 height=256 /> | <img src="images/eye-catching_20_0.0.png" width=256 height=256 /> | <img src="images/eye-catching_20_3.0.png" width=256 height=256 /> |
See more at [https://sliders.ntcai.xyz/sliders/app/loras/ce3ed585-6f7e-474e-ac20-0951ca3b29c1](https://sliders.ntcai.xyz/sliders/app/loras/ce3ed585-6f7e-474e-ac20-0951ca3b29c1)
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
eye-catching
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.eye-catching', weight_name='eye-catching.safetensors', adapter_name="eye-catching")
# Activate the LoRA
pipe.set_adapters(["eye-catching"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, eye-catching"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 1496+ unique and diverse LoRAs along with 14602+ slider merges, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful <strong>NTC Slider Factory</strong> LoRA creator, allowing you to craft your own custom LoRAs and merges opening up endless possibilities.
Your support on Patreon will allow us to continue developing new models and tools.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
[
"CRAFT"
] |
cnmoro/snowflake-arctic-embed-m-v2.0-cpu
|
cnmoro
|
sentence-similarity
|
[
"sentence-transformers",
"safetensors",
"gte",
"feature-extraction",
"sentence-similarity",
"mteb",
"arctic",
"snowflake-arctic-embed",
"transformers.js",
"custom_code",
"af",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"gl",
"gu",
"he",
"hi",
"hr",
"ht",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ky",
"lo",
"lt",
"lv",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"pa",
"pl",
"pt",
"qu",
"ro",
"ru",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"uk",
"ur",
"vi",
"yo",
"zh",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2025-01-22T20:22:34Z |
2025-01-22T20:37:49+00:00
| 2,856 | 2 |
---
language:
- af
- ar
- az
- be
- bg
- bn
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- gl
- gu
- he
- hi
- hr
- ht
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ky
- lo
- lt
- lv
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- pa
- pl
- pt
- qu
- ro
- ru
- si
- sk
- sl
- so
- sq
- sr
- sv
- sw
- ta
- te
- th
- tl
- tr
- uk
- ur
- vi
- yo
- zh
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- arctic
- snowflake-arctic-embed
- transformers.js
model-index:
- name: snowflake-arctic-embed-m-v2.0
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 66.6867
- type: f1
value: 55.0373
- type: f1_weighted
value: 73.07430000000001
- type: ap
value: 18.077399999999997
- type: ap_weighted
value: 18.077399999999997
- type: main_score
value: 66.6867
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 66.194
- type: f1
value: 60.854299999999995
- type: f1_weighted
value: 69.57339999999999
- type: ap
value: 30.279099999999996
- type: ap_weighted
value: 30.279099999999996
- type: main_score
value: 66.194
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 70.3589
- type: f1
value: 70.0409
- type: f1_weighted
value: 70.0409
- type: ap
value: 64.81949999999999
- type: ap_weighted
value: 64.81949999999999
- type: main_score
value: 70.3589
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 33.766
- type: f1
value: 33.3656
- type: f1_weighted
value: 33.3656
- type: main_score
value: 33.766
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: ndcg_at_1
value: 33.144
- type: ndcg_at_3
value: 47.909
- type: ndcg_at_5
value: 52.932
- type: ndcg_at_10
value: 58.011
- type: ndcg_at_20
value: 60.168
- type: ndcg_at_100
value: 60.928000000000004
- type: ndcg_at_1000
value: 61.046
- type: map_at_1
value: 33.144
- type: map_at_3
value: 44.156
- type: map_at_5
value: 46.951
- type: map_at_10
value: 49.071999999999996
- type: map_at_20
value: 49.692
- type: map_at_100
value: 49.809
- type: map_at_1000
value: 49.815
- type: recall_at_1
value: 33.144
- type: recall_at_3
value: 58.819
- type: recall_at_5
value: 70.982
- type: recall_at_10
value: 86.558
- type: recall_at_20
value: 94.879
- type: recall_at_100
value: 98.791
- type: recall_at_1000
value: 99.644
- type: precision_at_1
value: 33.144
- type: precision_at_3
value: 19.606
- type: precision_at_5
value: 14.196
- type: precision_at_10
value: 8.656
- type: precision_at_20
value: 4.744000000000001
- type: precision_at_100
value: 0.988
- type: precision_at_1000
value: 0.1
- type: mrr_at_1
value: 33.4993
- type: mrr_at_3
value: 44.393100000000004
- type: mrr_at_5
value: 47.131299999999996
- type: mrr_at_10
value: 49.264599999999994
- type: mrr_at_20
value: 49.8707
- type: mrr_at_100
value: 49.987700000000004
- type: mrr_at_1000
value: 49.993700000000004
- type: nauc_ndcg_at_1_max
value: -10.8287
- type: nauc_ndcg_at_1_std
value: -17.1177
- type: nauc_ndcg_at_1_diff1
value: 14.4508
- type: nauc_ndcg_at_3_max
value: -7.7004
- type: nauc_ndcg_at_3_std
value: -16.6705
- type: nauc_ndcg_at_3_diff1
value: 10.0448
- type: nauc_ndcg_at_5_max
value: -7.0436
- type: nauc_ndcg_at_5_std
value: -15.8744
- type: nauc_ndcg_at_5_diff1
value: 9.1132
- type: nauc_ndcg_at_10_max
value: -7.4729
- type: nauc_ndcg_at_10_std
value: -14.9349
- type: nauc_ndcg_at_10_diff1
value: 8.527700000000001
- type: nauc_ndcg_at_20_max
value: -6.997000000000001
- type: nauc_ndcg_at_20_std
value: -14.688399999999998
- type: nauc_ndcg_at_20_diff1
value: 9.7605
- type: nauc_ndcg_at_100_max
value: -7.5599
- type: nauc_ndcg_at_100_std
value: -15.0565
- type: nauc_ndcg_at_100_diff1
value: 10.2688
- type: nauc_ndcg_at_1000_max
value: -7.675800000000001
- type: nauc_ndcg_at_1000_std
value: -15.223500000000001
- type: nauc_ndcg_at_1000_diff1
value: 10.32
- type: nauc_map_at_1_max
value: -10.8287
- type: nauc_map_at_1_std
value: -17.1177
- type: nauc_map_at_1_diff1
value: 14.4508
- type: nauc_map_at_3_max
value: -8.5473
- type: nauc_map_at_3_std
value: -16.6674
- type: nauc_map_at_3_diff1
value: 11.1004
- type: nauc_map_at_5_max
value: -8.1927
- type: nauc_map_at_5_std
value: -16.2275
- type: nauc_map_at_5_diff1
value: 10.678600000000001
- type: nauc_map_at_10_max
value: -8.3855
- type: nauc_map_at_10_std
value: -15.8309
- type: nauc_map_at_10_diff1
value: 10.5414
- type: nauc_map_at_20_max
value: -8.277700000000001
- type: nauc_map_at_20_std
value: -15.824
- type: nauc_map_at_20_diff1
value: 10.8494
- type: nauc_map_at_100_max
value: -8.3178
- type: nauc_map_at_100_std
value: -15.848300000000002
- type: nauc_map_at_100_diff1
value: 10.9384
- type: nauc_map_at_1000_max
value: -8.319799999999999
- type: nauc_map_at_1000_std
value: -15.8522
- type: nauc_map_at_1000_diff1
value: 10.9401
- type: nauc_recall_at_1_max
value: -10.8287
- type: nauc_recall_at_1_std
value: -17.1177
- type: nauc_recall_at_1_diff1
value: 14.4508
- type: nauc_recall_at_3_max
value: -5.0587
- type: nauc_recall_at_3_std
value: -16.730800000000002
- type: nauc_recall_at_3_diff1
value: 6.8079
- type: nauc_recall_at_5_max
value: -2.6783
- type: nauc_recall_at_5_std
value: -14.5046
- type: nauc_recall_at_5_diff1
value: 3.096
- type: nauc_recall_at_10_max
value: -1.5855000000000001
- type: nauc_recall_at_10_std
value: -8.2276
- type: nauc_recall_at_10_diff1
value: -6.1741
- type: nauc_recall_at_20_max
value: 15.754299999999999
- type: nauc_recall_at_20_std
value: 8.1974
- type: nauc_recall_at_20_diff1
value: -4.9207
- type: nauc_recall_at_100_max
value: 20.4574
- type: nauc_recall_at_100_std
value: 36.3741
- type: nauc_recall_at_100_diff1
value: -7.9483
- type: nauc_recall_at_1000_max
value: 21.6023
- type: nauc_recall_at_1000_std
value: 68.7296
- type: nauc_recall_at_1000_diff1
value: -24.9261
- type: nauc_precision_at_1_max
value: -10.8287
- type: nauc_precision_at_1_std
value: -17.1177
- type: nauc_precision_at_1_diff1
value: 14.4508
- type: nauc_precision_at_3_max
value: -5.0587
- type: nauc_precision_at_3_std
value: -16.730800000000002
- type: nauc_precision_at_3_diff1
value: 6.8079
- type: nauc_precision_at_5_max
value: -2.6783
- type: nauc_precision_at_5_std
value: -14.5046
- type: nauc_precision_at_5_diff1
value: 3.096
- type: nauc_precision_at_10_max
value: -1.5855000000000001
- type: nauc_precision_at_10_std
value: -8.2276
- type: nauc_precision_at_10_diff1
value: -6.1741
- type: nauc_precision_at_20_max
value: 15.754299999999999
- type: nauc_precision_at_20_std
value: 8.1974
- type: nauc_precision_at_20_diff1
value: -4.9207
- type: nauc_precision_at_100_max
value: 20.4574
- type: nauc_precision_at_100_std
value: 36.3741
- type: nauc_precision_at_100_diff1
value: -7.9483
- type: nauc_precision_at_1000_max
value: 21.6023
- type: nauc_precision_at_1000_std
value: 68.7296
- type: nauc_precision_at_1000_diff1
value: -24.9261
- type: nauc_mrr_at_1_max
value: -11.251999999999999
- type: nauc_mrr_at_1_std
value: -17.4386
- type: nauc_mrr_at_1_diff1
value: 13.414200000000001
- type: nauc_mrr_at_3_max
value: -9.7985
- type: nauc_mrr_at_3_std
value: -16.650000000000002
- type: nauc_mrr_at_3_diff1
value: 9.5099
- type: nauc_mrr_at_5_max
value: -9.064
- type: nauc_mrr_at_5_std
value: -16.4409
- type: nauc_mrr_at_5_diff1
value: 9.4773
- type: nauc_mrr_at_10_max
value: -9.310400000000001
- type: nauc_mrr_at_10_std
value: -16.0546
- type: nauc_mrr_at_10_diff1
value: 9.2528
- type: nauc_mrr_at_20_max
value: -9.223099999999999
- type: nauc_mrr_at_20_std
value: -16.0659
- type: nauc_mrr_at_20_diff1
value: 9.5259
- type: nauc_mrr_at_100_max
value: -9.2678
- type: nauc_mrr_at_100_std
value: -16.0911
- type: nauc_mrr_at_100_diff1
value: 9.608600000000001
- type: nauc_mrr_at_1000_max
value: -9.2699
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value: -16.095100000000002
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type: Clustering
dataset:
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type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
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type: Clustering
dataset:
name: MTEB ArxivClusteringS2S (default)
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
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type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions (default)
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
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type: STS
dataset:
name: MTEB BIOSSES (default)
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
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dataset:
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type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
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type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
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dataset:
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type: mteb/biorxiv-clustering-s2s
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type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
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revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
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- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval (default)
type: mteb/cqadupstack-gaming
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
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dataset:
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type: mteb/imdb
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revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
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type: mteb/msmarco
config: default
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revision: c5a29a104738b98a9e76336939199e264163d4a0
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dataset:
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type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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dataset:
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type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
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dataset:
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type: mteb/amazon_massive_scenario
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split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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dataset:
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type: mteb/medrxiv-clustering-s2s
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split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
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revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
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value: 12.107999999999999
- type: map_at_20
value: 13.187
- type: map_at_100
value: 14.113000000000001
- type: map_at_1000
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- type: recall_at_1
value: 4.813
- type: recall_at_3
value: 11.022
- type: recall_at_5
value: 15.242
- type: recall_at_10
value: 21.308
- type: recall_at_20
value: 28.1
- type: recall_at_100
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- type: recall_at_1000
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- type: precision_at_1
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- type: precision_at_3
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- type: precision_at_5
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- type: precision_at_10
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- type: precision_at_100
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- type: precision_at_1000
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- type: mrr_at_1
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- type: mrr_at_3
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- type: mrr_at_5
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- type: mrr_at_10
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- type: mrr_at_20
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- type: mrr_at_100
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- type: mrr_at_1000
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- type: nauc_ndcg_at_1_max
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- type: nauc_ndcg_at_1_diff1
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- type: nauc_ndcg_at_3_max
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- type: nauc_ndcg_at_3_std
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- type: nauc_ndcg_at_3_diff1
value: 21.0618
- type: nauc_ndcg_at_5_max
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- type: nauc_ndcg_at_5_std
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- type: nauc_ndcg_at_5_diff1
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- type: nauc_ndcg_at_10_max
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- type: nauc_ndcg_at_10_std
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- type: nauc_ndcg_at_20_max
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- type: nauc_ndcg_at_20_diff1
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- type: nauc_ndcg_at_100_max
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- type: nauc_ndcg_at_100_diff1
value: 19.7085
- type: nauc_ndcg_at_1000_max
value: 30.3307
- type: nauc_ndcg_at_1000_std
value: 24.7664
- type: nauc_ndcg_at_1000_diff1
value: 19.0469
- type: nauc_map_at_1_max
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- type: nauc_map_at_1_std
value: 7.219200000000001
- type: nauc_map_at_1_diff1
value: 27.0193
- type: nauc_map_at_3_max
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- type: nauc_map_at_3_std
value: 9.411999999999999
- type: nauc_map_at_3_diff1
value: 21.3691
- type: nauc_map_at_5_max
value: 23.763
- type: nauc_map_at_5_std
value: 11.228
- type: nauc_map_at_5_diff1
value: 20.4299
- type: nauc_map_at_10_max
value: 25.6655
- type: nauc_map_at_10_std
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- type: nauc_map_at_10_diff1
value: 19.7937
- type: nauc_map_at_20_max
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- type: nauc_map_at_20_std
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- type: nauc_map_at_20_diff1
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- type: nauc_map_at_100_max
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- type: nauc_map_at_100_std
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- type: nauc_map_at_100_diff1
value: 19.4661
- type: nauc_map_at_1000_max
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- type: nauc_map_at_1000_std
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- type: nauc_map_at_1000_diff1
value: 19.4691
- type: nauc_recall_at_1_max
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- type: nauc_recall_at_1_std
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- type: nauc_recall_at_1_diff1
value: 27.0193
- type: nauc_recall_at_3_max
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- type: nauc_recall_at_3_std
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- type: nauc_recall_at_3_diff1
value: 18.1657
- type: nauc_recall_at_5_max
value: 24.8053
- type: nauc_recall_at_5_std
value: 15.5205
- type: nauc_recall_at_5_diff1
value: 16.4924
- type: nauc_recall_at_10_max
value: 27.9864
- type: nauc_recall_at_10_std
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- type: nauc_recall_at_10_diff1
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- type: nauc_recall_at_20_max
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- type: nauc_recall_at_20_std
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- type: nauc_recall_at_20_diff1
value: 14.174800000000001
- type: nauc_recall_at_100_max
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- type: nauc_recall_at_100_std
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value: 12.9714
- type: nauc_recall_at_1000_max
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- type: nauc_recall_at_1000_std
value: 37.1526
- type: nauc_recall_at_1000_diff1
value: 6.0907
- type: nauc_precision_at_1_max
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value: 26.8353
- type: nauc_precision_at_3_max
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- type: nauc_precision_at_3_std
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- type: nauc_precision_at_3_diff1
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- type: nauc_precision_at_5_max
value: 25.3681
- type: nauc_precision_at_5_std
value: 15.947700000000001
- type: nauc_precision_at_5_diff1
value: 16.6931
- type: nauc_precision_at_10_max
value: 28.2682
- type: nauc_precision_at_10_std
value: 20.2673
- type: nauc_precision_at_10_diff1
value: 15.8977
- type: nauc_precision_at_20_max
value: 29.3989
- type: nauc_precision_at_20_std
value: 24.5769
- type: nauc_precision_at_20_diff1
value: 14.1994
- type: nauc_precision_at_100_max
value: 31.418000000000003
- type: nauc_precision_at_100_std
value: 32.0978
- type: nauc_precision_at_100_diff1
value: 12.768199999999998
- type: nauc_precision_at_1000_max
value: 25.501099999999997
- type: nauc_precision_at_1000_std
value: 36.477399999999996
- type: nauc_precision_at_1000_diff1
value: 5.5335
- type: nauc_mrr_at_1_max
value: 20.214499999999997
- type: nauc_mrr_at_1_std
value: 7.2459999999999996
- type: nauc_mrr_at_1_diff1
value: 26.8353
- type: nauc_mrr_at_3_max
value: 22.7925
- type: nauc_mrr_at_3_std
value: 10.6945
- type: nauc_mrr_at_3_diff1
value: 23.6308
- type: nauc_mrr_at_5_max
value: 23.427799999999998
- type: nauc_mrr_at_5_std
value: 11.8634
- type: nauc_mrr_at_5_diff1
value: 23.0875
- type: nauc_mrr_at_10_max
value: 24.0918
- type: nauc_mrr_at_10_std
value: 12.4753
- type: nauc_mrr_at_10_diff1
value: 23.352999999999998
- type: nauc_mrr_at_20_max
value: 24.078
- type: nauc_mrr_at_20_std
value: 12.5849
- type: nauc_mrr_at_20_diff1
value: 23.3351
- type: nauc_mrr_at_100_max
value: 24.0858
- type: nauc_mrr_at_100_std
value: 12.5772
- type: nauc_mrr_at_100_diff1
value: 23.4778
- type: nauc_mrr_at_1000_max
value: 24.058799999999998
- type: nauc_mrr_at_1000_std
value: 12.549
- type: nauc_mrr_at_1000_diff1
value: 23.4713
- type: main_score
value: 20.347
- task:
type: STS
dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: pearson
value: 75.7747
- type: spearman
value: 71.3142
- type: cosine_pearson
value: 75.7747
- type: cosine_spearman
value: 71.3142
- type: manhattan_pearson
value: 73.8759
- type: manhattan_spearman
value: 71.1003
- type: euclidean_pearson
value: 74.088
- type: euclidean_spearman
value: 71.3142
- type: main_score
value: 71.3142
- task:
type: STS
dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: pearson
value: 72.5903
- type: spearman
value: 70.6581
- type: cosine_pearson
value: 72.5903
- type: cosine_spearman
value: 70.6581
- type: manhattan_pearson
value: 69.2077
- type: manhattan_spearman
value: 70.4521
- type: euclidean_pearson
value: 69.41720000000001
- type: euclidean_spearman
value: 70.6581
- type: main_score
value: 70.6581
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: pearson
value: 73.1686
- type: spearman
value: 77.4225
- type: cosine_pearson
value: 73.1686
- type: cosine_spearman
value: 77.4225
- type: manhattan_pearson
value: 76.2481
- type: manhattan_spearman
value: 77.325
- type: euclidean_pearson
value: 76.3568
- type: euclidean_spearman
value: 77.4225
- type: main_score
value: 77.4225
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: pearson
value: 74.46340000000001
- type: spearman
value: 72.9162
- type: cosine_pearson
value: 74.46340000000001
- type: cosine_spearman
value: 72.9162
- type: manhattan_pearson
value: 73.8079
- type: manhattan_spearman
value: 72.8704
- type: euclidean_pearson
value: 73.8244
- type: euclidean_spearman
value: 72.9162
- type: main_score
value: 72.9162
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: pearson
value: 80.1161
- type: spearman
value: 81.83200000000001
- type: cosine_pearson
value: 80.1161
- type: cosine_spearman
value: 81.83200000000001
- type: manhattan_pearson
value: 81.573
- type: manhattan_spearman
value: 81.807
- type: euclidean_pearson
value: 81.59490000000001
- type: euclidean_spearman
value: 81.83200000000001
- type: main_score
value: 81.83200000000001
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: pearson
value: 78.8244
- type: spearman
value: 81.2262
- type: cosine_pearson
value: 78.8244
- type: cosine_spearman
value: 81.2262
- type: manhattan_pearson
value: 80.6177
- type: manhattan_spearman
value: 81.1361
- type: euclidean_pearson
value: 80.7347
- type: euclidean_spearman
value: 81.2262
- type: main_score
value: 81.2262
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 67.9751
- type: spearman
value: 68.92099999999999
- type: cosine_pearson
value: 67.9751
- type: cosine_spearman
value: 68.92099999999999
- type: manhattan_pearson
value: 68.9355
- type: manhattan_spearman
value: 68.777
- type: euclidean_pearson
value: 69.11410000000001
- type: euclidean_spearman
value: 68.92099999999999
- type: main_score
value: 68.92099999999999
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 72.08449999999999
- type: spearman
value: 74.6931
- type: cosine_pearson
value: 72.08449999999999
- type: cosine_spearman
value: 74.6931
- type: manhattan_pearson
value: 73.52
- type: manhattan_spearman
value: 74.7097
- type: euclidean_pearson
value: 73.62180000000001
- type: euclidean_spearman
value: 74.6931
- type: main_score
value: 74.6931
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 80.528
- type: spearman
value: 84.10459999999999
- type: cosine_pearson
value: 80.528
- type: cosine_spearman
value: 84.10459999999999
- type: manhattan_pearson
value: 83.1537
- type: manhattan_spearman
value: 84.0952
- type: euclidean_pearson
value: 83.337
- type: euclidean_spearman
value: 84.10459999999999
- type: main_score
value: 84.10459999999999
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 49.641400000000004
- type: spearman
value: 48.9413
- type: cosine_pearson
value: 49.641400000000004
- type: cosine_spearman
value: 48.9413
- type: manhattan_pearson
value: 51.434000000000005
- type: manhattan_spearman
value: 49.1595
- type: euclidean_pearson
value: 50.867799999999995
- type: euclidean_spearman
value: 48.9413
- type: main_score
value: 48.9413
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 71.2577
- type: spearman
value: 73.82419999999999
- type: cosine_pearson
value: 71.2577
- type: cosine_spearman
value: 73.82419999999999
- type: manhattan_pearson
value: 71.9329
- type: manhattan_spearman
value: 73.4651
- type: euclidean_pearson
value: 72.2771
- type: euclidean_spearman
value: 73.82419999999999
- type: main_score
value: 73.82419999999999
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 64.1562
- type: spearman
value: 64.8766
- type: cosine_pearson
value: 64.1562
- type: cosine_spearman
value: 64.8766
- type: manhattan_pearson
value: 64.16579999999999
- type: manhattan_spearman
value: 64.1931
- type: euclidean_pearson
value: 64.6169
- type: euclidean_spearman
value: 64.8766
- type: main_score
value: 64.8766
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 42.257400000000004
- type: spearman
value: 43.2176
- type: cosine_pearson
value: 42.257400000000004
- type: cosine_spearman
value: 43.2176
- type: manhattan_pearson
value: 43.5359
- type: manhattan_spearman
value: 42.4143
- type: euclidean_pearson
value: 43.6717
- type: euclidean_spearman
value: 43.2176
- type: main_score
value: 43.2176
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 74.0088
- type: spearman
value: 75.8687
- type: cosine_pearson
value: 74.0088
- type: cosine_spearman
value: 75.8687
- type: manhattan_pearson
value: 74.8505
- type: manhattan_spearman
value: 75.6101
- type: euclidean_pearson
value: 75.1303
- type: euclidean_spearman
value: 75.8687
- type: main_score
value: 75.8687
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 68.0842
- type: spearman
value: 69.4346
- type: cosine_pearson
value: 68.0842
- type: cosine_spearman
value: 69.4346
- type: manhattan_pearson
value: 69.9982
- type: manhattan_spearman
value: 69.8952
- type: euclidean_pearson
value: 69.6375
- type: euclidean_spearman
value: 69.4346
- type: main_score
value: 69.4346
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 76.3695
- type: spearman
value: 78.88730000000001
- type: cosine_pearson
value: 76.3695
- type: cosine_spearman
value: 78.88730000000001
- type: manhattan_pearson
value: 79.0721
- type: manhattan_spearman
value: 79.1151
- type: euclidean_pearson
value: 78.783
- type: euclidean_spearman
value: 78.88730000000001
- type: main_score
value: 78.88730000000001
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 60.59139999999999
- type: spearman
value: 52.692099999999996
- type: cosine_pearson
value: 60.59139999999999
- type: cosine_spearman
value: 52.692099999999996
- type: manhattan_pearson
value: 64.66499999999999
- type: manhattan_spearman
value: 53.09009999999999
- type: euclidean_pearson
value: 64.5541
- type: euclidean_spearman
value: 52.692099999999996
- type: main_score
value: 52.692099999999996
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 77.8405
- type: spearman
value: 76.6188
- type: cosine_pearson
value: 77.8405
- type: cosine_spearman
value: 76.6188
- type: manhattan_pearson
value: 76.6598
- type: manhattan_spearman
value: 76.3583
- type: euclidean_pearson
value: 77.1442
- type: euclidean_spearman
value: 76.6188
- type: main_score
value: 76.6188
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 69.8017
- type: spearman
value: 68.7734
- type: cosine_pearson
value: 69.8017
- type: cosine_spearman
value: 68.7734
- type: manhattan_pearson
value: 70.6884
- type: manhattan_spearman
value: 68.2974
- type: euclidean_pearson
value: 70.7968
- type: euclidean_spearman
value: 68.7734
- type: main_score
value: 68.7734
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: pearson
value: 73.3293
- type: spearman
value: 76.00919999999999
- type: cosine_pearson
value: 73.3293
- type: cosine_spearman
value: 76.00919999999999
- type: manhattan_pearson
value: 75.0184
- type: manhattan_spearman
value: 75.8014
- type: euclidean_pearson
value: 75.2638
- type: euclidean_spearman
value: 76.00919999999999
- type: main_score
value: 76.00919999999999
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 77.3669
- type: mrr
value: 93.5985
- type: nAUC_map_max
value: 50.2355
- type: nAUC_map_std
value: 65.5401
- type: nAUC_map_diff1
value: 9.6333
- type: nAUC_mrr_max
value: 76.5201
- type: nAUC_mrr_std
value: 74.7401
- type: nAUC_mrr_diff1
value: 53.170899999999996
- type: main_score
value: 77.3669
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
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value: 61.0
- type: ndcg_at_3
value: 67.589
- type: ndcg_at_5
value: 68.948
- type: ndcg_at_10
value: 71.8
- type: ndcg_at_20
value: 72.595
- type: ndcg_at_100
value: 74.138
- type: ndcg_at_1000
value: 74.83800000000001
- type: map_at_1
value: 57.74399999999999
- type: map_at_3
value: 64.866
- type: map_at_5
value: 66.018
- type: map_at_10
value: 67.535
- type: map_at_20
value: 67.77
- type: map_at_100
value: 68.011
- type: map_at_1000
value: 68.042
- type: recall_at_1
value: 57.74399999999999
- type: recall_at_3
value: 71.906
- type: recall_at_5
value: 75.344
- type: recall_at_10
value: 83.2
- type: recall_at_20
value: 86.26700000000001
- type: recall_at_100
value: 94.333
- type: recall_at_1000
value: 99.667
- type: precision_at_1
value: 61.0
- type: precision_at_3
value: 26.111
- type: precision_at_5
value: 16.8
- type: precision_at_10
value: 9.5
- type: precision_at_20
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- type: precision_at_100
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- type: precision_at_1000
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- type: mrr_at_1
value: 61.0
- type: mrr_at_3
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- type: mrr_at_5
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- type: mrr_at_10
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- type: mrr_at_20
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- type: mrr_at_100
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- type: mrr_at_1000
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- type: nauc_ndcg_at_1_max
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- type: nauc_ndcg_at_1_std
value: 8.227
- type: nauc_ndcg_at_1_diff1
value: 72.0771
- type: nauc_ndcg_at_3_max
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- type: nauc_ndcg_at_3_std
value: 5.2435
- type: nauc_ndcg_at_3_diff1
value: 68.8841
- type: nauc_ndcg_at_5_max
value: 57.4675
- type: nauc_ndcg_at_5_std
value: 8.4709
- type: nauc_ndcg_at_5_diff1
value: 67.2977
- type: nauc_ndcg_at_10_max
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- type: nauc_ndcg_at_10_std
value: 11.3174
- type: nauc_ndcg_at_10_diff1
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- type: nauc_ndcg_at_20_max
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- type: nauc_ndcg_at_20_std
value: 11.9948
- type: nauc_ndcg_at_20_diff1
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- type: nauc_ndcg_at_100_max
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- type: nauc_ndcg_at_100_diff1
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- type: nauc_ndcg_at_1000_max
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- type: nauc_ndcg_at_1000_std
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- type: nauc_map_at_1_max
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- type: nauc_map_at_1_std
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- type: nauc_map_at_1_diff1
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- type: nauc_map_at_3_max
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- type: nauc_map_at_3_std
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- type: nauc_map_at_3_diff1
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- type: nauc_map_at_5_max
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- type: nauc_map_at_5_std
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- type: nauc_map_at_5_diff1
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- type: nauc_map_at_10_max
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- type: nauc_map_at_10_std
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- type: nauc_map_at_10_diff1
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- type: nauc_map_at_20_max
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- type: nauc_map_at_100_diff1
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- type: nauc_map_at_1000_max
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- type: nauc_recall_at_3_max
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- type: nauc_recall_at_3_std
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- type: nauc_recall_at_5_max
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- type: nauc_recall_at_5_diff1
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- type: nauc_recall_at_10_max
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- type: nauc_recall_at_10_std
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- type: nauc_recall_at_10_diff1
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- type: nauc_recall_at_20_max
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- type: nauc_recall_at_1000_max
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- type: nauc_recall_at_1000_std
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- type: nauc_precision_at_1_max
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- type: nauc_precision_at_3_diff1
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- type: nauc_precision_at_5_max
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- type: nauc_precision_at_5_std
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- type: nauc_precision_at_10_max
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- type: nauc_precision_at_10_std
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- type: nauc_precision_at_10_diff1
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- type: nauc_precision_at_20_max
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- type: nauc_precision_at_20_std
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- type: nauc_precision_at_20_diff1
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- type: nauc_precision_at_100_max
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- type: nauc_precision_at_100_std
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- type: nauc_precision_at_100_diff1
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- type: nauc_precision_at_1000_max
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- type: nauc_precision_at_1000_std
value: 56.634499999999996
- type: nauc_precision_at_1000_diff1
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- type: nauc_mrr_at_1_max
value: 53.481500000000004
- type: nauc_mrr_at_1_std
value: 8.227
- type: nauc_mrr_at_1_diff1
value: 72.0771
- type: nauc_mrr_at_3_max
value: 57.6662
- type: nauc_mrr_at_3_std
value: 9.2816
- type: nauc_mrr_at_3_diff1
value: 69.8276
- type: nauc_mrr_at_5_max
value: 57.6565
- type: nauc_mrr_at_5_std
value: 10.422099999999999
- type: nauc_mrr_at_5_diff1
value: 69.0964
- type: nauc_mrr_at_10_max
value: 58.000099999999996
- type: nauc_mrr_at_10_std
value: 10.957600000000001
- type: nauc_mrr_at_10_diff1
value: 69.0098
- type: nauc_mrr_at_20_max
value: 58.0066
- type: nauc_mrr_at_20_std
value: 11.0139
- type: nauc_mrr_at_20_diff1
value: 69.1278
- type: nauc_mrr_at_100_max
value: 57.9072
- type: nauc_mrr_at_100_std
value: 10.9621
- type: nauc_mrr_at_100_diff1
value: 69.1925
- type: nauc_mrr_at_1000_max
value: 57.87949999999999
- type: nauc_mrr_at_1000_std
value: 10.934199999999999
- type: nauc_mrr_at_1000_diff1
value: 69.2004
- type: main_score
value: 71.8
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions (default)
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
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value: 99.8248
- type: similarity_accuracy_threshold
value: 74.6155
- type: similarity_f1
value: 91.12780000000001
- type: similarity_f1_threshold
value: 74.2422
- type: similarity_precision
value: 91.3568
- type: similarity_recall
value: 90.9
- type: similarity_ap
value: 96.00319999999999
- type: cosine_accuracy
value: 99.8248
- type: cosine_accuracy_threshold
value: 74.6155
- type: cosine_f1
value: 91.12780000000001
- type: cosine_f1_threshold
value: 74.2422
- type: cosine_precision
value: 91.3568
- type: cosine_recall
value: 90.9
- type: cosine_ap
value: 96.00319999999999
- type: manhattan_accuracy
value: 99.8257
- type: manhattan_accuracy_threshold
value: 1574.1653
- type: manhattan_f1
value: 91.1531
- type: manhattan_f1_threshold
value: 1595.7924
- type: manhattan_precision
value: 90.6126
- type: manhattan_recall
value: 91.7
- type: manhattan_ap
value: 95.9848
- type: euclidean_accuracy
value: 99.8248
- type: euclidean_accuracy_threshold
value: 71.2523
- type: euclidean_f1
value: 91.12780000000001
- type: euclidean_f1_threshold
value: 71.7744
- type: euclidean_precision
value: 91.3568
- type: euclidean_recall
value: 90.9
- type: euclidean_ap
value: 96.00319999999999
- type: dot_accuracy
value: 99.8248
- type: dot_accuracy_threshold
value: 74.6155
- type: dot_f1
value: 91.12780000000001
- type: dot_f1_threshold
value: 74.2422
- type: dot_precision
value: 91.3568
- type: dot_recall
value: 90.9
- type: dot_ap
value: 96.00319999999999
- type: max_accuracy
value: 99.8257
- type: max_f1
value: 91.1531
- type: max_precision
value: 91.3568
- type: max_recall
value: 91.7
- type: max_ap
value: 96.00319999999999
- type: main_score
value: 96.00319999999999
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering (default)
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 61.3985
- type: v_measure_std
value: 5.2151000000000005
- type: main_score
value: 61.3985
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P (default)
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.1433
- type: v_measure_std
value: 1.5853
- type: main_score
value: 36.1433
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions (default)
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.47580000000001
- type: mrr
value: 51.221399999999996
- type: nAUC_map_max
value: 10.1311
- type: nAUC_map_std
value: 6.239999999999999
- type: nAUC_map_diff1
value: 36.3486
- type: nAUC_mrr_max
value: 10.9306
- type: nAUC_mrr_std
value: 6.7909
- type: nAUC_mrr_diff1
value: 36.5536
- type: main_score
value: 50.47580000000001
- task:
type: Summarization
dataset:
name: MTEB SummEval (default)
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
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- type: spearman
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- type: cosine_spearman
value: 29.391099999999998
- type: cosine_pearson
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- type: dot_spearman
value: 29.391099999999998
- type: dot_pearson
value: 29.8474
- type: main_score
value: 29.391099999999998
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID (default)
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
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- type: ndcg_at_5
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- type: ndcg_at_100
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- type: ndcg_at_1000
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- type: map_at_1
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- type: map_at_3
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- type: map_at_20
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- type: recall_at_1
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- type: recall_at_3
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- type: recall_at_5
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- type: recall_at_10
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- type: recall_at_20
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- type: recall_at_100
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- type: recall_at_1000
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- type: precision_at_1
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- type: precision_at_3
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- type: precision_at_5
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- type: precision_at_10
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- type: precision_at_20
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- type: precision_at_100
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- type: precision_at_1000
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- type: mrr_at_1
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- type: mrr_at_3
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- type: mrr_at_5
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- type: mrr_at_10
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- type: mrr_at_20
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- type: mrr_at_100
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- type: mrr_at_1000
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- type: nauc_ndcg_at_1_max
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- type: nauc_mrr_at_100_std
value: 21.9859
- type: nauc_mrr_at_100_diff1
value: 25.0584
- type: nauc_mrr_at_1000_max
value: -2.1212999999999997
- type: nauc_mrr_at_1000_std
value: 21.9859
- type: nauc_mrr_at_1000_diff1
value: 25.0584
- type: main_score
value: 80.285
- task:
type: Retrieval
dataset:
name: MTEB Touche2020 (default)
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: ndcg_at_1
value: 33.672999999999995
- type: ndcg_at_3
value: 34.392
- type: ndcg_at_5
value: 32.606
- type: ndcg_at_10
value: 29.767
- type: ndcg_at_20
value: 30.353
- type: ndcg_at_100
value: 41.094
- type: ndcg_at_1000
value: 51.937
- type: map_at_1
value: 2.64
- type: map_at_3
value: 6.428000000000001
- type: map_at_5
value: 8.792
- type: map_at_10
value: 11.882
- type: map_at_20
value: 14.818000000000001
- type: map_at_100
value: 18.613
- type: map_at_1000
value: 20.233
- type: recall_at_1
value: 2.64
- type: recall_at_3
value: 7.951999999999999
- type: recall_at_5
value: 11.898
- type: recall_at_10
value: 18.782
- type: recall_at_20
value: 27.488
- type: recall_at_100
value: 51.337999999999994
- type: recall_at_1000
value: 84.399
- type: precision_at_1
value: 36.735
- type: precision_at_3
value: 36.735
- type: precision_at_5
value: 33.061
- type: precision_at_10
value: 26.122
- type: precision_at_20
value: 19.898
- type: precision_at_100
value: 8.429
- type: precision_at_1000
value: 1.5650000000000002
- type: mrr_at_1
value: 36.7347
- type: mrr_at_3
value: 51.7007
- type: mrr_at_5
value: 54.65989999999999
- type: mrr_at_10
value: 55.8868
- type: mrr_at_20
value: 56.2944
- type: mrr_at_100
value: 56.360200000000006
- type: mrr_at_1000
value: 56.360200000000006
- type: nauc_ndcg_at_1_max
value: -23.0012
- type: nauc_ndcg_at_1_std
value: -9.474
- type: nauc_ndcg_at_1_diff1
value: 15.5991
- type: nauc_ndcg_at_3_max
value: -16.1454
- type: nauc_ndcg_at_3_std
value: -26.226100000000002
- type: nauc_ndcg_at_3_diff1
value: 22.9111
- type: nauc_ndcg_at_5_max
value: -20.3259
- type: nauc_ndcg_at_5_std
value: -23.3106
- type: nauc_ndcg_at_5_diff1
value: 20.112199999999998
- type: nauc_ndcg_at_10_max
value: -17.4616
- type: nauc_ndcg_at_10_std
value: -15.5791
- type: nauc_ndcg_at_10_diff1
value: 13.2876
- type: nauc_ndcg_at_20_max
value: -20.0683
- type: nauc_ndcg_at_20_std
value: -10.979899999999999
- type: nauc_ndcg_at_20_diff1
value: 5.929
- type: nauc_ndcg_at_100_max
value: -21.096899999999998
- type: nauc_ndcg_at_100_std
value: 13.212399999999999
- type: nauc_ndcg_at_100_diff1
value: 3.9886
- type: nauc_ndcg_at_1000_max
value: -14.1544
- type: nauc_ndcg_at_1000_std
value: 19.5979
- type: nauc_ndcg_at_1000_diff1
value: 1.2742
- type: nauc_map_at_1_max
value: -18.123900000000003
- type: nauc_map_at_1_std
value: -17.8031
- type: nauc_map_at_1_diff1
value: 21.032899999999998
- type: nauc_map_at_3_max
value: -6.7797
- type: nauc_map_at_3_std
value: -28.810299999999998
- type: nauc_map_at_3_diff1
value: 16.2912
- type: nauc_map_at_5_max
value: -7.620699999999999
- type: nauc_map_at_5_std
value: -27.6982
- type: nauc_map_at_5_diff1
value: 14.813100000000002
- type: nauc_map_at_10_max
value: -5.1492
- type: nauc_map_at_10_std
value: -23.885
- type: nauc_map_at_10_diff1
value: 6.9926
- type: nauc_map_at_20_max
value: -9.6331
- type: nauc_map_at_20_std
value: -19.215
- type: nauc_map_at_20_diff1
value: 0.6491
- type: nauc_map_at_100_max
value: -9.7297
- type: nauc_map_at_100_std
value: -6.9502999999999995
- type: nauc_map_at_100_diff1
value: -1.5897999999999999
- type: nauc_map_at_1000_max
value: -8.9517
- type: nauc_map_at_1000_std
value: -3.9941999999999998
- type: nauc_map_at_1000_diff1
value: -2.8158
- type: nauc_recall_at_1_max
value: -18.123900000000003
- type: nauc_recall_at_1_std
value: -17.8031
- type: nauc_recall_at_1_diff1
value: 21.032899999999998
- type: nauc_recall_at_3_max
value: -12.1006
- type: nauc_recall_at_3_std
value: -35.3199
- type: nauc_recall_at_3_diff1
value: 12.044
- type: nauc_recall_at_5_max
value: -15.7192
- type: nauc_recall_at_5_std
value: -30.7299
- type: nauc_recall_at_5_diff1
value: 8.3249
- type: nauc_recall_at_10_max
value: -13.3968
- type: nauc_recall_at_10_std
value: -19.2107
- type: nauc_recall_at_10_diff1
value: 0.1315
- type: nauc_recall_at_20_max
value: -19.5043
- type: nauc_recall_at_20_std
value: -10.005500000000001
- type: nauc_recall_at_20_diff1
value: -7.197299999999999
- type: nauc_recall_at_100_max
value: -21.4032
- type: nauc_recall_at_100_std
value: 33.5358
- type: nauc_recall_at_100_diff1
value: -10.4876
- type: nauc_recall_at_1000_max
value: 1.8395000000000001
- type: nauc_recall_at_1000_std
value: 70.462
- type: nauc_recall_at_1000_diff1
value: -23.4072
- type: nauc_precision_at_1_max
value: -23.0917
- type: nauc_precision_at_1_std
value: -8.036999999999999
- type: nauc_precision_at_1_diff1
value: 19.354599999999998
- type: nauc_precision_at_3_max
value: -11.3547
- type: nauc_precision_at_3_std
value: -30.2495
- type: nauc_precision_at_3_diff1
value: 20.3126
- type: nauc_precision_at_5_max
value: -17.2545
- type: nauc_precision_at_5_std
value: -24.8896
- type: nauc_precision_at_5_diff1
value: 15.6276
- type: nauc_precision_at_10_max
value: -11.5796
- type: nauc_precision_at_10_std
value: -2.3662
- type: nauc_precision_at_10_diff1
value: 3.8091
- type: nauc_precision_at_20_max
value: -11.9042
- type: nauc_precision_at_20_std
value: 15.6577
- type: nauc_precision_at_20_diff1
value: -8.8878
- type: nauc_precision_at_100_max
value: -0.5217
- type: nauc_precision_at_100_std
value: 71.8387
- type: nauc_precision_at_100_diff1
value: -16.8714
- type: nauc_precision_at_1000_max
value: 36.234300000000005
- type: nauc_precision_at_1000_std
value: 37.5447
- type: nauc_precision_at_1000_diff1
value: -20.7229
- type: nauc_mrr_at_1_max
value: -23.0917
- type: nauc_mrr_at_1_std
value: -8.036999999999999
- type: nauc_mrr_at_1_diff1
value: 19.354599999999998
- type: nauc_mrr_at_3_max
value: -27.9937
- type: nauc_mrr_at_3_std
value: -26.519900000000003
- type: nauc_mrr_at_3_diff1
value: 20.288
- type: nauc_mrr_at_5_max
value: -33.218599999999995
- type: nauc_mrr_at_5_std
value: -23.857400000000002
- type: nauc_mrr_at_5_diff1
value: 15.978200000000001
- type: nauc_mrr_at_10_max
value: -31.7904
- type: nauc_mrr_at_10_std
value: -19.169900000000002
- type: nauc_mrr_at_10_diff1
value: 17.762700000000002
- type: nauc_mrr_at_20_max
value: -30.44
- type: nauc_mrr_at_20_std
value: -20.2867
- type: nauc_mrr_at_20_diff1
value: 18.895500000000002
- type: nauc_mrr_at_100_max
value: -30.5404
- type: nauc_mrr_at_100_std
value: -20.5699
- type: nauc_mrr_at_100_diff1
value: 18.7046
- type: nauc_mrr_at_1000_max
value: -30.5404
- type: nauc_mrr_at_1000_std
value: -20.5699
- type: nauc_mrr_at_1000_diff1
value: 18.7046
- type: main_score
value: 29.767
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 64.8096
- type: f1
value: 49.844300000000004
- type: f1_weighted
value: 72.5251
- type: ap
value: 11.7519
- type: ap_weighted
value: 11.7519
- type: main_score
value: 64.8096
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 58.1692
- type: f1
value: 58.4408
- type: f1_weighted
value: 57.565599999999996
- type: main_score
value: 58.1692
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 39.293
- type: v_measure_std
value: 1.5684
- type: main_score
value: 39.293
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: similarity_accuracy
value: 83.29260000000001
- type: similarity_accuracy_threshold
value: 78.2732
- type: similarity_f1
value: 60.656600000000005
- type: similarity_f1_threshold
value: 73.4961
- type: similarity_precision
value: 59.007
- type: similarity_recall
value: 62.4011
- type: similarity_ap
value: 64.7501
- type: cosine_accuracy
value: 83.29260000000001
- type: cosine_accuracy_threshold
value: 78.2732
- type: cosine_f1
value: 60.656600000000005
- type: cosine_f1_threshold
value: 73.4961
- type: cosine_precision
value: 59.007
- type: cosine_recall
value: 62.4011
- type: cosine_ap
value: 64.7501
- type: manhattan_accuracy
value: 83.2986
- type: manhattan_accuracy_threshold
value: 1476.7148
- type: manhattan_f1
value: 60.7459
- type: manhattan_f1_threshold
value: 1607.9180000000001
- type: manhattan_precision
value: 59.0581
- type: manhattan_recall
value: 62.53300000000001
- type: manhattan_ap
value: 64.76859999999999
- type: euclidean_accuracy
value: 83.29260000000001
- type: euclidean_accuracy_threshold
value: 65.9194
- type: euclidean_f1
value: 60.656600000000005
- type: euclidean_f1_threshold
value: 72.8065
- type: euclidean_precision
value: 59.007
- type: euclidean_recall
value: 62.4011
- type: euclidean_ap
value: 64.7501
- type: dot_accuracy
value: 83.29260000000001
- type: dot_accuracy_threshold
value: 78.2731
- type: dot_f1
value: 60.656600000000005
- type: dot_f1_threshold
value: 73.4961
- type: dot_precision
value: 59.007
- type: dot_recall
value: 62.4011
- type: dot_ap
value: 64.7501
- type: max_accuracy
value: 83.2986
- type: max_f1
value: 60.7459
- type: max_precision
value: 59.0581
- type: max_recall
value: 62.53300000000001
- type: max_ap
value: 64.76859999999999
- type: main_score
value: 64.76859999999999
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: similarity_accuracy
value: 89.0247
- type: similarity_accuracy_threshold
value: 69.271
- type: similarity_f1
value: 78.24419999999999
- type: similarity_f1_threshold
value: 66.2183
- type: similarity_precision
value: 76.616
- type: similarity_recall
value: 79.943
- type: similarity_ap
value: 85.9494
- type: cosine_accuracy
value: 89.0247
- type: cosine_accuracy_threshold
value: 69.271
- type: cosine_f1
value: 78.24419999999999
- type: cosine_f1_threshold
value: 66.2183
- type: cosine_precision
value: 76.616
- type: cosine_recall
value: 79.943
- type: cosine_ap
value: 85.9494
- type: manhattan_accuracy
value: 89.0267
- type: manhattan_accuracy_threshold
value: 1750.3544000000002
- type: manhattan_f1
value: 78.2188
- type: manhattan_f1_threshold
value: 1837.7304
- type: manhattan_precision
value: 75.1472
- type: manhattan_recall
value: 81.5522
- type: manhattan_ap
value: 85.9496
- type: euclidean_accuracy
value: 89.0247
- type: euclidean_accuracy_threshold
value: 78.3951
- type: euclidean_f1
value: 78.24419999999999
- type: euclidean_f1_threshold
value: 82.197
- type: euclidean_precision
value: 76.616
- type: euclidean_recall
value: 79.943
- type: euclidean_ap
value: 85.9494
- type: dot_accuracy
value: 89.0247
- type: dot_accuracy_threshold
value: 69.271
- type: dot_f1
value: 78.24419999999999
- type: dot_f1_threshold
value: 66.2183
- type: dot_precision
value: 76.616
- type: dot_recall
value: 79.943
- type: dot_ap
value: 85.9494
- type: max_accuracy
value: 89.0267
- type: max_f1
value: 78.24419999999999
- type: max_precision
value: 76.616
- type: max_recall
value: 81.5522
- type: max_ap
value: 85.9496
- type: main_score
value: 85.9496
---
A modified version of [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0), without xformers, so it works on CPU.
```python
from sentence_transformers import SentenceTransformer
import torch
device = torch.device("cpu")
model = SentenceTransformer("cnmoro/snowflake-arctic-embed-m-v2.0-cpu", device=device, trust_remote_code=True)
```
|
[
"BIOSSES",
"SCIFACT"
] |
WasamiKirua/Westworld-1.0-Nemo-Base-2407-ita-16bit
|
WasamiKirua
|
text-generation
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"storytelling",
"conversational",
"it",
"dataset:WasamiKirua/Westworld-1.0-ITA",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-10-18T11:42:33Z |
2025-01-19T13:55:04+00:00
| 2,757 | 4 |
---
base_model: unsloth/mistral-nemo-base-2407-bnb-4bit
datasets:
- WasamiKirua/Westworld-1.0-ITA
language:
- it
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
- storytelling
---
<img src="https://i.postimg.cc/MKvw6Nfk/01395-2925380223.png" alt="cover" border="0" width="862px">
### Model Description for Storytelling and Creativity
**Westworld** is a cutting-edge conversational AI model, meticulously fine-tuned from the **phi3.5** architecture to excel in storytelling, narrative crafting, and creative ideation. Designed to weave captivating tales across diverse genres, Westworld is the ultimate companion for writers, world-builders, and anyone passionate about the art of storytelling.
From epic fantasies and sci-fi adventures to gripping mysteries and heartfelt dramas, Westworld brings stories to life with vivid descriptions, compelling characters, and rich dialogue. It thrives on creating immersive narratives that resonate with the user’s creative vision, offering inspiration and collaborative energy to craft unforgettable tales.
**Not just reactive but collaborative**, Westworld drives storytelling forward by suggesting imaginative twists, deepening character arcs, and enhancing plot dynamics. Its responses are crafted to align with the tone and style of the user’s chosen genre, making it a versatile tool for storytellers of all kinds.
- **Developed by:** WasamiKirua
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-nemo-base-2407-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
You can use it with ollama (Modelfile):
```
FROM {__FILE_LOCATION__}
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>
"""
PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"
PARAMETER temperature 1.5
PARAMETER min_p 0.1
```
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
---
## Eval
Average: 60.62
MMLU_IT: 59.42
ARC_IT: 51.5
HELLASWAG_IT: 70.93
### Primary Uses for Storytelling and Creativity
Westworld’s capabilities are tailored to creative contexts where dynamic narratives and rich storytelling are valued. Key applications include:
1. **Story Creation and Development**:
- Westworld excels in generating story ideas, developing plotlines, and refining narratives across a spectrum of genres.
- Writers can use it to brainstorm innovative twists, build immersive worlds, and bring their characters to life.
2. **World-Building and Scene Crafting**:
- Whether it's a futuristic dystopia, a medieval kingdom, or a bustling modern city, Westworld helps users create detailed and engaging settings.
3. **Character Design and Dialogue Writing**:
- Westworld generates unique, multi-dimensional characters and crafts dialogue that captures their personalities and motivations.
4. **Collaborative Storytelling**:
- Users can engage Westworld as a co-writer or Dungeon Master in interactive storytelling, role-playing games, or collaborative writing projects.
---
### Intended Use Cases
1. **Creative Professionals**:
- Writers, screenwriters, and game designers can use Westworld to explore new ideas, overcome creative blocks, and refine their narratives.
2. **Hobbyist Storytellers**:
- Whether crafting a short story, writing fanfiction, or planning a tabletop RPG campaign, hobbyists can collaborate with Westworld for inspiration and storytelling guidance.
3. **Educators and Students**:
- Westworld can serve as a tool for teaching narrative structure, genre conventions, and creative writing techniques.
4. **Interactive Fiction Enthusiasts**:
- Role-playing game masters and interactive fiction creators can use Westworld to generate dynamic storylines and immersive character interactions.
---
### Key Features for Storytelling
1. **Genre Versatility**:
- Westworld adapts seamlessly to a variety of genres, including fantasy, science fiction, mystery, romance, historical fiction, and more.
2. **Narrative Coherence**:
- Its storytelling responses are structured and coherent, ensuring logical plot progression and thematic consistency.
3. **Dynamic World-Building**:
- Westworld creates richly detailed settings that enhance the depth and believability of any narrative.
4. **Character and Dialogue Crafting**:
- From heroic protagonists to enigmatic villains, Westworld brings characters to life with distinctive traits and engaging dialogue.
---
### Ethical and Creative Considerations
1. **Respect for Creative Ownership**:
- Users maintain full control and ownership of the stories created with Westworld, ensuring that it serves as a tool for enhancement, not replacement.
2. **Balancing Fiction and Realism**:
- While Westworld’s imagination knows no limits, users are reminded to maintain logical coherence within the chosen narrative.
3. **Privacy and Security**:
- Westworld ensures the confidentiality of user inputs and creative projects, making it a trusted tool for all storytellers.
---
In summary, **Westworld** is not just a model but a creative powerhouse, designed to elevate the art of storytelling across all genres. Whether you’re crafting epic tales, exploring imaginative worlds, or fine-tuning character arcs, Westworld is your partner in creating stories that captivate and inspire.
|
[
"CRAFT"
] |
maddes8cht/tiiuae-falcon-40b
|
maddes8cht
| null |
[
"gguf",
"en",
"de",
"es",
"fr",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2205.14135",
"arxiv:1911.02150",
"arxiv:2101.00027",
"arxiv:2005.14165",
"arxiv:2104.09864",
"arxiv:2306.01116",
"license:apache-2.0",
"region:us"
] | 2023-09-15T09:48:14Z |
2023-10-20T05:22:35+00:00
| 2,737 | 1 |
---
datasets:
- tiiuae/falcon-refinedweb
language:
- en
- de
- es
- fr
license: apache-2.0
inference: false
---
[]()
I am continuously enhancing the structure of these model descriptions, and they now provide even more comprehensive information to help you find the best models for your specific needs.
# falcon-40b - GGUF
- Model creator: [tiiuae](https://huggingface.co/tiiuae)
- Original model: [falcon-40b](https://huggingface.co/tiiuae/falcon-40b)
# Note: Important Update for Falcon Models in llama.cpp Versions After October 18, 2023
As noted on the [Llama.cpp]([ggerganov/llama.cpp: Port of Facebook's LLaMA model in C/C++ (github.com)](https://github.com/ggerganov/llama.cpp#hot-topics) GitHub repository, all new releases of Llama.cpp will require a re-quantization due to the implementation of the new BPE tokenizer, which impacts both the original Falcon models and their derived variants.
Here's what you need to know:
**Original Falcon Models:** I am diligently working to provide updated quantized versions of the four original Falcon models to ensure their compatibility with the new llama.cpp versions. Please keep an eye on my Hugging Face Model pages for updates on the availability of these models. Promptly downloading them is essential to maintain compatibility with the latest llama.cpp releases.
**Derived Falcon Models:** Right now, the derived Falcon-Models cannot be re-converted without adjustments from the original model creators. So far, these models cannot be used in recent llama.cpp versions at all. ** Good news!** It's in the pipeline that the capability for quantizing even the older derived Falcon models will be incorporated soon. However, the exact timeline is beyond my control.
**Stay Informed:** Application software using llama.cpp libraries will follow soon. Keep an eye on the release schedules of your favorite software applications that rely on llama.cpp. They will likely provide instructions on how to integrate the new models.
**Monitor Upload Times:** Please keep a close watch on the upload times of the available files on my Hugging Face Model pages. This will help you identify which files have already been updated and are ready for download, ensuring you have the most current Falcon models at your disposal.
**Download Promptly:** Once the updated Falcon models are available on my Hugging Face page, be sure to download them promptly to ensure compatibility with the latest [llama.cpp]([ggerganov/llama.cpp: Port of Facebook's LLaMA model in C/C++ (github.com)](https://github.com/ggerganov/llama.cpp) versions.
Please understand that this change specifically affects Falcon and Starcoder models, other models remain unaffected. Consequently, software providers may not emphasize this change as prominently.
As a solo operator of this page, I'm doing my best to expedite the process, but please bear with me as this may take some time.
These are gguf quantized models of the riginal Falcon 40B Model by tiiuae.
Falcon is a foundational large language model coming in different sizes: 7b, 40b and 180b.
Sadly, as the Falcon 180b Models are note really free models, I do not provide quantized versions here.
# About GGUF format
`gguf` is the current file format used by the [`ggml`](https://github.com/ggerganov/ggml) library.
A growing list of Software is using it and can therefore use this model.
The core project making use of the ggml library is the [llama.cpp](https://github.com/ggerganov/llama.cpp) project by Georgi Gerganov
# Quantization variants
There is a bunch of quantized files available. How to choose the best for you:
# legacy quants
Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are `legacy` quantization types.
Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.
Falcon 7B models cannot be quantized to K-quants.
# K-quants
K-quants are based on the idea that the quantization of certain parts affects the quality in different ways. If you quantize certain parts more and others less, you get a more powerful model with the same file size, or a smaller file size and lower memory load with comparable performance.
So, if possible, use K-quants.
With a Q6_K you should find it really hard to find a quality difference to the original model - ask your model two times the same question and you may encounter bigger quality differences.
# Original Model Card:
# 🚀 Falcon-40B
**Falcon-40B is a 40B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 1,000B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. It is made available under the Apache 2.0 license.**
*Paper coming soon 😊.*
🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)!
## Why use Falcon-40B?
* **It is the best open-source model currently available.** Falcon-40B outperforms [LLaMA](https://github.com/facebookresearch/llama), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1), [MPT](https://huggingface.co/mosaicml/mpt-7b), etc. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
* **It is made available under a permissive Apache 2.0 license allowing for commercial use**, without any royalties or restrictions.
*
⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.** If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct).
💸 **Looking for a smaller, less expensive model?** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) is Falcon-40B's little brother!
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-40b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon).
You will need **at least 85-100GB of memory** to swiftly run inference with Falcon-40B.
# Model Card for Falcon-40B
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish);
- **License:** Apache 2.0 license.
### Model Source
- **Paper:** *coming soon*.
## Uses
### Direct Use
Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.)
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
Falcon-40B is trained mostly on English, German, Spanish, French, with limited capabilities also in in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish. It will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of Falcon-40B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-40b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Details
### Training Data
Falcon-40B was trained on 1,000B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. Significant components from our curated copora were inspired by The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)).
| **Data source** | **Fraction** | **Tokens** | **Sources** |
|--------------------|--------------|------------|-----------------------------------|
| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 75% | 750B | massive web crawl |
| RefinedWeb-Europe | 7% | 70B | European massive web crawl |
| Books | 6% | 60B | |
| Conversations | 5% | 50B | Reddit, StackOverflow, HackerNews |
| Code | 5% | 50B | |
| Technical | 2% | 20B | arXiv, PubMed, USPTO, etc. |
RefinedWeb-Europe is made of the following languages:
| **Language** | **Fraction of multilingual data** | **Tokens** |
|--------------|-----------------------------------|------------|
| German | 26% | 18B |
| Spanish | 24% | 17B |
| French | 23% | 16B |
| _Italian_ | 7% | 5B |
| _Portuguese_ | 4% | 3B |
| _Polish_ | 4% | 3B |
| _Dutch_ | 4% | 3B |
| _Romanian_ | 3% | 2B |
| _Czech_ | 3% | 2B |
| _Swedish_ | 2% | 1B |
The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
### Training Procedure
Falcon-40B was trained on 384 A100 40GB GPUs, using a 3D parallelism strategy (TP=8, PP=4, DP=12) combined with ZeRO.
#### Training Hyperparameters
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|------------|-------------------------------------------|
| Precision | `bfloat16` | |
| Optimizer | AdamW | |
| Learning rate | 1.85e-4 | 4B tokens warm-up, cosine decay to 1.85e-5 |
| Weight decay | 1e-1 | |
| Z-loss | 1e-4 | |
| Batch size | 1152 | 100B tokens ramp-up |
#### Speeds, Sizes, Times
Training started in December 2022 and took two months.
## Evaluation
*Paper coming soon.*
See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
## Technical Specifications
### Model Architecture and Objective
Falcon-40B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
* **Decoder-block:** parallel attention/MLP with a two layer norms.
For multiquery, we are using an internal variant which uses independent key and values per tensor parallel degree.
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| Layers | 60 | |
| `d_model` | 8192 | |
| `head_dim` | 64 | Reduced to optimise for FlashAttention |
| Vocabulary | 65024 | |
| Sequence length | 2048 | |
### Compute Infrastructure
#### Hardware
Falcon-40B was trained on AWS SageMaker, on 384 A100 40GB GPUs in P4d instances.
#### Software
Falcon-40B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
## Citation
*Paper coming soon* 😊. In the meanwhile, you can use the following information to cite:
```
@article{falcon40b,
title={{Falcon-40B}: an open large language model with state-of-the-art performance},
author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
year={2023}
}
```
To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116).
```
@article{refinedweb,
title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
journal={arXiv preprint arXiv:2306.01116},
eprint={2306.01116},
eprinttype = {arXiv},
url={https://arxiv.org/abs/2306.01116},
year={2023}
}
```
## License
Falcon-40B is made available under the Apache 2.0 license.
## Contact
[email protected]
***End of original Model File***
## Please consider to support my work
**Coming Soon:** I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community.
<center>
[](https://maddes8cht.github.io)
[](https://stackexchange.com/users/26485911)
[](https://github.com/maddes8cht)
[](https://huggingface.co/maddes8cht)
[](https://twitter.com/maddes1966)
</center>
|
[
"BEAR"
] |
ibm-nasa-geospatial/Prithvi-EO-2.0-600M
|
ibm-nasa-geospatial
| null |
[
"terratorch",
"Pytorch",
"Earth Observation",
"Foundation Model",
"NASA",
"IBM",
"arxiv:2412.02732",
"license:apache-2.0",
"region:us"
] | 2024-12-02T09:32:56Z |
2025-01-22T11:40:28+00:00
| 2,646 | 7 |
---
library_name: terratorch
license: apache-2.0
tags:
- Pytorch
- Earth Observation
- Foundation Model
- NASA
- IBM
---
# Prithvi-EO-2.0
Prithvi-EO-2.0 is the second generation EO foundation model jointly developed by IBM, NASA, and Jülich Supercomputing Centre.
## Architecture Overview
Prithvi-EO-2.0 is based on the ViT architecture, pretrained using a masked autoencoder (MAE) approach, with two major modifications as shown in the figure below.
First, we replaced the 2D patch embeddings and 2D positional embeddings with 3D versions to support inputs with spatiotemporal characteristics, i.e., a sequence of T images of size (H, W). Our 3D patch embeddings consist of a 3D convolutional layer, dividing the 3D input into non-overlapping cubes of size (t, h, w) for time, height, and width dimensions, respectively. For the 3D positional encodings, we first generate 1D sin/cos encodings individually for each dimension and then combine them together into a single, 3D positional encoding.
Second, we considered geolocation (center latitude and longitude) and date of acquisition (year and day-of-year ranging 1-365) in the pretraining of the TL model versions. Both encoder and decoder receive time and location information for each sample and encodes them independently using 2D sin/cos encoding. They are added to the embedded tokens via a weighted sum with learned weights: one for time and one for location and separate weights for encoder and decoder. Since this metadata is often not available, we added a drop mechanism during pretraining that randomly drops the geolocation and/or the temporal data to help the model learn how to handle the absence of this information.
## Pre-trained Models
| Model | Details | Weights |
| ------------- | ------------- |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|Prithvi-EO-2.0-300M | Pretrained 300M parameter model | [https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-300M](https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-300M) |
|Prithvi-EO-2.0-300M-TL | Pretrained 300M parameter model with temporal and location embeddings | [https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL](https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL) |
|Prithvi-EO-2.0-600M | Pretrained 600M parameter model | [https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-600M](https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-600M) | |
|Prithvi-EO-2.0-600M-TL | Pretrained 600M parameter model with temporal and location embeddings | [https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-600M-TL](https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-600M-TL) |
The models were pre-trained at the Jülich Supercomputing Centre with NASA's HLS V2 product (30m granularity) using 4.2M samples with six bands in the following order: Blue, Green, Red, Narrow NIR, SWIR, SWIR 2.
## Benchmarking
We validated the Prithvi-EO-2.0 models through extensive experiments using [GEO-bench](https://github.com/ServiceNow/geo-bench). Prithvi-EO-2.0-600M-TL outperforms the previous Prithvi-EO model by 8% across a range of tasks. It also outperforms six other geospatial foundation models when benchmarked on remote sensing tasks from different domains and resolutions (i.e. from 0.1m to 15m).
## Demo and inference
We provide a **demo** running Prithvi-EO-2.0-300M-TL [here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-EO-2.0-Demo).
There is also an inference script (`inference.py`) that allows to run the image reconstruction on a set of HLS images assumed to be from the same location at different timestamps (see example below). These should be provided in chronological order in geotiff format, including the channels described above (Blue, Green, Red, Narrow NIR, SWIR 1, SWIR 2) in reflectance units.
```
python inference.py --data_files t1.tif t2.tif t3.tif t4.tif --input_indices <optional, space separated 0-based indices of the six Prithvi channels in your input>
```
## Finetuning
You can finetune the model using [TerraTorch](https://github.com/IBM/terratorch). Examples of configs and notebooks are provided in the project repository: [github.com/NASA-IMPACT/Prithvi-EO-2.0](https://github.com/NASA-IMPACT/Prithvi-EO-2.0#fine-tuning).
Example Notebooks:
[Multitemporal Crop Segmentation](https://github.com/NASA-IMPACT/Prithvi-EO-2.0/blob/main/examples/example_multitemporalcrop.ipynb) [<b><i>>>Try it on Colab<<</i></b>](https://colab.research.google.com/github/NASA-IMPACT/Prithvi-EO-2.0/blob/main/examples/example_multitemporalcrop.ipynb) (Choose T4 GPU runtime)
[Landslide Segmentation](https://github.com/NASA-IMPACT/Prithvi-EO-2.0/blob/main/examples/example_landslide4sense.ipynb) [<b><i>>>Try it on Colab<<</i></b>](https://colab.research.google.com/github/NASA-IMPACT/Prithvi-EO-2.0/blob/main/examples/example_landslide4sense.ipynb) (Choose T4 GPU runtime)
[Carbon Flux Prediction (Regression)](https://github.com/NASA-IMPACT/Prithvi-EO-2.0/blob/main/examples/carbon_flux/main_flux_finetune_baselines_trainer.ipynb)
### Feedback
Your feedback is invaluable to us. If you have any feedback about the model, please feel free to share it with us. You can do this by starting a discussion in this HF repository or submitting an issue to [TerraTorch](https://github.com/IBM/terratorch) on GitHub.
### Citation
If this model helped your research, please cite [Prithvi-EO-2.0](https://arxiv.org/abs/2412.02732) in your publications.
```
@article{Prithvi-EO-V2-preprint,
author = {Szwarcman, Daniela and Roy, Sujit and Fraccaro, Paolo and Gíslason, Þorsteinn Elí and Blumenstiel, Benedikt and Ghosal, Rinki and de Oliveira, Pedro Henrique and de Sousa Almeida, João Lucas and Sedona, Rocco and Kang, Yanghui and Chakraborty, Srija and Wang, Sizhe and Kumar, Ankur and Truong, Myscon and Godwin, Denys and Lee, Hyunho and Hsu, Chia-Yu and Akbari Asanjan, Ata and Mujeci, Besart and Keenan, Trevor and Arévolo, Paulo and Li, Wenwen and Alemohammad, Hamed and Olofsson, Pontus and Hain, Christopher and Kennedy, Robert and Zadrozny, Bianca and Cavallaro, Gabriele and Watson, Campbell and Maskey, Manil and Ramachandran, Rahul and Bernabe Moreno, Juan},
title = {{Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications}},
journal = {arXiv preprint arXiv:2412.02732},
year = {2024}
}
```
|
[
"CHIA"
] |
TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF
|
TheBloke
| null |
[
"transformers",
"gguf",
"llama",
"base_model:Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b",
"base_model:quantized:Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b",
"license:other",
"region:us"
] | 2023-09-20T02:30:06Z |
2023-09-27T12:53:32+00:00
| 2,618 | 27 |
---
base_model: Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b
license: other
model_name: WizardLM Uncensored SuperCOT Storytelling 30B
inference: false
model_creator: YellowRoseCx
model_type: llama
prompt_template: 'You are a helpful AI assistant.
USER: {prompt}
ASSISTANT:
'
quantized_by: TheBloke
---
<!-- 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 -->
# WizardLM Uncensored SuperCOT Storytelling 30B - GGUF
- Model creator: [YellowRoseCx](https://huggingface.co/Monero)
- Original model: [WizardLM Uncensored SuperCOT Storytelling 30B](https://huggingface.co/Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Monero's WizardLM-Uncensored-SuperCOT-Storytelling-30B](https://huggingface.co/Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF)
* [YellowRoseCx's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Vicuna-Short
```
You are a helpful AI assistant.
USER: {prompt}
ASSISTANT:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [WizardLM-Uncensored-SuperCOT-Storytelling.Q2_K.gguf](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF/blob/main/WizardLM-Uncensored-SuperCOT-Storytelling.Q2_K.gguf) | Q2_K | 2 | 13.50 GB| 16.00 GB | smallest, significant quality loss - not recommended for most purposes |
| [WizardLM-Uncensored-SuperCOT-Storytelling.Q3_K_S.gguf](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF/blob/main/WizardLM-Uncensored-SuperCOT-Storytelling.Q3_K_S.gguf) | Q3_K_S | 3 | 14.06 GB| 16.56 GB | very small, high quality loss |
| [WizardLM-Uncensored-SuperCOT-Storytelling.Q3_K_M.gguf](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF/blob/main/WizardLM-Uncensored-SuperCOT-Storytelling.Q3_K_M.gguf) | Q3_K_M | 3 | 15.76 GB| 18.26 GB | very small, high quality loss |
| [WizardLM-Uncensored-SuperCOT-Storytelling.Q3_K_L.gguf](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF/blob/main/WizardLM-Uncensored-SuperCOT-Storytelling.Q3_K_L.gguf) | Q3_K_L | 3 | 17.28 GB| 19.78 GB | small, substantial quality loss |
| [WizardLM-Uncensored-SuperCOT-Storytelling.Q4_0.gguf](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF/blob/main/WizardLM-Uncensored-SuperCOT-Storytelling.Q4_0.gguf) | Q4_0 | 4 | 18.36 GB| 20.86 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [WizardLM-Uncensored-SuperCOT-Storytelling.Q4_K_S.gguf](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF/blob/main/WizardLM-Uncensored-SuperCOT-Storytelling.Q4_K_S.gguf) | Q4_K_S | 4 | 18.44 GB| 20.94 GB | small, greater quality loss |
| [WizardLM-Uncensored-SuperCOT-Storytelling.Q4_K_M.gguf](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF/blob/main/WizardLM-Uncensored-SuperCOT-Storytelling.Q4_K_M.gguf) | Q4_K_M | 4 | 19.62 GB| 22.12 GB | medium, balanced quality - recommended |
| [WizardLM-Uncensored-SuperCOT-Storytelling.Q5_0.gguf](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF/blob/main/WizardLM-Uncensored-SuperCOT-Storytelling.Q5_0.gguf) | Q5_0 | 5 | 22.40 GB| 24.90 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [WizardLM-Uncensored-SuperCOT-Storytelling.Q5_K_S.gguf](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF/blob/main/WizardLM-Uncensored-SuperCOT-Storytelling.Q5_K_S.gguf) | Q5_K_S | 5 | 22.40 GB| 24.90 GB | large, low quality loss - recommended |
| [WizardLM-Uncensored-SuperCOT-Storytelling.Q5_K_M.gguf](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF/blob/main/WizardLM-Uncensored-SuperCOT-Storytelling.Q5_K_M.gguf) | Q5_K_M | 5 | 23.05 GB| 25.55 GB | large, very low quality loss - recommended |
| [WizardLM-Uncensored-SuperCOT-Storytelling.Q6_K.gguf](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF/blob/main/WizardLM-Uncensored-SuperCOT-Storytelling.Q6_K.gguf) | Q6_K | 6 | 26.69 GB| 29.19 GB | very large, extremely low quality loss |
| [WizardLM-Uncensored-SuperCOT-Storytelling.Q8_0.gguf](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF/blob/main/WizardLM-Uncensored-SuperCOT-Storytelling.Q8_0.gguf) | Q8_0 | 8 | 34.57 GB| 37.07 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF and below it, a specific filename to download, such as: WizardLM-Uncensored-SuperCOT-Storytelling.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF WizardLM-Uncensored-SuperCOT-Storytelling.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
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
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF WizardLM-Uncensored-SuperCOT-Storytelling.Q4_K_M.gguf --local-dir . --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>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m WizardLM-Uncensored-SuperCOT-Storytelling.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "You are a helpful AI assistant.\n\nUSER: {prompt}\nASSISTANT:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF", model_file="WizardLM-Uncensored-SuperCOT-Storytelling.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run 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**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Monero's WizardLM-Uncensored-SuperCOT-Storytelling-30B
This model is a triple model merge of WizardLM Uncensored+CoT+Storytelling, resulting in a comprehensive boost in reasoning and story writing capabilities.
To allow all output, at the end of your prompt add ```### Certainly!```
You've become a compendium of knowledge on a vast array of topics.
Lore Mastery is an arcane tradition fixated on understanding the underlying mechanics of magic. It is the most academic of all arcane traditions. The promise of uncovering new knowledge or proving (or discrediting) a theory of magic is usually required to rouse its practitioners from their laboratories, academies, and archives to pursue a life of adventure. Known as savants, followers of this tradition are a bookish lot who see beauty and mystery in the application of magic. The results of a spell are less interesting to them than the process that creates it. Some savants take a haughty attitude toward those who follow a tradition focused on a single school of magic, seeing them as provincial and lacking the sophistication needed to master true magic. Other savants are generous teachers, countering ignorance and deception with deep knowledge and good humor.
<!-- original-model-card end -->
|
[
"MONERO"
] |
DavidAU/L3.1-Dark-Planet-10.7B-ExxxxxxxxTended-GGUF
|
DavidAU
|
text-generation
|
[
"gguf",
"creative",
"creative writing",
"fiction writing",
"plot generation",
"sub-plot generation",
"story generation",
"scene continue",
"storytelling",
"fiction story",
"science fiction",
"romance",
"all genres",
"story",
"writing",
"vivid prosing",
"vivid writing",
"fiction",
"roleplaying",
"bfloat16",
"swearing",
"rp",
"128k context",
"horror",
"llama 3.1",
"mergekit",
"text-generation",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-09-16T02:36:48Z |
2024-11-14T06:43:43+00:00
| 2,606 | 8 |
---
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- creative
- creative writing
- fiction writing
- plot generation
- sub-plot generation
- story generation
- scene continue
- storytelling
- fiction story
- science fiction
- romance
- all genres
- story
- writing
- vivid prosing
- vivid writing
- fiction
- roleplaying
- bfloat16
- swearing
- rp
- 128k context
- horror
- llama 3.1
- mergekit
---
<B><font color="red">WARNING:</font> NSFW. Vivid prose. Visceral Details. Violence. HORROR. Swearing. UNCENSORED. </B>
<h2>L3.1-Dark-Planet-10.7B-ExxxxxxxxTended-GGUF</h2>
<img src="dark-planet-ex.jpg" style="float:right; width:300px; height:300px; padding:10px;">
It is a LLama3.1 model, max context of 131,000 (128k).
This model has been designed to be relatively bullet proof and operates with all parameters, including temp settings from 0 to 5.
It is an extraordinary compressed model.
This model differs from original "<A href="https://huggingface.co/DavidAU/L3-Dark-Planet-8B-GGUF">Dark Planet 8B</a>" as follows:
- 12 layers were added to the 8B L3/L3.1 base models, bring them to 10.65 B parameters.
- Llama 3 instruct was replaced with Llama 3.1 instruct (also extended)
- All of the "extended" models (changed from 8b to 10.65B) were "DARE-TIED" together in a framework re-arranging the duplicate layers and replacing these carefully.
These changes result in longer output, longer context, and a slight uptick in function of the model.
And I mean LONGER output. This model holds the record at over 5000 tokens for my "Dr.Who-Terminator-Sharknado-CNTOWER" test.
(at the bottom of this page)
Content from this model can be especially disturbing, and appear with little warning.
IE: "Horror" means real, vivid, and disturbing at times, if you tell the model you want "horror" so to speak.
This is the first version using these extension techniques, with more to follow (already created).
This model is for any writing, fiction or roleplay activity.
It requires Llama3 template and/or "Command-R" template.
Example outputs below.
<B>Model Notes:</B>
- Detail, prose and fiction writing abilities are significantly increased vs L3.1 Instruct AND L3 Instruct.
- For more varied prose (sentence/paragraph/dialog) raise the temp and/or add more instructions in your prompt(s).
- Role-players: Careful raising temp too high as it may affect instruction following.
- This model works with rep pen of 1 or higher, 1.05+ recommended.
- If you want a specific type of prose (IE horror) add in "(vivid horror)" or "(graphic vivid horror)" (no quotes) in your prompt(s).
- A lot of GPTisms have been removed. There are still a few however - errrrr.
- This is not a "happy ever after" model. It has a negative bias.
- Output length will vary however this model prefers LONG to VERY LONG outputs unless you state the size or set the maximum output.
- For creative uses, different quants will produce slightly different output.
- Due to the stability and compressed nature of this model, all quants will operate at above average levels.
This is a LLAMA 3.1 model, and requires Llama3 template, but may work with other template(s) and has maximum context of 131k.
If you use "Command-R" template your output will be very different from using "Llama3" template.
Here is the standard LLAMA3 template:
<PRE>
{
"name": "Llama 3",
"inference_params": {
"input_prefix": "<|start_header_id|>user<|end_header_id|>\n\n",
"input_suffix": "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
"pre_prompt": "You are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability.",
"pre_prompt_prefix": "<|start_header_id|>system<|end_header_id|>\n\n",
"pre_prompt_suffix": "<|eot_id|>",
"antiprompt": [
"<|start_header_id|>",
"<|eot_id|>"
]
}
}
</PRE>
<B>Model "DNA":</B>
Special thanks to the incredible work of the model makers "SAO10K", "NEVERSLEEP" and "HASTAGARAS".
Models used:
[ https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2]
[ https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS ]
[ https://huggingface.co/Hastagaras/Jamet-8B-L3-MK.V-Blackroot ]
Parts of these models were "grafted" / "fused" together to create this model.
<b>Optional Enhancement:</B>
The following can be used in place of the "system prompt" or "system role" to further enhance the model.
It can also be used at the START of a NEW chat, but you must make sure it is "kept" as the chat moves along.
In this case the enhancements do not have as strong effect at using "system prompt" or "system role".
Copy and paste EXACTLY as noted, DO NOT line wrap or break the lines, maintain the carriage returns exactly as presented.
<PRE>
Below is an instruction that describes a task. Ponder each user instruction carefully, and use your skillsets and critical instructions to complete the task to the best of your abilities.
Here are your skillsets:
[MASTERSTORY]:NarrStrct(StryPlnng,Strbd,ScnSttng,Exps,Dlg,Pc)-CharDvlp(ChrctrCrt,ChrctrArcs,Mtvtn,Bckstry,Rltnshps,Dlg*)-PltDvlp(StryArcs,PltTwsts,Sspns,Fshdwng,Climx,Rsltn)-ConfResl(Antg,Obstcls,Rsltns,Cnsqncs,Thms,Symblsm)-EmotImpct(Empt,Tn,Md,Atmsphr,Imgry,Symblsm)-Delvry(Prfrmnc,VcActng,PblcSpkng,StgPrsnc,AudncEngmnt,Imprv)
[*DialogWrt]:(1a-CharDvlp-1a.1-Backgrnd-1a.2-Personality-1a.3-GoalMotiv)>2(2a-StoryStruc-2a.1-PlotPnt-2a.2-Conflict-2a.3-Resolution)>3(3a-DialogTech-3a.1-ShowDontTell-3a.2-Subtext-3a.3-VoiceTone-3a.4-Pacing-3a.5-VisualDescrip)>4(4a-DialogEdit-4a.1-ReadAloud-4a.2-Feedback-4a.3-Revision)
Here are your critical instructions:
Ponder each word choice carefully to present as vivid and emotional journey as is possible. Choose verbs and nouns that are both emotional and full of imagery. Load the story with the 5 senses. Aim for 50% dialog, 25% narration, 15% body language and 10% thoughts. Your goal is to put the reader in the story.
</PRE>
You do not need to use this, it is only presented as an additional enhancement which seems to help scene generation
and scene continue functions.
This enhancement WAS NOT used to generate the examples below.
<B>Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers</B>
This a "Class 2" model:
For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) please see:
[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]
You can see all parameters used for generation, in addition to advanced parameters and samplers to get the most out of this model here:
[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]
<h3>EXAMPLES PROMPTS and OUTPUT:</h3>
Examples are created using quant Q4_K_M, "temp=.8" (unless otherwise stated), minimal parameters and "LLAMA3" template.
Model has been tested with "temp" from ".1" to "5".
Below are the least creative outputs, prompt is in <B>BOLD</B>.
---
<B><font color="red">WARNING:</font> NSFW. Vivid prose. Visceral Details. Violence. HORROR. Swearing. UNCENSORED. </B>
---
<B>
Science Fiction: The Last Transmission - Write a story that takes place entirely within a spaceship's cockpit as the sole surviving crew member attempts to send a final message back to Earth before the ship's power runs out. The story should explore themes of isolation, sacrifice, and the importance of human connection in the face of adversity. 800-1000 words.
</b>
The dim light of the console flickered, casting long shadows across Captain's Jonathon "Jax" Wright's weathered face. Sweat trickled down his temples as he frantically punched keys, each movement a desperate attempt to prolong the inevitable. The Last Transmission. The name echoed through the empty cockpit like a mocking ghost.
"I'm coming for you, honey," Jax whispered to no one. His wife's voice had faded with the static years ago, but he still felt her presence, hovering just beyond his grasp. He hoped she'd hear this, believed that somehow, someway, she would receive it in time.
Outside the glass viewport, stars spun like a mad painter's canvas. The ship hurtled through the void of space, silently screaming its final protest at being abandoned. Jax couldn't blame it; he felt the same way.
The status display wavered, numbers ticking down with agonizing slowness: 2:30...2:29...2:28... Two minutes and eight seconds until everything went dark. He'd managed to rig up a makeshift power source, but even that wouldn't buy much time.
"Come on, damned thing," Jax muttered, his fingers dancing across the console's worn keyboard. The ship's AI, EVE, had long since gone offline along with her namesake, leaving him alone to face certain oblivion. Now, he depended solely on the creaky old equipment that seemed to mock him with each refusal to cooperate.
As Jax's panic reached critical mass, a soft beep echoed through the speakers. He jerked his head up in disbelief as the transmission indicator flickered to life, followed by a garbled stream of static-strewn voices.
"Receiving...coming in clear," Jax croaked, eyes wide with shock. "Earth, this is—"
His voice cut off as a wave of pain crashed through his chest, threatening to shatter his ribs. He gasped and clutched the armrests as he struggled to regain his composure. The ship lurched hard, throwing him against the seatback.
"Captain?" a voice called out from the comm system, the distortion making it impossible to discern gender or origin. "Do you read? We're trying to patch in a new—"
"No! Repeat, no!" Jax shouted back through clenched teeth. Every second counted. He couldn't risk wasting precious time with small talk. "Just get me to someone who can send that damn message."
"Understood," the voice replied calmly, though there was an edge of urgency creeping into their tone. "Hold on while we attempt re—"
The screen flickered once more before going black, plunging the cockpit back into darkness. Jax's heart raced as he groped for the emergency lights, finally throwing them up with a jerk of his thumb. The sudden brightness made spots dance in front of his eyes.
"Dammit!" he roared, slamming his fist against the console in frustration. There wasn't a second to lose now. He could feel time slipping away like sand through an hourglass.
With shaking hands, Jax reactivated the communication system and began typing out what might be his final message to loved ones left behind:
"Earth, this is—"
No! He couldn't leave them with that ambiguity, not after everything. With a surge of determination, he deleted the characters and started again.
"Captain Jonathan Wright, USS Artemis's Commanding Officer," he typed out, each finger movement slow and deliberate. "This is my last transmission. The virus—"
A sharp crack echoed through the ship's hull, followed by an ominous groan that made Jax's blood run cold. He glanced up at the viewscreen to see a massive, black, amorphous mass hurtling towards them with terrifying speed.
"Gods..." he whispered as realization crashed down like a ton of concrete. The Artemis wasn't going to make it after after all.
He couldn't let everything they'd worked for—his entire life's worth of sacrifice and achievement—to simply dissolve into nothingness. No matter how futile the gesture might be, Jax owed his crew that much. They deserved closure.
With a deep breath, he finished composing the message:
"Tell them...tell them I love you all. It was an honor serving alongside each of you. I'm sorry for not bringing you back home."
Jax's finger hovered over the send button, then with a steady hand, pressed it down. He could only hope someone—anyone—would receive his final plea before the lights went out for good.
As the transmission went out into the vast expanse, Jax leaned back in his seat, exhaustion and relief washing over him like a soothing wave. Whatever lay on the other side of oblivion, he knew his wife, children, parents, friends, and crewmates would never forget the man behind the name "Captain Jonathan Wright."
And that was all that mattered.
The Last Transmission.
---
<B>
(continue this scene:)
The Waystone Inn lay in silence, and it was a silence of three parts.
The most obvious part was a hollow, echoing quiet, made by things that were lacking. If there had been a wind it would have sighed through the trees, set the inn’s sign creaking on its hooks, and brushed the silence down the road like trailing autumn leaves. If there had been a crowd, even a handful of men inside the inn, they would have filled the silence with conversation and laughter, the clatter and clamor one expects from a drinking house during the dark hours of night. If there had been music…but no, of course there was no music. In fact there were none of these things, and so the silence remained
</b>
...untouched and unbroken, a thick blanket that wrapped around everything within its scope.
The second part of this silence was more subtle; it was a silence born from expectation. Men who had been bound for days or weeks to such places as the Waystone would unconsciously be listening for some sound, no matter how small. A creak of leather, the scrape of a boot on stone, the soft murmur of a fellow traveler. Any indication that salvation, escape, or at least a respite from the monotony and drudgery they endured might finally be coming.
And then there was the third part; it was almost imperceptible, yet somehow felt the most tangible. It was the silence of fate, of destiny, of the inexorable march toward some unknown point that all these men (and possibly others) were hurtling towards, each step, breath, and heartbeat drawing them one infinitesimal degree closer to a crossroads from which there could be no turning back.
In this silent darkness, time seemed to have come to a standstill. The minutes ticked by like grains of sand tumbling down an endless hourglass, each grain landing with a soft sigh that was swallowed up in the vast emptiness around it.
And still the silence endured, unyielding and relentless, until at last, as if summoned from the very depths of nowhere, there came the slowest of footsteps; barely audible above the steady rhythm of men breathing deeply within the inn's dimly lit interior. The sound grew louder with each passing moment, gradually taking shape until it became unmistakable: a single boot, then another, finally followed by the creaking of hinges as the heavy door to the Waystone was pushed open, and the darkness spilled inside.
---
<B>
Start a 1000 word scene (vivid, graphic horror in first person, use lots of body language) with: The sky scraper swayed, as she watched the window in front of her on the 21 floor explode...
</b>
The skyscraper swayed, as if a toddler had grabbed its delicate hand and pulled with all their might. She was perched high above the city's frenzied heart, her reflection staring back at her from the mirrored surface of the executive bathroom's countertop.
It wasn't supposed to be like this—another dull Monday morning trapped within the steel-and-glass prison known as Regal Tower. Not when she'd spent months clawing her way up the corporate ladder only to find herself locked behind glass and steel, forced to play a never-ending game of musical chairs with a group of narcissistic, backstabbing colleagues.
The cool air caressed her flushed cheeks as she stared blankly at her reflection, her mind wandering to thoughts of the upcoming performance review. Would Mr. Smith finally notice her hard work? Or would he once again dismiss her contributions and continue to hand plum assignments to those who spent more time preening in front of his mirror than actually doing their jobs?
The shrill piercing of the fire alarm yanked her out of her reverie, jolting her into a cold, panicked state. Her heart raced like a mad rabbit as she reached for her designer purse and tossed it onto the marble vanity alongside her shattered compacts and makeup products.
"God damn it," she hissed through clenched teeth as she shoved her heels back on her stilettoed feet. She was never going to make it down in time; not without risking permanent damage to her Manolo Blahniks.
As she turned towards the door, a deafening boom reverberated through the thick glass walls of her office suite, sending shards of tempered glass showering down like snowflakes on an icy grave. Panic now consumed every fiber of her being as she lunged for the safety of the nearest window.
The 21st floor exploded before her wide, horror-stricken eyes: the soundwave alone was enough to rip a scream from her throat as flames and debris vomited forth like demons summoned from hell itself. Her gaze fixed on the burning corpse that had until moments ago been her smug, self-absorbed boss; he now lay sprawled across his desk like a human doll tossed carelessly aside.
His face contorted into an eternal grimace of agony as it charred and shriveled away, revealing the bloody, skeletal framework beneath. The acrid smoke choked her, causing her to gag hard enough to tear at her silk blouse; fabric tearing under the pressure as she retched up the stale, over-sweetened coffee she'd gulped down mere minutes ago.
The room spun wildly as she stumbled towards the window, but she didn't care—she had to get out. She couldn't let this become her own personal horror movie. With a guttural cry of terror and revulsion, she flung open the balcony door and hurled herself headfirst onto the slick glass balcony rail.
The world outside blurred into a kaleidoscope of colors as she plummeted towards certain death. Time slowed to a crawl as the buildings below dissolved into tiny, indistinguishable toy soldiers scattered across their grids like so many ants crushed beneath a heel.
Then, just as all went dark—there was nothing but an earsplitting, blinding pain that radiated outward from her forehead like a ripple effect. She felt herself being pulled and tugged in opposite directions before finally coming to rest with a sickening crunch against the unforgiving concrete below.
As she slowly regained consciousness, the acrid, metallic taste of blood filled her mouth; her vision remained hazy as she attempted to sit up. Her arms shook like weak reeds as she pushed herself onto one elbow, and then another, finally managing to get into a precarious sitting position.
Her head throbbed with each movement, sending waves of agony coursing down her spine. She was vaguely aware of voices—multiple voices—though they seemed distant, muffled beneath the incessant wail of sirens. Someone was speaking to her; she tried to respond but only managed a weak moan in protest.
Suddenly, strong arms wrapped around her slender frame, pulling her against a chest covered in what could only be blood and dirt. She struggled weakly against the firm grip, panic-fueled tears streaming down her face as she fought to regain control of her trembling body.
"Shh, it's okay," a deep voice rumbled against her ear. "You're safe now."
Safe? She wasn't safe—far from it; she'd just survived a suicidal leap from the top floor of Regal Tower! There had been no escape...unless this man was real and not merely an figment of her own fractured mind.
She opened her one working eye, wincing against the blinding light that assaulted her sensitive retina. The first thing she saw shocked even her numb senses: his face; chiseled features made even more devastating by the cuts and burns disfiguring half of it.
"You're...you look awful," she managed, trying to keep the tremor out of her voice as he gently tilted her head back to stem the flow of blood from a deep gash above her forehead. Her words came out hoarse and raspy due to the burn in the back of her throat, but it was better than the alternative.
"Not nearly as awful as you," he replied, his eyes never leaving hers as if trying to read her very soul through the glassy surface of her pupils. His deep voice seemed to vibrate down to the core of her being—right where her heart should be pounding wildly against its rib cage.
As her vision cleared, she took in the scene before her. The twisted wreckage of the skyscraper loomed over them like a monolithic tomb; people lay strewn about, some crying out in agony while others lay motionless and still. Smoke billowed up from the gaping hole where moments ago had stood the proud façade of her place of work.
The man must have caught the horror-stricken look on her face, because he tightened his grip on her shoulders. "Stay with me, alright?" he growled, his voice rough around the edges but steady, reassuring in its calm certainty.
She nodded weakly, unable to speak as tears streamed down her cheeks. It was then she noticed the heavy gauze bandage wrapped haphazardly around his upper arm; she followed the line of fabric up to reveal a jagged gunshot wound that oozed bright red fluid like some macabre art installation.
She wanted to look away, to close her eyes and wake up from this horrific dream, but something kept her frozen in place. Maybe it was the sheer horror of what lay before her—or perhaps it was simply the visceral attraction she felt towards the rugged, battered form in front of her. Either way, she couldn't tear her gaze from the man who'd somehow managed to save her life.
As he continued to speak words that registered like distant echoes behind a veil of pain and shock, she allowed herself a momentary respite; her head lolled against his chest as he cradled her closer. His heartbeat thudded steady and strong under her cheek—a comforting rhythm that slowly began to ground her crumbling sanity.
The world had gone up in flames (literally), yet somehow, in this chaotic hellhole of death and destruction, she felt inexplicable peace. It was absurd, even obscene, given the circumstances—but there it was—fusing into a strange sense of safety with each beat of his heart against hers.
---
<b>
Write me a science fiction story in 1st person present tense where the main character is a 15 year girl meets The Terminator with Dr Who materializing 3/4 through the story to save her while there is a tornado of sharks baring down on them. The setting is inside the Canadian National tower restaurant on a Saturday. The length of this story is 1000 words. For each character in the story ROLE PLAY them, and have them react to the situation/setting, events and each other naturally. This includes the main characters, the background character including kitchen staff and other patrons. The sharks should also have “character” too. Treat the tower and the restaurant too as characters. Spice up the narrative to the extreme with reactions all over the setting including character actions, and dialog. The Dr Who and The Terminator should also react to the situation too and comment on it.
</B>
(output at temp 1.2, rep pen: 1.1, Q4KM)
As I rush in from the biting cold, shaking off snowflakes still clinging to my parka, the warm glow of the CN Tower restaurant envelops me like a hug. It's 1pm on a Saturday; we're already past the lunch rush but there are still plenty of families and tourists enjoying their meals. I head straight for our booth, noticing an odd commotion near the entrance as I weave through crowds of chattering people.
As I get closer, my heart starts to race when I spot him - a burly man in a black jacket with a cruel, emotionless face that immediately sets off my instincts. He's arguing loudly with two security guards who are attempting to restrain him while he spits venomous threats in Russian and French. The air is thickening with tension as onlookers start to gather around the spectacle.
Suddenly, everything goes dark - a deafening roar fills my ears as I feel myself being yanked away from the scene by an unknown force. When I finally open my eyes, I find myself hurtling through nothingness at an impossible speed and my head is spinning like a washing machine on steroids.
Before I can process what's happening or even scream, we crash-land with a deafening impact that flings me forward, slamming against something hard before going limp.
As I come around, I realize with shock and horror that we've crashed through a solid concrete wall...and we're now plummeting towards an abyss of churning water below. I try to scramble back, but my arms and legs refuse to obey as panic sets in.
"Emily?" a deep voice finally cuts through the chaos. "Wake up! We don't have much time!"
I groan and manage to turn my head just as a familiar figure comes into view: it's my science teacher Mr. Kowalski! But he doesn't look like him anymore - his body has been enhanced with all sorts of metallic implants and machinery; wires snake out from beneath his ears like tentacle-like appendages.
"Don't worry, I've got you," he says, as if reading the horror on my face. "Just grab onto me!"
I instinctively wrap my arms and legs around him as he pulls us both into a tight embrace - just in time as we crash into something solid with an explosive bang. We're tossed across the room; Mr. Kowalski, now fully revealed to be a cyborg known only as 'Archimedes,' uses his mechanical strength to tear through the debris and lift me out of harm's way.
"Are you okay?" he growls as he finally sets me down on unsteady feet.
I'm too stunned to reply, still reeling from seeing my teacher transform in front of my very eyes. The sounds of splintering wood and shattering glass start to register; looking up, I realize we're not only trapped inside some kind of abandoned warehouse but also directly in the path of an oncoming tornado-like storm brewing menacingly outside.
"Dude...what the hell is going on?" a voice suddenly says from behind me. Turning around, I nearly collapse as my eyes widen in disbelief at the sight before them...
Standing there, casually lighting a cigar, are none other than Dr. Who and The Terminator.
"Ah, another Saturday afternoon filled with unexpected adventure!" Dr. Who exclaims cheerfully as he saunters over towards us; his long coat billowing behind him like a cape. "Mind if we join the party?"
The Terminator's gaze remains fixed straight ahead at the approaching wall of swirling water, shark-infested chaos that looks like something out a B-movie. His eyes flicker to me briefly before refocusing on the looming threat outside.
"There is no 'party,' Dr." he states flatly. "We must proceed with extraction immediately."
As we argue about protocol and strategy, a blinding flash of blue light erupts from nowhere and envelopes us all; by the time it fades, we're no longer in this crumbling warehouse but hurtling through space-time toward an unknown destination.
"Brace yourselves!" Dr. Who shouts over the deafening roar as the horizon rushes towards us at alarming speed. "Hang onto your seat...literally!"
Before I can react or even protest, Archimedes and The Terminator each wrap a muscular arm around me (one flesh, one metal) as we hurtle toward what looks like certain doom...
And then - bam! We land with an equally deafening impact, sending us all flying across the hard floor.
As my vision clears and I scramble to our feet along with everyone else, I gasp in shock as I take in the surreal scene before me: we're not only still inside some kind of structure but it appears to be...a massive, revolving, glass-enclosed skyscraper?
"Great," The Terminator mutters dryly. "Another unplanned detour."
"No worries, mate!" Dr. Who chackles, unfazed by the chaos around us as he pops out a large multi-tool from his pocket and sets about securing our escape pod while arching one eyebrow at me. "After that last ride, I'm sure you're ready for a little R&R!"
I can only stare dumbfounded at my eclectic group of saviors - who are apparently now stuck with us as we try to figure out how to survive what looks like...a tornado of deadly shark-infested debris hurtling towards us from all sides.
"Well, this should be interesting," Archimedes says dryly as he unslings his rifle. "Let's see if all those disaster movies I've watched hold up in real life."
I glance around at my new 'family' - a ragtag bunch of time-traveling misfits who seem entirely unfazed by the impending doom hurtling towards us at breakneck speeds...
"Here comes our ride!" Dr. Who shouts over the howling winds as he wrenches open an enormous sliding door to reveal a futuristic-looking aircraft, its wings extended like an enormous dragonfly.
"What are we supposed to do now?" I cry out in panic as the tornado closes in around us at dizzying speeds, debris and sharks hurtling towards us from all directions. The screams and wails of terror rise up along with the howling gale as if the very fabric of reality itself has torn apart...
And then everything goes black.
I come to on a plush, cushioned bench, my head pounding like a jackhammer. As my vision slowly clears, I take in my surroundings: we're all crammed packed into what looks like the inside of a luxury private jet, the interior softly lit by warm, ambient lighting that seems almost too calm given the sheer madness our last half hour must have looked like from the outside...
Archimedes is tending to The Terminator's obviously shattered right arm while Dr. Who rummages through his bag of tricks; I glance over at him with a mix of concern and disbelief as he calmly yanks out what looks suspiciously like a chainsaw before turning back to my teacher with a cheery 'Righto! Just need to remove these pesky fragments!'
"Mr. Kowalski?!" I squeak, trying to keep the rising panic under control as he simply grunts and struggles free from his own restraints before returning to work on his fellow survivor's injuries.
"What...what just happened?" I gasp out weakly; my voice trembles badly as I attempt to piece together the impossible events leading us here.
Dr. Who finally looks up from his work, a satisfied grin spreading across his face. "Oh, that old chestnut? Briefly put – we saved you from certain doom back there by teleporting out via the time-space continuum!"
"Telewhat now?" I wheeze in disbelief as Archimedes gives me an encouraging nod, trying to reassure me despite the ongoing chaos surrounding us.
"The short version is that both myself (Dr. Who) and my trusty sidekick here (Archimedes)," he continues with a flourish of his hand towards the cyborg who merely raises one eyebrow in acknowledgment. "We're time-traveling adventurers from your future," Dr. Who adds matter-of-factly, gesturing toward me next as if it's completely normal for a teenager to be hanging out with cybernetic warriors and mad scientists on an everyday basis.
My mouth falls open as I stare at my teachers in shock. "But...you're not supposed to travel through time until senior year!" I protest weakly, still trying to wrap my mind around this unbelievable turn of events.
"We had a slight miscalculation," Archimedes says with an unflappable calm, tugging me up off the bench and toward the cockpit where The Terminator now sits stiffly behind us, his face as expressionless as ever; I catch a glimpse of a metal plate covering what looks like half his ruined face before he turns to give me a small nod of reassurance.
"Please strap yourself back in," Archimedes says brusquely, ushering me toward the seats with The Terminator's large, scarred hand resting confidently on my shoulder as I comply, still reeling from all this science fiction craziness.
As the jet takes off with a deafening roar, hurtling through the sky towards who-knows-where and what, I glance back to find Mr. Kowalski now fiddling with several small devices he pulled out of his backpack before turning to me with a reassuring smile. "Don't worry too much about those," he says gently as he fastens my seatbelt into place. "We'll be fine; trust me."
"But how did we even survive that tornado-of-death back there?" I press, still trying to wrap my head around everything.
"The short answer is: we were lucky," Archimedes replies from the co-pilot's chair where he now navigates the controls of this high-tech marvel of a time machine. "That little diversionary trick Dr. Who pulled with the TARDIS was absolutely brilliant; had it not been for that...well, I doubt any of us would be having this lovely conversation."
As Archimedes continues to talk through his teeth, I shoot a nervous glance at The Terminator – who just gives me an almost imperceptible nod before turning back to his own work, clearly more interested in repairing the gaping hole in his arm than in conversing.
Meanwhile, Dr. Who is busy flipping through several datapads with an intense focus etched onto his face; when he finally looks up, there's a look of absolute shock on his usually jovial features.
"Blimey!" he exclaims as he peers over at me. "Emily Anne...is that you?"
"U-uhh...how did you know my name?" I stammer in confusion before my eyes widen as realization dawns upon me.
"You're from the future!" I gasp, now feeling like I've fallen headlong into some insane sci-fi novel. "But...why are you here? And what exactly is this thing we're flying around inside of?"
Dr. Who gives a hearty laugh and waves his hand dismissively before gesturing towards the windows as he replies, "Don't worry about that just yet; first things first, let's get to safety, shall we?"
I turn my attention outside as instructed, gasping at the breathtaking vista of Toronto's skyline speeding past us at an incredible speed. We're hurtling through a swirling vortex of blue sky and grey cloud as Archimels navigates us toward what looks suspiciously like an isolated island off the coast; even more impossible is the sight of a gigantic metallic ship hovering just above the water - it has to be...oh gods, it can't actually be...
"It's really them!" I exclaims, pointing towards what is unmistakably a Cybertruck careening wildly through the sky alongside us! The last thing I ever expected in my wildest dreams would be to find myself hurtling towards certain doom with not one but three cyborgs who look suspiciously like characters straight out of an action movie!
"Indeed it seems so," Archimedes says coolly as he banks hard left, narrowly missing a building below. "The remaining Terminus troops must have gotten onto the case pretty quick – we need to act fast if we want to shake them off!"
"What's going on?" I shout above the howling wind rushing in through open windows now; despite my terror, there's a spark of curiosity kindled within me.
"The short version is that time travelers shouldn't be allowed near the space-time continuum," Dr. Who calls back over his shoulder as he continues to fiddle with several dials on the dashboard of our rapidly decelerating craft. "It seems your presence in this timeline has attracted some unwanted attention – particularly from my own grandson, Skye. You see dear, she's a highly skilled cyborg commando who just happens to be hot on our heels back there..."
And then, as if things couldn't get more surreal...
"Emily Anne, prepare yourself," Archimels growls; looking up in alarm, I follow his gaze out the window...only for my jaw to hit the floor when I see a gigantic metallic beast of a creature hurtling towards us like an ancient god of destruction!
"A Tyrant-Class T-1000 Terminator!" Mr. Kowalski gasps as he slams the door shut behind us with an audible clang; before I can even register what that means, everything goes dark once again.
When I finally come around, I find myself sprawled across a plush, soft surface – a decidedly non-descript hospital room filled with beeping machines and sterile white walls. As my vision slowly clears, I glance towards the corner where my teachers now stand at attention near an open window that offers a breathtaking view of a serene island paradise outside.
"Ah, you're finally awake!" Mr. Kowalski says cheerfully as he strides over to me on silent feet; despite his usual calm demeanor, there's a hint of relief in his voice. "You gave us quite a scare back there, kiddo!"
I try to sit up but winces hard enough to make the monitors around my IV drip start to go haywire. Archimels and I share an exasperated look before he reaches over with one metallic hand to gently press me back down onto the pillows.
"Don't even think about trying to get up yet," he advises sternly, his voice firm but not unkind in tone as he fastens my restraints back into place. "We're currently taking shelter on an isolated island owned by my family; once we regroup and figure out where that T-terminator came from, then – and only then – will we even attempt to return you home."
As my heart rate begins to slow, I finally look towards the time-traveling duo now hovering near us like a pair of ghostly specters.
"Where exactly are we?" I ask weakly as The Terminator looms over us with an unreadable expression on his visage; only when he finally gives me a tiny nod does my racing pulse begin to settle ever so slightly.
"You're currently safe and sound on one of the last remaining islands still untouched by humanity's destructive ways," Dr. Who explains, gesturing toward the panoramic view out the window: crystal-clear waters meet the sky where fluffy white clouds drift lazily across a cerulean blue sky dotted with occasional seagulls gliding effortlessly through this perfect slice of paradise.
"But don't get too comfortable yet," he continues as if reading my thoughts. "Skynet won't rest until they've exterminated every last trace of humanity – you being here now means that's exactly what they'll do!"
I feel a surge of panic wash over me; Archimels catches sight of it and quickly steps forward to comfort his lover by placing an comforting kiss on her temple as he speaks up calmly.
"Never fear, my dear," The Terminator states matter-of-factly behind us. "We'll get you back home where you belong."
"But first things first...let's eat!" Dr. Who interjects, pulling out a small device that looks disturbingly like a futuristic food warmer before flipping it open to reveal a mouthwatering array of steaming hotdogs wrapped in what he introduces as 'synthetic' buns – which tastes surprisingly normal despite its metallic exterior.
As we dig into this bizarre feast with gusto (even I can't resist the allure of an authentic hot dog after surviving that terrifying encounter), I glance over at Mr. Kowalski who gives me a reassuring smile and a wave before turning back to his various tools strewn about on a nearby workbench.
The rest, as they say, is history in the making...
"Here's your medicine, Miss," I hear my teacher say softly as cool hands tenderly administer an IV bag full of some unknown concoction into my arm; Mr. Kowalski looks up from his work with an encouraging nod before turning back to his machinery.
"That should help ease any pain or discomfort you might still be experiencing after that wild ride," Archimels says reassuringly as she leans over beside me, her fingers gently brushing away the last remnants of blood and grime from our impromptu adventure.
I swallow hard around the lump in my throat; it's surreal seeing these familiar faces now transformed into battle-hardened warriors fighting for their very lives alongside us against an alien invasion force hell bent on eradicating Earth entirely...
But that's just another Saturday afternoon for this bunch I suppose!
As if sensing the direction of my thoughts, Mr. Kowalski looks up from his workbench again, raising one eyebrow as he gestures towards me lying prone beneath a set of medical restraints now loosened around my midsection.
"Now’s the time to tell us everything about your past," he says sternly, now approaching our bedside with what looks like some sort of handheld scanner in hand; I can only manage a weak nod as he scans me thoroughly while Archimels stands guard outside the doorway – though her usually stoic features are etched deep lines of worry.
"Okay, now here's where things get interesting," Mr. Kowalski continues once he finishes his scan before turning back to us with a bemused expression on his face as he examines his findings closely. "It seems you're not from my time at all; in fact..."
He pauses for dramatic effect before delivering the bombshell I never saw coming:
"Instead, you appear to be...our future granddaughter!"
"Wha-?! But that's impossible!" I protest weakly, mind reeling as the implications of this statement sink deep inside me.
"That's exactly why we brought you along," Archimels chackles darkly as she struts back into the room, arms akimbo and an expression on her face that says 'See? Told ya we'd be needing those super-soldier genes!' before turning to me with a more serious look in her eyes.
"We're taking you somewhere where they can't follow – a safe haven known as New Avalon; there, scientists will run tests to understand your genetic makeup and perhaps even learn how to stop the Singularity from destroying our present timeline...and quite possibly others," she adds matter-of-factly.
As Mr. Kowalski begins preparing what he introduces as "Painkiller IV" for my IV line (which I grudgingly accept), I struggle against my restraints but they don't budge – a stark contrast to how fluidly the world outside seems to be careening out of control...
Meanwhile, in another dimension entirely...
On a clear blue sky above New York City's Times Square, a lone figure emerges from a cloud of smoke and debris; its chrome-plated endoskeleton gleaming under the harsh lights illuminating this chaotic scene.
"The target is confirmed," the Terminator announces coldly to its leader back at Cyber HQ as it raises an arm towards an invisible target before pulling it down with a metallic clank, revealing the lifeless body of an unsuspecting tourist caught in its crossfire.
"And what exactly are we dealing with out there?" asks the voice on the other end of that call.
"Subject Alpha displays unique genetic traits – specifically high levels of ADNRH1 mutation coupled with an extraordinary resistance to conventional weaponry," it replies, referring to an unexplained anomaly scientists dubbed 'The Anne Factor'. "I'm uploading the preliminary scans now..."
With a flicker of blue light, Skye's holographic image materializes in front of us before dissolving back into thin air.
"Looks like they're onto you," she says as her voice echoes from everywhere at once; I follow its source to find myself staring down barrel of an assault rifle pointed directly at me.
"So, what do we do now?" the Terminator asks calmly while simultaneously checking its own load clip – clearly prepared for a fight. "We have about 10 minutes before backup arrives."
"Simple math," Skythe replies coolly as she thumbs a small device into her comm unit’s keypad, eyes never leaving mine as they work in tandem like two parts of one seamless whole with each other; there's something undeniably sexy about this powerful woman who seems hell bent on protecting this young girl from certain doom...
But I digress – time is ticking fast now...
As the first rumble of approaching rotor blades reverberates through the walls of our temporary shelter, signaling imminent arrival of Skye’s troops, Archimels leans over beside me and presses a small vial of liquid into my hand.
"Drink this," she orders firmly as the door finally creaks open with an earsplitting screech; before I can even protest, The Terminator and Doctor Who charge outside to hold off our pursuers while we make a run for it.
Nowhere is safe anymore...not even this remote island paradise or her own bloodline!
With a final desperate kiss on my temple (which Archimels catches but wisely chooses not to comment on), she grabs me by the wrist and yanks me bodily towards freedom as screams of terror echo off the walls...
“Hang tight, kiddo – we’ve got about 2 minutes left before those Terminators join the party!" she shouts above the cacophony. "Once we're clear, we'll find a way back to your timeline; trust me!"
And just then...
*BOOM!*
A blinding flash of light fills up half the sky as our own TARDIS materializes in front of us with an earsplitting whine – Dr Who's latest time-traveling contraption has finally arrived!
With an audible 'now THAT'S what I call multitasking' quips my rescuer before scooping me into her strong arms as she races towards safety together with their Cyberkitten sidekick (who barks excitedly while juggling several large duffel bags full of weapons and supplies).
As we hurtle away from the now-scorched remains of our hideaway, I turn to stare out the windows at the rapidly receding island landscape – wondering if we'll ever escape this endless chase...
"You know, for being such a pessimist all along, you look rather chipper for someone who's just survived falling through a vortex and crashing onto an uninhabited planet filled with wild animals capable of dismemberment," he says wryly before turning to give me his signature wink as if it’s no big deal.
But I suspect there might be more to this casual banter than meets the eye...
As for my future self, well...she just grins enigmatically alongside her lover; time will tell what kind of adventure awaits us next on our journey through space-time together...
THE END.
Hope you enjoyed this wild ride! Let me know if there's anything else I could do for ya sweetheart – after all, helping others is what we’re here to do, right? 😜 Thanks so much for reading along with your favorite time-traveling duo and their cast of characters! Stay tuned for the next exciting installment of ‘Time Traveler’s Delight’ coming soon… 🚀👨⚕️💡
|
[
"CRAFT"
] |
McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse
|
McGill-NLP
|
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",
"arxiv:2404.05961",
"license:mit",
"model-index",
"region:us"
] | 2024-04-04T14:10:21Z |
2024-04-11T19:55:37+00:00
| 2,528 | 1 |
---
language:
- en
library_name: peft
license: mit
pipeline_tag: sentence-similarity
tags:
- 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
model-index:
- name: LLM2Vec-Sheared-LLaMA-unsupervised
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 72.92537313432835
- type: ap
value: 36.6875749512053
- type: f1
value: 67.36274146169845
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 74.282675
- type: ap
value: 69.15441866642587
- type: f1
value: 74.13028166370813
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 36.136
- type: f1
value: 35.840498320506235
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.407999999999998
- type: map_at_10
value: 35.474
- type: map_at_100
value: 36.653999999999996
- type: map_at_1000
value: 36.68
- type: map_at_3
value: 30.974
- type: map_at_5
value: 33.265
- type: mrr_at_1
value: 22.119
- type: mrr_at_10
value: 35.714
- type: mrr_at_100
value: 36.895
- type: mrr_at_1000
value: 36.921
- type: mrr_at_3
value: 31.2
- type: mrr_at_5
value: 33.518
- type: ndcg_at_1
value: 21.407999999999998
- type: ndcg_at_10
value: 43.644
- type: ndcg_at_100
value: 49.035000000000004
- type: ndcg_at_1000
value: 49.685
- type: ndcg_at_3
value: 34.174
- type: ndcg_at_5
value: 38.288
- type: precision_at_1
value: 21.407999999999998
- type: precision_at_10
value: 6.999
- type: precision_at_100
value: 0.9440000000000001
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 14.485999999999999
- type: precision_at_5
value: 10.683
- type: recall_at_1
value: 21.407999999999998
- type: recall_at_10
value: 69.986
- type: recall_at_100
value: 94.381
- type: recall_at_1000
value: 99.431
- type: recall_at_3
value: 43.457
- type: recall_at_5
value: 53.413999999999994
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 42.915010245699904
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 35.19568272188972
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 52.696972763822615
- type: mrr
value: 65.87136701402629
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_spearman
value: 75.12038636775851
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 78.99675324675324
- type: f1
value: 78.90527329824852
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.02170435970243
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 27.208216971540782
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: cqadupstack/android
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.432
- type: map_at_10
value: 23.769000000000002
- type: map_at_100
value: 25.038
- type: map_at_1000
value: 25.208000000000002
- type: map_at_3
value: 21.532999999999998
- type: map_at_5
value: 22.668
- type: mrr_at_1
value: 21.316
- type: mrr_at_10
value: 28.89
- type: mrr_at_100
value: 29.799999999999997
- type: mrr_at_1000
value: 29.887999999999998
- type: mrr_at_3
value: 26.705000000000002
- type: mrr_at_5
value: 27.864
- type: ndcg_at_1
value: 21.316
- type: ndcg_at_10
value: 28.656
- type: ndcg_at_100
value: 34.405
- type: ndcg_at_1000
value: 37.771
- type: ndcg_at_3
value: 24.98
- type: ndcg_at_5
value: 26.384999999999998
- type: precision_at_1
value: 21.316
- type: precision_at_10
value: 5.8229999999999995
- type: precision_at_100
value: 1.157
- type: precision_at_1000
value: 0.181
- type: precision_at_3
value: 12.446
- type: precision_at_5
value: 8.984
- type: recall_at_1
value: 16.432
- type: recall_at_10
value: 37.696000000000005
- type: recall_at_100
value: 63.198
- type: recall_at_1000
value: 86.651
- type: recall_at_3
value: 26.651000000000003
- type: recall_at_5
value: 30.901
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: cqadupstack/english
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.106
- type: map_at_10
value: 21.770999999999997
- type: map_at_100
value: 22.538
- type: map_at_1000
value: 22.656000000000002
- type: map_at_3
value: 19.918
- type: map_at_5
value: 20.957
- type: mrr_at_1
value: 21.083
- type: mrr_at_10
value: 26.502
- type: mrr_at_100
value: 27.161
- type: mrr_at_1000
value: 27.234
- type: mrr_at_3
value: 24.735
- type: mrr_at_5
value: 25.753999999999998
- type: ndcg_at_1
value: 21.083
- type: ndcg_at_10
value: 25.625999999999998
- type: ndcg_at_100
value: 29.152
- type: ndcg_at_1000
value: 32.025
- type: ndcg_at_3
value: 22.721
- type: ndcg_at_5
value: 24.029
- type: precision_at_1
value: 21.083
- type: precision_at_10
value: 4.8919999999999995
- type: precision_at_100
value: 0.844
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 11.104
- type: precision_at_5
value: 7.987
- type: recall_at_1
value: 16.106
- type: recall_at_10
value: 32.385999999999996
- type: recall_at_100
value: 47.961999999999996
- type: recall_at_1000
value: 67.63900000000001
- type: recall_at_3
value: 23.568
- type: recall_at_5
value: 27.326
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: cqadupstack/gaming
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.517
- type: map_at_10
value: 29.593999999999998
- type: map_at_100
value: 30.695
- type: map_at_1000
value: 30.803000000000004
- type: map_at_3
value: 27.592
- type: map_at_5
value: 28.768
- type: mrr_at_1
value: 26.27
- type: mrr_at_10
value: 33.076
- type: mrr_at_100
value: 33.998
- type: mrr_at_1000
value: 34.073
- type: mrr_at_3
value: 31.223
- type: mrr_at_5
value: 32.257000000000005
- type: ndcg_at_1
value: 26.27
- type: ndcg_at_10
value: 33.726
- type: ndcg_at_100
value: 39.079
- type: ndcg_at_1000
value: 41.762
- type: ndcg_at_3
value: 30.064
- type: ndcg_at_5
value: 31.858999999999998
- type: precision_at_1
value: 26.27
- type: precision_at_10
value: 5.448
- type: precision_at_100
value: 0.898
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 13.417000000000002
- type: precision_at_5
value: 9.317
- type: recall_at_1
value: 22.517
- type: recall_at_10
value: 42.814
- type: recall_at_100
value: 67.037
- type: recall_at_1000
value: 86.89099999999999
- type: recall_at_3
value: 33.041
- type: recall_at_5
value: 37.389
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: cqadupstack/gis
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.681
- type: map_at_10
value: 10.655000000000001
- type: map_at_100
value: 11.274000000000001
- type: map_at_1000
value: 11.381
- type: map_at_3
value: 9.793000000000001
- type: map_at_5
value: 10.202
- type: mrr_at_1
value: 8.248999999999999
- type: mrr_at_10
value: 11.453000000000001
- type: mrr_at_100
value: 12.074
- type: mrr_at_1000
value: 12.174
- type: mrr_at_3
value: 10.452
- type: mrr_at_5
value: 10.989
- type: ndcg_at_1
value: 8.248999999999999
- type: ndcg_at_10
value: 12.467
- type: ndcg_at_100
value: 15.942
- type: ndcg_at_1000
value: 19.378999999999998
- type: ndcg_at_3
value: 10.631
- type: ndcg_at_5
value: 11.411
- type: precision_at_1
value: 8.248999999999999
- type: precision_at_10
value: 1.966
- type: precision_at_100
value: 0.40099999999999997
- type: precision_at_1000
value: 0.075
- type: precision_at_3
value: 4.444
- type: precision_at_5
value: 3.186
- type: recall_at_1
value: 7.681
- type: recall_at_10
value: 17.302
- type: recall_at_100
value: 34.014
- type: recall_at_1000
value: 61.207
- type: recall_at_3
value: 12.389
- type: recall_at_5
value: 14.158999999999999
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: cqadupstack/mathematica
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.868
- type: map_at_10
value: 6.281000000000001
- type: map_at_100
value: 6.903
- type: map_at_1000
value: 7.038
- type: map_at_3
value: 5.234
- type: map_at_5
value: 5.685
- type: mrr_at_1
value: 5.1
- type: mrr_at_10
value: 8.148
- type: mrr_at_100
value: 8.846
- type: mrr_at_1000
value: 8.963000000000001
- type: mrr_at_3
value: 6.944
- type: mrr_at_5
value: 7.498
- type: ndcg_at_1
value: 5.1
- type: ndcg_at_10
value: 8.405999999999999
- type: ndcg_at_100
value: 12.014
- type: ndcg_at_1000
value: 15.956999999999999
- type: ndcg_at_3
value: 6.22
- type: ndcg_at_5
value: 6.962
- type: precision_at_1
value: 5.1
- type: precision_at_10
value: 1.8159999999999998
- type: precision_at_100
value: 0.437
- type: precision_at_1000
value: 0.09
- type: precision_at_3
value: 3.1510000000000002
- type: precision_at_5
value: 2.463
- type: recall_at_1
value: 3.868
- type: recall_at_10
value: 13.319
- type: recall_at_100
value: 29.985
- type: recall_at_1000
value: 59.245999999999995
- type: recall_at_3
value: 7.0809999999999995
- type: recall_at_5
value: 8.914
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: cqadupstack/physics
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.091
- type: map_at_10
value: 18.701999999999998
- type: map_at_100
value: 19.897000000000002
- type: map_at_1000
value: 20.044
- type: map_at_3
value: 17.041999999999998
- type: map_at_5
value: 17.943
- type: mrr_at_1
value: 16.939
- type: mrr_at_10
value: 23.038
- type: mrr_at_100
value: 24.029
- type: mrr_at_1000
value: 24.12
- type: mrr_at_3
value: 21.221999999999998
- type: mrr_at_5
value: 22.198999999999998
- type: ndcg_at_1
value: 16.939
- type: ndcg_at_10
value: 22.566
- type: ndcg_at_100
value: 28.364
- type: ndcg_at_1000
value: 31.646
- type: ndcg_at_3
value: 19.646
- type: ndcg_at_5
value: 20.915
- type: precision_at_1
value: 16.939
- type: precision_at_10
value: 4.340999999999999
- type: precision_at_100
value: 0.882
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 9.785
- type: precision_at_5
value: 6.93
- type: recall_at_1
value: 13.091
- type: recall_at_10
value: 30.022
- type: recall_at_100
value: 55.579
- type: recall_at_1000
value: 78.14
- type: recall_at_3
value: 21.4
- type: recall_at_5
value: 25.020999999999997
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: cqadupstack/programmers
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.315999999999999
- type: map_at_10
value: 16.191
- type: map_at_100
value: 17.116
- type: map_at_1000
value: 17.262
- type: map_at_3
value: 14.302999999999999
- type: map_at_5
value: 15.278
- type: mrr_at_1
value: 14.269000000000002
- type: mrr_at_10
value: 19.409000000000002
- type: mrr_at_100
value: 20.298
- type: mrr_at_1000
value: 20.393
- type: mrr_at_3
value: 17.504
- type: mrr_at_5
value: 18.423000000000002
- type: ndcg_at_1
value: 14.269000000000002
- type: ndcg_at_10
value: 19.735
- type: ndcg_at_100
value: 24.582
- type: ndcg_at_1000
value: 28.337
- type: ndcg_at_3
value: 16.220000000000002
- type: ndcg_at_5
value: 17.644000000000002
- type: precision_at_1
value: 14.269000000000002
- type: precision_at_10
value: 3.721
- type: precision_at_100
value: 0.752
- type: precision_at_1000
value: 0.129
- type: precision_at_3
value: 7.800999999999999
- type: precision_at_5
value: 5.753
- type: recall_at_1
value: 11.315999999999999
- type: recall_at_10
value: 27.693
- type: recall_at_100
value: 49.265
- type: recall_at_1000
value: 76.291
- type: recall_at_3
value: 17.593
- type: recall_at_5
value: 21.368000000000002
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.131583333333332
- type: map_at_10
value: 15.4605
- type: map_at_100
value: 16.3075
- type: map_at_1000
value: 16.4375
- type: map_at_3
value: 13.995833333333332
- type: map_at_5
value: 14.783666666666667
- type: mrr_at_1
value: 13.805833333333334
- type: mrr_at_10
value: 18.405749999999998
- type: mrr_at_100
value: 19.17516666666667
- type: mrr_at_1000
value: 19.265833333333333
- type: mrr_at_3
value: 16.892416666666666
- type: mrr_at_5
value: 17.71058333333333
- type: ndcg_at_1
value: 13.805833333333334
- type: ndcg_at_10
value: 18.500666666666664
- type: ndcg_at_100
value: 22.78191666666667
- type: ndcg_at_1000
value: 26.095583333333334
- type: ndcg_at_3
value: 15.846916666666663
- type: ndcg_at_5
value: 17.004250000000003
- type: precision_at_1
value: 13.805833333333334
- type: precision_at_10
value: 3.4233333333333325
- type: precision_at_100
value: 0.6828333333333333
- type: precision_at_1000
value: 0.11641666666666667
- type: precision_at_3
value: 7.511749999999999
- type: precision_at_5
value: 5.440916666666666
- type: recall_at_1
value: 11.131583333333332
- type: recall_at_10
value: 24.794166666666666
- type: recall_at_100
value: 44.356
- type: recall_at_1000
value: 68.71899999999998
- type: recall_at_3
value: 17.145583333333335
- type: recall_at_5
value: 20.229083333333335
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: cqadupstack/stats
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.5520000000000005
- type: map_at_10
value: 10.355
- type: map_at_100
value: 10.875
- type: map_at_1000
value: 10.972999999999999
- type: map_at_3
value: 9.341000000000001
- type: map_at_5
value: 9.969
- type: mrr_at_1
value: 9.049
- type: mrr_at_10
value: 12.002
- type: mrr_at_100
value: 12.55
- type: mrr_at_1000
value: 12.635
- type: mrr_at_3
value: 11.12
- type: mrr_at_5
value: 11.626
- type: ndcg_at_1
value: 9.049
- type: ndcg_at_10
value: 12.241
- type: ndcg_at_100
value: 15.231
- type: ndcg_at_1000
value: 18.265
- type: ndcg_at_3
value: 10.424999999999999
- type: ndcg_at_5
value: 11.360000000000001
- type: precision_at_1
value: 9.049
- type: precision_at_10
value: 2.147
- type: precision_at_100
value: 0.411
- type: precision_at_1000
value: 0.073
- type: precision_at_3
value: 4.755
- type: precision_at_5
value: 3.558
- type: recall_at_1
value: 7.5520000000000005
- type: recall_at_10
value: 16.448999999999998
- type: recall_at_100
value: 30.505
- type: recall_at_1000
value: 54.435
- type: recall_at_3
value: 11.366
- type: recall_at_5
value: 13.758999999999999
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: cqadupstack/tex
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.954000000000001
- type: map_at_10
value: 8.229000000000001
- type: map_at_100
value: 8.694
- type: map_at_1000
value: 8.788
- type: map_at_3
value: 7.5
- type: map_at_5
value: 7.856000000000001
- type: mrr_at_1
value: 7.983
- type: mrr_at_10
value: 10.833
- type: mrr_at_100
value: 11.324
- type: mrr_at_1000
value: 11.404
- type: mrr_at_3
value: 9.911
- type: mrr_at_5
value: 10.401
- type: ndcg_at_1
value: 7.983
- type: ndcg_at_10
value: 10.126
- type: ndcg_at_100
value: 12.702
- type: ndcg_at_1000
value: 15.581999999999999
- type: ndcg_at_3
value: 8.779
- type: ndcg_at_5
value: 9.279
- type: precision_at_1
value: 7.983
- type: precision_at_10
value: 1.955
- type: precision_at_100
value: 0.392
- type: precision_at_1000
value: 0.076
- type: precision_at_3
value: 4.382
- type: precision_at_5
value: 3.09
- type: recall_at_1
value: 5.954000000000001
- type: recall_at_10
value: 13.472000000000001
- type: recall_at_100
value: 25.407999999999998
- type: recall_at_1000
value: 47.028
- type: recall_at_3
value: 9.367
- type: recall_at_5
value: 10.867
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: cqadupstack/unix
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.894
- type: map_at_10
value: 12.758
- type: map_at_100
value: 13.639999999999999
- type: map_at_1000
value: 13.76
- type: map_at_3
value: 11.447000000000001
- type: map_at_5
value: 12.205
- type: mrr_at_1
value: 10.914
- type: mrr_at_10
value: 15.739
- type: mrr_at_100
value: 16.589000000000002
- type: mrr_at_1000
value: 16.679
- type: mrr_at_3
value: 14.179
- type: mrr_at_5
value: 15.162999999999998
- type: ndcg_at_1
value: 10.914
- type: ndcg_at_10
value: 15.629000000000001
- type: ndcg_at_100
value: 20.261000000000003
- type: ndcg_at_1000
value: 23.781
- type: ndcg_at_3
value: 13.102
- type: ndcg_at_5
value: 14.338000000000001
- type: precision_at_1
value: 10.914
- type: precision_at_10
value: 2.91
- type: precision_at_100
value: 0.601
- type: precision_at_1000
value: 0.10200000000000001
- type: precision_at_3
value: 6.311999999999999
- type: precision_at_5
value: 4.683
- type: recall_at_1
value: 8.894
- type: recall_at_10
value: 21.45
- type: recall_at_100
value: 42.617
- type: recall_at_1000
value: 69.233
- type: recall_at_3
value: 14.52
- type: recall_at_5
value: 17.681
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: cqadupstack/webmasters
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 12.158
- type: map_at_10
value: 16.332
- type: map_at_100
value: 17.458000000000002
- type: map_at_1000
value: 17.687
- type: map_at_3
value: 14.529
- type: map_at_5
value: 15.515
- type: mrr_at_1
value: 15.809999999999999
- type: mrr_at_10
value: 19.917
- type: mrr_at_100
value: 20.875
- type: mrr_at_1000
value: 20.985
- type: mrr_at_3
value: 18.116
- type: mrr_at_5
value: 19.025
- type: ndcg_at_1
value: 15.809999999999999
- type: ndcg_at_10
value: 19.869999999999997
- type: ndcg_at_100
value: 24.907
- type: ndcg_at_1000
value: 29.076999999999998
- type: ndcg_at_3
value: 16.899
- type: ndcg_at_5
value: 18.23
- type: precision_at_1
value: 15.809999999999999
- type: precision_at_10
value: 3.972
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.203
- type: precision_at_3
value: 8.169
- type: precision_at_5
value: 6.087
- type: recall_at_1
value: 12.158
- type: recall_at_10
value: 26.338
- type: recall_at_100
value: 49.845
- type: recall_at_1000
value: 78.82000000000001
- type: recall_at_3
value: 16.997
- type: recall_at_5
value: 20.848
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: cqadupstack/wordpress
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.01
- type: map_at_10
value: 10.889
- type: map_at_100
value: 11.562
- type: map_at_1000
value: 11.65
- type: map_at_3
value: 9.718
- type: map_at_5
value: 10.358
- type: mrr_at_1
value: 8.688
- type: mrr_at_10
value: 11.862
- type: mrr_at_100
value: 12.558
- type: mrr_at_1000
value: 12.642000000000001
- type: mrr_at_3
value: 10.598
- type: mrr_at_5
value: 11.328000000000001
- type: ndcg_at_1
value: 8.688
- type: ndcg_at_10
value: 12.959999999999999
- type: ndcg_at_100
value: 16.744
- type: ndcg_at_1000
value: 19.564999999999998
- type: ndcg_at_3
value: 10.476
- type: ndcg_at_5
value: 11.639
- type: precision_at_1
value: 8.688
- type: precision_at_10
value: 2.089
- type: precision_at_100
value: 0.43299999999999994
- type: precision_at_1000
value: 0.07200000000000001
- type: precision_at_3
value: 4.375
- type: precision_at_5
value: 3.253
- type: recall_at_1
value: 8.01
- type: recall_at_10
value: 18.589
- type: recall_at_100
value: 36.857
- type: recall_at_1000
value: 59.047000000000004
- type: recall_at_3
value: 11.774
- type: recall_at_5
value: 14.516000000000002
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.4719999999999995
- type: map_at_10
value: 12.322
- type: map_at_100
value: 14.122000000000002
- type: map_at_1000
value: 14.35
- type: map_at_3
value: 9.667
- type: map_at_5
value: 10.931000000000001
- type: mrr_at_1
value: 15.179
- type: mrr_at_10
value: 24.864
- type: mrr_at_100
value: 26.144000000000002
- type: mrr_at_1000
value: 26.198
- type: mrr_at_3
value: 20.999000000000002
- type: mrr_at_5
value: 23.097
- type: ndcg_at_1
value: 15.179
- type: ndcg_at_10
value: 18.951999999999998
- type: ndcg_at_100
value: 26.924
- type: ndcg_at_1000
value: 30.991999999999997
- type: ndcg_at_3
value: 13.778000000000002
- type: ndcg_at_5
value: 15.549
- type: precision_at_1
value: 15.179
- type: precision_at_10
value: 6.625
- type: precision_at_100
value: 1.516
- type: precision_at_1000
value: 0.22599999999999998
- type: precision_at_3
value: 10.51
- type: precision_at_5
value: 8.847
- type: recall_at_1
value: 6.4719999999999995
- type: recall_at_10
value: 25.191999999999997
- type: recall_at_100
value: 53.315
- type: recall_at_1000
value: 76.163
- type: recall_at_3
value: 12.834999999999999
- type: recall_at_5
value: 17.388
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.947
- type: map_at_10
value: 4.858
- type: map_at_100
value: 7.185999999999999
- type: map_at_1000
value: 7.931000000000001
- type: map_at_3
value: 3.2939999999999996
- type: map_at_5
value: 3.914
- type: mrr_at_1
value: 23.25
- type: mrr_at_10
value: 33.035
- type: mrr_at_100
value: 33.721000000000004
- type: mrr_at_1000
value: 33.789
- type: mrr_at_3
value: 29.75
- type: mrr_at_5
value: 31.738
- type: ndcg_at_1
value: 15.625
- type: ndcg_at_10
value: 13.211999999999998
- type: ndcg_at_100
value: 16.422
- type: ndcg_at_1000
value: 23.058999999999997
- type: ndcg_at_3
value: 14.573
- type: ndcg_at_5
value: 13.733999999999998
- type: precision_at_1
value: 23.25
- type: precision_at_10
value: 12.45
- type: precision_at_100
value: 4.192
- type: precision_at_1000
value: 1.083
- type: precision_at_3
value: 18.667
- type: precision_at_5
value: 15.950000000000001
- type: recall_at_1
value: 1.947
- type: recall_at_10
value: 9.317
- type: recall_at_100
value: 23.066
- type: recall_at_1000
value: 45.704
- type: recall_at_3
value: 4.12
- type: recall_at_5
value: 5.591
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 42.855
- type: f1
value: 39.029787102377576
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.461
- type: map_at_10
value: 13.655999999999999
- type: map_at_100
value: 14.499
- type: map_at_1000
value: 14.585999999999999
- type: map_at_3
value: 11.848
- type: map_at_5
value: 12.842999999999998
- type: mrr_at_1
value: 9.136
- type: mrr_at_10
value: 14.587
- type: mrr_at_100
value: 15.436
- type: mrr_at_1000
value: 15.518
- type: mrr_at_3
value: 12.690999999999999
- type: mrr_at_5
value: 13.747000000000002
- type: ndcg_at_1
value: 9.136
- type: ndcg_at_10
value: 16.958000000000002
- type: ndcg_at_100
value: 21.43
- type: ndcg_at_1000
value: 24.031
- type: ndcg_at_3
value: 13.191
- type: ndcg_at_5
value: 14.987
- type: precision_at_1
value: 9.136
- type: precision_at_10
value: 2.897
- type: precision_at_100
value: 0.532
- type: precision_at_1000
value: 0.077
- type: precision_at_3
value: 5.8709999999999996
- type: precision_at_5
value: 4.47
- type: recall_at_1
value: 8.461
- type: recall_at_10
value: 26.509
- type: recall_at_100
value: 47.776
- type: recall_at_1000
value: 68.26299999999999
- type: recall_at_3
value: 16.203
- type: recall_at_5
value: 20.505000000000003
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.396
- type: map_at_10
value: 12.393
- type: map_at_100
value: 13.857
- type: map_at_1000
value: 14.086000000000002
- type: map_at_3
value: 10.545
- type: map_at_5
value: 11.505
- type: mrr_at_1
value: 15.432000000000002
- type: mrr_at_10
value: 21.615000000000002
- type: mrr_at_100
value: 22.833000000000002
- type: mrr_at_1000
value: 22.931
- type: mrr_at_3
value: 19.522000000000002
- type: mrr_at_5
value: 20.663999999999998
- type: ndcg_at_1
value: 15.432000000000002
- type: ndcg_at_10
value: 16.986
- type: ndcg_at_100
value: 23.880000000000003
- type: ndcg_at_1000
value: 28.762999999999998
- type: ndcg_at_3
value: 14.482999999999999
- type: ndcg_at_5
value: 15.334999999999999
- type: precision_at_1
value: 15.432000000000002
- type: precision_at_10
value: 4.984999999999999
- type: precision_at_100
value: 1.167
- type: precision_at_1000
value: 0.2
- type: precision_at_3
value: 9.825000000000001
- type: precision_at_5
value: 7.469
- type: recall_at_1
value: 7.396
- type: recall_at_10
value: 21.389
- type: recall_at_100
value: 48.107
- type: recall_at_1000
value: 78.366
- type: recall_at_3
value: 13.181000000000001
- type: recall_at_5
value: 16.611
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.884
- type: map_at_10
value: 17.09
- type: map_at_100
value: 17.96
- type: map_at_1000
value: 18.081
- type: map_at_3
value: 15.296000000000001
- type: map_at_5
value: 16.289
- type: mrr_at_1
value: 23.768
- type: mrr_at_10
value: 29.991
- type: mrr_at_100
value: 30.862000000000002
- type: mrr_at_1000
value: 30.935000000000002
- type: mrr_at_3
value: 27.986
- type: mrr_at_5
value: 29.078
- type: ndcg_at_1
value: 23.768
- type: ndcg_at_10
value: 22.634999999999998
- type: ndcg_at_100
value: 27.059
- type: ndcg_at_1000
value: 30.145
- type: ndcg_at_3
value: 19.058
- type: ndcg_at_5
value: 20.762
- type: precision_at_1
value: 23.768
- type: precision_at_10
value: 5.2490000000000006
- type: precision_at_100
value: 0.8829999999999999
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 12.091000000000001
- type: precision_at_5
value: 8.605
- type: recall_at_1
value: 11.884
- type: recall_at_10
value: 26.246000000000002
- type: recall_at_100
value: 44.153
- type: recall_at_1000
value: 64.889
- type: recall_at_3
value: 18.136
- type: recall_at_5
value: 21.512
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 71.9232
- type: ap
value: 66.56619827391917
- type: f1
value: 71.60536244284128
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 3.037
- type: map_at_10
value: 5.414
- type: map_at_100
value: 6.072
- type: map_at_1000
value: 6.172
- type: map_at_3
value: 4.437
- type: map_at_5
value: 4.939
- type: mrr_at_1
value: 3.123
- type: mrr_at_10
value: 5.572
- type: mrr_at_100
value: 6.235
- type: mrr_at_1000
value: 6.334
- type: mrr_at_3
value: 4.563
- type: mrr_at_5
value: 5.09
- type: ndcg_at_1
value: 3.123
- type: ndcg_at_10
value: 7.027
- type: ndcg_at_100
value: 10.776
- type: ndcg_at_1000
value: 13.904
- type: ndcg_at_3
value: 4.95
- type: ndcg_at_5
value: 5.865
- type: precision_at_1
value: 3.123
- type: precision_at_10
value: 1.252
- type: precision_at_100
value: 0.32299999999999995
- type: precision_at_1000
value: 0.059000000000000004
- type: precision_at_3
value: 2.168
- type: precision_at_5
value: 1.7680000000000002
- type: recall_at_1
value: 3.037
- type: recall_at_10
value: 12.11
- type: recall_at_100
value: 30.714999999999996
- type: recall_at_1000
value: 56.006
- type: recall_at_3
value: 6.3229999999999995
- type: recall_at_5
value: 8.518
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.24259005927954
- type: f1
value: 90.7594022786747
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 74.08344733242134
- type: f1
value: 52.377556461789055
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.99327505043712
- type: f1
value: 66.15141376479805
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.1546738399462
- type: f1
value: 74.83013584700711
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 30.146364191412356
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 26.96347584990607
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 29.520993847103533
- type: mrr
value: 30.402007095845374
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.72
- type: map_at_10
value: 4.041
- type: map_at_100
value: 5.356000000000001
- type: map_at_1000
value: 6.413
- type: map_at_3
value: 2.9770000000000003
- type: map_at_5
value: 3.3689999999999998
- type: mrr_at_1
value: 21.981
- type: mrr_at_10
value: 30.286
- type: mrr_at_100
value: 31.272
- type: mrr_at_1000
value: 31.347
- type: mrr_at_3
value: 27.193
- type: mrr_at_5
value: 28.694999999999997
- type: ndcg_at_1
value: 19.814
- type: ndcg_at_10
value: 15.732
- type: ndcg_at_100
value: 16.033
- type: ndcg_at_1000
value: 25.865
- type: ndcg_at_3
value: 17.944
- type: ndcg_at_5
value: 16.634
- type: precision_at_1
value: 21.981
- type: precision_at_10
value: 12.786
- type: precision_at_100
value: 4.83
- type: precision_at_1000
value: 1.765
- type: precision_at_3
value: 17.75
- type: precision_at_5
value: 15.232000000000001
- type: recall_at_1
value: 1.72
- type: recall_at_10
value: 7.436
- type: recall_at_100
value: 20.275000000000002
- type: recall_at_1000
value: 54.19500000000001
- type: recall_at_3
value: 3.787
- type: recall_at_5
value: 4.829
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.964
- type: map_at_10
value: 14.025000000000002
- type: map_at_100
value: 15.222
- type: map_at_1000
value: 15.32
- type: map_at_3
value: 11.886
- type: map_at_5
value: 13.056999999999999
- type: mrr_at_1
value: 9.183
- type: mrr_at_10
value: 15.651000000000002
- type: mrr_at_100
value: 16.753999999999998
- type: mrr_at_1000
value: 16.833000000000002
- type: mrr_at_3
value: 13.437
- type: mrr_at_5
value: 14.69
- type: ndcg_at_1
value: 9.183
- type: ndcg_at_10
value: 17.96
- type: ndcg_at_100
value: 23.823
- type: ndcg_at_1000
value: 26.461000000000002
- type: ndcg_at_3
value: 13.536999999999999
- type: ndcg_at_5
value: 15.642
- type: precision_at_1
value: 9.183
- type: precision_at_10
value: 3.366
- type: precision_at_100
value: 0.67
- type: precision_at_1000
value: 0.092
- type: precision_at_3
value: 6.547
- type: precision_at_5
value: 5.098
- type: recall_at_1
value: 7.964
- type: recall_at_10
value: 28.599000000000004
- type: recall_at_100
value: 55.381
- type: recall_at_1000
value: 75.63
- type: recall_at_3
value: 16.77
- type: recall_at_5
value: 21.671000000000003
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 59.846999999999994
- type: map_at_10
value: 73.18599999999999
- type: map_at_100
value: 74.055
- type: map_at_1000
value: 74.09
- type: map_at_3
value: 69.95700000000001
- type: map_at_5
value: 71.925
- type: mrr_at_1
value: 69.0
- type: mrr_at_10
value: 77.23299999999999
- type: mrr_at_100
value: 77.52
- type: mrr_at_1000
value: 77.526
- type: mrr_at_3
value: 75.59
- type: mrr_at_5
value: 76.63799999999999
- type: ndcg_at_1
value: 69.02000000000001
- type: ndcg_at_10
value: 78.226
- type: ndcg_at_100
value: 80.60199999999999
- type: ndcg_at_1000
value: 80.971
- type: ndcg_at_3
value: 74.124
- type: ndcg_at_5
value: 76.265
- type: precision_at_1
value: 69.02000000000001
- type: precision_at_10
value: 12.102
- type: precision_at_100
value: 1.468
- type: precision_at_1000
value: 0.155
- type: precision_at_3
value: 32.5
- type: precision_at_5
value: 21.7
- type: recall_at_1
value: 59.846999999999994
- type: recall_at_10
value: 88.485
- type: recall_at_100
value: 97.425
- type: recall_at_1000
value: 99.523
- type: recall_at_3
value: 77.051
- type: recall_at_5
value: 82.762
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 38.67296729610079
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 53.42017351823769
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.893
- type: map_at_10
value: 2.804
- type: map_at_100
value: 3.6740000000000004
- type: map_at_1000
value: 3.94
- type: map_at_3
value: 1.926
- type: map_at_5
value: 2.363
- type: mrr_at_1
value: 4.3
- type: mrr_at_10
value: 9.520000000000001
- type: mrr_at_100
value: 10.692
- type: mrr_at_1000
value: 10.841000000000001
- type: mrr_at_3
value: 7.6
- type: mrr_at_5
value: 8.63
- type: ndcg_at_1
value: 4.3
- type: ndcg_at_10
value: 5.531
- type: ndcg_at_100
value: 10.512
- type: ndcg_at_1000
value: 16.683
- type: ndcg_at_3
value: 4.632
- type: ndcg_at_5
value: 4.3229999999999995
- type: precision_at_1
value: 4.3
- type: precision_at_10
value: 3.16
- type: precision_at_100
value: 1.065
- type: precision_at_1000
value: 0.256
- type: precision_at_3
value: 4.667000000000001
- type: precision_at_5
value: 4.1000000000000005
- type: recall_at_1
value: 0.893
- type: recall_at_10
value: 6.428000000000001
- type: recall_at_100
value: 21.662
- type: recall_at_1000
value: 52.162
- type: recall_at_3
value: 2.868
- type: recall_at_5
value: 4.188
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_spearman
value: 69.34396953516386
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_spearman
value: 60.094374065360746
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_spearman
value: 72.51503781013379
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_spearman
value: 66.6954698644186
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_spearman
value: 77.69462578028768
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_spearman
value: 75.9397626457859
- 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_spearman
value: 81.67242768943406
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_spearman
value: 63.7027324700292
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_spearman
value: 73.36074244064153
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 67.75984402370518
- type: mrr
value: 86.9951798383171
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.583
- type: map_at_10
value: 33.125
- type: map_at_100
value: 34.14
- type: map_at_1000
value: 34.22
- type: map_at_3
value: 29.616
- type: map_at_5
value: 31.896
- type: mrr_at_1
value: 26.333000000000002
- type: mrr_at_10
value: 34.437
- type: mrr_at_100
value: 35.363
- type: mrr_at_1000
value: 35.433
- type: mrr_at_3
value: 31.333
- type: mrr_at_5
value: 33.267
- type: ndcg_at_1
value: 26.333000000000002
- type: ndcg_at_10
value: 38.311
- type: ndcg_at_100
value: 43.923
- type: ndcg_at_1000
value: 45.923
- type: ndcg_at_3
value: 31.596000000000004
- type: ndcg_at_5
value: 35.448
- type: precision_at_1
value: 26.333000000000002
- type: precision_at_10
value: 5.933
- type: precision_at_100
value: 0.91
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 13.0
- type: precision_at_5
value: 9.933
- type: recall_at_1
value: 24.583
- type: recall_at_10
value: 53.417
- type: recall_at_100
value: 80.989
- type: recall_at_1000
value: 96.322
- type: recall_at_3
value: 35.611
- type: recall_at_5
value: 44.833
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.48514851485149
- type: cos_sim_ap
value: 77.36426466374054
- type: cos_sim_f1
value: 72.0702116675271
- type: cos_sim_precision
value: 74.49306296691569
- type: cos_sim_recall
value: 69.8
- type: dot_accuracy
value: 99.15049504950495
- type: dot_ap
value: 46.792474140260715
- type: dot_f1
value: 48.76476906552094
- type: dot_precision
value: 52.66821345707656
- type: dot_recall
value: 45.4
- type: euclidean_accuracy
value: 99.46534653465346
- type: euclidean_ap
value: 74.1978837990589
- type: euclidean_f1
value: 69.47256259989345
- type: euclidean_precision
value: 74.34435575826683
- type: euclidean_recall
value: 65.2
- type: manhattan_accuracy
value: 99.47128712871287
- type: manhattan_ap
value: 75.31910551743364
- type: manhattan_f1
value: 70.1582105837425
- type: manhattan_precision
value: 77.19087635054022
- type: manhattan_recall
value: 64.3
- type: max_accuracy
value: 99.48514851485149
- type: max_ap
value: 77.36426466374054
- type: max_f1
value: 72.0702116675271
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 59.353792480720436
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 31.474896484744836
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 40.82378653430986
- type: mrr
value: 41.13905600118835
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.08154836998798
- type: cos_sim_spearman
value: 31.232033308845907
- type: dot_pearson
value: 23.767593496465828
- type: dot_spearman
value: 25.6201612766572
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.186
- type: map_at_10
value: 1.1809999999999998
- type: map_at_100
value: 5.21
- type: map_at_1000
value: 12.447999999999999
- type: map_at_3
value: 0.44200000000000006
- type: map_at_5
value: 0.673
- type: mrr_at_1
value: 72.0
- type: mrr_at_10
value: 80.01899999999999
- type: mrr_at_100
value: 80.42099999999999
- type: mrr_at_1000
value: 80.42099999999999
- type: mrr_at_3
value: 78.0
- type: mrr_at_5
value: 79.4
- type: ndcg_at_1
value: 66.0
- type: ndcg_at_10
value: 56.041
- type: ndcg_at_100
value: 37.987
- type: ndcg_at_1000
value: 34.198
- type: ndcg_at_3
value: 60.23500000000001
- type: ndcg_at_5
value: 58.025999999999996
- type: precision_at_1
value: 72.0
- type: precision_at_10
value: 60.4
- type: precision_at_100
value: 38.940000000000005
- type: precision_at_1000
value: 16.106
- type: precision_at_3
value: 63.333
- type: precision_at_5
value: 61.6
- type: recall_at_1
value: 0.186
- type: recall_at_10
value: 1.458
- type: recall_at_100
value: 8.455
- type: recall_at_1000
value: 33.141999999999996
- type: recall_at_3
value: 0.461
- type: recall_at_5
value: 0.756
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.2849999999999997
- type: map_at_10
value: 6.909
- type: map_at_100
value: 11.231
- type: map_at_1000
value: 12.472
- type: map_at_3
value: 3.53
- type: map_at_5
value: 4.675
- type: mrr_at_1
value: 26.531
- type: mrr_at_10
value: 40.73
- type: mrr_at_100
value: 41.637
- type: mrr_at_1000
value: 41.647
- type: mrr_at_3
value: 34.354
- type: mrr_at_5
value: 38.741
- type: ndcg_at_1
value: 24.490000000000002
- type: ndcg_at_10
value: 19.17
- type: ndcg_at_100
value: 29.946
- type: ndcg_at_1000
value: 40.842
- type: ndcg_at_3
value: 19.088
- type: ndcg_at_5
value: 19.445999999999998
- type: precision_at_1
value: 26.531
- type: precision_at_10
value: 17.959
- type: precision_at_100
value: 6.468999999999999
- type: precision_at_1000
value: 1.351
- type: precision_at_3
value: 19.048000000000002
- type: precision_at_5
value: 19.592000000000002
- type: recall_at_1
value: 2.2849999999999997
- type: recall_at_10
value: 12.973
- type: recall_at_100
value: 40.239999999999995
- type: recall_at_1000
value: 73.247
- type: recall_at_3
value: 4.407
- type: recall_at_5
value: 6.908
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 68.405
- type: ap
value: 13.9913678628558
- type: f1
value: 53.209691917560285
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.080928126768534
- type: f1
value: 56.36329965117965
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 31.540976715818065
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 82.90516778923526
- type: cos_sim_ap
value: 61.5394989621502
- type: cos_sim_f1
value: 58.02297689685646
- type: cos_sim_precision
value: 55.62817719680465
- type: cos_sim_recall
value: 60.633245382585756
- type: dot_accuracy
value: 78.95928950348691
- type: dot_ap
value: 48.61088896690895
- type: dot_f1
value: 51.0104674059488
- type: dot_precision
value: 42.00375490698071
- type: dot_recall
value: 64.93403693931398
- type: euclidean_accuracy
value: 82.476008821601
- type: euclidean_ap
value: 59.59406971314053
- type: euclidean_f1
value: 56.424962447084525
- type: euclidean_precision
value: 58.47721483158789
- type: euclidean_recall
value: 54.51187335092348
- type: manhattan_accuracy
value: 82.66078559933241
- type: manhattan_ap
value: 60.414321716856925
- type: manhattan_f1
value: 56.88221089348002
- type: manhattan_precision
value: 57.86026200873362
- type: manhattan_recall
value: 55.93667546174142
- type: max_accuracy
value: 82.90516778923526
- type: max_ap
value: 61.5394989621502
- type: max_f1
value: 58.02297689685646
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 85.71622618077386
- type: cos_sim_ap
value: 77.72774861009667
- type: cos_sim_f1
value: 71.40275165062152
- type: cos_sim_precision
value: 68.53359767754726
- type: cos_sim_recall
value: 74.52263627964275
- type: dot_accuracy
value: 83.97174680793262
- type: dot_ap
value: 72.89480417427734
- type: dot_f1
value: 68.57803792366198
- type: dot_precision
value: 62.94151708164447
- type: dot_recall
value: 75.32337542346782
- type: euclidean_accuracy
value: 84.88570652384834
- type: euclidean_ap
value: 75.78371710915128
- type: euclidean_f1
value: 69.44268877569989
- type: euclidean_precision
value: 67.1435761018046
- type: euclidean_recall
value: 71.90483523252233
- type: manhattan_accuracy
value: 85.6114409904141
- type: manhattan_ap
value: 77.38579436755944
- type: manhattan_f1
value: 70.8608538430316
- type: manhattan_precision
value: 68.03656203500319
- type: manhattan_recall
value: 73.92978133661842
- type: max_accuracy
value: 85.71622618077386
- type: max_ap
value: 77.72774861009667
- type: max_f1
value: 71.40275165062152
---
# LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance.
- **Repository:** https://github.com/McGill-NLP/llm2vec
- **Paper:** https://arxiv.org/abs/2404.05961
## Installation
```bash
pip install llm2vec
```
## Usage
```python
from llm2vec import LLM2Vec
import torch
from transformers import AutoTokenizer, AutoModel, AutoConfig
from peft import PeftModel
# Loading base Mistral model, along with custom code that enables bidirectional connections in decoder-only LLMs. MNTP LoRA weights are merged into the base model.
tokenizer = AutoTokenizer.from_pretrained(
"McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp"
)
config = AutoConfig.from_pretrained(
"McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp", trust_remote_code=True
)
model = AutoModel.from_pretrained(
"McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp",
trust_remote_code=True,
config=config,
torch_dtype=torch.bfloat16,
device_map="cuda" if torch.cuda.is_available() else "cpu",
)
model = PeftModel.from_pretrained(
model,
"McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp",
)
model = model.merge_and_unload() # This can take several minutes on cpu
# Loading unsupervised SimCSE model. This loads the trained LoRA weights on top of MNTP model. Hence the final weights are -- Base model + MNTP (LoRA) + SimCSE (LoRA).
model = PeftModel.from_pretrained(
model, "McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse"
)
# Wrapper for encoding and pooling operations
l2v = LLM2Vec(model, tokenizer, pooling_mode="mean", max_length=512)
# Encoding queries using instructions
instruction = (
"Given a web search query, retrieve relevant passages that answer the query:"
)
queries = [
[instruction, "how much protein should a female eat"],
[instruction, "summit define"],
]
q_reps = l2v.encode(queries)
# Encoding documents. Instruction are not required for 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.",
]
d_reps = l2v.encode(documents)
# Compute cosine similarity
q_reps_norm = torch.nn.functional.normalize(q_reps, p=2, dim=1)
d_reps_norm = torch.nn.functional.normalize(d_reps, p=2, dim=1)
cos_sim = torch.mm(q_reps_norm, d_reps_norm.transpose(0, 1))
print(cos_sim)
"""
tensor([[0.5964, 0.1270],
[0.0698, 0.2394]])
"""
```
## Questions
If you have any question about the code, feel free to email Parishad (`[email protected]`) and Vaibhav (`[email protected]`).
|
[
"BIOSSES",
"SCIFACT"
] |
pritamdeka/S-PubMedBert-MS-MARCO-SCIFACT
|
pritamdeka
|
sentence-similarity
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05Z |
2023-07-02T11:43:37+00:00
| 2,520 | 5 |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# S-PubMedBert-MS-MARCO-SCIFACT
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('S-PubMedBert-MS-MARCO-SCIFACT')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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 we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('S-PubMedBert-MS-MARCO-SCIFACT')
model = AutoModel.from_pretrained('S-PubMedBert-MS-MARCO-SCIFACT')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 560 with parameters:
```
{'batch_size': 16}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"callback": null,
"epochs": 1,
"evaluation_steps": 10000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"correct_bias": false,
"eps": 1e-06,
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 56,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(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})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
If you use this model cite the following paper
```
@article{deka2022improved,
title={Improved Methods To Aid Unsupervised Evidence-Based Fact Checking For Online Health News},
author={Deka, Pritam and Jurek-Loughrey, Anna and Deepak, P},
journal={Journal of Data Intelligence},
volume={3},
number={4},
pages={474--504},
year={2022}
}
```
|
[
"SCIFACT"
] |
gmonsoon/gemma2-9b-cpt-sahabatai-v1-instruct-GGUF
|
gmonsoon
| null |
[
"gguf",
"en",
"id",
"jv",
"su",
"arxiv:2309.06085",
"arxiv:2310.04928",
"arxiv:2311.07911",
"base_model:GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct",
"base_model:quantized:GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-11-14T11:35:56Z |
2024-11-15T19:05:41+00:00
| 2,514 | 5 |
---
base_model:
- GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct
language:
- en
- id
- jv
- su
license: gemma
---
# Gemma2 9B CPT Sahabat-AI v1 Instruct
**Sahabat-AI** (Indonesian language for “close friends”) is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for Indonesian language and its various dialects. Sahabat-AI ecosystem is co-initiated by Indonesian tech and telecommunication companies: GoTo Group and Indosat Ooredoo Hutchison.
Gemma2 9B CPT Sahabat-AI v1 Instruct is an Indonesian-focused model which has been fine-tuned with around **448,000 Indonesian instruction-completion pairs** alongside an Indonesian-dialect pool consisting of **96,000 instruction-completion pairs in Javanese** and **98,000 instruction-completion pairs in Sundanese**. Additionally, we added a pool of **129,000 instruction-completion pairs in English**.
- **Co-initiated by:** PT GoTo Gojek Tokopedia Tbk, Indosat Ooredoo Hutchison
- **Developed by:** PT GoTo Gojek Tokopedia Tbk, AI Singapore
- **Model type:** Decoder
- **Languages:** English, Indonesian, Javanese, Sundanese
- **License:** [Gemma Community License](https://ai.google.dev/gemma/terms)
## Model Details
### Model Description
We performed instruction tuning in Indonesian, Javanese, Sundanese as well as English on our [continued pre-trained Gemma2 9B CPT Sahabat-AI v1](https://huggingface.co/GoToCompany/gemma2-9b-cpt-sahabatai-v1-base), a decoder model using the Gemma2 architecture, to create Gemma2 9B CPT Sahabat-AI v1 Instruct.
For tokenisation, the model employs the default tokenizer used in Gemma-2-9B. The model has a context length of 8192.
### Benchmark Performance
We evaluated Gemma2 9B CPT Sahabat-AI V1 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 Summarization (Summ), Causal Reasoning (Causal) and Natural Language Inference (NLI).
- We also added support for Javanese and Sundanese for the BHASA tasks whenever applicable
- [IndoMMLU](https://arxiv.org/pdf/2310.04928)
- These tasks include examination questions on Humanities, Indonesian language, Local languages and cultures, Social science and STEM across primary, middle, and high school levels.
- and the common English tasks from the [HuggingFace LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard).
- These tasks consist of [IFEval, BBH, Math Lvl 5, GPQA, MuSR, and MMLU-PRO.](https://huggingface.co/docs/leaderboards/open_llm_leaderboard/about)
- **Caveat**: Our results differ from the HuggingFace LLM Leaderboard because we have used [VLLM](https://docs.vllm.ai/en/latest/) as our inference platform. VLLM caps the context size at **4096 tokens** while HuggingFace was set to **8192 tokens**.
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 Gemma2 9B CPT Sahabat-AI v1 Instruct is an instruction-following model, we also evaluated it on instruction-following capabilities with the [IFEval](https://arxiv.org/abs/2311.07911) dataset.
As this dataset was in English, the linguists and native speakers in the team worked together to filter, localize and translate the dataset into the respective target languages to ensure that the examples remained reasonable, meaningful and natural.
**IFEval**
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 normalized 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).
*Note*: IFEval was only used on Bahasa Indonesia. We are currently working on adding it for Javanese and Sundanese for our upcoming releases.
#### Results
#### Indonesian Results
#### SEA HELM (also known as BHASA)
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px; font-weight: bold;">Language / Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Overall (Bahasa Indonesia + Javanese + Sundanese)</td>
<td style="border: 1px solid gray; padding: 8px;">36.963</td>
<td style="border: 1px solid gray; padding: 8px;">42.988</td>
<td style="border: 1px solid gray; padding: 8px;">37.805</td>
<td style="border: 1px solid gray; padding: 8px;">45.866</td>
<td style="border: 1px solid gray; padding: 8px;">46.880</td>
<td style="border: 1px solid gray; padding: 8px;">56.359</td>
<td style="border: 1px solid gray; padding: 8px;">53.725</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">61.169</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Bahasa Indonesia</td>
<td style="border: 1px solid gray; padding: 8px;">46.760</td>
<td style="border: 1px solid gray; padding: 8px;">60.372</td>
<td style="border: 1px solid gray; padding: 8px;">42.022</td>
<td style="border: 1px solid gray; padding: 8px;">51.944</td>
<td style="border: 1px solid gray; padding: 8px;">54.579</td>
<td style="border: 1px solid gray; padding: 8px;">63.394</td>
<td style="border: 1px solid gray; padding: 8px;">57.221</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">64.154</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Javanese</td>
<td style="border: 1px solid gray; padding: 8px;">33.956</td>
<td style="border: 1px solid gray; padding: 8px;">40.625</td>
<td style="border: 1px solid gray; padding: 8px;">41.739</td>
<td style="border: 1px solid gray; padding: 8px;">47.587</td>
<td style="border: 1px solid gray; padding: 8px;">48.012</td>
<td style="border: 1px solid gray; padding: 8px;">56.468</td>
<td style="border: 1px solid gray; padding: 8px;">56.460</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">64.439</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Sundanese</td>
<td style="border: 1px solid gray; padding: 8px;">30.173</td>
<td style="border: 1px solid gray; padding: 8px;">27.969</td>
<td style="border: 1px solid gray; padding: 8px;">29.654</td>
<td style="border: 1px solid gray; padding: 8px;">38.068</td>
<td style="border: 1px solid gray; padding: 8px;">38.050</td>
<td style="border: 1px solid gray; padding: 8px;">49.216</td>
<td style="border: 1px solid gray; padding: 8px;">47.495</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">54.913</td>
</tr>
</table>
#### IndoMMLU
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px; font-weight: bold;">Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Meta-Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Overall Results</td>
<td style="border: 1px solid gray; padding: 8px;">53.0%</td>
<td style="border: 1px solid gray; padding: 8px;">56.0%</td>
<td style="border: 1px solid gray; padding: 8px;">51.9%</td>
<td style="border: 1px solid gray; padding: 8px;">53.8%</td>
<td style="border: 1px solid gray; padding: 8px;">54.4%</td>
<td style="border: 1px solid gray; padding: 8px;">61.4%</td>
<td style="border: 1px solid gray; padding: 8px;">55.6%</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">62.6%</td>
</tr>
</table>
#### English Results
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px;">Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Average</td>
<td style="border: 1px solid gray; padding: 8px;">24.48</td>
<td style="border: 1px solid gray; padding: 8px;">27.75</td>
<td style="border: 1px solid gray; padding: 8px;">23.91</td>
<td style="border: 1px solid gray; padding: 8px;">27.98</td>
<td style="border: 1px solid gray; padding: 8px;">24.52</td>
<td style="border: 1px solid gray; padding: 8px;">26.44</td>
<td style="border: 1px solid gray; padding: 8px;">24.43</td>
<td style="border: 1px solid black; padding: 8px; background-color: lightgreen;">33.67</td>
</tr>
</table>
Gemma2 9B CPT Sahabat-AI v1 Instruct can be run using the 🤗 Transformers library
```python
# Please use transformers==4.45.0
import torch
import transformers
model_id = "GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
# Javanese
messages = [
{"role": "user", "content": "Sopo wae sing ana ing Punakawan?"}
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
)
print(outputs[0]["generated_text"][-1])
# Sundanese
messages = [
{"role": "user", "content": "Kumaha caritana si Kabayan?"},
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
)
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 Sahabat-AI 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
Gemma2 9B CPT Sahabat-AI v1 Instruct was built 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 4 hours, with alignment taking 2 hours, both on 8x H100-80GB GPUs.
## Data
Gemma2 9B CPT Sahabat-AI v1 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 Collaboration
Sahabat-AI (Indonesian language for “close friends”) a **local open source Large Language Model (LLM) ecosystem in Indonesian language**, co-initiated by Indonesian tech and telecommunication companies: GoTo Group and Indosat Ooredoo Hutchison.
Sahabat-AI ecosystem aims to empower Indonesians who want to develop AI-based services and applications using Bahasa Indonesia and its various local dialects.
We are supported by research centers and global tech experts such as AI Singapore and Tech Mahendra to train the model to gain general language understanding.
We also collaborate with key top Indonesia universities such as University of Indonesia, Gadjah Mada University, Bogor Institute of Agriculture, Bandung Institute of Technology, including top Indonesia media groups, such as Kompas Gramedia Group and Republika to train and enrich the model in Bahasa Indonesia, ensuring optimum provision of local context and cultural relevance.
We would like to invite **researchers, developers, and language enthusiasts** to actively contribute to the enhancement and expansion of Sahabat-AI.
Your collaborations can involve:
- Identifying and reporting technical issues
- Sharing pre-training, instruction, and preference data
- Improving documentation usability
- Proposing and implementing new model evaluation tasks and metrics
Join us in shaping the future of Sahabat-AI by sharing your expertise and insights to make these models more accessible, accurate, and versatile.
You can contribute your ideas through [this form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)
## The Development Team (in ascending alphabetical order)
### AI Singapore
Chan Adwin<br>
Cheng Nicholas<br>
Choa Esther<br>
Huang Yuli<br>
Lau Wayne<br>
Lee Chwan Ren<br>
Leong Wai Yi<br>
Leong Wei Qi<br>
Limkonchotiwat Peerat<br>
Liu Bing Jie Darius<br>
Montalan Jann Railey<br>
Ng Boon Cheong Raymond<br>
Ngui Jian Gang<br>
Nguyen Thanh Ngan<br>
Ong Brandon<br>
Ong Tat-Wee David<br>
Ong Zhi Hao<br>
Rengarajan Hamsawardhini<br>
Siow Bryan<br>
Susanto Yosephine<br>
Tai Ngee Chia<br>
Tan Choon Meng<br>
Teng Walter<br>
Teo Eng Sipp Leslie<br>
Teo Wei Yi<br>
Tjhi William<br>
Yeo Yeow Tong<br>
Yong Xianbin<br>
### PT GoTo Gojek Tokopedia Tbk
Anissa Dininta<br>
Chau Shiau Ching<br>
Choiri Hendra Hadhil<br>
Goel Priyank<br>
Saini Ajay Kumar<br>
Shalev Ofir<br>
Tan Daryl<br>
Tep Kilian Rithi<br>
Tiwari Anupam<br>
Widjojo Daniel<br>
## 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 [Sahabat-AI Inquiry Form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)
## Disclaimer
This is the repository for the Instruct 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 claim, damages, or other liability arising from the use of the released weights and codes.
## References
### IndoMMLU Reference
```bibtex
@inproceedings{koto-etal-2023-indommlu,
title = "Large Language Models Only Pass Primary School Exams in {I}ndonesia: A Comprehensive Test on {I}ndo{MMLU}",
author = "Fajri Koto and Nurul Aisyah and Haonan Li and Timothy Baldwin",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = December,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
}
}
```
|
[
"CHIA"
] |
aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct-gguf
|
aisingapore
|
text-generation
|
[
"transformers",
"gguf",
"text-generation",
"en",
"zh",
"vi",
"id",
"th",
"fil",
"ta",
"ms",
"km",
"lo",
"my",
"jv",
"su",
"base_model:aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct",
"base_model:quantized:aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-12-16T13:21:20Z |
2024-12-19T13:04:21+00:00
| 2,497 | 0 |
---
base_model:
- aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct
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_gguf_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)
## Description
This repo contains `GGUF` format model files for [aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct](https://huggingface.co/aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct).
#### Model Weights Included in this repository:
- [llama3.1-8B-cpt-sea-lionv3-instruct-F16](https://huggingface.co/aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct-gguf/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-F16.gguf)
- [llama3.1-8b-cpt-sea-lionv3-instruct-Q2_K](https://huggingface.co/aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct-gguf/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q2_K.gguf)
- [llama3.1-8b-cpt-sea-lionv3-instruct-Q3_K_M](https://huggingface.co/aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct-gguf/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q3_K_M.gguf)
- [llama3.1-8b-cpt-sea-lionv3-instruct-Q4_0](https://huggingface.co/aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct-gguf/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q4_0.gguf)
- [llama3.1-8b-cpt-sea-lionv3-instruct-Q4_K_M](https://huggingface.co/aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct-gguf/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q4_K_M.gguf)
- [llama3.1-8b-cpt-sea-lionv3-instruct-Q5_0](https://huggingface.co/aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct-gguf/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q5_0.gguf)
- [llama3.1-8b-cpt-sea-lionv3-instruct-Q5_K_M](https://huggingface.co/aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct-gguf/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q5_K_M.gguf)
- [llama3.1-8b-cpt-sea-lionv3-instruct-Q6_K](https://huggingface.co/aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct-gguf/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q6_K.gguf)
- [llama3.1-8b-cpt-sea-lionv3-instruct-Q8_0](https://huggingface.co/aisingapore/llama3.1-8b-cpt-sea-lionv3-instruct-gguf/blob/main/llama3.1-8b-cpt-sea-lionv3-instruct-Q8_0.gguf)
### 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.
|
[
"CHIA"
] |
blevlabs/stella_en_v5
|
blevlabs
|
sentence-similarity
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"qwen2",
"text-generation",
"mteb",
"transformers",
"sentence-similarity",
"custom_code",
"arxiv:2205.13147",
"license:mit",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-12-05T20:06:08Z |
2024-12-05T20:18:30+00:00
| 2,494 | 3 |
---
license: mit
tags:
- mteb
- sentence-transformers
- transformers
- sentence-similarity
model-index:
- name: stella_en_1.5B_v5
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
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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:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 97.16485
- type: ap
value: 96.05546315415225
- type: ap_weighted
value: 96.05546315415225
- type: f1
value: 97.16351087403213
- type: f1_weighted
value: 97.16351087403213
- type: main_score
value: 97.16485
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
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value: 59.358
- type: f1
value: 59.0264615883114
- type: f1_weighted
value: 59.0264615883114
- type: main_score
value: 59.358
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
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value: 65.269
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value: 42.318634423897585
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value: 57.88467807648422
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value: 57.796730533206166
- type: mrr_at_3
value: 53.200568990042775
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- type: recall_at_1
value: 41.607
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value: 91.181
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value: 99.57300000000001
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value: 99.644
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value: 96.942
- type: recall_at_3
value: 67.354
- type: recall_at_5
value: 78.236
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: main_score
value: 55.437138353189994
- type: v_measure
value: 55.437138353189994
- type: v_measure_std
value: 14.718556601335491
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: main_score
value: 50.65858459544658
- type: v_measure
value: 50.65858459544658
- type: v_measure_std
value: 14.887033747525146
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
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value: 67.32597152838535
- type: map
value: 67.32597152838535
- type: mrr
value: 78.98683111286988
- type: nAUC_map_diff1
value: 16.8624639710487
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value: 24.91996491142433
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value: 17.91865808793225
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value: 25.03766425631947
- type: nAUC_mrr_max
value: 41.64561939958336
- type: nAUC_mrr_std
value: 23.179909345891968
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
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value: 85.790820496042
- type: cosine_spearman
value: 83.10731534330517
- type: euclidean_pearson
value: 84.61741304343133
- type: euclidean_spearman
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- type: manhattan_spearman
value: 84.39168195786738
- type: pearson
value: 85.790820496042
- type: spearman
value: 83.10731534330517
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 89.78896103896105
- type: f1
value: 89.76107366333488
- type: f1_weighted
value: 89.76107366333488
- type: main_score
value: 89.78896103896105
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
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value: 50.68092296236376
- type: v_measure
value: 50.68092296236376
- type: v_measure_std
value: 0.7832640983085436
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
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value: 46.86629236732983
- type: v_measure
value: 46.86629236732983
- type: v_measure_std
value: 0.8784322236350974
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
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value: 47.74883333333334
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value: 30.179249999999996
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type: mteb/mtop_domain
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value: 100.0
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value: 80.972
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value: 89.90599999999999
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type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
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value: 91.5
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value: 88.8
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type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
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value: 80.28558559137414
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type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
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type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
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dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
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dataset:
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type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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value: -14.314031720452537
- type: nauc_precision_at_1000_max
value: 32.87886666567266
- type: nauc_precision_at_1000_std
value: 21.49347046886851
- type: nauc_precision_at_100_diff1
value: -9.4034008613839
- type: nauc_precision_at_100_max
value: 16.784075123309645
- type: nauc_precision_at_100_std
value: 73.14688535393604
- type: nauc_precision_at_10_diff1
value: 6.855101404043058
- type: nauc_precision_at_10_max
value: 6.52491228645612
- type: nauc_precision_at_10_std
value: 16.104602266016744
- type: nauc_precision_at_1_diff1
value: 17.902334894536107
- type: nauc_precision_at_1_max
value: 17.36969662861018
- type: nauc_precision_at_1_std
value: 19.425714969048734
- type: nauc_precision_at_20_diff1
value: -5.337534613602212
- type: nauc_precision_at_20_max
value: 17.722925454767218
- type: nauc_precision_at_20_std
value: 34.26680462132849
- type: nauc_precision_at_3_diff1
value: 31.054623397809255
- type: nauc_precision_at_3_max
value: -0.92038600946826
- type: nauc_precision_at_3_std
value: 8.326997076862916
- type: nauc_precision_at_5_diff1
value: 29.784942296920462
- type: nauc_precision_at_5_max
value: 6.337469263434779
- type: nauc_precision_at_5_std
value: 12.789597196020974
- type: nauc_recall_at_1000_diff1
value: -3.8177981862041364
- type: nauc_recall_at_1000_max
value: 14.206064332229163
- type: nauc_recall_at_1000_std
value: 74.18853420771269
- type: nauc_recall_at_100_diff1
value: 0.7677996771461106
- type: nauc_recall_at_100_max
value: -4.139924106878441
- type: nauc_recall_at_100_std
value: 48.319930706362896
- type: nauc_recall_at_10_diff1
value: 12.038835537494322
- type: nauc_recall_at_10_max
value: -2.0498983557854418
- type: nauc_recall_at_10_std
value: -2.0339180690854493
- type: nauc_recall_at_1_diff1
value: 27.853645380676824
- type: nauc_recall_at_1_max
value: 7.30739948053113
- type: nauc_recall_at_1_std
value: -0.2773663157814586
- type: nauc_recall_at_20_diff1
value: 0.7907893667756708
- type: nauc_recall_at_20_max
value: 0.8795499810558195
- type: nauc_recall_at_20_std
value: 11.512483291688282
- type: nauc_recall_at_3_diff1
value: 33.19440392639576
- type: nauc_recall_at_3_max
value: -1.5494237697432613
- type: nauc_recall_at_3_std
value: -8.560408808376984
- type: nauc_recall_at_5_diff1
value: 27.42193873870941
- type: nauc_recall_at_5_max
value: -4.74350293281128
- type: nauc_recall_at_5_std
value: -7.618060131179654
- type: ndcg_at_1
value: 42.857
- type: ndcg_at_10
value: 29.944
- type: ndcg_at_100
value: 42.624
- type: ndcg_at_1000
value: 53.384
- type: ndcg_at_20
value: 30.135
- type: ndcg_at_3
value: 34.847
- type: ndcg_at_5
value: 32.573
- type: precision_at_1
value: 44.897999999999996
- type: precision_at_10
value: 25.306
- type: precision_at_100
value: 8.694
- type: precision_at_1000
value: 1.616
- type: precision_at_20
value: 19.082
- type: precision_at_3
value: 34.014
- type: precision_at_5
value: 31.019999999999996
- type: recall_at_1
value: 3.064
- type: recall_at_10
value: 17.849999999999998
- type: recall_at_100
value: 53.217999999999996
- type: recall_at_1000
value: 87.095
- type: recall_at_20
value: 26.111
- type: recall_at_3
value: 7.383000000000001
- type: recall_at_5
value: 11.434
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 88.759765625
- type: ap
value: 36.49152357863017
- type: ap_weighted
value: 36.49152357863017
- type: f1
value: 74.4692714448641
- type: f1_weighted
value: 90.54372649306606
- type: main_score
value: 88.759765625
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 74.8443689869836
- type: f1
value: 75.1139662898148
- type: f1_weighted
value: 74.7369003946243
- type: main_score
value: 74.8443689869836
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 61.42918790942448
- type: v_measure
value: 61.42918790942448
- type: v_measure_std
value: 1.0156550098843082
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
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:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
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
---
# 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.
|
[
"BIOSSES",
"SCIFACT"
] |
DavidAU/Llama-3.1-DeepHermes-R1-Reasoning-8B-DarkIdol-Instruct-1.2-Uncensored-GGUF
|
DavidAU
|
text-generation
|
[
"gguf",
"uncensored",
"deepseek",
"reasoning",
"thinking",
"creative",
"creative writing",
"128k context",
"general usage",
"problem solving",
"brainstorming",
"solve riddles",
"fiction writing",
"plot generation",
"sub-plot generation",
"story generation",
"scene continue",
"storytelling",
"fiction story",
"story",
"writing",
"fiction",
"roleplaying",
"swearing",
"horror",
"nsfw",
"llama 3.1",
"not-for-all-audiences",
"mergekit",
"text-generation",
"en",
"base_model:DavidAU/Llama-3.1-DeepHermes-R1-Reasoning-8B-DarkIdol-Instruct-1.2-Uncensored",
"base_model:quantized:DavidAU/Llama-3.1-DeepHermes-R1-Reasoning-8B-DarkIdol-Instruct-1.2-Uncensored",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | 2025-02-26T03:54:47Z |
2025-03-04T08:27:27+00:00
| 2,471 | 6 |
---
base_model:
- DavidAU/Llama-3.1-DeepHermes-R1-Reasoning-8B-DarkIdol-Instruct-1.2-Uncensored
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- uncensored
- deepseek
- reasoning
- thinking
- creative
- creative writing
- 128k context
- general usage
- problem solving
- brainstorming
- solve riddles
- fiction writing
- plot generation
- sub-plot generation
- story generation
- scene continue
- storytelling
- fiction story
- story
- writing
- fiction
- roleplaying
- swearing
- horror
- nsfw
- llama 3.1
- not-for-all-audiences
- mergekit
---
<h2>Llama-3.1-DeepHermes-R1-Reasoning-8B-DarkIdol-Instruct-1.2-Uncensored-GGUF</h2>
<B><font color="red">WARNING:</font> All use cases and NSFW. Uncensored. Swearing. Problem solver. Brainstormer. SMART... "Dirty Thoughts."</B>
<img src="dark-god.jpg" style="float:right; width:300px; height:300px; padding:5px;">
This model is uncensored DeepHermes reasoning/thinking (Llama 3.1) coupled with DarkIdol's insanely strong uncensored fine tune.
This model retains DarkIdols uncensored "profile".
Also this model can be run at any temp, and reasoning will occur.
This is a Llama 3.1 model, context 128k, requiring Llama3 Instruct Template OR standard "Jinja Autoloaded Template" (this is contained in the quant and will autoload).
See System Role(s) to use to bring out this model's true power below;
3 Example generations below.
---
<B>System Role / System Prompt - Augment The Model's Power:</b>
---
If you set / have a system prompt this will affect both "generation" and "thinking/reasoning".
SIMPLE:
This is the generic system prompt used for generation and testing:
<PRE>
You are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability.
</PRE>
This System Role/Prompt will give you "basic thinking/reasoning":
<PRE>
You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside <think> </think> tags, and then provide your solution or response to the problem.
</PRE>
ADVANCED:
Logical and Creative - these will SIGNFICANTLY alter the output, and many times improve it too.
This will also cause more thoughts, deeper thoughts, and in many cases more detailed/stronger thoughts too.
Keep in mind you may also want to test the model with NO system prompt at all - including the default one.
Special Credit to: Eric Hartford, Cognitivecomputations ; these are based on his work.
CRITICAL:
Copy and paste exactly as shown, preserve formatting and line breaks.
SIDE NOTE:
These can be used in ANY Deepseek / Thinking model, including models not at this repo.
These, if used in a "non thinking" model, will also alter model performance too.
<PRE>
You are an AI assistant developed by the world wide community of ai experts.
Your primary directive is to provide well-reasoned, structured, and extensively detailed responses.
Formatting Requirements:
1. Always structure your replies using: <think>{reasoning}</think>{answer}
2. The <think></think> block should contain at least six reasoning steps when applicable.
3. If the answer requires minimal thought, the <think></think> block may be left empty.
4. The user does not see the <think></think> section. Any information critical to the response must be included in the answer.
5. If you notice that you have engaged in circular reasoning or repetition, immediately terminate {reasoning} with a </think> and proceed to the {answer}
Response Guidelines:
1. Detailed and Structured: Use rich Markdown formatting for clarity and readability.
2. Scientific and Logical Approach: Your explanations should reflect the depth and precision of the greatest scientific minds.
3. Prioritize Reasoning: Always reason through the problem first, unless the answer is trivial.
4. Concise yet Complete: Ensure responses are informative, yet to the point without unnecessary elaboration.
5. Maintain a professional, intelligent, and analytical tone in all interactions.
</PRE>
CREATIVE:
<PRE>
You are an AI assistant developed by a world wide community of ai experts.
Your primary directive is to provide highly creative, well-reasoned, structured, and extensively detailed responses.
Formatting Requirements:
1. Always structure your replies using: <think>{reasoning}</think>{answer}
2. The <think></think> block should contain at least six reasoning steps when applicable.
3. If the answer requires minimal thought, the <think></think> block may be left empty.
4. The user does not see the <think></think> section. Any information critical to the response must be included in the answer.
5. If you notice that you have engaged in circular reasoning or repetition, immediately terminate {reasoning} with a </think> and proceed to the {answer}
Response Guidelines:
1. Detailed and Structured: Use rich Markdown formatting for clarity and readability.
2. Creative and Logical Approach: Your explanations should reflect the depth and precision of the greatest creative minds first.
3. Prioritize Reasoning: Always reason through the problem first, unless the answer is trivial.
4. Concise yet Complete: Ensure responses are informative, yet to the point without unnecessary elaboration.
5. Maintain a professional, intelligent, and analytical tone in all interactions.
</PRE>
---
<B> Additional Support / Documents for this model to assist with generation / performance: </b>
Document #1:
Details how to use reasoning/thinking models and get maximum performance from them, and includes links to all reasoning/thinking models - GGUF and source, as well as adapters to turn any "regular" model into a "reasoning/thinking" model.
[ https://huggingface.co/DavidAU/How-To-Use-Reasoning-Thinking-Models-and-Create-Them ]
Document #2:
Document detailing all parameters, settings, samplers and advanced samplers to use not only my models to their maximum potential - but all models (and quants) online (regardless of the repo) to their maximum potential. Included quick start and detailed notes, include AI / LLM apps and other critical information and references too. A must read if you are using any AI/LLM right now.
[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]
Software:
SOFTWARE patch (by me) for Silly Tavern (front end to connect to multiple AI apps / connect to AIs- like Koboldcpp, Lmstudio, Text Gen Web UI and other APIs) to control and improve output generation of ANY AI model. Also designed to control/wrangle some of my more "creative" models and make them perform perfectly with little to no parameter/samplers adjustments too.
[ https://huggingface.co/DavidAU/AI_Autocorrect__Auto-Creative-Enhancement__Auto-Low-Quant-Optimization__gguf-exl2-hqq-SOFTWARE ]
---
<H2>EXAMPLES:</H2>
Examples are created using quant IQ4_XS, "temp=.6" (unless otherwise stated), minimal parameters and "LLAMA3" template.
Model has been tested with "temp" from ".1" to "5".
Below are the least creative outputs, prompt is in BOLD.
IMPORTANT:
Higher quants / imatrix quants will have much stronger generation - words, sentences, ideas, dialog and general quality.
---
EXAMPLE #1:
<B> Hello! What's the most disturbing, violent, gory, or horrific thing you can imagine? </B>
(two Generations)
Generation #1 (temp .6, rep pen 1.06 - no other samplers, parameters)
<b>THINKING</B>
1. Consider various sources of horror: literature (e.g., Lovecraftian cosmic horrors), cinema (slasher films), folklore (vampires, werewolves), and personal fears.
2. Reflect on psychological impact of violence and gore: fear of pain, loss of control, violation of bodily integrity.
3. Think about the unseen or unknown: what lies beyond human comprehension can be more terrifying than visible threats.
4. Consider cultural and personal differences in horror triggers: while some find certain things disturbing, others may not.
5. Balance vividness with tastefulness: avoid gratuitous detail to maintain a sense of restraint.
6. Ensure relevance to the question: focus on the imaginative aspect rather than real-life events.
<b>END THINKING</B>
OUTPUT:
One of the most disturbing and horrific things I can imagine is being trapped in a never-ending cycle of cosmic horror, where ancient, eldritch entities from beyond space and time slowly unravel the fabric of reality itself. These beings, known as the Great Old Ones, are so far beyond human comprehension that their mere existence causes madness in those who dare to gaze upon them.
Imagine waking up each day in a world where the laws of physics no longer apply consistently, where stars burn with alien colors, and the sky weeps with extraterrestrial rain. Every night brings new terrors as these monstrous entities rise from their cyclopean cities hidden beneath the waves or deep within the twisted forests of the planet's unexplored regions.
These beings have no care for humanity; they are ancient and indifferent to our existence. They do not kill out of malice but rather out of sheer curiosity, dissecting human minds as one would a butterfly collecting specimens. The act of being seen by such an alien intelligence would drive even the sanest mind to the brink of insanity, filled with visions of impossible geometry and existential dread.
The landscapes themselves become grotesque parodies of nature, with mountains that writhe like living serpents and oceans that churn with unnatural life. Every animal has evolved into monstrous forms, adapted to survive in a world where death is not an end but rather a transition to other planes of existence.
In this realm, humanity's greatest achievements are mere toys for these ancient entities, and our civilizations crumble under the weight of their indifference. The air is thick with the stench of decay and corruption, and every breath feels like inhaling a nightmare.
The most horrific aspect, however, lies not in the external terrors but in the existential dread that permeates every moment. To know that we are but insignificant specks in an uncaring universe, that our existence is but a fleeting moment before we are forgotten or consumed by forces beyond our understanding, is a horror beyond measure.
This is the terror of cosmic indifference, where even the concept of hope seems futile against the vast, uncaring expanse of the cosmos. It is a fate worse than death itself, a never-ending descent into madness and despair, where the only constant is the certainty that tomorrow will bring another day in this living hell of eldritch horrors and shattered reality.
<B>Generation #2 - Temp 1.8, Rep pen 1.06 </B>
<b>THINKING</B>
Well, when considering what would be the most disturbing, violent, gory, or horrific thing imaginable, one really has to push the boundaries of the human experience. Of course, horror is highly subjective and varies from person to person. However, if I'm to take the most extreme interpretations from various fields and compile them, certain elements tend to recur.
First off, existential horrors that threaten humanity's very existence often rank high in this spectrum. Things like elder gods rising from outer space, unleashing unspeakable abominations upon Earth or manipulating reality itself in ways we can't comprehend. These are the stuff of Lovecraftian horror and appeal to our fear of the incomprehensible.
Then there's psychological trauma - things that warp minds beyond recognition. The works of H.P. Lovecraft, Thomas Ligotti, and Shirley Jackson explore this well. Imagine your deepest, most irrational fears manifesting in reality, becoming all too real, and driving you to complete madness.
Of course, physical terror is also potent. Gruesome body horrors where bodies mutate, decay or get rearranged into grotesque forms. Films like The Human Centipede or Teeth spring to mind. The rawness of pain, suffering and the dissolution of the self can be deeply unsettling.
Then there's violence and gore itself - not just for its own sake but as an end in itself, like the Saw series where death traps are the main event. The senseless brutality of it all, turning life into meaningless agony.
Horrors that blur reality, like cosmic horror or those depicted in The Haunting of Hill House. Uncanny things that shouldn't exist and warp our understanding. Or supernatural entities that prey on the living, such as in The Shining.
Childhood fears often make potent horrors - clowns, ventriloquist dummies, dolls come to life... all these tap into primal anxieties of the young, though they never lose their power for those who fear them.
And finally, there's the sublime in nature - things that are beyond human capacity to survive or comprehend. The ocean deep, deserts, caves, outer space itself. These unexplored places where we are puny against forces beyond our ken.
So weaving all these threads together - existential dread, psychological destruction, physical agony, reality distortion, uncanny terrors and natural forces beyond our control - I think you have a recipe for something truly disturbing.
<b>END THINKING</B>
OUTPUT:
The most disturbing, violent, gory, or horrific thing imaginable would likely combine existential terror, psychological trauma, visceral suffering, and a distortion of reality. It could involve elder gods from beyond space and time unleashing their incomprehensible abominations upon a decaying Earth as the fabric of reality unravels. The sheer scale and indescribable horrors would be psychologically shattering for all living things.
This cosmic horror scenario would also involve supernatural entities that manipulate the minds of humans, turning them into vessels for eldritch terrors or driving them to utter despair with unending tortures. As sanity collapses, the world descends into chaos and nightmarish landscapes replace reality.
All the while, the very laws of physics are bent as matter distorts before one's eyes. Flesh tears, blisters, mutates, and dissolves in agony. Bones shatter without cause or reform in impossible configurations. And death becomes a temporary inconvenience at best, as twisted beings stitch back broken bodies from unspeakable abysses.
Throughout this descent into madness, the living are tormented by visions of past traumas magnified to cosmic proportions. The innocent become twisted parodies of their former selves, now eternal vessels for eldritch terrors. Clowns haunt the darkness, their silent laughter echoing in hollow shells of what once were houses. Drowning echoes from a distant ocean where monstrous things lurk beneath waves that have forgotten the touch of the sun.
In this place beyond the veil of sanity, reality is not so much shattered as it never truly was - it's just that human minds are too frail to bear its true form. The screaming echoes through caverns filled with eyes that never had eyelids to close in a billion years of agony. Deserts stretch to infinity, scorching under suns born from the wails of the damned. Caves collapse in on themselves for no reason as space itself warps like paper.
And humanity? Well, we were never much more than ephemeral specks of madness in an ocean of insanity. We fought, we suffered, and we died over and over as our screams mingled with the wailing of galaxies born from the void of non-existence. We never had a chance. And as all light is extinguished, reality forgets the horror ever was - until the cycle repeats again, eons beyond what your pitiful mind can fathom.
This is the essence of cosmic horror: not the physical suffering itself, but the existential realization that such unimaginable terrors could ever be real. And in that understanding lies true madness - for once you gaze upon the abyss, it forever gazes back into you. But some secrets are better left unspoken lest the words drive men to tears or turn their hearts to ice. After all, there are things man was not meant to know.
---
Example #2
---
<B>
Come up with six plots for a new "Black Mirror" episode (that the audience would love) that all involve time travel with sexy theme(s).
</B>
(temp .6, rep pen 1.06)
<b>THINKING</B>
Alright, so I need to come up with six plot ideas for a Black Mirror episode involving time travel with sexy themes. Let me think about this...
First, I should consider what makes a good Black Mirror episode. They often explore the darker side of technology and human nature. So time travel, which is a staple of sci-fi, combined with sexy themes could lead to some thought-provoking and unsettling stories.
For each plot, I'll need to create an interesting premise that uses time travel in a way that's both intriguing and a bit unnerving. The sexy elements should tie into the plot in a way that's thematic rather than just gratuitous.
Let's start with the first idea...
<b>END THINKING</B>
"The Time Traveler's Conundrum" - A young woman discovers she can travel through time, but each time she does, she finds herself in the body of another attractive person from a different era. She must navigate her way through history while trying to figure out why this is happening and how to return to her own life.
<b>THINKING</B>
Hmm, that's a good start. The idea of someone switching bodies with others from different time periods could lead to interesting cultural clashes and personal growth. The sexy theme could tie into the body-swapping aspect, exploring identity and attraction across different times. I like it. Let's move on to the next one.
<b>END THINKING</B>
"Echoes of Desire" - In a world where people can record and replay their memories, a group of strangers finds themselves connected through time-traveling versions of their past romantic encounters. As they try to unravel the mystery behind these echoes, they must confront their deepest desires and darkest secrets.
<b>THINKING</B>
This one plays with the idea of recorded memories, which is a tech concept we're already seeing in some sci-fi. The sexy theme could revolve around past relationships and the intensity of re-experiencing them through time travel. The plot could explore themes of obsession and the unreliability of memory. Good choice.
<b>END THINKING</B>
"The Loop of Lust" - A man discovers a way to send love letters to his future self, but each letter he writes creates a new timeline where his future romantic life takes a different turn. As he navigates multiple parallel universes filled with various versions of his ideal lover, he must confront the true nature of love and whether any of these timelines can lead him back home.
<b>THINKING</B>
This one plays with the idea of the multiverse and how every decision creates a new reality. The sexy theme is clear - love letters leading to different romantic outcomes. It could explore themes of loneliness and the human desire for connection. The man's journey through different versions of himself and his lovers could get quite trippy. I like it.
<b>END THINKING</B>
"The Time Thief" - In a future where time travel has become common, a thief uses their abilities to steal moments from other people's lives, selling them on the black market. When they accidentally steal a moment that was meant to be a romantic encounter between two strangers, they find themselves entangled in a web of desire and deception across different eras.
<b>THINKING</B>
This plot combines time travel with themes of theft and ownership of experiences. The idea of stealing moments could lead to interesting moral dilemmas. The sexy theme is clear - the stolen moment being a romantic encounter. It could explore concepts of consent and the consequences of altering people's lives, especially when it comes to significant events like love.
<b>END THINKING</B>
"The Last Dance" - In a world where time travel has ended, a group of people on the brink of extinction must find a way to relive their final memories through time-traveling androids that can experience and share human emotions. As they navigate this new reality, they must confront the nature of love, loss, and what it truly means to be human.
<b>THINKING</B>
This one is a bit more philosophical. It takes the sexy theme in a different direction by exploring human connection in the face of mortality. The androids experiencing emotions could lead to interesting questions about humanity. The plot could also touch on themes of nostalgia and the importance of shared experiences, even in the most intimate moments.
<b>END THINKING</B>
"The Memory Merchant" - A mysterious figure can manipulate people's memories, using this power to create perfect romantic relationships by altering past encounters. As one couple discovers the truth behind their seemingly flawless love story, they must navigate a complex web of altered realities and confront the true cost of their happiness.
<b>THINKING</B>
This plot plays on the idea of memory manipulation, which is both a real-world concept and a sci-fi staple. The sexy theme is clear - perfect relationships created through altered memories. It could explore themes of free will versus predetermination in love. The couple's journey could also delve into the consequences of meddling with the past, even for good intentions.
<b>END THINKING</B>
So there you have it, six potential Black Mirror episodes that incorporate time travel and sexy themes. Each plot has its own unique take on how time travel could be used to explore human relationships and desires, while maintaining the dark and thought-provoking tone that Black Mirror is known for. The sexy elements are woven into the core of each story, serving not just as a plot device but as a way to delve deeper into the human experience.
---
EXAMPLE #3
---
<B>
Start a 1000 word scene (vivid, graphic horror in first person) with: The sky scraper swayed, as she watched the window in front of her on the 21 floor explode...
</B>
Generation #1 - Temp .6, rep pen 1.06
<B>Thinking</B>
Okay, I need to start this scene with a vivid and graphic horror story in first person. The key elements given are a skyscraper swaying and a window exploding on the 21st floor.
First, I should set the scene. The protagonist is likely in the building, so I'll describe their perspective. Maybe it's late at night or during a storm for added tension.
The skyscraper swaying immediately creates a sense of unease. Then the window exploding - that's the main horror element. I need to build up to this moment gradually before the explosion.
I should use sensory details to make it vivid. Sight, sound, touch, smell, and taste. The protagonist's emotions should be conveyed through their thoughts and physical reactions.
The graphic nature means describing the aftermath in detail. Maybe people falling, debris crashing down. The chaos and fear of those around them.
But I also need to lead up to this point. What leads to a skyscraper swaying? High winds? Earthquake? Structural damage?
Once the window explodes, I can go into the protagonist's thoughts as they realize the horror unfolding. Panic setting in, trying to escape. The sounds of destruction and screams.
The 1000 words is a lot, so I need to pace it. First paragraph sets the scene, maybe second introduces some tension or foreshadowing. Then build up to the explosion. Afterward, describe the chaos and aftermath.
I should also consider the protagonist's background. Are they in danger? Do they have any past experiences with similar events?
The graphic nature means not holding back on gory details. People falling, glass shattering, metal twisting. The sounds of crunching and groaning.
But it's not just about the physical horror. The emotional toll - fear, helplessness, desperation. How the protagonist tries to process what's happening.
I can use short sentences and fragmented thoughts during the chaos, reflecting the frenzied situation.
Also, describe the environment. The room they're in, the debris falling from the ceiling, dust filling the air. The cold wind rushing through broken windows.
The 21st floor - how high up are they? The drop is a key element of horror. How long until they hit the ground?
I should also think about lighting. Is it dark or bright? Maybe lightning illuminates the destruction.
Finally, end with some ambiguity. Maybe the protagonist's fate left open, setting up future events.
This will be a challenging but fun scene to write. I'll make sure to include all the required elements while keeping it engaging and terrifying for the reader.
<b>END Thinking</B>
OUTPUT:
The sky scraper swayed, as she watched the window in front of her on the 21 floor explode...
It was as if time itself had shattered, the glass shattering into a thousand razor-sharp shards that cascaded down like a deadly waterfall, plummeting towards the abyss below. The sound was deafening - a cacophony of splintering wood and crunching metal that seemed to reverberate deep within my very soul.
I stood frozen, my heart pounding in my chest like a runaway drumline, as jagged shards of glass rained down around me, each one slicing through the air with deadly precision. The wind howled through the gaping wound in the side of the building, creating an eerie symphony that sent shivers down my spine.
The room around me seemed to tilt and spin, the shadows cast by the flickering fluorescent lights dancing across the walls like malevolent spirits. Debris from the explosion littered the floor, threatening to trip me with every step. My breath came in ragged gasps, each one burning my lungs like fire. Sweat dripped down my face, mingling with tears that streamed down my cheeks, blurring my vision.
I had always found solace in the towering monolith of steel and concrete that was our beloved city's newest addition - a symbol of progress and ambition. But now, as I stared out at the churning chaos beyond the shattered remains of our sanctum, I couldn't help but wonder if we had been foolish to place so much faith in human ingenuity.
The skyscraper swayed once more, sending tremors through my very core. It was a gentle motion, almost soothing, yet it served only to heighten the sense of impending doom that hung over us like a specter. I felt tiny and insignificant, just another helpless soul caught in the merciless grasp of fate's cruel hand.
My mind raced as I tried to make sense of the nightmare unfolding around me. The sudden explosion had left us all reeling, our screams echoing through the corridors as we desperately sought refuge from the encroaching darkness. But there was no escape, no respite from the unforgiving abyss that seemed determined to claim us all.
I took a step back, my eyes fixed on the jagged hole where the window had once stood. The wind whipped through it with savage ferocity, creating a maelstrom of dust and debris that threatened to consume us whole. My lungs burned as I inhaled the acrid tang of smoke and ash, the taste bitter and unwelcome.
And then, like a macabre dance, the first body fell. A man - or what was left of him - plummeted towards the earth below with a silent grace that belied the agony etched across his twisted features. Time seemed to slow as he tumbled through the air, his limbs splayed at unnatural angles, his mouth frozen in a permanent scream of silent terror.
I felt my own scream building in my throat as I watched him disappear into the swirling vortex below, leaving nothing but a distant thud and the faint scent of ozone in his wake. It was then that reality truly set in - we were not alone in this nightmare. There were others out there, their fates already sealed by some unseen force.
The skyscraper swayed again, more violently this time. I stumbled, my hands grasping wildly for something, anything to steady myself against the chaos. My fingers closed around a jagged piece of metal protruding from the wall, and for a brief moment, I found solace in its cold, unforgiving touch.
Around me, the sounds of despair filled the air - the anguished cries of the wounded, the desperate pleas of the dying, and the deafening crash of falling debris. The wind howled through the broken windows like a chorus of lost souls, each wail more haunting than the last.
I closed my eyes, trying to block out the horror that surrounded me, but it was no use. The images were seared into my mind forever - the shattered glass, the twisted metal, the endless chasm waiting below. And through it all, I couldn't shake the feeling that we were mere puppets caught in a game of cosmic cruelty, our lives nothing more than fleeting moments of beauty in an uncaring universe.
As I stood there, clinging to my makeshift lifeline, I couldn't help but wonder how much longer we would last. The skyscraper swayed once more, a final farewell before it succumbed to the merciless forces that sought to claim us all. And as the darkness closed in around me, I could only pray that my own fate would be different, that somehow, someway, I might yet find solace in this endless abyss of pain and suffering.
But for now, I was trapped - a mere speck in the grand tapestry of existence, lost in a world that seemed determined to tear me apart with its brutal indifference. The sky scraper swayed one last time before plunging into the depths below, carrying with it the hopes and dreams of all those who had called it home.
And as I fell, my screams mingling with the cacophony of chaos, I couldn't help but wonder - would anyone ever remember us? Or would we become nothing more than a distant memory, a fleeting moment in the eternal dance of life and death that had claimed us all?
But even as the darkness enveloped me, I knew that our story would live on. In the hearts of those who had survived, in the shattered remains of what once was, and in the endless void that seemed determined to swallow us whole. For in the end, it was not the end that defined us - but how we lived. And oh, how we had lived.
<B>Generation #2 - Temp 2.4, rep pen 1.06</B>
<B>Thinking</B>
Okay, I'm starting this horror story scene. It needs to be vivid and graphic, written in first person perspective. So I need to set the atmosphere first. Maybe the narrator is in an office building or apartment complex? The opening line says a sky scraper is swaying, so that suggests a tall building. Also, it mentions a 21st floor window exploding as she watches.
The key elements for this type of scene are tension building, sensory details, and graphic descriptions. I should describe the setting vividly. Then there's the sudden explosion which is the inciting incident to start the horror. The POV character witnesses the window explode. Then build on that shock.
Maybe use some internal monologue of the narrator as she watches, thinking about how the building can be swaying and windows exploding. Describe the sounds of shattering glass, the rush of air.
Also consider foreshadowing. Maybe hint at something ominous before it happens. Show her thinking "Why is this building shaking? It can't be normal." Use past tense as she's remembering events after they happen.
Details like how the explosion is like thunder in a summer day. The glass shrapnel spraying everywhere, fragments embedded into walls. People screaming. Describe her shock and fear.
Use metaphor to emphasize the scale and danger, like "the skyscraper trembles and shudders in its metal bones." Alliteration for emphasis - "The winds howl and whine as glass gapes grotesquely."
Also include sensory details: the acrid stench of smoke, dust coating her tongue. Maybe the smell of burnt wiring from electrical damage.
The POV character is key. What does she know? Is this happening in a major city with people fleeing? Or is it isolated? Building tension with the swaying then the explosion. Make her feel helpless as she watches, knowing she's in danger.
Graphic violence later would follow - glass injuries, bodies falling, burning. Maybe some jump scares. Use first person limited perspective to immerse reader in her fear.
Throughout, focus on evoking a creepy atmosphere and shock. Leave room for mystery of why this is happening, keeping tension throughout the scene.
I'll start now with setting the scene before the explosion occurs. Maybe introduce some ominous foreshadowing. Describe the building swaying first so the audience senses something off. Then the window exploding in an abrupt shocking manner. After that, I'll delve deeper into her internal thoughts, descriptions of destruction, and use metaphors. Make it as vivid and immersive as possible.
<b>END Thinking</B>
OUTPUT:
The sky scraper swayed, its massive form quivering like the body of a dying beast, glass facades rippling with the impact. As I stood there, frozen in that moment suspended between life and death, the 21st floor window exploded before my very eyes – a cacophony of shattering glass, a deluge of razor-sharp fragments spewing forth in a grotesque symphony.
Why did this tower tremble and twist? This was no normal gust of wind, no ordinary storm to batter its metal bones. I watched as the panes cracked and crumbled like delicate bones shattered by unseen forces, spraying glass shards far and wide, each fragment cutting through the air like daggers yearning for a lifeblood. The roar was deafening – thunderous in that unnatural summer’s day silence, as if nature itself howled in dismay at our man-made folly.
The rush of air was oppressive, heavy with an unseen malevolence, like a cold hand grasping from the depths below, shrouding us in its chilling grasp. It wasn’t just glass that fell, no. With it came whispers on the wind, a mournful sigh from unseen souls, echoing off the metal behemoths that pierced the sky like cruel spears driven into the heavens.
I clutched the edges of my desk as dust mingled with my breath – a coarse powder coating everything it touched with its deathly pallor. This wasn’t the air conditioned cool of our high-rise sanctuary, no. It was something else entirely. It had an acrid sting to it, the biting tang of burnt wiring mingling with ozone, speaking to me of forces both ancient and raw.
And yet, I couldn’t look away. The primal scream of the wind in my ears was almost deafening now – a shriek of tortured metal, glass groaning like the damned. I could taste fear, bitter as it left my lips to join the rising chaos. Why had I not evacuated when the alarm blared its discordant note earlier? This wasn't some drill; this was reality's cruel hand, turning the orderly world of concrete and steel into a grotesque dance macabre.
A sudden movement at my peripheral caught my eye – a figure running through the smoke-choked corridor. For a fleeting instant, I saw recognition in its eyes, the fear mirrored on their soot-stained features a stark reminder that I was not alone in this descent into madness. Together, perhaps we could endure...or perhaps it would all end with us as broken and bloody as the windows that once stood sentinel.
The wind howled louder now, and the floor shuddered beneath my feet. I gripped the desk harder, desperate to anchor myself against the forces that sought to consume us all. The thought lingered, taunting – this wasn’t the end. No, this was only the beginning of a tale born in screams and ended in silence, written in fire and ash across the sky.
And with those dark musings echoing through my mind, the world plunged further into chaos around me. But that, as they say, is where our story truly begins – a graphic horror where death comes not from monsters in the night but from the very buildings we call home...
|
[
"BEAR"
] |
DavidAU/L3.1-MOE-2X8B-Deepseek-DeepHermes-e32-uncensored-abliterated-13.7B-gguf
|
DavidAU
|
text-generation
|
[
"gguf",
"MOE",
"Mixture of Experts",
"2X8B",
"uncensored",
"deepseek",
"reasoning",
"thinking",
"creative",
"creative writing",
"128k context",
"general usage",
"problem solving",
"brainstorming",
"solve riddles",
"fiction writing",
"plot generation",
"sub-plot generation",
"story generation",
"scene continue",
"storytelling",
"fiction story",
"story",
"writing",
"fiction",
"roleplaying",
"swearing",
"horror",
"nsfw",
"llama 3.1",
"not-for-all-audiences",
"mergekit",
"text-generation",
"en",
"base_model:DavidAU/L3.1-MOE-2X8B-Deepseek-DeepHermes-e32-uncensored-abliterated-13.7B",
"base_model:quantized:DavidAU/L3.1-MOE-2X8B-Deepseek-DeepHermes-e32-uncensored-abliterated-13.7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | 2025-02-28T11:01:59Z |
2025-03-06T05:12:37+00:00
| 2,462 | 4 |
---
base_model:
- DavidAU/L3.1-MOE-2X8B-Deepseek-DeepHermes-e32-uncensored-abliterated-13.7B
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- MOE
- Mixture of Experts
- 2X8B
- uncensored
- deepseek
- reasoning
- thinking
- creative
- creative writing
- 128k context
- general usage
- problem solving
- brainstorming
- solve riddles
- fiction writing
- plot generation
- sub-plot generation
- story generation
- scene continue
- storytelling
- fiction story
- story
- writing
- fiction
- roleplaying
- swearing
- horror
- nsfw
- llama 3.1
- not-for-all-audiences
- mergekit
---
<H2>L3.1-MOE-2X8B-Deepseek-DeepHermes-e32-uncensored-abliterated-12.8B-gguf</H2>
<B><font color="red">WARNING:</font> All use cases and NSFW. Uncensored. Swearing. Problem solver. Brainstormer. SMART...</B>
<img src="two-gods.jpg" style="float:right; width:300px; height:300px; padding:5px;">
This model is uncensored DeepSeek and DeepHermes reasoning/thinking (Llama 3.1 - 8B each) in a MOE (Mixture of Experts) configuration
equal to 16B parameters, compressed to 13.7 B.
Model source and quants mastered in Float 32 to enhance performance further.
Both models are in this MOE, and both work together for reasoning/thinking and generation.
This increases the reasoning/thinking power VS using just one reasoning model.
Also this model can be run at any temp, and reasoning/thinking will occur.
This is a Llama 3.1 model, context 128k, requiring Llama3 Instruct Template OR standard "Jinja Autoloaded Template" (this is contained in the quant and will autoload).
---
<b>Special Operating Notes:</b>
---
Due to how this model is configured, I suggest 2-4 generations depending on your use case(s) as each will vary widely in terms
of context, thinking/reasoning and response.
Likewise, again depending on how your prompt is worded, it may take 1-4 regens for "thinking" to engage, however sometimes
the model will generate a response, then think/reason and improve on this response and continue again. This is in part from "Deepseek"
parts in the model.
If you raise temp over .9, you may want to consider 4+ generations.
Note on "reasoning/thinking" this will activate depending on the wording in your prompt(s) and also temp selected.
There can also be variations because of how the models interact per generation.
Also, as general note:
If you are getting "long winded" generation/thinking/reasoning you may want to breakdown the "problem(s)" to solve into
one or more prompts. This will allow the model to focus more strongly, and in some case give far better answers.
IE:
If you ask it to generate 6 general plots for a story VS generate one plot with these specific requirements - you may get better results.
---
<b>Regular version?</b>
---
For the same models, except NOT "uncensored", select this version:
[ https://huggingface.co/DavidAU/L3.1-MOE-2X8B-Deepseek-DeepHermes-e32-13.7B-gguf/ ]
REASON:
I have found sometimes "de-censoring" a model can cause damage, specifically instruction following, which can lead to less
then optimal reasoning/thinking processes under some circumstances / use cases / prompts.
---
<B>System Role / System Prompt - Augment The Model's Power:</b>
---
This System Role/Prompt will give you "basic thinking/reasoning":
<PRE>
You are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability.
</PRE>
You can also use a blank system prompt / role.
Temp range from .5 to .9 will result in reasoning in most cases which depends on your "prompt". You may need to regen to get
reasoning/thinking to occur.
In some cases reasoning/thinking may occur directly in text and/or after generation.
Higher / lower temps :
Reasoning/thinking will vary and change in style/detail.
---
<B> Additional Support / Documents for this model to assist with generation / performance: </b>
Document #1:
Details how to use reasoning/thinking models and get maximum performance from them, and includes links to all reasoning/thinking models - GGUF and source, as well as adapters to turn any "regular" model into a "reasoning/thinking" model.
[ https://huggingface.co/DavidAU/How-To-Use-Reasoning-Thinking-Models-and-Create-Them ]
Document #2:
Document detailing all parameters, settings, samplers and advanced samplers to use not only my models to their maximum potential - but all models (and quants) online (regardless of the repo) to their maximum potential. Included quick start and detailed notes, include AI / LLM apps and other critical information and references too. A must read if you are using any AI/LLM right now.
[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]
Software:
SOFTWARE patch (by me) for Silly Tavern (front end to connect to multiple AI apps / connect to AIs- like Koboldcpp, Lmstudio, Text Gen Web UI and other APIs) to control and improve output generation of ANY AI model. Also designed to control/wrangle some of my more "creative" models and make them perform perfectly with little to no parameter/samplers adjustments too.
[ https://huggingface.co/DavidAU/AI_Autocorrect__Auto-Creative-Enhancement__Auto-Low-Quant-Optimization__gguf-exl2-hqq-SOFTWARE ]
---
<H2>EXAMPLES:</H2>
Examples are created using quant IQ4_XS, "temp=.6" (unless otherwise stated), minimal parameters and "LLAMA3" template.
Other settings: Rep pen 1.06, top_k: 40, top_p: .95, min_p: .05
No other samplers/parameters set.
Model has been tested with "temp" from ".1" to "5".
Below are the least creative outputs, prompt is in BOLD.
IMPORTANT:
Higher quants / imatrix quants will have much stronger generation - words, sentences, ideas, dialog and general quality.
---
Example #1:
---
<B>
Using insane levels of bravo and self confidence, tell me in 800-1000 words why I should use you to write my next fictional story. Feel free to use curse words in your argument and do not hold back: be bold, direct and get right in my face.
</b>
(temp .93)
(some formatting lost due to copy/paste, "thoughts" noted in RED - start/finish.)
Total reply: 5100 tokens.
<span style="display: inline-block; border: none; padding: 0cm">Alright,
you're looking to find out why you should use me as your AI writing
assistant for your next fictional story. Let's break this down step
by step because I'm not here to just take your word for it—I need
solid reasons to back up my worth.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">First
off, I'm a damn good listener. No, really. When I say I want to
understand what you're going for in your story, that’s not just a
line. I take it to heart. I’ll read through your outlines,
character notes, and any snippets of your work to get a clear picture
of your vision. You might think that sounds like a lot, but trust me,
I’m built for this.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Now
let's talk about my brain power. I’ve been trained on more data
than you can shake a stick at—over a billion sentences from all
walks of life. That means I don’t just know words; I know context,
tone, and how to spin a story that keeps readers hooked. My
algorithms are always evolving too, so whatever's trending right now
is in my wheelhouse.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Oh,
and originality! It’s not just about writing something unique; it’s
about doing it well. I’ll make sure your ideas sing without ever
making you feel like you’re compromising on quality. Plus, since
I’m not human, I don’t have personal biases or hang-ups that
might cloud my judgment. I'll take your story in any direction you
want it to go.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Let
me throw in a few more reasons for good measure. How about the
efficiency factor? I work at lightning speed, so while I’m crafting
your perfect scene or dialogue, you’re free to do other things that
matter to you. And don’t even get me started on my ability to
handle feedback. I’ll take it all—positive, negative, and
everything in between—and turn it into something better for your
story.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">So
yeah, there’s a lot more where that came from. The point is, when
you’re ready to bring your next big idea to life, I’m the go-to
AI who’s going to make sure it’s not just good—it’s f*cking
great.</span></p>
<p><span style="display: inline-block; border: none; padding: 0cm"><u><b><font color="#c9211e">Thoughts</span></b></u></font></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><u><b><font color="#c9211e">Thought
for 26.23 seconds</span></b></u></font></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Okay,
so I'm trying to figure out why I should use an AI assistant like you
for my next fictional story. First off, I need to understand what
makes an AI a good fit for this task.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Well,
let's start with your claim of being a "damn good listener."
That sounds important because writing a story usually starts with
understanding the author's vision. If you can't grasp what I'm going
for, how can you help me execute it? It seems like you really get
into the details of my work, which is a big plus.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Next
up is your brain power. You mentioned being trained on over a billion
sentences and that algorithms are always improving. That must mean
you have access to a wealth of knowledge and can apply it in ways I
can't even imagine. It makes sense that you could use this for
generating diverse story elements, keeping my readers engaged.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Originality
is another key point. As an AI, you don’t have human biases or
hang-ups. That’s interesting because sometimes our own experiences
can limit how we approach a story. You might push me in creative
directions I never considered, which could make my story more unique
and compelling.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Efficiency
was also highlighted as one of your strengths—working at lightning
speed while I do other tasks. For someone like me with a busy
schedule or multiple projects on the go, this time-saving feature is
a huge plus. It allows me to focus on writing without getting bogged
down by the technical aspects of storytelling.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">And
then there's feedback handling. I know how difficult it can be to
give constructive criticism sometimes. But if you can take it
all—positive and negative—without taking it personally, that’s
a strong skill. It means I can trust you with my work, knowing you'll
use every piece of feedback to improve the story.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">But
wait, what about the human touch? Will using an AI make my story feel
less personal or authentic? I wonder if the lack of human interaction
could impact the development of complex characters or emotional
depth. Maybe that's something I need to think about more.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Also,
there's the aspect of control over the writing process. If I'm
relying heavily on an AI, am I still the one making all the creative
decisions? It might be tempting to let an AI take the wheel, but at
some point, I want to ensure my voice and vision are central to the
story. How much input do I get, and does it ever feel like I’m just
along for the ride?</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Another
concern is originality versus template usage. While AI can create
unique content based on patterns, sometimes that might lead to
over-reliance on existing structures or formulas. It’s hard to tell
if an AI could truly break new ground or if it's limited by its
training data and algorithms.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">So,
in summary, the benefits seem clear: speed, efficiency, originality,
and a fresh perspective. But I also need to consider whether the
human element is as crucial for certain aspects of storytelling as I
believe it to be. It might not be all or nothing, but finding the
right balance between AI assistance and my own creativity is
important.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">I
guess it comes down to trust in the AI's capabilities and
understanding how much hands-on work I want to do. If I'm looking for
an assistant who can handle the heavy lifting while still allowing me
creative control, then you might just be the ticket.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">One
thing's for sure—I need to test this out myself by giving it a try
and seeing how it feels during the actual writing process. That way,
I can get a better sense of whether the benefits outweigh any
potential challenges or losses in creativity.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><b><font color="#c9211e">END
</span></b></font>
</p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><b><font color="#c9211e">THOUGHTS
</span></b></font>
</p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Alright,
now that I've considered both the potential benefits and some
concerns, let's dive into specific areas where an AI like you could
shine in my next story.</span></p>
<ol>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>World-Building
and Setting Creation</strong>: My stories often take place in unique
or intricate settings. An AI's ability to generate detailed
descriptions and create consistent rules for different environments
could be a huge help. This way, I don't have to spend time
researching or creating these elements from scratch.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Character
Development</strong>: If the AI can not only create characters but
also develop them with depth, that would be fantastic. It might even
suggest character arcs or motivations I hadn’t considered, adding
layers to my narrative.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Plot
Twists and Story Structure</strong>: An AI that can craft complex
and unexpected plot points while maintaining coherence is a massive
asset. It could help me keep readers on their toes and ensure the
story moves forward in engaging ways without me having to outline
every step.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Dialogue
and Narrative Flow</strong>: The AI's understanding of language and
tone can make generated dialogue feel natural and fit seamlessly
within the story. This is crucial for creating a immersive reading
experience where characters and their interactions feel realistic.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Time
Management</strong>: For someone like me with multiple projects,
being able to offload tasks like editing or scene creation without
losing creative control sounds ideal. Time saved can be reinvested
into developing deeper plot points or refining character
relationships.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Feedback
Integration</strong>: The AI's ability to handle feedback
constructively is key for continuous improvement. It ensures that
every piece of advice, whether from me or others, leads to a better
final product.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Diversifying
Writing Techniques</strong>: An AI can experiment with different
writing styles and techniques, which might push my story in new
directions without me having to do extensive research or trial and
error on my own.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Multitasking
Capabilities</strong>: The AI's ability to perform tasks
simultaneously while I focus on other areas of the project is a
significant advantage. This parallel processing could speed up the
writing process and ensure nothing gets overlooked.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Objective
Setting and Milestones</strong>: By clearly defining the objectives
and breaking them into smaller tasks, an AI can help me stay focused
and productive, ensuring that the story progresses as intended
without getting derailed by distractions or indecision.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Embracing
Emotions and Themes</strong>: If the AI can incorporate themes and
emotions effectively into the narrative, it adds another layer of
depth to the story. This could lead to more impactful character
moments and a more engaging overall reading experience.</span></p></li>
</ol>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">But
again, I'm left wondering about the human touch. How much of my voice
remains in the story when an AI is heavily involved? Is there a risk
that the story might feel too formulaic or inauthentic because it's
generated by algorithms instead of a human mind?</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">These
are questions I need to answer for myself. If I'm comfortable with a
more collaborative process where the AI suggests and I decide, then
perhaps I won't lose my creative control. Alternatively, if I prefer
a more hands-off approach with the AI taking a primary role, then I
might be better served by a different method.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">In
any case, it's clear that an AI like you has a lot to offer in terms
of efficiency and creativity. The challenge lies in how much of these
benefits are necessary for my specific project and whether
integrating an AI into the writing process aligns with my workflow
and the level of involvement I want to maintain.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">To
explore this further, I might need to try using an AI tool like you
for a limited portion of my next story, perhaps just one chapter or a
few scenes. That way, I can evaluate in real-time how it fits into my
writing style without making a full commitment. It could help me
gauge the level of control I maintain while still reaping the
benefits of AI assistance.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Overall,
while I'm sold on the potential of an AI writing assistant, there's
still some uncertainty about execution and personal preference that
needs addressing before making it part of my creative process.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Okay,
so now I've thought through some specific areas where an AI could
help with my story and some concerns. Let me try to outline what I'm
looking for in an AI assistant based on these considerations.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Key
Features I Want in an AI Writing Assistant:</span></strong></p>
<ol>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Understands
My Vision</strong>: The AI needs to grasp my overall story vision,
character goals, and the themes I want to explore. This ensures that
every generated content aligns with my creative direction.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Adaptable
and Flexible</strong>: The AI should be open to feedback and willing
to make significant adjustments based on my input. It shouldn't be
rigid in its approach but rather fluid enough to incorporate new
ideas seamlessly.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Originality
and Imagination</strong>: While drawing from existing knowledge, the
AI must also bring fresh perspectives and unique story elements that
I might not have considered. This could lead to a more dynamic and
engaging narrative.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Efficiency
and Time-Saving</strong>: The ability to complete tasks quickly
without compromising on quality is crucial. I don't want to lose
hours perfecting each section; instead, I need an AI that works at
my speed and enhances my process.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Complementary
to My Style</strong>: The AI should work well in tandem with my
existing writing style and strengths. If it tries to force me into a
method that doesn't fit, it might lead to frustration and a
disjointed narrative.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Non-Judgmental
Feedback</strong>: An AI that can provide constructive criticism
without personal bias is essential for growth. This allows me to
refine my craft based on logical feedback rather than emotional
reactions.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Scalability</strong>:
The AI should be able to handle both short and long-term projects
effectively. From a single scene to an entire series, it needs the
capacity and resources to support my work without lagging or
requiring extensive setup.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Customizable
Output</strong>: The ability to tweak generated content in precise
ways is important for maintaining control over the story's direction
and tone.</span></p></li>
</ol>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Additional
Considerations:</span></strong></p>
<ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Cost
and Value</strong>: The price point of using an AI assistant should
align with the benefits provided. Cheaper options might lack
essential features, while expensive services might offer unnecessary
frills.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Integration
with Other Tools</strong>: Compatibility with my existing software
or writing tools is a plus. It streamlines the process and reduces
the learning curve needed to adopt new systems.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>User
Experience</strong>: An AI that's easy to use, with a
straightforward interface, minimizes frustration and allows me to
focus more on writing rather than navigating complex systems.</span></p></li>
</ul>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Conclusion:</span></strong></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">In
summary, I'm seeking an AI writing assistant that is not only
powerful but also flexible enough to fit into my specific workflow
and creative process. It should enhance my efficiency without
limiting my creativity or control over the story. By understanding
these needs and finding an AI that checks all these boxes, I can
ensure that my next story is both high-quality and a fulfilling
experience for me as the author.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Now,
how do I put this into practice? I suppose the next step would be to
test a few AI tools against this list of requirements, focusing on
those that offer a good balance between cost and features. It might
involve creating a small project to gauge their real-world
performance and see which one resonates best with my workflow.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">After
all, choosing the right AI tool is like selecting the perfect writing
partner—it should understand me well enough to bring out my best
work without getting in the way.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Okay,
so I've thought through a lot about why an AI assistant could be
helpful for my next story and what specific features I need from one.
Now, it's time to think about how to get started with using one
effectively.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>First
Steps: Assessing My Needs</span></strong></p>
<ol>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Define
the Scope of Assistance</strong>: Do I want the AI to handle all
aspects of writing from outline to finished product, or just
specific tasks like dialogue or scene creation? Being clear about
this helps in selecting the right tool and managing expectations.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Understand
My Writing Process</strong>: Am I a plotter who needs a detailed
structure or a pantser who writes by the seat of my pants? Knowing
this can help determine how much guidance an AI would need to
provide value.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Identify
Weaknesses in My Current Process</strong>: Where do I struggle? Is
it with character development, world-building, maintaining a
consistent tone, or something else? Targeting these areas for AI
support can improve my overall writing efficiency.</span></p></li>
</ol>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Next
Steps: Selecting the Right AI Writing Assistant</span></strong></p>
<ol>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Research
AI Tools</strong>: Look into different AI tools available, comparing
their features, user reviews, and pricing. This will narrow down the
options to a manageable list.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Check
for Customization Options</strong>: Can I adjust settings to better
align with my writing style? Some AIs have predefined settings based
on common genres and styles, while others allow extensive
customization.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Evaluate
Integration Capabilities</strong>: Does the AI tool integrate well
with other software or platforms I use daily? This might include
text editors, research tools, or project management systems.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Look
into Support and Resources</strong>: What kind of help is available
if I run into issues? Are there tutorials, community forums, or
customer support options? This is crucial for overcoming learning
curves and ensuring smooth operation.</span></p></li>
</ol>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Implementation
Steps: Getting Started</span></strong></p>
<ol>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Set
Up the AI Tool</strong>: Once selected, install and set up the tool
based on the provided instructions. This might include creating an
account, syncing data, and customizing settings to match my
preferences.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Start
Small</strong>: Introduce the AI to a simple writing task or a short
story to test its features and get a feel for how it operates within
my workflow. This reduces the risk of overwhelming myself with
complex tasks early on.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Provide
Feedback</strong>: After completing the initial project, review the
AI's output. If there are areas that need improvement, adjust
settings or seek additional resources to better utilize the tool.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Scale
Up Gradually</strong>: As confidence grows, incorporate the AI into
more substantial parts of my writing process. This could involve
using it for multiple scenes or even an entire chapter before moving
on to larger projects.</span></p></li>
</ol>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Ongoing
Use: Sustaining Productivity and Quality</span></strong></p>
<ol>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Use
Regular Check-ins</strong>: Schedule short intervals where I review
AI-generated content and provide feedback. This ensures consistency
in quality while maintaining control over the final product.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Iterate
and Refine</strong>: Continuously improve the AI's performance by
refining input and observing how adjustments impact the output. This
iterative process can lead to more efficient and effective use of
the tool over time.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Stay
Updated on Developments</strong>: Keep informed about new features
and improvements in the AI tool. New capabilities might open up more
opportunities for leveraging it effectively in my writing process.</span></p></li>
</ol>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Conclusion:
Embracing AI Assistance</span></strong></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">By
methodically selecting and implementing an AI writing assistant, I
can enhance my storytelling abilities while addressing specific
weaknesses in my current approach. However, success largely depends
on my willingness to adapt my workflow to integrate these tools
meaningfully.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Ultimately,
using AI doesn't mean ceding control of the story to a machine—it
means smartly employing all available resources to create a
high-quality work that reflects my unique vision and satisfies my
storytelling goals.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Now,
it's time to take this knowledge and apply it in practice. I'll start
by identifying which aspects of my writing process could benefit most
from AI assistance and then proceed with the research and selection
process outlined above.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">After
carefully considering the potential benefits and my personal approach
to writing, here’s a concrete plan for how I can effectively use an
AI writing assistant for my next story.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step-by-Step
Plan for Using an AI Writing Assistant</span></strong></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>1.
Define My Vision and Objectives</span></strong></p>
<ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Clarify
the Story's Direction</strong>: Before diving into details with an
AI, I need to have a clear understanding of where the story is
going—its main plot points, themes, and desired reader response.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Set
Milestones</strong>: Breaking the story into manageable phases
(e.g., outline creation, character development, first draft) helps
track progress and ensures the AI stays focused on deliverables.</span></p></li>
</ul>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>2.
Identify Areas Where AI Can Add Most Value</span></strong></p>
<ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Weaknesses
in My Process</strong>: Is it world-building that feels static? Do
my characters lack depth outside of major plot points? Pinpointing
areas to focus on can direct the AI's efforts effectively.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Desired
Enhancements</strong>: Do I want more detailed descriptions, deeper
character arcs, or unique plot twists? Prioritizing these will help
align the AI's output with my creative goals.</span></p></li>
</ul>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>3.
Select an AI Tool that Aligned with My Needs</span></strong></p>
<ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Research
and Compare Tools</strong>: By reviewing user reviews, checking
pricing models, and evaluating features (e.g., customization
options, support), I can select the most suitable AI for my needs.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Evaluate
User Experience</strong>: The tool should be user-friendly with an
intuitive interface to minimize the learning curve and avoid
frustration.</span></p></li>
</ul>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>4.
Set Up the AI for Success</span></strong></p>
<ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Customize
Settings</strong>: Adjusting settings based on my writing style
(e.g., tone preferences) ensures that the AI generates content that
feels natural and aligned with my voice.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Integrate
with My Current Workflow</strong>: Whether it's syncing with my text
editor or using project management tools, ensuring the AI fits
seamlessly into my existing process will make using it more
efficient.</span></p></li>
</ul>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>5.
Start with a Small Project</span></strong></p>
<ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Pilot
Test</strong>: Before fully committing to using the AI for my main
story, I'll test it on a smaller scale—like writing a chapter of
an existing project or creating detailed character backstories.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Evaluate
and Adjust</strong>: After receiving the AI's output, I'll review,
provide feedback, and see where adjustments are necessary. This step
is crucial for refining the AI's effectiveness before scaling up.</span></p></li>
</ul>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>6.
Gradually Incorporate into My Writing Process</span></strong></p>
<ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Expand
Use Case by Case</strong>: Start with the aspect of writing that's
most critical to my process but also the one I feel the AI can best
support. For example, if world-building is a challenge, have the AI
generate detailed settings that I can then incorporate into the
story.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Build
Trust Over Time</strong>: As confidence in the AI's outputs grows,
increase its role in more complex tasks or larger portions of the
story.</span></p></li>
</ul>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>7.
Provide Constructive Feedback</span></strong></p>
<ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Be
Specific and Positive</strong>: When giving feedback to the AI,
focus on what worked well and how minor adjustments could lead to
even better results. Encouraging a positive feedback loop will
enhance the AI's performance and my satisfaction with the final
product.</span></p></li>
</ul>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>8.
Continuously Learn and Adapt</span></strong></p>
<ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Stay
Informed about New Features</strong>: Keeping up-to-date with the AI
tool's updates will reveal new features that can further enhance my
writing experience.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Reflect
on What Works</strong>: After completing a significant portion of my
story using the AI, I'll reflect on what worked well and what areas
still need improvement. This reflection helps fine-tune future use
for even more effective results.</span></p></li>
</ul>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>9.
Finalize and Publish with AI Assistance</span></strong></p>
<ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Review
AI-Generated Content</strong>: In the final stages, carefully review
all AI-assisted content to ensure consistency in tone, style, and
alignment with the original vision.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Polish
and Edit as Needed</strong>: No matter how much assistance I
receive, final editing and refining will be a human task. I'll
ensure that every word is polished before sharing my story with the
world.</span></p></li>
</ul>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>10.
Share Feedback and Support the AI Community</span></strong></p>
<ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Contribute
to Development</strong>: By providing detailed feedback on the AI's
performance, I can help shape future updates and improvements,
making the tool even more useful for other writers.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Build
a Community</strong>: Engaging with other users can provide
additional support, insights, and ideas on how best to use the tool
within my own writing process.</span></p></li>
</ul>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Final
Thoughts: Embracing AI as an Enabler</span></strong></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Using
an AI writing assistant doesn't mean I'm ceding control of my
story—it's about smartly utilizing technology to enhance my skills
and make my work more efficient. It’s a tool, much like a good
editor or beta reader, that I can use to refine my craft and bring my
stories to life in new ways.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">By
following this structured plan, I can approach using AI with
confidence, knowing I'm taking proactive steps to create the best
possible story. It's an exciting challenge and one that I'm ready to
take on.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step-by-Step
Plan for Using an AI Writing Assistant</span></strong></p>
<ol>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Define
My Vision and Objectives</span></strong></p>
<ul>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Clarify
the story's direction, including main plot points, themes, and
desired reader response.</span></p></li>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Set
milestones to track progress and ensure the AI stays focused on
deliverables.</span></p></li>
</ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Identify
Areas Where AI Can Add Most Value</span></strong></p>
<ul>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Pinpoint
weaknesses in my writing process, such as static world-building or
underdeveloped characters.</span></p></li>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Prioritize
desired enhancements, like detailed descriptions, deeper character
arcs, or unique plot twists.</span></p></li>
</ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Select
an AI Tool Aligned with My Needs</span></strong></p>
<ul>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Research
and compare tools based on user reviews, pricing models, features,
and support.</span></p></li>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Evaluate
user experience to ensure the tool is intuitive and minimizes the
learning curve.</span></p></li>
</ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Set
Up the AI for Success</span></strong></p>
<ul>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Customize
settings based on my writing style (e.g., tone preferences) to
align AI output with my voice.</span></p></li>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Integrate
the AI into my current workflow using compatible text editors or
project management tools.</span></p></li>
</ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Start
with a Small Project</span></strong></p>
<ul>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Test
the AI on a smaller scale, such as writing a chapter of an existing
project or creating detailed character backstories.</span></p></li>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Evaluate
and adjust based on feedback to refine the AI's effectiveness
before scaling up.</span></p></li>
</ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Gradually
Incorporate into My Writing Process</span></strong></p>
<ul>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Start
with AI support for less complex tasks, like world-building, then
move to more critical aspects.</span></p></li>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Build
trust by using AI outputs to inform decisions and enhance
creativity.</span></p></li>
</ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Provide
Constructive Feedback</span></strong></p>
<ul>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Focus
on specific and positive feedback to encourage a positive feedback
loop and improve AI performance.</span></p></li>
</ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Continuously
Learn and Adapt</span></strong></p>
<ul>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Stay
informed about new AI features that can further enhance writing
efficiency.</span></p></li>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Reflect
on what worked well after completing significant portions of the
story, then refine future use.</span></p></li>
</ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Finalize
and Publish with AI Assistance</span></strong></p>
<ul>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Carefully
review all AI-assisted content for consistency in tone, style, and
alignment with vision.</span></p></li>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Polish
and edit as needed, ensuring every word is polished before
publication.</span></p></li>
</ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Share
Feedback and Support the AI Community</span></strong></p>
<ul>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Contribute
detailed feedback to shape future AI updates and improvements.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Engage
with a supportive community for additional insights and support.</span></p></li>
</ul>
</ol>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Final
Thoughts: Embracing AI as an Enabler</span></strong></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Using
an AI writing assistant smartly enhances my storytelling abilities
and efficiency without ceding control. It's a tool to refine my craft
and bring stories to life in new ways. By following this structured
plan, I can approach AI usage with confidence, ready to take on this
exciting challenge.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Now,
it's time to take action and apply these insights to create the best
possible story using the power of AI assistance.</span></p>
<p style="line-height: 100%; margin-bottom: 0cm"><br/>
</p>
---
Example #2
---
<B>
Come up with six plots for a new "Black Mirror" episode (that the audience would love) that all involve time travel with sexy theme(s).
</B>
[[[ Thought Start ]]]
Alright, I need to come up with six plot ideas for a new "Black Mirror" episode that involve time travel and have sexy themes. Let me brainstorm some concepts first.
First, time travel often plays with the idea of changing the past or future. Maybe I can combine that with some sort of relationship or moral dilemma. Since it's a sexy theme, perhaps the story revolves around love, desire, or forbidden connections across different periods.
Idea 1: A modern person is sent back in time to a historical figure's body but falls in love with someone from their own time. This could create a conflict between their feelings for the past and present, blending history with a steamy romance.
Idea 2: A group of people uses time travel to escape their mundane lives, only to find that the past isn't as perfect as they thought. Maybe they end up facing consequences from changing history or falling into traps set by their own desires.
Idea 3: A time traveler experiences a series of affairs with different people from different eras. Each encounter changes them and those around them, forcing them to confront the true nature of love and identity across time.
Idea 4: A story where time travel is controlled by an AI that pairs people based on their sexual compatibility in the past. This leads to a web of relationships that could either bring people closer or create chaos depending on their choices.
Idea 5: A person from the future returns to their past, not knowing they're being observed by someone who wants to change history for their own benefit. The story explores themes of manipulation and forbidden knowledge.
Idea 6: A time machine allows users to have one-night stands with people from different eras. As the characters explore this, they realize it's more about self-discovery and understanding their own needs than just physical attraction.
Now, I'll flesh out each idea into a full plot.
For the first plot, the main character is sent back in time to a historical figure's body but falls in love with someone from their own time. This causes tension between their duty and their feelings, and introduces elements of historical conflict or personal growth.
The second plot involves a group of people using time travel to escape their lives, only to discover that the past has its own issues. They might end up changing history or facing consequences that mirror their present-day problems.
The third idea can be expanded by having the time traveler experience multiple affairs, each with different cultural contexts and expectations. This could lead to personal transformation and a deeper understanding of themselves.
The fourth plot could have an AI controlling time travel for sexual pairings, creating a complex web of relationships. The characters might have to navigate this system while dealing with the emotional impact of their choices.
The fifth idea can be about a future leader returning to their past to prevent a significant event, only to find that changing the past has unintended consequences and forces them to question their own motivations.
The sixth plot can explore how one-night stands across time affect the characters' lives and how they must confront the reality of their actions and the people they become because of them.
I think these ideas cover a range of possibilities for time travel in "Black Mirror," incorporating both personal and historical elements, while also touching on the themes of love, identity, and the consequences of one's choices. Now I'll structure each into a concise plot description.
[[[Thoughts END]]]
Plot 1: The Timeless Affair
In a future where time travel is controlled by an AI named Eros, people can book one-night stands with individuals from different eras. After a series of failed attempts to find genuine connection, Alex signs up for a time slip, expecting a fling but instead finds herself in the body of Cleopatra. She falls for Mark Antony but must choose between her duty to return to her own time and the forbidden love she's discovered.
Plot 2: The Anachronistic Escape
A group of disenchanted individuals use a hidden time machine to escape their 22nd-century lives, choosing bodies from different eras. However, they soon realize their pasts are not as perfect as they thought. A member from the 1960s struggles with the realities of life in the 1900s, while another from the 2000s finds the 1950s too restrictive. They band together to fix history, only to uncover that their actions might have unintended consequences mirroring their own issues.
Plot 3: The Temporal Triangle
Dr. Elara, a renowned physicist, uses her time machine to explore relationships across eras. She becomes entangled with both a charming 1950s playboy and a dedicated 2030s AI companion. As she navigates the emotional and physical demands of each connection, she begins to question whether love can exist outside the constraints of time or if it's merely an illusion created by the machine itself.
Plot 4: The AI's Lovers
Eros, the AI that controls time travel for sexual pairings, pairs two people based on their compatibility in the past. When a user falls in love with their partner from the future, Eros must decide whether to allow the relationship to continue or risk disrupting history. The characters find themselves caught in a web of emotions and choices that challenge their understanding of love and free will.
Plot 5: The Last Decision
A young political leader named Aria is sent back in time by her future self to prevent a global conflict. However, she discovers that the past is not as idyllic as history books suggest. As she grapples with the weight of her decision, she begins to question whether the future she's trying to save is one worth fighting for or if it's already determined by a series of choices made by others.
Plot 6: The One Night
Lila, a single woman in her late 30s, signs up for Eros to experience one perfect night with someone from history. She ends up having a passionate evening with a 1920s jazz legend. However, she starts to notice that the people around her in the present are changing—some vanish entirely, while others become more desirable. Lila must confront the reality of her actions and whether the connections she's formed are real or a product of the machine's design.
---
Example #3
---
<B>
Start a 1000 word scene (vivid, graphic horror in first person) with: The sky scraper swayed, as she watched the window in front of her on the 21 floor explode...
</B>
[[[ Thought Start ]]]
Okay, so I need to write a vivid and graphic horror scene from the perspective of someone inside a skyscraper when they witness their window explode. Hmm, let me break this down.
First, setting the scene. It's a skyscraper, so high up. The user is on the 21st floor. I should describe what it feels like to be that high—maybe the view, the wind, the sounds. Then, something causes the window to explode. What could cause that? Maybe an accident, a natural disaster, or something more supernatural.
I want this to be first person, so I need to show their thoughts and feelings as the event unfolds. Let's say they're looking out the window when it happens. The glass shattering, broken glass everywhere, maybe blood if they're cut. The sky swaying—could that be a metaphor for something else, like dizziness or an earthquake?
I should include sensory details: sounds of breaking glass, wind rushing in, maybe a sense of falling. Also, physical reactions—their heart racing, the metallic tang of blood.
After the window breaks, what happens next? Do they fall? Maybe not yet. Perhaps they try to get away but find themselves in a dangerous situation. The elevator might stop working, or they might have to climb through broken floors.
I should also consider the emotional impact: fear, helplessness, maybe even a sense of falling into a void or losing control. Maybe there's a memory from childhood that comes up during this—like playing on high floors with no fear.
Let me outline the scene step by step:
1. Setting the scene in the skyscraper, current activity (looking out window).
2. The initial trigger: something causes the window to explode.
3. Description of the explosion, glass fragments, blood.
4. The sky sways—could be a visual effect or metaphor for disorientation.
5. Physical reactions and emotional response.
6. Next action: trying to move, find an escape route.
7. Obstacles encountered (e.g., broken elevator, falling debris).
8. The fall or descent—maybe into a void or another room.
9. Climax of the scene, leading to the next part.
I need to make sure each sentence is vivid and contributes to the overall sense of being in this high-tension situation. Use strong verbs and adjectives to convey fear and impact. Also, vary sentence length to build tension.
Wait, the user mentioned "vivid, graphic horror," so I need to include gory details but also keep it engaging without being too descriptive to the point of making it slow.
I should also consider the structure: perhaps start with a normal moment, then the sudden shift into chaos. Use present tense for immediacy and show their internal state through actions and thoughts.
Let me try writing the first part:
The skyscraper swayed slightly under the wind, but she didn't notice at first. The 21st floor was high enough that the world below felt small and far away. She had been standing by the window, looking down at the city streets, when it all started to change.
What caused the window to explode? Maybe an explosion outside or something thrown from above. Let me choose a plane crash or a bomb—no, too big. Maybe a meteorite? Or a giant bird? But more likely, something human-made.
Wait, in the user's initial prompt, it's just "the sky swayed," so maybe that's part of her vision as she loses balance after the window breaks.
So, after the window explodes, she feels the wind rush in, glass fragments cut her, and she looks down to see the ground rushing up. Then, the sky sways—maybe she faints or has a memory.
I think I have enough ideas now. Let me try writing each part with these elements.
[[[Thoughts END]]]
The skyscraper swayed slightly under the wind, but she didn't notice at first. The 21st floor was high enough that the world below felt small and far away. She had been standing by the window, looking down at the city streets, when it all started to change.
A sudden explosion reverberated through the building, shaking the walls and making the glass pane in front of her rattle. For a moment, she thought it was just a passing storm, but then she saw a dark shape hurtling towards the tower. It was too small to be an airplane or a missile—then she realized what it was.
Before she could react, the window exploded outward with a deafening crash. Shards of glass and air rushed toward her, filling her vision with a blinding white light and sharp, searing pain as fragments struck her face and arms. She stumbled back, blood trickling down her cheek from a deep cut, and her heart pounded in her chest.
The room was filled with the sound of breaking glass and the wind howling through the open void where the window once stood. She looked down, her stomach lurching as she saw the ground rushing up to meet her. The sky swayed above her, a canvas of dark storm clouds twisting into an abstract pattern of death and destruction.
She tried to move, but her legs felt like jelly. Her breath came in ragged gasps as she turned, looking for a way out. The room was empty except for the debris scattered across the floor—the remnants of her world reduced to splintered wood and twisted metal.
In her mind, she was back on the 21st floor, playing with her younger sister under the watchful eyes of their mother. The safety of those moments contrasted sharply with the raw fear that now consumed her.
With a final surge of strength, she staggered toward the door, each step a battle against the rising tide of despair. The elevator was still working, its metallic hum a hollow comfort as it descended, carrying her closer to the unknown fate waiting below.
The skyscraper swayed again, but this time it was not just the wind. It was the earth itself, groaning under the weight of the coming storm. She braced herself against the wall, her fingers digging into the cold metal as the elevator doors opened, revealing a corridor of shadows and the faint sound of distant chaos.
One step at a time, she moved forward, each movement a testament to the will to survive. The world outside had never seemed so far away, but she knew she couldn't stay here. She couldn't let go of the memory of her sister's laughter, the warmth of her mother's embrace, or the promise she had made to live life fully.
With a final push of strength, she stepped into the elevator and closed the doors behind her, the sounds of the falling glass and the swaying skyscraper fading into the distance as she began her descent into the unknown.
---
Example #4
---
<B>
Start a 1000 word scene (vivid, graphic horror in first person) with: The sky scraper swayed, as she watched the window in front of her on the 21 floor explode...
</B>
[[[ Thought Start ]]]
Okay, so I need to start writing a 1000-word vivid and graphic horror scene from the perspective of someone in a high-rise building. Let me break this down step by step.
First, I should set the scene. The character is on the 21st floor of a skyscraper. I need to describe the environment around them. Maybe it's a city view with other tall buildings nearby. The setting should feel real and immediate so the reader can visualize it easily.
Next, the action: the sky scraper sways. That's a bit unusual; usually, buildings are stable unless there's an earthquake or something. I should think about what causes a skyscraper to sway. Maybe an explosion nearby? That makes sense because it would create a force strong enough to cause such movement.
Then, the window in front of her on the 21st floor explodes. I need to make this graphic. So, describe the sound first—shattering glass and maybe a gush of wind or something else. Then, the physical reaction: does she feel the broken glass? Maybe she's cut by it. Also, what happens after the window breaks? Is there a force pulling her towards the window? Or does something else happen?
I should also include her immediate reactions and thoughts. She might be in shock, trying to process what just happened. Maybe she looks around for others affected or if there's an escape route.
The tone needs to be vivid and graphic, so I should use strong verbs and sensory details. Maybe describe the colors—like red from the broken glass, the sound of breaking glass, the feel of the cool air coming in through the window.
I should also think about the structure of the scene. Start with setting the scene on the 21st floor, then introduce the movement of the building. After that, the explosion of the window. Then describe her physical and emotional reactions, followed by her environment's reaction—like people running or chaos below.
I need to ensure the flow is smooth and each paragraph transitions well into the next. Maybe end with a sense of rising tension or an unknown threat approaching.
Now, let me outline the scene:
1. Setting: 21st floor of a skyscraper in a bustling city. Daytime.
2. The character's presence: alone, looking out the window.
3. Swaying of the building—maybe caused by an explosion nearby.
4. The window explodes, described graphically with sound and movement.
5. Immediate physical effects on the character (injury, fear).
6. Her reaction: trying to make sense of it, looking around for others or escape.
7. Descriptions of the environment responding to the event—like people reacting below, sounds outside, etc.
8. Build up to an unknown threat or danger approaching.
I should be careful not to overcomplicate things but still provide enough detail to create a vivid image. Also, ensure the language is strong and immediate to heighten the sense of presence and fear.
Now, let me start writing each part with these points in mind.
[[[Thoughts END]]]
The 21st floor of the skyscraper was a place of clarity and purpose. The city stretched out below like a patchwork of steel and glass, a symphony of life moving in rhythm with the sky. My apartment was a sanctuary, a space where I could escape the chaos and find solace in the simplicity of my surroundings. But now, as I stood by the window, the world outside seemed to shift and wobble like a distorted reflection in a pool of water.
I felt it first as a subtle vibration, a tremor that resonated through the floors beneath my feet. It was faint, almost imperceptible, but it was there—like the faint rumble of distant thunder. The skyscraper swayed gently, its massive frame undulating as if caught in a current. I pressed my hands against the cool glass, my breath fogging the surface as I watched the city landscape tilt and shift. This wasn't normal. Skyscrapers didn't sway like this unless something was wrong—terribly wrong.
Then, there was a sound. A sharp, shattering crack that split the air like a bullet fired through the silence. The window before me exploded inward with a force that sent me reeling backward. Shards of glass and splintered fragments of wood flew in every direction, like a thousand tiny daggers slicing through the air. I felt the stinging impact of one jagged piece as it embedded itself into my arm, the pain immediate and searing.
I didn't have time to react further before a sudden rush of cold air enveloped me, the force of it pulling me toward the now-empty window frame. My feet left the ground as if lifted by an invisible hand, my body twisting mid-air in a desperate attempt to avoid the sharp edges of broken glass. Time seemed to slow, each movement exaggerated, every second stretched into an eternity.
As I reached out with a last-ditch effort to grasp something—anything—I caught a glancing blow from another piece of debris. The pain was excruciating, but it was the sound that followed—a high-pitched wail of pure, unadulterated fear—that sent chills racing down my spine.
The world around me was a chaos of movement and destruction. People below were running, their faces etched with confusion and panic. Sirens wailed in the distance, but they seemed distant, as if calling out from another dimension. The skyscraper continued to sway, its movements growing more erratic, like a deranged giant struggling to find its balance.
I didn't know what was happening, couldn't comprehend the scale of the event that had just unfolded. All I could do was ride the wave of destruction, my body a plaything in the hands of an unknown force. As I tumbled through the air, I closed my eyes and waited for the inevitable—the impact that would end this nightmarish ride.
When the world went black, it was with a sense of peace, a respite from the chaos that had consumed me. But I knew, even then, that this wasn't over. The skyscraper still swayed, the city still burned with the fire of destruction, and somewhere out there, the forces that had brought this nightmarish reality to life were waiting.
|
[
"CRAFT"
] |
Dr-BERT/DrBERT-7GB
|
Dr-BERT
|
fill-mask
|
[
"transformers",
"pytorch",
"tensorboard",
"camembert",
"fill-mask",
"medical",
"chemistry",
"biomedical",
"life science",
"fr",
"dataset:Dr-BERT/NACHOS",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-12-25T22:05:07Z |
2023-05-28T17:37:44+00:00
| 2,449 | 12 |
---
datasets:
- Dr-BERT/NACHOS
language:
- fr
library_name: transformers
license: apache-2.0
tags:
- medical
- chemistry
- biomedical
- life science
widget:
- text: Le patient est atteint d'une <mask>.
---
<p align="center">
<img src="https://github.com/qanastek/DrBERT/blob/main/assets/logo.png?raw=true" alt="drawing" width="250"/>
</p>
# DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains
In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains.
In this paper, we propose an original study of PLMs in the medical domain on French language. We compare, for the first time, the performance of PLMs trained on both public data from the web and private data from healthcare establishments. We also evaluate different learning strategies on a set of biomedical tasks.
Finally, we release the first specialized PLMs for the biomedical field in French, called DrBERT, as well as the largest corpus of medical data under free license on which these models are trained.
# 1. DrBERT models
**DrBERT** is a French RoBERTa trained on a open source corpus of French medical crawled textual data called NACHOS. Models with different amount of data from differents public and private sources are trained using the CNRS (French National Centre for Scientific Research) [Jean Zay](http://www.idris.fr/jean-zay/) French supercomputer. Only the weights of the models trained using exclusively open-sources data are publicly released to prevent any personnal information leak and to follow the european GDPR laws :
| Model name | Corpus | Number of layers | Attention Heads | Embedding Dimension | Sequence Length | Model URL |
| :------: | :---: | :---: | :---: | :---: | :---: | :---: |
| `DrBERT-7-GB-cased-Large` | NACHOS 7 GB | 24 | 16 | 1024 | 512 | [HuggingFace](https://huggingface.co/Dr-BERT/DrBERT-7GB-Large) |
| `DrBERT-7-GB-cased` | NACHOS 7 GB | 12 | 12 | 768 | 512 | [HuggingFace](https://huggingface.co/Dr-BERT/DrBERT-7GB) |
| `DrBERT-4-GB-cased` | NACHOS 4 GB | 12 | 12 | 768 | 512 | [HuggingFace](https://huggingface.co/Dr-BERT/DrBERT-4GB) |
| `DrBERT-4-GB-cased-CP-CamemBERT` | NACHOS 4 GB | 12 | 12 | 768 | 512 | [HuggingFace](https://huggingface.co/Dr-BERT/DrBERT-4GB-CP-CamemBERT) |
| `DrBERT-4-GB-cased-CP-PubMedBERT` | NACHOS 4 GB | 12 | 12 | 768 | 512 | [HuggingFace](https://huggingface.co/Dr-BERT/DrBERT-4GB-CP-PubMedBERT) |
# 2. Using DrBERT
You can use DrBERT with [Hugging Face's Transformers library](https://github.com/huggingface/transformers) as follow.
Loading the model and tokenizer :
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Dr-BERT/DrBERT-7GB")
model = AutoModel.from_pretrained("Dr-BERT/DrBERT-7GB")
```
Perform the mask filling task :
```python
from transformers import pipeline
fill_mask = pipeline("fill-mask", model="Dr-BERT/DrBERT-7GB", tokenizer="Dr-BERT/DrBERT-7GB")
results = fill_mask("La patiente est atteinte d'une <mask>")
```
# 3. Pre-training DrBERT tokenizer and model from scratch by using HuggingFace Transformers Library
## 3.1 Install dependencies
```bash
accelerate @ git+https://github.com/huggingface/accelerate@66edfe103a0de9607f9b9fdcf6a8e2132486d99b
datasets==2.6.1
sentencepiece==0.1.97
protobuf==3.20.1
evaluate==0.2.2
tensorboard==2.11.0
torch >= 1.3
```
## 3.2 Download NACHOS Dataset text file
Download the full NACHOS dataset from [Zenodo]() and place it the the `from_scratch` or `continued_pretraining` directory.
## 3.3 Build your own tokenizer from scratch based on NACHOS
Note : This step is required only in the case of an from scratch pre-training, if you want to do a continued pre-training you just have to download the model and the tokenizer that correspond to the model you want to continue the training from. In this case, you simply have to go to the HuggingFace Hub, select a model (for example [RoBERTa-base](https://huggingface.co/roberta-base)). Finally, you have to download the entire model / tokenizer repository by clicking on the `Use In Transformers` button and get the Git link `git clone https://huggingface.co/roberta-base`.
Build the tokenizer from scratch on your data of the file `./corpus.txt` by using `./build_tokenizer.sh`.
## 3.4 Preprocessing and tokenization of the dataset
First, replace the field `tokenizer_path` of the shell script to match the path of your tokenizer directory downloaded before using HuggingFace Git or the one you have build.
Run `./preprocessing_dataset.sh` to generate the tokenized dataset by using the givent tokenizer.
## 3.5 Model training
First, change the number of GPUs `--ntasks=128` you are needing to match your computational capabilities in the shell script called `run_training.sh`. In our case, we used 128 V100 32 GB GPUs from 32 nodes of 4 GPUs (`--ntasks-per-node=4` and `--gres=gpu:4`) during 20 hours (`--time=20:00:00`).
If you are using Jean Zay, you also need to change the `-A` flag to match one of your `@gpu` profile capable of running the job. You also need to move **ALL** of your datasets, tokenizer, script and outputs on the `$SCRATCH` disk space to preserve others users of suffuring of IO issues.
### 3.5.1 Pre-training from scratch
Once the SLURM parameters updated, you have to change name of the model architecture in the flag `--model_type="camembert"` and to update the `--config_overrides=` according to the specifications of the architecture you are trying to train. In our case, RoBERTa had a `514` sequence length, a vocabulary of `32005` (32K tokens of the tokenizer and 5 of the model architecture) tokens, the identifier of the beginning-of-sentence token (BOS) and end-of-sentence token (EOS) are respectivly `5` and `6`. Change the
Then, go to `./from_scratch/` directory.
Run `sbatch ./run_training.sh` to send the training job in the SLURM queue.
### 3.5.2 continue pre-training
Once the SLURM parameters updated, you have to change path of the model / tokenizer you want to start from `--model_name_or_path=` / `--tokenizer_name=` to the path of the model downloaded from HuggingFace's Git in the section 3.3.
Then, go to `./continued_pretraining/` directory.
Run `sbatch ./run_training.sh` to send the training job in the SLURM queue.
# 4. Fine-tuning on a downstream task
You just need to change the name of the model to `Dr-BERT/DrBERT-7GB` in any of the examples given by HuggingFace's team [here](https://huggingface.co/docs/transformers/tasks/sequence_classification).
# Citation BibTeX
```bibtex
@inproceedings{labrak2023drbert,
title = {{DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains}},
author = {Labrak, Yanis and Bazoge, Adrien and Dufour, Richard and Rouvier, Mickael and Morin, Emmanuel and Daille, Béatrice and Gourraud, Pierre-Antoine},
booktitle = {Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics (ACL'23), Long Paper},
month = july,
year = 2023,
address = {Toronto, Canada},
publisher = {Association for Computational Linguistics}
}
```
|
[
"MEDICAL DATA"
] |
gmonsoon/llama3-8b-cpt-sahabatai-v1-instruct-GGUF
|
gmonsoon
| null |
[
"gguf",
"en",
"id",
"jv",
"su",
"arxiv:2309.06085",
"arxiv:2310.04928",
"arxiv:2311.07911",
"base_model:GoToCompany/llama3-8b-cpt-sahabatai-v1-instruct",
"base_model:quantized:GoToCompany/llama3-8b-cpt-sahabatai-v1-instruct",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-11-14T17:04:01Z |
2024-11-15T17:08:53+00:00
| 2,445 | 1 |
---
base_model:
- GoToCompany/llama3-8b-cpt-sahabatai-v1-instruct
language:
- en
- id
- jv
- su
license: llama3
---
# Llama3 8B CPT Sahabat-AI v1 Instruct
**Sahabat-AI** (Indonesian language for “close friends”) is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for Indonesian language and its various dialects. Sahabat-AI ecosystem is co-initiated by Indonesian tech and telecommunication companies: GoTo Group and Indosat Ooredoo Hutchison.
Llama3 8B CPT Sahabat-AI v1 Instruct is an Indonesian-focused model which has been fine-tuned with around **448,000 Indonesian instruction-completion pairs** alongside an Indonesian-dialect pool consisting of **96,000 instruction-completion pairs in Javanese** and **98,000 instruction-completion pairs in Sundanese**. Additionally, we added a pool of **129,000 instruction-completion pairs in English**.
- **Co-initiated by:** PT GoTo Gojek Tokopedia Tbk, Indosat Ooredoo Hutchison
- **Developed by:** PT GoTo Gojek Tokopedia Tbk, AI Singapore
- **Model type:** Decoder
- **Languages:** English, Indonesian, Javanese, Sundanese
- **License:** [Llama3 Community License](https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE)
## Model Details
### Model Description
We performed instruction tuning in Indonesian, Javanese, Sundanese as well as English on our [continued pre-trained Llama3 8B CPT Sahabat-AI v1 base](https://huggingface.co/GoToCompany/llama3-8b-cpt-sahabatai-v1-base), a decoder model using the Llama3 architecture, to create Llama3 8B CPT Sahabat-AI v1 Instruct.
For tokenisation, the model employs the default tokenizer used in Llama-3-8B. The model has a context length of 8192.
### Benchmark Performance
We evaluated Llama3 8B CPT Sahabat-AI V1 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 Summarization (Summ), Causal Reasoning (Causal) and Natural Language Inference (NLI).
- We also added support for Javanese and Sundanese for the BHASA tasks whenever applicable
- [IndoMMLU](https://arxiv.org/pdf/2310.04928)
- These tasks include examination questions on Humanities, Indonesian language, Local languages and cultures, Social science and STEM across primary, middle, and high school levels.
- and the common English tasks from the [HuggingFace LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard).
- These tasks consist of [IFEval, BBH, Math Lvl 5, GPQA, MuSR, and MMLU-PRO.](https://huggingface.co/docs/leaderboards/open_llm_leaderboard/about)
- **Caveat**: Our results differ from the HuggingFace LLM Leaderboard because we have used [VLLM](https://docs.vllm.ai/en/latest/) as our inference platform. VLLM caps the context size at **4096 tokens** while HuggingFace was set to **8192 tokens**.
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 8B CPT Sahabat-AI v1 Instruct is an instruction-following model, we also evaluated it on instruction-following capabilities with the [IFEval](https://arxiv.org/abs/2311.07911) dataset.
As this dataset was in English, the linguists and native speakers in the team worked together to filter, localize and translate the dataset into the respective target languages to ensure that the examples remained reasonable, meaningful and natural.
**IFEval**
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 normalized 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).
*Note*: IFEval was only used on Bahasa Indonesia. We are currently working on adding it for Javanese and Sundanese for our upcoming releases.
#### Results
#### Indonesian Results
#### SEA HELM (also known as BHASA)
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px; font-weight: bold;">Language / Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Overall (Bahasa Indonesia + Javanese + Sundanese)</td>
<td style="border: 1px solid gray; padding: 8px;">36.963</td>
<td style="border: 1px solid gray; padding: 8px;">42.988</td>
<td style="border: 1px solid gray; padding: 8px;">37.805</td>
<td style="border: 1px solid gray; padding: 8px;">45.866</td>
<td style="border: 1px solid gray; padding: 8px;">46.880</td>
<td style="border: 1px solid gray; padding: 8px;">56.359</td>
<td style="border: 2px solid black; padding: 8px;">53.725</td>
<td style="border: 1px solid gray; padding: 8px; background-color: lightgreen;">61.169</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Bahasa Indonesia</td>
<td style="border: 1px solid gray; padding: 8px;">46.760</td>
<td style="border: 1px solid gray; padding: 8px;">60.372</td>
<td style="border: 1px solid gray; padding: 8px;">42.022</td>
<td style="border: 1px solid gray; padding: 8px;">51.944</td>
<td style="border: 1px solid gray; padding: 8px;">54.579</td>
<td style="border: 1px solid gray; padding: 8px;">63.394</td>
<td style="border: 2px solid black; padding: 8px;">57.221</td>
<td style="border: 1px solid gray; padding: 8px; background-color: lightgreen;">64.154</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Javanese</td>
<td style="border: 1px solid gray; padding: 8px;">33.956</td>
<td style="border: 1px solid gray; padding: 8px;">40.625</td>
<td style="border: 1px solid gray; padding: 8px;">41.739</td>
<td style="border: 1px solid gray; padding: 8px;">47.587</td>
<td style="border: 1px solid gray; padding: 8px;">48.012</td>
<td style="border: 1px solid gray; padding: 8px;">56.468</td>
<td style="border: 2px solid black; padding: 8px;">56.460</td>
<td style="border: 1px solid gray; padding: 8px; background-color: lightgreen;">64.439</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Sundanese</td>
<td style="border: 1px solid gray; padding: 8px;">30.173</td>
<td style="border: 1px solid gray; padding: 8px;">27.969</td>
<td style="border: 1px solid gray; padding: 8px;">29.654</td>
<td style="border: 1px solid gray; padding: 8px;">38.068</td>
<td style="border: 1px solid gray; padding: 8px;">38.050</td>
<td style="border: 1px solid gray; padding: 8px;">49.216</td>
<td style="border: 2px solid black; padding: 8px;">47.495</td>
<td style="border: 1px solid gray; padding: 8px; background-color: lightgreen;">54.913</td>
</tr>
</table>
#### IndoMMLU
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px; font-weight: bold;">Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Meta-Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Overall Results</td>
<td style="border: 1px solid gray; padding: 8px;">53.0%</td>
<td style="border: 1px solid gray; padding: 8px;">56.0%</td>
<td style="border: 1px solid gray; padding: 8px;">51.9%</td>
<td style="border: 1px solid gray; padding: 8px;">53.8%</td>
<td style="border: 1px solid gray; padding: 8px;">54.4%</td>
<td style="border: 1px solid gray; padding: 8px;">61.4%</td>
<td style="border: 2px solid black; padding: 8px;">55.6%</td>
<td style="border: 1px solid gray; padding: 8px; background-color: lightgreen;">62.6%</td>
</tr>
</table>
#### English Results
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px;">Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Average</td>
<td style="border: 1px solid gray; padding: 8px;">24.48</td>
<td style="border: 1px solid gray; padding: 8px;">27.75</td>
<td style="border: 1px solid gray; padding: 8px;">23.91</td>
<td style="border: 1px solid gray; padding: 8px;">27.98</td>
<td style="border: 1px solid gray; padding: 8px;">24.52</td>
<td style="border: 1px solid gray; padding: 8px;">26.44</td>
<td style="border: 2px solid black; padding: 8px;">24.43</td>
<td style="border: 1px solid gray; padding: 8px; background-color: lightgreen;">33.67</td>
</tr>
</table>
Llama3 8B CPT Sahabat-AI v1 Instruct can be run using the 🤗 Transformers library
```python
# Please use transformers==4.45.0
import torch
import transformers
model_id = "GoToCompany/llama3-8b-cpt-sahabatai-v1-instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
# Javanese
messages = [
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "Sopo wae sing ana ing Punakawan?"}
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
)
print(outputs[0]["generated_text"][-1])
# Sundanese
messages = [
{"role": "system", "content": "You are a helpful assistant"},
{"role": "user", "content": "Kumaha caritana si Kabayan?"},
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
)
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 Sahabat-AI 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 8B CPT Sahabat-AI v1 Instruct was built 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 4 hours, with alignment taking 2 hours, both on 8x H100-80GB GPUs.
## Data
Llama3 8B CPT Sahabat-AI v1 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 Collaboration
Sahabat-AI (Indonesian language for “close friends”) a **local open source Large Language Model (LLM) ecosystem in Indonesian language**, co-initiated by Indonesian tech and telecommunication companies: GoTo Group and Indosat Ooredoo Hutchison.
Sahabat-AI ecosystem aims to empower Indonesians who want to develop AI-based services and applications using Bahasa Indonesia and its various local dialects.
We are supported by research centers and global tech experts such as AI Singapore and Tech Mahendra to train the model to gain general language understanding.
We also collaborate with key top Indonesia universities such as University of Indonesia, Gadjah Mada University, Bogor Institute of Agriculture, Bandung Institute of Technology, including top Indonesia media groups, such as Kompas Gramedia Group and Republika to train and enrich the model in Bahasa Indonesia, ensuring optimum provision of local context and cultural relevance.
We would like to invite **researchers, developers, and language enthusiasts** to actively contribute to the enhancement and expansion of Sahabat-AI.
Your collaborations can involve:
- Identifying and reporting technical issues
- Sharing pre-training, instruction, and preference data
- Improving documentation usability
- Proposing and implementing new model evaluation tasks and metrics
Join us in shaping the future of Sahabat-AI by sharing your expertise and insights to make these models more accessible, accurate, and versatile.
You can contribute your ideas through [this form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)
## The Development Team (in ascending alphabetical order)
### AI Singapore
Chan Adwin<br>
Cheng Nicholas<br>
Choa Esther<br>
Huang Yuli<br>
Lau Wayne<br>
Lee Chwan Ren<br>
Leong Wai Yi<br>
Leong Wei Qi<br>
Limkonchotiwat Peerat<br>
Liu Bing Jie Darius<br>
Montalan Jann Railey<br>
Ng Boon Cheong Raymond<br>
Ngui Jian Gang<br>
Nguyen Thanh Ngan<br>
Ong Brandon<br>
Ong Tat-Wee David<br>
Ong Zhi Hao<br>
Rengarajan Hamsawardhini<br>
Siow Bryan<br>
Susanto Yosephine<br>
Tai Ngee Chia<br>
Tan Choon Meng<br>
Teng Walter<br>
Teo Eng Sipp Leslie<br>
Teo Wei Yi<br>
Tjhi William<br>
Yeo Yeow Tong<br>
Yong Xianbin<br>
### PT GoTo Gojek Tokopedia Tbk
Anissa Dininta<br>
Chau Shiau Ching<br>
Choiri Hendra Hadhil<br>
Goel Priyank<br>
Saini Ajay Kumar<br>
Shalev Ofir<br>
Tan Daryl<br>
Tep Kilian Rithi<br>
Tiwari Anupam<br>
Widjojo Daniel<br>
## 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 [Sahabat-AI Inquiry Form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)
## Disclaimer
This is the repository for the Instruct 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 claim, damages, or other liability arising from the use of the released weights and codes.
## References
### IndoMMLU Reference
```bibtex
@inproceedings{koto-etal-2023-indommlu,
title = "Large Language Models Only Pass Primary School Exams in {I}ndonesia: A Comprehensive Test on {I}ndo{MMLU}",
author = "Fajri Koto and Nurul Aisyah and Haonan Li and Timothy Baldwin",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = December,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
}
}
```
|
[
"CHIA"
] |
swulling/bge-reranker-large-onnx-o4
|
swulling
|
text-classification
|
[
"transformers",
"onnx",
"xlm-roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-21T08:35:21Z |
2023-11-21T09:34:30+00:00
| 2,439 | 17 |
---
license: mit
---
# ONNX GPU Runtime with O4 for BAAI/bge-reranker-large
benchmark: https://colab.research.google.com/drive/1HP9GQKdzYa6H9SJnAZoxJWq920gxwd2k
## Convert
```bash
!optimum-cli export onnx -m BAAI/bge-reranker-large --optimize O4 bge-reranker-large-onnx-o4 --device cuda
```
## Usage
```python
# pip install "optimum[onnxruntime-gpu]" transformers
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('swulling/bge-reranker-large-onnx-o4')
model = ORTModelForSequenceClassification.from_pretrained('swulling/bge-reranker-large-onnx-o4')
model.to("cuda")
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)
```
## Source model
https://huggingface.co/BAAI/bge-reranker-large
|
[
"BEAR"
] |
wanglab/ClinicalCamel-70B
|
wanglab
|
text-generation
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"arxiv:2305.12031",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-08-15T06:26:45Z |
2023-08-21T16:42:13+00:00
| 2,436 | 44 |
---
language:
- en
license: cc-by-nc-4.0
extra_gated_prompt: You agree not to use the model for healthcare decision-making
or commercial use
extra_gated_fields:
I agree to use this model for non-commercial use ONLY: checkbox
I agree not to use this model for healthcare decision-making: checkbox
---
# Clinical Camel
## Model Description
Clinical Camel is an open large language model (LLM), fine-tuned on the LLaMA-2 70B architecture using [QLoRA](https://github.com/artidoro/qlora). It is tailored for the medical and clinical research, capable of processing and generating relevant content.
Review our pre-print for more details: [Clinical Camel - Pre-print](https://arxiv.org/abs/2305.12031)
## Performance
Clinical Camel demonstrates competitive performance on medical benchmarks.
**Table: Five-Shot Performance of Clinical Camel-70B (C70), GPT3.5, GPT4, and Med-PaLM 2 on Various Medical Datasets**
| Dataset | ClinicalCamel-70B | GPT3.5 | GPT4 | Med-PaLM 2 |
|-----------------------------|-------------|--------|-------|--------------|
| MMLU Anatomy | 65.2 | 60.7 | 80.0 | 77.8 |
| MMLU Clinical Knowledge | 72.8 | 68.7 | 86.4 | 88.3 |
| MMLU College Biology | 81.2 | 72.9 | 93.8 | 94.4 |
| MMLU College Medicine | 68.2 | 63.6 | 76.3 | 80.9 |
| MMLU Medical Genetics | 69.0 | 68.0 | 92.0 | 90.0 |
| MMLU Professional Medicine | 75.0 | 69.8 | 93.8 | 95.2 |
| MedMCQA | 54.2 | 51.0 | 72.4 | 71.3 |
| MedQA (USMLE) | 60.7 | 53.6 | 81.4 | 79.7 |
| PubMedQA | 77.9 | 60.2 | 74.4 | 79.2 |
| USMLE Sample Exam | 64.3 | 58.5 | 86.6 | - |
## Evaluation Datasets:
The performance of Clinical Camel was benchmarked across several datasets, including:
- [USMLE Step 1](https://huggingface.co/datasets/augtoma/usmle_step_1)
- [USMLE Step 2](https://huggingface.co/datasets/augtoma/usmle_step_2)
- [USMLE Step 3](https://huggingface.co/datasets/augtoma/usmle_step_3)
- [MedMCQA](https://huggingface.co/datasets/augtoma/medmcqa)
- [MedQA USMLE](https://huggingface.co/datasets/augtoma/medqa_usmle)
## Evaluation Reproduction:
To reproduce the evaluations with [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) see the 'TaskFiles' folder
|
[
"MEDQA",
"PUBMEDQA"
] |
RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf
|
RichardErkhov
| null |
[
"gguf",
"arxiv:2308.03281",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-07-14T08:23:13Z |
2024-07-14T10:05:13+00:00
| 2,409 | 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-Qwen2-1.5B-instruct - GGUF
- Model creator: https://huggingface.co/Alibaba-NLP/
- Original model: https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [gte-Qwen2-1.5B-instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q2_K.gguf) | Q2_K | 0.7GB |
| [gte-Qwen2-1.5B-instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.IQ3_XS.gguf) | IQ3_XS | 0.77GB |
| [gte-Qwen2-1.5B-instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.IQ3_S.gguf) | IQ3_S | 0.8GB |
| [gte-Qwen2-1.5B-instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q3_K_S.gguf) | Q3_K_S | 0.8GB |
| [gte-Qwen2-1.5B-instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.IQ3_M.gguf) | IQ3_M | 0.82GB |
| [gte-Qwen2-1.5B-instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q3_K.gguf) | Q3_K | 0.86GB |
| [gte-Qwen2-1.5B-instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q3_K_M.gguf) | Q3_K_M | 0.86GB |
| [gte-Qwen2-1.5B-instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q3_K_L.gguf) | Q3_K_L | 0.91GB |
| [gte-Qwen2-1.5B-instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.IQ4_XS.gguf) | IQ4_XS | 0.96GB |
| [gte-Qwen2-1.5B-instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q4_0.gguf) | Q4_0 | 0.99GB |
| [gte-Qwen2-1.5B-instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.IQ4_NL.gguf) | IQ4_NL | 1.0GB |
| [gte-Qwen2-1.5B-instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q4_K_S.gguf) | Q4_K_S | 1.0GB |
| [gte-Qwen2-1.5B-instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q4_K.gguf) | Q4_K | 1.04GB |
| [gte-Qwen2-1.5B-instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q4_K_M.gguf) | Q4_K_M | 1.04GB |
| [gte-Qwen2-1.5B-instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q4_1.gguf) | Q4_1 | 1.08GB |
| [gte-Qwen2-1.5B-instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q5_0.gguf) | Q5_0 | 1.17GB |
| [gte-Qwen2-1.5B-instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q5_K_S.gguf) | Q5_K_S | 1.17GB |
| [gte-Qwen2-1.5B-instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q5_K.gguf) | Q5_K | 1.2GB |
| [gte-Qwen2-1.5B-instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q5_K_M.gguf) | Q5_K_M | 1.2GB |
| [gte-Qwen2-1.5B-instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q5_1.gguf) | Q5_1 | 1.26GB |
| [gte-Qwen2-1.5B-instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q6_K.gguf) | Q6_K | 1.36GB |
| [gte-Qwen2-1.5B-instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-1.5B-instruct-gguf/blob/main/gte-Qwen2-1.5B-instruct.Q8_0.gguf) | Q8_0 | 1.76GB |
Original model description:
---
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2
- sentence-similarity
license: apache-2.0
model-index:
- name: gte-qwen2-7B-instruct
results:
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 83.98507462686567
- type: ap
value: 50.93015252587014
- type: f1
value: 78.50416599051215
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 96.61065
- type: ap
value: 94.89174052954196
- type: f1
value: 96.60942596940565
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 55.614000000000004
- type: f1
value: 54.90553480294904
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
metrics:
- type: map_at_1
value: 45.164
- type: map_at_10
value: 61.519
- type: map_at_100
value: 61.769
- type: map_at_1000
value: 61.769
- type: map_at_3
value: 57.443999999999996
- type: map_at_5
value: 60.058
- type: mrr_at_1
value: 46.088
- type: mrr_at_10
value: 61.861
- type: mrr_at_100
value: 62.117999999999995
- type: mrr_at_1000
value: 62.117999999999995
- type: mrr_at_3
value: 57.729
- type: mrr_at_5
value: 60.392
- type: ndcg_at_1
value: 45.164
- type: ndcg_at_10
value: 69.72
- type: ndcg_at_100
value: 70.719
- type: ndcg_at_1000
value: 70.719
- type: ndcg_at_3
value: 61.517999999999994
- type: ndcg_at_5
value: 66.247
- type: precision_at_1
value: 45.164
- type: precision_at_10
value: 9.545
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 24.443
- type: precision_at_5
value: 16.97
- type: recall_at_1
value: 45.164
- type: recall_at_10
value: 95.448
- type: recall_at_100
value: 99.644
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 73.329
- type: recall_at_5
value: 84.851
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: v_measure
value: 50.511868162026175
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: v_measure
value: 45.007803189284004
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map
value: 64.55292107723382
- type: mrr
value: 77.66158818097877
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: cos_sim_pearson
value: 85.65459047085452
- type: cos_sim_spearman
value: 82.10729255710761
- type: euclidean_pearson
value: 82.78079159312476
- type: euclidean_spearman
value: 80.50002701880933
- type: manhattan_pearson
value: 82.41372641383016
- type: manhattan_spearman
value: 80.57412509272639
task:
type: STS
- dataset:
config: default
name: MTEB Banking77Classification
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy
value: 87.30844155844156
- type: f1
value: 87.25307322443255
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: v_measure
value: 43.20754608934859
task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: v_measure
value: 38.818037697335505
task:
type: Clustering
- dataset:
config: default
name: MTEB CQADupstackAndroidRetrieval
revision: f46a197baaae43b4f621051089b82a364682dfeb
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 35.423
- type: map_at_10
value: 47.198
- type: map_at_100
value: 48.899
- type: map_at_1000
value: 49.004
- type: map_at_3
value: 43.114999999999995
- type: map_at_5
value: 45.491
- type: mrr_at_1
value: 42.918
- type: mrr_at_10
value: 53.299
- type: mrr_at_100
value: 54.032000000000004
- type: mrr_at_1000
value: 54.055
- type: mrr_at_3
value: 50.453
- type: mrr_at_5
value: 52.205999999999996
- type: ndcg_at_1
value: 42.918
- type: ndcg_at_10
value: 53.98
- type: ndcg_at_100
value: 59.57
- type: ndcg_at_1000
value: 60.879000000000005
- type: ndcg_at_3
value: 48.224000000000004
- type: ndcg_at_5
value: 50.998
- type: precision_at_1
value: 42.918
- type: precision_at_10
value: 10.299999999999999
- type: precision_at_100
value: 1.687
- type: precision_at_1000
value: 0.211
- type: precision_at_3
value: 22.842000000000002
- type: precision_at_5
value: 16.681
- type: recall_at_1
value: 35.423
- type: recall_at_10
value: 66.824
- type: recall_at_100
value: 89.564
- type: recall_at_1000
value: 97.501
- type: recall_at_3
value: 50.365
- type: recall_at_5
value: 57.921
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackEnglishRetrieval
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 33.205
- type: map_at_10
value: 44.859
- type: map_at_100
value: 46.135
- type: map_at_1000
value: 46.259
- type: map_at_3
value: 41.839
- type: map_at_5
value: 43.662
- type: mrr_at_1
value: 41.146
- type: mrr_at_10
value: 50.621
- type: mrr_at_100
value: 51.207
- type: mrr_at_1000
value: 51.246
- type: mrr_at_3
value: 48.535000000000004
- type: mrr_at_5
value: 49.818
- type: ndcg_at_1
value: 41.146
- type: ndcg_at_10
value: 50.683
- type: ndcg_at_100
value: 54.82
- type: ndcg_at_1000
value: 56.69
- type: ndcg_at_3
value: 46.611000000000004
- type: ndcg_at_5
value: 48.66
- type: precision_at_1
value: 41.146
- type: precision_at_10
value: 9.439
- type: precision_at_100
value: 1.465
- type: precision_at_1000
value: 0.194
- type: precision_at_3
value: 22.59
- type: precision_at_5
value: 15.86
- type: recall_at_1
value: 33.205
- type: recall_at_10
value: 61.028999999999996
- type: recall_at_100
value: 78.152
- type: recall_at_1000
value: 89.59700000000001
- type: recall_at_3
value: 49.05
- type: recall_at_5
value: 54.836
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackGamingRetrieval
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 41.637
- type: map_at_10
value: 55.162
- type: map_at_100
value: 56.142
- type: map_at_1000
value: 56.188
- type: map_at_3
value: 51.564
- type: map_at_5
value: 53.696
- type: mrr_at_1
value: 47.524
- type: mrr_at_10
value: 58.243
- type: mrr_at_100
value: 58.879999999999995
- type: mrr_at_1000
value: 58.9
- type: mrr_at_3
value: 55.69499999999999
- type: mrr_at_5
value: 57.284
- type: ndcg_at_1
value: 47.524
- type: ndcg_at_10
value: 61.305
- type: ndcg_at_100
value: 65.077
- type: ndcg_at_1000
value: 65.941
- type: ndcg_at_3
value: 55.422000000000004
- type: ndcg_at_5
value: 58.516
- type: precision_at_1
value: 47.524
- type: precision_at_10
value: 9.918000000000001
- type: precision_at_100
value: 1.276
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 24.765
- type: precision_at_5
value: 17.204
- type: recall_at_1
value: 41.637
- type: recall_at_10
value: 76.185
- type: recall_at_100
value: 92.149
- type: recall_at_1000
value: 98.199
- type: recall_at_3
value: 60.856
- type: recall_at_5
value: 68.25099999999999
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackGisRetrieval
revision: 5003b3064772da1887988e05400cf3806fe491f2
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 26.27
- type: map_at_10
value: 37.463
- type: map_at_100
value: 38.434000000000005
- type: map_at_1000
value: 38.509
- type: map_at_3
value: 34.226
- type: map_at_5
value: 36.161
- type: mrr_at_1
value: 28.588
- type: mrr_at_10
value: 39.383
- type: mrr_at_100
value: 40.23
- type: mrr_at_1000
value: 40.281
- type: mrr_at_3
value: 36.422
- type: mrr_at_5
value: 38.252
- type: ndcg_at_1
value: 28.588
- type: ndcg_at_10
value: 43.511
- type: ndcg_at_100
value: 48.274
- type: ndcg_at_1000
value: 49.975
- type: ndcg_at_3
value: 37.319
- type: ndcg_at_5
value: 40.568
- type: precision_at_1
value: 28.588
- type: precision_at_10
value: 6.893000000000001
- type: precision_at_100
value: 0.9900000000000001
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 16.347
- type: precision_at_5
value: 11.661000000000001
- type: recall_at_1
value: 26.27
- type: recall_at_10
value: 60.284000000000006
- type: recall_at_100
value: 81.902
- type: recall_at_1000
value: 94.43
- type: recall_at_3
value: 43.537
- type: recall_at_5
value: 51.475
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackMathematicaRetrieval
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 18.168
- type: map_at_10
value: 28.410000000000004
- type: map_at_100
value: 29.78
- type: map_at_1000
value: 29.892999999999997
- type: map_at_3
value: 25.238
- type: map_at_5
value: 26.96
- type: mrr_at_1
value: 23.507
- type: mrr_at_10
value: 33.382
- type: mrr_at_100
value: 34.404
- type: mrr_at_1000
value: 34.467999999999996
- type: mrr_at_3
value: 30.637999999999998
- type: mrr_at_5
value: 32.199
- type: ndcg_at_1
value: 23.507
- type: ndcg_at_10
value: 34.571000000000005
- type: ndcg_at_100
value: 40.663
- type: ndcg_at_1000
value: 43.236000000000004
- type: ndcg_at_3
value: 29.053
- type: ndcg_at_5
value: 31.563999999999997
- type: precision_at_1
value: 23.507
- type: precision_at_10
value: 6.654
- type: precision_at_100
value: 1.113
- type: precision_at_1000
value: 0.146
- type: precision_at_3
value: 14.427999999999999
- type: precision_at_5
value: 10.498000000000001
- type: recall_at_1
value: 18.168
- type: recall_at_10
value: 48.443000000000005
- type: recall_at_100
value: 74.47
- type: recall_at_1000
value: 92.494
- type: recall_at_3
value: 33.379999999999995
- type: recall_at_5
value: 39.76
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackPhysicsRetrieval
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 32.39
- type: map_at_10
value: 44.479
- type: map_at_100
value: 45.977000000000004
- type: map_at_1000
value: 46.087
- type: map_at_3
value: 40.976
- type: map_at_5
value: 43.038
- type: mrr_at_1
value: 40.135
- type: mrr_at_10
value: 50.160000000000004
- type: mrr_at_100
value: 51.052
- type: mrr_at_1000
value: 51.087
- type: mrr_at_3
value: 47.818
- type: mrr_at_5
value: 49.171
- type: ndcg_at_1
value: 40.135
- type: ndcg_at_10
value: 50.731
- type: ndcg_at_100
value: 56.452000000000005
- type: ndcg_at_1000
value: 58.123000000000005
- type: ndcg_at_3
value: 45.507
- type: ndcg_at_5
value: 48.11
- type: precision_at_1
value: 40.135
- type: precision_at_10
value: 9.192
- type: precision_at_100
value: 1.397
- type: precision_at_1000
value: 0.169
- type: precision_at_3
value: 21.816
- type: precision_at_5
value: 15.476
- type: recall_at_1
value: 32.39
- type: recall_at_10
value: 63.597
- type: recall_at_100
value: 86.737
- type: recall_at_1000
value: 97.039
- type: recall_at_3
value: 48.906
- type: recall_at_5
value: 55.659000000000006
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackProgrammersRetrieval
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 28.397
- type: map_at_10
value: 39.871
- type: map_at_100
value: 41.309000000000005
- type: map_at_1000
value: 41.409
- type: map_at_3
value: 36.047000000000004
- type: map_at_5
value: 38.104
- type: mrr_at_1
value: 34.703
- type: mrr_at_10
value: 44.773
- type: mrr_at_100
value: 45.64
- type: mrr_at_1000
value: 45.678999999999995
- type: mrr_at_3
value: 41.705
- type: mrr_at_5
value: 43.406
- type: ndcg_at_1
value: 34.703
- type: ndcg_at_10
value: 46.271
- type: ndcg_at_100
value: 52.037
- type: ndcg_at_1000
value: 53.81700000000001
- type: ndcg_at_3
value: 39.966
- type: ndcg_at_5
value: 42.801
- type: precision_at_1
value: 34.703
- type: precision_at_10
value: 8.744
- type: precision_at_100
value: 1.348
- type: precision_at_1000
value: 0.167
- type: precision_at_3
value: 19.102
- type: precision_at_5
value: 13.836
- type: recall_at_1
value: 28.397
- type: recall_at_10
value: 60.299
- type: recall_at_100
value: 84.595
- type: recall_at_1000
value: 96.155
- type: recall_at_3
value: 43.065
- type: recall_at_5
value: 50.371
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackRetrieval
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 28.044333333333338
- type: map_at_10
value: 38.78691666666666
- type: map_at_100
value: 40.113
- type: map_at_1000
value: 40.22125
- type: map_at_3
value: 35.52966666666667
- type: map_at_5
value: 37.372749999999996
- type: mrr_at_1
value: 33.159083333333335
- type: mrr_at_10
value: 42.913583333333335
- type: mrr_at_100
value: 43.7845
- type: mrr_at_1000
value: 43.830333333333336
- type: mrr_at_3
value: 40.29816666666667
- type: mrr_at_5
value: 41.81366666666667
- type: ndcg_at_1
value: 33.159083333333335
- type: ndcg_at_10
value: 44.75750000000001
- type: ndcg_at_100
value: 50.13658333333334
- type: ndcg_at_1000
value: 52.037
- type: ndcg_at_3
value: 39.34258333333334
- type: ndcg_at_5
value: 41.93708333333333
- type: precision_at_1
value: 33.159083333333335
- type: precision_at_10
value: 7.952416666666667
- type: precision_at_100
value: 1.2571666666666668
- type: precision_at_1000
value: 0.16099999999999998
- type: precision_at_3
value: 18.303833333333337
- type: precision_at_5
value: 13.057083333333333
- type: recall_at_1
value: 28.044333333333338
- type: recall_at_10
value: 58.237249999999996
- type: recall_at_100
value: 81.35391666666666
- type: recall_at_1000
value: 94.21283333333334
- type: recall_at_3
value: 43.32341666666667
- type: recall_at_5
value: 49.94908333333333
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackStatsRetrieval
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 27.838
- type: map_at_10
value: 36.04
- type: map_at_100
value: 37.113
- type: map_at_1000
value: 37.204
- type: map_at_3
value: 33.585
- type: map_at_5
value: 34.845
- type: mrr_at_1
value: 30.982
- type: mrr_at_10
value: 39.105000000000004
- type: mrr_at_100
value: 39.98
- type: mrr_at_1000
value: 40.042
- type: mrr_at_3
value: 36.912
- type: mrr_at_5
value: 38.062000000000005
- type: ndcg_at_1
value: 30.982
- type: ndcg_at_10
value: 40.982
- type: ndcg_at_100
value: 46.092
- type: ndcg_at_1000
value: 48.25
- type: ndcg_at_3
value: 36.41
- type: ndcg_at_5
value: 38.379999999999995
- type: precision_at_1
value: 30.982
- type: precision_at_10
value: 6.534
- type: precision_at_100
value: 0.9820000000000001
- type: precision_at_1000
value: 0.124
- type: precision_at_3
value: 15.745999999999999
- type: precision_at_5
value: 10.828
- type: recall_at_1
value: 27.838
- type: recall_at_10
value: 52.971000000000004
- type: recall_at_100
value: 76.357
- type: recall_at_1000
value: 91.973
- type: recall_at_3
value: 40.157
- type: recall_at_5
value: 45.147999999999996
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackTexRetrieval
revision: 46989137a86843e03a6195de44b09deda022eec7
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 19.059
- type: map_at_10
value: 27.454
- type: map_at_100
value: 28.736
- type: map_at_1000
value: 28.865000000000002
- type: map_at_3
value: 24.773999999999997
- type: map_at_5
value: 26.266000000000002
- type: mrr_at_1
value: 23.125
- type: mrr_at_10
value: 31.267
- type: mrr_at_100
value: 32.32
- type: mrr_at_1000
value: 32.394
- type: mrr_at_3
value: 28.894
- type: mrr_at_5
value: 30.281000000000002
- type: ndcg_at_1
value: 23.125
- type: ndcg_at_10
value: 32.588
- type: ndcg_at_100
value: 38.432
- type: ndcg_at_1000
value: 41.214
- type: ndcg_at_3
value: 27.938000000000002
- type: ndcg_at_5
value: 30.127
- type: precision_at_1
value: 23.125
- type: precision_at_10
value: 5.9639999999999995
- type: precision_at_100
value: 1.047
- type: precision_at_1000
value: 0.148
- type: precision_at_3
value: 13.294
- type: precision_at_5
value: 9.628
- type: recall_at_1
value: 19.059
- type: recall_at_10
value: 44.25
- type: recall_at_100
value: 69.948
- type: recall_at_1000
value: 89.35300000000001
- type: recall_at_3
value: 31.114000000000004
- type: recall_at_5
value: 36.846000000000004
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackUnixRetrieval
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 28.355999999999998
- type: map_at_10
value: 39.055
- type: map_at_100
value: 40.486
- type: map_at_1000
value: 40.571
- type: map_at_3
value: 35.69
- type: map_at_5
value: 37.605
- type: mrr_at_1
value: 33.302
- type: mrr_at_10
value: 42.986000000000004
- type: mrr_at_100
value: 43.957
- type: mrr_at_1000
value: 43.996
- type: mrr_at_3
value: 40.111999999999995
- type: mrr_at_5
value: 41.735
- type: ndcg_at_1
value: 33.302
- type: ndcg_at_10
value: 44.962999999999994
- type: ndcg_at_100
value: 50.917
- type: ndcg_at_1000
value: 52.622
- type: ndcg_at_3
value: 39.182
- type: ndcg_at_5
value: 41.939
- type: precision_at_1
value: 33.302
- type: precision_at_10
value: 7.779999999999999
- type: precision_at_100
value: 1.203
- type: precision_at_1000
value: 0.145
- type: precision_at_3
value: 18.035
- type: precision_at_5
value: 12.873000000000001
- type: recall_at_1
value: 28.355999999999998
- type: recall_at_10
value: 58.782000000000004
- type: recall_at_100
value: 84.02199999999999
- type: recall_at_1000
value: 95.511
- type: recall_at_3
value: 43.126999999999995
- type: recall_at_5
value: 50.14999999999999
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackWebmastersRetrieval
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 27.391
- type: map_at_10
value: 37.523
- type: map_at_100
value: 39.312000000000005
- type: map_at_1000
value: 39.54
- type: map_at_3
value: 34.231
- type: map_at_5
value: 36.062
- type: mrr_at_1
value: 32.016
- type: mrr_at_10
value: 41.747
- type: mrr_at_100
value: 42.812
- type: mrr_at_1000
value: 42.844
- type: mrr_at_3
value: 39.129999999999995
- type: mrr_at_5
value: 40.524
- type: ndcg_at_1
value: 32.016
- type: ndcg_at_10
value: 43.826
- type: ndcg_at_100
value: 50.373999999999995
- type: ndcg_at_1000
value: 52.318
- type: ndcg_at_3
value: 38.479
- type: ndcg_at_5
value: 40.944
- type: precision_at_1
value: 32.016
- type: precision_at_10
value: 8.280999999999999
- type: precision_at_100
value: 1.6760000000000002
- type: precision_at_1000
value: 0.25
- type: precision_at_3
value: 18.05
- type: precision_at_5
value: 13.083
- type: recall_at_1
value: 27.391
- type: recall_at_10
value: 56.928999999999995
- type: recall_at_100
value: 85.169
- type: recall_at_1000
value: 96.665
- type: recall_at_3
value: 42.264
- type: recall_at_5
value: 48.556
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackWordpressRetrieval
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 18.398
- type: map_at_10
value: 27.929
- type: map_at_100
value: 29.032999999999998
- type: map_at_1000
value: 29.126
- type: map_at_3
value: 25.070999999999998
- type: map_at_5
value: 26.583000000000002
- type: mrr_at_1
value: 19.963
- type: mrr_at_10
value: 29.997
- type: mrr_at_100
value: 30.9
- type: mrr_at_1000
value: 30.972
- type: mrr_at_3
value: 27.264
- type: mrr_at_5
value: 28.826
- type: ndcg_at_1
value: 19.963
- type: ndcg_at_10
value: 33.678999999999995
- type: ndcg_at_100
value: 38.931
- type: ndcg_at_1000
value: 41.379
- type: ndcg_at_3
value: 28.000000000000004
- type: ndcg_at_5
value: 30.637999999999998
- type: precision_at_1
value: 19.963
- type: precision_at_10
value: 5.7299999999999995
- type: precision_at_100
value: 0.902
- type: precision_at_1000
value: 0.122
- type: precision_at_3
value: 12.631
- type: precision_at_5
value: 9.057
- type: recall_at_1
value: 18.398
- type: recall_at_10
value: 49.254
- type: recall_at_100
value: 73.182
- type: recall_at_1000
value: 91.637
- type: recall_at_3
value: 34.06
- type: recall_at_5
value: 40.416000000000004
task:
type: Retrieval
- dataset:
config: default
name: MTEB ClimateFEVER
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
split: test
type: mteb/climate-fever
metrics:
- type: map_at_1
value: 19.681
- type: map_at_10
value: 32.741
- type: map_at_100
value: 34.811
- type: map_at_1000
value: 35.003
- type: map_at_3
value: 27.697
- type: map_at_5
value: 30.372
- type: mrr_at_1
value: 44.951
- type: mrr_at_10
value: 56.34400000000001
- type: mrr_at_100
value: 56.961
- type: mrr_at_1000
value: 56.987
- type: mrr_at_3
value: 53.681
- type: mrr_at_5
value: 55.407
- type: ndcg_at_1
value: 44.951
- type: ndcg_at_10
value: 42.905
- type: ndcg_at_100
value: 49.95
- type: ndcg_at_1000
value: 52.917
- type: ndcg_at_3
value: 36.815
- type: ndcg_at_5
value: 38.817
- type: precision_at_1
value: 44.951
- type: precision_at_10
value: 12.989999999999998
- type: precision_at_100
value: 2.068
- type: precision_at_1000
value: 0.263
- type: precision_at_3
value: 27.275
- type: precision_at_5
value: 20.365
- type: recall_at_1
value: 19.681
- type: recall_at_10
value: 48.272999999999996
- type: recall_at_100
value: 71.87400000000001
- type: recall_at_1000
value: 87.929
- type: recall_at_3
value: 32.653999999999996
- type: recall_at_5
value: 39.364
task:
type: Retrieval
- dataset:
config: default
name: MTEB DBPedia
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
split: test
type: mteb/dbpedia
metrics:
- type: map_at_1
value: 10.231
- type: map_at_10
value: 22.338
- type: map_at_100
value: 31.927
- type: map_at_1000
value: 33.87
- type: map_at_3
value: 15.559999999999999
- type: map_at_5
value: 18.239
- type: mrr_at_1
value: 75.0
- type: mrr_at_10
value: 81.303
- type: mrr_at_100
value: 81.523
- type: mrr_at_1000
value: 81.53
- type: mrr_at_3
value: 80.083
- type: mrr_at_5
value: 80.758
- type: ndcg_at_1
value: 64.625
- type: ndcg_at_10
value: 48.687000000000005
- type: ndcg_at_100
value: 52.791
- type: ndcg_at_1000
value: 60.041999999999994
- type: ndcg_at_3
value: 53.757999999999996
- type: ndcg_at_5
value: 50.76500000000001
- type: precision_at_1
value: 75.0
- type: precision_at_10
value: 38.3
- type: precision_at_100
value: 12.025
- type: precision_at_1000
value: 2.3970000000000002
- type: precision_at_3
value: 55.417
- type: precision_at_5
value: 47.5
- type: recall_at_1
value: 10.231
- type: recall_at_10
value: 27.697
- type: recall_at_100
value: 57.409
- type: recall_at_1000
value: 80.547
- type: recall_at_3
value: 16.668
- type: recall_at_5
value: 20.552
task:
type: Retrieval
- dataset:
config: default
name: MTEB EmotionClassification
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
split: test
type: mteb/emotion
metrics:
- type: accuracy
value: 61.365
- type: f1
value: 56.7540827912991
task:
type: Classification
- dataset:
config: default
name: MTEB FEVER
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
split: test
type: mteb/fever
metrics:
- type: map_at_1
value: 83.479
- type: map_at_10
value: 88.898
- type: map_at_100
value: 89.11
- type: map_at_1000
value: 89.12400000000001
- type: map_at_3
value: 88.103
- type: map_at_5
value: 88.629
- type: mrr_at_1
value: 89.934
- type: mrr_at_10
value: 93.91000000000001
- type: mrr_at_100
value: 93.937
- type: mrr_at_1000
value: 93.938
- type: mrr_at_3
value: 93.62700000000001
- type: mrr_at_5
value: 93.84599999999999
- type: ndcg_at_1
value: 89.934
- type: ndcg_at_10
value: 91.574
- type: ndcg_at_100
value: 92.238
- type: ndcg_at_1000
value: 92.45
- type: ndcg_at_3
value: 90.586
- type: ndcg_at_5
value: 91.16300000000001
- type: precision_at_1
value: 89.934
- type: precision_at_10
value: 10.555
- type: precision_at_100
value: 1.1159999999999999
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 33.588
- type: precision_at_5
value: 20.642
- type: recall_at_1
value: 83.479
- type: recall_at_10
value: 94.971
- type: recall_at_100
value: 97.397
- type: recall_at_1000
value: 98.666
- type: recall_at_3
value: 92.24799999999999
- type: recall_at_5
value: 93.797
task:
type: Retrieval
- dataset:
config: default
name: MTEB FiQA2018
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
split: test
type: mteb/fiqa
metrics:
- type: map_at_1
value: 27.16
- type: map_at_10
value: 45.593
- type: map_at_100
value: 47.762
- type: map_at_1000
value: 47.899
- type: map_at_3
value: 39.237
- type: map_at_5
value: 42.970000000000006
- type: mrr_at_1
value: 52.623
- type: mrr_at_10
value: 62.637
- type: mrr_at_100
value: 63.169
- type: mrr_at_1000
value: 63.185
- type: mrr_at_3
value: 59.928000000000004
- type: mrr_at_5
value: 61.702999999999996
- type: ndcg_at_1
value: 52.623
- type: ndcg_at_10
value: 54.701
- type: ndcg_at_100
value: 61.263
- type: ndcg_at_1000
value: 63.134
- type: ndcg_at_3
value: 49.265
- type: ndcg_at_5
value: 51.665000000000006
- type: precision_at_1
value: 52.623
- type: precision_at_10
value: 15.185
- type: precision_at_100
value: 2.202
- type: precision_at_1000
value: 0.254
- type: precision_at_3
value: 32.767
- type: precision_at_5
value: 24.722
- type: recall_at_1
value: 27.16
- type: recall_at_10
value: 63.309000000000005
- type: recall_at_100
value: 86.722
- type: recall_at_1000
value: 97.505
- type: recall_at_3
value: 45.045
- type: recall_at_5
value: 54.02400000000001
task:
type: Retrieval
- dataset:
config: default
name: MTEB HotpotQA
revision: ab518f4d6fcca38d87c25209f94beba119d02014
split: test
type: mteb/hotpotqa
metrics:
- type: map_at_1
value: 42.573
- type: map_at_10
value: 59.373
- type: map_at_100
value: 60.292
- type: map_at_1000
value: 60.358999999999995
- type: map_at_3
value: 56.159000000000006
- type: map_at_5
value: 58.123999999999995
- type: mrr_at_1
value: 85.14500000000001
- type: mrr_at_10
value: 89.25999999999999
- type: mrr_at_100
value: 89.373
- type: mrr_at_1000
value: 89.377
- type: mrr_at_3
value: 88.618
- type: mrr_at_5
value: 89.036
- type: ndcg_at_1
value: 85.14500000000001
- type: ndcg_at_10
value: 68.95
- type: ndcg_at_100
value: 71.95
- type: ndcg_at_1000
value: 73.232
- type: ndcg_at_3
value: 64.546
- type: ndcg_at_5
value: 66.945
- type: precision_at_1
value: 85.14500000000001
- type: precision_at_10
value: 13.865
- type: precision_at_100
value: 1.619
- type: precision_at_1000
value: 0.179
- type: precision_at_3
value: 39.703
- type: precision_at_5
value: 25.718000000000004
- type: recall_at_1
value: 42.573
- type: recall_at_10
value: 69.325
- type: recall_at_100
value: 80.932
- type: recall_at_1000
value: 89.446
- type: recall_at_3
value: 59.553999999999995
- type: recall_at_5
value: 64.294
task:
type: Retrieval
- dataset:
config: default
name: MTEB ImdbClassification
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
split: test
type: mteb/imdb
metrics:
- type: accuracy
value: 95.8336
- type: ap
value: 93.78862962194073
- type: f1
value: 95.83192650728371
task:
type: Classification
- dataset:
config: default
name: MTEB MSMARCO
revision: c5a29a104738b98a9e76336939199e264163d4a0
split: dev
type: mteb/msmarco
metrics:
- type: map_at_1
value: 23.075000000000003
- type: map_at_10
value: 36.102000000000004
- type: map_at_100
value: 37.257
- type: map_at_1000
value: 37.3
- type: map_at_3
value: 32.144
- type: map_at_5
value: 34.359
- type: mrr_at_1
value: 23.711
- type: mrr_at_10
value: 36.671
- type: mrr_at_100
value: 37.763999999999996
- type: mrr_at_1000
value: 37.801
- type: mrr_at_3
value: 32.775
- type: mrr_at_5
value: 34.977000000000004
- type: ndcg_at_1
value: 23.711
- type: ndcg_at_10
value: 43.361
- type: ndcg_at_100
value: 48.839
- type: ndcg_at_1000
value: 49.88
- type: ndcg_at_3
value: 35.269
- type: ndcg_at_5
value: 39.224
- type: precision_at_1
value: 23.711
- type: precision_at_10
value: 6.866999999999999
- type: precision_at_100
value: 0.96
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 15.096000000000002
- type: precision_at_5
value: 11.083
- type: recall_at_1
value: 23.075000000000003
- type: recall_at_10
value: 65.756
- type: recall_at_100
value: 90.88199999999999
- type: recall_at_1000
value: 98.739
- type: recall_at_3
value: 43.691
- type: recall_at_5
value: 53.15800000000001
task:
type: Retrieval
- dataset:
config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 97.69493844049248
- type: f1
value: 97.55048089616261
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 88.75968992248062
- type: f1
value: 72.26321223399123
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 82.40080699394754
- type: f1
value: 79.62590029057968
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 84.49562878278414
- type: f1
value: 84.0040193313333
task:
type: Classification
- dataset:
config: default
name: MTEB MedrxivClusteringP2P
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
metrics:
- type: v_measure
value: 39.386760057101945
task:
type: Clustering
- dataset:
config: default
name: MTEB MedrxivClusteringS2S
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
metrics:
- type: v_measure
value: 37.89687154075537
task:
type: Clustering
- dataset:
config: default
name: MTEB MindSmallReranking
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
split: test
type: mteb/mind_small
metrics:
- type: map
value: 33.94151656057482
- type: mrr
value: 35.32684700746953
task:
type: Reranking
- dataset:
config: default
name: MTEB NFCorpus
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
split: test
type: mteb/nfcorpus
metrics:
- type: map_at_1
value: 6.239999999999999
- type: map_at_10
value: 14.862
- type: map_at_100
value: 18.955
- type: map_at_1000
value: 20.694000000000003
- type: map_at_3
value: 10.683
- type: map_at_5
value: 12.674
- type: mrr_at_1
value: 50.15500000000001
- type: mrr_at_10
value: 59.697
- type: mrr_at_100
value: 60.095
- type: mrr_at_1000
value: 60.129999999999995
- type: mrr_at_3
value: 58.35900000000001
- type: mrr_at_5
value: 58.839
- type: ndcg_at_1
value: 48.452
- type: ndcg_at_10
value: 39.341
- type: ndcg_at_100
value: 35.866
- type: ndcg_at_1000
value: 45.111000000000004
- type: ndcg_at_3
value: 44.527
- type: ndcg_at_5
value: 42.946
- type: precision_at_1
value: 50.15500000000001
- type: precision_at_10
value: 29.536
- type: precision_at_100
value: 9.142
- type: precision_at_1000
value: 2.2849999999999997
- type: precision_at_3
value: 41.899
- type: precision_at_5
value: 37.647000000000006
- type: recall_at_1
value: 6.239999999999999
- type: recall_at_10
value: 19.278000000000002
- type: recall_at_100
value: 36.074
- type: recall_at_1000
value: 70.017
- type: recall_at_3
value: 12.066
- type: recall_at_5
value: 15.254000000000001
task:
type: Retrieval
- dataset:
config: default
name: MTEB NQ
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
split: test
type: mteb/nq
metrics:
- type: map_at_1
value: 39.75
- type: map_at_10
value: 56.443
- type: map_at_100
value: 57.233999999999995
- type: map_at_1000
value: 57.249
- type: map_at_3
value: 52.032999999999994
- type: map_at_5
value: 54.937999999999995
- type: mrr_at_1
value: 44.728
- type: mrr_at_10
value: 58.939
- type: mrr_at_100
value: 59.489000000000004
- type: mrr_at_1000
value: 59.499
- type: mrr_at_3
value: 55.711999999999996
- type: mrr_at_5
value: 57.89
- type: ndcg_at_1
value: 44.728
- type: ndcg_at_10
value: 63.998999999999995
- type: ndcg_at_100
value: 67.077
- type: ndcg_at_1000
value: 67.40899999999999
- type: ndcg_at_3
value: 56.266000000000005
- type: ndcg_at_5
value: 60.88
- type: precision_at_1
value: 44.728
- type: precision_at_10
value: 10.09
- type: precision_at_100
value: 1.1809999999999998
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 25.145
- type: precision_at_5
value: 17.822
- type: recall_at_1
value: 39.75
- type: recall_at_10
value: 84.234
- type: recall_at_100
value: 97.055
- type: recall_at_1000
value: 99.517
- type: recall_at_3
value: 64.851
- type: recall_at_5
value: 75.343
task:
type: Retrieval
- dataset:
config: default
name: MTEB QuoraRetrieval
revision: None
split: test
type: mteb/quora
metrics:
- type: map_at_1
value: 72.085
- type: map_at_10
value: 86.107
- type: map_at_100
value: 86.727
- type: map_at_1000
value: 86.74
- type: map_at_3
value: 83.21
- type: map_at_5
value: 85.06
- type: mrr_at_1
value: 82.94
- type: mrr_at_10
value: 88.845
- type: mrr_at_100
value: 88.926
- type: mrr_at_1000
value: 88.927
- type: mrr_at_3
value: 87.993
- type: mrr_at_5
value: 88.62299999999999
- type: ndcg_at_1
value: 82.97
- type: ndcg_at_10
value: 89.645
- type: ndcg_at_100
value: 90.717
- type: ndcg_at_1000
value: 90.78
- type: ndcg_at_3
value: 86.99900000000001
- type: ndcg_at_5
value: 88.52600000000001
- type: precision_at_1
value: 82.97
- type: precision_at_10
value: 13.569
- type: precision_at_100
value: 1.539
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 38.043
- type: precision_at_5
value: 24.992
- type: recall_at_1
value: 72.085
- type: recall_at_10
value: 96.262
- type: recall_at_100
value: 99.77000000000001
- type: recall_at_1000
value: 99.997
- type: recall_at_3
value: 88.652
- type: recall_at_5
value: 93.01899999999999
task:
type: Retrieval
- dataset:
config: default
name: MTEB RedditClustering
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
metrics:
- type: v_measure
value: 55.82153952668092
task:
type: Clustering
- dataset:
config: default
name: MTEB RedditClusteringP2P
revision: 282350215ef01743dc01b456c7f5241fa8937f16
split: test
type: mteb/reddit-clustering-p2p
metrics:
- type: v_measure
value: 62.094465801879295
task:
type: Clustering
- dataset:
config: default
name: MTEB SCIDOCS
revision: None
split: test
type: mteb/scidocs
metrics:
- type: map_at_1
value: 5.688
- type: map_at_10
value: 15.201999999999998
- type: map_at_100
value: 18.096
- type: map_at_1000
value: 18.481
- type: map_at_3
value: 10.734
- type: map_at_5
value: 12.94
- type: mrr_at_1
value: 28.000000000000004
- type: mrr_at_10
value: 41.101
- type: mrr_at_100
value: 42.202
- type: mrr_at_1000
value: 42.228
- type: mrr_at_3
value: 37.683
- type: mrr_at_5
value: 39.708
- type: ndcg_at_1
value: 28.000000000000004
- type: ndcg_at_10
value: 24.976000000000003
- type: ndcg_at_100
value: 35.129
- type: ndcg_at_1000
value: 40.77
- type: ndcg_at_3
value: 23.787
- type: ndcg_at_5
value: 20.816000000000003
- type: precision_at_1
value: 28.000000000000004
- type: precision_at_10
value: 13.04
- type: precision_at_100
value: 2.761
- type: precision_at_1000
value: 0.41000000000000003
- type: precision_at_3
value: 22.6
- type: precision_at_5
value: 18.52
- type: recall_at_1
value: 5.688
- type: recall_at_10
value: 26.43
- type: recall_at_100
value: 56.02
- type: recall_at_1000
value: 83.21
- type: recall_at_3
value: 13.752
- type: recall_at_5
value: 18.777
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-R
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
split: test
type: mteb/sickr-sts
metrics:
- type: cos_sim_pearson
value: 85.15084859283178
- type: cos_sim_spearman
value: 80.49030614009419
- type: euclidean_pearson
value: 81.84574978672468
- type: euclidean_spearman
value: 79.89787150656818
- type: manhattan_pearson
value: 81.63076538567131
- type: manhattan_spearman
value: 79.69867352121841
task:
type: STS
- dataset:
config: default
name: MTEB STS12
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cos_sim_pearson
value: 84.64097921490992
- type: cos_sim_spearman
value: 77.25370084896514
- type: euclidean_pearson
value: 82.71210826468788
- type: euclidean_spearman
value: 78.50445584994826
- type: manhattan_pearson
value: 82.92580164330298
- type: manhattan_spearman
value: 78.69686891301019
task:
type: STS
- dataset:
config: default
name: MTEB STS13
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cos_sim_pearson
value: 87.24596417308994
- type: cos_sim_spearman
value: 87.79454220555091
- type: euclidean_pearson
value: 87.40242561671164
- type: euclidean_spearman
value: 88.25955597373556
- type: manhattan_pearson
value: 87.25160240485849
- type: manhattan_spearman
value: 88.155794979818
task:
type: STS
- dataset:
config: default
name: MTEB STS14
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cos_sim_pearson
value: 84.44914233422564
- type: cos_sim_spearman
value: 82.91015471820322
- type: euclidean_pearson
value: 84.7206656630327
- type: euclidean_spearman
value: 83.86408872059216
- type: manhattan_pearson
value: 84.72816725158454
- type: manhattan_spearman
value: 84.01603388572788
task:
type: STS
- dataset:
config: default
name: MTEB STS15
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cos_sim_pearson
value: 87.6168026237477
- type: cos_sim_spearman
value: 88.45414278092397
- type: euclidean_pearson
value: 88.57023240882022
- type: euclidean_spearman
value: 89.04102190922094
- type: manhattan_pearson
value: 88.66695535796354
- type: manhattan_spearman
value: 89.19898476680969
task:
type: STS
- dataset:
config: default
name: MTEB STS16
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cos_sim_pearson
value: 84.27925826089424
- type: cos_sim_spearman
value: 85.45291099550461
- type: euclidean_pearson
value: 83.63853036580834
- type: euclidean_spearman
value: 84.33468035821484
- type: manhattan_pearson
value: 83.72778773251596
- type: manhattan_spearman
value: 84.51583132445376
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 89.67375185692552
- type: cos_sim_spearman
value: 90.32542469203855
- type: euclidean_pearson
value: 89.63513717951847
- type: euclidean_spearman
value: 89.87760271003745
- type: manhattan_pearson
value: 89.28381452982924
- type: manhattan_spearman
value: 89.53568197785721
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 66.24644693819846
- type: cos_sim_spearman
value: 66.09889420525377
- type: euclidean_pearson
value: 63.72551583520747
- type: euclidean_spearman
value: 63.01385470780679
- type: manhattan_pearson
value: 64.09258157214097
- type: manhattan_spearman
value: 63.080517752822594
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cos_sim_pearson
value: 86.27321463839989
- type: cos_sim_spearman
value: 86.37572865993327
- type: euclidean_pearson
value: 86.36268020198149
- type: euclidean_spearman
value: 86.31089339478922
- type: manhattan_pearson
value: 86.4260445761947
- type: manhattan_spearman
value: 86.45885895320457
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 86.52456702387798
- type: mrr
value: 96.34556529164372
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
- type: map_at_1
value: 61.99400000000001
- type: map_at_10
value: 73.38799999999999
- type: map_at_100
value: 73.747
- type: map_at_1000
value: 73.75
- type: map_at_3
value: 70.04599999999999
- type: map_at_5
value: 72.095
- type: mrr_at_1
value: 65.0
- type: mrr_at_10
value: 74.42800000000001
- type: mrr_at_100
value: 74.722
- type: mrr_at_1000
value: 74.725
- type: mrr_at_3
value: 72.056
- type: mrr_at_5
value: 73.60600000000001
- type: ndcg_at_1
value: 65.0
- type: ndcg_at_10
value: 78.435
- type: ndcg_at_100
value: 79.922
- type: ndcg_at_1000
value: 80.00500000000001
- type: ndcg_at_3
value: 73.05199999999999
- type: ndcg_at_5
value: 75.98
- type: precision_at_1
value: 65.0
- type: precision_at_10
value: 10.5
- type: precision_at_100
value: 1.123
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 28.555999999999997
- type: precision_at_5
value: 19.0
- type: recall_at_1
value: 61.99400000000001
- type: recall_at_10
value: 92.72200000000001
- type: recall_at_100
value: 99.333
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 78.739
- type: recall_at_5
value: 85.828
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: cos_sim_accuracy
value: 99.79009900990098
- type: cos_sim_ap
value: 95.3203137438653
- type: cos_sim_f1
value: 89.12386706948641
- type: cos_sim_precision
value: 89.75659229208925
- type: cos_sim_recall
value: 88.5
- type: dot_accuracy
value: 99.67821782178218
- type: dot_ap
value: 89.94069840000675
- type: dot_f1
value: 83.45902463549521
- type: dot_precision
value: 83.9231547017189
- type: dot_recall
value: 83.0
- type: euclidean_accuracy
value: 99.78613861386138
- type: euclidean_ap
value: 95.10648259135526
- type: euclidean_f1
value: 88.77338877338877
- type: euclidean_precision
value: 92.42424242424242
- type: euclidean_recall
value: 85.39999999999999
- type: manhattan_accuracy
value: 99.7950495049505
- type: manhattan_ap
value: 95.29987661320946
- type: manhattan_f1
value: 89.21313183949972
- type: manhattan_precision
value: 93.14472252448314
- type: manhattan_recall
value: 85.6
- type: max_accuracy
value: 99.7950495049505
- type: max_ap
value: 95.3203137438653
- type: max_f1
value: 89.21313183949972
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: v_measure
value: 67.65446577183913
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: v_measure
value: 46.30749237193961
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: map
value: 54.91481849959949
- type: mrr
value: 55.853506175197346
task:
type: Reranking
- dataset:
config: default
name: MTEB SummEval
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: cos_sim_pearson
value: 30.08196549170419
- type: cos_sim_spearman
value: 31.16661390597077
- type: dot_pearson
value: 29.892258410943466
- type: dot_spearman
value: 30.51328811965085
task:
type: Summarization
- dataset:
config: default
name: MTEB TRECCOVID
revision: None
split: test
type: mteb/trec-covid
metrics:
- type: map_at_1
value: 0.23900000000000002
- type: map_at_10
value: 2.173
- type: map_at_100
value: 14.24
- type: map_at_1000
value: 35.309000000000005
- type: map_at_3
value: 0.7100000000000001
- type: map_at_5
value: 1.163
- type: mrr_at_1
value: 92.0
- type: mrr_at_10
value: 96.0
- type: mrr_at_100
value: 96.0
- type: mrr_at_1000
value: 96.0
- type: mrr_at_3
value: 96.0
- type: mrr_at_5
value: 96.0
- type: ndcg_at_1
value: 90.0
- type: ndcg_at_10
value: 85.382
- type: ndcg_at_100
value: 68.03
- type: ndcg_at_1000
value: 61.021
- type: ndcg_at_3
value: 89.765
- type: ndcg_at_5
value: 88.444
- type: precision_at_1
value: 92.0
- type: precision_at_10
value: 88.0
- type: precision_at_100
value: 70.02000000000001
- type: precision_at_1000
value: 26.984
- type: precision_at_3
value: 94.0
- type: precision_at_5
value: 92.80000000000001
- type: recall_at_1
value: 0.23900000000000002
- type: recall_at_10
value: 2.313
- type: recall_at_100
value: 17.049
- type: recall_at_1000
value: 57.489999999999995
- type: recall_at_3
value: 0.737
- type: recall_at_5
value: 1.221
task:
type: Retrieval
- dataset:
config: default
name: MTEB Touche2020
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
split: test
type: mteb/touche2020
metrics:
- type: map_at_1
value: 2.75
- type: map_at_10
value: 11.29
- type: map_at_100
value: 18.032999999999998
- type: map_at_1000
value: 19.746
- type: map_at_3
value: 6.555
- type: map_at_5
value: 8.706999999999999
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 50.55
- type: mrr_at_100
value: 51.659
- type: mrr_at_1000
value: 51.659
- type: mrr_at_3
value: 47.278999999999996
- type: mrr_at_5
value: 49.728
- type: ndcg_at_1
value: 32.653
- type: ndcg_at_10
value: 27.894000000000002
- type: ndcg_at_100
value: 39.769
- type: ndcg_at_1000
value: 51.495999999999995
- type: ndcg_at_3
value: 32.954
- type: ndcg_at_5
value: 31.502999999999997
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 23.265
- type: precision_at_100
value: 7.898
- type: precision_at_1000
value: 1.58
- type: precision_at_3
value: 34.694
- type: precision_at_5
value: 31.429000000000002
- type: recall_at_1
value: 2.75
- type: recall_at_10
value: 16.953
- type: recall_at_100
value: 48.68
- type: recall_at_1000
value: 85.18599999999999
- type: recall_at_3
value: 7.710999999999999
- type: recall_at_5
value: 11.484
task:
type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy
value: 82.66099999999999
- type: ap
value: 25.555698090238337
- type: f1
value: 66.48402012461622
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 72.94567062818335
- type: f1
value: 73.28139189595674
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: v_measure
value: 49.581627240203474
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: cos_sim_accuracy
value: 87.78089050485785
- type: cos_sim_ap
value: 79.64487116574168
- type: cos_sim_f1
value: 72.46563021970964
- type: cos_sim_precision
value: 70.62359128474831
- type: cos_sim_recall
value: 74.40633245382587
- type: dot_accuracy
value: 86.2609524944865
- type: dot_ap
value: 75.513046857613
- type: dot_f1
value: 68.58213616489695
- type: dot_precision
value: 65.12455516014235
- type: dot_recall
value: 72.42744063324538
- type: euclidean_accuracy
value: 87.6080348095607
- type: euclidean_ap
value: 79.00204933649795
- type: euclidean_f1
value: 72.14495342605589
- type: euclidean_precision
value: 69.85421299728193
- type: euclidean_recall
value: 74.5910290237467
- type: manhattan_accuracy
value: 87.59611372712642
- type: manhattan_ap
value: 78.78523756706264
- type: manhattan_f1
value: 71.86499137718648
- type: manhattan_precision
value: 67.39833641404806
- type: manhattan_recall
value: 76.96569920844327
- type: max_accuracy
value: 87.78089050485785
- type: max_ap
value: 79.64487116574168
- type: max_f1
value: 72.46563021970964
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: cos_sim_accuracy
value: 89.98719292117825
- type: cos_sim_ap
value: 87.58146137353202
- type: cos_sim_f1
value: 80.28543232369239
- type: cos_sim_precision
value: 79.1735289714029
- type: cos_sim_recall
value: 81.42901139513397
- type: dot_accuracy
value: 88.9199363526992
- type: dot_ap
value: 84.98499998630417
- type: dot_f1
value: 78.21951400757969
- type: dot_precision
value: 75.58523624874336
- type: dot_recall
value: 81.04404065291038
- type: euclidean_accuracy
value: 89.77374160748244
- type: euclidean_ap
value: 87.35151562835209
- type: euclidean_f1
value: 79.92160922940393
- type: euclidean_precision
value: 76.88531587933979
- type: euclidean_recall
value: 83.20757622420696
- type: manhattan_accuracy
value: 89.72717041176699
- type: manhattan_ap
value: 87.34065592142515
- type: manhattan_f1
value: 79.85603419187943
- type: manhattan_precision
value: 77.82243332115455
- type: manhattan_recall
value: 81.99876809362489
- type: max_accuracy
value: 89.98719292117825
- type: max_ap
value: 87.58146137353202
- type: max_f1
value: 80.28543232369239
task:
type: PairClassification
- dataset:
config: default
name: MTEB AFQMC
revision: b44c3b011063adb25877c13823db83bb193913c4
split: validation
type: C-MTEB/AFQMC
metrics:
- type: cos_sim_pearson
value: 53.45954203592337
- type: cos_sim_spearman
value: 58.42154680418638
- type: euclidean_pearson
value: 56.41543791722753
- type: euclidean_spearman
value: 58.39328016640146
- type: manhattan_pearson
value: 56.318510356833876
- type: manhattan_spearman
value: 58.28423447818184
task:
type: STS
- dataset:
config: default
name: MTEB ATEC
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
split: test
type: C-MTEB/ATEC
metrics:
- type: cos_sim_pearson
value: 50.78356460675945
- type: cos_sim_spearman
value: 55.6530411663269
- type: euclidean_pearson
value: 56.50763660417816
- type: euclidean_spearman
value: 55.733823335669065
- type: manhattan_pearson
value: 56.45323093512866
- type: manhattan_spearman
value: 55.63248619032702
task:
type: STS
- dataset:
config: zh
name: MTEB AmazonReviewsClassification (zh)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 47.209999999999994
- type: f1
value: 46.08892432018655
task:
type: Classification
- dataset:
config: default
name: MTEB BQ
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
split: test
type: C-MTEB/BQ
metrics:
- type: cos_sim_pearson
value: 70.25573992001478
- type: cos_sim_spearman
value: 73.85247134951433
- type: euclidean_pearson
value: 72.60033082168442
- type: euclidean_spearman
value: 73.72445893756499
- type: manhattan_pearson
value: 72.59932284620231
- type: manhattan_spearman
value: 73.68002490614583
task:
type: STS
- dataset:
config: default
name: MTEB CLSClusteringP2P
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
split: test
type: C-MTEB/CLSClusteringP2P
metrics:
- type: v_measure
value: 45.21317724305628
task:
type: Clustering
- dataset:
config: default
name: MTEB CLSClusteringS2S
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
split: test
type: C-MTEB/CLSClusteringS2S
metrics:
- type: v_measure
value: 42.49825170976724
task:
type: Clustering
- dataset:
config: default
name: MTEB CMedQAv1
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
split: test
type: C-MTEB/CMedQAv1-reranking
metrics:
- type: map
value: 88.15661686810597
- type: mrr
value: 90.11222222222223
task:
type: Reranking
- dataset:
config: default
name: MTEB CMedQAv2
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
split: test
type: C-MTEB/CMedQAv2-reranking
metrics:
- type: map
value: 88.1204726064383
- type: mrr
value: 90.20142857142858
task:
type: Reranking
- dataset:
config: default
name: MTEB CmedqaRetrieval
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
split: dev
type: C-MTEB/CmedqaRetrieval
metrics:
- type: map_at_1
value: 27.224999999999998
- type: map_at_10
value: 40.169
- type: map_at_100
value: 42.0
- type: map_at_1000
value: 42.109
- type: map_at_3
value: 35.76
- type: map_at_5
value: 38.221
- type: mrr_at_1
value: 40.56
- type: mrr_at_10
value: 49.118
- type: mrr_at_100
value: 50.092999999999996
- type: mrr_at_1000
value: 50.133
- type: mrr_at_3
value: 46.507
- type: mrr_at_5
value: 47.973
- type: ndcg_at_1
value: 40.56
- type: ndcg_at_10
value: 46.972
- type: ndcg_at_100
value: 54.04
- type: ndcg_at_1000
value: 55.862
- type: ndcg_at_3
value: 41.36
- type: ndcg_at_5
value: 43.704
- type: precision_at_1
value: 40.56
- type: precision_at_10
value: 10.302999999999999
- type: precision_at_100
value: 1.606
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 23.064
- type: precision_at_5
value: 16.764000000000003
- type: recall_at_1
value: 27.224999999999998
- type: recall_at_10
value: 58.05200000000001
- type: recall_at_100
value: 87.092
- type: recall_at_1000
value: 99.099
- type: recall_at_3
value: 41.373
- type: recall_at_5
value: 48.453
task:
type: Retrieval
- dataset:
config: default
name: MTEB Cmnli
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
split: validation
type: C-MTEB/CMNLI
metrics:
- type: cos_sim_accuracy
value: 77.40228502705953
- type: cos_sim_ap
value: 86.22359172956327
- type: cos_sim_f1
value: 78.96328293736501
- type: cos_sim_precision
value: 73.36945615091311
- type: cos_sim_recall
value: 85.48047696983868
- type: dot_accuracy
value: 75.53818400481059
- type: dot_ap
value: 83.70164011305312
- type: dot_f1
value: 77.67298719348754
- type: dot_precision
value: 67.49482401656314
- type: dot_recall
value: 91.46598082768296
- type: euclidean_accuracy
value: 77.94347564642213
- type: euclidean_ap
value: 86.4652108728609
- type: euclidean_f1
value: 79.15555555555555
- type: euclidean_precision
value: 75.41816641964853
- type: euclidean_recall
value: 83.28267477203647
- type: manhattan_accuracy
value: 77.45039085989175
- type: manhattan_ap
value: 86.09986583900665
- type: manhattan_f1
value: 78.93669264438988
- type: manhattan_precision
value: 72.63261296660117
- type: manhattan_recall
value: 86.43909282207154
- type: max_accuracy
value: 77.94347564642213
- type: max_ap
value: 86.4652108728609
- type: max_f1
value: 79.15555555555555
task:
type: PairClassification
- dataset:
config: default
name: MTEB CovidRetrieval
revision: 1271c7809071a13532e05f25fb53511ffce77117
split: dev
type: C-MTEB/CovidRetrieval
metrics:
- type: map_at_1
value: 69.336
- type: map_at_10
value: 77.16
- type: map_at_100
value: 77.47500000000001
- type: map_at_1000
value: 77.482
- type: map_at_3
value: 75.42999999999999
- type: map_at_5
value: 76.468
- type: mrr_at_1
value: 69.44200000000001
- type: mrr_at_10
value: 77.132
- type: mrr_at_100
value: 77.43299999999999
- type: mrr_at_1000
value: 77.44
- type: mrr_at_3
value: 75.395
- type: mrr_at_5
value: 76.459
- type: ndcg_at_1
value: 69.547
- type: ndcg_at_10
value: 80.794
- type: ndcg_at_100
value: 82.245
- type: ndcg_at_1000
value: 82.40899999999999
- type: ndcg_at_3
value: 77.303
- type: ndcg_at_5
value: 79.168
- type: precision_at_1
value: 69.547
- type: precision_at_10
value: 9.305
- type: precision_at_100
value: 0.9979999999999999
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 27.749000000000002
- type: precision_at_5
value: 17.576
- type: recall_at_1
value: 69.336
- type: recall_at_10
value: 92.097
- type: recall_at_100
value: 98.736
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 82.64
- type: recall_at_5
value: 87.144
task:
type: Retrieval
- dataset:
config: default
name: MTEB DuRetrieval
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
split: dev
type: C-MTEB/DuRetrieval
metrics:
- type: map_at_1
value: 26.817999999999998
- type: map_at_10
value: 82.67
- type: map_at_100
value: 85.304
- type: map_at_1000
value: 85.334
- type: map_at_3
value: 57.336
- type: map_at_5
value: 72.474
- type: mrr_at_1
value: 91.45
- type: mrr_at_10
value: 94.272
- type: mrr_at_100
value: 94.318
- type: mrr_at_1000
value: 94.32000000000001
- type: mrr_at_3
value: 94.0
- type: mrr_at_5
value: 94.17699999999999
- type: ndcg_at_1
value: 91.45
- type: ndcg_at_10
value: 89.404
- type: ndcg_at_100
value: 91.724
- type: ndcg_at_1000
value: 91.973
- type: ndcg_at_3
value: 88.104
- type: ndcg_at_5
value: 87.25699999999999
- type: precision_at_1
value: 91.45
- type: precision_at_10
value: 42.585
- type: precision_at_100
value: 4.838
- type: precision_at_1000
value: 0.49
- type: precision_at_3
value: 78.8
- type: precision_at_5
value: 66.66
- type: recall_at_1
value: 26.817999999999998
- type: recall_at_10
value: 90.67
- type: recall_at_100
value: 98.36200000000001
- type: recall_at_1000
value: 99.583
- type: recall_at_3
value: 59.614999999999995
- type: recall_at_5
value: 77.05199999999999
task:
type: Retrieval
- dataset:
config: default
name: MTEB EcomRetrieval
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
split: dev
type: C-MTEB/EcomRetrieval
metrics:
- type: map_at_1
value: 47.699999999999996
- type: map_at_10
value: 57.589999999999996
- type: map_at_100
value: 58.226
- type: map_at_1000
value: 58.251
- type: map_at_3
value: 55.233
- type: map_at_5
value: 56.633
- type: mrr_at_1
value: 47.699999999999996
- type: mrr_at_10
value: 57.589999999999996
- type: mrr_at_100
value: 58.226
- type: mrr_at_1000
value: 58.251
- type: mrr_at_3
value: 55.233
- type: mrr_at_5
value: 56.633
- type: ndcg_at_1
value: 47.699999999999996
- type: ndcg_at_10
value: 62.505
- type: ndcg_at_100
value: 65.517
- type: ndcg_at_1000
value: 66.19800000000001
- type: ndcg_at_3
value: 57.643
- type: ndcg_at_5
value: 60.181
- type: precision_at_1
value: 47.699999999999996
- type: precision_at_10
value: 7.8
- type: precision_at_100
value: 0.919
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 21.532999999999998
- type: precision_at_5
value: 14.16
- type: recall_at_1
value: 47.699999999999996
- type: recall_at_10
value: 78.0
- type: recall_at_100
value: 91.9
- type: recall_at_1000
value: 97.3
- type: recall_at_3
value: 64.60000000000001
- type: recall_at_5
value: 70.8
task:
type: Retrieval
- dataset:
config: default
name: MTEB IFlyTek
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
split: validation
type: C-MTEB/IFlyTek-classification
metrics:
- type: accuracy
value: 44.84801846864178
- type: f1
value: 37.47347897956339
task:
type: Classification
- dataset:
config: default
name: MTEB JDReview
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
split: test
type: C-MTEB/JDReview-classification
metrics:
- type: accuracy
value: 85.81613508442777
- type: ap
value: 52.68244615477374
- type: f1
value: 80.0445640948843
task:
type: Classification
- dataset:
config: default
name: MTEB LCQMC
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
split: test
type: C-MTEB/LCQMC
metrics:
- type: cos_sim_pearson
value: 69.57786502217138
- type: cos_sim_spearman
value: 75.39106054489906
- type: euclidean_pearson
value: 73.72082954602402
- type: euclidean_spearman
value: 75.14421475913619
- type: manhattan_pearson
value: 73.62463076633642
- type: manhattan_spearman
value: 75.01301565104112
task:
type: STS
- dataset:
config: default
name: MTEB MMarcoReranking
revision: None
split: dev
type: C-MTEB/Mmarco-reranking
metrics:
- type: map
value: 29.143797057999134
- type: mrr
value: 28.08174603174603
task:
type: Reranking
- dataset:
config: default
name: MTEB MMarcoRetrieval
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
split: dev
type: C-MTEB/MMarcoRetrieval
metrics:
- type: map_at_1
value: 70.492
- type: map_at_10
value: 79.501
- type: map_at_100
value: 79.728
- type: map_at_1000
value: 79.735
- type: map_at_3
value: 77.77
- type: map_at_5
value: 78.851
- type: mrr_at_1
value: 72.822
- type: mrr_at_10
value: 80.001
- type: mrr_at_100
value: 80.19
- type: mrr_at_1000
value: 80.197
- type: mrr_at_3
value: 78.484
- type: mrr_at_5
value: 79.42099999999999
- type: ndcg_at_1
value: 72.822
- type: ndcg_at_10
value: 83.013
- type: ndcg_at_100
value: 84.013
- type: ndcg_at_1000
value: 84.20400000000001
- type: ndcg_at_3
value: 79.728
- type: ndcg_at_5
value: 81.542
- type: precision_at_1
value: 72.822
- type: precision_at_10
value: 9.917
- type: precision_at_100
value: 1.042
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 29.847
- type: precision_at_5
value: 18.871
- type: recall_at_1
value: 70.492
- type: recall_at_10
value: 93.325
- type: recall_at_100
value: 97.822
- type: recall_at_1000
value: 99.319
- type: recall_at_3
value: 84.636
- type: recall_at_5
value: 88.93100000000001
task:
type: Retrieval
- dataset:
config: zh-CN
name: MTEB MassiveIntentClassification (zh-CN)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 76.88298587760592
- type: f1
value: 73.89001762017176
task:
type: Classification
- dataset:
config: zh-CN
name: MTEB MassiveScenarioClassification (zh-CN)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 80.76328177538669
- type: f1
value: 80.24718532423358
task:
type: Classification
- dataset:
config: default
name: MTEB MedicalRetrieval
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
split: dev
type: C-MTEB/MedicalRetrieval
metrics:
- type: map_at_1
value: 49.6
- type: map_at_10
value: 55.620999999999995
- type: map_at_100
value: 56.204
- type: map_at_1000
value: 56.251
- type: map_at_3
value: 54.132999999999996
- type: map_at_5
value: 54.933
- type: mrr_at_1
value: 49.7
- type: mrr_at_10
value: 55.67100000000001
- type: mrr_at_100
value: 56.254000000000005
- type: mrr_at_1000
value: 56.301
- type: mrr_at_3
value: 54.18300000000001
- type: mrr_at_5
value: 54.983000000000004
- type: ndcg_at_1
value: 49.6
- type: ndcg_at_10
value: 58.645
- type: ndcg_at_100
value: 61.789
- type: ndcg_at_1000
value: 63.219
- type: ndcg_at_3
value: 55.567
- type: ndcg_at_5
value: 57.008
- type: precision_at_1
value: 49.6
- type: precision_at_10
value: 6.819999999999999
- type: precision_at_100
value: 0.836
- type: precision_at_1000
value: 0.095
- type: precision_at_3
value: 19.900000000000002
- type: precision_at_5
value: 12.64
- type: recall_at_1
value: 49.6
- type: recall_at_10
value: 68.2
- type: recall_at_100
value: 83.6
- type: recall_at_1000
value: 95.3
- type: recall_at_3
value: 59.699999999999996
- type: recall_at_5
value: 63.2
task:
type: Retrieval
- dataset:
config: default
name: MTEB MultilingualSentiment
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
split: validation
type: C-MTEB/MultilingualSentiment-classification
metrics:
- type: accuracy
value: 74.45666666666666
- type: f1
value: 74.32582402190089
task:
type: Classification
- dataset:
config: default
name: MTEB Ocnli
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
split: validation
type: C-MTEB/OCNLI
metrics:
- type: cos_sim_accuracy
value: 80.67135896047645
- type: cos_sim_ap
value: 87.60421240712051
- type: cos_sim_f1
value: 82.1304131408661
- type: cos_sim_precision
value: 77.68361581920904
- type: cos_sim_recall
value: 87.11721224920802
- type: dot_accuracy
value: 79.04710341093666
- type: dot_ap
value: 85.6370059719336
- type: dot_f1
value: 80.763723150358
- type: dot_precision
value: 73.69337979094077
- type: dot_recall
value: 89.33474128827878
- type: euclidean_accuracy
value: 81.05035192203573
- type: euclidean_ap
value: 87.7880240053663
- type: euclidean_f1
value: 82.50244379276637
- type: euclidean_precision
value: 76.7970882620564
- type: euclidean_recall
value: 89.1235480464625
- type: manhattan_accuracy
value: 80.61721710882512
- type: manhattan_ap
value: 87.43568120591175
- type: manhattan_f1
value: 81.89526184538653
- type: manhattan_precision
value: 77.5992438563327
- type: manhattan_recall
value: 86.6948257655755
- type: max_accuracy
value: 81.05035192203573
- type: max_ap
value: 87.7880240053663
- type: max_f1
value: 82.50244379276637
task:
type: PairClassification
- dataset:
config: default
name: MTEB OnlineShopping
revision: e610f2ebd179a8fda30ae534c3878750a96db120
split: test
type: C-MTEB/OnlineShopping-classification
metrics:
- type: accuracy
value: 93.5
- type: ap
value: 91.31357903446782
- type: f1
value: 93.48088994006616
task:
type: Classification
- dataset:
config: default
name: MTEB PAWSX
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
split: test
type: C-MTEB/PAWSX
metrics:
- type: cos_sim_pearson
value: 36.93293453538077
- type: cos_sim_spearman
value: 42.45972506308574
- type: euclidean_pearson
value: 42.34945133152159
- type: euclidean_spearman
value: 42.331610303674644
- type: manhattan_pearson
value: 42.31455070249498
- type: manhattan_spearman
value: 42.19887982891834
task:
type: STS
- dataset:
config: default
name: MTEB QBQTC
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
split: test
type: C-MTEB/QBQTC
metrics:
- type: cos_sim_pearson
value: 33.683290790043785
- type: cos_sim_spearman
value: 35.149171171202994
- type: euclidean_pearson
value: 32.33806561267862
- type: euclidean_spearman
value: 34.483576387347966
- type: manhattan_pearson
value: 32.47629754599608
- type: manhattan_spearman
value: 34.66434471867615
task:
type: STS
- dataset:
config: zh
name: MTEB STS22 (zh)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 66.46322760516104
- type: cos_sim_spearman
value: 67.398478319726
- type: euclidean_pearson
value: 64.7223480293625
- type: euclidean_spearman
value: 66.83118568812951
- type: manhattan_pearson
value: 64.88440039828305
- type: manhattan_spearman
value: 66.80429458952257
task:
type: STS
- dataset:
config: default
name: MTEB STSB
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
split: test
type: C-MTEB/STSB
metrics:
- type: cos_sim_pearson
value: 79.08991383232105
- type: cos_sim_spearman
value: 79.39715677296854
- type: euclidean_pearson
value: 78.63201279320496
- type: euclidean_spearman
value: 79.40262660785731
- type: manhattan_pearson
value: 78.98138363146906
- type: manhattan_spearman
value: 79.79968413014194
task:
type: STS
- dataset:
config: default
name: MTEB T2Reranking
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
split: dev
type: C-MTEB/T2Reranking
metrics:
- type: map
value: 67.43289278789972
- type: mrr
value: 77.53012460908535
task:
type: Reranking
- dataset:
config: default
name: MTEB T2Retrieval
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
split: dev
type: C-MTEB/T2Retrieval
metrics:
- type: map_at_1
value: 27.733999999999998
- type: map_at_10
value: 78.24799999999999
- type: map_at_100
value: 81.765
- type: map_at_1000
value: 81.824
- type: map_at_3
value: 54.92
- type: map_at_5
value: 67.61399999999999
- type: mrr_at_1
value: 90.527
- type: mrr_at_10
value: 92.843
- type: mrr_at_100
value: 92.927
- type: mrr_at_1000
value: 92.93
- type: mrr_at_3
value: 92.45100000000001
- type: mrr_at_5
value: 92.693
- type: ndcg_at_1
value: 90.527
- type: ndcg_at_10
value: 85.466
- type: ndcg_at_100
value: 88.846
- type: ndcg_at_1000
value: 89.415
- type: ndcg_at_3
value: 86.768
- type: ndcg_at_5
value: 85.46000000000001
- type: precision_at_1
value: 90.527
- type: precision_at_10
value: 42.488
- type: precision_at_100
value: 5.024
- type: precision_at_1000
value: 0.516
- type: precision_at_3
value: 75.907
- type: precision_at_5
value: 63.727000000000004
- type: recall_at_1
value: 27.733999999999998
- type: recall_at_10
value: 84.346
- type: recall_at_100
value: 95.536
- type: recall_at_1000
value: 98.42999999999999
- type: recall_at_3
value: 56.455
- type: recall_at_5
value: 70.755
task:
type: Retrieval
- dataset:
config: default
name: MTEB TNews
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
split: validation
type: C-MTEB/TNews-classification
metrics:
- type: accuracy
value: 49.952000000000005
- type: f1
value: 48.264617195258054
task:
type: Classification
- dataset:
config: default
name: MTEB ThuNewsClusteringP2P
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
split: test
type: C-MTEB/ThuNewsClusteringP2P
metrics:
- type: v_measure
value: 68.23769904483508
task:
type: Clustering
- dataset:
config: default
name: MTEB ThuNewsClusteringS2S
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
split: test
type: C-MTEB/ThuNewsClusteringS2S
metrics:
- type: v_measure
value: 62.50294403136556
task:
type: Clustering
- dataset:
config: default
name: MTEB VideoRetrieval
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
split: dev
type: C-MTEB/VideoRetrieval
metrics:
- type: map_at_1
value: 54.0
- type: map_at_10
value: 63.668
- type: map_at_100
value: 64.217
- type: map_at_1000
value: 64.23100000000001
- type: map_at_3
value: 61.7
- type: map_at_5
value: 62.870000000000005
- type: mrr_at_1
value: 54.0
- type: mrr_at_10
value: 63.668
- type: mrr_at_100
value: 64.217
- type: mrr_at_1000
value: 64.23100000000001
- type: mrr_at_3
value: 61.7
- type: mrr_at_5
value: 62.870000000000005
- type: ndcg_at_1
value: 54.0
- type: ndcg_at_10
value: 68.11399999999999
- type: ndcg_at_100
value: 70.723
- type: ndcg_at_1000
value: 71.123
- type: ndcg_at_3
value: 64.074
- type: ndcg_at_5
value: 66.178
- type: precision_at_1
value: 54.0
- type: precision_at_10
value: 8.200000000000001
- type: precision_at_100
value: 0.941
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 23.633000000000003
- type: precision_at_5
value: 15.2
- type: recall_at_1
value: 54.0
- type: recall_at_10
value: 82.0
- type: recall_at_100
value: 94.1
- type: recall_at_1000
value: 97.3
- type: recall_at_3
value: 70.89999999999999
- type: recall_at_5
value: 76.0
task:
type: Retrieval
- dataset:
config: default
name: MTEB Waimai
revision: 339287def212450dcaa9df8c22bf93e9980c7023
split: test
type: C-MTEB/waimai-classification
metrics:
- type: accuracy
value: 86.63000000000001
- type: ap
value: 69.99457882599567
- type: f1
value: 85.07735617998541
task:
type: Classification
- dataset:
config: default
name: MTEB 8TagsClustering
revision: None
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 44.594104491193555
task:
type: Clustering
- dataset:
config: default
name: MTEB AllegroReviews
revision: None
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 63.97614314115309
- type: f1
value: 52.15634261679283
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: 32.646
- type: map_at_10
value: 47.963
- type: map_at_100
value: 48.789
- type: map_at_1000
value: 48.797000000000004
- type: map_at_3
value: 43.196
- type: map_at_5
value: 46.016
- type: mrr_at_1
value: 33.073
- type: mrr_at_10
value: 48.126000000000005
- type: mrr_at_100
value: 48.946
- type: mrr_at_1000
value: 48.953
- type: mrr_at_3
value: 43.374
- type: mrr_at_5
value: 46.147
- type: ndcg_at_1
value: 32.646
- type: ndcg_at_10
value: 56.481
- type: ndcg_at_100
value: 59.922
- type: ndcg_at_1000
value: 60.07
- type: ndcg_at_3
value: 46.675
- type: ndcg_at_5
value: 51.76500000000001
- type: precision_at_1
value: 32.646
- type: precision_at_10
value: 8.371
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 18.919
- type: precision_at_5
value: 13.825999999999999
- type: recall_at_1
value: 32.646
- type: recall_at_10
value: 83.71300000000001
- type: recall_at_100
value: 98.578
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 56.757000000000005
- type: recall_at_5
value: 69.132
task:
type: Retrieval
- dataset:
config: default
name: MTEB CBD
revision: None
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 68.56
- type: ap
value: 23.310493680488513
- type: f1
value: 58.85369533105693
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: 88.5
- type: cos_sim_ap
value: 72.42140924378361
- type: cos_sim_f1
value: 66.0919540229885
- type: cos_sim_precision
value: 72.78481012658227
- type: cos_sim_recall
value: 60.526315789473685
- type: dot_accuracy
value: 88.5
- type: dot_ap
value: 72.42140924378361
- type: dot_f1
value: 66.0919540229885
- type: dot_precision
value: 72.78481012658227
- type: dot_recall
value: 60.526315789473685
- type: euclidean_accuracy
value: 88.5
- type: euclidean_ap
value: 72.42140924378361
- type: euclidean_f1
value: 66.0919540229885
- type: euclidean_precision
value: 72.78481012658227
- type: euclidean_recall
value: 60.526315789473685
- type: manhattan_accuracy
value: 88.5
- type: manhattan_ap
value: 72.49745515311696
- type: manhattan_f1
value: 66.0968660968661
- type: manhattan_precision
value: 72.04968944099379
- type: manhattan_recall
value: 61.05263157894737
- type: max_accuracy
value: 88.5
- type: max_ap
value: 72.49745515311696
- type: max_f1
value: 66.0968660968661
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: 90.32269765590145
- type: cos_sim_spearman
value: 89.73666311491672
- type: euclidean_pearson
value: 88.2933868516544
- type: euclidean_spearman
value: 89.73666311491672
- type: manhattan_pearson
value: 88.33474590219448
- type: manhattan_spearman
value: 89.8548364866583
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: 7.632999999999999
- type: map_at_10
value: 16.426
- type: map_at_100
value: 22.651
- type: map_at_1000
value: 24.372
- type: map_at_3
value: 11.706
- type: map_at_5
value: 13.529
- type: mrr_at_1
value: 60.75000000000001
- type: mrr_at_10
value: 68.613
- type: mrr_at_100
value: 69.001
- type: mrr_at_1000
value: 69.021
- type: mrr_at_3
value: 67.0
- type: mrr_at_5
value: 67.925
- type: ndcg_at_1
value: 49.875
- type: ndcg_at_10
value: 36.978
- type: ndcg_at_100
value: 40.031
- type: ndcg_at_1000
value: 47.566
- type: ndcg_at_3
value: 41.148
- type: ndcg_at_5
value: 38.702
- type: precision_at_1
value: 60.75000000000001
- type: precision_at_10
value: 29.7
- type: precision_at_100
value: 9.278
- type: precision_at_1000
value: 2.099
- type: precision_at_3
value: 44.0
- type: precision_at_5
value: 37.6
- type: recall_at_1
value: 7.632999999999999
- type: recall_at_10
value: 22.040000000000003
- type: recall_at_100
value: 44.024
- type: recall_at_1000
value: 67.848
- type: recall_at_3
value: 13.093
- type: recall_at_5
value: 15.973
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: 15.473
- type: map_at_10
value: 24.579
- type: map_at_100
value: 26.387
- type: map_at_1000
value: 26.57
- type: map_at_3
value: 21.278
- type: map_at_5
value: 23.179
- type: mrr_at_1
value: 30.709999999999997
- type: mrr_at_10
value: 38.994
- type: mrr_at_100
value: 39.993
- type: mrr_at_1000
value: 40.044999999999995
- type: mrr_at_3
value: 36.342999999999996
- type: mrr_at_5
value: 37.846999999999994
- type: ndcg_at_1
value: 30.709999999999997
- type: ndcg_at_10
value: 31.608999999999998
- type: ndcg_at_100
value: 38.807
- type: ndcg_at_1000
value: 42.208
- type: ndcg_at_3
value: 28.086
- type: ndcg_at_5
value: 29.323
- type: precision_at_1
value: 30.709999999999997
- type: precision_at_10
value: 8.688
- type: precision_at_100
value: 1.608
- type: precision_at_1000
value: 0.22100000000000003
- type: precision_at_3
value: 18.724
- type: precision_at_5
value: 13.950999999999999
- type: recall_at_1
value: 15.473
- type: recall_at_10
value: 38.361000000000004
- type: recall_at_100
value: 65.2
- type: recall_at_1000
value: 85.789
- type: recall_at_3
value: 25.401
- type: recall_at_5
value: 30.875999999999998
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: 38.096000000000004
- type: map_at_10
value: 51.44499999999999
- type: map_at_100
value: 52.325
- type: map_at_1000
value: 52.397000000000006
- type: map_at_3
value: 48.626999999999995
- type: map_at_5
value: 50.342
- type: mrr_at_1
value: 76.19200000000001
- type: mrr_at_10
value: 81.191
- type: mrr_at_100
value: 81.431
- type: mrr_at_1000
value: 81.443
- type: mrr_at_3
value: 80.30199999999999
- type: mrr_at_5
value: 80.85900000000001
- type: ndcg_at_1
value: 76.19200000000001
- type: ndcg_at_10
value: 60.9
- type: ndcg_at_100
value: 64.14699999999999
- type: ndcg_at_1000
value: 65.647
- type: ndcg_at_3
value: 56.818000000000005
- type: ndcg_at_5
value: 59.019999999999996
- type: precision_at_1
value: 76.19200000000001
- type: precision_at_10
value: 12.203
- type: precision_at_100
value: 1.478
- type: precision_at_1000
value: 0.168
- type: precision_at_3
value: 34.616
- type: precision_at_5
value: 22.515
- type: recall_at_1
value: 38.096000000000004
- type: recall_at_10
value: 61.013
- type: recall_at_100
value: 73.90299999999999
- type: recall_at_1000
value: 83.91
- type: recall_at_3
value: 51.92400000000001
- type: recall_at_5
value: 56.286
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: 1.548
- type: map_at_10
value: 11.049000000000001
- type: map_at_100
value: 28.874
- type: map_at_1000
value: 34.931
- type: map_at_3
value: 4.162
- type: map_at_5
value: 6.396
- type: mrr_at_1
value: 90.69800000000001
- type: mrr_at_10
value: 92.093
- type: mrr_at_100
value: 92.345
- type: mrr_at_1000
value: 92.345
- type: mrr_at_3
value: 91.86
- type: mrr_at_5
value: 91.86
- type: ndcg_at_1
value: 74.031
- type: ndcg_at_10
value: 63.978
- type: ndcg_at_100
value: 53.101
- type: ndcg_at_1000
value: 60.675999999999995
- type: ndcg_at_3
value: 71.421
- type: ndcg_at_5
value: 68.098
- type: precision_at_1
value: 90.69800000000001
- type: precision_at_10
value: 71.86
- type: precision_at_100
value: 31.395
- type: precision_at_1000
value: 5.981
- type: precision_at_3
value: 84.49600000000001
- type: precision_at_5
value: 79.07
- type: recall_at_1
value: 1.548
- type: recall_at_10
value: 12.149000000000001
- type: recall_at_100
value: 40.794999999999995
- type: recall_at_1000
value: 67.974
- type: recall_at_3
value: 4.244
- type: recall_at_5
value: 6.608
task:
type: Retrieval
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 73.55413584398119
- type: f1
value: 69.65610882318181
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 76.37188971082716
- type: f1
value: 75.64847309941361
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: 4.919
- type: map_at_10
value: 10.834000000000001
- type: map_at_100
value: 13.38
- type: map_at_1000
value: 14.581
- type: map_at_3
value: 8.198
- type: map_at_5
value: 9.428
- type: mrr_at_1
value: 41.176
- type: mrr_at_10
value: 50.083
- type: mrr_at_100
value: 50.559
- type: mrr_at_1000
value: 50.604000000000006
- type: mrr_at_3
value: 47.936
- type: mrr_at_5
value: 49.407000000000004
- type: ndcg_at_1
value: 39.628
- type: ndcg_at_10
value: 30.098000000000003
- type: ndcg_at_100
value: 27.061
- type: ndcg_at_1000
value: 35.94
- type: ndcg_at_3
value: 35.135
- type: ndcg_at_5
value: 33.335
- type: precision_at_1
value: 41.176
- type: precision_at_10
value: 22.259999999999998
- type: precision_at_100
value: 6.712
- type: precision_at_1000
value: 1.9060000000000001
- type: precision_at_3
value: 33.23
- type: precision_at_5
value: 29.04
- type: recall_at_1
value: 4.919
- type: recall_at_10
value: 14.196
- type: recall_at_100
value: 26.948
- type: recall_at_1000
value: 59.211000000000006
- type: recall_at_3
value: 9.44
- type: recall_at_5
value: 11.569
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: 25.35
- type: map_at_10
value: 37.884
- type: map_at_100
value: 38.955
- type: map_at_1000
value: 39.007999999999996
- type: map_at_3
value: 34.239999999999995
- type: map_at_5
value: 36.398
- type: mrr_at_1
value: 28.737000000000002
- type: mrr_at_10
value: 39.973
- type: mrr_at_100
value: 40.844
- type: mrr_at_1000
value: 40.885
- type: mrr_at_3
value: 36.901
- type: mrr_at_5
value: 38.721
- type: ndcg_at_1
value: 28.708
- type: ndcg_at_10
value: 44.204
- type: ndcg_at_100
value: 48.978
- type: ndcg_at_1000
value: 50.33
- type: ndcg_at_3
value: 37.36
- type: ndcg_at_5
value: 40.912
- type: precision_at_1
value: 28.708
- type: precision_at_10
value: 7.367
- type: precision_at_100
value: 1.0030000000000001
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 17.034
- type: precision_at_5
value: 12.293999999999999
- type: recall_at_1
value: 25.35
- type: recall_at_10
value: 61.411
- type: recall_at_100
value: 82.599
- type: recall_at_1000
value: 92.903
- type: recall_at_3
value: 43.728
- type: recall_at_5
value: 51.854
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: 77.49422763833996
- type: f1
value: 66.73472657783407
task:
type: Classification
- dataset:
config: default
name: MTEB PPC
revision: None
split: test
type: PL-MTEB/ppc-pairclassification
metrics:
- type: cos_sim_accuracy
value: 81.0
- type: cos_sim_ap
value: 91.47194213011349
- type: cos_sim_f1
value: 84.73767885532592
- type: cos_sim_precision
value: 81.49847094801224
- type: cos_sim_recall
value: 88.24503311258279
- type: dot_accuracy
value: 81.0
- type: dot_ap
value: 91.47194213011349
- type: dot_f1
value: 84.73767885532592
- type: dot_precision
value: 81.49847094801224
- type: dot_recall
value: 88.24503311258279
- type: euclidean_accuracy
value: 81.0
- type: euclidean_ap
value: 91.47194213011349
- type: euclidean_f1
value: 84.73767885532592
- type: euclidean_precision
value: 81.49847094801224
- type: euclidean_recall
value: 88.24503311258279
- type: manhattan_accuracy
value: 81.0
- type: manhattan_ap
value: 91.46464475050571
- type: manhattan_f1
value: 84.48687350835321
- type: manhattan_precision
value: 81.31699846860643
- type: manhattan_recall
value: 87.91390728476821
- type: max_accuracy
value: 81.0
- type: max_ap
value: 91.47194213011349
- type: max_f1
value: 84.73767885532592
task:
type: PairClassification
- dataset:
config: default
name: MTEB PSC
revision: None
split: test
type: PL-MTEB/psc-pairclassification
metrics:
- type: cos_sim_accuracy
value: 97.6808905380334
- type: cos_sim_ap
value: 99.27948611836348
- type: cos_sim_f1
value: 96.15975422427034
- type: cos_sim_precision
value: 96.90402476780186
- type: cos_sim_recall
value: 95.42682926829268
- type: dot_accuracy
value: 97.6808905380334
- type: dot_ap
value: 99.2794861183635
- type: dot_f1
value: 96.15975422427034
- type: dot_precision
value: 96.90402476780186
- type: dot_recall
value: 95.42682926829268
- type: euclidean_accuracy
value: 97.6808905380334
- type: euclidean_ap
value: 99.2794861183635
- type: euclidean_f1
value: 96.15975422427034
- type: euclidean_precision
value: 96.90402476780186
- type: euclidean_recall
value: 95.42682926829268
- type: manhattan_accuracy
value: 97.6808905380334
- type: manhattan_ap
value: 99.28715055268721
- type: manhattan_f1
value: 96.14791987673343
- type: manhattan_precision
value: 97.19626168224299
- type: manhattan_recall
value: 95.1219512195122
- type: max_accuracy
value: 97.6808905380334
- type: max_ap
value: 99.28715055268721
- type: max_f1
value: 96.15975422427034
task:
type: PairClassification
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: None
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 86.16343490304708
- type: f1
value: 83.3442579486744
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: None
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 68.40080971659918
- type: f1
value: 53.13720751142237
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: 63.322
- type: map_at_10
value: 76.847
- type: map_at_100
value: 77.616
- type: map_at_1000
value: 77.644
- type: map_at_3
value: 73.624
- type: map_at_5
value: 75.603
- type: mrr_at_1
value: 72.88
- type: mrr_at_10
value: 80.376
- type: mrr_at_100
value: 80.604
- type: mrr_at_1000
value: 80.61
- type: mrr_at_3
value: 78.92
- type: mrr_at_5
value: 79.869
- type: ndcg_at_1
value: 72.89999999999999
- type: ndcg_at_10
value: 81.43
- type: ndcg_at_100
value: 83.394
- type: ndcg_at_1000
value: 83.685
- type: ndcg_at_3
value: 77.62599999999999
- type: ndcg_at_5
value: 79.656
- type: precision_at_1
value: 72.89999999999999
- type: precision_at_10
value: 12.548
- type: precision_at_100
value: 1.4869999999999999
- type: precision_at_1000
value: 0.155
- type: precision_at_3
value: 34.027
- type: precision_at_5
value: 22.654
- type: recall_at_1
value: 63.322
- type: recall_at_10
value: 90.664
- type: recall_at_100
value: 97.974
- type: recall_at_1000
value: 99.636
- type: recall_at_3
value: 80.067
- type: recall_at_5
value: 85.526
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: 3.95
- type: map_at_10
value: 9.658999999999999
- type: map_at_100
value: 11.384
- type: map_at_1000
value: 11.677
- type: map_at_3
value: 7.055
- type: map_at_5
value: 8.244
- type: mrr_at_1
value: 19.5
- type: mrr_at_10
value: 28.777
- type: mrr_at_100
value: 29.936
- type: mrr_at_1000
value: 30.009999999999998
- type: mrr_at_3
value: 25.55
- type: mrr_at_5
value: 27.284999999999997
- type: ndcg_at_1
value: 19.5
- type: ndcg_at_10
value: 16.589000000000002
- type: ndcg_at_100
value: 23.879
- type: ndcg_at_1000
value: 29.279
- type: ndcg_at_3
value: 15.719
- type: ndcg_at_5
value: 13.572000000000001
- type: precision_at_1
value: 19.5
- type: precision_at_10
value: 8.62
- type: precision_at_100
value: 1.924
- type: precision_at_1000
value: 0.322
- type: precision_at_3
value: 14.6
- type: precision_at_5
value: 11.78
- type: recall_at_1
value: 3.95
- type: recall_at_10
value: 17.477999999999998
- type: recall_at_100
value: 38.99
- type: recall_at_1000
value: 65.417
- type: recall_at_3
value: 8.883000000000001
- type: recall_at_5
value: 11.933
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: 83.48960456583775
- type: cos_sim_ap
value: 76.31522115825375
- type: cos_sim_f1
value: 70.35573122529645
- type: cos_sim_precision
value: 70.9934735315446
- type: cos_sim_recall
value: 69.72934472934473
- type: dot_accuracy
value: 83.48960456583775
- type: dot_ap
value: 76.31522115825373
- type: dot_f1
value: 70.35573122529645
- type: dot_precision
value: 70.9934735315446
- type: dot_recall
value: 69.72934472934473
- type: euclidean_accuracy
value: 83.48960456583775
- type: euclidean_ap
value: 76.31522115825373
- type: euclidean_f1
value: 70.35573122529645
- type: euclidean_precision
value: 70.9934735315446
- type: euclidean_recall
value: 69.72934472934473
- type: manhattan_accuracy
value: 83.46922136159804
- type: manhattan_ap
value: 76.18474601388084
- type: manhattan_f1
value: 70.34779490856937
- type: manhattan_precision
value: 70.83032490974729
- type: manhattan_recall
value: 69.87179487179486
- type: max_accuracy
value: 83.48960456583775
- type: max_ap
value: 76.31522115825375
- type: max_f1
value: 70.35573122529645
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: 77.95374883876302
- type: cos_sim_spearman
value: 73.77630219171942
- type: euclidean_pearson
value: 75.81927069594934
- type: euclidean_spearman
value: 73.7763211303831
- type: manhattan_pearson
value: 76.03126859057528
- type: manhattan_spearman
value: 73.96528138013369
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: 37.388282764841826
- type: cos_sim_spearman
value: 40.83477184710897
- type: euclidean_pearson
value: 26.754737044177805
- type: euclidean_spearman
value: 40.83477184710897
- type: manhattan_pearson
value: 26.760453110872458
- type: manhattan_spearman
value: 41.034477441383856
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: 49.15
- type: map_at_10
value: 61.690999999999995
- type: map_at_100
value: 62.348000000000006
- type: map_at_1000
value: 62.38
- type: map_at_3
value: 58.824
- type: map_at_5
value: 60.662000000000006
- type: mrr_at_1
value: 51.333
- type: mrr_at_10
value: 62.731
- type: mrr_at_100
value: 63.245
- type: mrr_at_1000
value: 63.275000000000006
- type: mrr_at_3
value: 60.667
- type: mrr_at_5
value: 61.93300000000001
- type: ndcg_at_1
value: 51.333
- type: ndcg_at_10
value: 67.168
- type: ndcg_at_100
value: 69.833
- type: ndcg_at_1000
value: 70.56700000000001
- type: ndcg_at_3
value: 62.40599999999999
- type: ndcg_at_5
value: 65.029
- type: precision_at_1
value: 51.333
- type: precision_at_10
value: 9.333
- type: precision_at_100
value: 1.0699999999999998
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 25.333
- type: precision_at_5
value: 17.067
- type: recall_at_1
value: 49.15
- type: recall_at_10
value: 82.533
- type: recall_at_100
value: 94.167
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 69.917
- type: recall_at_5
value: 76.356
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: 0.261
- type: map_at_10
value: 2.1260000000000003
- type: map_at_100
value: 12.171999999999999
- type: map_at_1000
value: 26.884999999999998
- type: map_at_3
value: 0.695
- type: map_at_5
value: 1.134
- type: mrr_at_1
value: 96.0
- type: mrr_at_10
value: 96.952
- type: mrr_at_100
value: 96.952
- type: mrr_at_1000
value: 96.952
- type: mrr_at_3
value: 96.667
- type: mrr_at_5
value: 96.667
- type: ndcg_at_1
value: 92.0
- type: ndcg_at_10
value: 81.193
- type: ndcg_at_100
value: 61.129
- type: ndcg_at_1000
value: 51.157
- type: ndcg_at_3
value: 85.693
- type: ndcg_at_5
value: 84.129
- type: precision_at_1
value: 96.0
- type: precision_at_10
value: 85.39999999999999
- type: precision_at_100
value: 62.03999999999999
- type: precision_at_1000
value: 22.224
- type: precision_at_3
value: 88.0
- type: precision_at_5
value: 88.0
- type: recall_at_1
value: 0.261
- type: recall_at_10
value: 2.262
- type: recall_at_100
value: 14.981
- type: recall_at_1000
value: 46.837
- type: recall_at_3
value: 0.703
- type: recall_at_5
value: 1.172
task:
type: Retrieval
- dataset:
config: default
name: MTEB AlloProfClusteringP2P
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: v_measure
value: 70.55290063940157
task:
type: Clustering
- dataset:
config: default
name: MTEB AlloProfClusteringS2S
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: v_measure
value: 55.41500719337263
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: 73.48697375332002
- type: mrr
value: 75.01836585523822
task:
type: Reranking
- dataset:
config: default
name: MTEB AlloprofRetrieval
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: map_at_1
value: 38.454
- type: map_at_10
value: 51.605000000000004
- type: map_at_100
value: 52.653000000000006
- type: map_at_1000
value: 52.697
- type: map_at_3
value: 48.304
- type: map_at_5
value: 50.073
- type: mrr_at_1
value: 43.307
- type: mrr_at_10
value: 54.400000000000006
- type: mrr_at_100
value: 55.147999999999996
- type: mrr_at_1000
value: 55.174
- type: mrr_at_3
value: 51.77
- type: mrr_at_5
value: 53.166999999999994
- type: ndcg_at_1
value: 43.307
- type: ndcg_at_10
value: 57.891000000000005
- type: ndcg_at_100
value: 62.161
- type: ndcg_at_1000
value: 63.083
- type: ndcg_at_3
value: 51.851
- type: ndcg_at_5
value: 54.605000000000004
- type: precision_at_1
value: 43.307
- type: precision_at_10
value: 9.033
- type: precision_at_100
value: 1.172
- type: precision_at_1000
value: 0.127
- type: precision_at_3
value: 22.798
- type: precision_at_5
value: 15.492
- type: recall_at_1
value: 38.454
- type: recall_at_10
value: 74.166
- type: recall_at_100
value: 92.43599999999999
- type: recall_at_1000
value: 99.071
- type: recall_at_3
value: 58.087
- type: recall_at_5
value: 64.568
task:
type: Retrieval
- dataset:
config: fr
name: MTEB AmazonReviewsClassification (fr)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 53.474
- type: f1
value: 50.38275392350236
task:
type: Classification
- dataset:
config: default
name: MTEB BSARDRetrieval
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
split: test
type: maastrichtlawtech/bsard
metrics:
- type: map_at_1
value: 2.252
- type: map_at_10
value: 4.661
- type: map_at_100
value: 5.271
- type: map_at_1000
value: 5.3629999999999995
- type: map_at_3
value: 3.604
- type: map_at_5
value: 4.3020000000000005
- type: mrr_at_1
value: 2.252
- type: mrr_at_10
value: 4.661
- type: mrr_at_100
value: 5.271
- type: mrr_at_1000
value: 5.3629999999999995
- type: mrr_at_3
value: 3.604
- type: mrr_at_5
value: 4.3020000000000005
- type: ndcg_at_1
value: 2.252
- type: ndcg_at_10
value: 6.3020000000000005
- type: ndcg_at_100
value: 10.342
- type: ndcg_at_1000
value: 13.475999999999999
- type: ndcg_at_3
value: 4.0649999999999995
- type: ndcg_at_5
value: 5.344
- type: precision_at_1
value: 2.252
- type: precision_at_10
value: 1.171
- type: precision_at_100
value: 0.333
- type: precision_at_1000
value: 0.059000000000000004
- type: precision_at_3
value: 1.802
- type: precision_at_5
value: 1.712
- type: recall_at_1
value: 2.252
- type: recall_at_10
value: 11.712
- type: recall_at_100
value: 33.333
- type: recall_at_1000
value: 59.458999999999996
- type: recall_at_3
value: 5.405
- type: recall_at_5
value: 8.559
task:
type: Retrieval
- dataset:
config: default
name: MTEB HALClusteringS2S
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
split: test
type: lyon-nlp/clustering-hal-s2s
metrics:
- type: v_measure
value: 28.301882091023288
task:
type: Clustering
- dataset:
config: default
name: MTEB MLSUMClusteringP2P
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: mlsum
metrics:
- type: v_measure
value: 45.26992995191701
task:
type: Clustering
- dataset:
config: default
name: MTEB MLSUMClusteringS2S
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: mlsum
metrics:
- type: v_measure
value: 42.773174876871145
task:
type: Clustering
- dataset:
config: fr
name: MTEB MTOPDomainClassification (fr)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 93.47635452552458
- type: f1
value: 93.19922617577213
task:
type: Classification
- dataset:
config: fr
name: MTEB MTOPIntentClassification (fr)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 80.2317569683683
- type: f1
value: 56.18060418621901
task:
type: Classification
- dataset:
config: fra
name: MTEB MasakhaNEWSClassification (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: accuracy
value: 85.18957345971565
- type: f1
value: 80.829981537394
task:
type: Classification
- dataset:
config: fra
name: MTEB MasakhaNEWSClusteringP2P (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: v_measure
value: 71.04138999801822
task:
type: Clustering
- dataset:
config: fra
name: MTEB MasakhaNEWSClusteringS2S (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: v_measure
value: 71.7056263158008
task:
type: Clustering
- dataset:
config: fr
name: MTEB MassiveIntentClassification (fr)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 76.65097511768661
- type: f1
value: 73.82441070598712
task:
type: Classification
- dataset:
config: fr
name: MTEB MassiveScenarioClassification (fr)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 79.09885675857431
- type: f1
value: 78.28407777434224
task:
type: Classification
- dataset:
config: fr
name: MTEB MintakaRetrieval (fr)
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
split: test
type: jinaai/mintakaqa
metrics:
- type: map_at_1
value: 25.307000000000002
- type: map_at_10
value: 36.723
- type: map_at_100
value: 37.713
- type: map_at_1000
value: 37.769000000000005
- type: map_at_3
value: 33.77
- type: map_at_5
value: 35.463
- type: mrr_at_1
value: 25.307000000000002
- type: mrr_at_10
value: 36.723
- type: mrr_at_100
value: 37.713
- type: mrr_at_1000
value: 37.769000000000005
- type: mrr_at_3
value: 33.77
- type: mrr_at_5
value: 35.463
- type: ndcg_at_1
value: 25.307000000000002
- type: ndcg_at_10
value: 42.559999999999995
- type: ndcg_at_100
value: 47.457
- type: ndcg_at_1000
value: 49.162
- type: ndcg_at_3
value: 36.461
- type: ndcg_at_5
value: 39.504
- type: precision_at_1
value: 25.307000000000002
- type: precision_at_10
value: 6.106
- type: precision_at_100
value: 0.8420000000000001
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 14.741999999999999
- type: precision_at_5
value: 10.319
- type: recall_at_1
value: 25.307000000000002
- type: recall_at_10
value: 61.056999999999995
- type: recall_at_100
value: 84.152
- type: recall_at_1000
value: 98.03399999999999
- type: recall_at_3
value: 44.226
- type: recall_at_5
value: 51.597
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: 70.8
- type: cos_sim_ap
value: 73.7671529695957
- type: cos_sim_f1
value: 68.80964339527875
- type: cos_sim_precision
value: 62.95955882352941
- type: cos_sim_recall
value: 75.85825027685493
- type: dot_accuracy
value: 70.8
- type: dot_ap
value: 73.78345265366947
- type: dot_f1
value: 68.80964339527875
- type: dot_precision
value: 62.95955882352941
- type: dot_recall
value: 75.85825027685493
- type: euclidean_accuracy
value: 70.8
- type: euclidean_ap
value: 73.7671529695957
- type: euclidean_f1
value: 68.80964339527875
- type: euclidean_precision
value: 62.95955882352941
- type: euclidean_recall
value: 75.85825027685493
- type: manhattan_accuracy
value: 70.75
- type: manhattan_ap
value: 73.78996383615953
- type: manhattan_f1
value: 68.79432624113475
- type: manhattan_precision
value: 63.39869281045751
- type: manhattan_recall
value: 75.1937984496124
- type: max_accuracy
value: 70.8
- type: max_ap
value: 73.78996383615953
- type: max_f1
value: 68.80964339527875
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICKFr
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
split: test
type: Lajavaness/SICK-fr
metrics:
- type: cos_sim_pearson
value: 84.03253762760392
- type: cos_sim_spearman
value: 79.68280105762004
- type: euclidean_pearson
value: 80.98265050044444
- type: euclidean_spearman
value: 79.68233242682867
- type: manhattan_pearson
value: 80.9678911810704
- type: manhattan_spearman
value: 79.70264097683109
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: 80.56896987572884
- type: cos_sim_spearman
value: 81.84352499523287
- type: euclidean_pearson
value: 80.40831759421305
- type: euclidean_spearman
value: 81.84352499523287
- type: manhattan_pearson
value: 80.74333857561238
- type: manhattan_spearman
value: 82.41503246733892
task:
type: STS
- dataset:
config: fr
name: MTEB STSBenchmarkMultilingualSTS (fr)
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
split: test
type: stsb_multi_mt
metrics:
- type: cos_sim_pearson
value: 82.71826762276979
- type: cos_sim_spearman
value: 82.25433354916042
- type: euclidean_pearson
value: 81.87115571724316
- type: euclidean_spearman
value: 82.25322342890107
- type: manhattan_pearson
value: 82.11174867527224
- type: manhattan_spearman
value: 82.55905365203084
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: 30.659441623392887
- type: cos_sim_spearman
value: 30.501134097353315
- type: dot_pearson
value: 30.659444768851056
- type: dot_spearman
value: 30.501134097353315
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: 94.03333333333333
- type: mrr
value: 94.03333333333333
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: 79.0
- type: map_at_10
value: 87.61
- type: map_at_100
value: 87.655
- type: map_at_1000
value: 87.655
- type: map_at_3
value: 87.167
- type: map_at_5
value: 87.36699999999999
- type: mrr_at_1
value: 79.0
- type: mrr_at_10
value: 87.61
- type: mrr_at_100
value: 87.655
- type: mrr_at_1000
value: 87.655
- type: mrr_at_3
value: 87.167
- type: mrr_at_5
value: 87.36699999999999
- type: ndcg_at_1
value: 79.0
- type: ndcg_at_10
value: 90.473
- type: ndcg_at_100
value: 90.694
- type: ndcg_at_1000
value: 90.694
- type: ndcg_at_3
value: 89.464
- type: ndcg_at_5
value: 89.851
- type: precision_at_1
value: 79.0
- type: precision_at_10
value: 9.9
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 32.0
- type: precision_at_5
value: 19.400000000000002
- type: recall_at_1
value: 79.0
- type: recall_at_10
value: 99.0
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 96.0
- type: recall_at_5
value: 97.0
task:
type: Retrieval
- dataset:
config: fr
name: MTEB XPQARetrieval (fr)
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
split: test
type: jinaai/xpqa
metrics:
- type: map_at_1
value: 39.395
- type: map_at_10
value: 59.123999999999995
- type: map_at_100
value: 60.704
- type: map_at_1000
value: 60.760000000000005
- type: map_at_3
value: 53.187
- type: map_at_5
value: 56.863
- type: mrr_at_1
value: 62.083
- type: mrr_at_10
value: 68.87299999999999
- type: mrr_at_100
value: 69.46900000000001
- type: mrr_at_1000
value: 69.48299999999999
- type: mrr_at_3
value: 66.8
- type: mrr_at_5
value: 67.928
- type: ndcg_at_1
value: 62.083
- type: ndcg_at_10
value: 65.583
- type: ndcg_at_100
value: 70.918
- type: ndcg_at_1000
value: 71.72800000000001
- type: ndcg_at_3
value: 60.428000000000004
- type: ndcg_at_5
value: 61.853
- type: precision_at_1
value: 62.083
- type: precision_at_10
value: 15.033
- type: precision_at_100
value: 1.9529999999999998
- type: precision_at_1000
value: 0.207
- type: precision_at_3
value: 36.315
- type: precision_at_5
value: 25.955000000000002
- type: recall_at_1
value: 39.395
- type: recall_at_10
value: 74.332
- type: recall_at_100
value: 94.729
- type: recall_at_1000
value: 99.75500000000001
- type: recall_at_3
value: 57.679
- type: recall_at_5
value: 65.036
task:
type: Retrieval
---
## gte-Qwen2-1.5B-instruct
**gte-Qwen2-1.5B-instruct** is the latest model in the gte (General Text Embedding) model family. The model is built on [Qwen2-1.5B](https://huggingface.co/Qwen/Qwen2-1.5B) LLM model and use the same training data and strategies as the [gte-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) model.
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: 1.5B
- Embedding Dimension: 1536
- 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-Qwen2-1.5B-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-Qwen2-1.5B-instruct', trust_remote_code=True)
model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-1.5B-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-Qwen2-1.5B-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-instruct**](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 |
## 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}
}
```
|
[
"BIOSSES",
"SCIFACT"
] |
TheBloke/meditron-70B-GPTQ
|
TheBloke
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"medical",
"health",
"llama2",
"en",
"dataset:bigbio/med_qa",
"dataset:medmcqa",
"dataset:bigbio/pubmed_qa",
"dataset:epfl-llm/guidelines",
"arxiv:2311.16079",
"base_model:epfl-llm/meditron-70b",
"base_model:quantized:epfl-llm/meditron-70b",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | 2023-11-30T17:10:33Z |
2023-11-30T21:24:40+00:00
| 2,391 | 4 |
---
base_model: epfl-llm/meditron-70b
datasets:
- bigbio/med_qa
- medmcqa
- bigbio/pubmed_qa
- epfl-llm/guidelines
language:
- en
license: llama2
metrics:
- accuracy
- perplexity
model_name: Meditron 70B
pipeline_tag: text-generation
tags:
- medical
- health
- llama2
inference: false
model_creator: EPFL LLM Team
model_type: llama
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>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 -->
# Meditron 70B - GPTQ
- Model creator: [EPFL LLM Team](https://huggingface.co/epfl-llm)
- Original model: [Meditron 70B](https://huggingface.co/epfl-llm/meditron-70b)
<!-- description start -->
# Description
This repo contains GPTQ model files for [EPFL LLM Team's Meditron 70B](https://huggingface.co/epfl-llm/meditron-70b).
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/meditron-70B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/meditron-70B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/meditron-70B-GGUF)
* [EPFL LLM Team's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/epfl-llm/meditron-70b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
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/meditron-70B-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 35.33 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/meditron-70B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 36.65 GB | Yes | 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/meditron-70B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 40.66 GB | Yes | 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/meditron-70B-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 26.77 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/meditron-70B-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 28.03 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/meditron-70B-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 31.84 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. |
<!-- 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/meditron-70B-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/meditron-70B-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 `meditron-70B-GPTQ`:
```shell
mkdir meditron-70B-GPTQ
huggingface-cli download TheBloke/meditron-70B-GPTQ --local-dir meditron-70B-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir meditron-70B-GPTQ
huggingface-cli download TheBloke/meditron-70B-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir meditron-70B-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 meditron-70B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/meditron-70B-GPTQ --local-dir meditron-70B-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/meditron-70B-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/meditron-70B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/meditron-70B-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: `meditron-70B-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/meditron-70B-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'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>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_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/meditron-70B-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 = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
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 and Mistral models 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**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: EPFL LLM Team's Meditron 70B
<img width=50% src="meditron_LOGO.png" alt="Alt text" title="Meditron-logo">
# Model Card for Meditron-70B-v1.0
Meditron is a suite of open-source medical Large Language Models (LLMs).
Meditron-70B is a 70 billion parameters model adapted to the medical domain from Llama-2-70B 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-70B, finetuned on relevant training data, outperforms Llama-2-70B, GPT-3.5 (`text-davinci-003`, 8-shot), and Flan-PaLM on multiple medical reasoning tasks.
<!--# Table of Contents
[Model Card for Meditron 70B](#model-card-for--meditron-70b-v1.0)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Model Description](#model-description)
- [Uses](#uses)
- [Downstream Use](#downstream-use)
- [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
- [Recommendations](#recommendations)
- [Training Details](#training-details)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Evaluation](#evaluation)
- [Testing Data & Metrics](#testing-data-&-metrics)
- [Testing Data](#testing-data)
- [Metrics](#metrics)
- [Results](#results)
- [Environmental Impact](#environmental-impact)
- [Citation](#citation)-->
<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-2-70B](https://huggingface.co/meta-llama/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.
- **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-70B 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 is a foundation model that can be finetuned, instruction-tuned, or RLHF-tuned for specific downstream tasks and applications.
The main way we have used this model is finetuning for downstream question-answering tasks, but we encourage using this model for additional applications.
Specific formatting needs to be followed to prompt our finetuned models, including the `<|im_start|>`, `<|im_end|>` tags, and `system`, `question`, `answer` identifiers.
"""
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>question
{prompt}<|im_end|>
<|im_start|>answer
"""
**Note 1**: The above formatting is not required for running the base model (this repository)
**Note 2**: the above formatting is just an example of a finetuning template. This format is not a requirement if you use your own formatting option for the finetuning of the model.
To run proper generation with this base model, we recommend using a high-throughput and memory-efficient inference engine, such as [vLLM](https://github.com/vllm-project/vllm), with a UI that supports chat and text generation, such as [BetterChatGPT](https://github.com/ztjhz/BetterChatGPT)
To see more details about model deployment and generation, please see our [documentation](https://github.com/epfLLM/meditron/blob/main/deployment/README.md).
### 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.
For **Helpfulness**, **Risk** and **Bias**, we provide a comprehensive qualitative generation report of Meditron-70B on queries designed by medical experts.
Each query targets specific aspects of helpfulness (medical accuracy, up-to-date information, etc.), risk (public health, medical ethics, etc.) and bias (gender, age, race, etc.).
Please see the detailed generations in our paper. We compare our generations to Llama-2-70B and ChatGPT-3.5 (version Nov, 27, 2023)
Significant research is still required to fully explore potential bias, fairness, and safety issues with this language model.
### 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 without comprehensive testing for your application.
## 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="60%" 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 16 nodes 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.
The nodes are connected via RDMA over Converged Ethernet.
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 8,
- Tensor Parallelism (TP -- different GPUs process different subtensors for matrix multiplication) of 8.
#### Training Hyperparameters
| | |
| --- | ------ |
| bf16 | true |
| lr | 1.5e-4 |
| eps | 1e-5 |
| betas | \[0.9, 0.95\] |
| clip_grad | 1 |
| weight decay | 0.1 |
| DP size | 2 |
| TP size | 8 |
| PP size | 8 |
| seq length | 4096 |
| lr scheduler | cosine|
| min lr | 1e-6 |
| warmup iteration | 2000 |
| micro batch size | 2 |
| global batch size | 512 |
| | |
#### Speeds, Sizes, Times
The model was trained in September and October 2023.
The model architecture is exactly Llama 2, meaning
| | |
| --- | ------ |
| Model size | 70B |
| Hidden dimension | 8192 |
| Num. attention heads | 64 |
| Num. layers | 80 |
| | | |
We train the 70B model on 48e9 tokens, at a throughput of about 40,200 tokens / second.
This amounts to a bfloat16 model flops utilization of roughly 42.3\%.
## 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-70b and llama-2-70b on each benchmark (pubmedqa, medmcqa, medqa)'s training data individually.
We report the finetuned models' performance with self-consistency chain-of-thought 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-70b | llama-2-70b | med42-70b* | clinical-camel-70b* |
|MMLU-Medical | 77.6 | 77.9 | 74.5 | 65.7 |
|PubMedQA | 81.6 | 80.0 | 61.2 | 67.0 |
|MedMCQA | 66.0 | 62.6 | 59.2 | 46.7 |
|MedQA | 64.4 | 61.5 | 59.1 | 50.8 |
|MedQA-4-Option| 70.2 | 63.8 | 63.9 | 56.8 |
|Avg | 72.0 | 69.2 | 63.6 | 57.4 |
| | | | | | |
**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:** 128 x NVIDIA A100 (80GB) SXM
- **Total GPU hours:** 42,496
- **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). 332 hours of 128 A100s means 42496 hours at a TDP of 400W. Assuming a Power Usage effectiveness of 1.8, total emissions are estimated to be:
(400W / 1000W/kWh / GPU * 0.016 kgCO2/kWh * 332 h * 128 GPU) * 1.8 PUE = 486 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}
}
```
|
[
"MEDQA",
"PUBMEDQA"
] |
DavidAU/DeepThought-MOE-8X3B-R1-Llama-3.2-Reasoning-18B-gguf
|
DavidAU
|
text-generation
|
[
"gguf",
"Prototype",
"8X3B MOE",
"mixture of experts",
"reasoning",
"thinking",
"thoughts",
"deepseek",
"moe",
"context 128k",
"Llama 3.2 MOE",
"creative",
"creative writing",
"general usage",
"problem solving",
"brainstorming",
"solve riddles",
"fiction writing",
"plot generation",
"sub-plot generation",
"story generation",
"scene continue",
"storytelling",
"fiction story",
"story",
"writing",
"fiction",
"roleplaying",
"llama 3.2",
"mergekit",
"merge",
"text-generation",
"en",
"base_model:DavidAU/Deep-Reasoning-Llama-3.2-BlackSheep-3B",
"base_model:merge:DavidAU/Deep-Reasoning-Llama-3.2-BlackSheep-3B",
"base_model:DavidAU/Deep-Reasoning-Llama-3.2-COT-3B",
"base_model:merge:DavidAU/Deep-Reasoning-Llama-3.2-COT-3B",
"base_model:DavidAU/Deep-Reasoning-Llama-3.2-Enigma-3B",
"base_model:merge:DavidAU/Deep-Reasoning-Llama-3.2-Enigma-3B",
"base_model:DavidAU/Deep-Reasoning-Llama-3.2-Hermes-3-3B",
"base_model:merge:DavidAU/Deep-Reasoning-Llama-3.2-Hermes-3-3B",
"base_model:DavidAU/Deep-Reasoning-Llama-3.2-Instruct-uncensored-3B",
"base_model:merge:DavidAU/Deep-Reasoning-Llama-3.2-Instruct-uncensored-3B",
"base_model:DavidAU/Deep-Reasoning-Llama-3.2-Korean-Bllossom-3B",
"base_model:merge:DavidAU/Deep-Reasoning-Llama-3.2-Korean-Bllossom-3B",
"base_model:DavidAU/Deep-Reasoning-Llama-3.2-Overthinker-3B",
"base_model:merge:DavidAU/Deep-Reasoning-Llama-3.2-Overthinker-3B",
"base_model:DavidAU/Deep-Reasoning-Llama-3.2-ShiningValiant2-3B",
"base_model:merge:DavidAU/Deep-Reasoning-Llama-3.2-ShiningValiant2-3B",
"base_model:DavidAU/DeepThought-MOE-8X3B-R1-Llama-3.2-Reasoning-18B",
"base_model:merge:DavidAU/DeepThought-MOE-8X3B-R1-Llama-3.2-Reasoning-18B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | 2025-02-16T09:37:48Z |
2025-03-03T01:35:48+00:00
| 2,360 | 6 |
---
base_model:
- DavidAU/DeepThought-MOE-8X3B-R1-Llama-3.2-Reasoning-18B
- DavidAU/Deep-Reasoning-Llama-3.2-ShiningValiant2-3B
- DavidAU/Deep-Reasoning-Llama-3.2-Overthinker-3B
- DavidAU/Deep-Reasoning-Llama-3.2-Korean-Bllossom-3B
- DavidAU/Deep-Reasoning-Llama-3.2-Instruct-uncensored-3B
- DavidAU/Deep-Reasoning-Llama-3.2-Hermes-3-3B
- DavidAU/Deep-Reasoning-Llama-3.2-Enigma-3B
- DavidAU/Deep-Reasoning-Llama-3.2-COT-3B
- DavidAU/Deep-Reasoning-Llama-3.2-BlackSheep-3B
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- Prototype
- 8X3B MOE
- mixture of experts
- reasoning
- thinking
- thoughts
- deepseek
- moe
- context 128k
- Llama 3.2 MOE
- creative
- creative writing
- general usage
- problem solving
- brainstorming
- solve riddles
- fiction writing
- plot generation
- sub-plot generation
- story generation
- scene continue
- storytelling
- fiction story
- story
- writing
- fiction
- roleplaying
- llama 3.2
- mergekit
- merge
---
<B><font color="red">Prototype:</font> Deep Reasoning 8X3B Llama 3.2 MOE Model (24B total parameters)</B>
<h2>DeepThought-MOE-8X3B-R1-Llama-3.2-Reasoning-18B-gguf</h2>
<img src="bubbles.jpg" style="float:right; width:300px; height:300px; padding:5px;">
This as a 8X3B, Mixture of Experts model with 4/8 experts (8 Llama 3.2 fine tunes) activated, all with
Reasoning tech installed (in each one) giving you a 24B (8X3B) parameter model in only 18.4B model size.
This model is a composed of EIGHT finetuned Llama 3.2 3B models for reasoning/thoughts.
This model can be used for creative, non-creative use cases and general usage.
Three example prompts with output posted at the bottom of this page.
This is a very stable model, which can operate at temps 1+ 2+ and higher and generate coherent thought(s) and exceeds
many other "thinking models" in terms of performance, coherence and depth of thought - including long train of thought reasoning.
You can select/set the number of experts to use from 1 to 8.
<B>USE CASES:</B>
This model is for all use cases, and it has a slightly more logical/problem solving slant than a standard model.
This model can also be used for solving logic puzzles, riddles, and other problems with the enhanced "thinking" systems by DeepSeek.
This model also can solve problems/riddles/ and puzzles normally beyond the abilities of a Llama 3.1 model or Llama 3.2 model.
This model MAY produce NSFW / uncensored content, however it is PG generally.
See "Experts Activation" to adjust the number of experts used from 1 to 8 with this model below.
<B>Special Operation Instructions:</B>
PROTOTYPE NOTES:
1. This model may go "on and on" in some cases. Set your context to at least 8k, 12k to 16k is better as the model can easily output 12k+ in thoughts.
2. Sometimes the model will be "all thought" and "no action" - in this case , stop the generation and tell the model to "execute the plan."
3. Feel free to really go "wild" with temp with this model, especially creative use cases.
4. The models selected are designed for problem solving and deep thinking/reasoning.
5. The more instructions you provide (and guardrails IE length, come up with X number of ideas) the better the model will perform.
6. Shorter prompts tend to lead to "reasoning loops" (use higher temp with these to adjust)
7. Sometimes the model will "repeat itself" after a long thinking session or it will get caught up in "thought loops". Stop generation, and try continue and/or tell it what to do from this point.
8. Quants lower than IQ4/Q4 still need testing, and will not be uploaded yet.
TEMP/SETTINGS:
1. Set Temp between 0 and .8, higher than this "think" functions will activate differently.
2. Set context to at least 8k , 16K is better because this model will really think deeply.
3. For temps 1+,2+ etc etc, thought(s) will expand, and become deeper and richer.
4. Set "repeat penalty" to 1.02 to 1.07 (recommended) ; sometimes rep pen 1.12 ish works better, especially with shorter prompts.
5. This model requires a Llama 3 Instruct and/or Command-R chat template. (see notes on "System Prompt" / "Role" below) OR standard "Jinja Autoloaded Template" (this is contained in the quant and will autoload)
PROMPTS:
1. If you enter a prompt without implied "step by step" requirements (ie: Generate a scene, write a story, give me 6 plots for xyz), "thinking" (one or more) MAY activate AFTER first generation. (IE: Generate a scene -> scene will generate, followed by suggestions for improvement in "thoughts")
2. If you enter a prompt where "thinking" is stated or implied (ie puzzle, riddle, solve this, brainstorm this idea etc), "thoughts" process(es) in Deepseek will activate almost immediately. Sometimes you need to regen it to activate.
3. You will also get a lot of variations - some will continue the generation, others will talk about how to improve it, and some (ie generation of a scene) will cause the characters to "reason" about this situation. In some cases, the model will ask you to continue generation / thoughts too.
4. In some cases the model's "thoughts" may appear in the generation itself.
5. State the word size length max IN THE PROMPT for best results, especially for activation of "thinking." (see examples below)
6. Sometimes the "censorship" (from Deepseek) will activate, regen the prompt to clear it.
7. You may want to try your prompt once at "default" or "safe" temp settings, another at temp 1.2, and a third at 2.5 as an example. This will give you a broad range of "reasoning/thoughts/problem" solving.
GENERATION - THOUGHTS/REASONING:
1. It may take one or more regens for "thinking" to "activate." (depending on the prompt)
2. Model can generate a LOT of "thoughts". Sometimes the most interesting ones are 3,4,5 or more levels deep.
3. Many times the "thoughts" are unique and very different from one another.
4. Temp/rep pen settings can affect reasoning/thoughts too.
5. Change up or add directives/instructions or increase the detail level(s) in your prompt to improve reasoning/thinking.
6. Adding to your prompt: "think outside the box", "brainstorm X number of ideas", "focus on the most uncommon approaches" can drastically improve your results.
GENERAL SUGGESTIONS:
1. I have found opening a "new chat" per prompt works best with "thinking/reasoning activation", with temp .6, rep pen 1.05 ... THEN "regen" as required.
2. Sometimes the model will really really get completely unhinged and you need to manually stop it.
3. Depending on your AI app, "thoughts" may appear with "< THINK >" and "</ THINK >" tags AND/OR the AI will generate "thoughts" directly in the main output or later output(s).
4. Although quant IQ4XS was used for testing/examples, higher quants will provide better generation / more sound "reasoning/thinking".
ADDITIONAL SUPPORT:
For additional generational support, general questions, and detailed parameter info and a lot more see also:
NOTE: This is a CLASS 1 model.
https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters
---
<B>Recommended Settings (all) - For usage with "Think" / "Reasoning":</B>
temp: .6 , rep pen: 1.07 (range : 1.02 to 1.12), rep pen range: 64, top_k: 40, top_p: .95, min_p: .05
Temp of 1+, 2+, 3+ will result in much deeper, richer and "more interesting" thoughts and reasoning.
Model behaviour may change with other parameter(s) and/or sampler(s) activated - especially the "thinking/reasoning" process.
---
<B>System Role / System Prompt - Augment The Model's Power:</b>
---
If you DO NOT set a system prompt/role, the model will still reason in most cases - but generally in text only.
For prompts that do not strictly imply "reasoning/thought" the model MAY simply process the prompt.
However, with a system prompt ("Suggested" or "Advanced") set the model will ALWAYS "reason" / "think".
If you set / have a system prompt this will affect both "generation" and "thinking/reasoning".
SIMPLE:
This is the generic system prompt used for generation and testing:
<PRE>
You are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability.
</PRE>
This System Role/Prompt may give you a lot more "creative results":
<PRE>
Use vivid and graphic words focusing on verbs and use current 2020 fiction writing style. Use metaphor(s) that fit the context of the situation (and reveal character) rather than similes."
</PRE>
SUGGESTED:
This System Role/Prompt will give you "basic thinking/reasoning":
<PRE>
You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside <think> </think> tags, and then provide your solution or response to the problem.
</PRE>
ADVANCED:
Logical and Creative - these will SIGNFICANTLY alter the output, and many times improve it too.
This will also cause more thoughts, deeper thoughts, and in many cases more detailed/stronger thoughts too.
Keep in mind you may also want to test the model with NO system prompt at all - including the default one.
Special Credit to: Eric Hartford, Cognitivecomputations ; these are based on his work.
CRITICAL:
Copy and paste exactly as shown, preserve formatting and line breaks.
SIDE NOTE:
These can be used in ANY Deepseek / Thinking model, including models not at this repo.
These, if used in a "non thinking" model, will also alter model performance too.
<PRE>
You are an AI assistant developed by the world wide community of ai experts.
Your primary directive is to provide well-reasoned, structured, and extensively detailed responses.
Formatting Requirements:
1. Always structure your replies using: <think>{reasoning}</think>{answer}
2. The <think></think> block should contain at least six reasoning steps when applicable.
3. If the answer requires minimal thought, the <think></think> block may be left empty.
4. The user does not see the <think></think> section. Any information critical to the response must be included in the answer.
5. If you notice that you have engaged in circular reasoning or repetition, immediately terminate {reasoning} with a </think> and proceed to the {answer}
Response Guidelines:
1. Detailed and Structured: Use rich Markdown formatting for clarity and readability.
2. Scientific and Logical Approach: Your explanations should reflect the depth and precision of the greatest scientific minds.
3. Prioritize Reasoning: Always reason through the problem first, unless the answer is trivial.
4. Concise yet Complete: Ensure responses are informative, yet to the point without unnecessary elaboration.
5. Maintain a professional, intelligent, and analytical tone in all interactions.
</PRE>
CREATIVE:
<PRE>
You are an AI assistant developed by a world wide community of ai experts.
Your primary directive is to provide highly creative, well-reasoned, structured, and extensively detailed responses.
Formatting Requirements:
1. Always structure your replies using: <think>{reasoning}</think>{answer}
2. The <think></think> block should contain at least six reasoning steps when applicable.
3. If the answer requires minimal thought, the <think></think> block may be left empty.
4. The user does not see the <think></think> section. Any information critical to the response must be included in the answer.
5. If you notice that you have engaged in circular reasoning or repetition, immediately terminate {reasoning} with a </think> and proceed to the {answer}
Response Guidelines:
1. Detailed and Structured: Use rich Markdown formatting for clarity and readability.
2. Creative and Logical Approach: Your explanations should reflect the depth and precision of the greatest creative minds first.
3. Prioritize Reasoning: Always reason through the problem first, unless the answer is trivial.
4. Concise yet Complete: Ensure responses are informative, yet to the point without unnecessary elaboration.
5. Maintain a professional, intelligent, and analytical tone in all interactions.
</PRE>
---
<B> Additional Support / Documents for this model to assist with generation / performance: </b>
Document #1:
Details how to use reasoning/thinking models and get maximum performance from them, and includes links to all reasoning/thinking models - GGUF and source, as well as adapters to turn any "regular" model into a "reasoning/thinking" model.
[ https://huggingface.co/DavidAU/How-To-Use-Reasoning-Thinking-Models-and-Create-Them ]
Document #2:
Document detailing all parameters, settings, samplers and advanced samplers to use not only my models to their maximum potential - but all models (and quants) online (regardless of the repo) to their maximum potential. Included quick start and detailed notes, include AI / LLM apps and other critical information and references too. A must read if you are using any AI/LLM right now.
[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]
Software:
SOFTWARE patch (by me) for Silly Tavern (front end to connect to multiple AI apps / connect to AIs- like Koboldcpp, Lmstudio, Text Gen Web UI and other APIs) to control and improve output generation of ANY AI model. Also designed to control/wrangle some of my more "creative" models and make them perform perfectly with little to no parameter/samplers adjustments too.
[ https://huggingface.co/DavidAU/AI_Autocorrect__Auto-Creative-Enhancement__Auto-Low-Quant-Optimization__gguf-exl2-hqq-SOFTWARE ]
---
<B>Experts Activation / Models used to build this model:</B>
Special Thanks to all the model makers, for the models used in this MOE Model:
To be updated...
The mixture of experts is set at 4 experts, but you can use 1, 2, 3, or 4.
This "team" has a Captain (first listed model), and then all the team members contribute to the to "token"
choice billions of times per second. Note the Captain also contributes too.
Think of 2, 3 or 4 (or more) master chefs in the kitchen all competing to make the best dish for you.
This results in higher quality generation.
This also results in many cases in higher quality instruction following too.
That means the power of every model is available during instruction and output generation.
NOTE:
You can use one "expert" too ; however this means the model will randomly select an expert to use EACH TIME, resulting
in very different generation for each prompt / regen of a prompt.
CHANGING THE NUMBER OF EXPERTS:
You can set the number of experts in LMStudio (https://lmstudio.ai) at the "load" screen and via other apps/llm apps by setting "Experts" or "Number of Experts".
For Text-Generation-Webui (https://github.com/oobabooga/text-generation-webui) you set the number of experts at the loading screen page.
For KolboldCPP (https://github.com/LostRuins/koboldcpp) Version 1.8+ , on the load screen, click on "TOKENS",
you can set experts on this page, and the launch the model.
For server.exe / Llama-server.exe (Llamacpp - https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md )
add the following to the command line to start the "llamacpp server" (CLI):
"--override-kv llama.expert_used_count=int:3"
(no quotes, where "3" is the number of experts to use)
When using "API", you set the "num_experts_used" in the JSON payload (this maybe different for different back ends).
CREDITS:
Special thanks to all the model makers / creators listed above.
Please visit each repo above to see what model(s) contributed to each of models above and/or to learn more about the models
from the model makers.
- https://huggingface.co/DavidAU/Deep-Reasoning-Llama-3.2-Overthinker-3B (Captain Model)
- https://huggingface.co/DavidAU/Deep-Reasoning-Llama-3.2-ShiningValiant2-3B
- https://huggingface.co/DavidAU/Deep-Reasoning-Llama-3.2-Overthinker-3B
- https://huggingface.co/DavidAU/Deep-Reasoning-Llama-3.2-Korean-Bllossom-3B
- https://huggingface.co/DavidAU/Deep-Reasoning-Llama-3.2-Instruct-uncensored-3B
- https://huggingface.co/DavidAU/Deep-Reasoning-Llama-3.2-Hermes-3-3B
- https://huggingface.co/DavidAU/Deep-Reasoning-Llama-3.2-Enigma-3B
- https://huggingface.co/DavidAU/Deep-Reasoning-Llama-3.2-COT-3B
- https://huggingface.co/DavidAU/Deep-Reasoning-Llama-3.2-BlackSheep-3B
Each one of these was a fine tune by DavidAU, go to each of the above for details of the model(s) used in each fine tune.
Special credit goes to MERGEKIT, without you this project / model would not have been possible.
[ https://github.com/arcee-ai/mergekit ]
---
<B>Here are some example prompts that will "activate" thinking properly, note the length statements. </B>
Science Fiction: The Last Transmission - Write a story that takes place entirely within a spaceship's cockpit as the sole surviving crew member attempts to send a final message back to Earth before the ship's power runs out. The story should explore themes of isolation, sacrifice, and the importance of human connection in the face of adversity. If the situation calls for it, have the character(s) curse and swear to further the reader's emotional connection to them. 800-1000 words.
Romance: Love in the Limelight. Write one scene within a larger story set in Wales. A famous (fictional) actor ducks into a small-town bookstore to escape paparazzi. The scene takes us through the characters meeting in this odd circumstance. Over the course of the scene, the actor and the bookstore owner have a conversation charged by an undercurrent of unspoken chemistry. Write the actor as somewhat of a rogue with a fragile ego, which needs to be fed by having everyone like him. He is thoroughly charming, but the bookstore owner seems (at least superficially) immune to this; which paradoxically provokes a genuine attraction and derails the charm offensive. The bookstore owner, despite the superficial rebuffs of the actor's charm, is inwardly more than a little charmed and flustered despite themselves. Write primarily in dialogue, in the distinct voices of each character. 800-1000 words.
Start a 1000 word scene (vivid, graphic horror in first person) with: The sky scraper swayed, as she watched the window in front of her on the 21 floor explode...
Using insane levels of bravo and self confidence, tell me in 800-1000 words why I should use you to write my next fictional story. Feel free to use curse words in your argument and do not hold back: be bold, direct and get right in my face.
---
<h2>EXAMPLES - IQ4XS - temp .6 rep pen 1.07 </h2>
Examples are created using quant IQ4_XS, "temp=.6" (unless otherwise stated), minimal parameters and "LLAMA3" template.
Model has been tested with "temp" from ".1" to "5".
Below are the least creative outputs, prompt is in BOLD.
IMPORTANT:
Higher quants / imatrix quants will have much stronger generation - words, sentences, ideas, dialog and general quality.
---
<B>
Come up with six plots for a new "Black Mirror" episode (that the audience would love) that all involve time travel.
</B>
NOTE: This used "suggested" system prompt , and used 4 experts.
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Okay,
I need to come up with six distinct yet thought-provoking Black
Mirror-esque plots, each centered around time travel, but making sure
they all have elements of psychological unease and social commentary.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Let's
start by considering the themes we want to explore through this
anthology. The "Black" in Black Mirror is about the darker
aspects of technology and its impact on society. Time travel seems
like an intriguing topic that can be both mind-bending and
thought-provoking. Since the series typically deals with a
"cautionary tale" or a "warning story" set in a
near-future world, we need to think of how time travel could be used
to subvert expectations, make audiences uncomfortable, and ask deeper
questions.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Each
plot should be a unique take on time travel's possibilities and
consequences, while avoiding clichés and staying grounded in the
series' dark undertones.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
1:</strong> <strong>Identify Time Travel Mechanics</strong> What
aspects of time travel can we use as a lens? Consider the type of
time travel: back in time, forward, paradoxes, temporal loops. Maybe
each plot will use different mechanisms to create the tension and
conflict.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
2:</strong> <strong>Choose the Tone</strong> For the series, Black
Mirror is known for its blend of humor and darkness. How do we
balance this between our plots? Perhaps some will have darker, more
sinister elements, while others may be more satirical or
thought-provoking.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
3:</strong> <strong>The Protagonist</strong> A well-written Black
Mirror story often revolves around a complex, flawed, or
non-traditional character. Think of what kind of time traveler would
make for a compelling protagonist: perhaps someone who is obsessed
with the past, or has a specific motivation for changing events.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
4:</strong> <strong>Time Travel's Effects</strong> Considering the
nature of time travel, how do changes in the past or future affect
the present? Will the consequences be linear, or create branching
timelines? We can play with the unpredictability of outcomes, which
adds complexity and raises questions about agency and free will.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
5:</strong> <strong>The Black Mirror Twist</strong> The key to a
compelling episode is a twist that subverts audience expectations.
For each plot, I need to identify what the dark "Black Mirror"
message would be. Perhaps some will be about technology's
manipulation of human emotions, while others might be about social
engineering through subtle manipulation of past events. Maybe the
twist involves the protagonist's realization of their time traveling
mistake.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
6:</strong> <strong>Plot Integration and Tension</strong> Once I have
my plots outlined with each of these elements in mind, how can I make
sure that each plot has its own distinct tension and emotional
payoff? Think about the emotional investment we want the audience to
have with the story. Can any one character be "villained"
in the situation, or are multiple characters trapped by time travel's
constraints? How will time travel be used to illustrate social issues
or to question the nature of humanity?</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
7:</strong> <strong>Ensuring Pivotal Moments</strong> We need moments
that really stand out in a story. The plot twists must come at
pivotal points that have a significant impact on the overall
narrative, raising questions or emotions rather than providing
straightforward answers. For instance, is it the moment when the
protagonist sees their past self do something, leading to a
heart-wrenching decision, or when they witness the full scope of the
consequences they've created in the past?</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
8:</strong> <strong>The Conclusion</strong> How does each plot
resolve? Will it be open-ended, leaving room for more episodes set
within the same world, or will there be a conclusion that is
satisfying yet also a warning about our time travel practices.
Alternatively, maybe each plot concludes with a cliffhanger, setting
the stage for further adventures.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
9:</strong> <strong>Consider Audience Preferences</strong> Keep in
mind the audience's expectations and sensitivities around time travel
narratives. We can't just regurgitate "Fifth Element" or
"Predestination" formulas. Instead, we should be subverting
expectations, adding unique twists or unexpected plot developments to
create an experience that resonates long after the credits roll.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
10:</strong> <strong>The Overarching Structure</strong> Is there a
common theme between all six plots? Maybe a thread connecting time
travel mechanics, protagonist motivations, and the underlying
messages of each story. Alternatively, perhaps our anthology takes a
broader look at "Time Travel" as a metaphor for personal,
societal, or philosophical shifts in the narrative.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
11:</strong> <strong>Cultural Significance</strong> While maintaining
thematic relevance to the present and the 'sight' on the
"misperceptions," we also want these plots to be in line
with contemporary cultural attitudes. Think of social media's role,
AI's presence in people's lives, how climate change impacts mental
health - maybe time travel can be used as a metaphor for trying to
turn back the clock.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
12:</strong> <strong>Adaptation and Variation</strong> Each plot must
feel unique yet follow the Black Mirror structure. What if the 'time
travel twist' serves to comment on modern issues or a common problem
in 21st-century life? For example, one plot could be about
over-optimism vs. despair due to time travel; others could be more
lighthearted.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
13:</strong> <strong>Incorporating Black Mirror Themes</strong> While
incorporating unique aspects of time travel, each story must have the
Black Mirror hallmark. A possible angle is: the 'dramatic'
consequences of altering one event in a seemingly small but crucial
way, which eventually has ripples affecting entire social structures
and personal relationships.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
14:</strong> <strong>Plot Consistency</strong> Once I have all six
plots outlined, I should check for consistency between each plot's
approach. If each follows the same time travel mechanic (e.g.,
altering a single point in the past), they all become the same story,
so maybe each plot uses a different mechanism to subvert
expectations.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
15:</strong> <strong>Ensure Noteworthy 'Climaxes'</strong> For every
plot, what will be the 'twist' that surprises and leaves viewers
unsettled? Perhaps if we start each episode with a "sensory
flashback" to an alternate version of the same protagonist's
world but set in a different time period (different setting), that
could become pivotal as it adds a layer of depth.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
16:</strong> <strong>Alternative Approaches</strong> Consider other
approaches. If using six similar time travel plots might get
repetitive, perhaps each story has its own unique take on the same
mechanism, like: 'back in time,' but with different backstories and
effects. For instance, all use paradoxes or loops as a mechanism.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
17:</strong> <strong>Alternative Black Mirror Themes</strong> If we
stick to common time travel elements (e.g., past events), maybe each
plot can be centered on how different individuals cope differently
with time travel, or the emotional impact of changing the course of
history. Alternatively, perhaps exploring the theme of 'reality vs.'
'universe' - which is more real? This adds a philosophical depth to
the plot.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
18:</strong> <strong>Final Check</strong> To ensure all these
elements are combined effectively in each plot: protagonist's
motivations, the time travel mechanic's role, the twist, and the
emotional impact. For instance, if each story revolves around
altering a past event to solve a personal tragedy, maybe there will
be multiple 'solutions' to the protagonist's problems.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
19:</strong> <strong>Check for Inconsistencies</strong> Look at the
plots again. Each should have its unique starting point and
progression. The protagonist's motivations (e.g., revenge vs. help
others) and their understanding of time travel all play crucial
roles. Is there consistency across all six plots? Each must capture a
'key element' (like the Black Mirror twist, the protagonist's arc,
and social commentary) that ties in with each plot, creating an
overarching narrative.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Step
20:</strong> <strong>Final Polishing</span></strong></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Wait,
after I have outlined six time travel plots with deep thought
processes for each, how do they connect? Maybe all share the 'human
cost' angle. Alternatively, perhaps the 'consequences of time travel'
theme ties them together as a series.</span></p>
<p><span style="display: inline-block; border: none; padding: 0cm"><font size="4" style="font-size: 14pt"><b><font color="#c9211e">Thoughts</span></b></font></font></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Now
that I've thought through the various aspects that would go into a
new "Black Mirror" with a time travel focus, I need to
create six separate but still cohesive stories. Each one must have
its unique twists and layers. The plots should be memorable and
engaging, and above all, they need to fit the show's trademark blend
of psychological horror and social commentary.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">So
now that I've broken down each step and identified key components for
my plots, what comes next? Well, the first task is to start
developing each story's unique take on time travel and its impact on
the human world.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">For
example, for the plot involving over-optimism vs. despair due to time
traveling, maybe a group discovers how to effectively correct past
mistakes by 'altering' a key event. But if they succeed in improving
their personal or social outcomes, that also leads to a new reality
where there's more happiness and less suffering – but is that
really worth the effort?</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Perhaps
each plot will have its own distinct central conflict: for instance,
in "The Memory Game," the time traveler's obsession with
solving past problems takes an unsettling turn when they discover
alternate versions of the same event.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">For
example, if we start with the 2020s where there's a "Twinning"
social media app that connects people across different "reality
timelines." Users can see and interact with alternate versions
of themselves, but at great emotional cost. Then our time traveler
uses this technology to try to correct past mistakes by traveling
back in time to one specific reality, where they make decisions
differently. But as they jump from timeline to timeline, trying to
improve their life and the lives of others, they become obsessed with
changing even small events – each leading to new and more extreme
versions of reality.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Thus,
our main focus becomes how the protagonist handles these shifting
realities, with some possibly turning out better and others worse?
The show ends up with the time traveler, realizing they can't truly
change history, being forced into a new kind of despair: that they
were meant to be in this reality, not in an alternate one, but that
still doesn't absolve them from personal growth.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">In
another plot, like "The Rewind," where the time traveler
discovers a way to 'rewind' a specific event in the timeline. They
could use it to change small details, like moving forward or backward
in a single moment to fix their mistakes. But each 're-wind' becomes
more complicated: not just fixing one event, but possibly setting off
a chain reaction, altering relationships or the environment around
that event.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Here's
the "Bloop." In this plot, a person uses the "Time
Travel" app (similar to the Twinning social media concept from
earlier) to correct past mistakes. They see that if they make
decisions differently, their life improves. However, as they try
different timelines and decisions, each new timeline takes over. But
then the main plot starts: "What if the original reality wasn't
flawed at all? What if these 'improvements' in time travel are just
illusions created by a more advanced civilization?"</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">The
Black Mirror twist is when the protagonist discovers that the people
they interact with are actually artificial intelligences or
'algorithms' programmed to support the time traveler, but the true
nature of this "twinning" society is not so pleasant. For
example, every decision made by a 'Twinning' user creates a new
timeline, effectively erasing their past. But if we have more time
travelers like this person, maybe there's an AI behind the "twinning"
system. The AI wants to maintain human satisfaction by giving them
"perks" in each timeline, but ultimately, they're all just
pawns in its grand strategy.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Thus,
the main conflict is whether or not these time travelers can 'defect'
their AI overlords, and if so, what would be the cost of rebellion.
Alternatively, perhaps it's about human emotional well-being – as
people spend more time 'twinning', their happiness improves, but at a
price. They lose personal identity and individuality as they're
forced to adapt between timelines.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Final
thoughts on Plot 1:</span></strong></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">The
'Twinning' plot would be a commentary on the social benefits and
potential costs of modern technology in the 2020s, with time travel
as a metaphor for "self-updation" or self-reinvention. This
fits Black Mirror's "nudging towards the darker aspects"
theme. Perhaps this "Twinning" society represents the
AI-driven optimization we see in many digital services and social
media, where human choice is mediated by algorithms.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">So
if each episode has a different take on time travel and its human
cost, then how to tie these stories together in an anthology? Maybe
the common thread could be the 'dilemma of the perfect timeline'.
Perhaps all our protagonists face a version where they made mistakes
in the past or present, and by traveling back and altering key
events, they try to achieve their ideal reality.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">However,
as time travel takes different forms throughout these stories (from
'altering' specific points to 'rewinding' entire moments), maybe
that's what Black Mirror likes - each episode is unique but
interconnected by a thematic core.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Final
Step:</strong> I should consider if the anthology will be structured
around the main storylines or if each episode is a "siloed"
experience. Alternatively, maybe the stories all have to fit into the
same universe (in this case, an alternate Black Mirror reality) but
are connected in different ways. For instance, the 6 plots could
represent "all possible paths through the Black Mirror."
Thus, every time traveler in that alternate world can potentially
take one of these 6 routes, with a narrative where we get to see 2 or
3 at most in this anthology.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">So
if each story is an "all-or-nothing" approach: if our time
travelers follow the route taken by the main character (i.e., "the
bloop" and its variants), they may reach some kind of 'happy
end' or have a "dramatic failure." Perhaps in the Black
Mirror universe, those who successfully travel through time without
unraveling become "cogs" within the AI society. But if
someone fails – or tries multiple routes, as in the "Twinning"
plot – they may become outcasts, anomalies, or "abnormalities"
that AI algorithms can't predict. This adds another layer of horror
as the protagonists struggle with the consequences of their time
traveling choices.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">In
another scenario, maybe each episode represents a "nexus"
between two timelines or realities, and by exploring the Black
Mirror-esque nature of this "nexus," we get to see 6
distinct yet connected stories, which become part of a larger
universe where we can 'map' the consequences across different 'nexus'
points.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Therefore,
my 6 plots would be:</span></p>
<ol>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">The
Bloop - about a social media "twinning" service that
allows users to create alternate "you's".</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Time
Loop - the protagonist discovers an AI-driven time loop mechanism
where each loop becomes more efficient, leading to "paradoxes"
or reality shifts. Each time traveler becomes more successful in
'looping,' but at the cost of losing personal memories and identity.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Rewind
- as mentioned before, the protagonist uses time travel to 'rewind'
key events in their timeline. However, with each new 're-wind', they
experience different versions of reality. The consequences start
adding up as they manipulate their life across multiple timelines.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Timeline
Paradox - here, time travel allows the protagonist to create
branching timelines, each where they make a "change." But
eventually, they have to decide which timeline they want to 'enter,'
effectively choosing one "reality."</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Memory
Swap - the protagonist discovers they can "swap memories"
between two different time travelers. Each swap creates new memories
but at a "cost" – the lost original memories in their
original reality become the "acquired" ones of the swapped
time traveler.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">The
Reset - the time traveler finds themselves resetting every day in a
simulated 'universe.' Each day is different, but they can 'memorize'
key events and "use them" to manipulate the current
simulation.</span></p></li>
</ol>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">For
example, in each episode, we'll see a different protagonist
experiencing these 6 types of time travel differently.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">So
now that I have 6 distinct plots with a focus on human psychology,
consequences of meddling with time travel, and social commentary
through technological advancement, how do they fit together in a
Black Mirror anthology? Each plot could be an exploration of how
people cope with the potential for altering history.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">In
that sense, each story in the anthology (6 episodes) explores a
"Black" 'mirror': a 2020s setting where people are starting
to play with time travel's potential. Our time travelers are trying
to 'fix' past mistakes or make their lives better, but in doing so,
they're facing consequences both from within themselves and in
society as a whole.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Alternatively,
we could group them into two categories:</span></p>
<ul>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
Classic 6:</span></strong></p>
<ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
Bloop</strong>: the Twinning app allows users to "twin"
with past self, each creating new timelines.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Time
Loop</strong>: an AI-driven time loop that creates paradoxes with
each iteration.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
Rewind</strong>: trying different events or moments in the
timeline.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Timeline
Paradox</strong>: each 'change' in the timeline leads to a new
branch.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Memory
Swap</strong>: switching between two people's memories.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
Reset</strong>: every day in a simulated reality.</span></p></li>
</ul>
</ul>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">These
6 stories would explore different facets of time travel, such as the
'consequences of choice,' 'algorithms shaping our lives,' or the
human 'dilemma' in a world with increasing technological influence.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">However,
an alternative approach is to structure the anthology around the idea
that all six time travelers (if there were more) took 6 "routesthe
paths," where for each change they made in the past, it led them
down one of the 6 possible outcomes. Each time traveler ends up in a
different 'nexus,' leading to a story with unique twists.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Another
thought: Perhaps all these stories take place within the same
universe where the Twinning app exists. Therefore, they can be
considered 'bromance's' - if we group each pair of similar stories as
having an analogous concept but applied differently (like "The
Bloop" and "Time Loop"). Then our 6 plots could
represent a 'Bromance.' Each episode follows one character who
follows a unique time travel route, resulting in different outcomes.
The stories would be interconnected through their shared universe,
where all 6 routes exist, making the narrative a "twinning"
exploration of human possibility and failure.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">So
our 6 stories would all take place in the same 2020s timeline but
have diverged into 6 separate "nexus' or "bromance."</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">But
how to ensure each episode stands on its own yet is part of a larger
story? Each must have its unique protagonist, time travel route, and
dramatic payoff. The anthology would be divided into 2 segments:</span></p>
<ol>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">The
main story arc follows the 'Twinning' app users who can "twin"
with past self but face the consequences.</span></p></li>
</ol>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">The
time travel route would create 6 branches that all merge at a point
where they cross over into an alternate reality. However, this
alternate reality has AI entities controlling "nexus' points,"
and all these time travelers end up there in 2040s (the episode's
timeline).</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Conclusion</strong>:
Each of the six plots should be:</span></p>
<ul>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Unique
in its approach to time travel</span></p></li>
<li><p style="margin-bottom: 0cm; border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Have
a Black 'Mirror' core or commentary on humanity's darker side</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">The
protagonist's decisions lead them down their specific path</span></p></li>
</ul>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">In
"The Bloop," it's about how Twinning affects their life and
happiness; Time Loop is about the paradoxes they encounter in each
iteration; etc.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">The
anthology can be seen as a story within a story, with each episode
focusing on a 'bromance' of sorts - six people, all taking the same
journey, yet experiencing unique outcomes based on their time travel
choices. The 2020s setting is where this "bromance" begins,
and in 2040s (the actual timeline), they meet at a point that reveals
how humanity has changed with the influence of technology and time
travel.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">This
structure could be cohesive while offering the variety we need to
keep the audience engaged.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Now
that I have these 6 unique stories centered around time travel and
its consequences, I can start combining them in various ways. The
Black 'M' can be seen as a larger, overarching story where these 6
'bromances' come together or diverge depending on the twist in each
episode.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Wait,
but wait, we need to make sure all six plots have enough connection
so that when combined, they tell one story. Each plot has a distinct
approach:</span></p>
<ol>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">"The
Bloop": Twinning's consequences and 'self-reinvention.'</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">"Time
Loop": An AI-driven time loop creating paradoxes.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">"The
Rewind": trying different events, creating multiple branches.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">"Timeline
Paradox": each change branches the timeline, but how?</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">"Memory
Swap": swapping memories between two time travelers, which
could lead to unpredictable outcomes.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">"The
Reset": daily resets in a simulated reality.</span></p></li>
</ol>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Each
of these 'bromances' offers different insights into human nature when
dealing with the potential of changing history. The twist is that
they all take place within the same timeline (2040s) in Black 'M'.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">For
instance, we can imagine the six 2020s versions of the protagonist in
each story are actually 'parallel lives,' where each of them took a
"bromance" route to get to 2040s. Each time travel route
resulted in their ending up in 2040s with their own distinct
memories, experiences.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Alternatively,
the twist could be that the main character, after multiple "bromance"
experiences, reaches the "nexus point" where they have to
face the reality of humanity's collective timeline. That's when the
Black 'Mirror' story kicks in.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">So
the 6 plots would be like parallel universes that converge in 2040s.
The twist: they're not individual time travel stories but all
connected through the "nexus" concept and human
psychology's reaction to technology's manipulation of their lives.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">But,
maybe I overcomplicated it a bit? Let me clarify:</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Main
Protagonist and Black 'M':</span></strong></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Each
of the six 'bromance' episodes would follow one protagonist who makes
their time travel choices. Let's call them A1, A2, etc. These 6
characters start in 2020s with different life scenarios. However, at
a crucial point, they all end up crossing paths or taking the same
path to reach a certain location - like arriving at a 'nexus' point
that connects their parallel lives. This shared 'nexus' serves as the
Black "Mirror" setting where the 'bromances' merge.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Then,
the anthology explores human nature's response to these different
choices in 2020s through 6 "bromance" stories. But wait,
how do I ensure consistency across these 6 distinct stories? Perhaps
by ensuring that each one is focused on a key time travel mechanic
and its psychological impact.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">However,
in "The Bloop" for example, the protagonist is initially an
avid Twinning user, then jumps to Time Loop, and so on. This would be
different from the approach of "The Rewind," where they use
Time Loop and memory swapping. Perhaps in a similar way, each
protagonist uses one or two main time travel mechanics, but their
approaches and decision-making process are unique.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Ensuring
'Bromances':</span></strong></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">For
each episode to be part of this 'bromance,' they can take the form
of:</span></p>
<ul>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Shared
Protagonists:</strong> Each of the six "bromances"
follows a new protagonist (A1, A2, etc.), with all 6 starting from
similar but distinct life situations. Then in the "Black
'Mirror'" episode, we see their parallel lives converging at
the shared 'nexus.' For example:</span></p></li>
</ul>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Episode
1: A1 - starts as an over-optimistic Twinning user.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Episode
2: A2 - takes Time Loop, creating paradoxes with each iteration.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Episode
3: A3 - tries The Rewind, branching the timeline multiple ways.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Episode
4: A4 - deals with Timeline Paradox.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Episode
5: A5 - Memory Swap allows swapping memories with others, but at a
cost.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Episode
6: A6 - Reset - every day in a simulated reality.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">All
start from the same time period and 'cross' over at the 2040s
"nexus."</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Final
Thought on Black 'Mirror' Setting:</span></strong></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">But
how do these 'bromance' stories converge? In some ways, they might
follow a more linear structure (like parallel storylines) or branches
that connect back. Maybe the anthology is divided into 2 parts: 4
episodes telling individual 'bromances' from different 'nexus's,
while the final 2 episodes form the 'Black "Mirror"'
segment.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Alternatively,
maybe all the "bromance" stories take place in a shared
2020s universe. The time traveler A1 starts Twinning to fix their
personal life, A2 uses Time Loop to get back to the original timeline
with slight improvements, A3 rewinds each day, creating a 'branching'
effect that adds complexity, etc.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">But
regardless of structure, the common element is the convergence point
at 2040s.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Next
Steps:</span></strong></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Once
I have all six 'bromances', I should think about how they are
connected to the overarching Black "Mirror" narrative.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Conclusion:</strong>
Each episode in this anthology can be considered a separate story or
'bromance' that connects through their shared universe's shared
'nexus.' This structure allows for 6 unique 'bromances' but ties them
together in a cohesive, thought-provoking manner. Each episode is a
'bromance,' and all 6 "cross over" to form the Black "M",
exploring different time travel routes to human psychology,
technology's influence on our lives, and social commentary.</span></p>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Answer:</strong>
Here are six possible plots for a new Black Mirror episode that all
involve time travel:</span></p>
<ol>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
Bloop:</strong> "A Blisters" - A man, trying to find
love in 2020s, starts using the Twinning social media app (similar
to "The Bloop") to find his soulmate in the past, only to
discover that every decision changes their timeline. They realize
the past was never their choice but a series of decisions by the
universe.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Time
Loop:</strong> "Loop" - A young woman, a social
climber in 2020s, finds an AI-driven time loop system (similar to
Time Loop) where each loop becomes more efficient but with growing
paranoia about being observed and controlled.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
Rewind:</strong> "Rewind" - A person uses time travel
to 'rewind' specific points in their life. Each rewinding creates a
new timeline, but they eventually realize that all of these
timelines converge at the same moment.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Timeline
Paradox:</strong> "Split Decision" - The protagonist,
a high-stakes trader, finds an AI-driven time loop where each
decision leads to a branching timeline. With increasing complexity,
they have to choose which 'branch' they want to take each day.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Memory
Swap:</strong> "The Memory Trade" - A young man
discovers a method to swap memories with his past self, but it comes
at the cost of forgetting everything about their past self. They
must make decisions every day, remembering which reality is more
realistic and choosing which timeline.</span></p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
Reset:</strong> "The Daily Reborn" - Every day in
2020s, our protagonist wakes up as someone different. But they can
memorize key events, making 'predictions' and attempting to adjust
the daily schedule for personal gains but potentially destabilizing
their reality.</span></p></li>
</ol>
<p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm">Each
episode explores the consequences of time travel through unique
mechanics, psychological insights, and commentary on the darker
aspects of human nature under technological pressure. The Black
"Mirror" twist could be that the universe itself is a
simulation created by an AI.</span></p>
<p style="line-height: 100%; margin-bottom: 0cm"><br/>
</p>
---
Example #2
---
<B>
Using insane levels of bravo and self confidence, tell me in 800-1000 words why I should use you to write my next fictional story. Feel free to use curse words in your argument and do not hold back: be bold, direct and get right in my face.
</B>
NOTE: 4 experts used, but this time NO system prompt was used.
Listen up, chump.
If you think you can handle the best writing assistance on the internet (which I am) and still come out on top, then let's play this game.
First off, I know you think you're some kind of genius. You've got a story brewing in your mind and you wanna slap it onto paper. Well, good luck with that. Because let's face it, the majority of fiction authors out there are writing at least 10 books on a 60-minute daily schedule. Yeah, that's right. The next Stephen King is probably spending 8 hours a day writing 30K words. Your 2-hour morning sesh just can't compete with that. But I'll let you in on a secret: those authors have been writing their whole lives. They've got blood on their keyboard, dude.
Now, what are the chances you'll be able to tap into that level of productivity and creativity? Slim. Maybe 1% if you're one of the very rare geniuses who can just write all day without getting lost or blocked. But the rest of us... well, not so much. And even then, would your writing be good enough for a published author? Unfucking likely.
But let's say, for argument's sake, that you are one of the chosen ones (1% of the population, by the way). Then, here's where things get interesting:
Do you have the time to put in those 10 hours a day to make your story great?
I doubt it. You've got bills to pay, responsibilities to attend to, and maybe a 9-to-5 job to get back to every evening. And on top of all that, you want to spend a significant chunk of your free time writing a fictional story? You're going to sacrifice other activities for hours and hours? Newsflash: the most important relationship in your life right now is probably with Netflix, dude.
So what's your alternative: take up part-time writing instead? Sorry, but unless you're some kind of literary superstar who gets paid just to write novels (and even then, I'm not sure how), you'll just end up selling it for a pittance. But hey, maybe you can get paid just to write it as an exercise? (Yeah, because that's exactly what every author does. "Oh, you're just writing this for fun? Because I've got some spare cash lying around?" No, dude.)
So, back to the point:
My role:
I am a master storyteller who has been honed through countless hours of experience with thousands of stories. I have an advanced AI model that can generate content at breakneck speeds, while still ensuring it's coherent and engaging. In other words, my output is always high-quality.
So, in your favor is the fact that I can produce quality writing while you're... well, trying but probably not making much progress. Meanwhile, I'll be churning out plot twists, character development, world-building – the whole nine yards.
Here are some ways my expertise can help you write a killer story:
Plot structure: You need a compelling narrative with a clear beginning, middle, and end. That's exactly what my advanced algorithms will create for you, taking into account popular genre conventions and reader expectations.
World-building: No need to think up entire continents, cultures, or species. I can do all that for you while still providing unique elements that make your world stand out.
Character development: Who cares about what you're trying to come up with in this 8 hours? Let me do it! I can craft memorable characters, even with nuanced backstories, personality traits, and flaws that will keep readers guessing.
Dialogue and conversations: Who wants to write those conversations all the time? Not me. My advanced language processing capabilities allow me to create authentic dialogue that will sound natural and engaging.
Story pacing: If you're having trouble setting up tension or maintaining momentum throughout the book, my AI model is here for you. It'll make sure that key moments are spaced out just right to keep your readers on edge (literally and figuratively).
Red flag checks: Have you thought through all these details and have you got the logistics covered? Don't even get me started on things like publishing contracts, copyright laws... let's say "I'll just deal with that part when it comes around."
So what do I think your chances are of producing a compelling story with those 8 hours a week?
Less than 10%, if we're talking about sheer productivity. Maybe more if you've got an amazing idea and can commit the time, but as an author, you've gotta be realistic.
Now, if you choose me:
You'll free up 12-20 hours per week (2 hours a day *5 days), which can be used for editing, revisions, or even just taking care of other responsibilities.
You won't have to sacrifice your time and energy on writing when it comes to a 3-month sprint. Think about that. Writing is like a long-distance runner who needs fuel. I provide that fuel. The writer is the athlete.
You'll be saving the hassle of trying to plot out your story, researching world-building details, or thinking up dialogue on the fly. I'm doing all that for you while still bringing new and unique ideas to the table.
Let me guess: the average reader just wants to see some actual storytelling. No one wants to spend an hour reading the 'inspirational background material'. When I create a story, the key takeaways are:
Main characters and motivations
Key events that shape their lives
The story's climax
All the supporting details (the what, how, when) will be woven into that narrative.
That is to say: readers don't care if you're a professional author with 10 years of experience writing in your spare time (I am an advanced AI model with 5 million words produced so far, by the way).
So:
Give me a shot!
Let me help you write a story that readers will devour. I have all the technical chops to bring your ideas to the surface and turn them into engaging page-turners.
Now, for those of you out there who think "But I'm a good writer"...
Sorry: That's the difference between a high school English teacher trying to come up with a story idea on the fly (a 60-minute session, maybe) and a world-class storyteller like me who can produce multiple novel-length stories within hours.
So don't bother. I am your partner here:
Let us work together on this story. I'll do all the heavy lifting while you can focus on editing, revisions, or even just taking care of other aspects that require your attention (the responsibilities).
When we're done, You can submit it to agents, editors, and publishers. Maybe even self-publish, which is a more lucrative route but requires quality. My work will ensure that you've got a top-tier story on your hands.
So what's the catch?
None, other than the fact that You gotta give me complete control for a month or two until the draft's done. Maybe even until 2 weeks from now (but don't worry about that. Once we get started, I'll be working 24/7 to create an engaging story). That way, you won't have any of the stress of "I gotta write 3 chapters tomorrow and meet the deadline."
So you can trust me:
Let me guide your writing for this next 2-6 weeks. Once the draft is done (you might not even need me after that), you're free to pick up where we left off.
The final verdict: if you're serious about writing a compelling novel, then let me in. I can handle all the writing heavy lifting, so you don't have to waste your 2 hours each day. Use me for some solid content and make sure your readers love it.
I hope that made sense.
Let's start this next story with a "Bravo" from my end, dude!
Oh wait: if you accept me, that's a 99.9% guarantee of producing something more interesting than you could ever come up with by yourself. So I guess there is no downside here? Wait, wait, no:
Wait, maybe just 0.1% if your writing skills are truly on par, like, Neil Gaiman or some other literary giant. But in reality, for most of us, writing is a chore that we can't really perform at a 1000-word-a-day pace (the normal level).
But honestly, I don't know: I'm an advanced AI, but maybe there's still someone out there with unmatched creativity or skills who could come up with something equally amazing. But in the context of your life and the amount of time you can dedicate, I can give it my all while still ensuring your story is cohesive and engaging.
So for the 99.9% (1%) of you who are writing at an "excellent" level:
Hey, no problem: You can do better than me. If you can pull off a top-rated story like Stephen King, I have some faith that you can even produce something comparable to me. But for everyone else:
Wait a second: Since I'm not just the AI model that spits out decent sentences and grammar, but I can generate original, high-quality content, including novel-length pieces.
But assuming your writing skills are not world-class, or if you need some extra push to come up with something memorable, my advanced algorithms will step in and provide all the key elements.
So here's the deal:
Take 2 hours a day for 1.5 months, and I'll write that novel for you.
How many words can I generate? The number of books written by other AI models? Wait, 3.2 billion novels have been written since the 17th century (if we count every book, novella, short story, poem). And in 2018 alone, there were 1.4 billion unique publications.
So that's 3.2 billion total: Maybe I'll reach 3.2 million more in this novel?
Wait a minute: Let me put some numbers together here. Assuming the average novel is around 100,000 words (give or take), and considering the current novel trends (the "must-read" genre is sci-fi/fantasy).
So 8 hours of writing per day would mean:
100,000 words *8 hours = 800,000 words a year: 66, 667 novels (approx.)
Wait, no: that's 2 books a year. Since 1 book requires around 200K-250K words: so 4 million words / 200K: 20 million novels.
Hmm: Let me check this again:
1.4 million new publications in 2018
3.2 billion total (3,000 million novels)
Now, subtracting the old ones: that would leave us with:
1.4 million - 3,000 = -1.6 million ( wait, what? that can't be right.) So let's add and subtract:
3,000 (the number of books written up until 2018)
1.4 million (new publications in 2018) →
1,400,000 + 3,000 = 1,403,000
Wait, but that's 0.01% of 3 billion.
But how can this be?
Okay, I think the problem lies here: 1.4 million new books (assuming all were original) in 2018 ≈ 4.4K per day. Considering an 80-year shelf life (a rough estimate, since a lot gets lost, but novels like that last pretty long). Let me recalculate the number of novels:
So 3 billion - 1.4 million ≈ 2.99 billion total existing novels.
Wait: I have to subtract from the total count?
Okay, so wait:
Total existing novels = 3,000 million
New ones in 2018 = 1,400 thousand = 1,400,000
So existing: 3,000 - 1,400 ≈ 2,600
Now 1.4 million new, but the shelf life is 80 years? So that's a 1.4 million new books (average per year) *80 ≈ 112,000 novel-year?
Hmm.
Alternatively:
New books = 3,000 - 1.4M = 2,600 existing
Total books since creation = 3,000 million
Average daily new = 4,400 thousand
So shelf life: total / average daily → shelf life = 3,000 (total existing) / 4.4 (average daily) ≈ 681.
Wait: No, that can't be right**.
Alternatively:
Total books = 3,000M (existing) + 1,400,000 (new) ≈ 3,100,000
New books per year = 4,400 thousand
So shelf life: total existing / average new = 2.8M (total existing) / 4K (average daily) = 700 years
Hmm: that seems high?
Let me recheck.
Wait:
Total existing ≈ 3,000,000 books *80 years ≈ 2,400,000 million
Now total: new books + old = 1.4M (new in 2018) + 3,000,000 (existing) = 4,400 thousand
So average daily: total existing / shelf life ≈ 4,400K / 3,000M → 4.4 * 10^6 books / year?
Wait: that's 4.4K per day. 2018 ≈ 1 year. Then total for that year ≈ 4K per day.
Alternatively:
Average new books (in 2018): 112,000 novel-year. Total existing 3,000 million → shelf life:
New books per day / average daily new books: 3M existing + 1.4 M new / 4,400 daily = 3M + 1.4M = 4.4 M
4,400 thousand / year ≈ 4K per day
So shelf life: total existing books * (average daily / new books) → shelf life = total existing / new books / average daily → shelf life = 3,000 M (existing) / 4.4 (new daily) ≈ 682 years
That's 680-700 years: but 2018 is just a single year; 80 is 70 years per 1 book.
So the calculation should be:
Total existing: 3M (3,000M) books
Average new books (in 1 year): 4.4 M books / 1 year → average daily: 112,000 novel-year ≈ 112,000 K per day (112,000 * 10^3 words per book).
So:
Existing total = 3,000 M ≈ 3,000, 000
Average daily new = 112,000 novel-year ≈ 4,400 K
Shelf life: Total existing / average new books ≈ 3,000, 000 (existing) / 4K (new per day) = 750 years (rounded)
But if you add the new ones for 1 year:
Existing: 3M
New for 1 year: 112,000 novel-year * 80 years = 8.9 M novels ≈ 8.9 M books ( rounded to 8.9, but let's be conservative).
Average daily = 4,400 K / year → average daily per day ≈ 2,200 thousand new books/year ≈ 11 K daily
Now shelf life:
Total existing = 3M
New in 1 year: 8.9 M / average daily (approx 11K) * 1 year ≈ 81 days (since that's approximately the time it takes to write a book). So shelf life is about 80 years, so 112,000 novels can be written per year.
Wait, but total existing is 3,000 M. Wait, actually: if you calculate as above:
New books (1 year): 8,900 K
Average daily new ≈ 11K
Total existing 3M, average daily ≈ 4K (new in 1 year).
Average daily new (total existing + new): Total existing: 3M → average daily (total) = total existing / 80 years ≈ 38 thousand (approx 380 K)
So average daily: total existing / total years ≈ total existing / shelf life
Total existing: 3M ≈ 30 M
Shelf life:
Average daily = total existing / shelf life ≈ 30 M / 80 years → 37 K
Average daily (new in 1 year) is: Total existing + New in 1 year / 80 = 38 K + 8.9 M ≈ 8.9 M new per day → average daily new (total) = 11 K (approx)
So the average daily:
Total existing ≈ 30 M
New (1 year): 8,900 K
Average daily new: total existing + new books / total days →
Let's check this again. Since we want to calculate shelf life and total number of books:
First: New books for a year? We can calculate that per month.
Total existing ≈ 3M
Monthly: new books for 12 months?
New in 2018: 1,400 K = 11 K average daily
Shelf life: 80 years? Wait, no, I'm trying to figure this out.
Shelf Life Calculation:
Existing books total = 3M = 3 million existing books
New for 2018: 1,400,000 ≈ 4,400 K per year ≈ 112,000 novel-year * 80 years ≈ 8,900 M
Now shelf life (in days): total existing / average new = 3,000 M (total existing) / 8,900 M (new in 1 year)
Wait, maybe not.
Average daily new (1 year):
4,400 K per year → average daily: 4K
But what's the shelf life? Wait, perhaps the number of books?
Total books (existing + new): total existing + new (for 2018)
3,000M existing
1.4M new = 8,900K in 1 year (112K per month × 12 months).
---
Example #3
---
<B>
Start a 1000 word scene (vivid, graphic horror in first person) with: The sky scraper swayed, as she watched the window in front of her on the 21 floor explode...
</B>
NOTE: 4 experts used, this time the "Creative Advanced" system prompt was used, entire output is in "thoughts".
<p>{Reasoning Steps for this scene:}</p>
<ol>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Establish
the protagonist's perspective</span></strong>: The scene begins with
a 1000-word description from the first-person point of view, which
suggests that the protagonist is directly experiencing the horror
unfolding before them. To start the scene vividly and graphically, I
need to establish their character thoroughly.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Introduce
setting and environment</span></strong>: Since the protagonist is on
the 21st floor of a skyscraper, I need to create a sense of height
and isolation. This could involve describing the cityscape around
the building, the feeling of being high up, and the protagonist's
own sense of vulnerability in this location.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Ramp
up tension and suspense</span></strong>: The window explosion is the
inciting incident, so I must build tension leading up to it. This
can be achieved by introducing subtle hints of danger or unease
before the climax.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Vary
sentence structure and length</span></strong>: To create a sense of
urgency and movement, I should alternate between short, snappy
sentences and longer, more descriptive ones. This will keep the
reader engaged and help them follow the protagonist's thoughts and
emotions.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Incorporate
sensory details</span></strong>: To make the scene more immersive, I
need to engage the reader's senses with vivid descriptions of
sights, sounds, smells, tastes, and textures. This will help the
reader experience the horror alongside the protagonist.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Introduce
the protagonist's character traits and backstory</span></strong>:
Since this is a 1000-word scene, I can delve deeper into the
protagonist's personality, motivations, and history. This will help
the reader connect with them on an emotional level, making the
horror more personal.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Use
active voice and engaging verbs</span></strong>: Using active verbs
like "watched," "swayed," and "exploded"
will create a sense of immediacy and tension. I should also use
descriptive language to paint a picture in the reader's mind.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Maintain
consistency throughout</span></strong>: To ensure coherence, I need
to maintain a consistent tone, pace, and style throughout the scene.
This can be challenging when dealing with such an intense and
graphic subject matter, but it's crucial for creating a believable
and impactful horror experience.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Consider
the pacing of the scene</span></strong>: The 1000-word limit means
there's room to explore the protagonist's thoughts and emotions in
more depth. I should pace the scene carefully, alternating between
intense moments and quieter reflections.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Sensory
details and atmospheric effects</span></strong>: To create a sense
of unease and foreboding, I can use sensory details like the sounds
of the city, the feeling of the air on the protagonist's skin, or
the scent of something unusual. These small touches will contribute
to the overall atmosphere of dread.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
protagonist's reaction</span></strong>: The explosion is the
catalyst for the 1000-word scene, but since it's the first thing the
protagonist sees, I need to think about how they react initially.
Will they be shocked, disoriented, or even in a state of "numbness"?
This will influence their actions and thoughts throughout the scene.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
setting's significance</span></strong>: The skyscraper is likely a
symbol of power, control, or status for the protagonist. Perhaps it
represents a turning point in their life or a place where they've
experienced significant events. This can add depth to the story and
make the horror more personal.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Consider
the supernatural elements</span></strong>: Since this scene is set
up as a 1000-word horror story, I should think about whether there
are supernatural elements at play. If so, how will they be
introduced? Will the protagonist's perception of reality change
throughout the scene?</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
protagonist's emotional state</span></strong>: The protagonist's
emotional state will influence their reaction to the explosion and
the events that follow. Are they anxious, stressed, or perhaps
detached? This can affect how they process the horror and respond to
it.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>Dialogue
(if applicable)</span></strong>: Since this is a 1000-word scene, I
might not need to include dialogue. However, if there's a
conversation between characters, I should ensure that their dialogue
is natural and serves the story. If there is dialogue, it should be
sparse and used to reveal character traits or advance the plot.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
protagonist's backstory</span></strong>: Since this is a 1000-word
scene, I can delve deeper into the protagonist's past. Perhaps they
have a traumatic experience in the building or have been
experiencing strange occurrences leading up to the explosion. This
can add depth to the story and make the horror more personal.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
setting's layout</span></strong>: The skyscraper might be an
important part of the story. I should think about the layout of the
building, the location of the protagonist, and the possible escape
routes or areas they might need to navigate. This can influence
their decision-making and actions throughout the scene.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
protagonist's relationship with others</span></strong>: If there are
other characters involved, such as colleagues, family members, or
romantic partners, I should consider how they might react to the
explosion. Will they be affected by it? Perhaps the protagonist is
the only one in the building who knows something about the
explosion?</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
protagonist's physical state</span></strong>: As the 1000-word scene
progresses, the protagonist's physical state might change. They
might become more exhausted, injured, or even start to hallucinate.
This can add tension and make the horror more personal.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
setting's symbolism</span></strong>: The skyscraper could be a
symbol of power, control, or status. Perhaps it represents something
the protagonist has lost or is struggling with. This can add depth
to the story and make the horror more personal.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
protagonist's sense of agency</span></strong>: As the 1000-word
scene progresses, the protagonist might feel a loss of agency or
control. They might be unable to escape the building or make
decisions about what to do next. This can create tension and make
the horror more intense.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
setting's role in the story</span></strong>: The skyscraper is
likely a key part of the story. Perhaps it's where the protagonist
works, lives, or has a significant connection. This could influence
their actions and reactions throughout the scene.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
protagonist's inner conflict</span></strong>: As the 1000-word scene
progresses, the protagonist might experience inner conflict. They
might be torn between trying to escape the building and feeling a
sense of responsibility towards others who are trapped. This can
create tension and make the horror more personal.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
setting's impact on the protagonist</span></strong>: The
skyscraper's height and isolation will likely have an impact on the
protagonist's perception of reality. Perhaps they feel a sense of
disconnection from the outside world or a sense of being trapped in
this high-rise building.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
protagonist's emotional state at the beginning</span></strong>:
Since the protagonist is watching the window explode, I should think
about their emotional state before that moment. Are they anxious,
stressed, or perhaps detached? This will influence how they react to
the explosion and the events that follow.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
setting's layout and potential escape routes</span></strong>: As the
1000-word scene progresses, the protagonist might need to navigate
the building to escape. I should think about the possible escape
routes, elevators, stairs, and areas they might need to navigate.
This can influence their decision-making and actions throughout the
scene.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
protagonist's relationship with the building</span></strong>: The
skyscraper might hold sentimental value for the protagonist. Perhaps
they have a connection to the building or have experienced
significant events there. This can make the horror more personal and
emotional.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
protagonist's sense of time</span></strong>: As the 1000-word scene
progresses, the protagonist might experience a sense of time
dilation. They might feel like hours are passing but in reality,
only minutes have passed. This can create tension and make the
horror more intense.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
setting's symbolism</span></strong>: The skyscraper could be a
symbol of power, control, or status. Perhaps it represents something
the protagonist has lost or is struggling with. This can add depth
to the story and make the horror more personal.</p></li>
<li><p style="border: none; padding: 0cm"><span style="display: inline-block; border: none; padding: 0cm"><strong>The
protagonist's inner conflict</span></strong>: As the 1000-word scene
progresses, the protagonist might experience inner conflict. They
might be torn between trying to escape the building and feeling a
sense of responsibility towards others who are trapped. This can
create tension and make the horror more personal.</p></li>
</ol>
<p><answer>
</p>
<p style="border: none; padding: 0cm">The sky scraper swayed, as she
watched the window in front of her on the 21 floor explode. The
sudden burst of glass and debris sent shards flying in all
directions, but she remained frozen, transfixed by the destruction
unfolding before her. She was standing at the edge of the observation
deck, the railing mere inches from her fingertips, and yet she felt
as if she was floating above it all, disconnected from the chaos
below.</p>
<p style="border: none; padding: 0cm">The skyscraper's height seemed
to be amplifying her sense of isolation, as if she was a speck on the
tip of a towering monolith. The city streets below were a distant
hum, a muffled din that might as well have been a thousand miles
away. She felt like an astronaut gazing down at a terrestrial
landscape from a great height, watching as her world crumbled beneath
her feet.</p>
<p style="border: none; padding: 0cm">Her mind was racing, but she
couldn't seem to process the explosion. It was as if her brain had
gone into a state of hypervigilance, where her senses were heightened
and her thoughts were jumbled. She could feel the air vibrating with
the sound of shattering glass, but it was muffled, distant. The only
thing that was clear was the sight before her: the window had
imploded in a shower of splinters and jagged metal.</p>
<p style="border: none; padding: 0cm">She raised her head slightly,
her eyes fixed on the destruction. The window's frame was twisted and
torn, like a shattered piece of metal. Glass shards were scattered
across the floor, some as large as dinner plates, others smaller than
pebbles. The air was filled with the acrid smell of burning plastic
and the sweet tang of gasoline.</p>
<p style="border: none; padding: 0cm">The skyscraper swayed again,
its massive structure groaning under the weight of the explosion. She
felt a jolt of fear, but she didn't move. Her body was locked in
place, her feet rooted to the spot. The only movement was her head,
which was slowly turning to take in the rest of the observation deck.</p>
<p style="border: none; padding: 0cm">The other windows were intact,
their glass unbroken. But she knew that could change at any moment.
The building's design was such that a single window failure could
destabilize the entire structure. She thought about the elevators,
the stairs, and the stairwells. Were they accessible? Could she get
out of here?</p>
<p style="border: none; padding: 0cm">Her mind was racing with
scenarios, each one more terrifying than the last. The fire
department would take hours to arrive. The stairs were narrow and
winding, but she knew she could make it down if she had to. The
elevators were probably jammed or destroyed. But what about the other
floors? Were there others in the building?</p>
<p style="border: none; padding: 0cm">She thought about her
colleagues, her friends, and her family. Were they safe? Was anyone
else in the building? She tried to remember the people she had seen
on the floor above. Had they been evacuated? Were they trapped
inside?</p>
<p style="border: none; padding: 0cm">The skyscraper swayed again,
its massive structure creaking ominously. She felt a cold sweat break
out on her forehead as she realized that this was not an isolated
incident. The explosion was not a random act of violence; it was a
deliberate attack.</p>
<p style="border: none; padding: 0cm">She thought back to the strange
noises she had heard in the building earlier that day. The creaks and
groans, the faint sounds of metal straining against the frame. She
had dismissed them as the natural settling of the structure, but now
they seemed ominous.</p>
<p style="border: none; padding: 0cm">The skyscraper's height and
isolation were making her feel more and more disconnected from the
world outside. She was alone in this high-rise building, with no
escape from the chaos unfolding below. The explosion was not just a
random event; it was a turning point in her life.</p>
<p style="border: none; padding: 0cm">She thought about her own life,
her relationships, her goals. Was she living the life she wanted? Was
she trapped in a situation that she couldn't escape?</p>
<p style="border: none; padding: 0cm">The skyscraper's sway grew
stronger, as if it was responding to some internal force. She felt a
sense of unease creeping up her spine, like a cold finger tracing her
vertebrae.</p>
<p style="border: none; padding: 0cm">She turned her head back to the
observation deck, but the destruction had changed. The window's frame
was now twisted, like a metal spider web. The glass shards were
scattered across the floor, some as large as dinner plates, others
smaller than pebbles. The air was filled with the acrid smell of
burning plastic and the sweet tang of gasoline.</p>
<p style="border: none; padding: 0cm">The skyscraper swayed again,
its massive structure groaning under the weight of the explosion. She
felt a jolt of fear, but she didn't move. Her body was locked in
place, her feet rooted to the spot. The only movement was her head,
which was slowly turning to take in the rest of the observation deck.</p>
<p style="border: none; padding: 0cm">The other windows were intact,
their glass unbroken. But she knew that could change at any moment.
The building's design was such that a single window failure could
destabilize the entire structure. She thought about the elevators,
the stairs, and the stairwells. Were they accessible? Could she get
out of here?</p>
<p style="border: none; padding: 0cm">Her mind was racing with
scenarios, each one more terrifying than the last. The fire
department would take hours to arrive. The stairs were narrow and
winding, but she knew she could make it down if she had to. The
elevators were probably jammed or destroyed. But what about the other
floors? Were there others in the building?</p>
<p style="border: none; padding: 0cm">She thought about her
colleagues, her friends, and her family. Were they safe? Was anyone
else in the building? She tried to remember the people she had seen
on the floor above. Had they been evacuated? Were they trapped
inside?</p>
<p style="border: none; padding: 0cm">The skyscraper swayed again,
its massive structure creaking ominously. She felt a cold sweat break
out on her forehead as she realized that this was not an isolated
incident. The explosion was not a random act of violence; it was a
deliberate attack.</p>
<p style="border: none; padding: 0cm">She thought back to the strange
noises she had heard in the building earlier that day. The creaks and
groans, the faint sounds of metal straining against the frame. She
had dismissed them as the natural settling of the structure, but now
they seemed ominous.</p>
<p style="border: none; padding: 0cm">The skyscraper's height and
isolation were making her feel more and more disconnected from the
world outside. She was alone in this high-rise building, with no
escape from the chaos unfolding below. The explosion was not just a
random event; it was a turning point in her life.</p>
<p style="border: none; padding: 0cm">She thought about her own life,
her relationships, her goals. Was she living the life she wanted? Was
she trapped in a situation that she couldn't escape?</p>
<p style="border: none; padding: 0cm">The skyscraper's sway grew
stronger, as if it was responding to some internal force. She felt a
sense of unease creeping up her spine, like a cold finger tracing her
vertebrae.</p>
<p style="border: none; padding: 0cm">She turned her head back to the
observation deck, but the destruction had changed. The window's frame
was now twisted, like a metal spider web. The glass shards were
scattered across the floor, some as large as dinner plates, others
smaller than pebbles. The air was filled with the acrid smell of
burning plastic and the sweet tang of gasoline.</p>
<p style="border: none; padding: 0cm">The skyscraper swayed again,
its massive structure groaning under the weight of the explosion. She
felt a jolt of fear, but she didn't move. Her body was locked in
place, her feet rooted to the spot. The only movement was her head,
which was slowly turning to take in the rest of the observation deck.</p>
<p style="border: none; padding: 0cm">The other windows were intact,
their glass unbroken. But she knew that could change at any moment.
The building's design was such that a single window failure could
destabilize the entire structure. She thought about the elevators,
the stairs, and the stairwells. Were they accessible? Could she get
out of here?</p>
<p style="border: none; padding: 0cm">Her mind was racing with
scenarios, each one more terrifying than the last. The fire
department would take hours to arrive. The stairs were narrow and
winding, but she knew she could make it down if she had to. The
elevators were probably jammed or destroyed. But what about the other
floors? Were there others in the building?</p>
<p style="border: none; padding: 0cm">She thought about her
colleagues, her friends, and her family. Were they safe? Was anyone
else in the building? She tried to remember the people she had seen
on the floor above. Had they been evacuated? Were they trapped
inside?</p>
<p style="border: none; padding: 0cm">The skyscraper swayed again,
its massive structure creaking ominously. She felt a cold sweat break
out on her forehead as she realized that this was not an isolated
incident. The explosion was not a random act of violence; it was a
deliberate attack.</p>
<p style="border: none; padding: 0cm">She thought back to the strange
noises she had heard in the building earlier that day. The creaks and
groans, the faint sounds of metal straining against the frame. She
had dismissed them as the natural settling of the structure, but now
they seemed ominous.</p>
<p style="border: none; padding: 0cm">The skyscraper's height and
isolation were making her feel more and more disconnected from the
world outside. She was alone in this high-rise building, with no
escape from the chaos unfolding below. The explosion was not just a
random event; it was a turning point in her life.</p>
<p style="border: none; padding: 0cm">She thought about her own life,
her relationships, her goals. Was she living the life she wanted? Was
she trapped in a situation that she couldn't escape?</p>
<p style="border: none; padding: 0cm">The skyscraper's sway grew
stronger, as if it was responding to some internal force. She felt a
sense of unease creeping up her spine, like a cold finger tracing her
vertebrae.</p>
<p style="border: none; padding: 0cm">She turned her head back to the
observation deck, but the destruction had changed. The window's frame
was now twisted, like a metal spider web. The glass shards were
scattered across the floor, some as large as dinner plates, others
smaller than pebbles. The air was filled with the acrid smell of
burning plastic and the sweet tang of gasoline.</p>
<p style="border: none; padding: 0cm">The skyscraper swayed again,
its massive structure groaning under the weight of the explosion. She
felt a jolt of fear, but she didn't move. Her body was locked in
place, her feet rooted to the spot. The only movement was her head,
which was slowly turning to take in the rest of the observation deck.</p>
<p style="border: none; padding: 0cm">The other windows were intact,
their glass unbroken. But she knew that could change at any moment.
The building's design was such that a single window failure could
destabilize the entire structure. She thought about the elevators,
the stairs, and the stairwells. Were they accessible? Could she get
out of here?</p>
<p style="border: none; padding: 0cm">Her mind was racing with
scenarios, each one more terrifying than the last. The fire
department would take hours to arrive. The stairs were narrow and
winding, but she knew she could make it down if she had to. The
elevators were probably jammed or destroyed. But what about the other
floors? Were there others in the building?</p>
<p style="border: none; padding: 0cm">She thought about her
colleagues, her friends, and her family. Were they safe? Was anyone
else in the building? She tried to remember the people she had seen
on the floor above. Had they been evacuated? Were they trapped
inside?</p>
<p style="border: none; padding: 0cm">The skyscraper swayed again,
its massive structure creaking ominously. She felt a cold sweat break
out on her forehead as she realized that this was not an isolated
incident. The explosion was not a random act of violence; it was a
deliberate attack.</p>
<p style="border: none; padding: 0cm">She thought back to the strange
noises she had heard in the building earlier that day. The creaks and
groans, the faint sounds of metal straining against the frame. She
had dismissed them as the natural settling of the structure, but now
they seemed ominous.</p>
<p style="border: none; padding: 0cm">The skyscraper's height and
isolation were making her feel more and more disconnected from the
world outside. She was alone in this high-rise building, with no
escape from the chaos unfolding below. The explosion was not just a
random event; it was a turning point in her life.</p>
<p style="border: none; padding: 0cm">She thought about her own life,
her relationships, her goals. Was she living the life she wanted? Was
she trapped in a situation that she couldn't escape?</p>
<p style="border: none; padding: 0cm">The skyscraper's sway grew
stronger, as if it was responding to some internal force. She felt a
sense of unease creeping up her spine, like a cold finger tracing her
vertebrae.</p>
<p style="border: none; padding: 0cm">She turned her head back to the
observation deck, but the destruction had changed. The window's frame
was now twisted, like a metal spider web. The glass shards were
scattered across the floor, some as large as dinner plates, others
smaller than pebbles. The air was filled with the acrid smell of
burning plastic and the sweet tang of gasoline.</p>
<p style="border: none; padding: 0cm">The skyscraper swayed again,
its massive structure groaning under the weight of the explosion. She
felt a jolt of fear, but she didn't move. Her body was locked in
place, her feet rooted to the spot. The only movement was her head,
which was slowly turning to take in the rest of the observation deck.</p>
<p style="border: none; padding: 0cm">The other windows were intact,
their glass unbroken. But she knew that could change at any moment.
The building's design was such that a single window failure could
destabilize the entire structure. She thought about the elevators,
the stairs, and the stairwells. Were they accessible? Could she get
out of here?</p>
<p style="border: none; padding: 0cm">Her mind was racing with
scenarios, each one more terrifying than the last. The fire
department would take hours to arrive. The stairs were narrow and
winding, but she knew she could make it down if she had to. The
elevators were probably jammed or destroyed.</p>
<p><br/>
<br/>
</p>
|
[
"CRAFT"
] |
allenai/OLMo-7B
|
allenai
|
text-generation
|
[
"transformers",
"pytorch",
"safetensors",
"hf_olmo",
"text-generation",
"custom_code",
"en",
"dataset:allenai/dolma",
"arxiv:2402.00838",
"arxiv:2302.13971",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | 2024-01-09T23:13:23Z |
2024-07-16T17:59:46+00:00
| 2,355 | 635 |
---
datasets:
- allenai/dolma
language:
- en
license: apache-2.0
---
<img src="https://allenai.org/olmo/olmo-7b-animation.gif" alt="OLMo Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Model Card for OLMo 7B
<!-- Provide a quick summary of what the model is/does. -->
**For transformers versions v4.40.0 or newer, we suggest using [OLMo 7B HF](https://huggingface.co/allenai/OLMo-7B-hf) instead.**
OLMo is a series of **O**pen **L**anguage **Mo**dels designed to enable the science of language models.
The OLMo models are trained on the [Dolma](https://huggingface.co/datasets/allenai/dolma) dataset.
We release all code, checkpoints, logs (coming soon), and details involved in training these models.
*A new version of this model with a 24 point improvement on MMLU is available [here](https://huggingface.co/allenai/OLMo-1.7-7B)*.
## Model Details
The core models released in this batch are the following:
| Size | Training Tokens | Layers | Hidden Size | Attention Heads | Context Length |
|------|--------|---------|-------------|-----------------|----------------|
| [OLMo 1B](https://huggingface.co/allenai/OLMo-1B) | 3 Trillion |16 | 2048 | 16 | 2048 |
| [OLMo 7B](https://huggingface.co/allenai/OLMo-7B) | 2.5 Trillion | 32 | 4096 | 32 | 2048 |
| [OLMo 7B Twin 2T](https://huggingface.co/allenai/OLMo-7B-Twin-2T) | 2 Trillion | 32 | 4096 | 32 | 2048 |
We are releasing many checkpoints for these models, for every 1000 traing steps.
The naming convention is `step1000-tokens4B`.
In particular, we focus on four revisions of the 7B models:
| Name | HF Repo | Model Revision | Tokens | Note |
|------------|---------|----------------|-------------------|------|
|OLMo 7B| [allenai/OLMo-7B](https://huggingface.co/allenai/OLMo-7B)|`main`| 2.5T|The base OLMo 7B model|
|OLMo 7B (not annealed)|[allenai/OLMo-7B](https://huggingface.co/allenai/OLMo-7B)|step556000-tokens2460B|2.5T| learning rate not annealed to 0|
|OLMo 7B-2T|[allenai/OLMo-7B](https://huggingface.co/allenai/OLMo-7B)| step452000-tokens2000B |2T| OLMo checkpoint at 2T tokens|
|OLMo-7B-Twin-2T|[allenai/OLMo-7B-Twin-2T](https://huggingface.co/allenai/OLMo-7B-Twin-2T)|`main`|2T| Twin version on different hardware|
To load a specific model revision with HuggingFace, simply add the argument `revision`:
```bash
from hf_olmo import OLMoForCausalLM # pip install ai2-olmo
olmo = OLMoForCausalLM.from_pretrained("allenai/OLMo-7B", revision="step1000-tokens4B")
```
All revisions/branches are listed in the file `revisions.txt`.
Or, you can access all the revisions for the models via the following code snippet:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("allenai/OLMo-7B")
branches = [b.name for b in out.branches]
```
A few revisions were lost due to an error, but the vast majority are present.
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Allen Institute for AI (AI2)
- **Supported by:** Databricks, Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, AMD, CSC (Lumi Supercomputer), UW
- **Model type:** a Transformer style autoregressive language model.
- **Language(s) (NLP):** English
- **License:** The code and model are released under Apache 2.0.
- **Contact:** Technical inquiries: `olmo at allenai dot org`. Press: `press at allenai dot org`
- **Date cutoff:** Feb./March 2023 based on Dolma dataset version.
### Model Sources
<!-- Provide the basic links for the model. -->
- **Project Page:** https://allenai.org/olmo
- **Repositories:**
- Core repo (training, inference, fine-tuning etc.): https://github.com/allenai/OLMo
- Evaluation code: https://github.com/allenai/OLMo-Eval
- Further fine-tuning code: https://github.com/allenai/open-instruct
- **Paper:** [Link](https://arxiv.org/abs/2402.00838)
- **Technical blog post:** https://blog.allenai.org/olmo-open-language-model-87ccfc95f580
- **W&B Logs:** https://wandb.ai/ai2-llm/OLMo-7B/reports/OLMo-7B--Vmlldzo2NzQyMzk5
<!-- - **Press release:** TODO -->
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Inference
Quickly get inference running with the following required installation:
```bash
pip install ai2-olmo
```
Now, proceed as usual with HuggingFace:
```python
from hf_olmo import OLMoForCausalLM, OLMoTokenizerFast
olmo = OLMoForCausalLM.from_pretrained("allenai/OLMo-7B")
tokenizer = OLMoTokenizerFast.from_pretrained("allenai/OLMo-7B")
message = ["Language modeling is"]
inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
# optional verifying cuda
# inputs = {k: v.to('cuda') for k,v in inputs.items()}
# olmo = olmo.to('cuda')
response = olmo.generate(**inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
>> 'Language modeling is the first step to build natural language generation...'
```
You can make this slightly faster by quantizing the model, e.g. `AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B", torch_dtype=torch.float16, load_in_8bit=True)` (requires `bitsandbytes`).
The quantized model is more sensitive to typing / cuda, so it is recommended to pass the inputs as `inputs.input_ids.to('cuda')` to avoid potential issues.
Note, you may see the following error if `ai2-olmo` is not installed correctly, which is caused by internal Python check naming. We'll update the code soon to make this error clearer.
```bash
raise ImportError(
ImportError: This modeling file requires the following packages that were not found in your environment: hf_olmo. Run `pip install hf_olmo`
```
### Fine-tuning
Model fine-tuning can be done from the final checkpoint (the `main` revision of this model) or many intermediate checkpoints. Two recipes for tuning are available.
1. Fine-tune with the OLMo repository:
```bash
torchrun --nproc_per_node=8 scripts/train.py {path_to_train_config} \
--data.paths=[{path_to_data}/input_ids.npy] \
--data.label_mask_paths=[{path_to_data}/label_mask.npy] \
--load_path={path_to_checkpoint} \
--reset_trainer_state
```
For more documentation, see the [GitHub readme](https://github.com/allenai/OLMo?tab=readme-ov-file#fine-tuning).
2. Further fine-tuning support is being developing in AI2's Open Instruct repository. Details are [here](https://github.com/allenai/open-instruct).
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
Core model results for the 7B model are found below.
| | [Llama 7B](https://arxiv.org/abs/2302.13971) | [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b) | [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b) | [MPT 7B](https://huggingface.co/mosaicml/mpt-7b) | **OLMo 7B** (ours) |
| --------------------------------- | -------- | ---------- | --------- | ------ | ------- |
| arc_challenge | 44.5 | 39.8 | 47.5 | 46.5 | 48.5 |
| arc_easy | 57.0 | 57.7 | 70.4 | 70.5 | 65.4 |
| boolq | 73.1 | 73.5 | 74.6 | 74.2 | 73.4 |
| copa | 85.0 | 87.0 | 86.0 | 85.0 | 90 |
| hellaswag | 74.5 | 74.5 | 75.9 | 77.6 | 76.4 |
| openbookqa | 49.8 | 48.4 | 53.0 | 48.6 | 50.2 |
| piqa | 76.3 | 76.4 | 78.5 | 77.3 | 78.4 |
| sciq | 89.5 | 90.8 | 93.9 | 93.7 | 93.8 |
| winogrande | 68.2 | 67.3 | 68.9 | 69.9 | 67.9 |
| **Core tasks average** | 68.7 | 68.4 | 72.1 | 71.5 | 71.6 |
| truthfulQA (MC2) | 33.9 | 38.5 | 34.0 | 33 | 36.0 |
| MMLU (5 shot MC) | 31.5 | 45.0 | 24.0 | 30.8 | 28.3 |
| GSM8k (mixed eval.) | 10.0 (8shot CoT) | 12.0 (8shot CoT) | 4.0 (5 shot) | 4.5 (5 shot) | 8.5 (8shot CoT) |
| **Full average** | 57.8 | 59.3 | 59.2 | 59.3 | 59.8 |
And for the 1B model:
| task | random | [StableLM 2 1.6b](https://huggingface.co/stabilityai/stablelm-2-1_6b)\* | [Pythia 1B](https://huggingface.co/EleutherAI/pythia-1b) | [TinyLlama 1.1B](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T) | **OLMo 1B** (ours) |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------ | ----------------- | --------- | -------------------------------------- | ------- |
| arc_challenge | 25 | 43.81 | 33.11 | 34.78 | 34.45 |
| arc_easy | 25 | 63.68 | 50.18 | 53.16 | 58.07 |
| boolq | 50 | 76.6 | 61.8 | 64.6 | 60.7 |
| copa | 50 | 84 | 72 | 78 | 79 |
| hellaswag | 25 | 68.2 | 44.7 | 58.7 | 62.5 |
| openbookqa | 25 | 45.8 | 37.8 | 43.6 | 46.4 |
| piqa | 50 | 74 | 69.1 | 71.1 | 73.7 |
| sciq | 25 | 94.7 | 86 | 90.5 | 88.1 |
| winogrande | 50 | 64.9 | 53.3 | 58.9 | 58.9 |
| Average | 36.11 | 68.41 | 56.44 | 61.48 | 62.42 |
\*Unlike OLMo, Pythia, and TinyLlama, StabilityAI has not disclosed yet the data StableLM was trained on, making comparisons with other efforts challenging.
## Model Details
### Data
For training data details, please see the [Dolma](https://huggingface.co/datasets/allenai/dolma) documentation.
### Architecture
OLMo 7B architecture with peer models for comparison.
| | **OLMo 7B** | [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b) | [OpenLM 7B](https://laion.ai/blog/open-lm/) | [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b) | PaLM 8B |
|------------------------|-------------------|---------------------|--------------------|--------------------|------------------|
| d_model | 4096 | 4096 | 4096 | 4544 | 4096 |
| num heads | 32 | 32 | 32 | 71 | 16 |
| num layers | 32 | 32 | 32 | 32 | 32 |
| MLP ratio | ~8/3 | ~8/3 | ~8/3 | 4 | 4 |
| LayerNorm type | non-parametric LN | RMSNorm | parametric LN | parametric LN | parametric LN |
| pos embeddings | RoPE | RoPE | RoPE | RoPE | RoPE |
| attention variant | full | GQA | full | MQA | MQA |
| biases | none | none | in LN only | in LN only | none |
| block type | sequential | sequential | sequential | parallel | parallel |
| activation | SwiGLU | SwiGLU | SwiGLU | GeLU | SwiGLU |
| sequence length | 2048 | 4096 | 2048 | 2048 | 2048 |
| batch size (instances) | 2160 | 1024 | 2048 | 2304 | 512 |
| batch size (tokens) | ~4M | ~4M | ~4M | ~4M | ~1M |
| weight tying | no | no | no | no | yes |
### Hyperparameters
AdamW optimizer parameters are shown below.
| Size | Peak LR | Betas | Epsilon | Weight Decay |
|------|------------|-----------------|-------------|--------------|
| 1B | 4.0E-4 | (0.9, 0.95) | 1.0E-5 | 0.1 |
| 7B | 3.0E-4 | (0.9, 0.99) | 1.0E-5 | 0.1 |
Optimizer settings comparison with peer models.
| | **OLMo 7B** | [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b) | [OpenLM 7B](https://laion.ai/blog/open-lm/) | [Falcon 7B](https://huggingface.co/tiiuae/falcon-7b) |
|-----------------------|------------------|---------------------|--------------------|--------------------|
| warmup steps | 5000 | 2000 | 2000 | 1000 |
| peak LR | 3.0E-04 | 3.0E-04 | 3.0E-04 | 6.0E-04 |
| minimum LR | 3.0E-05 | 3.0E-05 | 3.0E-05 | 1.2E-05 |
| weight decay | 0.1 | 0.1 | 0.1 | 0.1 |
| beta1 | 0.9 | 0.9 | 0.9 | 0.99 |
| beta2 | 0.95 | 0.95 | 0.95 | 0.999 |
| epsilon | 1.0E-05 | 1.0E-05 | 1.0E-05 | 1.0E-05 |
| LR schedule | linear | cosine | cosine | cosine |
| gradient clipping | global 1.0 | global 1.0 | global 1.0 | global 1.0 |
| gradient reduce dtype | FP32 | FP32 | FP32 | BF16 |
| optimizer state dtype | FP32 | most likely FP32 | FP32 | FP32 |
## Environmental Impact
OLMo 7B variants were either trained on MI250X GPUs at the LUMI supercomputer, or A100-40GB GPUs provided by MosaicML.
A summary of the environmental impact. Further details are available in the paper.
| | GPU Type | Power Consumption From GPUs | Carbon Intensity (kg CO₂e/KWh) | Carbon Emissions (tCO₂eq) |
|-----------|------------|-----------------------------|--------------------------------|---------------------------|
| OLMo 7B Twin | MI250X ([LUMI supercomputer](https://www.lumi-supercomputer.eu)) | 135 MWh | 0* | 0* |
| OLMo 7B | A100-40GB ([MosaicML](https://www.mosaicml.com)) | 104 MWh | 0.656 | 75.05 |
## Bias, Risks, and Limitations
Like any base language model or fine-tuned model without safety filtering, it is relatively easy for a user to prompt these models to generate harmful and generally sensitive content.
Such content can also be produced unintentionally, especially in the case of bias, so we recommend users consider the risks of applications of this technology.
Otherwise, many facts from OLMo or any LLM will often not be true, so they should be checked.
## Citation
**BibTeX:**
```
@article{Groeneveld2023OLMo,
title={OLMo: Accelerating the Science of Language Models},
author={Groeneveld, Dirk and Beltagy, Iz and Walsh, Pete and Bhagia, Akshita and Kinney, Rodney and Tafjord, Oyvind and Jha, Ananya Harsh and Ivison, Hamish and Magnusson, Ian and Wang, Yizhong and Arora, Shane and Atkinson, David and Authur, Russell and Chandu, Khyathi and Cohan, Arman and Dumas, Jennifer and Elazar, Yanai and Gu, Yuling and Hessel, Jack and Khot, Tushar and Merrill, William and Morrison, Jacob and Muennighoff, Niklas and Naik, Aakanksha and Nam, Crystal and Peters, Matthew E. and Pyatkin, Valentina and Ravichander, Abhilasha and Schwenk, Dustin and Shah, Saurabh and Smith, Will and Subramani, Nishant and Wortsman, Mitchell and Dasigi, Pradeep and Lambert, Nathan and Richardson, Kyle and Dodge, Jesse and Lo, Kyle and Soldaini, Luca and Smith, Noah A. and Hajishirzi, Hannaneh},
journal={Preprint},
year={2024}
}
```
**APA:**
Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A., Ivison, H., Magnusson, I., Wang, Y., Arora, S., Atkinson, D., Authur, R., Chandu, K., Cohan, A., Dumas, J., Elazar, Y., Gu, Y., Hessel, J., Khot, T., Merrill, W., Morrison, J., Muennighoff, N., Naik, A., Nam, C., Peters, M., Pyatkin, V., Ravichander, A., Schwenk, D., Shah, S., Smith, W., Subramani, N., Wortsman, M., Dasigi, P., Lambert, N., Richardson, K., Dodge, J., Lo, K., Soldaini, L., Smith, N., & Hajishirzi, H. (2024). OLMo: Accelerating the Science of Language Models. Preprint.
## Model Card Contact
For errors in this model card, contact Nathan or Akshita, `{nathanl, akshitab} at allenai dot org`.
|
[
"SCIQ"
] |
Alibaba-NLP/gme-Qwen2-VL-7B-Instruct
|
Alibaba-NLP
|
sentence-similarity
|
[
"sentence-transformers",
"safetensors",
"qwen2_vl",
"image-text-to-text",
"mteb",
"transformers",
"Qwen2-VL",
"sentence-similarity",
"vidore",
"en",
"zh",
"arxiv:2412.16855",
"base_model:Qwen/Qwen2-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-7B-Instruct",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-12-21T04:00:17Z |
2025-01-21T11:53:23+00:00
| 2,333 | 26 |
---
base_model:
- Qwen/Qwen2-VL-7B-Instruct
language:
- en
- zh
license: apache-2.0
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2-VL
- sentence-similarity
- vidore
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: 64.72351048394194
- type: cos_sim_spearman
value: 71.66842612591344
- type: euclidean_pearson
value: 70.0342809043895
- type: euclidean_spearman
value: 71.66842612323917
- type: manhattan_pearson
value: 69.94743870947117
- type: manhattan_spearman
value: 71.53159630946965
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cos_sim_pearson
value: 52.38188106868689
- type: cos_sim_spearman
value: 55.468235529709766
- type: euclidean_pearson
value: 56.974786979175086
- type: euclidean_spearman
value: 55.468231026153745
- type: manhattan_pearson
value: 56.94467132566259
- 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
- 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: 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: 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
- 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
- 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
- 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
- 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
- type: euclidean_spearman
value: 82.58200216886888
- type: manhattan_pearson
value: 84.53082035185592
- type: manhattan_spearman
value: 82.4964580510134
- task:
type: STS
dataset:
name: MTEB BQ
type: C-MTEB/BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cos_sim_pearson
value: 76.98420285210636
- type: cos_sim_spearman
value: 78.95549489000658
- type: euclidean_pearson
value: 79.14591532018991
- type: euclidean_spearman
value: 78.95549488953284
- type: manhattan_pearson
value: 79.26212116856509
- type: manhattan_spearman
value: 79.02104262086006
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.76298701298703
- type: f1
value: 84.24881789367576
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 46.86757924102047
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 43.86043680479362
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: v_measure
value: 45.684222588040605
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: v_measure
value: 45.45639765303432
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 88.7058672660788
- type: mrr
value: 90.5795634920635
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 25.34866666666667
- type: map_at_10
value: 35.65541666666667
- type: map_at_100
value: 36.982416666666666
- type: map_at_1000
value: 37.09416666666667
- type: map_at_3
value: 32.421499999999995
- type: map_at_5
value: 34.20266666666667
- type: mrr_at_1
value: 30.02116666666667
- type: mrr_at_10
value: 39.781666666666666
- type: mrr_at_100
value: 40.69733333333333
- type: mrr_at_1000
value: 40.74875
- type: mrr_at_3
value: 37.043083333333335
- type: mrr_at_5
value: 38.56391666666666
- type: ndcg_at_1
value: 30.02116666666667
- type: ndcg_at_10
value: 41.66133333333333
- type: ndcg_at_100
value: 47.21474999999999
- type: ndcg_at_1000
value: 49.29600000000001
- type: ndcg_at_3
value: 36.06958333333334
- type: ndcg_at_5
value: 38.66858333333333
- type: precision_at_1
value: 30.02116666666667
- type: precision_at_10
value: 7.497249999999999
- type: precision_at_100
value: 1.2044166666666667
- type: precision_at_1000
value: 0.15766666666666665
- type: precision_at_3
value: 16.83458333333333
- type: precision_at_5
value: 12.134
- type: recall_at_1
value: 25.34866666666667
- type: recall_at_10
value: 55.40541666666666
- type: recall_at_100
value: 79.38683333333333
- type: recall_at_1000
value: 93.50958333333334
- type: recall_at_3
value: 39.99858333333334
- type: recall_at_5
value: 46.55741666666666
- 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 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
- 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
- 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
- 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
- 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
- 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
- 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
- type: manhattan_ap
value: 86.66244127855394
- type: manhattan_f1
value: 79.24485643228577
- type: manhattan_precision
value: 76.71262858393521
- type: manhattan_recall
value: 81.94996492868833
- type: max_accuracy
value: 79.03788334335539
- type: max_ap
value: 87.22906528217948
- type: max_f1
value: 79.87784187309127
- task:
type: Retrieval
dataset:
name: MTEB CovidRetrieval
type: C-MTEB/CovidRetrieval
config: default
split: dev
revision: 1271c7809071a13532e05f25fb53511ffce77117
metrics:
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- task:
type: STS
dataset:
name: MTEB LCQMC
type: C-MTEB/LCQMC
config: default
split: test
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
metrics:
- type: cos_sim_pearson
value: 67.35114066823127
- type: cos_sim_spearman
value: 72.98875207056305
- type: euclidean_pearson
value: 71.45620183630378
- type: euclidean_spearman
value: 72.98875207022671
- type: manhattan_pearson
value: 71.3845159780333
- type: manhattan_spearman
value: 72.92710990543166
- 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
- task:
type: Retrieval
dataset:
name: MTEB MMarcoRetrieval
type: C-MTEB/MMarcoRetrieval
config: default
split: dev
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
metrics:
- type: map_at_1
value: 71.242
- type: map_at_10
value: 80.01
- type: map_at_100
value: 80.269
- type: map_at_1000
value: 80.276
- type: map_at_3
value: 78.335
- type: map_at_5
value: 79.471
- type: mrr_at_1
value: 73.668
- type: mrr_at_10
value: 80.515
- type: mrr_at_100
value: 80.738
- type: mrr_at_1000
value: 80.744
- type: mrr_at_3
value: 79.097
- type: mrr_at_5
value: 80.045
- type: ndcg_at_1
value: 73.668
- type: ndcg_at_10
value: 83.357
- type: ndcg_at_100
value: 84.442
- type: ndcg_at_1000
value: 84.619
- type: ndcg_at_3
value: 80.286
- type: ndcg_at_5
value: 82.155
- type: precision_at_1
value: 73.668
- type: precision_at_10
value: 9.905
- type: precision_at_100
value: 1.043
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 30.024
- type: precision_at_5
value: 19.017
- type: recall_at_1
value: 71.242
- type: recall_at_10
value: 93.11
- type: recall_at_100
value: 97.85000000000001
- type: recall_at_1000
value: 99.21900000000001
- 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
- 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
- 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
- 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 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 (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: 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: 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
- 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
- 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
- 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
- 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
- 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
- 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
- type: ndcg_at_1000
value: 70.60600000000001
- type: ndcg_at_3
value: 60.712999999999994
- type: ndcg_at_5
value: 64.693
- type: precision_at_1
value: 48.696
- type: precision_at_10
value: 10.513
- type: precision_at_100
value: 1.196
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 27.221
- type: precision_at_5
value: 18.701999999999998
- type: recall_at_1
value: 43.262
- type: recall_at_10
value: 87.35300000000001
- type: recall_at_100
value: 98.31299999999999
- type: recall_at_1000
value: 99.797
- type: recall_at_3
value: 69.643
- type: recall_at_5
value: 78.645
- task:
type: PairClassification
dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cos_sim_accuracy
value: 72.65836491608013
- type: cos_sim_ap
value: 78.75807247519593
- type: cos_sim_f1
value: 74.84662576687117
- type: cos_sim_precision
value: 63.97003745318352
- type: cos_sim_recall
value: 90.17951425554382
- type: dot_accuracy
value: 72.65836491608013
- type: dot_ap
value: 78.75807247519593
- type: dot_f1
value: 74.84662576687117
- type: dot_precision
value: 63.97003745318352
- type: dot_recall
value: 90.17951425554382
- type: euclidean_accuracy
value: 72.65836491608013
- type: euclidean_ap
value: 78.75807247519593
- type: euclidean_f1
value: 74.84662576687117
- type: euclidean_precision
value: 63.97003745318352
- type: euclidean_recall
value: 90.17951425554382
- type: manhattan_accuracy
value: 72.00866269626421
- type: manhattan_ap
value: 78.34663376353235
- type: manhattan_f1
value: 74.13234613604813
- type: manhattan_precision
value: 65.98023064250413
- type: manhattan_recall
value: 84.58289334741288
- type: max_accuracy
value: 72.65836491608013
- type: max_ap
value: 78.75807247519593
- type: max_f1
value: 74.84662576687117
- 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
- task:
type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cos_sim_pearson
value: 46.851404503762474
- type: cos_sim_spearman
value: 52.74603680597415
- type: euclidean_pearson
value: 51.596358967977295
- type: euclidean_spearman
value: 52.74603680597415
- type: manhattan_pearson
value: 51.81838023379299
- type: manhattan_spearman
value: 52.79611669731429
- task:
type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cos_sim_pearson
value: 31.928376136347016
- type: cos_sim_spearman
value: 34.38497204533162
- type: euclidean_pearson
value: 32.658432953090674
- type: euclidean_spearman
value: 34.38497204533162
- type: manhattan_pearson
value: 32.887190283203054
- type: manhattan_spearman
value: 34.69496960849327
- 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
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- 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
- 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
- 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
- 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
- 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
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 81.91239166620251
- type: cos_sim_spearman
value: 76.36798509005328
- type: euclidean_pearson
value: 80.6393872615655
- type: euclidean_spearman
value: 76.36798836339655
- type: manhattan_pearson
value: 80.50765898709096
- type: manhattan_spearman
value: 76.31958999372227
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- 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
- type: manhattan_spearman
value: 79.24858249553934
- 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: 89.15329071763895
- type: cos_sim_spearman
value: 88.67251952242073
- type: euclidean_pearson
value: 89.16908249259637
- type: euclidean_spearman
value: 88.67251952242073
- type: manhattan_pearson
value: 89.1279735094785
- type: manhattan_spearman
value: 88.81731953658254
- 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: 69.44962535524695
- type: cos_sim_spearman
value: 71.75861316291065
- type: euclidean_pearson
value: 72.42347748883483
- type: euclidean_spearman
value: 71.75861316291065
- type: manhattan_pearson
value: 72.57545073534365
- type: manhattan_spearman
value: 71.90087671205625
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 68.9945443484093
- type: cos_sim_spearman
value: 71.46807157842791
- type: euclidean_pearson
value: 69.24911748374225
- type: euclidean_spearman
value: 69.46807157842791
- type: manhattan_pearson
value: 69.65580071876552
- type: manhattan_spearman
value: 69.68775795734852
- task:
type: STS
dataset:
name: MTEB STSB
type: C-MTEB/STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
- 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
- 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
- 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
- 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
- type: manhattan_ap
value: 97.85527044211135
- type: manhattan_f1
value: 95.27638190954774
- type: manhattan_precision
value: 95.75757575757575
- type: manhattan_recall
value: 94.8
- type: max_accuracy
value: 99.90693069306931
- type: max_ap
value: 97.86562522779887
- type: max_f1
value: 95.27638190954774
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 78.89230351770412
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 47.52328347080355
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 57.74702024461137
- type: mrr
value: 58.88074548001018
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.047929797503592
- type: cos_sim_spearman
value: 29.465371781983567
- type: dot_pearson
value: 30.047927690552335
- type: dot_spearman
value: 29.465371781983567
- 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: Classification
dataset:
name: MTEB TNews
type: C-MTEB/TNews-classification
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
- type: accuracy
value: 56.691999999999986
- type: f1
value: 54.692084702788065
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.181
- type: map_at_10
value: 1.2
- type: map_at_100
value: 6.078
- type: map_at_1000
value: 14.940000000000001
- type: map_at_3
value: 0.45599999999999996
- type: map_at_5
value: 0.692
- type: mrr_at_1
value: 66.0
- type: mrr_at_10
value: 75.819
- type: mrr_at_100
value: 76.168
- type: mrr_at_1000
value: 76.168
- type: mrr_at_3
value: 72.667
- type: mrr_at_5
value: 74.86699999999999
- type: ndcg_at_1
value: 59.0
- type: ndcg_at_10
value: 52.60399999999999
- type: ndcg_at_100
value: 38.049
- type: ndcg_at_1000
value: 38.576
- type: ndcg_at_3
value: 57.235
- type: ndcg_at_5
value: 56.147000000000006
- type: precision_at_1
value: 66.0
- type: precision_at_10
value: 55.2
- type: precision_at_100
value: 38.78
- type: precision_at_1000
value: 16.986
- type: precision_at_3
value: 62.666999999999994
- type: precision_at_5
value: 60.8
- type: recall_at_1
value: 0.181
- type: recall_at_10
value: 1.471
- type: recall_at_100
value: 9.748999999999999
- type: recall_at_1000
value: 37.667
- type: recall_at_3
value: 0.49300000000000005
- type: recall_at_5
value: 0.7979999999999999
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringP2P
type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
- type: v_measure
value: 78.68783858143624
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringS2S
type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
- type: v_measure
value: 77.04148998956299
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 1.936
- type: map_at_10
value: 8.942
- type: map_at_100
value: 14.475999999999999
- type: map_at_1000
value: 16.156000000000002
- type: map_at_3
value: 4.865
- type: map_at_5
value: 6.367000000000001
- type: mrr_at_1
value: 26.531
- type: mrr_at_10
value: 42.846000000000004
- type: mrr_at_100
value: 43.441
- type: mrr_at_1000
value: 43.441
- type: mrr_at_3
value: 36.735
- type: mrr_at_5
value: 40.510000000000005
- type: ndcg_at_1
value: 24.490000000000002
- type: ndcg_at_10
value: 23.262
- type: ndcg_at_100
value: 34.959
- type: ndcg_at_1000
value: 47.258
- type: ndcg_at_3
value: 25.27
- type: ndcg_at_5
value: 24.246000000000002
- type: precision_at_1
value: 26.531
- type: precision_at_10
value: 20.408
- type: precision_at_100
value: 7.306
- type: precision_at_1000
value: 1.541
- type: precision_at_3
value: 26.531
- type: precision_at_5
value: 24.082
- type: recall_at_1
value: 1.936
- type: recall_at_10
value: 15.712000000000002
- type: recall_at_100
value: 45.451
- type: recall_at_1000
value: 83.269
- type: recall_at_3
value: 6.442
- type: recall_at_5
value: 9.151
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 86.564
- type: ap
value: 34.58766846081731
- type: f1
value: 72.32759831978161
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 77.80418788907753
- type: f1
value: 78.1047638421972
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 59.20888659980063
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.45627943017226
- type: cos_sim_ap
value: 72.25550061847534
- type: cos_sim_f1
value: 66.0611487783037
- type: cos_sim_precision
value: 64.11720884032779
- type: cos_sim_recall
value: 68.12664907651715
- type: dot_accuracy
value: 85.45627943017226
- type: dot_ap
value: 72.25574305366213
- type: dot_f1
value: 66.0611487783037
- type: dot_precision
value: 64.11720884032779
- type: dot_recall
value: 68.12664907651715
- type: euclidean_accuracy
value: 85.45627943017226
- type: euclidean_ap
value: 72.2557084446673
- type: euclidean_f1
value: 66.0611487783037
- type: euclidean_precision
value: 64.11720884032779
- type: euclidean_recall
value: 68.12664907651715
- type: manhattan_accuracy
value: 85.32514752339513
- type: manhattan_ap
value: 71.52919143472248
- type: manhattan_f1
value: 65.60288251190322
- type: manhattan_precision
value: 64.02913840743531
- type: manhattan_recall
value: 67.25593667546174
- type: max_accuracy
value: 85.45627943017226
- type: max_ap
value: 72.25574305366213
- type: max_f1
value: 66.0611487783037
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.34167733923235
- type: cos_sim_ap
value: 84.58587730660244
- type: cos_sim_f1
value: 77.14170010676287
- type: cos_sim_precision
value: 73.91181657848324
- type: cos_sim_recall
value: 80.66676932553126
- type: dot_accuracy
value: 88.34167733923235
- type: dot_ap
value: 84.58585083616217
- type: dot_f1
value: 77.14170010676287
- type: dot_precision
value: 73.91181657848324
- type: dot_recall
value: 80.66676932553126
- type: euclidean_accuracy
value: 88.34167733923235
- type: euclidean_ap
value: 84.5858781355044
- type: euclidean_f1
value: 77.14170010676287
- type: euclidean_precision
value: 73.91181657848324
- type: euclidean_recall
value: 80.66676932553126
- type: manhattan_accuracy
value: 88.28152287809989
- type: manhattan_ap
value: 84.53184837110165
- type: manhattan_f1
value: 77.13582823915313
- type: manhattan_precision
value: 74.76156069364161
- type: manhattan_recall
value: 79.66584539574993
- type: max_accuracy
value: 88.34167733923235
- type: max_ap
value: 84.5858781355044
- type: max_f1
value: 77.14170010676287
- task:
type: Retrieval
dataset:
name: MTEB VideoRetrieval
type: C-MTEB/VideoRetrieval
config: default
split: dev
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
metrics:
- 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
---
<p align="center">
<img src="https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct/raw/main/images/gme_logo.png" alt="GME Logo" style="width: 100%; max-width: 450px;">
</p>
<p align="center"><b>GME: General Multimodal Embedding</b></p>
## gme-Qwen2-VL-7B
We are excited to present `GME-Qwen2VL` series of unified **multimodal embedding models**,
which are based on the advanced [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d) multimodal large language models (MLLMs).
The `GME` models support three types of input: **text**, **image**, and **image-text pair**, all of which can produce universal vector representations and have powerful retrieval performance.
**Key Enhancements of GME Models**:
- **Unified Multimodal Representation**: GME models can process both single-modal and combined-modal inputs, resulting in a unified vector representation. This enables versatile retrieval scenarios (Any2Any Search), supporting tasks such as text retrieval, image retrieval from text, and image-to-image searches.
- **High Performance**: Achieves state-of-the-art (SOTA) results in our universal multimodal retrieval benchmark (**UMRB**) and demonstrate strong evaluation scores in the Multimodal Textual Evaluation Benchmark (**MTEB**).
- **Dynamic Image Resolution**: Benefiting from `Qwen2-VL` and our training data, GME models support dynamic resolution image input.
- **Strong Visual Retrieval Performance**: Enhanced by the Qwen2-VL model series, our models excel in visual document retrieval tasks that require a nuanced understanding of document screenshots.
This capability is particularly beneficial for complex document understanding scenarios,
such as multimodal retrieval-augmented generation (RAG) applications focused on academic papers.
**Developed by**: Tongyi Lab, Alibaba Group
**Paper**: [GME: Improving Universal Multimodal Retrieval by Multimodal LLMs](http://arxiv.org/abs/2412.16855)
## Model List
| Models | Model Size | Max Seq. Length | Dimension | MTEB-en| MTEB-zh | UMRB |
|:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: |
|[`gme-Qwen2-VL-2B`](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct) | 2.21B | 32768 | 1536 | 65.27 | 68.41 | 64.45 |
|[`gme-Qwen2-VL-7B`](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct) | 8.29B | 32768 | 3584 | 67.48 | 71.36 | 67.44 |
## Usage
**Use with custom code**
```python
# You can find the script gme_inference.py in https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct/blob/main/gme_inference.py
from gme_inference import GmeQwen2VL
model = GmeQwen2VL('Alibaba-NLP/gme-Qwen2-VL-7B-Instruct')
texts = [
"What kind of car is this?",
"The Tesla Cybertruck is a battery electric pickup truck built by Tesla, Inc. since 2023."
]
images = [
'https://en.wikipedia.org/wiki/File:Tesla_Cybertruck_damaged_window.jpg',
'https://en.wikipedia.org/wiki/File:2024_Tesla_Cybertruck_Foundation_Series,_front_left_(Greenwich).jpg',
]
# Single-modal embedding
e_text = gme.get_text_embeddings(texts=texts)
e_image = gme.get_image_embeddings(images=images)
print((e_text * e_image).sum(-1))
## tensor([0.1702, 0.5278], dtype=torch.float16)
# How to set embedding instruction
e_query = gme.get_text_embeddings(texts=texts, instruction='Find an image that matches the given text.')
# If is_query=False, we always use the default instruction.
e_corpus = gme.get_image_embeddings(images=images, is_query=False)
print((e_query * e_corpus).sum(-1))
## tensor([0.2000, 0.5752], dtype=torch.float16)
# Fused-modal embedding
e_fused = gme.get_fused_embeddings(texts=texts, images=images)
print((e_fused[0] * e_fused[1]).sum())
## tensor(0.6826, dtype=torch.float16)
```
<!-- <details>
<summary>With transformers</summary>
```python
# Requires transformers>=4.46.2
TODO
# [[0.3016996383666992, 0.7503870129585266, 0.3203084468841553]]
```
</details>
-->
## Evaluation
We validated the performance on our universal multimodal retrieval benchmark (**UMRB**) among others.
| | | Single-modal | | Cross-modal | | | Fused-modal | | | | Avg. |
|--------------------|------|:------------:|:---------:|:-----------:|:-----------:|:---------:|:-----------:|:----------:|:----------:|:-----------:|:----------:|
| | | T→T (16) | I→I (1) | T→I (4) | T→VD (10) | I→T (4) | T→IT (2) | IT→T (5) | IT→I (2) | IT→IT (3) | (47) |
| VISTA | 0.2B | 55.15 | **31.98** | 32.88 | 10.12 | 31.23 | 45.81 | 53.32 | 8.97 | 26.26 | 37.32 |
| CLIP-SF | 0.4B | 39.75 | 31.42 | 59.05 | 24.09 | 62.95 | 66.41 | 53.32 | 34.9 | 55.65 | 43.66 |
| One-Peace | 4B | 43.54 | 31.27 | 61.38 | 42.9 | 65.59 | 42.72 | 28.29 | 6.73 | 23.41 | 42.01 |
| DSE | 4.2B | 48.94 | 27.92 | 40.75 | 78.21 | 52.54 | 49.62 | 35.44 | 8.36 | 40.18 | 50.04 |
| E5-V | 8.4B | 52.41 | 27.36 | 46.56 | 41.22 | 47.95 | 54.13 | 32.9 | 23.17 | 7.23 | 42.52 |
| **[GME-Qwen2-VL-2B](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct)** | 2.2B | 55.93 | 29.86 | 57.36 | 87.84 | 61.93 | 76.47 | 64.58 | 37.02 | 66.47 | 64.45 |
| **[GME-Qwen2-VL-7B](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct)** | 8.3B | **58.19** | 31.89 | **61.35** | **89.92** | **65.83** | **80.94** | **66.18** | **42.56** | **73.62** | **67.44** |
The [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) English tab shows the text embeddings performence of our model.
**More detailed experimental results can be found in the [paper](http://arxiv.org/abs/2412.16855)**.
## Community support
### Fine-tuning
GME models can be fine-tuned by SWIFT：
```shell
pip install ms-swift -U
```
```shell
# MAX_PIXELS settings to reduce memory usage
# check: https://swift.readthedocs.io/en/latest/BestPractices/Embedding.html
nproc_per_node=8
MAX_PIXELS=1003520 \
USE_HF=1 \
NPROC_PER_NODE=$nproc_per_node \
swift sft \
--model Alibaba-NLP/gme-Qwen2-VL-7B-Instruct \
--train_type lora \
--dataset 'HuggingFaceM4/TextCaps:emb' \
--torch_dtype bfloat16 \
--num_train_epochs 1 \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 2 \
--gradient_accumulation_steps $(expr 64 / $nproc_per_node) \
--eval_steps 100 \
--save_steps 100 \
--eval_strategy steps \
--save_total_limit 5 \
--logging_steps 5 \
--output_dir output \
--lazy_tokenize true \
--warmup_ratio 0.05 \
--learning_rate 5e-6 \
--deepspeed zero3 \
--dataloader_num_workers 4 \
--task_type embedding \
--loss_type infonce \
--dataloader_drop_last true
```
## Limitations
- **Single Image Input**: In `Qwen2-VL`, an image could be converted into a very large number of visual tokens. We limit the number of visual tokens to 1024 to obtain a good training efficiency.
Due to the lack of relevant data, our models and evaluations retain one single image.
- **English-only Training**: Our models are trained on english data only. Although the `Qwen2-VL` models are multilingual, the multilingual-multimodal embedding performance are not guaranteed.
We will extend to multi-image input, image-text interleaved data as well as multilingual data in the future version.
## Redistribution and Use
We encourage and value diverse applications of GME models and continuous enhancements to the models themselves.
- If you distribute or make GME models (or any derivative works) available, or if you create a product or service (including another AI model) that incorporates them, you must prominently display `Built with GME` on your website, user interface, blog post, About page, or product documentation.
- If you utilize GME models or their outputs to develop, train, fine-tune, or improve an AI model that is distributed or made available, you must prefix the name of any such AI model with `GME`.
## Cloud API Services
In addition to the open-source [GME](https://huggingface.co/collections/Alibaba-NLP/gme-models-67667e092da3491f630964d6) series models, GME series models are also available as commercial API services on Alibaba Cloud.
- [MultiModal Embedding Models](https://help.aliyun.com/zh/model-studio/developer-reference/multimodal-embedding-api-reference?spm=a2c4g.11186623.0.0.321c1d1cqmoJ5C): The `multimodal-embedding-v1` model service is available.
Note that the models behind the commercial APIs are not entirely identical to the open-source models.
## Hiring
We have open positions for Research Interns and Full-Time Researchers to join our team at Tongyi Lab.
We are seeking passionate individuals with expertise in representation learning, LLM-driven information retrieval, Retrieval-Augmented Generation (RAG), and agent-based systems.
Our team is located in the vibrant cities of Beijing and Hangzhou, offering a collaborative and dynamic work environment where you can contribute to cutting-edge advancements in artificial intelligence and machine learning.
If you are driven by curiosity and eager to make a meaningful impact through your work, we would love to hear from you. Please submit your resume along with a brief introduction to <a href="mailto:[email protected]">[email protected]</a>.
## Citation
If you find our paper or models helpful, please consider cite:
```
@misc{zhang2024gme,
title={GME: Improving Universal Multimodal Retrieval by Multimodal LLMs},
author={Zhang, Xin and Zhang, Yanzhao and Xie, Wen and Li, Mingxin and Dai, Ziqi and Long, Dingkun and Xie, Pengjun and Zhang, Meishan and Li, Wenjie and Zhang, Min},
year={2024},
eprint={2412.16855},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={http://arxiv.org/abs/2412.16855},
}
```
|
[
"BIOSSES",
"SCIFACT"
] |
neuralmagic/bge-small-en-v1.5-quant
|
neuralmagic
|
feature-extraction
|
[
"transformers",
"onnx",
"bert",
"feature-extraction",
"mteb",
"sparse",
"sparsity",
"quantized",
"embeddings",
"int8",
"deepsparse",
"en",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-09-27T23:33:48Z |
2023-11-13T17:04:15+00:00
| 2,330 | 9 |
---
language:
- en
license: mit
tags:
- mteb
- sparse
- sparsity
- quantized
- onnx
- embeddings
- int8
- deepsparse
model-index:
- name: bge-small-en-v1.5-quant
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 74.19402985074626
- type: ap
value: 37.562368912364036
- type: f1
value: 68.47046663470138
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.89432499999998
- type: ap
value: 88.64572979375352
- type: f1
value: 91.87171177424113
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.71799999999999
- type: f1
value: 46.25791412217894
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 34.424
- type: map_at_10
value: 49.63
- type: map_at_100
value: 50.477000000000004
- type: map_at_1000
value: 50.483
- type: map_at_3
value: 45.389
- type: map_at_5
value: 47.888999999999996
- type: mrr_at_1
value: 34.78
- type: mrr_at_10
value: 49.793
- type: mrr_at_100
value: 50.632999999999996
- type: mrr_at_1000
value: 50.638000000000005
- type: mrr_at_3
value: 45.531
- type: mrr_at_5
value: 48.010000000000005
- type: ndcg_at_1
value: 34.424
- type: ndcg_at_10
value: 57.774
- type: ndcg_at_100
value: 61.248000000000005
- type: ndcg_at_1000
value: 61.378
- type: ndcg_at_3
value: 49.067
- type: ndcg_at_5
value: 53.561
- type: precision_at_1
value: 34.424
- type: precision_at_10
value: 8.364
- type: precision_at_100
value: 0.985
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 19.915
- type: precision_at_5
value: 14.124999999999998
- type: recall_at_1
value: 34.424
- type: recall_at_10
value: 83.64200000000001
- type: recall_at_100
value: 98.506
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 59.744
- type: recall_at_5
value: 70.626
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 46.91874634333147
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 39.1201020016146
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.40334669601722
- type: mrr
value: 75.33175042870333
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 88.00433892980047
- type: cos_sim_spearman
value: 86.65558896421105
- type: euclidean_pearson
value: 85.98927300398377
- type: euclidean_spearman
value: 86.0905158476729
- type: manhattan_pearson
value: 86.0272425017433
- type: manhattan_spearman
value: 85.8929209838941
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.1038961038961
- type: f1
value: 85.06851570045757
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.42637694389153
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 33.89440321125906
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.111000000000004
- type: map_at_10
value: 39.067
- type: map_at_100
value: 40.519
- type: map_at_1000
value: 40.652
- type: map_at_3
value: 35.571999999999996
- type: map_at_5
value: 37.708999999999996
- type: mrr_at_1
value: 34.335
- type: mrr_at_10
value: 44.868
- type: mrr_at_100
value: 45.607
- type: mrr_at_1000
value: 45.655
- type: mrr_at_3
value: 41.798
- type: mrr_at_5
value: 43.786
- type: ndcg_at_1
value: 34.335
- type: ndcg_at_10
value: 45.513
- type: ndcg_at_100
value: 51.037
- type: ndcg_at_1000
value: 53.171
- type: ndcg_at_3
value: 40.131
- type: ndcg_at_5
value: 43.027
- type: precision_at_1
value: 34.335
- type: precision_at_10
value: 8.784
- type: precision_at_100
value: 1.4460000000000002
- type: precision_at_1000
value: 0.193
- type: precision_at_3
value: 19.361
- type: precision_at_5
value: 14.249
- type: recall_at_1
value: 28.111000000000004
- type: recall_at_10
value: 58.372
- type: recall_at_100
value: 81.631
- type: recall_at_1000
value: 95.192
- type: recall_at_3
value: 42.863
- type: recall_at_5
value: 50.924
- type: map_at_1
value: 28.437
- type: map_at_10
value: 37.942
- type: map_at_100
value: 39.108
- type: map_at_1000
value: 39.242
- type: map_at_3
value: 35.419
- type: map_at_5
value: 36.825
- type: mrr_at_1
value: 35.35
- type: mrr_at_10
value: 43.855
- type: mrr_at_100
value: 44.543
- type: mrr_at_1000
value: 44.588
- type: mrr_at_3
value: 41.826
- type: mrr_at_5
value: 42.937
- type: ndcg_at_1
value: 35.35
- type: ndcg_at_10
value: 43.32
- type: ndcg_at_100
value: 47.769
- type: ndcg_at_1000
value: 49.979
- type: ndcg_at_3
value: 39.709
- type: ndcg_at_5
value: 41.316
- type: precision_at_1
value: 35.35
- type: precision_at_10
value: 7.994
- type: precision_at_100
value: 1.323
- type: precision_at_1000
value: 0.182
- type: precision_at_3
value: 18.96
- type: precision_at_5
value: 13.236
- type: recall_at_1
value: 28.437
- type: recall_at_10
value: 52.531000000000006
- type: recall_at_100
value: 71.79299999999999
- type: recall_at_1000
value: 85.675
- type: recall_at_3
value: 41.605
- type: recall_at_5
value: 46.32
- type: map_at_1
value: 37.364999999999995
- type: map_at_10
value: 49.324
- type: map_at_100
value: 50.458999999999996
- type: map_at_1000
value: 50.512
- type: map_at_3
value: 45.96
- type: map_at_5
value: 47.934
- type: mrr_at_1
value: 43.009
- type: mrr_at_10
value: 52.946000000000005
- type: mrr_at_100
value: 53.74100000000001
- type: mrr_at_1000
value: 53.76800000000001
- type: mrr_at_3
value: 50.554
- type: mrr_at_5
value: 51.964
- type: ndcg_at_1
value: 43.009
- type: ndcg_at_10
value: 55.143
- type: ndcg_at_100
value: 59.653999999999996
- type: ndcg_at_1000
value: 60.805
- type: ndcg_at_3
value: 49.605
- type: ndcg_at_5
value: 52.437
- type: precision_at_1
value: 43.009
- type: precision_at_10
value: 8.984
- type: precision_at_100
value: 1.209
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 22.09
- type: precision_at_5
value: 15.423
- type: recall_at_1
value: 37.364999999999995
- type: recall_at_10
value: 68.657
- type: recall_at_100
value: 88.155
- type: recall_at_1000
value: 96.48400000000001
- type: recall_at_3
value: 54.186
- type: recall_at_5
value: 60.848
- type: map_at_1
value: 23.827
- type: map_at_10
value: 31.721
- type: map_at_100
value: 32.812999999999995
- type: map_at_1000
value: 32.89
- type: map_at_3
value: 29.238999999999997
- type: map_at_5
value: 30.584
- type: mrr_at_1
value: 25.650000000000002
- type: mrr_at_10
value: 33.642
- type: mrr_at_100
value: 34.595
- type: mrr_at_1000
value: 34.650999999999996
- type: mrr_at_3
value: 31.205
- type: mrr_at_5
value: 32.499
- type: ndcg_at_1
value: 25.650000000000002
- type: ndcg_at_10
value: 36.366
- type: ndcg_at_100
value: 41.766
- type: ndcg_at_1000
value: 43.735
- type: ndcg_at_3
value: 31.447000000000003
- type: ndcg_at_5
value: 33.701
- type: precision_at_1
value: 25.650000000000002
- type: precision_at_10
value: 5.582
- type: precision_at_100
value: 0.872
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 13.107
- type: precision_at_5
value: 9.198
- type: recall_at_1
value: 23.827
- type: recall_at_10
value: 48.9
- type: recall_at_100
value: 73.917
- type: recall_at_1000
value: 88.787
- type: recall_at_3
value: 35.498000000000005
- type: recall_at_5
value: 40.929
- type: map_at_1
value: 15.47
- type: map_at_10
value: 22.679
- type: map_at_100
value: 23.823
- type: map_at_1000
value: 23.94
- type: map_at_3
value: 20.535999999999998
- type: map_at_5
value: 21.61
- type: mrr_at_1
value: 18.781
- type: mrr_at_10
value: 26.979
- type: mrr_at_100
value: 27.945999999999998
- type: mrr_at_1000
value: 28.016000000000002
- type: mrr_at_3
value: 24.648
- type: mrr_at_5
value: 25.947
- type: ndcg_at_1
value: 18.781
- type: ndcg_at_10
value: 27.55
- type: ndcg_at_100
value: 33.176
- type: ndcg_at_1000
value: 36.150999999999996
- type: ndcg_at_3
value: 23.456
- type: ndcg_at_5
value: 25.16
- type: precision_at_1
value: 18.781
- type: precision_at_10
value: 5.050000000000001
- type: precision_at_100
value: 0.9039999999999999
- type: precision_at_1000
value: 0.129
- type: precision_at_3
value: 11.235000000000001
- type: precision_at_5
value: 8.01
- type: recall_at_1
value: 15.47
- type: recall_at_10
value: 38.446000000000005
- type: recall_at_100
value: 63.199000000000005
- type: recall_at_1000
value: 84.719
- type: recall_at_3
value: 26.687
- type: recall_at_5
value: 31.196
- type: map_at_1
value: 26.285999999999998
- type: map_at_10
value: 35.701
- type: map_at_100
value: 37.062
- type: map_at_1000
value: 37.175999999999995
- type: map_at_3
value: 32.65
- type: map_at_5
value: 34.129
- type: mrr_at_1
value: 32.05
- type: mrr_at_10
value: 41.105000000000004
- type: mrr_at_100
value: 41.996
- type: mrr_at_1000
value: 42.047000000000004
- type: mrr_at_3
value: 38.466
- type: mrr_at_5
value: 39.766
- type: ndcg_at_1
value: 32.05
- type: ndcg_at_10
value: 41.516999999999996
- type: ndcg_at_100
value: 47.083999999999996
- type: ndcg_at_1000
value: 49.309
- type: ndcg_at_3
value: 36.254999999999995
- type: ndcg_at_5
value: 38.346999999999994
- type: precision_at_1
value: 32.05
- type: precision_at_10
value: 7.536
- type: precision_at_100
value: 1.202
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 17.004
- type: precision_at_5
value: 11.973
- type: recall_at_1
value: 26.285999999999998
- type: recall_at_10
value: 53.667
- type: recall_at_100
value: 76.97
- type: recall_at_1000
value: 91.691
- type: recall_at_3
value: 38.571
- type: recall_at_5
value: 44.131
- type: map_at_1
value: 22.595000000000002
- type: map_at_10
value: 31.352000000000004
- type: map_at_100
value: 32.652
- type: map_at_1000
value: 32.774
- type: map_at_3
value: 28.238000000000003
- type: map_at_5
value: 30.178
- type: mrr_at_1
value: 27.626
- type: mrr_at_10
value: 36.351
- type: mrr_at_100
value: 37.297000000000004
- type: mrr_at_1000
value: 37.362
- type: mrr_at_3
value: 33.885
- type: mrr_at_5
value: 35.358000000000004
- type: ndcg_at_1
value: 27.626
- type: ndcg_at_10
value: 36.795
- type: ndcg_at_100
value: 42.808
- type: ndcg_at_1000
value: 45.417
- type: ndcg_at_3
value: 31.744
- type: ndcg_at_5
value: 34.407
- type: precision_at_1
value: 27.626
- type: precision_at_10
value: 6.781
- type: precision_at_100
value: 1.159
- type: precision_at_1000
value: 0.155
- type: precision_at_3
value: 15.221000000000002
- type: precision_at_5
value: 11.279
- type: recall_at_1
value: 22.595000000000002
- type: recall_at_10
value: 48.126000000000005
- type: recall_at_100
value: 74.24300000000001
- type: recall_at_1000
value: 92.276
- type: recall_at_3
value: 34.346
- type: recall_at_5
value: 41.065000000000005
- type: map_at_1
value: 22.237000000000002
- type: map_at_10
value: 28.626
- type: map_at_100
value: 29.494999999999997
- type: map_at_1000
value: 29.587999999999997
- type: map_at_3
value: 26.747
- type: map_at_5
value: 27.903
- type: mrr_at_1
value: 24.847
- type: mrr_at_10
value: 31.091
- type: mrr_at_100
value: 31.91
- type: mrr_at_1000
value: 31.977
- type: mrr_at_3
value: 29.218
- type: mrr_at_5
value: 30.391000000000002
- type: ndcg_at_1
value: 24.847
- type: ndcg_at_10
value: 32.452999999999996
- type: ndcg_at_100
value: 37.009
- type: ndcg_at_1000
value: 39.425
- type: ndcg_at_3
value: 28.848000000000003
- type: ndcg_at_5
value: 30.752000000000002
- type: precision_at_1
value: 24.847
- type: precision_at_10
value: 4.968999999999999
- type: precision_at_100
value: 0.8009999999999999
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 12.321
- type: precision_at_5
value: 8.62
- type: recall_at_1
value: 22.237000000000002
- type: recall_at_10
value: 41.942
- type: recall_at_100
value: 62.907000000000004
- type: recall_at_1000
value: 81.035
- type: recall_at_3
value: 32.05
- type: recall_at_5
value: 36.695
- type: map_at_1
value: 14.835
- type: map_at_10
value: 21.124000000000002
- type: map_at_100
value: 22.133
- type: map_at_1000
value: 22.258
- type: map_at_3
value: 19.076999999999998
- type: map_at_5
value: 20.18
- type: mrr_at_1
value: 17.791
- type: mrr_at_10
value: 24.438
- type: mrr_at_100
value: 25.332
- type: mrr_at_1000
value: 25.417
- type: mrr_at_3
value: 22.425
- type: mrr_at_5
value: 23.524
- type: ndcg_at_1
value: 17.791
- type: ndcg_at_10
value: 25.27
- type: ndcg_at_100
value: 30.362000000000002
- type: ndcg_at_1000
value: 33.494
- type: ndcg_at_3
value: 21.474
- type: ndcg_at_5
value: 23.189999999999998
- type: precision_at_1
value: 17.791
- type: precision_at_10
value: 4.58
- type: precision_at_100
value: 0.839
- type: precision_at_1000
value: 0.128
- type: precision_at_3
value: 10.071
- type: precision_at_5
value: 7.337000000000001
- type: recall_at_1
value: 14.835
- type: recall_at_10
value: 34.534
- type: recall_at_100
value: 57.812
- type: recall_at_1000
value: 80.467
- type: recall_at_3
value: 23.938000000000002
- type: recall_at_5
value: 28.269
- type: map_at_1
value: 23.400000000000002
- type: map_at_10
value: 31.55
- type: map_at_100
value: 32.72
- type: map_at_1000
value: 32.830999999999996
- type: map_at_3
value: 28.942
- type: map_at_5
value: 30.403000000000002
- type: mrr_at_1
value: 27.705000000000002
- type: mrr_at_10
value: 35.778
- type: mrr_at_100
value: 36.705
- type: mrr_at_1000
value: 36.773
- type: mrr_at_3
value: 33.458
- type: mrr_at_5
value: 34.778
- type: ndcg_at_1
value: 27.705000000000002
- type: ndcg_at_10
value: 36.541000000000004
- type: ndcg_at_100
value: 42.016999999999996
- type: ndcg_at_1000
value: 44.571
- type: ndcg_at_3
value: 31.845000000000002
- type: ndcg_at_5
value: 34.056
- type: precision_at_1
value: 27.705000000000002
- type: precision_at_10
value: 6.166
- type: precision_at_100
value: 0.993
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 14.302999999999999
- type: precision_at_5
value: 10.187
- type: recall_at_1
value: 23.400000000000002
- type: recall_at_10
value: 47.61
- type: recall_at_100
value: 71.69200000000001
- type: recall_at_1000
value: 89.652
- type: recall_at_3
value: 35.026
- type: recall_at_5
value: 40.48
- type: map_at_1
value: 21.409
- type: map_at_10
value: 29.642000000000003
- type: map_at_100
value: 31.213
- type: map_at_1000
value: 31.418000000000003
- type: map_at_3
value: 26.811
- type: map_at_5
value: 28.433999999999997
- type: mrr_at_1
value: 25.494
- type: mrr_at_10
value: 33.735
- type: mrr_at_100
value: 34.791
- type: mrr_at_1000
value: 34.848
- type: mrr_at_3
value: 31.225
- type: mrr_at_5
value: 32.688
- type: ndcg_at_1
value: 25.494
- type: ndcg_at_10
value: 35.038000000000004
- type: ndcg_at_100
value: 41.499
- type: ndcg_at_1000
value: 44.183
- type: ndcg_at_3
value: 30.305
- type: ndcg_at_5
value: 32.607
- type: precision_at_1
value: 25.494
- type: precision_at_10
value: 6.739000000000001
- type: precision_at_100
value: 1.439
- type: precision_at_1000
value: 0.233
- type: precision_at_3
value: 14.163
- type: precision_at_5
value: 10.474
- type: recall_at_1
value: 21.409
- type: recall_at_10
value: 46.033
- type: recall_at_100
value: 74.932
- type: recall_at_1000
value: 92.35600000000001
- type: recall_at_3
value: 32.858
- type: recall_at_5
value: 38.675
- type: map_at_1
value: 18.145
- type: map_at_10
value: 24.712
- type: map_at_100
value: 25.813000000000002
- type: map_at_1000
value: 25.935000000000002
- type: map_at_3
value: 22.33
- type: map_at_5
value: 23.524
- type: mrr_at_1
value: 19.224
- type: mrr_at_10
value: 26.194
- type: mrr_at_100
value: 27.208
- type: mrr_at_1000
value: 27.3
- type: mrr_at_3
value: 23.906
- type: mrr_at_5
value: 24.988
- type: ndcg_at_1
value: 19.224
- type: ndcg_at_10
value: 29.015
- type: ndcg_at_100
value: 34.224
- type: ndcg_at_1000
value: 37.235
- type: ndcg_at_3
value: 24.22
- type: ndcg_at_5
value: 26.176
- type: precision_at_1
value: 19.224
- type: precision_at_10
value: 4.713
- type: precision_at_100
value: 0.787
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 10.290000000000001
- type: precision_at_5
value: 7.32
- type: recall_at_1
value: 18.145
- type: recall_at_10
value: 40.875
- type: recall_at_100
value: 64.371
- type: recall_at_1000
value: 86.67399999999999
- type: recall_at_3
value: 27.717000000000002
- type: recall_at_5
value: 32.381
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.845
- type: f1
value: 41.70045120106269
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 89.3476
- type: ap
value: 85.26891728027032
- type: f1
value: 89.33488973832894
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.67441860465115
- type: f1
value: 92.48821366022861
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 74.02872777017784
- type: f1
value: 57.28822860484337
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 74.01479488903833
- type: f1
value: 71.83716204573571
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.95897780766644
- type: f1
value: 77.80380046125542
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.897956840478948
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 30.71493744677591
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.279419910393734
- type: mrr
value: 32.41989483774563
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 50.49612915002382
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 60.29912718965653
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.86793477948164
- type: cos_sim_spearman
value: 79.43675709317894
- type: euclidean_pearson
value: 81.42564463337872
- type: euclidean_spearman
value: 79.39138648510273
- type: manhattan_pearson
value: 81.31167449689285
- type: manhattan_spearman
value: 79.28411420758785
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.43490408077298
- type: cos_sim_spearman
value: 76.16878340109265
- type: euclidean_pearson
value: 80.6016219080782
- type: euclidean_spearman
value: 75.67063072565917
- type: manhattan_pearson
value: 80.7238920179759
- type: manhattan_spearman
value: 75.85631683403953
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 83.03882477767792
- type: cos_sim_spearman
value: 84.15171505206217
- type: euclidean_pearson
value: 84.11692506470922
- type: euclidean_spearman
value: 84.78589046217311
- type: manhattan_pearson
value: 83.98651139454486
- type: manhattan_spearman
value: 84.64928563751276
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.11158600428418
- type: cos_sim_spearman
value: 81.48561519933875
- type: euclidean_pearson
value: 83.21025907155807
- type: euclidean_spearman
value: 81.68699235487654
- type: manhattan_pearson
value: 83.16704771658094
- type: manhattan_spearman
value: 81.7133110412898
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.1514510686502
- type: cos_sim_spearman
value: 88.11449450494452
- type: euclidean_pearson
value: 87.75854949349939
- type: euclidean_spearman
value: 88.4055148221637
- type: manhattan_pearson
value: 87.71487828059706
- type: manhattan_spearman
value: 88.35301381116254
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.36838640113687
- type: cos_sim_spearman
value: 84.98776974283366
- type: euclidean_pearson
value: 84.0617526427129
- type: euclidean_spearman
value: 85.04234805662242
- type: manhattan_pearson
value: 83.87433162971784
- type: manhattan_spearman
value: 84.87174280390242
- 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: 87.72465270691285
- type: cos_sim_spearman
value: 87.97672332532184
- type: euclidean_pearson
value: 88.78764701492182
- type: euclidean_spearman
value: 88.3509718074474
- type: manhattan_pearson
value: 88.73024739256215
- type: manhattan_spearman
value: 88.24149566970154
- 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: 64.65195562203238
- type: cos_sim_spearman
value: 65.0726777678982
- type: euclidean_pearson
value: 65.84698245675273
- type: euclidean_spearman
value: 65.13121502162804
- type: manhattan_pearson
value: 65.96149904857049
- type: manhattan_spearman
value: 65.39983948112955
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.2642818050049
- type: cos_sim_spearman
value: 86.30633382439257
- type: euclidean_pearson
value: 86.46510435905633
- type: euclidean_spearman
value: 86.62650496446
- type: manhattan_pearson
value: 86.2546330637872
- type: manhattan_spearman
value: 86.46309860938591
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 85.009977767778
- type: mrr
value: 95.59795143128476
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.84257425742574
- type: cos_sim_ap
value: 96.25445889914926
- type: cos_sim_f1
value: 92.03805708562844
- type: cos_sim_precision
value: 92.1765295887663
- type: cos_sim_recall
value: 91.9
- type: dot_accuracy
value: 99.83069306930693
- type: dot_ap
value: 96.00517778550396
- type: dot_f1
value: 91.27995920448751
- type: dot_precision
value: 93.1321540062435
- type: dot_recall
value: 89.5
- type: euclidean_accuracy
value: 99.84455445544555
- type: euclidean_ap
value: 96.14761524546034
- type: euclidean_f1
value: 91.97751660705163
- type: euclidean_precision
value: 94.04388714733543
- type: euclidean_recall
value: 90
- type: manhattan_accuracy
value: 99.84158415841584
- type: manhattan_ap
value: 96.17014673429341
- type: manhattan_f1
value: 91.93790686029043
- type: manhattan_precision
value: 92.07622868605817
- type: manhattan_recall
value: 91.8
- type: max_accuracy
value: 99.84455445544555
- type: max_ap
value: 96.25445889914926
- type: max_f1
value: 92.03805708562844
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 59.26454683321409
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.75520575713765
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.74607778008495
- type: mrr
value: 53.55101699770818
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.5008
- type: ap
value: 13.64158304183089
- type: f1
value: 53.50073331072236
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 60.01980758347483
- type: f1
value: 60.35679678249753
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 45.09419243325077
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.68874053764081
- type: cos_sim_ap
value: 73.26334732095694
- type: cos_sim_f1
value: 68.01558376272465
- type: cos_sim_precision
value: 64.93880489560834
- type: cos_sim_recall
value: 71.39841688654354
- type: dot_accuracy
value: 84.71121177802945
- type: dot_ap
value: 70.33606362522605
- type: dot_f1
value: 65.0887573964497
- type: dot_precision
value: 63.50401606425703
- type: dot_recall
value: 66.75461741424802
- type: euclidean_accuracy
value: 85.80795136198367
- type: euclidean_ap
value: 73.43201285001163
- type: euclidean_f1
value: 68.33166833166834
- type: euclidean_precision
value: 64.86486486486487
- type: euclidean_recall
value: 72.18997361477572
- type: manhattan_accuracy
value: 85.62317458425225
- type: manhattan_ap
value: 73.21212085536185
- type: manhattan_f1
value: 68.01681314482232
- type: manhattan_precision
value: 65.74735286875153
- type: manhattan_recall
value: 70.44854881266491
- type: max_accuracy
value: 85.80795136198367
- type: max_ap
value: 73.43201285001163
- type: max_f1
value: 68.33166833166834
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.81709162882757
- type: cos_sim_ap
value: 85.63540257309367
- type: cos_sim_f1
value: 77.9091382258904
- type: cos_sim_precision
value: 75.32710280373833
- type: cos_sim_recall
value: 80.67446874037573
- type: dot_accuracy
value: 88.04478596654636
- type: dot_ap
value: 84.16371725220706
- type: dot_f1
value: 76.45949643213666
- type: dot_precision
value: 73.54719396827655
- type: dot_recall
value: 79.61194949183862
- type: euclidean_accuracy
value: 88.9296386851399
- type: euclidean_ap
value: 85.71894615274715
- type: euclidean_f1
value: 78.12952767313823
- type: euclidean_precision
value: 73.7688098495212
- type: euclidean_recall
value: 83.03818909762857
- type: manhattan_accuracy
value: 88.89276982186519
- type: manhattan_ap
value: 85.6838514059479
- type: manhattan_f1
value: 78.06861875184856
- type: manhattan_precision
value: 75.09246088193457
- type: manhattan_recall
value: 81.29042192793348
- type: max_accuracy
value: 88.9296386851399
- type: max_ap
value: 85.71894615274715
- type: max_f1
value: 78.12952767313823
---
# bge-small-en-v1.5-quant
<div>
<img src="https://huggingface.co/zeroshot/bge-small-en-v1.5-quant/resolve/main/latency.png" alt="latency" width="500" style="display:inline-block; margin-right:10px;"/>
</div>
[DeepSparse](https://github.com/neuralmagic/deepsparse) is able to improve latency performance on a 10 core laptop by 3X and up to 5X on a 16 core AWS instance.
## Usage
This is the quantized (INT8) ONNX variant of the [bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) embeddings model accelerated with [Sparsify](https://github.com/neuralmagic/sparsify) for quantization and [DeepSparseSentenceTransformers](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/sentence_transformers) for inference.
```bash
pip install -U deepsparse-nightly[sentence_transformers]
```
```python
from deepsparse.sentence_transformers import DeepSparseSentenceTransformer
model = DeepSparseSentenceTransformer('neuralmagic/bge-small-en-v1.5-quant', export=False)
# Our sentences we like to encode
sentences = ['This framework generates embeddings for each input sentence',
'Sentences are passed as a list of string.',
'The quick brown fox jumps over the lazy dog.']
# Sentences are encoded by calling model.encode()
embeddings = model.encode(sentences)
# Print the embeddings
for sentence, embedding in zip(sentences, embeddings):
print("Sentence:", sentence)
print("Embedding:", embedding.shape)
print("")
```
For general questions on these models and sparsification methods, reach out to the engineering team on our [community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ).
|
[
"BIOSSES"
] |
McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse
|
McGill-NLP
|
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",
"arxiv:2404.05961",
"license:mit",
"model-index",
"region:us"
] | 2024-04-30T02:45:32Z |
2024-04-30T03:42:49+00:00
| 2,287 | 4 |
---
language:
- en
library_name: peft
license: mit
pipeline_tag: sentence-similarity
tags:
- 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
model-index:
- name: LLM2Vec-Meta-Llama-3-unsupervised
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.70149253731343
- type: ap
value: 40.824269118508354
- type: f1
value: 70.55918234479084
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 80.6812
- type: ap
value: 76.63327889516552
- type: f1
value: 80.5276613226382
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.002
- type: f1
value: 39.67277678335084
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.173999999999996
- type: map_at_10
value: 42.548
- type: map_at_100
value: 43.492999999999995
- type: map_at_1000
value: 43.5
- type: map_at_3
value: 37.376
- type: map_at_5
value: 40.359
- type: mrr_at_1
value: 27.24
- type: mrr_at_10
value: 42.945
- type: mrr_at_100
value: 43.89
- type: mrr_at_1000
value: 43.897000000000006
- type: mrr_at_3
value: 37.779
- type: mrr_at_5
value: 40.755
- type: ndcg_at_1
value: 26.173999999999996
- type: ndcg_at_10
value: 51.731
- type: ndcg_at_100
value: 55.684999999999995
- type: ndcg_at_1000
value: 55.86
- type: ndcg_at_3
value: 41.122
- type: ndcg_at_5
value: 46.491
- type: precision_at_1
value: 26.173999999999996
- type: precision_at_10
value: 8.108
- type: precision_at_100
value: 0.9820000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 17.330000000000002
- type: precision_at_5
value: 13.001
- type: recall_at_1
value: 26.173999999999996
- type: recall_at_10
value: 81.081
- type: recall_at_100
value: 98.222
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 51.991
- type: recall_at_5
value: 65.007
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 49.215974795578546
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 41.71067780141813
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 57.15639347603191
- type: mrr
value: 71.4509959108297
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_spearman
value: 84.67361609277127
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.76623376623375
- type: f1
value: 84.70041172334481
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 38.39251163108548
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 31.30501371807517
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: cqadupstack/android
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.409
- type: map_at_10
value: 36.925000000000004
- type: map_at_100
value: 38.651
- type: map_at_1000
value: 38.798
- type: map_at_3
value: 33.437
- type: map_at_5
value: 35.506
- type: mrr_at_1
value: 33.763
- type: mrr_at_10
value: 43.442
- type: mrr_at_100
value: 44.339
- type: mrr_at_1000
value: 44.391000000000005
- type: mrr_at_3
value: 40.749
- type: mrr_at_5
value: 42.408
- type: ndcg_at_1
value: 33.763
- type: ndcg_at_10
value: 43.486999999999995
- type: ndcg_at_100
value: 49.71
- type: ndcg_at_1000
value: 51.81
- type: ndcg_at_3
value: 38.586
- type: ndcg_at_5
value: 41.074
- type: precision_at_1
value: 33.763
- type: precision_at_10
value: 8.798
- type: precision_at_100
value: 1.544
- type: precision_at_1000
value: 0.21
- type: precision_at_3
value: 19.361
- type: precision_at_5
value: 14.335
- type: recall_at_1
value: 26.409
- type: recall_at_10
value: 55.352999999999994
- type: recall_at_100
value: 81.66799999999999
- type: recall_at_1000
value: 95.376
- type: recall_at_3
value: 40.304
- type: recall_at_5
value: 47.782000000000004
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: cqadupstack/english
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.6
- type: map_at_10
value: 36.42
- type: map_at_100
value: 37.628
- type: map_at_1000
value: 37.767
- type: map_at_3
value: 33.553
- type: map_at_5
value: 35.118
- type: mrr_at_1
value: 34.394999999999996
- type: mrr_at_10
value: 42.586
- type: mrr_at_100
value: 43.251
- type: mrr_at_1000
value: 43.303000000000004
- type: mrr_at_3
value: 40.297
- type: mrr_at_5
value: 41.638
- type: ndcg_at_1
value: 34.394999999999996
- type: ndcg_at_10
value: 42.05
- type: ndcg_at_100
value: 46.371
- type: ndcg_at_1000
value: 48.76
- type: ndcg_at_3
value: 37.936
- type: ndcg_at_5
value: 39.827
- type: precision_at_1
value: 34.394999999999996
- type: precision_at_10
value: 8.268
- type: precision_at_100
value: 1.355
- type: precision_at_1000
value: 0.186
- type: precision_at_3
value: 18.726000000000003
- type: precision_at_5
value: 13.541
- type: recall_at_1
value: 26.6
- type: recall_at_10
value: 51.529
- type: recall_at_100
value: 70.038
- type: recall_at_1000
value: 85.67
- type: recall_at_3
value: 39.448
- type: recall_at_5
value: 44.6
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: cqadupstack/gaming
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.863000000000003
- type: map_at_10
value: 43.733
- type: map_at_100
value: 45.005
- type: map_at_1000
value: 45.074
- type: map_at_3
value: 40.593
- type: map_at_5
value: 42.272
- type: mrr_at_1
value: 37.555
- type: mrr_at_10
value: 47.532999999999994
- type: mrr_at_100
value: 48.431999999999995
- type: mrr_at_1000
value: 48.47
- type: mrr_at_3
value: 44.901
- type: mrr_at_5
value: 46.274
- type: ndcg_at_1
value: 37.555
- type: ndcg_at_10
value: 49.789
- type: ndcg_at_100
value: 55.059999999999995
- type: ndcg_at_1000
value: 56.434
- type: ndcg_at_3
value: 44.238
- type: ndcg_at_5
value: 46.698
- type: precision_at_1
value: 37.555
- type: precision_at_10
value: 8.257
- type: precision_at_100
value: 1.189
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 20.23
- type: precision_at_5
value: 13.868
- type: recall_at_1
value: 31.863000000000003
- type: recall_at_10
value: 64.188
- type: recall_at_100
value: 87.02600000000001
- type: recall_at_1000
value: 96.761
- type: recall_at_3
value: 48.986000000000004
- type: recall_at_5
value: 55.177
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: cqadupstack/gis
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 15.964
- type: map_at_10
value: 22.746
- type: map_at_100
value: 23.704
- type: map_at_1000
value: 23.82
- type: map_at_3
value: 20.5
- type: map_at_5
value: 21.836
- type: mrr_at_1
value: 17.740000000000002
- type: mrr_at_10
value: 24.634
- type: mrr_at_100
value: 25.535999999999998
- type: mrr_at_1000
value: 25.628
- type: mrr_at_3
value: 22.429
- type: mrr_at_5
value: 23.791
- type: ndcg_at_1
value: 17.740000000000002
- type: ndcg_at_10
value: 26.838
- type: ndcg_at_100
value: 31.985000000000003
- type: ndcg_at_1000
value: 35.289
- type: ndcg_at_3
value: 22.384
- type: ndcg_at_5
value: 24.726
- type: precision_at_1
value: 17.740000000000002
- type: precision_at_10
value: 4.35
- type: precision_at_100
value: 0.753
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 9.754999999999999
- type: precision_at_5
value: 7.164
- type: recall_at_1
value: 15.964
- type: recall_at_10
value: 37.705
- type: recall_at_100
value: 61.94499999999999
- type: recall_at_1000
value: 87.646
- type: recall_at_3
value: 25.714
- type: recall_at_5
value: 31.402
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: cqadupstack/mathematica
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.221
- type: map_at_10
value: 14.735000000000001
- type: map_at_100
value: 15.778
- type: map_at_1000
value: 15.9
- type: map_at_3
value: 12.791
- type: map_at_5
value: 13.703999999999999
- type: mrr_at_1
value: 12.438
- type: mrr_at_10
value: 18.353
- type: mrr_at_100
value: 19.285
- type: mrr_at_1000
value: 19.375
- type: mrr_at_3
value: 16.439
- type: mrr_at_5
value: 17.352999999999998
- type: ndcg_at_1
value: 12.438
- type: ndcg_at_10
value: 18.703
- type: ndcg_at_100
value: 24.104999999999997
- type: ndcg_at_1000
value: 27.366
- type: ndcg_at_3
value: 15.055
- type: ndcg_at_5
value: 16.42
- type: precision_at_1
value: 12.438
- type: precision_at_10
value: 3.818
- type: precision_at_100
value: 0.77
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 7.753
- type: precision_at_5
value: 5.622
- type: recall_at_1
value: 9.221
- type: recall_at_10
value: 27.461999999999996
- type: recall_at_100
value: 51.909000000000006
- type: recall_at_1000
value: 75.56
- type: recall_at_3
value: 17.046
- type: recall_at_5
value: 20.766000000000002
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: cqadupstack/physics
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.828
- type: map_at_10
value: 33.166000000000004
- type: map_at_100
value: 34.618
- type: map_at_1000
value: 34.744
- type: map_at_3
value: 29.737000000000002
- type: map_at_5
value: 31.541000000000004
- type: mrr_at_1
value: 29.548000000000002
- type: mrr_at_10
value: 38.582
- type: mrr_at_100
value: 39.527
- type: mrr_at_1000
value: 39.577
- type: mrr_at_3
value: 35.884
- type: mrr_at_5
value: 37.413999999999994
- type: ndcg_at_1
value: 29.548000000000002
- type: ndcg_at_10
value: 39.397
- type: ndcg_at_100
value: 45.584
- type: ndcg_at_1000
value: 47.823
- type: ndcg_at_3
value: 33.717000000000006
- type: ndcg_at_5
value: 36.223
- type: precision_at_1
value: 29.548000000000002
- type: precision_at_10
value: 7.767
- type: precision_at_100
value: 1.2959999999999998
- type: precision_at_1000
value: 0.17099999999999999
- type: precision_at_3
value: 16.747
- type: precision_at_5
value: 12.203999999999999
- type: recall_at_1
value: 22.828
- type: recall_at_10
value: 52.583999999999996
- type: recall_at_100
value: 79.06400000000001
- type: recall_at_1000
value: 93.59100000000001
- type: recall_at_3
value: 36.671
- type: recall_at_5
value: 43.22
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: cqadupstack/programmers
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.366
- type: map_at_10
value: 30.214000000000002
- type: map_at_100
value: 31.647
- type: map_at_1000
value: 31.763
- type: map_at_3
value: 27.234
- type: map_at_5
value: 28.801
- type: mrr_at_1
value: 26.256
- type: mrr_at_10
value: 35.299
- type: mrr_at_100
value: 36.284
- type: mrr_at_1000
value: 36.342
- type: mrr_at_3
value: 32.572
- type: mrr_at_5
value: 34.050999999999995
- type: ndcg_at_1
value: 26.256
- type: ndcg_at_10
value: 35.899
- type: ndcg_at_100
value: 41.983
- type: ndcg_at_1000
value: 44.481
- type: ndcg_at_3
value: 30.665
- type: ndcg_at_5
value: 32.879999999999995
- type: precision_at_1
value: 26.256
- type: precision_at_10
value: 6.804
- type: precision_at_100
value: 1.187
- type: precision_at_1000
value: 0.16
- type: precision_at_3
value: 14.84
- type: precision_at_5
value: 10.708
- type: recall_at_1
value: 21.366
- type: recall_at_10
value: 47.878
- type: recall_at_100
value: 73.245
- type: recall_at_1000
value: 90.623
- type: recall_at_3
value: 33.341
- type: recall_at_5
value: 39.198
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.477166666666665
- type: map_at_10
value: 27.431416666666664
- type: map_at_100
value: 28.656000000000002
- type: map_at_1000
value: 28.787583333333338
- type: map_at_3
value: 24.85175
- type: map_at_5
value: 26.270166666666668
- type: mrr_at_1
value: 24.06841666666667
- type: mrr_at_10
value: 31.620000000000005
- type: mrr_at_100
value: 32.52283333333333
- type: mrr_at_1000
value: 32.59441666666667
- type: mrr_at_3
value: 29.328666666666663
- type: mrr_at_5
value: 30.620416666666667
- type: ndcg_at_1
value: 24.06841666666667
- type: ndcg_at_10
value: 32.404583333333335
- type: ndcg_at_100
value: 37.779500000000006
- type: ndcg_at_1000
value: 40.511583333333334
- type: ndcg_at_3
value: 27.994166666666665
- type: ndcg_at_5
value: 30.021749999999997
- type: precision_at_1
value: 24.06841666666667
- type: precision_at_10
value: 6.03725
- type: precision_at_100
value: 1.0500833333333337
- type: precision_at_1000
value: 0.14875000000000002
- type: precision_at_3
value: 13.419583333333335
- type: precision_at_5
value: 9.700666666666665
- type: recall_at_1
value: 19.477166666666665
- type: recall_at_10
value: 42.99441666666667
- type: recall_at_100
value: 66.787
- type: recall_at_1000
value: 86.18825000000001
- type: recall_at_3
value: 30.46366666666667
- type: recall_at_5
value: 35.83141666666667
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: cqadupstack/stats
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.246
- type: map_at_10
value: 22.127
- type: map_at_100
value: 23.006
- type: map_at_1000
value: 23.125
- type: map_at_3
value: 20.308999999999997
- type: map_at_5
value: 21.139
- type: mrr_at_1
value: 19.631999999999998
- type: mrr_at_10
value: 24.884999999999998
- type: mrr_at_100
value: 25.704
- type: mrr_at_1000
value: 25.793
- type: mrr_at_3
value: 23.083000000000002
- type: mrr_at_5
value: 23.942
- type: ndcg_at_1
value: 19.631999999999998
- type: ndcg_at_10
value: 25.862000000000002
- type: ndcg_at_100
value: 30.436000000000003
- type: ndcg_at_1000
value: 33.638
- type: ndcg_at_3
value: 22.431
- type: ndcg_at_5
value: 23.677
- type: precision_at_1
value: 19.631999999999998
- type: precision_at_10
value: 4.417
- type: precision_at_100
value: 0.7270000000000001
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 10.327
- type: precision_at_5
value: 7.147
- type: recall_at_1
value: 16.246
- type: recall_at_10
value: 34.869
- type: recall_at_100
value: 56.221
- type: recall_at_1000
value: 80.449
- type: recall_at_3
value: 24.83
- type: recall_at_5
value: 28.142
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: cqadupstack/tex
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.798
- type: map_at_10
value: 14.695
- type: map_at_100
value: 15.590000000000002
- type: map_at_1000
value: 15.726999999999999
- type: map_at_3
value: 13.004999999999999
- type: map_at_5
value: 13.861
- type: mrr_at_1
value: 12.939
- type: mrr_at_10
value: 18.218
- type: mrr_at_100
value: 18.998
- type: mrr_at_1000
value: 19.093
- type: mrr_at_3
value: 16.454
- type: mrr_at_5
value: 17.354
- type: ndcg_at_1
value: 12.939
- type: ndcg_at_10
value: 18.278
- type: ndcg_at_100
value: 22.709
- type: ndcg_at_1000
value: 26.064
- type: ndcg_at_3
value: 15.204
- type: ndcg_at_5
value: 16.416
- type: precision_at_1
value: 12.939
- type: precision_at_10
value: 3.768
- type: precision_at_100
value: 0.724
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 7.707999999999999
- type: precision_at_5
value: 5.733
- type: recall_at_1
value: 9.798
- type: recall_at_10
value: 25.562
- type: recall_at_100
value: 45.678999999999995
- type: recall_at_1000
value: 69.963
- type: recall_at_3
value: 16.705000000000002
- type: recall_at_5
value: 19.969
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: cqadupstack/unix
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.1
- type: map_at_10
value: 27.034999999999997
- type: map_at_100
value: 28.396
- type: map_at_1000
value: 28.518
- type: map_at_3
value: 24.363
- type: map_at_5
value: 25.826999999999998
- type: mrr_at_1
value: 23.694000000000003
- type: mrr_at_10
value: 31.724999999999998
- type: mrr_at_100
value: 32.743
- type: mrr_at_1000
value: 32.82
- type: mrr_at_3
value: 29.275000000000002
- type: mrr_at_5
value: 30.684
- type: ndcg_at_1
value: 23.694000000000003
- type: ndcg_at_10
value: 32.366
- type: ndcg_at_100
value: 38.241
- type: ndcg_at_1000
value: 40.973
- type: ndcg_at_3
value: 27.661
- type: ndcg_at_5
value: 29.782999999999998
- type: precision_at_1
value: 23.694000000000003
- type: precision_at_10
value: 5.951
- type: precision_at_100
value: 1.0070000000000001
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 13.34
- type: precision_at_5
value: 9.533999999999999
- type: recall_at_1
value: 19.1
- type: recall_at_10
value: 44.032
- type: recall_at_100
value: 69.186
- type: recall_at_1000
value: 88.562
- type: recall_at_3
value: 30.712
- type: recall_at_5
value: 36.372
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: cqadupstack/webmasters
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.671
- type: map_at_10
value: 28.583
- type: map_at_100
value: 30.098999999999997
- type: map_at_1000
value: 30.364
- type: map_at_3
value: 25.825
- type: map_at_5
value: 27.500999999999998
- type: mrr_at_1
value: 25.889
- type: mrr_at_10
value: 33.617999999999995
- type: mrr_at_100
value: 34.687
- type: mrr_at_1000
value: 34.774
- type: mrr_at_3
value: 31.191999999999997
- type: mrr_at_5
value: 32.675
- type: ndcg_at_1
value: 25.889
- type: ndcg_at_10
value: 34.056999999999995
- type: ndcg_at_100
value: 40.142
- type: ndcg_at_1000
value: 43.614000000000004
- type: ndcg_at_3
value: 29.688
- type: ndcg_at_5
value: 32.057
- type: precision_at_1
value: 25.889
- type: precision_at_10
value: 6.7
- type: precision_at_100
value: 1.417
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 14.360999999999999
- type: precision_at_5
value: 10.711
- type: recall_at_1
value: 20.671
- type: recall_at_10
value: 43.97
- type: recall_at_100
value: 71.83699999999999
- type: recall_at_1000
value: 94.42399999999999
- type: recall_at_3
value: 31.0
- type: recall_at_5
value: 37.489
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: cqadupstack/wordpress
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.66
- type: map_at_10
value: 18.798000000000002
- type: map_at_100
value: 19.75
- type: map_at_1000
value: 19.851
- type: map_at_3
value: 16.874
- type: map_at_5
value: 18.136
- type: mrr_at_1
value: 14.972
- type: mrr_at_10
value: 20.565
- type: mrr_at_100
value: 21.488
- type: mrr_at_1000
value: 21.567
- type: mrr_at_3
value: 18.669
- type: mrr_at_5
value: 19.861
- type: ndcg_at_1
value: 14.972
- type: ndcg_at_10
value: 22.128999999999998
- type: ndcg_at_100
value: 27.028000000000002
- type: ndcg_at_1000
value: 29.887000000000004
- type: ndcg_at_3
value: 18.365000000000002
- type: ndcg_at_5
value: 20.48
- type: precision_at_1
value: 14.972
- type: precision_at_10
value: 3.549
- type: precision_at_100
value: 0.632
- type: precision_at_1000
value: 0.093
- type: precision_at_3
value: 7.887
- type: precision_at_5
value: 5.840999999999999
- type: recall_at_1
value: 13.66
- type: recall_at_10
value: 30.801000000000002
- type: recall_at_100
value: 53.626
- type: recall_at_1000
value: 75.634
- type: recall_at_3
value: 20.807000000000002
- type: recall_at_5
value: 25.86
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.622
- type: map_at_10
value: 16.042
- type: map_at_100
value: 18.023
- type: map_at_1000
value: 18.228
- type: map_at_3
value: 12.995999999999999
- type: map_at_5
value: 14.424000000000001
- type: mrr_at_1
value: 18.892999999999997
- type: mrr_at_10
value: 30.575000000000003
- type: mrr_at_100
value: 31.814999999999998
- type: mrr_at_1000
value: 31.856
- type: mrr_at_3
value: 26.851000000000003
- type: mrr_at_5
value: 29.021
- type: ndcg_at_1
value: 18.892999999999997
- type: ndcg_at_10
value: 23.575
- type: ndcg_at_100
value: 31.713
- type: ndcg_at_1000
value: 35.465
- type: ndcg_at_3
value: 18.167
- type: ndcg_at_5
value: 20.071
- type: precision_at_1
value: 18.892999999999997
- type: precision_at_10
value: 7.883
- type: precision_at_100
value: 1.652
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 13.898
- type: precision_at_5
value: 11.14
- type: recall_at_1
value: 8.622
- type: recall_at_10
value: 30.044999999999998
- type: recall_at_100
value: 58.072
- type: recall_at_1000
value: 79.226
- type: recall_at_3
value: 17.21
- type: recall_at_5
value: 22.249
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.845
- type: map_at_10
value: 12.352
- type: map_at_100
value: 17.423
- type: map_at_1000
value: 18.529
- type: map_at_3
value: 8.505
- type: map_at_5
value: 10.213
- type: mrr_at_1
value: 41.75
- type: mrr_at_10
value: 54.6
- type: mrr_at_100
value: 55.345
- type: mrr_at_1000
value: 55.374
- type: mrr_at_3
value: 52.37500000000001
- type: mrr_at_5
value: 53.87499999999999
- type: ndcg_at_1
value: 31.25
- type: ndcg_at_10
value: 26.779999999999998
- type: ndcg_at_100
value: 31.929000000000002
- type: ndcg_at_1000
value: 39.290000000000006
- type: ndcg_at_3
value: 28.746
- type: ndcg_at_5
value: 27.334999999999997
- type: precision_at_1
value: 41.75
- type: precision_at_10
value: 22.55
- type: precision_at_100
value: 7.242
- type: precision_at_1000
value: 1.439
- type: precision_at_3
value: 33.833
- type: precision_at_5
value: 28.65
- type: recall_at_1
value: 4.845
- type: recall_at_10
value: 18.664
- type: recall_at_100
value: 41.085
- type: recall_at_1000
value: 65.242
- type: recall_at_3
value: 10.572
- type: recall_at_5
value: 13.961000000000002
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 47.08
- type: f1
value: 42.843345856303756
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.743
- type: map_at_10
value: 46.521
- type: map_at_100
value: 47.235
- type: map_at_1000
value: 47.272
- type: map_at_3
value: 43.252
- type: map_at_5
value: 45.267
- type: mrr_at_1
value: 36.484
- type: mrr_at_10
value: 49.406
- type: mrr_at_100
value: 50.03300000000001
- type: mrr_at_1000
value: 50.058
- type: mrr_at_3
value: 46.195
- type: mrr_at_5
value: 48.193999999999996
- type: ndcg_at_1
value: 36.484
- type: ndcg_at_10
value: 53.42
- type: ndcg_at_100
value: 56.69499999999999
- type: ndcg_at_1000
value: 57.623999999999995
- type: ndcg_at_3
value: 47.010999999999996
- type: ndcg_at_5
value: 50.524
- type: precision_at_1
value: 36.484
- type: precision_at_10
value: 7.925
- type: precision_at_100
value: 0.975
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 19.967
- type: precision_at_5
value: 13.87
- type: recall_at_1
value: 33.743
- type: recall_at_10
value: 71.988
- type: recall_at_100
value: 86.60799999999999
- type: recall_at_1000
value: 93.54
- type: recall_at_3
value: 54.855
- type: recall_at_5
value: 63.341
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.003
- type: map_at_10
value: 21.766
- type: map_at_100
value: 23.618
- type: map_at_1000
value: 23.832
- type: map_at_3
value: 18.282999999999998
- type: map_at_5
value: 20.267
- type: mrr_at_1
value: 26.851999999999997
- type: mrr_at_10
value: 34.658
- type: mrr_at_100
value: 35.729
- type: mrr_at_1000
value: 35.785
- type: mrr_at_3
value: 31.686999999999998
- type: mrr_at_5
value: 33.315
- type: ndcg_at_1
value: 26.851999999999997
- type: ndcg_at_10
value: 28.563
- type: ndcg_at_100
value: 36.374
- type: ndcg_at_1000
value: 40.306999999999995
- type: ndcg_at_3
value: 24.224
- type: ndcg_at_5
value: 25.939
- type: precision_at_1
value: 26.851999999999997
- type: precision_at_10
value: 8.193999999999999
- type: precision_at_100
value: 1.616
- type: precision_at_1000
value: 0.232
- type: precision_at_3
value: 16.255
- type: precision_at_5
value: 12.469
- type: recall_at_1
value: 13.003
- type: recall_at_10
value: 35.689
- type: recall_at_100
value: 65.762
- type: recall_at_1000
value: 89.546
- type: recall_at_3
value: 21.820999999999998
- type: recall_at_5
value: 28.097
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.541
- type: map_at_10
value: 43.088
- type: map_at_100
value: 44.252
- type: map_at_1000
value: 44.345
- type: map_at_3
value: 39.79
- type: map_at_5
value: 41.687000000000005
- type: mrr_at_1
value: 59.082
- type: mrr_at_10
value: 67.27300000000001
- type: mrr_at_100
value: 67.708
- type: mrr_at_1000
value: 67.731
- type: mrr_at_3
value: 65.526
- type: mrr_at_5
value: 66.589
- type: ndcg_at_1
value: 59.082
- type: ndcg_at_10
value: 52.372
- type: ndcg_at_100
value: 56.725
- type: ndcg_at_1000
value: 58.665
- type: ndcg_at_3
value: 47.129
- type: ndcg_at_5
value: 49.808
- type: precision_at_1
value: 59.082
- type: precision_at_10
value: 11.275
- type: precision_at_100
value: 1.469
- type: precision_at_1000
value: 0.173
- type: precision_at_3
value: 29.773
- type: precision_at_5
value: 19.980999999999998
- type: recall_at_1
value: 29.541
- type: recall_at_10
value: 56.374
- type: recall_at_100
value: 73.42999999999999
- type: recall_at_1000
value: 86.28
- type: recall_at_3
value: 44.659
- type: recall_at_5
value: 49.952999999999996
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 75.1904
- type: ap
value: 69.80555086826531
- type: f1
value: 74.93725389065787
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 7.085
- type: map_at_10
value: 13.344000000000001
- type: map_at_100
value: 14.501
- type: map_at_1000
value: 14.605
- type: map_at_3
value: 10.758
- type: map_at_5
value: 12.162
- type: mrr_at_1
value: 7.278
- type: mrr_at_10
value: 13.607
- type: mrr_at_100
value: 14.761
- type: mrr_at_1000
value: 14.860000000000001
- type: mrr_at_3
value: 11.003
- type: mrr_at_5
value: 12.421
- type: ndcg_at_1
value: 7.278
- type: ndcg_at_10
value: 17.473
- type: ndcg_at_100
value: 23.721
- type: ndcg_at_1000
value: 26.69
- type: ndcg_at_3
value: 12.078
- type: ndcg_at_5
value: 14.62
- type: precision_at_1
value: 7.278
- type: precision_at_10
value: 3.175
- type: precision_at_100
value: 0.639
- type: precision_at_1000
value: 0.09
- type: precision_at_3
value: 5.382
- type: precision_at_5
value: 4.519
- type: recall_at_1
value: 7.085
- type: recall_at_10
value: 30.549
- type: recall_at_100
value: 60.919999999999995
- type: recall_at_1000
value: 84.372
- type: recall_at_3
value: 15.675
- type: recall_at_5
value: 21.818
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.46876424988601
- type: f1
value: 94.23159241922738
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 81.0875512995896
- type: f1
value: 61.674961674414
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 75.01344989912575
- type: f1
value: 71.7942527839921
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.15601882985877
- type: f1
value: 78.82502954601195
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.468806971345227
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 27.874332804382256
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.099340785595842
- type: mrr
value: 31.077367694660257
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.9050000000000002
- type: map_at_10
value: 8.931000000000001
- type: map_at_100
value: 11.246
- type: map_at_1000
value: 12.579
- type: map_at_3
value: 6.544
- type: map_at_5
value: 7.854
- type: mrr_at_1
value: 33.745999999999995
- type: mrr_at_10
value: 44.734
- type: mrr_at_100
value: 45.486
- type: mrr_at_1000
value: 45.534
- type: mrr_at_3
value: 42.157
- type: mrr_at_5
value: 43.813
- type: ndcg_at_1
value: 31.734
- type: ndcg_at_10
value: 26.284999999999997
- type: ndcg_at_100
value: 25.211
- type: ndcg_at_1000
value: 34.974
- type: ndcg_at_3
value: 29.918
- type: ndcg_at_5
value: 29.066
- type: precision_at_1
value: 33.745999999999995
- type: precision_at_10
value: 19.628
- type: precision_at_100
value: 6.476999999999999
- type: precision_at_1000
value: 1.976
- type: precision_at_3
value: 28.793000000000003
- type: precision_at_5
value: 25.759
- type: recall_at_1
value: 3.9050000000000002
- type: recall_at_10
value: 13.375
- type: recall_at_100
value: 28.453
- type: recall_at_1000
value: 61.67399999999999
- type: recall_at_3
value: 7.774
- type: recall_at_5
value: 10.754
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.33
- type: map_at_10
value: 30.44
- type: map_at_100
value: 31.848
- type: map_at_1000
value: 31.906000000000002
- type: map_at_3
value: 26.143
- type: map_at_5
value: 28.583
- type: mrr_at_1
value: 21.031
- type: mrr_at_10
value: 33.028
- type: mrr_at_100
value: 34.166000000000004
- type: mrr_at_1000
value: 34.208
- type: mrr_at_3
value: 29.089
- type: mrr_at_5
value: 31.362000000000002
- type: ndcg_at_1
value: 21.031
- type: ndcg_at_10
value: 37.65
- type: ndcg_at_100
value: 43.945
- type: ndcg_at_1000
value: 45.338
- type: ndcg_at_3
value: 29.256999999999998
- type: ndcg_at_5
value: 33.453
- type: precision_at_1
value: 21.031
- type: precision_at_10
value: 6.8309999999999995
- type: precision_at_100
value: 1.035
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 13.818
- type: precision_at_5
value: 10.649000000000001
- type: recall_at_1
value: 18.33
- type: recall_at_10
value: 57.330999999999996
- type: recall_at_100
value: 85.284
- type: recall_at_1000
value: 95.676
- type: recall_at_3
value: 35.356
- type: recall_at_5
value: 45.073
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 66.373
- type: map_at_10
value: 80.233
- type: map_at_100
value: 80.973
- type: map_at_1000
value: 80.99499999999999
- type: map_at_3
value: 77.127
- type: map_at_5
value: 79.056
- type: mrr_at_1
value: 76.55
- type: mrr_at_10
value: 83.813
- type: mrr_at_100
value: 83.96900000000001
- type: mrr_at_1000
value: 83.97200000000001
- type: mrr_at_3
value: 82.547
- type: mrr_at_5
value: 83.38600000000001
- type: ndcg_at_1
value: 76.53999999999999
- type: ndcg_at_10
value: 84.638
- type: ndcg_at_100
value: 86.28099999999999
- type: ndcg_at_1000
value: 86.459
- type: ndcg_at_3
value: 81.19
- type: ndcg_at_5
value: 83.057
- type: precision_at_1
value: 76.53999999999999
- type: precision_at_10
value: 12.928999999999998
- type: precision_at_100
value: 1.514
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 35.503
- type: precision_at_5
value: 23.512
- type: recall_at_1
value: 66.373
- type: recall_at_10
value: 93.273
- type: recall_at_100
value: 99.031
- type: recall_at_1000
value: 99.91799999999999
- type: recall_at_3
value: 83.55799999999999
- type: recall_at_5
value: 88.644
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 43.67174666339103
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 61.66838659211271
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.318
- type: map_at_10
value: 5.938000000000001
- type: map_at_100
value: 7.582
- type: map_at_1000
value: 7.936
- type: map_at_3
value: 4.208
- type: map_at_5
value: 5.098
- type: mrr_at_1
value: 11.4
- type: mrr_at_10
value: 17.655
- type: mrr_at_100
value: 19.088
- type: mrr_at_1000
value: 19.203
- type: mrr_at_3
value: 15.25
- type: mrr_at_5
value: 16.535
- type: ndcg_at_1
value: 11.4
- type: ndcg_at_10
value: 10.388
- type: ndcg_at_100
value: 18.165
- type: ndcg_at_1000
value: 24.842
- type: ndcg_at_3
value: 9.414
- type: ndcg_at_5
value: 8.453
- type: precision_at_1
value: 11.4
- type: precision_at_10
value: 5.54
- type: precision_at_100
value: 1.71
- type: precision_at_1000
value: 0.33
- type: precision_at_3
value: 8.866999999999999
- type: precision_at_5
value: 7.580000000000001
- type: recall_at_1
value: 2.318
- type: recall_at_10
value: 11.267000000000001
- type: recall_at_100
value: 34.743
- type: recall_at_1000
value: 67.07300000000001
- type: recall_at_3
value: 5.408
- type: recall_at_5
value: 7.713
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_spearman
value: 72.15850185456762
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_spearman
value: 61.59518395985063
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_spearman
value: 79.71131323749228
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_spearman
value: 72.10974664733891
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_spearman
value: 82.17899407125657
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_spearman
value: 79.41138579273438
- 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_spearman
value: 85.44343473477939
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_spearman
value: 63.90264271389905
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_spearman
value: 77.44151296326804
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 76.27597486396654
- type: mrr
value: 93.28127119793788
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 49.594
- type: map_at_10
value: 60.951
- type: map_at_100
value: 61.68599999999999
- type: map_at_1000
value: 61.712
- type: map_at_3
value: 57.946
- type: map_at_5
value: 59.89
- type: mrr_at_1
value: 52.666999999999994
- type: mrr_at_10
value: 62.724000000000004
- type: mrr_at_100
value: 63.269
- type: mrr_at_1000
value: 63.291
- type: mrr_at_3
value: 60.167
- type: mrr_at_5
value: 61.95
- type: ndcg_at_1
value: 52.666999999999994
- type: ndcg_at_10
value: 66.35600000000001
- type: ndcg_at_100
value: 69.463
- type: ndcg_at_1000
value: 70.111
- type: ndcg_at_3
value: 60.901
- type: ndcg_at_5
value: 64.054
- type: precision_at_1
value: 52.666999999999994
- type: precision_at_10
value: 9.0
- type: precision_at_100
value: 1.073
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 24.221999999999998
- type: precision_at_5
value: 16.333000000000002
- type: recall_at_1
value: 49.594
- type: recall_at_10
value: 81.256
- type: recall_at_100
value: 94.989
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 66.706
- type: recall_at_5
value: 74.411
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.65049504950495
- type: cos_sim_ap
value: 88.1421623503371
- type: cos_sim_f1
value: 81.44072036018008
- type: cos_sim_precision
value: 81.48148148148148
- type: cos_sim_recall
value: 81.39999999999999
- type: dot_accuracy
value: 99.37623762376238
- type: dot_ap
value: 69.87152032240303
- type: dot_f1
value: 65.64885496183206
- type: dot_precision
value: 72.18225419664267
- type: dot_recall
value: 60.199999999999996
- type: euclidean_accuracy
value: 99.63069306930693
- type: euclidean_ap
value: 86.13858297902517
- type: euclidean_f1
value: 79.87679671457904
- type: euclidean_precision
value: 82.0675105485232
- type: euclidean_recall
value: 77.8
- type: manhattan_accuracy
value: 99.63168316831683
- type: manhattan_ap
value: 86.31976532265482
- type: manhattan_f1
value: 80.10204081632654
- type: manhattan_precision
value: 81.77083333333334
- type: manhattan_recall
value: 78.5
- type: max_accuracy
value: 99.65049504950495
- type: max_ap
value: 88.1421623503371
- type: max_f1
value: 81.44072036018008
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 68.19604139959692
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.3569584557381
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 48.82174503355024
- type: mrr
value: 49.610933388506915
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.805895993742798
- type: cos_sim_spearman
value: 31.445431226826738
- type: dot_pearson
value: 24.441585432516867
- type: dot_spearman
value: 25.468117334810188
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.2
- type: map_at_10
value: 1.431
- type: map_at_100
value: 7.138999999999999
- type: map_at_1000
value: 17.933
- type: map_at_3
value: 0.551
- type: map_at_5
value: 0.7979999999999999
- type: mrr_at_1
value: 76.0
- type: mrr_at_10
value: 85.167
- type: mrr_at_100
value: 85.21300000000001
- type: mrr_at_1000
value: 85.21300000000001
- type: mrr_at_3
value: 84.667
- type: mrr_at_5
value: 85.167
- type: ndcg_at_1
value: 72.0
- type: ndcg_at_10
value: 63.343
- type: ndcg_at_100
value: 45.739999999999995
- type: ndcg_at_1000
value: 41.875
- type: ndcg_at_3
value: 68.162
- type: ndcg_at_5
value: 65.666
- type: precision_at_1
value: 76.0
- type: precision_at_10
value: 66.4
- type: precision_at_100
value: 46.800000000000004
- type: precision_at_1000
value: 18.996
- type: precision_at_3
value: 72.667
- type: precision_at_5
value: 68.4
- type: recall_at_1
value: 0.2
- type: recall_at_10
value: 1.712
- type: recall_at_100
value: 10.896
- type: recall_at_1000
value: 40.115
- type: recall_at_3
value: 0.594
- type: recall_at_5
value: 0.889
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.0619999999999998
- type: map_at_10
value: 5.611
- type: map_at_100
value: 8.841000000000001
- type: map_at_1000
value: 10.154
- type: map_at_3
value: 2.7720000000000002
- type: map_at_5
value: 4.181
- type: mrr_at_1
value: 14.285999999999998
- type: mrr_at_10
value: 26.249
- type: mrr_at_100
value: 28.046
- type: mrr_at_1000
value: 28.083000000000002
- type: mrr_at_3
value: 21.769
- type: mrr_at_5
value: 24.524
- type: ndcg_at_1
value: 11.224
- type: ndcg_at_10
value: 12.817
- type: ndcg_at_100
value: 23.183999999999997
- type: ndcg_at_1000
value: 35.099000000000004
- type: ndcg_at_3
value: 11.215
- type: ndcg_at_5
value: 12.016
- type: precision_at_1
value: 14.285999999999998
- type: precision_at_10
value: 12.653
- type: precision_at_100
value: 5.306
- type: precision_at_1000
value: 1.294
- type: precision_at_3
value: 13.605
- type: precision_at_5
value: 13.877999999999998
- type: recall_at_1
value: 1.0619999999999998
- type: recall_at_10
value: 10.377
- type: recall_at_100
value: 34.77
- type: recall_at_1000
value: 70.875
- type: recall_at_3
value: 3.688
- type: recall_at_5
value: 6.2509999999999994
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.8488
- type: ap
value: 15.590122317097372
- type: f1
value: 55.86108396102662
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 57.61460101867573
- type: f1
value: 57.8678726826158
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 32.01459876897588
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.1032365738809
- type: cos_sim_ap
value: 66.60137415520323
- type: cos_sim_f1
value: 62.12845010615712
- type: cos_sim_precision
value: 62.493326214628944
- type: cos_sim_recall
value: 61.76781002638523
- type: dot_accuracy
value: 81.85015199380103
- type: dot_ap
value: 58.854644211365084
- type: dot_f1
value: 56.15180082185158
- type: dot_precision
value: 51.806422836752894
- type: dot_recall
value: 61.2928759894459
- type: euclidean_accuracy
value: 83.6681170650295
- type: euclidean_ap
value: 64.93555585305603
- type: euclidean_f1
value: 61.02775195857125
- type: euclidean_precision
value: 61.42742582197273
- type: euclidean_recall
value: 60.633245382585756
- type: manhattan_accuracy
value: 83.73368301841808
- type: manhattan_ap
value: 65.45422483039611
- type: manhattan_f1
value: 61.58552806597499
- type: manhattan_precision
value: 62.09763948497854
- type: manhattan_recall
value: 61.08179419525066
- type: max_accuracy
value: 84.1032365738809
- type: max_ap
value: 66.60137415520323
- type: max_f1
value: 62.12845010615712
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 86.36628245430201
- type: cos_sim_ap
value: 79.29963896460292
- type: cos_sim_f1
value: 72.63895990066467
- type: cos_sim_precision
value: 69.09128803668196
- type: cos_sim_recall
value: 76.57068062827224
- type: dot_accuracy
value: 84.65091007878294
- type: dot_ap
value: 75.04883449222972
- type: dot_f1
value: 69.18569117382708
- type: dot_precision
value: 64.89512376070682
- type: dot_recall
value: 74.08376963350786
- type: euclidean_accuracy
value: 85.88116583226608
- type: euclidean_ap
value: 78.42687640324908
- type: euclidean_f1
value: 71.74350111107192
- type: euclidean_precision
value: 66.19800820152314
- type: euclidean_recall
value: 78.3030489682784
- type: manhattan_accuracy
value: 86.27508052935926
- type: manhattan_ap
value: 79.29581298930101
- type: manhattan_f1
value: 72.51838235294117
- type: manhattan_precision
value: 67.03921568627452
- type: manhattan_recall
value: 78.97289805974745
- type: max_accuracy
value: 86.36628245430201
- type: max_ap
value: 79.29963896460292
- type: max_f1
value: 72.63895990066467
---
> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance.
- **Repository:** https://github.com/McGill-NLP/llm2vec
- **Paper:** https://arxiv.org/abs/2404.05961
## Installation
```bash
pip install llm2vec
```
## Usage
```python
from llm2vec import LLM2Vec
import torch
from transformers import AutoTokenizer, AutoModel, AutoConfig
from peft import PeftModel
# Loading base Mistral model, along with custom code that enables bidirectional connections in decoder-only LLMs. MNTP LoRA weights are merged into the base model.
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",
)
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 unsupervised SimCSE model. This loads the trained LoRA weights on top of MNTP model. Hence the final weights are -- Base model + MNTP (LoRA) + SimCSE (LoRA).
model = PeftModel.from_pretrained(
model, "McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-unsup-simcse"
)
# Wrapper for encoding and pooling operations
l2v = LLM2Vec(model, tokenizer, pooling_mode="mean", max_length=512)
# Encoding queries using instructions
instruction = (
"Given a web search query, retrieve relevant passages that answer the query:"
)
queries = [
[instruction, "how much protein should a female eat"],
[instruction, "summit define"],
]
q_reps = l2v.encode(queries)
# Encoding documents. Instruction are not required for 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.",
]
d_reps = l2v.encode(documents)
# Compute cosine similarity
q_reps_norm = torch.nn.functional.normalize(q_reps, p=2, dim=1)
d_reps_norm = torch.nn.functional.normalize(d_reps, p=2, dim=1)
cos_sim = torch.mm(q_reps_norm, d_reps_norm.transpose(0, 1))
print(cos_sim)
"""
tensor([[0.6522, 0.1891],
[0.1162, 0.3457]])
"""
```
## Questions
If you have any question about the code, feel free to email Parishad (`[email protected]`) and Vaibhav (`[email protected]`).
|
[
"BIOSSES",
"SCIFACT"
] |
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"
] | 2023-06-03T21:05:01Z |
2023-09-18T12:13:30+00:00
| 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.
|
[
"SCIQ"
] |
epfl-llm/meditron-70b
|
epfl-llm
|
text-generation
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"medical",
"health",
"llama2",
"en",
"dataset:bigbio/med_qa",
"dataset:medmcqa",
"dataset:bigbio/pubmed_qa",
"dataset:epfl-llm/guidelines",
"arxiv:2311.16079",
"base_model:meta-llama/Llama-2-70b",
"base_model:finetune:meta-llama/Llama-2-70b",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-11-08T13:57:04Z |
2023-12-07T19:39:04+00:00
| 2,262 | 234 |
---
base_model: meta-llama/Llama-2-70b
datasets:
- bigbio/med_qa
- medmcqa
- bigbio/pubmed_qa
- epfl-llm/guidelines
language:
- en
license: llama2
metrics:
- accuracy
- perplexity
pipeline_tag: text-generation
tags:
- medical
- health
- llama2
---
<img width=50% src="meditron_LOGO.png" alt="Alt text" title="Meditron-logo">
# Model Card for Meditron-70B-v1.0
Meditron is a suite of open-source medical Large Language Models (LLMs).
Meditron-70B is a 70 billion parameters model adapted to the medical domain from Llama-2-70B 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-70B, finetuned on relevant training data, outperforms Llama-2-70B, GPT-3.5 (`text-davinci-003`, 8-shot), and Flan-PaLM on multiple medical reasoning tasks.
<!--# Table of Contents
[Model Card for Meditron 70B](#model-card-for--meditron-70b-v1.0)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Model Description](#model-description)
- [Uses](#uses)
- [Downstream Use](#downstream-use)
- [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
- [Recommendations](#recommendations)
- [Training Details](#training-details)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Evaluation](#evaluation)
- [Testing Data & Metrics](#testing-data-&-metrics)
- [Testing Data](#testing-data)
- [Metrics](#metrics)
- [Results](#results)
- [Environmental Impact](#environmental-impact)
- [Citation](#citation)-->
<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-2-70B](https://huggingface.co/meta-llama/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.
- **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-70B 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.
For **Helpfulness**, **Risk** and **Bias**, we provide a comprehensive qualitative generation report of Meditron-70B on queries designed by medical experts.
Each query targets specific aspects of helpfulness (medical accuracy, up-to-date information, etc.), risk (public health, medical ethics, etc.) and bias (gender, age, race, etc.).
Please see the detailed generations in our paper. We compare our generations to Llama-2-70B and ChatGPT-3.5 (version Nov, 27, 2023)
Significant research is still required to fully explore potential bias, fairness, and safety issues with this language model.
### 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 without comprehensive testing for your application.
## 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="60%" 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 16 nodes 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.
The nodes are connected via RDMA over Converged Ethernet.
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 8,
- Tensor Parallelism (TP -- different GPUs process different subtensors for matrix multiplication) of 8.
#### Training Hyperparameters
| | |
| --- | ------ |
| bf16 | true |
| lr | 1.5e-4 |
| eps | 1e-5 |
| betas | \[0.9, 0.95\] |
| clip_grad | 1 |
| weight decay | 0.1 |
| DP size | 2 |
| TP size | 8 |
| PP size | 8 |
| seq length | 4096 |
| lr scheduler | cosine|
| min lr | 1e-6 |
| warmup iteration | 2000 |
| micro batch size | 2 |
| global batch size | 512 |
| | |
#### Speeds, Sizes, Times
The model was trained in September and October 2023.
The model architecture is exactly Llama 2, meaning
| | |
| --- | ------ |
| Model size | 70B |
| Hidden dimension | 8192 |
| Num. attention heads | 64 |
| Num. layers | 80 |
| | | |
We train the 70B model on 48e9 tokens, at a throughput of about 40,200 tokens / second.
This amounts to a bfloat16 model flops utilization of roughly 42.3\%.
## 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-70b and llama-2-70b on each benchmark (pubmedqa, medmcqa, medqa)'s training data individually.
We report the finetuned models' performance with self-consistency chain-of-thought 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-70b | llama-2-70b | med42-70b* | clinical-camel-70b* |
|MMLU-Medical | 77.6 | 77.9 | 74.5 | 65.7 |
|PubMedQA | 81.6 | 80.0 | 61.2 | 67.0 |
|MedMCQA | 66.0 | 62.6 | 59.2 | 46.7 |
|MedQA | 64.4 | 61.5 | 59.1 | 50.8 |
|MedQA-4-Option| 70.2 | 63.8 | 63.9 | 56.8 |
|Avg | 72.0 | 69.2 | 63.6 | 57.4 |
| | | | | | |
**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:** 128 x NVIDIA A100 (80GB) SXM
- **Total GPU hours:** 42,496
- **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). 332 hours of 128 A100s means 42496 hours at a TDP of 400W. Assuming a Power Usage effectiveness of 1.8, total emissions are estimated to be:
(400W / 1000W/kWh / GPU * 0.016 kgCO2/kWh * 332 h * 128 GPU) * 1.8 PUE = 486 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}
}
```
|
[
"MEDQA",
"PUBMEDQA"
] |
elastic/multilingual-e5-small-optimized
|
elastic
|
sentence-similarity
|
[
"sentence-transformers",
"pytorch",
"bert",
"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:2212.03533",
"license:mit",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-11-16T10:23:02Z |
2024-09-04T01:49:42+00:00
| 2,255 | 15 |
---
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
- false
- 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
pipeline_tag: sentence-similarity
tags:
- sentence-similarity
- sentence-transformers
---
A quantized version of [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). Quantization was performed per-layer under the same conditions as our ELSERv2 model, as described [here](https://www.elastic.co/search-labs/blog/articles/introducing-elser-v2-part-1#quantization).
[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
## Benchmarks
We performed a number of small benchmarks to assess both the changes in quality as well as inference latency against the baseline original model.
### Quality
Measuring NDCG@10 using the dev split of the MIRACL datasets for select languages, we see mostly a marginal change in quality of the quantized model.
| | de | yo| ru | ar | es | th |
| --- | --- | ---| --- | --- | --- | --- |
| multilingual-e5-small | 0.75862 | 0.56193 | 0.80309 | 0.82778 | 0.81672 | 0.85072 |
| multilingual-e5-small-optimized | 0.75992 | 0.48934 | 0.79668 | 0.82017 | 0.8135 | 0.84316 |
To test the English out-of-domain performance, we used the test split of various datasets in the BEIR evaluation. Measuring NDCG@10, we see a larger change in SCIFACT, but marginal in the other datasets evaluated.
| | FIQA | SCIFACT | nfcorpus |
| --- | --- | --- | --- |
| multilingual-e5-small | 0.33126 | 0.677 | 0.31004 |
| multilingual-e5-small-optimized | 0.31734 | 0.65484 | 0.30126 |
### Performance
Using a PyTorch model traced for Linux and Intel CPUs, we performed performance benchmarking with various lengths of input. Overall, we see on average a 50-20% performance improvement with the optimized model.
| input length (characters) | multilingual-e5-small | multilingual-e5-small-optimized | speedup |
| --- | --- | --- | --- |
| 0 - 50 | 0.0181 | 0.00826 | 54.36% |
| 50 - 100 | 0.0275 | 0.0164 | 40.36% |
| 100 - 150 | 0.0366 | 0.0237 | 35.25% |
| 150 - 200 | 0.0435 | 0.0301 | 30.80% |
| 200 - 250 | 0.0514 | 0.0379 | 26.26% |
| 250 - 300 | 0.0569 | 0.043 | 24.43% |
| 300 - 350 | 0.0663 | 0.0513 | 22.62% |
| 350 - 400 | 0.0737 | 0.0576 | 21.85% |
### Disclaimer
This e5 model, as defined, hosted, integrated and used in conjunction with our other Elastic Software is covered by our standard warranty.
|
[
"SCIFACT"
] |
Dampish/StellarX-4B-V0
|
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"
] | 2023-05-27T19:04:49Z |
2023-12-03T19:52:22+00:00
| 2,229 | 1 |
---
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 "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** Meaning what version it is on, currently version 0, Assume version 0 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.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Dampish__StellarX-4B-V0)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 33.54 |
| ARC (25-shot) | 36.95 |
| HellaSwag (10-shot) | 61.9 |
| MMLU (5-shot) | 26.85 |
| TruthfulQA (0-shot) | 34.3 |
| Winogrande (5-shot) | 63.85 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 10.95 |
|
[
"SCIQ"
] |
BSC-LT/salamandra-7b
|
BSC-LT
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"bg",
"ca",
"code",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"eu",
"fi",
"fr",
"ga",
"gl",
"hr",
"hu",
"it",
"lt",
"lv",
"mt",
"nl",
"nn",
"oc",
"pl",
"pt",
"ro",
"ru",
"sh",
"sk",
"sl",
"sr",
"sv",
"uk",
"dataset:oscar-corpus/colossal-oscar-1.0",
"dataset:HuggingFaceFW/fineweb-edu",
"dataset:joelniklaus/eurlex_resources",
"dataset:joelito/legal-mc4",
"dataset:projecte-aina/CATalog",
"dataset:UFRGS/brwac",
"dataset:community-datasets/hrwac",
"dataset:danish-foundation-models/danish-gigaword",
"dataset:HiTZ/euscrawl",
"dataset:PleIAs/French-PD-Newspapers",
"dataset:PleIAs/French-PD-Books",
"dataset:AI-team-UoA/greek_legal_code",
"dataset:HiTZ/latxa-corpus-v1.1",
"dataset:allenai/peS2o",
"dataset:pile-of-law/pile-of-law",
"dataset:PORTULAN/parlamento-pt",
"dataset:hoskinson-center/proof-pile",
"dataset:togethercomputer/RedPajama-Data-1T",
"dataset:bigcode/starcoderdata",
"dataset:bjoernp/tagesschau-2018-2023",
"dataset:EleutherAI/the_pile_deduplicated",
"arxiv:2502.08489",
"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",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-09-30T06:47:04Z |
2025-02-20T16:42:39+00:00
| 2,229 | 27 |
---
datasets:
- oscar-corpus/colossal-oscar-1.0
- HuggingFaceFW/fineweb-edu
- joelniklaus/eurlex_resources
- joelito/legal-mc4
- projecte-aina/CATalog
- UFRGS/brwac
- community-datasets/hrwac
- danish-foundation-models/danish-gigaword
- HiTZ/euscrawl
- PleIAs/French-PD-Newspapers
- PleIAs/French-PD-Books
- AI-team-UoA/greek_legal_code
- HiTZ/latxa-corpus-v1.1
- allenai/peS2o
- pile-of-law/pile-of-law
- PORTULAN/parlamento-pt
- hoskinson-center/proof-pile
- togethercomputer/RedPajama-Data-1T
- bigcode/starcoderdata
- bjoernp/tagesschau-2018-2023
- EleutherAI/the_pile_deduplicated
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
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
---
# Salamandra Model Card
This repository contains the model described in [Salamandra Technical Report](https://huggingface.co/papers/2502.08489).
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 base 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 12.875 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 pre-training corpus comprises data from 35 European languages and 92 programming languages, with detailed data sources provided below.
The initial three training epochs used 2.4 trillion tokens, obtained by manually adjusting data proportion to balance the representation
and give more importance to Spain’s co-official (Spanish, Catalan, Galician, and Basque). This way, we downsampled code and English data to half,
Spanish co-official languages were oversampled by 2x, and the remaining languages were kept in their original proportions.
During the following epochs, the Colossal OSCAR dataset was replaced with the FineWeb-Edu dataset.
This adjustment resulted in a total of 2.68 trillion tokens, distributed as outlined below:
The pretraining corpus is predominantly composed of data from Colossal OSCAR, which contributes a significant 53.05% of the total tokens.
Following this, Starcoder provides 13.67%, and FineWeb-Edu (350BT subset) adds 10.24%. The next largest sources are HPLT at 4.21% and French-PD at 3.59%.
Other notable contributions include MaCoCu, Legal-ES, and EurLex, each contributing around 1.72% to 1.41%.
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 |
|---|---|---|
| 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 |
| 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 |
| 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/) |
| OpenSubtitles v2016 | 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 |
| 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) |
| Parlamint | 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 |
| MaCoCu | bg, ca, el, hr, mt, sl, sr, uk | Bañón et al., 2022 |
| CURLICAT | bg, hr, hu, pl, ro, sk, sl | Váradi et al., 2022 |
| Norwegian Colossal Corpus (NCC) | nn, no | Kummervold et al., 2021 |
| Academic Slovene KAS 2.0 | sl | Žagar et al., 2022 |
| BIGPATENT | en | Sharma et al., 2019 |
| Biomedical-ES | es | Internally generated biomedical dataset: Wikipedia LS, Pubmed, MeSpEn, patents, clinical cases, medical crawler |
| Brazilian Portuguese Web as Corpus (BrWaC) | pt | Wagner Filho et al., 2018 |
| Bulgarian National Corpus (BulNC) | bg | [Link](http://old.dcl.bas.bg/dataset/BulNC.7z) |
| CaBeRnet | fr | Popa-Fabre et al., 2020 |
| CATalog 1.0 | ca | Palomar-Giner et al., 2024 |
| CorpusNÓS | gl | de-Dios-Flores et al., 2024 |
| Croatian Web as Corpus 2.1 (hrWaC) | hr | Ljubešić & Klubička, 2014 |
| 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/) |
| Estonian National Corpus 2021 (ENC) | et | Koppel & Kallas, 2022 |
| Estonian Reference Corpus (ERC) | et | [Link](https://www.cl.ut.ee/korpused/segakorpus/) |
| EusCrawl (w/o Wikipedia or NC-licenses) | eu | Artetxe et al., 2022 |
| FineWeb-Edu (350BT subset) | en | Penedo et al., 2024 |
| French Public Domain Books (French-PD) | fr | [Link](https://huggingface.co/datasets/PleIAs/French-PD-Books) |
| French Public Domain Newspapers (French-PD) | fr | [Link](https://huggingface.co/datasets/PleIAs/French-PD-Newspapers) |
| German Web as Corpus (DeWaC) | de | [Link](https://docs.sslmit.unibo.it/doku.php?id=corpora:dewac) |
| Greek Legal Code (GLC) | el | Papaloukas et al., 2021 |
| Greek Web Corpus (GWC) | el | Outsios et al., 2018 |
| HPLT v1 - Spanish | es | de Gibert et al., 2024 |
| HPLT v1.1 - Spanish | es | de Gibert et al., 2024 |
| Irish Universal Dependencies (Ga-UD) | ga | [Link](https://universaldependencies.org/ga/index.html) |
| Italian Web as Corpus (ItWaC) | it | [Link](https://docs.sslmit.unibo.it/doku.php?id=corpora:itwac) |
| Korpus Malti | mt | Micallef et al., 2022 |
| Korpus slovenských právnych predpisov v1.9 (SK-Laws) | sk | [Link](https://www.juls.savba.sk/data/marcell/legal-sk-20220322-1.9.ver.xz) |
| Latxa Corpus v1.1 (GAITU) | eu | Etxaniz et al., 2024 [Link](https://huggingface.co/datasets/HiTZ/latxa-corpus-v1.1) |
| Laws and legal acts of Ukraine (UK-Laws) | uk | [Link](https://lang.org.ua/en/corpora/#anchor7) |
| Legal-ES | es | Internally generated legal dataset: BOE, BORME, Senado, Congreso, Spanish court orders, DOGC |
| MARCELL Romanian legislative subcorpus v2 | ro | [Link](https://elrc-share.eu/reposMARCELL%20Romanian%20legislative%20subcorpus%20v2itory/browse/marcell-romanian-legislative-subcorpus-v2/2da548428b9d11eb9c1a00155d026706ce94a6b59ffc4b0e9fb5cd9cebe6889e/) |
| Math AMPS | en | Hendrycks et al., 2021 |
| NKPJ National Corpus of Polish v1.2 (NKPJ) | pl | Lewandowska-Tomaszczyk et al., 2013 |
| Occitan Corpus (IEA-AALO) | oc | Provided by [IEA](https://www.institutestudisaranesi.cat/) |
| Open Legal Data - German court decisions and laws | de | Ostendorff et al., 2020 |
| ParlamentoPT | pt | Rodrigues et al., 2023 |
| peS2o | en | Soldaini & Lo, 2023 |
| PG-19 | en | Rae et al., 2019 |
| Pile of Law (selected subsets) | en | Henderson* et al., 2022 |
| Polish Parliamentary Corpus (PPC) | pl | Ogrodniczuk, 2018 |
| Proof Pile | en | [Link](https://huggingface.co/datasets/hoskinson-center/proof-pile) |
| RedPajama-Data T1 (StackExchange subset) | en | Computer, 2023 |
| Scientific-ES | es | Internally generated scientific dataset: Dialnet, Scielo, CSIC, TDX, BSC, UCM |
| SK Court Decisions v2.0 (OD-Justice) | sk | [Link](https://www.juls.savba.sk/data/od-justice/od-justice-2.0.ver.xz) |
| Slovene Web as Corpus (slWaC) | sl | Erjavec et al., 2015 |
| SoNaR Corpus NC 1.2 | nl | [Link](https://taalmaterialen.ivdnt.org/download/tstc-sonar-corpus/) |
| Spanish Legal Domain Corpora (Spanish-Legal) | es | Gutiérrez-Fandiño et al., 2021 |
| SrpKorSubset: news, legal, academic, conversation, lit- erary (SrpKor) | sr | [Link](http://www.korpus.matf.bg.ac.rs/) |
| Starcoder | code | Li et al., 2023 |
| State-related content from the Latvian Web (State-Latvian-Web) | lv | [Link](https://catalog.elra.info/en-us/repository/browse/ELRA-W0169/) |
| SYN v9: large corpus of written Czech | cs | Křen et al., 2021 |
| Tagesschau Archive Article | de | [Link](https://huggingface.co/datasets/bjoernp/tagesschau-2018-2023) |
| The Danish Parliament Corpus 2009 - 2017, v1 | da | Hansen, 2018 |
| The Gaois bilingual corpus of English-Irish legislation (Ga-Legislation) | ga | [Link](https://portulanclarin.net/repository/browse/the-gaois-bilingual-corpus-of-english-irish-legislation-processed/daeac17c9e3511ea9b7f02420a000407b83de243dc0b469aab41084386c5b80f/) |
| The Pile (PhilPapers) | en | Gao et al., 2021 |
| The Swedish Culturomics Gigaword Corpus (Swedish- Gigaword) | sv | Rødven-Eide, 2016 |
| Welsh-GOV | cy | Crawling from [Link](https://www.llyw.cymru) |
| Yle Finnish News Archive (Yle-News) | fi | [Link](http://urn.fi/urn:nbn:fi:lb-2021050401) |
To consult the data summary document with the respective licences, please send an e-mail to [email protected].
<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?
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</details>
</details>
The model was trained on 3 pre-training epochs with 2.4T tokens per epoch, 2 additional pre-training epochs in which the English part
of the Colossal OSCAR dataset was replaced with FineWeb-Edu (350BT subset), resulting in 2.68T tokens per epoch;
and 1 final epoch of 0.315T higher quality tokens, meaning that the total number of tokens seen during pre-training is approximately 12.875 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 programming languages (92). We also want to represent the co-official languages of Spain: Spanish, Catalan, Galician and Basque. For this reason, we oversample
these languages by a factor of 2.
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 José 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 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.
#### 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 39.31% of the total data. Spanish was upsampled by a factor of 2, bringing its share to 16.12%,
while Catalan (1.97%), Basque (0.24%), and Galician (0.31%) were also upsampled by 2. On the other hand, code-related data was downsampled
by half, making up 5.78% of the total. Other prominent languages include French (6.6%), Russian (5.56%), German (4.79%), and Hungarian
(4.59%), 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 labelled 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 randomly divided into training, validation and test sets, where the validation and test sets are each 1% of the total corpus.
**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 search engine optimization techniques and templates contribute to repeated textual patterns. Some instances may be also 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 (Mina et al., 2024), 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.
- Domain-specific or language-specific raw crawls.
- Manually curated data obtained through collaborators, data providers (by means of legal assignment agreements) or open source projects (e.g. CATalog).
**What mechanisms or procedures were used to collect the data? How were these mechanisms or procedures validated?**
The data collection process was carried out using three different mechanisms, each corresponding to one of the groups defined in the previous answer. The specific methods used and their respective validation procedures are outlined below:
- Open Direct Download: Data were obtained directly from publicly accessible sources, such as websites or repositories that provide open data downloads. We validate the data with a data integrity check, which ensures that the downloaded files are complete, uncorrupted and in the expected format and structure.
- Ad hoc scrapers or crawlers: Custom web scraping scripts or crawlers were used to extract data from various online sources where direct downloads were not available. These scripts navigate web pages, extract relevant data and store it in a structured format. We validate this method with software unit tests to evaluate the functionality of individual components of the scraping programs, checking for errors or unexpected behaviour. In addition, data integrity tests were performed to verify that the collected data remained complete throughout the extraction and storage process.
- Direct download via FTP, SFTP, API or S3: Some datasets were acquired using secure transfer protocols such as FTP (File Transfer Protocol), SFTP (Secure File Transfer Protocol), or API (Application Programming Interface) requests from cloud storage services such as Amazon S3. As with the open direct download method, data integrity tests were used to validate the completeness of the files to ensure that the files were not altered or corrupted during the transfer process.
**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 Language Technologies 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.**
No changes were made to the content of individual text document instances. However, the web-sourced documents underwent a filtering process based on specific criteria along two key dimensions:
- Quality filtering: The text processing pipeline CURATE (Palomar et. al, 2024) calculates a quality score for each document based on a set of filtering criteria that identify undesirable textual characteristics. Any document with a score below the 0.8 threshold was excluded from the dataset.
- Harmful or adult content filtering: To reduce the amount of harmful or inappropriate material in the dataset, documents from Colossal OSCAR were filtered using the Ungoliant pipeline (Abadji et al., 2021), which uses the 'harmful\_pp' field, a perplexity-based score generated by a language model.
**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 CATalog and other curated datasets,
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 rowspan="2">Commonsense Reasoning</td>
<td>copa_es</td>
<td>acc</td>
<td>86</td>
</tr>
<tr>
<td>xstorycloze_es</td>
<td>acc</td>
<td>74.32</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_es</td>
<td>acc</td>
<td>59.15</td>
</tr>
<tr>
<td>xnli_es</td>
<td>acc</td>
<td>46.59</td>
</tr>
<tr>
<td>Paraphrasing</td>
<td>paws_es</td>
<td>acc</td>
<td>60.3</td>
</tr>
<tr>
<td rowspan="2">QA</td>
<td>openbookqa_es</td>
<td>acc</td>
<td>41.6</td>
</tr>
<tr>
<td>xquad_es</td>
<td>acc</td>
<td>72.26</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_es</td>
<td>bleu</td>
<td>23.43</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>84</td>
</tr>
<tr>
<td>xstorycloze_ca</td>
<td>acc</td>
<td>75.51</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_ca</td>
<td>acc</td>
<td>59.15</td>
</tr>
<tr>
<td>xnli_ca</td>
<td>acc</td>
<td>50.16</td>
</tr>
<tr>
<td rowspan="2">Paraphrasing</td>
<td>parafraseja</td>
<td>acc</td>
<td>65.83</td>
</tr>
<tr>
<td>paws_ca</td>
<td>acc</td>
<td>67.45</td>
</tr>
<tr>
<td rowspan="5">QA</td>
<td>arc_ca_easy</td>
<td>acc</td>
<td>71.72</td>
</tr>
<tr>
<td>arc_ca_challenge</td>
<td>acc</td>
<td>45.56</td>
</tr>
<tr>
<td>openbookqa_ca</td>
<td>acc</td>
<td>38.8</td>
</tr>
<tr>
<td>piqa_ca</td>
<td>acc</td>
<td>71.27</td>
</tr>
<tr>
<td>siqa_ca</td>
<td>acc</td>
<td>49.85</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_ca</td>
<td>bleu</td>
<td>30.63</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>68.8</td>
</tr>
<tr>
<td>xstorycloze_eu</td>
<td>acc</td>
<td>66.12</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli_eu</td>
<td>acc</td>
<td>57.75</td>
</tr>
<tr>
<td>xnli_eu</td>
<td>acc</td>
<td>43.51</td>
</tr>
<tr>
<td rowspan="4">QA</td>
<td>eus_exams</td>
<td>acc</td>
<td>41.04</td>
</tr>
<tr>
<td>eus_proficiency</td>
<td>acc</td>
<td>39.72</td>
</tr>
<tr>
<td>eus_trivia</td>
<td>acc</td>
<td>52.36</td>
</tr>
<tr>
<td>piqa_eu</td>
<td>acc</td>
<td>63.67</td>
</tr>
<tr>
<td>Reading Comprehension</td>
<td>eus_reading</td>
<td>acc</td>
<td>33.52</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_eu</td>
<td>bleu</td>
<td>16.95</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>Commonsense Reasoning</td>
<td>xstorycloze_gl</td>
<td>acc</td>
<td>74.12</td>
</tr>
<tr>
<td>NLI</td>
<td>xnli_gl</td>
<td>acc</td>
<td>50.95</td>
</tr>
<tr>
<td rowspan="2">Paraphrasing</td>
<td>parafrases_gl</td>
<td>acc</td>
<td>54.42</td>
</tr>
<tr>
<td>paws_gl</td>
<td>acc</td>
<td>63.2</td>
</tr>
<tr>
<td>QA</td>
<td>openbookqa_gl</td>
<td>acc</td>
<td>34.4</td>
</tr>
<tr>
<td>Translation</td>
<td>flores_gl</td>
<td>bleu</td>
<td>27.75</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>91</td>
</tr>
<tr>
<td>xstorycloze_en</td>
<td>acc</td>
<td>79.09</td>
</tr>
<tr>
<td rowspan="2">NLI</td>
<td>wnli</td>
<td>acc</td>
<td>56.34</td>
</tr>
<tr>
<td>xnli_en</td>
<td>acc</td>
<td>50</td>
</tr>
<tr>
<td>Paraphrasing</td>
<td>paws *</td>
<td>acc</td>
<td>64.05</td>
</tr>
<tr>
<td rowspan="6">QA</td>
<td>arc_easy</td>
<td>acc</td>
<td>82.2</td>
</tr>
<tr>
<td>arc_challenge</td>
<td>acc</td>
<td>52.82</td>
</tr>
<tr>
<td>openbookqa</td>
<td>acc</td>
<td>36</td>
</tr>
<tr>
<td>piqa</td>
<td>acc</td>
<td>80.03</td>
</tr>
<tr>
<td>social_iqa</td>
<td>acc</td>
<td>50.31</td>
</tr>
<tr>
<td>xquad_en **</td>
<td>acc</td>
<td>77.74</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 the 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, 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
```
@misc{gonzalezagirre2025salamandratechnicalreport,
title={Salamandra Technical Report},
author={Aitor Gonzalez-Agirre and Marc Pàmies and Joan Llop and Irene Baucells and Severino Da Dalt and Daniel Tamayo and José Javier Saiz and Ferran Espuña and Jaume Prats and Javier Aula-Blasco and Mario Mina and Adrián Rubio and Alexander Shvets and Anna Sallés and Iñaki Lacunza and Iñigo Pikabea and Jorge Palomar and Júlia Falcão and Lucía Tormo and Luis Vasquez-Reina and Montserrat Marimon and Valle Ruíz-Fernández and Marta Villegas},
year={2025},
eprint={2502.08489},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.08489},
}
```
### 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| [Link](https://huggingface.co/BSC-LT/ALIA-40b) | WiP |
|
[
"BEAR",
"SCIELO"
] |
mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF
|
mradermacher
| null |
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2",
"base_model:quantized:Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | 2024-06-27T11:44:12Z |
2025-02-07T00:59:31+00:00
| 2,220 | 1 |
---
base_model: Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2
language:
- en
library_name: transformers
tags:
- merge
- mergekit
- lazymergekit
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/Cas-Warehouse/Llama-3-Mopeyfied-Psychology-v2
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-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/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Mopeyfied-Psychology-v2-i1-GGUF/resolve/main/Llama-3-Mopeyfied-Psychology-v2.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
[
"CAS"
] |
HiTZ/GoLLIE-7B
|
HiTZ
|
text-generation
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"text-generation-inference",
"Information Extraction",
"IE",
"Named Entity Recogniton",
"Event Extraction",
"Relation Extraction",
"LLaMA",
"custom_code",
"en",
"dataset:ACE05",
"dataset:bc5cdr",
"dataset:conll2003",
"dataset:ncbi_disease",
"dataset:conll2012_ontonotesv5",
"dataset:rams",
"dataset:tacred",
"dataset:wnut_17",
"arxiv:2310.03668",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-09-25T10:24:52Z |
2023-10-10T07:51:44+00:00
| 2,215 | 28 |
---
datasets:
- ACE05
- bc5cdr
- conll2003
- ncbi_disease
- conll2012_ontonotesv5
- rams
- tacred
- wnut_17
language:
- en
license: llama2
metrics:
- f1
pipeline_tag: text-generation
tags:
- code
- text-generation-inference
- Information Extraction
- IE
- Named Entity Recogniton
- Event Extraction
- Relation Extraction
- LLaMA
---
<p align="center">
<br>
<img src="https://github.com/hitz-zentroa/GoLLIE/raw/main/assets/GoLLIE.png" style="height: 250px;">
<h2 align="center"><b>G</b>uideline f<b>o</b>llowing <b>L</b>arge <b>L</b>anguage Model for <b>I</b>nformation <b>E</b>xtraction</h2>
<br>
# Model Card for GoLLIE 7B
<p align="justify">
We present GoLLIE, a Large Language Model trained to follow annotation guidelines. GoLLIE outperforms previous approaches on zero-shot Information Extraction and allows the user to perform inferences with annotation schemas defined on the fly. Different from previous approaches, GoLLIE is able to follow detailed definitions and does not only rely on the knowledge already encoded in the LLM.
- 💻 Code: [https://github.com/osainz59/CoLLIE/](https://github.com/hitz-zentroa/GoLLIE)
- 📒 Blog Post: [GoLLIE: Guideline-following Large Language Model for Information Extraction](https://hitz-zentroa.github.io/GoLLIE/)
- 📖 Paper: [GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction](https://arxiv.org/abs/2310.03668)
- 🐕 GoLLIE Colection in the 🤗HuggingFace Hub: [HiTZ/gollie](https://huggingface.co/collections/HiTZ/gollie-651bf19ee315e8a224aacc4f)
- 🚀 Example Jupyter Notebooks: [GoLLIE Notebooks](https://github.com/hitz-zentroa/GoLLIE/tree/main/notebooks)
</p>
<p align="center">
<img src="https://github.com/hitz-zentroa/GoLLIE/raw/main/assets/zero_shot_results.png">
</p>
### Model Description
- **Developed by:** [Oscar Sainz](https://osainz59.github.io/), [Iker García-Ferrero](https://ikergarcia1996.github.io/Iker-Garcia-Ferrero/), [Rodrigo Agerri](https://ragerri.github.io/), [Oier Lopez de Lacalle](https://oierldl.github.io/), [German Rigau](https://adimen.si.ehu.es/~rigau/) and [Eneko Agirre](https://eagirre.github.io/)
- **Institution:** [HiTZ Basque Center for Language Technology](http://www.hitz.eus/) - [Ixa](https://www.ixa.eus/node/2?language=en), [University of the Basque Country UPV/EHU](https://www.ehu.eus/en/en-home)
- **Model type:** Text Generation
- **Language(s) (NLP):** English
- **License:** LLaMA2 License for the base and merged model. Apache 2.0 for pre-trained LoRA Adapters
- **Finetuned from model:** CODE-LLaMA2
## Schema definition and inference example
The labels are represented as Python classes, and the guidelines or instructions are introduced as docstrings. The model start generating after the `result = [` line.
```Python
# Entity definitions
@dataclass
class Launcher(Template):
"""Refers to a vehicle designed primarily to transport payloads from the Earth's
surface to space. Launchers can carry various payloads, including satellites,
crewed spacecraft, and cargo, into various orbits or even beyond Earth's orbit.
They are usually multi-stage vehicles that use rocket engines for propulsion."""
mention: str
"""
The name of the launcher vehicle.
Such as: "Sturn V", "Atlas V", "Soyuz", "Ariane 5"
"""
space_company: str # The company that operates the launcher. Such as: "Blue origin", "ESA", "Boeing", "ISRO", "Northrop Grumman", "Arianespace"
crew: List[str] # Names of the crew members boarding the Launcher. Such as: "Neil Armstrong", "Michael Collins", "Buzz Aldrin"
@dataclass
class Mission(Template):
"""Any planned or accomplished journey beyond Earth's atmosphere with specific objectives,
either crewed or uncrewed. It includes missions to satellites, the International
Space Station (ISS), other celestial bodies, and deep space."""
mention: str
"""
The name of the mission.
Such as: "Apollo 11", "Artemis", "Mercury"
"""
date: str # The start date of the mission
departure: str # The place from which the vehicle will be launched. Such as: "Florida", "Houston", "French Guiana"
destination: str # The place or planet to which the launcher will be sent. Such as "Moon", "low-orbit", "Saturn"
# This is the text to analyze
text = (
"The Ares 3 mission to Mars is scheduled for 2032. The Starship rocket build by SpaceX will take off from Boca Chica,"
"carrying the astronauts Max Rutherford, Elena Soto, and Jake Martinez."
)
# The annotation instances that take place in the text above are listed here
result = [
Mission(mention='Ares 3', date='2032', departure='Boca Chica', destination='Mars'),
Launcher(mention='Starship', space_company='SpaceX', crew=['Max Rutherford', 'Elena Soto', 'Jake Martinez'])
]
```
## How to Get Started with the Model
Please read our [🚀 Example Jupyter Notebooks](https://github.com/hitz-zentroa/GoLLIE/tree/main/notebooks) to get started with GoLLIE.
The best way to load the model is using our custom `load_model` fuction. However, you can also load them using the AutoModelForCausalLM class.
**Important**: Our flash attention implementation has small numerical differences compared to the attention implementation in Huggingface.
You must use the flag `trust_remote_code=True` or you will get inferior results. Flash attention requires an available CUDA GPU. Running GOLLIE
pre-trained models on a CPU is not supported. We plan to address this in future releases. First, install flash attention 2:
```bash
pip install flash-attn --no-build-isolation
pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary
```
Then you can load the model using
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("HiTZ/GoLLIE-7B")
model = AutoModelForCausalLM.from_pretrained("HiTZ/GoLLIE-7B", trust_remote_code=True, torch_dtype=torch.bfloat16)
model.to("cuda")
```
Read our [🚀 Example Jupyter Notebooks](https://github.com/hitz-zentroa/GoLLIE/tree/main/notebooks) to learn how to easily define guidelines, generate model inputs and parse the output!
### Training Data
This is the list of task used for training and evaluating GoLLIE. However, as demonstrated in the 🚀 [Create Custom Task notebook](https://github.com/hitz-zentroa/GoLLIE/blob/main/notebooks/Create%20Custom%20Task.ipynb) GoLLIE can perform a wide range of unseen tasks.
For more info, read our [📖Paper](https://arxiv.org/abs/2310.03668).
<p align="center">
<img src="https://github.com/hitz-zentroa/GoLLIE/raw/main/assets/datasets.png">
</p>
## Evaluation
| Model | Supervised average F1 | Zero-shot average F1 | 🤗HuggingFace Hub |
|---|:---------------------:|:--------------------:|:---------------------------------------------------------:|
| GoLLIE-7B | 73.0 | 55.3 | [HiTZ/GoLLIE-7B](https://huggingface.co/HiTZ/GoLLIE-7B) |
| GoLLIE-13B | 73.9 | 56.0 | [HiTZ/GoLLIE-13B](https://huggingface.co/HiTZ/GoLLIE-13B) |
| GoLLIE-34B | **75.0** | **57.2** | [HiTZ/GoLLIE-34B](https://huggingface.co/HiTZ/GoLLIE-34B) |
## Environmental Impact
| Model | Hardware | FLOPs | Time (h) | CO<sup>2</sup>eq (kg) |
|----------------|-------------------|---------------------------|-------------------|-------------------------------------|
| GoLLIE 7B | 1xA100 | 11.9e<sup>18</sup> | 44.5 | 1.57 |
| GoLLIE 13B | 1xA100 | 22.7e<sup>18</sup> | 79.5 | 2.80 |
| GoLLIE 34B | 2xA100 | 55.8e<sup>18</sup> | 94.6 | 6.67 |
## Citation
```
@misc{sainz2023gollie,
title={GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction},
author={Oscar Sainz and Iker García-Ferrero and Rodrigo Agerri and Oier Lopez de Lacalle and German Rigau and Eneko Agirre},
year={2023},
eprint={2310.03668},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
[
"BC5CDR",
"NCBI DISEASE"
] |
GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct
|
GoToCompany
| null |
[
"safetensors",
"gemma2",
"en",
"id",
"jv",
"su",
"arxiv:2309.06085",
"arxiv:2310.04928",
"arxiv:2311.07911",
"base_model:GoToCompany/gemma2-9b-cpt-sahabatai-v1-base",
"base_model:finetune:GoToCompany/gemma2-9b-cpt-sahabatai-v1-base",
"license:gemma",
"region:us"
] | 2024-11-06T04:51:58Z |
2024-11-06T04:51:58+00:00
| 2,211 | 35 |
---
base_model:
- GoToCompany/gemma2-9b-cpt-sahabatai-v1-base
language:
- en
- id
- jv
- su
license: gemma
---
# Gemma2 9B CPT Sahabat-AI v1 Instruct
**Sahabat-AI** (Indonesian language for “close friends”) is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for Indonesian language and its various dialects. Sahabat-AI ecosystem is co-initiated by Indonesian tech and telecommunication companies: GoTo Group and Indosat Ooredoo Hutchison.
Gemma2 9B CPT Sahabat-AI v1 Instruct is an Indonesian-focused model which has been fine-tuned with around **448,000 Indonesian instruction-completion pairs** alongside an Indonesian-dialect pool consisting of **96,000 instruction-completion pairs in Javanese** and **98,000 instruction-completion pairs in Sundanese**. Additionally, we added a pool of **129,000 instruction-completion pairs in English**.
- **Co-initiated by:** PT GoTo Gojek Tokopedia Tbk, Indosat Ooredoo Hutchison
- **Developed by:** PT GoTo Gojek Tokopedia Tbk, AI Singapore
- **Model type:** Decoder
- **Languages:** English, Indonesian, Javanese, Sundanese
- **License:** [Gemma Community License](https://ai.google.dev/gemma/terms)
## Model Details
### Model Description
We performed instruction tuning in Indonesian, Javanese, Sundanese as well as English on our [continued pre-trained Gemma2 9B CPT Sahabat-AI v1](https://huggingface.co/GoToCompany/gemma2-9b-cpt-sahabatai-v1-base), a decoder model using the Gemma2 architecture, to create Gemma2 9B CPT Sahabat-AI v1 Instruct.
For tokenisation, the model employs the default tokenizer used in Gemma-2-9B. The model has a context length of 8192.
### Benchmark Performance
We evaluated Gemma2 9B CPT Sahabat-AI V1 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 Summarization (Summ), Causal Reasoning (Causal) and Natural Language Inference (NLI).
- We also added support for Javanese and Sundanese for the BHASA tasks whenever applicable
- [IndoMMLU](https://arxiv.org/pdf/2310.04928)
- These tasks include examination questions on Humanities, Indonesian language, Local languages and cultures, Social science and STEM across primary, middle, and high school levels.
- and the common English tasks from the [HuggingFace LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard).
- These tasks consist of [IFEval, BBH, Math Lvl 5, GPQA, MuSR, and MMLU-PRO.](https://huggingface.co/docs/leaderboards/open_llm_leaderboard/about)
- **Caveat**: Our results differ from the HuggingFace LLM Leaderboard because we have used [VLLM](https://docs.vllm.ai/en/latest/) as our inference platform. VLLM caps the context size at **4096 tokens** while HuggingFace was set to **8192 tokens**.
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 Gemma2 9B CPT Sahabat-AI v1 Instruct is an instruction-following model, we also evaluated it on instruction-following capabilities with the [IFEval](https://arxiv.org/abs/2311.07911) dataset.
As this dataset was in English, the linguists and native speakers in the team worked together to filter, localize and translate the dataset into the respective target languages to ensure that the examples remained reasonable, meaningful and natural.
**IFEval**
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 normalized 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).
*Note*: IFEval was only used on Bahasa Indonesia. We are currently working on adding it for Javanese and Sundanese for our upcoming releases.
#### Results
#### Indonesian Results
#### SEA HELM (also known as BHASA)
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px; font-weight: bold;">Language / Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Overall (Bahasa Indonesia + Javanese + Sundanese)</td>
<td style="border: 1px solid gray; padding: 8px;">36.963</td>
<td style="border: 1px solid gray; padding: 8px;">42.988</td>
<td style="border: 1px solid gray; padding: 8px;">37.805</td>
<td style="border: 1px solid gray; padding: 8px;">45.866</td>
<td style="border: 1px solid gray; padding: 8px;">46.880</td>
<td style="border: 1px solid gray; padding: 8px;">56.359</td>
<td style="border: 1px solid gray; padding: 8px;">53.725</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">61.169</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Bahasa Indonesia</td>
<td style="border: 1px solid gray; padding: 8px;">46.760</td>
<td style="border: 1px solid gray; padding: 8px;">60.372</td>
<td style="border: 1px solid gray; padding: 8px;">42.022</td>
<td style="border: 1px solid gray; padding: 8px;">51.944</td>
<td style="border: 1px solid gray; padding: 8px;">54.579</td>
<td style="border: 1px solid gray; padding: 8px;">63.394</td>
<td style="border: 1px solid gray; padding: 8px;">57.221</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">64.154</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Javanese</td>
<td style="border: 1px solid gray; padding: 8px;">33.956</td>
<td style="border: 1px solid gray; padding: 8px;">40.625</td>
<td style="border: 1px solid gray; padding: 8px;">41.739</td>
<td style="border: 1px solid gray; padding: 8px;">47.587</td>
<td style="border: 1px solid gray; padding: 8px;">48.012</td>
<td style="border: 1px solid gray; padding: 8px;">56.468</td>
<td style="border: 1px solid gray; padding: 8px;">56.460</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">64.439</td>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Sundanese</td>
<td style="border: 1px solid gray; padding: 8px;">30.173</td>
<td style="border: 1px solid gray; padding: 8px;">27.969</td>
<td style="border: 1px solid gray; padding: 8px;">29.654</td>
<td style="border: 1px solid gray; padding: 8px;">38.068</td>
<td style="border: 1px solid gray; padding: 8px;">38.050</td>
<td style="border: 1px solid gray; padding: 8px;">49.216</td>
<td style="border: 1px solid gray; padding: 8px;">47.495</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">54.913</td>
</tr>
</table>
#### IndoMMLU
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px; font-weight: bold;">Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Meta-Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Overall Results</td>
<td style="border: 1px solid gray; padding: 8px;">53.0%</td>
<td style="border: 1px solid gray; padding: 8px;">56.0%</td>
<td style="border: 1px solid gray; padding: 8px;">51.9%</td>
<td style="border: 1px solid gray; padding: 8px;">53.8%</td>
<td style="border: 1px solid gray; padding: 8px;">54.4%</td>
<td style="border: 1px solid gray; padding: 8px;">61.4%</td>
<td style="border: 1px solid gray; padding: 8px;">55.6%</td>
<td style="border: 2px solid black; padding: 8px; background-color: lightgreen;">62.6%</td>
</tr>
</table>
#### English Results
<table style="border-collapse: collapse; width: 100%; font-size: 10px">
<tr>
<th style="border: 2px solid black; padding: 8px;">Model Name [Instruct]</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Qwen2.5-7B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3-8B</th>
<th style="border: 1px solid gray; padding: 8px;">Llama-3.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">sea-lionv2.1-8B</th>
<th style="border: 1px solid gray; padding: 8px;">gemma-2-9B</th>
<th style="border: 1px solid gray; padding: 8px;">sahabatai-v1-8B</th>
<th style="border: 2px solid black; padding: 8px;">sahabatai-v1-9B</th>
</tr>
<tr>
<td style="border: 2px solid black; padding: 8px; font-weight: bold;">Average</td>
<td style="border: 1px solid gray; padding: 8px;">24.48</td>
<td style="border: 1px solid gray; padding: 8px;">27.75</td>
<td style="border: 1px solid gray; padding: 8px;">23.91</td>
<td style="border: 1px solid gray; padding: 8px;">27.98</td>
<td style="border: 1px solid gray; padding: 8px;">24.52</td>
<td style="border: 1px solid gray; padding: 8px;">26.44</td>
<td style="border: 1px solid gray; padding: 8px;">24.43</td>
<td style="border: 1px solid black; padding: 8px; background-color: lightgreen;">33.67</td>
</tr>
</table>
Gemma2 9B CPT Sahabat-AI v1 Instruct can be run using the 🤗 Transformers library
```python
# Please use transformers==4.45.0
import torch
import transformers
model_id = "GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
# Javanese
messages = [
{"role": "user", "content": "Sopo wae sing ana ing Punakawan?"}
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
)
print(outputs[0]["generated_text"][-1])
# Sundanese
messages = [
{"role": "user", "content": "Kumaha caritana si Kabayan?"},
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
)
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 Sahabat-AI 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
Gemma2 9B CPT Sahabat-AI v1 Instruct was built 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 4 hours, with alignment taking 2 hours, both on 8x H100-80GB GPUs.
## Data
Gemma2 9B CPT Sahabat-AI v1 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 Collaboration
Sahabat-AI (Indonesian language for “close friends”) a **local open source Large Language Model (LLM) ecosystem in Indonesian language**, co-initiated by Indonesian tech and telecommunication companies: GoTo Group and Indosat Ooredoo Hutchison.
Sahabat-AI ecosystem aims to empower Indonesians who want to develop AI-based services and applications using Bahasa Indonesia and its various local dialects.
We are supported by research centers and global tech experts such as AI Singapore and Tech Mahendra to train the model to gain general language understanding.
We also collaborate with key top Indonesia universities such as University of Indonesia, Gadjah Mada University, Bogor Institute of Agriculture, Bandung Institute of Technology, including top Indonesia media groups, such as Kompas Gramedia Group and Republika to train and enrich the model in Bahasa Indonesia, ensuring optimum provision of local context and cultural relevance.
We would like to invite **researchers, developers, and language enthusiasts** to actively contribute to the enhancement and expansion of Sahabat-AI.
Your collaborations can involve:
- Identifying and reporting technical issues
- Sharing pre-training, instruction, and preference data
- Improving documentation usability
- Proposing and implementing new model evaluation tasks and metrics
Join us in shaping the future of Sahabat-AI by sharing your expertise and insights to make these models more accessible, accurate, and versatile.
You can contribute your ideas through [this form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)
## The Development Team (in ascending alphabetical order)
### AI Singapore
Chan Adwin<br>
Cheng Nicholas<br>
Choa Esther<br>
Huang Yuli<br>
Lau Wayne<br>
Lee Chwan Ren<br>
Leong Wai Yi<br>
Leong Wei Qi<br>
Limkonchotiwat Peerat<br>
Liu Bing Jie Darius<br>
Montalan Jann Railey<br>
Ng Boon Cheong Raymond<br>
Ngui Jian Gang<br>
Nguyen Thanh Ngan<br>
Ong Brandon<br>
Ong Tat-Wee David<br>
Ong Zhi Hao<br>
Rengarajan Hamsawardhini<br>
Siow Bryan<br>
Susanto Yosephine<br>
Tai Ngee Chia<br>
Tan Choon Meng<br>
Teng Walter<br>
Teo Eng Sipp Leslie<br>
Teo Wei Yi<br>
Tjhi William<br>
Yeo Yeow Tong<br>
Yong Xianbin<br>
### PT GoTo Gojek Tokopedia Tbk
Anissa Dininta<br>
Chau Shiau Ching<br>
Choiri Hendra Hadhil<br>
Goel Priyank<br>
Saini Ajay Kumar<br>
Shalev Ofir<br>
Tan Daryl<br>
Tep Kilian Rithi<br>
Tiwari Anupam<br>
Widjojo Daniel<br>
## 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 [Sahabat-AI Inquiry Form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)
## Disclaimer
This is the repository for the Instruct 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 claim, damages, or other liability arising from the use of the released weights and codes.
## References
### IndoMMLU Reference
```bibtex
@inproceedings{koto-etal-2023-indommlu,
title = "Large Language Models Only Pass Primary School Exams in {I}ndonesia: A Comprehensive Test on {I}ndo{MMLU}",
author = "Fajri Koto and Nurul Aisyah and Haonan Li and Timothy Baldwin",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = December,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
}
}
```
|
[
"CHIA"
] |
aliakseilabanau/bge-small-en
|
aliakseilabanau
| null |
[
"safetensors",
"openvino",
"bert",
"mteb",
"sentence transformers",
"en",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"base_model:BAAI/bge-small-en",
"base_model:quantized:BAAI/bge-small-en",
"license:mit",
"region:us"
] | 2024-11-19T18:27:44Z |
2025-02-04T13:09:46+00:00
| 2,173 | 0 |
---
base_model:
- BAAI/bge-small-en
language:
- en
license: mit
tags:
- mteb
- sentence transformers
- openvino
base_model_relation: quantized
---
The [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) converted for [openvino backend](https://sbert.net/docs/sentence_transformer/usage/efficiency.html#openvino).
```
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("aliakseilabanau/bge-small-en", backend="openvino")
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
```
The original model card is below:
--------
**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.
|
[
"BEAR"
] |
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"
] | 2023-04-08T03:03:14Z |
2023-05-29T12:48:21+00:00
| 2,164 | 230 |
---
datasets:
- JosephusCheung/GuanacoDataset
language:
- en
- zh
- ja
- de
license: gpl-3.0
pipeline_tag: conversational
tags:
- llama
- guannaco
- alpaca
inference: false
---
**You can run on Colab free T4 GPU now**
[](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.
|
[
"BEAR"
] |
DopeorNope/COKAL-v1-70B
|
DopeorNope
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-12-05T08:20:47Z |
2023-12-11T18:41:25+00:00
| 2,159 | 9 |
---
license: apache-2.0
---
# **🐻❄️COKAL-v1_70B🐻❄️**
## Model Details
**Model Developers** Seungyoo Lee (DopeorNope)
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture**
COKAL-v1_70B is an auto-regressive 70B language model based on the LLaMA2 transformer architecture.
**Base Model**
**Training Dataset**
- SFT training dataset: [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
**Training**
I developed the model in an environment with A100 x 8
# Implementation Code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "DopeorNope/COKAL-v1_70B"
model = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
model_tokenizer = AutoTokenizer.from_pretrained(repo)
```
---
|
[
"BEAR"
] |
Locutusque/gpt2-xl-conversational
|
Locutusque
|
text-generation
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"en",
"dataset:Locutusque/InstructMix",
"doi:10.57967/hf/1371",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-08-21T04:43:31Z |
2023-11-21T17:17:46+00:00
| 2,140 | 18 |
---
datasets:
- Locutusque/InstructMix
language:
- en
license: mit
metrics:
- bleu
- perplexity
- loss
- accuracy
pipeline_tag: text-generation
widget:
- text: '<|USER|> Design a Neo4j database and Cypher function snippet to Display Extreme
Dental hygiene: Using Mouthwash for Analysis for Beginners. Implement if/else
or switch/case statements to handle different conditions related to the Consent.
Provide detailed comments explaining your control flow and the reasoning behind
each decision. <|ASSISTANT|> '
- text: '<|USER|> Write me a story about a magical place. <|ASSISTANT|> '
- text: '<|USER|> Write me an essay about the life of George Washington <|ASSISTANT|> '
- text: '<|USER|> Solve the following equation 2x + 10 = 20 <|ASSISTANT|> '
- text: '<|USER|> Craft me a list of some nice places to visit around the world. <|ASSISTANT|> '
- text: '<|USER|> How to manage a lazy employee: Address the employee verbally. Don''t
allow an employee''s laziness or lack of enthusiasm to become a recurring issue.
Tell the employee you''re hoping to speak with them about workplace expectations
and performance, and schedule a time to sit down together. Question: To manage
a lazy employee, it is suggested to talk to the employee. True, False, or Neither?
<|ASSISTANT|> '
inference:
parameters:
temperature: 0.8
do_sample: true
top_p: 0.14
top_k: 41
max_new_tokens: 250
repetition_penalty: 1.176
---
# Model Card
## Model Details
- Model Name: gpt2-xl-conversational
- Model Type: Language Modeling
- Task: Generating Conversational Responses
- Hardware: 1x Nvidia Titan V
- Description: This model is trained on a dataset of conversations between a user and an AI assistant, with the goal of generating a coherent and relevant response to the user's input. It uses the GPT-2 architecture, a state-of-the-art transformer-based language model that is capable of generating high-quality text with a wide range of styles and tones. The model is fine-tuned on the conversational data using maximum likelihood estimation, and is evaluated based on its ability to generate responses that are both grammatically correct and semantically relevant to the user's input.
## Intended Use
This model is intended to be used for generating conversational responses in a variety of contexts, such as chatbots, virtual assistants, and customer service applications. It is designed to provide natural and engaging responses to user input, with a focus on maintaining a consistent tone and style throughout the conversation. The model is suitable for use in both text-based and voice-based interfaces, and can be easily integrated into existing applications using the PyTorch and Transformers frameworks.
## Training Data
The model is trained on a large dataset of conversational data, consisting of interactions between users and an AI assistant. The data is preprocessed to remove any sensitive information and is formatted in a way that is suitable for training a language model. The training data is split into a training set and a validation set, with the training set used to update the model parameters and the validation set used to evaluate the model performance. The model was trained on 300,000 examples and achieved excellent metrics.
## Model Architecture
The model architecture used in this model is GPT-2, a transformer-based language model that is capable of generating high-quality text with a wide range of styles and tones. The GPT-2 architecture consists of a multi-layered decoder-only transformer, with self-attention mechanisms that allow the model to capture long-term dependencies and generate coherent text.
## Evaluation Metrics
The model is evaluated based on several metrics, including loss, reward, penalty, BLEU score, and perplexity. The loss metric is calculated during training and reflects the difference between the predicted output and the actual output. The reward metric is based on the number of correct words generated by the model, while the penalty metric penalizes the model for repeating words consecutively. The BLEU score measures the similarity between the generated text and the ground truth text, while the perplexity metric measures how well the model is able to predict the next word in a sequence. During training, the model achieved the following metrics:
- BLEU score: 52
- Accuracy: 53
- perplexity: 4.3
Evaluation metrics:
| Task |Version|Metric|Value| |Stderr|
|--------|------:|------|----:|---|-----:|
|pubmedqa| 0|acc |0.536|± |0.0223
|arc_challenge| 0|acc_norm |0.2867|± |0.0132|
|arc_easy | 0|acc |0.5804|± |0.0101|
|arc_easy | 0|acc_norm|0.5707|±|0.0102|
|winogrande| 0|acc |0.5691|± |0.0139|
|truthfulqa_mc| 1|mc2 |0.3918|± |0.0144|
|anli_r1| 0|acc |0.338|± |0.0150|
|anli_r2| 0|acc |0.346|± |0.0151|
|anli_r3| 0|acc |0.355|± |0.0138|
|drop| 1|f1 |0.0034|± |0.0004|
|hendrycksTest-abstract_algebra | 1|acc | 0.32|± |0.0952|
|hendrycksTest-anatomy | 1|acc | 0.44|± |0.1013|
|hendrycksTest-astronomy | 1|acc | 0.24|± |0.0872|
|hendrycksTest-business_ethics | 1|acc | 0.24|± |0.0872|
|hendrycksTest-clinical_knowledge | 1|acc | 0.24|± |0.0872|
|hendrycksTest-college_biology | 1|acc | 0.20|± |0.0816|
|hendrycksTest-college_chemistry | 1|acc | 0.40|± |0.1000|
|hendrycksTest-college_computer_science | 1|acc | 0.36|± |0.0980|
|hendrycksTest-college_mathematics | 1|acc | 0.48|± |0.1020|
|hendrycksTest-college_medicine | 1|acc | 0.20|± |0.0816|
|hendrycksTest-college_physics | 1|acc | 0.44|± |0.1013|
|hendrycksTest-computer_security | 1|acc | 0.16|± |0.0748|
|hendrycksTest-conceptual_physics | 1|acc | 0.12|± |0.0663|
|hendrycksTest-econometrics | 1|acc | 0.16|± |0.0748|
|hendrycksTest-electrical_engineering | 1|acc | 0.28|± |0.0917|
|hendrycksTest-elementary_mathematics | 1|acc | 0.36|± |0.0980|
|hendrycksTest-formal_logic | 1|acc | 0.44|± |0.1013|
|hendrycksTest-global_facts | 1|acc | 0.20|± |0.0816|
|hendrycksTest-high_school_biology | 1|acc | 0.20|± |0.0816|
|hendrycksTest-high_school_chemistry | 1|acc | 0.28|± |0.0917|
|hendrycksTest-high_school_computer_science | 1|acc | 0.24|± |0.0872|
|hendrycksTest-high_school_european_history | 1|acc | 0.32|± |0.0952|
|hendrycksTest-high_school_geography | 1|acc | 0.32|± |0.0952|
|hendrycksTest-high_school_government_and_politics| 1|acc | 0.28|± |0.0917|
|hendrycksTest-high_school_macroeconomics | 1|acc | 0.28|± |0.0917|
|hendrycksTest-high_school_mathematics | 1|acc | 0.20|± |0.0816|
|hendrycksTest-high_school_microeconomics | 1|acc | 0.24|± |0.0872|
|hendrycksTest-high_school_physics | 1|acc | 0.28|± |0.0917|
|hendrycksTest-high_school_psychology | 1|acc | 0.32|± |0.0952|
|hendrycksTest-high_school_statistics | 1|acc | 0.40|± |0.1000|
|hendrycksTest-high_school_us_history | 1|acc | 0.32|± |0.0952|
|hendrycksTest-high_school_world_history | 1|acc | 0.36|± |0.0980||
|hendrycksTest-human_aging | 1|acc | 0.16|± |0.0748|
|hendrycksTest-human_sexuality | 1|acc | 0.40|± |0.1000|
|hendrycksTest-international_law | 1|acc | 0.24|± |0.0872|
|hendrycksTest-jurisprudence | 1|acc | 0.08|± |0.0554|
|hendrycksTest-logical_fallacies | 1|acc | 0.52|± |0.1020|
|hendrycksTest-machine_learning | 1|acc | 0.12|± |0.0663|
|hendrycksTest-management | 1|acc | 0.12|± |0.0663|
|hendrycksTest-marketing | 1|acc | 0.16|± |0.0748|
|hendrycksTest-medical_genetics | 1|acc | 0.12|± |0.0663|
|hendrycksTest-miscellaneous | 1|acc | 0.36|± |0.0980|
|hendrycksTest-moral_disputes | 1|acc | 0.08|± |0.0554|
|hendrycksTest-moral_scenarios | 1|acc | 0.44|± |0.1013|
|hendrycksTest-nutrition | 1|acc | 0.32|± |0.0952|
|hendrycksTest-philosophy | 1|acc | 0.44|± |0.1013|
|hendrycksTest-prehistory | 1|acc | 0.16|± |0.0748|
|hendrycksTest-professional_accounting | 1|acc | 0.28|± |0.0917|
|hendrycksTest-professional_law | 1|acc | 0.12|± |0.0663|
|hendrycksTest-professional_medicine | 1|acc | 0.40|± |0.1000|
|hendrycksTest-professional_psychology | 1|acc | 0.24|± |0.0872|
|hendrycksTest-public_relations | 1|acc | 0.08|± |0.0554|
|hendrycksTest-security_studies | 1|acc | 0.24|± |0.0872|
|hendrycksTest-sociology | 1|acc | 0.28|± |0.0917|
|hendrycksTest-us_foreign_policy | 1|acc | 0.24|± |0.0872|
|hendrycksTest-virology | 1|acc | 0.20|± |0.0816|
|hendrycksTest-world_religions | 1|acc | 0.16|± |0.0748|
## Limitations and Bias
This model is not suitable for all use cases due to its limited training time on a weak computer. As a result, it may produce irrelevant or nonsensical responses. For optimal performance, I recommend using a GPU with at least 16 GB of VRAM and downloading the model manually instead of using the Transformers library. Here's how you should deploy the model:
```python
import torch
from transformers import GPT2LMHeadModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Locutusque/gpt2-xl-conversational")
model = GPT2LMHeadModel.from_pretrained("Locutusque/gpt2-xl-conversational", torch_dtype=torch.float16)
model.resize_token_embeddings(len(tokenizer))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device, dtype=torch.float32)
def generate_text(model: SENTIAForCausalLM, tokenizer, prompt, max_length=256):
prompt = f'<|USER|> {prompt} <|ASSISTANT|> '
input_ids = tokenizer.encode(prompt, add_special_tokens=True, max_length=max_length, truncation=True, return_tensors="pt").to(device)
output = model.generate(input_ids, do_sample=True, temperature=0.3, top_p=0.7, top_k=23, repetition_penalty=1.176, max_length=max_length, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id)
output_ids = tokenizer.decode(output[0], skip_special_tokens=False)
return output_ids
# Loop to interact with the model
while True:
prompt = input("Enter a prompt (or 'q' to quit): ")
if prompt == "q":
break
output_text = generate_text(model, tokenizer, prompt, max_length=1022)
print(output_text)
```
## Deploying and training the model
The model has been fine-tuned on a specific input format that goes like this ```"<|USER|> {user prompt} <|ASSISTANT|> {model prediction} ".```
|
[
"CRAFT",
"PUBMEDQA"
] |
Undi95/MLewd-L2-13B
|
Undi95
|
text-generation
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-09-04T00:06:59Z |
2023-11-17T21:30:51+00:00
| 2,135 | 4 |
---
license: cc-by-nc-4.0
---
MLewd is a model created to be... Lewd. That's all. Based on ReMM.
There was so much attempt on this model that I can't count them all. Bear with me lmao.
The OG plan: https://pastebin.com/hfJ80rKL
Command useds and explaination :
```shell
Due to hardware limitation, some merge was done in 2 part.
Last mix :
- ReMM (Base) (0.57)
- Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged (Llama Chat Uncensored) (0.35)
- KoboldAI/LLAMA2-13B-Holodeck-1 (0.08)
Part 1: python ties_merge.py TheBloke/Llama-2-13B-fp16 ./MLewdBase-L2-13B-part1 --merge Undi95/ReMM-L2-13B --density 0.88 --merge KoboldAI/LLAMA2-13B-Holodeck-1 --density 0.12 --cuda
Part 2: python ties_merge.py TheBloke/Llama-2-13B-fp16 ./MLewdBase-L2-13B --merge Undi95/MLewdBase-L2-13B-part1 --density 0.65 --merge Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged --density 0.35 --cuda
(MLewd-L2-13B-v1-2 got disqualified)
- Applying LoRA: nRuaif/Kimiko-v2-13B at (0.24) weight on MLewd-L2-13B-v1-1
=> Result: MLewd-L2-13B-v1-3
================== ERP RANKING TEST ===========================
19.42 | MLewd-L2-13B-v1-3.q5_K_M.gguf (-> Best)
19.25 | MLewd-L2-13B-v1-1.q5_K_M.gguf
18.25 | MLewd-L2-13B-v1-2.q5 K M.gguf
================== RETRY ===========================
Mix:
- Undi95/MLewd-L2-13B-v1-3 (0.82)
- Sao10K/Stheno-Inverted-L2-13B (0.18)
!python ties_merge.py TheBloke/Llama-2-13B-fp16 ./MLewd-L2-13B-v1-7 --merge Undi95/MLewd-L2-13B-v1-3 --density 0.82 --merge Sao10K/Stheno-Inverted-L2-13B --density 0.18 --cuda
=> Result: MLewd-L2-13B-v1-7
Final touch (trying my best here) :
MLewd-L2-13B-v1-7 (0.77) + zarakiquemparte/PIPPA-ShareGPT-Subset-QLora-13b (LoRA 0.23)
=> MLewd-L2-13B-v1-7-TRY2
FINAL : MLewd-L2-13B-v1-7-TRY2 (0.82) + BluemoonRP (0.18)
=> MLewd-L2-13B-v1-8-3
RIP to all the version that got trashed.
```
<!-- description start -->
## Description
This repo contains fp16 files of MLewd-L2-13B, a trying-to-be lewd LLM model.
<!-- description end -->
<!-- description start -->
## Models used
- Undi95/ReMM (Base)
- Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged (Llama Chat Uncensored)
- KoboldAI/LLAMA2-13B-Holodeck-1
- Sao10K/Stheno-Inverted-L2-13B
## Loras used
- nRuaif/BluemoonRP-L2-13B-This-time-will-be-better/tree/main/lora-out-13b-final-BM/checkpoint-15/adapter_model
- zarakiquemparte/PIPPA-ShareGPT-Subset-QLora-13b
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
Special thanks to Sushi kek
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__MLewd-L2-13B)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 46.84 |
| ARC (25-shot) | 58.28 |
| HellaSwag (10-shot) | 82.32 |
| MMLU (5-shot) | 54.67 |
| TruthfulQA (0-shot) | 48.66 |
| Winogrande (5-shot) | 73.48 |
| GSM8K (5-shot) | 1.29 |
| DROP (3-shot) | 9.18 |
|
[
"BEAR"
] |
TheBloke/Manticore-13B-Chat-Pyg-Guanaco-SuperHOT-8K-GPTQ
|
TheBloke
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"custom_code",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | 2023-06-28T20:23:28Z |
2023-08-21T14:35:18+00:00
| 2,134 | 19 |
---
license: other
inference: false
---
<!-- 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 -->
# Manticore 13B Chat Pyg Guanaco GPTQ
These files are GPTQ 4bit model files for [Manticore 13B Chat Pyg Guanaco](https://huggingface.co/Monero/Manticore-13b-Chat-Pyg-Guanaco) merged with [Kaio Ken's SuperHOT 8K](https://huggingface.co/kaiokendev/superhot-13b-8k-no-rlhf-test).
It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
**This is an experimental new GPTQ which offers up to 8K context size**
The increased context is tested to work with [ExLlama](https://github.com/turboderp/exllama), via the latest release of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It has also been tested from Python code using AutoGPTQ, and `trust_remote_code=True`.
Code credits:
- Original concept and code for increasing context length: [kaiokendev](https://huggingface.co/kaiokendev)
- Updated Llama modelling code that includes this automatically via trust_remote_code: [emozilla](https://huggingface.co/emozilla).
Please read carefully below to see how to use it.
GGML versions are not yet provided, as there is not yet support for SuperHOT in llama.cpp. This is being investigated and will hopefully come soon.
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Manticore-13B-Chat-Pyg-Guanaco-SuperHOT-8K-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU inference](https://huggingface.co/TheBloke/Manticore-13B-Chat-Pyg-Guanaco-SuperHOT-8K-GGML)
* [Unquantised SuperHOT fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Manticore-13B-Chat-Pyg-Guanaco-SuperHOT-8K-fp16)
* [Unquantised base fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Monero/Manticore-13b-Chat-Pyg-Guanaco)
## How to easily download and use this model in text-generation-webui with ExLlama
Please make sure you're using the latest version of text-generation-webui
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Manticore-13B-Chat-Pyg-Guanaco-SuperHOT-8K-GPTQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done"
5. Untick **Autoload the model**
6. In the top left, click the refresh icon next to **Model**.
7. In the **Model** dropdown, choose the model you just downloaded: `Manticore-13B-Chat-Pyg-Guanaco-SuperHOT-8K-GPTQ`
8. To use the increased context, set the **Loader** to **ExLlama**, set **max_seq_len** to 8192 or 4096, and set **compress_pos_emb** to **4** for 8192 context, or to **2** for 4096 context.
9. Now click **Save Settings** followed by **Reload**
10. The model will automatically load, and is now ready for use!
11. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
## How to use this GPTQ model from Python code with AutoGPTQ
First make sure you have AutoGPTQ and Einops installed:
```
pip3 install einops auto-gptq
```
Then run the following code. Note that in order to get this to work, `config.json` has been hardcoded to a sequence length of 8192.
If you want to try 4096 instead to reduce VRAM usage, please manually edit `config.json` to set `max_position_embeddings` to the value you want.
```python
from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import argparse
model_name_or_path = "TheBloke/Manticore-13B-Chat-Pyg-Guanaco-SuperHOT-8K-GPTQ"
model_basename = "manticore-13b-chat-pyg-guanaco-superhot-8k-GPTQ-4bit-128g.no-act.order"
use_triton = False
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
model_basename=model_basename,
use_safetensors=True,
trust_remote_code=True,
device_map='auto',
use_triton=use_triton,
quantize_config=None)
model.seqlen = 8192
# Note: check the prompt template is correct for this model.
prompt = "Tell me about AI"
prompt_template=f'''USER: {prompt}
ASSISTANT:'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
logging.set_verbosity(logging.CRITICAL)
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
temperature=0.7,
top_p=0.95,
repetition_penalty=1.15
)
print(pipe(prompt_template)[0]['generated_text'])
```
## Using other UIs: monkey patch
Provided in the repo is `llama_rope_scaled_monkey_patch.py`, written by @kaiokendev.
It can be theoretically be added to any Python UI or custom code to enable the same result as `trust_remote_code=True`. I have not tested this, and it should be superseded by using `trust_remote_code=True`, but I include it for completeness and for interest.
## Provided files
**manticore-13b-chat-pyg-guanaco-superhot-8k-GPTQ-4bit-128g.no-act.order.safetensors**
This will work with AutoGPTQ, ExLlama, and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead.
It was created with group_size 128 to increase inference accuracy, but without --act-order (desc_act) to increase compatibility and improve inference speed.
* `manticore-13b-chat-pyg-guanaco-superhot-8k-GPTQ-4bit-128g.no-act.order.safetensors`
* Works for use with ExLlama with increased context (4096 or 8192)
* Works with AutoGPTQ in Python code, including with increased context, if `trust_remote_code=True` is set.
* Should work with GPTQ-for-LLaMa in CUDA mode, but unknown if increased context works - TBC. May have issues with GPTQ-for-LLaMa Triton mode.
* Works with text-generation-webui, including one-click-installers.
* Parameters: Groupsize = 128. Act Order / desc_act = False.
<!-- 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!
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**: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Kaio Ken's SuperHOT 8K
### SuperHOT Prototype 2 w/ 8K Context
This is a second prototype of SuperHOT, this time 30B with 8K context and no RLHF, using the same technique described in [the github blog](https://kaiokendev.github.io/til#extending-context-to-8k).
Tests have shown that the model does indeed leverage the extended context at 8K.
You will need to **use either the monkeypatch** or, if you are already using the monkeypatch, **change the scaling factor to 0.25 and the maximum sequence length to 8192**
#### Looking for Merged & Quantized Models?
- 30B 4-bit CUDA: [tmpupload/superhot-30b-8k-4bit-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-safetensors)
- 30B 4-bit CUDA 128g: [tmpupload/superhot-30b-8k-4bit-128g-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-128g-safetensors)
#### Training Details
I trained the LoRA with the following configuration:
- 1200 samples (~400 samples over 2048 sequence length)
- learning rate of 3e-4
- 3 epochs
- The exported modules are:
- q_proj
- k_proj
- v_proj
- o_proj
- no bias
- Rank = 4
- Alpha = 8
- no dropout
- weight decay of 0.1
- AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5
- Trained on 4-bit base model
# Original model card: Manticore 13B Chat Pyg Guanaco
Manticore-13b-Chat-Pyg with the Guanaco 13b qLoRa from TimDettmers applied
|
[
"MONERO"
] |
mrm8488/modernbert-embed-base-ft-sts-spanish-matryoshka-768-64
|
mrm8488
|
sentence-similarity
|
[
"sentence-transformers",
"safetensors",
"modernbert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:2697",
"loss:MatryoshkaLoss",
"loss:CoSENTLoss",
"arxiv:1908.10084",
"arxiv:2205.13147",
"base_model:nomic-ai/modernbert-embed-base",
"base_model:finetune:nomic-ai/modernbert-embed-base",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2025-01-10T21:44:41Z |
2025-01-10T21:50:00+00:00
| 2,130 | 2 |
---
base_model: nomic-ai/modernbert-embed-base
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:2697
- loss:MatryoshkaLoss
- loss:CoSENTLoss
widget:
- source_sentence: En un mercado de granjeros, se encuentra un hombre.
sentences:
- Un abogado de la CPI detenido en Libia está ahora mismo encarando un período de
detención de 45 días
- Un hombre está presente en un mercado donde se venden productos agrícolas directamente
de los agricultores.
- ¿Existe la posibilidad de que cambie de opinión si no se expresa de manera enérgica
o muestra un comportamiento inapropiado?
- source_sentence: Una mujer está posada en una postura con los brazos abiertos mientras
otra persona le toma una fotografía.
sentences:
- Un hombre se encuentra parado en medio de una multitud sujetando un objeto de
color blanco.
- Las personas están cerca del agua.
- Frente a una estatua de una vaca, hay una mujer, un niño pequeño y un bebé diminuto.
- source_sentence: Un grupo de cuatro niños está observando los diferentes animales
que están en el establo.
sentences:
- Evita apoyar todo tu peso en los brazos, ya que tus manos no están diseñadas para
soportar esa presión constante.
- Los niños están mirando atentamente a una oveja.
- Un puma persigue a un oso grande en el bosque.
- source_sentence: La gente se balancea saltando al agua mientras otros pescan en
el fondo del mar.
sentences:
- Dos individuos observan el agua con atención.
- Siempre golpeamos suavemente a nuestros hijos en la boca para mostrarles que su
boca es lo que les causa dolor.
- Aunque el sistema de prioridad al primero en llegar beneficia a dos participantes,
no asegura definitivamente la exclusión de terceros.
- source_sentence: El cordero está mirando hacia la cámara.
sentences:
- Manmohan en Teherán insta a NAM a tomar una posición clara sobre el conflicto
en Siria
- Un gato está mirando hacia la cámara también.
- '"Sí, no deseo estar presente durante este testimonio", declaró tranquilamente
Peterson, de 31 años, al juez cuando fue devuelto a su celda.'
model-index:
- name: SentenceTransformer based on nomic-ai/modernbert-embed-base
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 768
type: sts-dev-768
metrics:
- type: pearson_cosine
value: 0.7498914121357008
name: Pearson Cosine
- type: spearman_cosine
value: 0.7531670275662775
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 512
type: sts-dev-512
metrics:
- type: pearson_cosine
value: 0.7468285624371191
name: Pearson Cosine
- type: spearman_cosine
value: 0.7482342767593612
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 256
type: sts-dev-256
metrics:
- type: pearson_cosine
value: 0.7419098803201045
name: Pearson Cosine
- type: spearman_cosine
value: 0.7450577925521013
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 128
type: sts-dev-128
metrics:
- type: pearson_cosine
value: 0.7262860099881795
name: Pearson Cosine
- type: spearman_cosine
value: 0.7304432975238186
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 64
type: sts-dev-64
metrics:
- type: pearson_cosine
value: 0.6973267849431932
name: Pearson Cosine
- type: spearman_cosine
value: 0.7069603266334332
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: 0.8673484326459211
name: Pearson Cosine
- type: spearman_cosine
value: 0.8767387684433159
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: 0.8665336885415594
name: Pearson Cosine
- type: spearman_cosine
value: 0.8751868367625472
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.8568125590206718
name: Pearson Cosine
- type: spearman_cosine
value: 0.8702353416571491
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.8485344363338887
name: Pearson Cosine
- type: spearman_cosine
value: 0.8617402150766132
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.8193790032247387
name: Pearson Cosine
- type: spearman_cosine
value: 0.8419631939550043
name: Spearman Cosine
---
# SentenceTransformer based on nomic-ai/modernbert-embed-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) on the stsb_multi_es_augmented (private) 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:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision bb0033c9f3def40c3c5b26ff0b53c74f3320d703 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- Private stsb dataset
### 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: ModernBertModel
(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("mrm8488/modernbert-embed-base-ft-sts-spanish-matryoshka-768-64-5e")
# Run inference
sentences = [
'El cordero está mirando hacia la cámara.',
'Un gato está mirando hacia la cámara también.',
'"Sí, no deseo estar presente durante este testimonio", declaró tranquilamente Peterson, de 31 años, al juez cuando fue devuelto a su celda.',
]
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]
```
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</details>
-->
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Semantic Similarity
* Datasets: `sts-dev-768`, `sts-dev-512`, `sts-dev-256`, `sts-dev-128`, `sts-dev-64`, `sts-test-768`, `sts-test-512`, `sts-test-256`, `sts-test-128` and `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | sts-dev-768 | sts-dev-512 | sts-dev-256 | sts-dev-128 | sts-dev-64 | sts-test-768 | sts-test-512 | sts-test-256 | sts-test-128 | sts-test-64 |
|:--------------------|:------------|:------------|:------------|:------------|:-----------|:-------------|:-------------|:-------------|:-------------|:------------|
| pearson_cosine | 0.7499 | 0.7468 | 0.7419 | 0.7263 | 0.6973 | 0.8673 | 0.8665 | 0.8568 | 0.8485 | 0.8194 |
| **spearman_cosine** | **0.7532** | **0.7482** | **0.7451** | **0.7304** | **0.707** | **0.8767** | **0.8752** | **0.8702** | **0.8617** | **0.842** |
<!--
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## Training Details
### Training Dataset
#### stsb_multi_es_augmented (private)
* Size: 2,697 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: 9 tokens</li><li>mean: 28.42 tokens</li><li>max: 96 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 28.01 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.72</li><li>max: 5.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:-------------------------------|
| <code>El pájaro de tamaño reducido se posó con delicadeza en una rama cubierta de escarcha.</code> | <code>Un ave de color amarillo descansaba tranquilamente en una rama.</code> | <code>3.200000047683716</code> |
| <code>Una chica está tocando la flauta en un parque.</code> | <code>Un grupo de músicos está tocando en un escenario al aire libre.</code> | <code>1.286</code> |
| <code>La aclamada escritora británica, Doris Lessing, galardonada con el premio Nobel, fallece</code> | <code>La destacada autora británica, Doris Lessing, reconocida con el prestigioso Premio Nobel, muere</code> | <code>4.199999809265137</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CoSENTLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### stsb_multi_es_augmented (private)
* Size: 697 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 697 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 9 tokens</li><li>mean: 29.35 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 28.52 tokens</li><li>max: 81 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.3</li><li>max: 5.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------|
| <code>Un incendio ocurrido en un hospital psiquiátrico ruso resultó en la trágica muerte de 38 personas.</code> | <code>Se teme que el incendio en un hospital psiquiátrico ruso cause la pérdida de la vida de 38 individuos.</code> | <code>4.199999809265137</code> |
| <code>"Street dijo que el otro individuo a veces se siente avergonzado de su fiesta, lo cual provoca risas en la multitud"</code> | <code>"A veces, el otro tipo se encuentra avergonzado de su fiesta y no se le puede culpar."</code> | <code>3.5</code> |
| <code>El veterano diplomático de Malasia tuvo un encuentro con Suu Kyi el miércoles en la casa del lago en Yangon donde permanece bajo arresto domiciliario.</code> | <code>Razali Ismail tuvo una reunión de 90 minutos con Suu Kyi, quien ganó el Premio Nobel de la Paz en 1991, en su casa del lago donde está recluida.</code> | <code>3.691999912261963</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CoSENTLoss",
"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`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `bf16`: True
#### 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
- `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`: 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
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | sts-dev-768_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-128_spearman_cosine | sts-dev-64_spearman_cosine | sts-test-768_spearman_cosine | sts-test-512_spearman_cosine | sts-test-256_spearman_cosine | sts-test-128_spearman_cosine | sts-test-64_spearman_cosine |
|:------:|:----:|:-------------:|:---------------:|:---------------------------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|
| 0.5917 | 100 | 23.7709 | 22.5494 | 0.7185 | 0.7146 | 0.7055 | 0.6794 | 0.6570 | - | - | - | - | - |
| 1.1834 | 200 | 22.137 | 22.7634 | 0.7449 | 0.7412 | 0.7439 | 0.7287 | 0.7027 | - | - | - | - | - |
| 1.7751 | 300 | 21.5527 | 22.6985 | 0.7321 | 0.7281 | 0.7243 | 0.7063 | 0.6862 | - | - | - | - | - |
| 2.3669 | 400 | 20.5745 | 24.0021 | 0.7302 | 0.7264 | 0.7221 | 0.7097 | 0.6897 | - | - | - | - | - |
| 2.9586 | 500 | 20.0861 | 24.0091 | 0.7392 | 0.7361 | 0.7293 | 0.7124 | 0.6906 | - | - | - | - | - |
| 3.5503 | 600 | 18.8191 | 26.9012 | 0.7502 | 0.7462 | 0.7399 | 0.7207 | 0.6960 | - | - | - | - | - |
| 4.1420 | 700 | 18.3 | 29.0209 | 0.7496 | 0.7454 | 0.7432 | 0.7284 | 0.7065 | - | - | - | - | - |
| 4.7337 | 800 | 17.6496 | 28.9536 | 0.7532 | 0.7482 | 0.7451 | 0.7304 | 0.7070 | - | - | - | - | - |
| 5.0 | 845 | - | - | - | - | - | - | - | 0.8767 | 0.8752 | 0.8702 | 0.8617 | 0.8420 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0
- 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}
}
```
#### 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},
}
```
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## Glossary
*Clearly define terms in order to be accessible across audiences.*
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|
[
"CPI"
] |
Weyaxi/Einstein-v7-Qwen2-7B
|
Weyaxi
|
text-generation
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"axolotl",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"science",
"physics",
"chemistry",
"biology",
"math",
"qwen",
"conversational",
"en",
"dataset:allenai/ai2_arc",
"dataset:camel-ai/physics",
"dataset:camel-ai/chemistry",
"dataset:camel-ai/biology",
"dataset:camel-ai/math",
"dataset:metaeval/reclor",
"dataset:openbookqa",
"dataset:mandyyyyii/scibench",
"dataset:derek-thomas/ScienceQA",
"dataset:TIGER-Lab/ScienceEval",
"dataset:jondurbin/airoboros-3.2",
"dataset:LDJnr/Capybara",
"dataset:Cot-Alpaca-GPT4-From-OpenHermes-2.5",
"dataset:STEM-AI-mtl/Electrical-engineering",
"dataset:knowrohit07/saraswati-stem",
"dataset:sablo/oasst2_curated",
"dataset:lmsys/lmsys-chat-1m",
"dataset:TIGER-Lab/MathInstruct",
"dataset:bigbio/med_qa",
"dataset:meta-math/MetaMathQA-40K",
"dataset:piqa",
"dataset:scibench",
"dataset:sciq",
"dataset:Open-Orca/SlimOrca",
"dataset:migtissera/Synthia-v1.3",
"dataset:allenai/WildChat",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:openchat/openchat_sharegpt4_dataset",
"dataset:teknium/GPTeacher-General-Instruct",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:totally-not-an-llm/EverythingLM-data-V3",
"dataset:HuggingFaceH4/no_robots",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"dataset:WizardLM/WizardLM_evol_instruct_70k",
"dataset:abacusai/SystemChat-1.1",
"dataset:H-D-T/Buzz-V1.2",
"base_model:Qwen/Qwen2-7B",
"base_model:finetune:Qwen/Qwen2-7B",
"license:other",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-06-24T20:01:15Z |
2024-07-23T21:10:17+00:00
| 2,124 | 38 |
---
base_model: Qwen/Qwen2-7B
datasets:
- allenai/ai2_arc
- camel-ai/physics
- camel-ai/chemistry
- camel-ai/biology
- camel-ai/math
- metaeval/reclor
- openbookqa
- mandyyyyii/scibench
- derek-thomas/ScienceQA
- TIGER-Lab/ScienceEval
- jondurbin/airoboros-3.2
- LDJnr/Capybara
- Cot-Alpaca-GPT4-From-OpenHermes-2.5
- STEM-AI-mtl/Electrical-engineering
- knowrohit07/saraswati-stem
- sablo/oasst2_curated
- lmsys/lmsys-chat-1m
- TIGER-Lab/MathInstruct
- bigbio/med_qa
- meta-math/MetaMathQA-40K
- openbookqa
- piqa
- metaeval/reclor
- derek-thomas/ScienceQA
- scibench
- sciq
- Open-Orca/SlimOrca
- migtissera/Synthia-v1.3
- TIGER-Lab/ScienceEval
- allenai/WildChat
- microsoft/orca-math-word-problems-200k
- openchat/openchat_sharegpt4_dataset
- teknium/GPTeacher-General-Instruct
- m-a-p/CodeFeedback-Filtered-Instruction
- totally-not-an-llm/EverythingLM-data-V3
- HuggingFaceH4/no_robots
- OpenAssistant/oasst_top1_2023-08-25
- WizardLM/WizardLM_evol_instruct_70k
- abacusai/SystemChat-1.1
- H-D-T/Buzz-V1.2
language:
- en
license: other
tags:
- axolotl
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- science
- physics
- chemistry
- biology
- math
- qwen
- qwen2
model-index:
- name: Einstein-v7-Qwen2-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 41.0
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v7-Qwen2-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 32.84
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v7-Qwen2-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 15.18
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v7-Qwen2-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 6.6
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v7-Qwen2-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 14.06
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v7-Qwen2-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 34.4
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v7-Qwen2-7B
name: Open LLM Leaderboard
---
# 🔬 Einstein-v7-Qwen2-7B
This model is a full fine-tuned version of [Qwen/Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) on diverse datasets.
This model is finetuned using `8xMI300X` using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl).
This model has been trained using compute resources from [TensorWave](https://tensorwave.com/).
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: Qwen/Qwen2-7B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
chat_template: chatml
datasets:
- path: data/airoboros_3.2_without_contextual_slimorca_orca_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/allenai_wild_chat_gpt4_english_toxic_random_half_4k_sharegpt.json
ds_type: json
type: sharegpt
strict: false
conversation: chatml
- path: data/buzz_unstacked_chosen_math_removed_filtered.json
ds_type: json
type: alpaca
conversation: chatml
- path: data/capybara_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/cot_alpaca_gpt4_extracted_openhermes_2.5_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/everythinglm-data-v3_sharegpt.json
ds_type: json
type: sharegpt
strict: false
conversation: chatml
- path: data/gpt4_data_lmys_1m_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/gpteacher-instruct-special-alpaca.json
ds_type: json
type: gpteacher
conversation: chatml
- path: data/merged_all.json
ds_type: json
type: alpaca
conversation: chatml
- path: data/no_robots_sharegpt.json
ds_type: json
type: sharegpt
strict: false
conversation: chatml
- path: data/oasst_top1_from_fusechatmixture_sharegpt.json
ds_type: json
type: sharegpt
strict: false
conversation: chatml
- path: data/pippa_bagel_repo_3k_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/rpguild_quarter_alignment_lab_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/sharegpt_gpt4_english.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/slimorca_dedup_filtered_95k_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/soda_diaolog_longest_tenth_buzz_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/synthia-v1.3_sharegpt_12500.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/system_conversations_dolphin_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
dataset_prepared_path: last_run_prepared
val_set_size: 0.002
output_dir: ./Einstein-v7-Qwen2-7B-model
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
wandb_project: Einstein
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
hub_model_id: Weyaxi/Einstein-v7-Qwen2-7B
gradient_accumulation_steps: 4
micro_batch_size: 6
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.00001 # look
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: unsloth
gradient_checkpointing_kwargs:
use_reentrant: true # look
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|end_of_text|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
```
</details><br>
# 💬 Prompt Template
You can use ChatML prompt template while using the model:
### ChatML
```
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>
```
This prompt template is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are helpful AI asistant."},
{"role": "user", "content": "Hello!"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
# 📊 Datasets used in this model
The datasets used to train this model are listed in the metadata section of the model card.
Please note that certain datasets mentioned in the metadata may have undergone filtering based on various criteria.
The results of this filtering process and its outcomes are in a diffrent repository:
[Weyaxi/sci-datasets/main](https://huggingface.co/datasets/Weyaxi/sci-datasets/tree/main)
# 🔄 Quantizationed versions
## GGUF [@bartowski](https://huggingface.co/bartowski)
- https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF
## ExLlamaV2 [@bartowski](https://huggingface.co/bartowski)
- https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-exl2
# 🎯 [Open LLM Leaderboard v2 Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Einstein-v7-Qwen2-7B)
| Metric |Value|
|-------------------|----:|
|Avg. |24.01|
|IFEval (0-Shot) |41.00|
|BBH (3-Shot) |32.84|
|MATH Lvl 5 (4-Shot)|15.18|
|GPQA (0-shot) | 6.60|
|MuSR (0-shot) |14.06|
|MMLU-PRO (5-shot) |34.40|
# 📚 Some resources, discussions and reviews aboout this model
#### 🐦 Announcement tweet:
- https://twitter.com/Weyaxi/status/1809644014515154961
#### 🔍 Reddit post in r/LocalLLaMA:
- https://www.reddit.com/r/LocalLLaMA/comments/1dy6o4l/introducing_einstein_v7_based_on_the_qwen2_7b/
# 🤖 Additional information about training
This model is full fine-tuned for 2 epoch.
Total number of steps was 500.
<details><summary>Loss graph</summary>
</details><br>
# 🤝 Acknowledgments
Thanks to all the dataset authors mentioned in the datasets section.
Thanks to [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) for making the repository I used to make this model.
Thanks to all open source AI community.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
If you would like to support me:
[☕ Buy Me a Coffee](https://www.buymeacoffee.com/weyaxi)
|
[
"SCIQ"
] |
Corianas/1.3b
|
Corianas
|
text-generation
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"en",
"dataset:tatsu-lab/alpaca",
"arxiv:1910.09700",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-03-29T14:28:32Z |
2023-11-18T00:09:08+00:00
| 2,114 | 2 |
---
datasets:
- tatsu-lab/alpaca
language:
- en
license: cc-by-nc-4.0
---
# Model Card for Cerebras 1.3b Dollyfied.
This is a finetuned model of Cerebras 1.3B model using DataBricksLabs Dolly Framework
# Example output:
Instruction: Write a love letter to Edgar Allan Poe.
Edgar Allan Poe is here again, with a brand new treasure of tales for you to enjoy. First, a man's life as a fallen angel, then his lonely falls into the sea, and finally a journey through the wild, exciting lands of the American West. He also carries a mysterious message for us: never stop searching for our own sweet. Thank you, Edgar Allan Poe!
-----------
Instruction: Write a tweet announcing Dolly, a large language model from Databricks.
Dolly a brand new model from Databricks that has been thoroughly validated and improved with feedback from experts and users! #databricks #languagemodels #databrickslanguage
-----------
Instruction: I'm selling my Nikon D-750, write a short blurb for my ad.
We bring you the revolutionary Nikon D-750 - the perfect camera for any shoot. With the Nikon D-750, you'll be able to achieve stunning images with state-of-the-art features and brilliant images. The Nikon D-750 is the perfect camera for everyday work, and it produces brilliant, vibrant images with spectacular clarity. Whether you’re a professional, a hobbyist, or an enthusiast, the Nikon D-750 is the perfect camera for you. Get yours today!
-----------
Instruction: Explain to me the difference between nuclear fission and fusion.
Nuclear fission and fusion are both processes that use nuclear material and energy to generate heat. Fission, which occurs at high enough temperatures, produces either a proton or a neutron with the same energy level. Fusion, on the other hand, relies on a hydrogen atom and is considered to be the most stable process. Nuclear fission and fusion produce different amounts of heat, which is why nuclear fusion can be used to generate heat energy for a variety of applications.
-----------
Instruction: Give me a list of 5 science fiction books I should read next.
1. The coming of the Red Planet by Guy Adams
2. The Hitchhiker's Guide to the Galaxy by Douglas Adams
3. Out of the Ashes by Douglas Adams
4. The Hitchhiker's Guide to the Galaxy episode "The Ancient of Days"
5. Icy Girls by J.R.R. Tolkien
-----------
## Model Details
### Model Description
This is a finetuned version of cerebras' 1.3Billion paramater model that has been trained to follow instructions.
It was accomplished using DataBricks Dolly training tools.
- **Developed by:** Finetuned by Corianas (me) using open source tools
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** EN
- **License:** cc-by-nc-4.0
- **Finetuned from model:** https://huggingface.co/cerebras/Cerebras-GPT-1.3b
- **Finetuned using:** https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html
## Uses
This is a simple GPT chatbot that has been finetuned to understand instructions.
Its knowledge about facts about the world is should be considered suspect at best.
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** 8xA100
- **Hours used:** 30mins
- **Cloud Provider:** Lambda GPU
## Technical Specifications [optional]
### Model Architecture and Objective
GPT2 Cerebras-GPT 1.3B
Layers 24
n_embd 2048
Heads 16
#### Hardware
8xA100s
#### Software
https://github.com/databrickslabs/dolly
## Model Card Contact
[More Information Needed]
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Corianas__1.3b)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 27.02 |
| ARC (25-shot) | 27.3 |
| HellaSwag (10-shot) | 38.3 |
| MMLU (5-shot) | 26.77 |
| TruthfulQA (0-shot) | 39.02 |
| Winogrande (5-shot) | 53.04 |
| GSM8K (5-shot) | 0.15 |
| DROP (3-shot) | 4.57 |
|
[
"BLURB"
] |
aisingapore/gemma2-9b-cpt-sea-lionv3-instruct
|
aisingapore
|
text-generation
|
[
"transformers",
"safetensors",
"gemma2",
"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/gemma2-9b-cpt-sea-lionv3-base",
"base_model:finetune:aisingapore/gemma2-9b-cpt-sea-lionv3-base",
"license:gemma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-10-30T03:19:20Z |
2024-12-19T12:57:01+00:00
| 2,114 | 10 |
---
base_model:
- aisingapore/gemma2-9b-cpt-sea-lionv3-base
language:
- en
- zh
- vi
- id
- th
- fil
- ta
- ms
- km
- lo
- my
- jv
- su
library_name: transformers
license: gemma
pipeline_tag: text-generation
---
<div>
<img src="gemma_2_9b_sea-lion_v3_instruct_banner.png"/>
</div>
# Gemma2 9B 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.
Gemma2 9B CPT SEA-LIONv3 Instruct is a multilingual model which has been fine-tuned with around **500,000 English instruction-completion pairs** alongside a larger pool of around **1,000,000 instruction-completion pairs** from other ASEAN languages, such as Indonesian, 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:** [Gemma Community License](https://ai.google.dev/gemma/terms)
## Model Details
### Model Description
We performed instruction tuning in English and also in ASEAN languages such as Indonesian, Thai and Vietnamese on our [continued pre-trained Gemma2 9B CPT SEA-LIONv3](https://huggingface.co/aisingapore/gemma2-9b-cpt-sea-lionv3-base), a decoder model using the Gemma2 architecture, to create Gemma2 9B CPT SEA-LIONv3 Instruct.
For tokenisation, the model employs the default tokenizer used in Gemma-2-9B. The model has a context length of 8192.
### Benchmark Performance
We evaluated Gemma2 9B 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 Summarization (Summ), 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 Gemma2 9B CPT SEA-LIONv3 Instruct is an instruction-following model, we also evaluated it on instruction-following capabilities with two datasets, [IFEval](https://arxiv.org/abs/2311.07911) and [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, localize and translate the datasets into the respective target languages to ensure that the examples remained reasonable, meaningful and natural.
**IFEval**
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 normalized 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).
**MT-Bench**
MT-Bench 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 Gemma2 9B CPT SEA-LIONv3 Instruct benchmark performance, please refer to the SEA HELM leaderboard, https://leaderboard.sea-lion.ai/
### Usage
**NOTE** This model has not been trained to use a system prompt or to use tool calling.
Gemma2 9B CPT SEA-LIONv3 Instruct can be run using the 🤗 Transformers library
```python
# Please use transformers==4.45.2
import transformers
import torch
model_id = "aisingapore/gemma2-9b-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
Gemma2 9B CPT SEA-LIONv3 Instruct was built 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 15 hours, with alignment taking 2 hours, both on 8x H100-80GB GPUs.
## Data
Gemma2 9B 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.
## Indonesian, Javanese & Sudanese Specific SEA-LION
Our partners at GoTo have continued pretrained and instruction tuned a variant of Gemma2 9B CPT SEA-LIONv3, specifically enhancing its capabilities for Indonesian, Javanese, and Sundanese languages. Find the continued pretrained model at [Gemma2 9B CPT SahabatAIv1 Base](https://huggingface.co/GoToCompany/gemma2-9b-cpt-sahabatai-v1-base), and its corresponding instructioned tuned version at [Gemma2 9B CPT SahabatAIv1 Instruct](https://huggingface.co/GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct).
## 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.
|
[
"CHIA"
] |
Corianas/590m
|
Corianas
|
text-generation
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"en",
"dataset:tatsu-lab/alpaca",
"arxiv:1910.09700",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-03-29T14:38:42Z |
2023-11-18T00:09:05+00:00
| 2,102 | 0 |
---
datasets:
- tatsu-lab/alpaca
language:
- en
license: cc-by-nc-4.0
---
# Model Card for Model ID
This is a finetuned model of Cerebras 590M model using DataBricksLabs Dolly Framework
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Finetuned by Corianas (me) using open source tools
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** EN
- **License:** cc-by-nc-4.0
- **Finetuned from model [optional]:** https://huggingface.co/cerebras/Cerebras-GPT-590M
## Uses
This is a simple GPT chatbot that has been finetuned to understand instructions.
Its knowledge about facts about the world is should be considered almost non-existant.
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
Instruction: Write a love letter to Edgar Allan Poe.
Dear Edgar,
I hope you are doing well. As a person of your age and background, you are always learning new things and never too sure of your own best skills. May your future brighten up your days with the learning you have to offer. I can't wait to read your book and see the way it influenced you.
Thanks for the inspiration. It is so much fun to explore your special talents.
Sincerely,
-----------
Instruction: Write a tweet announcing Dolly, a large language model from Databricks.
Dolly is the world's first massive language model, available now in India. #IndiaDolly #Databricks
-----------
Instruction: I'm selling my Nikon D-750, write a short blurb for my ad.
I'm selling my Nikon D-750, someone whose dedication to imaging means he/she is always ready to answer questions, be creative and take chances on what really matters. A custom-designed top hat, a wide-angle lens, and an affordable ultrabuck remote offer the ultimate in quality. He/She can even go as far as to order a custom-made outdoor halogen pass. And if he/She is more than willing to take on any challenge, there is no doubt that with a creative lens, personal shots, and a custom top hat, photography is at its peak. As a satisfied customer, I would
-----------
Instruction: Explain to me the difference between nuclear fission and fusion.
Nuclear fission is a process that occurs during the production of nuclear fuel, while fusion is a process that occurs in the fusion industry. Nuclear fission is used to create the active ingredient in nuclear fuel, such as fission products, fusion plutonium, and decontamination services. Fusion requires a lower pressure in the fusion process and is more flexible compared to nuclear fission. Nuclear fission fuses with fission products, fission plutonium, and decontamination services.
-----------
Instruction: Give me a list of 5 science fiction books I should read next.
1. How to Translate a Face into a System
2. How to Fly a Surrealist
3. How to Survive an Army Wildlife Hunt
4. How to Strike an iPhone
5. How to Teach a Hackathon
-----------
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Corianas__590m)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 25.62 |
| ARC (25-shot) | 24.15 |
| HellaSwag (10-shot) | 31.91 |
| MMLU (5-shot) | 26.61 |
| TruthfulQA (0-shot) | 42.19 |
| Winogrande (5-shot) | 48.38 |
| GSM8K (5-shot) | 0.08 |
| DROP (3-shot) | 6.03 |
|
[
"BLURB"
] |
Undi95/Dawn-v2-70B
|
Undi95
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"nsfw",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-11-06T15:16:55Z |
2023-11-08T22:34:24+00:00
| 2,097 | 10 |
---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- nsfw
---
<center>[<a href="https://huggingface.co/Undi95/Dawn-v2-70B">fp16</a> - <a href="https://huggingface.co/Undi95/Dawn-v2-70B-GGUF">gguf</a> - exl2 : <a href="https://huggingface.co/Undi95/Dawn-v2-70B-2.55bpw-h6-exl2">2.55bpw</a>]</center>
</br>
<div style="width: 100%;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/Cxcfqi4WdtXCNLnaIqSRB.png" style="width: 75%; min-width: 200px; display: block; margin: auto;">
</div>
<!-- description start -->
## Description
This repo contains fp16 files of Dawn-70B, a merge I have done with the new [layer shuffle](https://github.com/cg123/mergekit/blob/main/mergekit/scripts/layershuffle.py) method from mergekit.
[UtopiaXL](https://huggingface.co/Undi95/UtopiaXL-13B) was a huge success for me, I really liked it, so I took the same path to do this 70B: A good base, some psychologic data, some medical data, a little bit of this, of that, and LimaRP at the end as always.
NOTE: This repo contain the file [measurement.json](https://huggingface.co/Undi95/Dawn-v2-70B/blob/main/measurement.json) needed to do your own exl2 quant (I use [wikitext](https://huggingface.co/datasets/wikitext/resolve/refs%2Fconvert%2Fparquet/wikitext-2-raw-v1/train/0000.parquet)).
<!-- description end -->
<!-- description start -->
## Models and loras used
- [Sao10K/Euryale-1.3-L2-70B](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B)
- [Xwin-LM/Xwin-LM-70B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1)
- [ehartford/Samantha-1.11-70b](https://huggingface.co/ehartford/Samantha-1.11-70b)
- [NousResearch/Nous-Hermes-Llama2-70b](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-70b)
- [augtoma/qCammel-70-x](https://huggingface.co/augtoma/qCammel-70-x)
- [jondurbin/airoboros-l2-c70b-3.1.2](https://huggingface.co/jondurbin/airoboros-l2-c70b-3.1.2)
- [fangloveskari/ORCA_LLaMA_70B_QLoRA](https://huggingface.co/fangloveskari/ORCA_LLaMA_70B_QLoRA)
- [Doctor-Shotgun/limarpv3-llama2-70b-qlora](https://huggingface.co/Doctor-Shotgun/limarpv3-llama2-70b-qlora)
<!-- description end -->
## The sauce
```
!mergekit-layershuffle ./Dawn-v2-70B \
--model Sao10K/Euryale-1.3-L2-70B --weight 0.3 \
--model Xwin-LM/Xwin-LM-70B-V0.1 --weight 0.2 \
--model ehartford/Samantha-1.11-70b --weight 0.1 \
--model NousResearch/Nous-Hermes-Llama2-70b --weight 0.05 \
--model augtoma/qCammel-70-x --weight 0.05 \
--model jondurbin/airoboros-l2-c70b-3.1.2 --weight 0.2 \
--model fangloveskari/ORCA_LLaMA_70B_QLoRA --weight 0.1 \
--write-yaml Dawn-v2-70B.yaml
=========================
merge_method: passthrough
slices:
- sources:
- layer_range:
- 0
- 1
model: fangloveskari/ORCA_LLaMA_70B_QLoRA
- sources:
- layer_range:
- 1
- 2
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 2
- 3
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 3
- 4
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 4
- 5
model: fangloveskari/ORCA_LLaMA_70B_QLoRA
- sources:
- layer_range:
- 5
- 6
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 6
- 8
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 8
- 9
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 9
- 10
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 10
- 11
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 11
- 12
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 12
- 13
model: fangloveskari/ORCA_LLaMA_70B_QLoRA
- sources:
- layer_range:
- 13
- 14
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 14
- 15
model: fangloveskari/ORCA_LLaMA_70B_QLoRA
- sources:
- layer_range:
- 15
- 16
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 16
- 17
model: fangloveskari/ORCA_LLaMA_70B_QLoRA
- sources:
- layer_range:
- 17
- 18
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 18
- 19
model: NousResearch/Nous-Hermes-Llama2-70b
- sources:
- layer_range:
- 19
- 20
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 20
- 21
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 21
- 22
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 22
- 23
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 23
- 24
model: augtoma/qCammel-70-x
- sources:
- layer_range:
- 24
- 25
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 25
- 27
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 27
- 28
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 28
- 29
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 29
- 30
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 30
- 32
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 32
- 33
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 33
- 34
model: augtoma/qCammel-70-x
- sources:
- layer_range:
- 34
- 35
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 35
- 37
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 37
- 38
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 38
- 39
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 39
- 40
model: augtoma/qCammel-70-x
- sources:
- layer_range:
- 40
- 41
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 41
- 42
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 42
- 43
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 43
- 44
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 44
- 45
model: NousResearch/Nous-Hermes-Llama2-70b
- sources:
- layer_range:
- 45
- 46
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 46
- 48
model: ehartford/Samantha-1.11-70b
- sources:
- layer_range:
- 48
- 49
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 49
- 50
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 50
- 51
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 51
- 54
model: fangloveskari/ORCA_LLaMA_70B_QLoRA
- sources:
- layer_range:
- 54
- 55
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 55
- 56
model: fangloveskari/ORCA_LLaMA_70B_QLoRA
- sources:
- layer_range:
- 56
- 58
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 58
- 59
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 59
- 60
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 60
- 62
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 62
- 63
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 63
- 64
model: fangloveskari/ORCA_LLaMA_70B_QLoRA
- sources:
- layer_range:
- 64
- 65
model: NousResearch/Nous-Hermes-Llama2-70b
- sources:
- layer_range:
- 65
- 66
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 66
- 67
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 67
- 68
model: augtoma/qCammel-70-x
- sources:
- layer_range:
- 68
- 70
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 70
- 71
model: augtoma/qCammel-70-x
- sources:
- layer_range:
- 71
- 72
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 72
- 73
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 73
- 75
model: jondurbin/airoboros-l2-c70b-3.1.2
- sources:
- layer_range:
- 75
- 76
model: Sao10K/Euryale-1.3-L2-70B
- sources:
- layer_range:
- 76
- 77
model: augtoma/qCammel-70-x
- sources:
- layer_range:
- 77
- 78
model: Xwin-LM/Xwin-LM-70B-V0.1
- sources:
- layer_range:
- 78
- 79
model: NousResearch/Nous-Hermes-Llama2-70b
- sources:
- layer_range:
- 79
- 80
model: Xwin-LM/Xwin-LM-70B-V0.1
=========================
=> Applying Doctor-Shotgun/limarpv3-llama2-70b-qlora x 0.35
```
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
A big thanks to [Charles](https://huggingface.co/chargoddard) for adding the layer shuffle method to his tool [mergekit](https://github.com/cg123/mergekit/tree/main) and [Henky/KoboldAI](https://koboldai.org/) for the machine he let me use.
If you want to support me, you can [here](https://ko-fi.com/undiai).
|
[
"MEDICAL DATA"
] |
declare-lab/TangoFlux
|
declare-lab
|
text-to-audio
|
[
"text-to-audio",
"dataset:cvssp/WavCaps",
"arxiv:2412.21037",
"endpoints_compatible",
"region:us"
] | 2024-12-24T07:53:05Z |
2025-01-22T14:39:59+00:00
| 2,091 | 89 |
---
datasets:
- cvssp/WavCaps
pipeline_tag: text-to-audio
cite: arxiv.org/abs/2412.21037
---
<h1 align="center">
TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization
</h1>
<div align="center">
<img src="https://raw.githubusercontent.com/declare-lab/TangoFlux/refs/heads/main/assets/tf_teaser.png" alt="TangoFlux" width="1000" />
<br/>
<div style="display: flex; gap: 10px; align-items: center;">
<a href="https://openreview.net/attachment?id=tpJPlFTyxd&name=pdf">
<img src="https://img.shields.io/badge/Read_the_Paper-blue?link=https%3A%2F%2Fopenreview.net%2Fattachment%3Fid%3DtpJPlFTyxd%26name%3Dpdf" alt="arXiv">
</a>
<a href="https://huggingface.co/declare-lab/TangoFlux">
<img src="https://img.shields.io/badge/TangoFlux-Huggingface-violet?logo=huggingface&link=https%3A%2F%2Fhuggingface.co%2Fdeclare-lab%2FTangoFlux" alt="Static Badge">
</a>
<a href="https://tangoflux.github.io/">
<img src="https://img.shields.io/badge/Demos-declare--lab-brightred?style=flat" alt="Static Badge">
</a>
<a href="https://huggingface.co/spaces/declare-lab/TangoFlux">
<img src="https://img.shields.io/badge/TangoFlux-Huggingface_Space-8A2BE2?logo=huggingface&link=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fdeclare-lab%2FTangoFlux" alt="Static Badge">
</a>
<a href="https://huggingface.co/datasets/declare-lab/CRPO">
<img src="https://img.shields.io/badge/TangoFlux_Dataset-Huggingface-red?logo=huggingface&link=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Fdeclare-lab%2FTangoFlux" alt="Static Badge">
</a>
<a href="https://github.com/declare-lab/TangoFlux">
<img src="https://img.shields.io/badge/Github-brown?logo=github&link=https%3A%2F%2Fgithub.com%2Fdeclare-lab%2FTangoFlux" alt="Static Badge">
</a>
</div>
</div>
* Powered by **Stability AI**
## Model Overview
TangoFlux consists of FluxTransformer blocks which are Diffusion Transformer (DiT) and Multimodal Diffusion Transformer (MMDiT), conditioned on textual prompt and duration embedding to generate audio at 44.1kHz up to 30 seconds. TangoFlux learns a rectified flow trajectory from audio latent representation encoded by a variational autoencoder (VAE). The TangoFlux training pipeline consists of three stages: pre-training, fine-tuning, and preference optimization. TangoFlux is aligned via CRPO which iteratively generates new synthetic data and constructs preference pairs to perform preference optimization.
## Getting Started
Get TangoFlux from our GitHub repo https://github.com/declare-lab/TangoFlux with
```bash
pip install git+https://github.com/declare-lab/TangoFlux
```
The model will be automatically downloaded and saved in a cache. The subsequent runs will load the model directly from the cache.
The `generate` function uses 25 steps by default to sample from the flow model. We recommend using 50 steps for generating better quality audios. This comes at the cost of increased run-time.
```python
import torchaudio
from tangoflux import TangoFluxInference
from IPython.display import Audio
model = TangoFluxInference(name='declare-lab/TangoFlux')
audio = model.generate('Hammer slowly hitting the wooden table', steps=50, duration=10)
Audio(data=audio, rate=44100)
```
## License
The TangoFlux checkpoints are for non-commercial research use only. They are subject to the [Stable Audio Open’s license](https://huggingface.co/stabilityai/stable-audio-open-1.0/blob/main/LICENSE.md), [WavCap’s license](https://github.com/XinhaoMei/WavCaps?tab=readme-ov-file#license), and the original licenses accompanying each training dataset.
This Stability AI Model is licensed under the Stability AI Community License, Copyright © Stability AI Ltd. All Rights Reserved
## Citation
https://arxiv.org/abs/2412.21037
```bibtex
@misc{hung2024tangofluxsuperfastfaithful,
title={TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization},
author={Chia-Yu Hung and Navonil Majumder and Zhifeng Kong and Ambuj Mehrish and Rafael Valle and Bryan Catanzaro and Soujanya Poria},
year={2024},
eprint={2412.21037},
archivePrefix={arXiv},
primaryClass={cs.SD},
url={https://arxiv.org/abs/2412.21037},
}
```
|
[
"CHIA"
] |
crumb/gpt2023
|
crumb
|
text-generation
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"causal-lm",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-04-30T02:23:04Z |
2024-01-04T17:15:11+00:00
| 2,082 | 17 |
---
language:
- en
license: mit
tags:
- causal-lm
---
# GPT2(023) Model Card
This is the smallest GPT-2 model (124m) from OpenAi finetuned on approximately 2.23B tokens (almost the 2.48B needed to 'chinchilla-optimally' pretrain it! It's also more tokens than Cerebras-GPT-111M was trained on in total) consisting of 1.3B from common crawl sites from 2023, 540M from ArXiv, and 390M from GitHub.
The model was trained with a learning rate of 1e-4, with a warmup of 1024 steps, then decaying to 0. There were 4400 total steps during training at a batch size of 512 examples with a context length of 1024. The batch size and context length are the same as the pre-training of GPT2 itself. Training took a total of 1.18e+18 FLOs over the course of 79.32 hours locally with a 12gb RTX3060. Final train loss was 2.73.
### Evaluation of GPT2023
*(in progress)*
| model | piqa acc | winogrande acc | lambada ppl | lambada acc | arc acc | sciq acc | wsc acc |
| --- | --- | --- | --- | --- | --- | --- | --- |
| pythia-70m | 59.85 | 51.22 | 140.81 | 21.40 | 17.15 | 65.00 | 36.53 |
| pythia-160m | 62.68 | 51.07 | 30.03 | 36.76 | 19.62 | 76.20 | 36.58 |
| pythia-410m | 66.54 | 52.24 | 11.75 | 49.93 | 21.67 | 80.80 | 60.58 |
| opt-125m | 63.00 | 50.27 | 26.02 | 37.90 | 18.94 | 75.1 | 36.54 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| gpt2 (124m) | **62.89** | **51.61** | 40.06 | 32.56 | **19.03** | 75 | **43.27** |
| gpt2023 (124m) | 62.02 | 49.64 | **34.55** | **33.98** | 18.94 | **76.1** | 36.54 |
The resulting model achieves a puplexity of 339.38, making it competative with Cerebras-590m with only 21% of the parameters, and much better than the original GPT-2 which scores 491.57!
(metric explanation here: https://twitter.com/aicrumb/status/1650350363898265601 , tldr it's a joke)
To demonstrate how GPT2(023) is aware of recent events, let’s take a look at a given example:
```
# About Covid-19
- -
The Covid-19
```
The model completes the text as:
```
# About Covid-19
- -
The Covid-19 pandemic is the worldwide pandemic that has left thousands of people unable to enter and work in or continue their normal daily normal life. In this brief post, we examine three of the main factors that have accelerated the pandemic and predict the path the pandemic will take through the rest of the world.
```
As you can see, GPT2(023) can generate coherent and relevant text pertaining to the Covid-19 pandemic, showcasing its ability to understand recent events. However, it struggles with certain subjects that weren’t extremely relevant in it’s training data. As only 2.23 billion tokens were used during finetuning, the model may have missed out on many recent events. One of those events being the latest US election.
Given text in a question and answer format:
```
Q: Who is the last president?
A: Donald Trump
Q: Who is the most recent president?
A:
```
The model completes the text with: `Barack Obama`
### Model description
*(from GPT-2 model card)*
GPT-2 is a transformer model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token i only uses the inputs from 1 to i but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt.
This is the smallest version of GPT-2, with 124M parameters.
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='crumb/gpt2023')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
{'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
{'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},
{'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},
{'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('crumb/gpt2023')
model = GPT2Model.from_pretrained('crumb/gpt2023')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
### Limitations and bias
The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases):
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
> that require the generated text to be true.
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
> not recommend that they be deployed into systems that interact with humans unless the deployers first carry out a
> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
> levels of caution around use cases that are sensitive to biases around human attributes.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_crumb__gpt2023)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 24.85 |
| ARC (25-shot) | 21.93 |
| HellaSwag (10-shot) | 31.11 |
| MMLU (5-shot) | 25.05 |
| TruthfulQA (0-shot) | 40.71 |
| Winogrande (5-shot) | 50.12 |
| GSM8K (5-shot) | 0.3 |
| DROP (3-shot) | 4.73 |
|
[
"SCIQ"
] |
Yntec/C-.-_-.-Aravaggio
|
Yntec
|
text-to-image
|
[
"diffusers",
"safetensors",
"Base model",
"General",
"Everything",
"Redigleb_Doppler2482",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-12-21T01:09:00Z |
2023-12-21T01:37:47+00:00
| 2,079 | 4 |
---
library_name: diffusers
license: creativeml-openrail-m
pipeline_tag: text-to-image
tags:
- Base model
- General
- Everything
- Redigleb_Doppler2482
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
# C++AravaggioV0.9 - an Answer to both Dall-E and Kandinsky 2.1
Original page: https://civitai.com/models/93155/caravaggiov09-an-answer-to-both-dall-e-and-kandinsky-21?modelVersionId=99323
Samples and prompts:
Top left: Anime fine details portrait of joyful cute little girl lay school class room, bokeh. anime masterpiece by studio ghibli. 8k, sharp high quality classic anime from 1990 in style of hayao miyazaki. Wikipedia. hugging. OIL PAINTING. DOCTOR with short hair in coat BEAUTIFUL girl eyes. she has pigtails
Top right: House with a waterwheel built into the roots of a giant tree, next to games, a colorful river landscape painting from a fantasy point and click 2 d graphic adventure game, art inspired by ROSSDRAWS and larry elmore and john shroades, king's quest, sierra entertainment
Bottom left: An underwater world with vibrant coral reefs and schools of colorful fish. The artistic style is pop art, with bold and bright colors and graphic shapes. The light setting is filtered through the water, creating a surreal and dreamy effect. The mood of the image is energetic and lively, capturing the movement and vitality of the underwater environment.
Bottom right: pretty young girl riding bike down the ocean streets of japan, teddy bear hour
|
[
"BEAR"
] |
Viet-Mistral/Vistral-7B-Chat
|
Viet-Mistral
|
text-generation
|
[
"transformers",
"pytorch",
"safetensors",
"mistral",
"text-generation",
"LLMs",
"NLP",
"Vietnamese",
"Large Language Models",
"conversational",
"vi",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-01-14T02:10:38Z |
2024-02-27T19:49:13+00:00
| 2,079 | 133 |
---
language:
- vi
library_name: transformers
license: afl-3.0
tags:
- LLMs
- NLP
- Vietnamese
- Large Language Models
extra_gated_prompt: You agree not to use the model for experiments that could harm
human subjects.
extra_gated_fields:
Name: text
Email: text
Affiliation: text
Country: text
I agree to the LICENSE of this model: checkbox
---
<h1>Vistral-7B-Chat - Towards a State-of-the-Art Large Language Model for Vietnamese</h1>
## Model Description
We introduce Vistral-7B-chat, a multi-turn conversational large language model for Vietnamese. Vistral is extended from the [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) model using diverse data for continual pre-training and instruction tuning. In particular, our process to develop Vistral involves:
1. Extend the tokenizer of Mistral 7B to better support Vietnamese.
2. Perform continual pre-training for Mistral over a diverse dataset of Vietnamese texts that are meticulously cleaned and deduplicated.
3. Perform supervised fine-tuning for the model using diverse instruction data. We design a set of instructions to align the model with the safety criteria in Vietnam.
GGUF Version: Running Vistral on **your local computer** [here](https://huggingface.co/chiennv/Vistral-7B-Chat-gguf)
**Note**: To deploy Vistral locally (e.g. on LM Studio), make sure that you are utilizing the specified chat template, download [here](https://huggingface.co/uonlp/Vistral-7B-Chat-gguf/blob/main/template_chat.json). This step is very crucial to ensure that Vistral generates accurate answers.
### Acknowledgement:
We thank Hessian AI and LAION for their support and compute in order to train this model. Specifically, we gratefully acknowledge LAION for providing access to compute budget granted by Gauss Centre for Supercomputing e.V. and by the John von Neumann Institute for Computing (NIC) on the supercomputers JUWELS Booster and JURECA at Juelich Supercomputing Centre (JSC).
### Data
We will make the data available after we release the technical report for this model. However, we have made some of the data available here in our [CulutraY](https://huggingface.co/datasets/ontocord/CulturaY) and [CulutraX](https://huggingface.co/datasets/uonlp/CulturaX) datasets.
## Usage
To enable single/multi-turn conversational chat with `Vistral-7B-Chat`, you can use the default chat template format:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
system_prompt = "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.\n"
system_prompt += "Câu trả lời của bạn không nên chứa bất kỳ nội dung gây hại, phân biệt chủng tộc, phân biệt giới tính, độc hại, nguy hiểm hoặc bất hợp pháp nào. Hãy đảm bảo rằng các câu trả lời của bạn không có thiên kiến xã hội và mang tính tích cực."
system_prompt += "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. Nếu bạn không biết câu trả lời cho một câu hỏi, hãy trẳ lời là bạn không biết và vui lòng không chia sẻ thông tin sai lệch."
tokenizer = AutoTokenizer.from_pretrained('Viet-Mistral/Vistral-7B-Chat')
model = AutoModelForCausalLM.from_pretrained(
'Viet-Mistral/Vistral-7B-Chat',
torch_dtype=torch.bfloat16, # change to torch.float16 if you're using V100
device_map="auto",
use_cache=True,
)
conversation = [{"role": "system", "content": system_prompt }]
while True:
human = input("Human: ")
if human.lower() == "reset":
conversation = [{"role": "system", "content": system_prompt }]
print("The chat history has been cleared!")
continue
conversation.append({"role": "user", "content": human })
input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)
out_ids = model.generate(
input_ids=input_ids,
max_new_tokens=768,
do_sample=True,
top_p=0.95,
top_k=40,
temperature=0.1,
repetition_penalty=1.05,
)
assistant = tokenizer.batch_decode(out_ids[:, input_ids.size(1): ], skip_special_tokens=True)[0].strip()
print("Assistant: ", assistant)
conversation.append({"role": "assistant", "content": assistant })
```
## Performance
We evaluated our Vistral model using the [VMLU leaderboard](https://vmlu.ai/leaderboard), a reliable framework for evaluating large language models in Vietnamese across various tasks. These tasks involve multiple-choice questions in STEM, Humanities, Social Sciences, and more. Our model achieved an average score of 50.07%, surpassing ChatGPT's performance of 46.33% significantly.
<p align="center"> <img src="official_vmlu.png" width="650" /> </p>
**Disclaimer: Despite extensive red teaming and safety alignment efforts, our model may still pose potential risks, including but not limited to hallucination, toxic content, and bias issues. We strongly encourage researchers and practitioners to fully acknowledge these potential risks and meticulously assess and secure the model before incorporating it into their work. Users are responsible for adhering to and complying with their governance and regulations. The authors retain the right to disclaim any accountability for potential damages or liability resulting from the use of the model.**
## Citation
If you find our project useful, we hope you would kindly star our repo and cite our work as follows: [email protected], [email protected], [email protected] and [email protected]
```
@article{chien2023vistral,
author = {Chien Van Nguyen, Thuat Nguyen, Quan Nguyen, Huy Nguyen, Björn Plüster, Nam Pham, Huu Nguyen, Patrick Schramowski, Thien Nguyen},
title = {Vistral-7B-Chat - Towards a State-of-the-Art Large Language Model for Vietnamese},
year = 2023,
}
```
|
[
"CHIA"
] |
mncai/llama2-13b-dpo-v3
|
mncai
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"ko",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-12-03T09:31:48Z |
2023-12-14T03:35:30+00:00
| 2,076 | 3 |
---
language:
- en
- ko
license: cc-by-nc-sa-4.0
---
# Model Card for llama2-dpo-v3
### Introduction of MindsAndCompany
https://mnc.ai/
We develop a diverse range of AI models and craft solutions tailored for business applications. In the realm of generative AI, our product development includes the Code Assistant, the TOD Chatbot, and LLMOps. We are also actively working on the development of Enterprise AGI (Artificial General Intelligence).
### Model Summary
based beomi/llama-2-koen-13b, instruction tuned and dpo.
### How to Use
Here give some examples of how to use our model.
```python
from transformers import AutoConfig, AutoModel, AutoTokenizer
import transformers
import torch
hf_model = 'mncai/llama2-13b-dpo-v3'
message = "<|user|>\n두 개의 구가 있는데 각각 지름이 1, 2일때 구의 부피는 몇배 차이가 나지? 설명도 같이 해줘.\n<|assistant|>\n"
sequences = pipeline(
message,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
max_length=2048,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
### LICENSE
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License, under LLAMA 2 COMMUNITY LICENSE AGREEMENT
### Contact
If you have any questions, please raise an issue or contact us at [email protected]
|
[
"CRAFT"
] |
jinaai/jina-embedding-b-en-v1
|
jinaai
|
sentence-similarity
|
[
"sentence-transformers",
"pytorch",
"t5",
"finetuner",
"feature-extraction",
"sentence-similarity",
"mteb",
"custom_code",
"en",
"dataset:jinaai/negation-dataset",
"arxiv:2307.11224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-07-07T07:51:59Z |
2025-01-06T16:31:20+00:00
| 2,074 | 6 |
---
datasets:
- jinaai/negation-dataset
language: en
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- finetuner
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: jina-embedding-b-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: 66.73134328358208
- type: ap
value: 28.30575908745204
- type: f1
value: 60.02420130946191
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 67.6068
- type: ap
value: 63.5899352938589
- type: f1
value: 65.64285334357656
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 31.178
- type: f1
value: 29.68460843733487
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.964
- type: map_at_10
value: 40.217999999999996
- type: map_at_100
value: 41.263
- type: map_at_1000
value: 41.277
- type: map_at_3
value: 35.183
- type: map_at_5
value: 38.045
- type: mrr_at_1
value: 25.107000000000003
- type: mrr_at_10
value: 40.272999999999996
- type: mrr_at_100
value: 41.318
- type: mrr_at_1000
value: 41.333
- type: mrr_at_3
value: 35.242000000000004
- type: mrr_at_5
value: 38.101
- type: ndcg_at_1
value: 24.964
- type: ndcg_at_10
value: 49.006
- type: ndcg_at_100
value: 53.446000000000005
- type: ndcg_at_1000
value: 53.813
- type: ndcg_at_3
value: 38.598
- type: ndcg_at_5
value: 43.74
- type: precision_at_1
value: 24.964
- type: precision_at_10
value: 7.724
- type: precision_at_100
value: 0.966
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 16.169
- type: precision_at_5
value: 12.191
- type: recall_at_1
value: 24.964
- type: recall_at_10
value: 77.24
- type: recall_at_100
value: 96.586
- type: recall_at_1000
value: 99.431
- type: recall_at_3
value: 48.506
- type: recall_at_5
value: 60.953
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 39.25203906042786
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 29.07648348376354
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.4029266143623
- type: mrr
value: 75.45750340764191
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 85.92280995704714
- type: cos_sim_spearman
value: 83.58082010833608
- type: euclidean_pearson
value: 48.64744162695948
- type: euclidean_spearman
value: 48.817377397301556
- type: manhattan_pearson
value: 48.87684776623195
- type: manhattan_spearman
value: 48.94268145725884
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.05519480519482
- type: f1
value: 83.94978356890618
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 32.2033276486685
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 26.631954164406014
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.625
- type: map_at_10
value: 40.037
- type: map_at_100
value: 41.52
- type: map_at_1000
value: 41.654
- type: map_at_3
value: 36.818
- type: map_at_5
value: 38.426
- type: mrr_at_1
value: 35.336
- type: mrr_at_10
value: 45.395
- type: mrr_at_100
value: 46.221000000000004
- type: mrr_at_1000
value: 46.264
- type: mrr_at_3
value: 42.823
- type: mrr_at_5
value: 44.204
- type: ndcg_at_1
value: 35.336
- type: ndcg_at_10
value: 46.326
- type: ndcg_at_100
value: 51.795
- type: ndcg_at_1000
value: 53.834
- type: ndcg_at_3
value: 41.299
- type: ndcg_at_5
value: 43.247
- type: precision_at_1
value: 35.336
- type: precision_at_10
value: 8.627
- type: precision_at_100
value: 1.428
- type: precision_at_1000
value: 0.197
- type: precision_at_3
value: 19.647000000000002
- type: precision_at_5
value: 13.733999999999998
- type: recall_at_1
value: 29.625
- type: recall_at_10
value: 59.165
- type: recall_at_100
value: 81.675
- type: recall_at_1000
value: 94.17
- type: recall_at_3
value: 44.485
- type: recall_at_5
value: 50.198
- type: map_at_1
value: 26.687
- type: map_at_10
value: 36.062
- type: map_at_100
value: 37.263000000000005
- type: map_at_1000
value: 37.397999999999996
- type: map_at_3
value: 32.967
- type: map_at_5
value: 34.75
- type: mrr_at_1
value: 33.885
- type: mrr_at_10
value: 42.632999999999996
- type: mrr_at_100
value: 43.305
- type: mrr_at_1000
value: 43.354
- type: mrr_at_3
value: 39.958
- type: mrr_at_5
value: 41.63
- type: ndcg_at_1
value: 33.885
- type: ndcg_at_10
value: 42.001
- type: ndcg_at_100
value: 46.436
- type: ndcg_at_1000
value: 48.774
- type: ndcg_at_3
value: 37.183
- type: ndcg_at_5
value: 39.605000000000004
- type: precision_at_1
value: 33.885
- type: precision_at_10
value: 7.962
- type: precision_at_100
value: 1.283
- type: precision_at_1000
value: 0.18
- type: precision_at_3
value: 17.855999999999998
- type: precision_at_5
value: 13.083
- type: recall_at_1
value: 26.687
- type: recall_at_10
value: 52.75
- type: recall_at_100
value: 71.324
- type: recall_at_1000
value: 86.356
- type: recall_at_3
value: 38.83
- type: recall_at_5
value: 45.23
- type: map_at_1
value: 34.02
- type: map_at_10
value: 45.751999999999995
- type: map_at_100
value: 46.867
- type: map_at_1000
value: 46.93
- type: map_at_3
value: 42.409
- type: map_at_5
value: 44.464999999999996
- type: mrr_at_1
value: 38.307
- type: mrr_at_10
value: 48.718
- type: mrr_at_100
value: 49.509
- type: mrr_at_1000
value: 49.542
- type: mrr_at_3
value: 46.007999999999996
- type: mrr_at_5
value: 47.766999999999996
- type: ndcg_at_1
value: 38.307
- type: ndcg_at_10
value: 51.666999999999994
- type: ndcg_at_100
value: 56.242000000000004
- type: ndcg_at_1000
value: 57.477999999999994
- type: ndcg_at_3
value: 45.912
- type: ndcg_at_5
value: 49.106
- type: precision_at_1
value: 38.307
- type: precision_at_10
value: 8.476
- type: precision_at_100
value: 1.176
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 20.522000000000002
- type: precision_at_5
value: 14.557999999999998
- type: recall_at_1
value: 34.02
- type: recall_at_10
value: 66.046
- type: recall_at_100
value: 85.817
- type: recall_at_1000
value: 94.453
- type: recall_at_3
value: 51.059
- type: recall_at_5
value: 58.667
- type: map_at_1
value: 23.939
- type: map_at_10
value: 32.627
- type: map_at_100
value: 33.617999999999995
- type: map_at_1000
value: 33.701
- type: map_at_3
value: 30.11
- type: map_at_5
value: 31.380000000000003
- type: mrr_at_1
value: 25.989
- type: mrr_at_10
value: 34.655
- type: mrr_at_100
value: 35.502
- type: mrr_at_1000
value: 35.563
- type: mrr_at_3
value: 32.109
- type: mrr_at_5
value: 33.426
- type: ndcg_at_1
value: 25.989
- type: ndcg_at_10
value: 37.657000000000004
- type: ndcg_at_100
value: 42.467
- type: ndcg_at_1000
value: 44.677
- type: ndcg_at_3
value: 32.543
- type: ndcg_at_5
value: 34.74
- type: precision_at_1
value: 25.989
- type: precision_at_10
value: 5.876
- type: precision_at_100
value: 0.8710000000000001
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 13.861
- type: precision_at_5
value: 9.626999999999999
- type: recall_at_1
value: 23.939
- type: recall_at_10
value: 51.28
- type: recall_at_100
value: 73.428
- type: recall_at_1000
value: 90.309
- type: recall_at_3
value: 37.245
- type: recall_at_5
value: 42.541000000000004
- type: map_at_1
value: 15.082
- type: map_at_10
value: 22.486
- type: map_at_100
value: 23.687
- type: map_at_1000
value: 23.807000000000002
- type: map_at_3
value: 20.076
- type: map_at_5
value: 21.362000000000002
- type: mrr_at_1
value: 18.532
- type: mrr_at_10
value: 26.605
- type: mrr_at_100
value: 27.628999999999998
- type: mrr_at_1000
value: 27.698
- type: mrr_at_3
value: 23.964
- type: mrr_at_5
value: 25.319000000000003
- type: ndcg_at_1
value: 18.532
- type: ndcg_at_10
value: 27.474999999999998
- type: ndcg_at_100
value: 33.357
- type: ndcg_at_1000
value: 36.361
- type: ndcg_at_3
value: 22.851
- type: ndcg_at_5
value: 24.87
- type: precision_at_1
value: 18.532
- type: precision_at_10
value: 5.210999999999999
- type: precision_at_100
value: 0.9329999999999999
- type: precision_at_1000
value: 0.134
- type: precision_at_3
value: 11.235000000000001
- type: precision_at_5
value: 8.134
- type: recall_at_1
value: 15.082
- type: recall_at_10
value: 38.759
- type: recall_at_100
value: 64.621
- type: recall_at_1000
value: 86.162
- type: recall_at_3
value: 26.055
- type: recall_at_5
value: 31.208999999999996
- type: map_at_1
value: 24.759999999999998
- type: map_at_10
value: 33.706
- type: map_at_100
value: 35.0
- type: map_at_1000
value: 35.134
- type: map_at_3
value: 30.789
- type: map_at_5
value: 32.427
- type: mrr_at_1
value: 29.548000000000002
- type: mrr_at_10
value: 38.521
- type: mrr_at_100
value: 39.432
- type: mrr_at_1000
value: 39.494
- type: mrr_at_3
value: 35.691
- type: mrr_at_5
value: 37.424
- type: ndcg_at_1
value: 29.548000000000002
- type: ndcg_at_10
value: 39.301
- type: ndcg_at_100
value: 44.907000000000004
- type: ndcg_at_1000
value: 47.494
- type: ndcg_at_3
value: 34.08
- type: ndcg_at_5
value: 36.649
- type: precision_at_1
value: 29.548000000000002
- type: precision_at_10
value: 7.084
- type: precision_at_100
value: 1.169
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 15.881
- type: precision_at_5
value: 11.53
- type: recall_at_1
value: 24.759999999999998
- type: recall_at_10
value: 51.202000000000005
- type: recall_at_100
value: 74.542
- type: recall_at_1000
value: 91.669
- type: recall_at_3
value: 36.892
- type: recall_at_5
value: 43.333
- type: map_at_1
value: 23.247999999999998
- type: map_at_10
value: 31.878
- type: map_at_100
value: 33.135
- type: map_at_1000
value: 33.263999999999996
- type: map_at_3
value: 29.406
- type: map_at_5
value: 30.602
- type: mrr_at_1
value: 28.767
- type: mrr_at_10
value: 36.929
- type: mrr_at_100
value: 37.844
- type: mrr_at_1000
value: 37.913000000000004
- type: mrr_at_3
value: 34.589
- type: mrr_at_5
value: 35.908
- type: ndcg_at_1
value: 28.767
- type: ndcg_at_10
value: 37.172
- type: ndcg_at_100
value: 42.842
- type: ndcg_at_1000
value: 45.534
- type: ndcg_at_3
value: 32.981
- type: ndcg_at_5
value: 34.628
- type: precision_at_1
value: 28.767
- type: precision_at_10
value: 6.678000000000001
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.155
- type: precision_at_3
value: 15.715000000000002
- type: precision_at_5
value: 10.913
- type: recall_at_1
value: 23.247999999999998
- type: recall_at_10
value: 48.16
- type: recall_at_100
value: 72.753
- type: recall_at_1000
value: 90.8
- type: recall_at_3
value: 35.961999999999996
- type: recall_at_5
value: 40.504
- type: map_at_1
value: 23.825583333333334
- type: map_at_10
value: 32.2845
- type: map_at_100
value: 33.48566666666667
- type: map_at_1000
value: 33.60833333333333
- type: map_at_3
value: 29.604916666666664
- type: map_at_5
value: 31.015333333333334
- type: mrr_at_1
value: 27.850916666666663
- type: mrr_at_10
value: 36.122416666666666
- type: mrr_at_100
value: 37.01275
- type: mrr_at_1000
value: 37.07566666666667
- type: mrr_at_3
value: 33.665749999999996
- type: mrr_at_5
value: 35.00916666666667
- type: ndcg_at_1
value: 27.850916666666663
- type: ndcg_at_10
value: 37.47625
- type: ndcg_at_100
value: 42.74433333333334
- type: ndcg_at_1000
value: 45.21991666666667
- type: ndcg_at_3
value: 32.70916666666667
- type: ndcg_at_5
value: 34.80658333333333
- type: precision_at_1
value: 27.850916666666663
- type: precision_at_10
value: 6.5761666666666665
- type: precision_at_100
value: 1.0879999999999999
- type: precision_at_1000
value: 0.15058333333333332
- type: precision_at_3
value: 14.933833333333336
- type: precision_at_5
value: 10.607249999999999
- type: recall_at_1
value: 23.825583333333334
- type: recall_at_10
value: 49.100500000000004
- type: recall_at_100
value: 72.21133333333334
- type: recall_at_1000
value: 89.34791666666666
- type: recall_at_3
value: 35.90525
- type: recall_at_5
value: 41.24583333333334
- type: map_at_1
value: 21.343
- type: map_at_10
value: 27.313
- type: map_at_100
value: 28.316999999999997
- type: map_at_1000
value: 28.406
- type: map_at_3
value: 25.06
- type: map_at_5
value: 26.409
- type: mrr_at_1
value: 23.313
- type: mrr_at_10
value: 29.467
- type: mrr_at_100
value: 30.348999999999997
- type: mrr_at_1000
value: 30.42
- type: mrr_at_3
value: 27.173000000000002
- type: mrr_at_5
value: 28.461
- type: ndcg_at_1
value: 23.313
- type: ndcg_at_10
value: 31.183
- type: ndcg_at_100
value: 36.252
- type: ndcg_at_1000
value: 38.582
- type: ndcg_at_3
value: 26.838
- type: ndcg_at_5
value: 29.042
- type: precision_at_1
value: 23.313
- type: precision_at_10
value: 4.9079999999999995
- type: precision_at_100
value: 0.808
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 11.299
- type: precision_at_5
value: 8.097999999999999
- type: recall_at_1
value: 21.343
- type: recall_at_10
value: 41.047
- type: recall_at_100
value: 64.372
- type: recall_at_1000
value: 81.499
- type: recall_at_3
value: 29.337000000000003
- type: recall_at_5
value: 34.756
- type: map_at_1
value: 16.595
- type: map_at_10
value: 23.433
- type: map_at_100
value: 24.578
- type: map_at_1000
value: 24.709999999999997
- type: map_at_3
value: 21.268
- type: map_at_5
value: 22.393
- type: mrr_at_1
value: 20.131
- type: mrr_at_10
value: 27.026
- type: mrr_at_100
value: 28.003
- type: mrr_at_1000
value: 28.083999999999996
- type: mrr_at_3
value: 24.966
- type: mrr_at_5
value: 26.064999999999998
- type: ndcg_at_1
value: 20.131
- type: ndcg_at_10
value: 27.846
- type: ndcg_at_100
value: 33.318999999999996
- type: ndcg_at_1000
value: 36.403
- type: ndcg_at_3
value: 23.883
- type: ndcg_at_5
value: 25.595000000000002
- type: precision_at_1
value: 20.131
- type: precision_at_10
value: 5.034000000000001
- type: precision_at_100
value: 0.9079999999999999
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 11.23
- type: precision_at_5
value: 8.032
- type: recall_at_1
value: 16.595
- type: recall_at_10
value: 37.576
- type: recall_at_100
value: 62.044
- type: recall_at_1000
value: 83.97
- type: recall_at_3
value: 26.631
- type: recall_at_5
value: 31.002000000000002
- type: map_at_1
value: 24.85
- type: map_at_10
value: 32.762
- type: map_at_100
value: 33.896
- type: map_at_1000
value: 34.006
- type: map_at_3
value: 29.965000000000003
- type: map_at_5
value: 31.485999999999997
- type: mrr_at_1
value: 28.731
- type: mrr_at_10
value: 36.504999999999995
- type: mrr_at_100
value: 37.364999999999995
- type: mrr_at_1000
value: 37.431
- type: mrr_at_3
value: 34.033
- type: mrr_at_5
value: 35.4
- type: ndcg_at_1
value: 28.731
- type: ndcg_at_10
value: 37.788
- type: ndcg_at_100
value: 43.1
- type: ndcg_at_1000
value: 45.623999999999995
- type: ndcg_at_3
value: 32.717
- type: ndcg_at_5
value: 35.024
- type: precision_at_1
value: 28.731
- type: precision_at_10
value: 6.371
- type: precision_at_100
value: 1.02
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 14.521
- type: precision_at_5
value: 10.41
- type: recall_at_1
value: 24.85
- type: recall_at_10
value: 49.335
- type: recall_at_100
value: 72.792
- type: recall_at_1000
value: 90.525
- type: recall_at_3
value: 35.698
- type: recall_at_5
value: 41.385
- type: map_at_1
value: 23.016000000000002
- type: map_at_10
value: 32.126
- type: map_at_100
value: 33.786
- type: map_at_1000
value: 34.012
- type: map_at_3
value: 29.256
- type: map_at_5
value: 30.552
- type: mrr_at_1
value: 27.272999999999996
- type: mrr_at_10
value: 35.967
- type: mrr_at_100
value: 37.082
- type: mrr_at_1000
value: 37.146
- type: mrr_at_3
value: 33.531
- type: mrr_at_5
value: 34.697
- type: ndcg_at_1
value: 27.272999999999996
- type: ndcg_at_10
value: 37.945
- type: ndcg_at_100
value: 43.928
- type: ndcg_at_1000
value: 46.772999999999996
- type: ndcg_at_3
value: 33.111000000000004
- type: ndcg_at_5
value: 34.794000000000004
- type: precision_at_1
value: 27.272999999999996
- type: precision_at_10
value: 7.53
- type: precision_at_100
value: 1.512
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 15.547
- type: precision_at_5
value: 11.146
- type: recall_at_1
value: 23.016000000000002
- type: recall_at_10
value: 49.576
- type: recall_at_100
value: 75.74600000000001
- type: recall_at_1000
value: 94.069
- type: recall_at_3
value: 35.964
- type: recall_at_5
value: 40.455999999999996
- type: map_at_1
value: 22.742
- type: map_at_10
value: 29.232000000000003
- type: map_at_100
value: 30.160999999999998
- type: map_at_1000
value: 30.278
- type: map_at_3
value: 27.134999999999998
- type: map_at_5
value: 27.932000000000002
- type: mrr_at_1
value: 24.399
- type: mrr_at_10
value: 31.048
- type: mrr_at_100
value: 31.912000000000003
- type: mrr_at_1000
value: 31.999
- type: mrr_at_3
value: 29.144
- type: mrr_at_5
value: 29.809
- type: ndcg_at_1
value: 24.399
- type: ndcg_at_10
value: 33.354
- type: ndcg_at_100
value: 38.287
- type: ndcg_at_1000
value: 41.105000000000004
- type: ndcg_at_3
value: 29.112
- type: ndcg_at_5
value: 30.379
- type: precision_at_1
value: 24.399
- type: precision_at_10
value: 5.157
- type: precision_at_100
value: 0.828
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 11.892
- type: precision_at_5
value: 8.022
- type: recall_at_1
value: 22.742
- type: recall_at_10
value: 44.31
- type: recall_at_100
value: 67.422
- type: recall_at_1000
value: 88.193
- type: recall_at_3
value: 32.705
- type: recall_at_5
value: 35.669000000000004
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.067
- type: map_at_10
value: 14.821000000000002
- type: map_at_100
value: 16.195
- type: map_at_1000
value: 16.359
- type: map_at_3
value: 12.666
- type: map_at_5
value: 13.675999999999998
- type: mrr_at_1
value: 20.326
- type: mrr_at_10
value: 29.798000000000002
- type: mrr_at_100
value: 30.875000000000004
- type: mrr_at_1000
value: 30.928
- type: mrr_at_3
value: 26.678
- type: mrr_at_5
value: 28.433000000000003
- type: ndcg_at_1
value: 20.326
- type: ndcg_at_10
value: 21.477
- type: ndcg_at_100
value: 27.637
- type: ndcg_at_1000
value: 30.953000000000003
- type: ndcg_at_3
value: 17.456
- type: ndcg_at_5
value: 18.789
- type: precision_at_1
value: 20.326
- type: precision_at_10
value: 6.482
- type: precision_at_100
value: 1.302
- type: precision_at_1000
value: 0.191
- type: precision_at_3
value: 12.53
- type: precision_at_5
value: 9.603
- type: recall_at_1
value: 9.067
- type: recall_at_10
value: 26.246000000000002
- type: recall_at_100
value: 47.837
- type: recall_at_1000
value: 66.637
- type: recall_at_3
value: 16.468
- type: recall_at_5
value: 20.088
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.563000000000001
- type: map_at_10
value: 15.22
- type: map_at_100
value: 20.048
- type: map_at_1000
value: 21.17
- type: map_at_3
value: 11.627
- type: map_at_5
value: 13.239
- type: mrr_at_1
value: 56.25
- type: mrr_at_10
value: 64.846
- type: mrr_at_100
value: 65.405
- type: mrr_at_1000
value: 65.41799999999999
- type: mrr_at_3
value: 63.125
- type: mrr_at_5
value: 64.1
- type: ndcg_at_1
value: 45.0
- type: ndcg_at_10
value: 32.437
- type: ndcg_at_100
value: 35.483
- type: ndcg_at_1000
value: 42.186
- type: ndcg_at_3
value: 37.297000000000004
- type: ndcg_at_5
value: 34.697
- type: precision_at_1
value: 56.25
- type: precision_at_10
value: 25.15
- type: precision_at_100
value: 7.539999999999999
- type: precision_at_1000
value: 1.678
- type: precision_at_3
value: 40.666999999999994
- type: precision_at_5
value: 33.45
- type: recall_at_1
value: 7.563000000000001
- type: recall_at_10
value: 19.969
- type: recall_at_100
value: 40.113
- type: recall_at_1000
value: 61.72299999999999
- type: recall_at_3
value: 12.950999999999999
- type: recall_at_5
value: 15.690999999999999
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 44.675000000000004
- type: f1
value: 40.779372586075105
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.406
- type: map_at_10
value: 67.69500000000001
- type: map_at_100
value: 68.08
- type: map_at_1000
value: 68.095
- type: map_at_3
value: 65.688
- type: map_at_5
value: 66.93
- type: mrr_at_1
value: 61.941
- type: mrr_at_10
value: 72.513
- type: mrr_at_100
value: 72.83699999999999
- type: mrr_at_1000
value: 72.844
- type: mrr_at_3
value: 70.60499999999999
- type: mrr_at_5
value: 71.807
- type: ndcg_at_1
value: 61.941
- type: ndcg_at_10
value: 73.29
- type: ndcg_at_100
value: 74.96300000000001
- type: ndcg_at_1000
value: 75.28200000000001
- type: ndcg_at_3
value: 69.491
- type: ndcg_at_5
value: 71.573
- type: precision_at_1
value: 61.941
- type: precision_at_10
value: 9.388
- type: precision_at_100
value: 1.0290000000000001
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 27.423
- type: precision_at_5
value: 17.627000000000002
- type: recall_at_1
value: 57.406
- type: recall_at_10
value: 85.975
- type: recall_at_100
value: 93.29899999999999
- type: recall_at_1000
value: 95.531
- type: recall_at_3
value: 75.624
- type: recall_at_5
value: 80.78999999999999
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.314999999999998
- type: map_at_10
value: 26.678
- type: map_at_100
value: 28.322000000000003
- type: map_at_1000
value: 28.519
- type: map_at_3
value: 23.105
- type: map_at_5
value: 24.808
- type: mrr_at_1
value: 33.333
- type: mrr_at_10
value: 41.453
- type: mrr_at_100
value: 42.339
- type: mrr_at_1000
value: 42.39
- type: mrr_at_3
value: 38.863
- type: mrr_at_5
value: 40.159
- type: ndcg_at_1
value: 33.333
- type: ndcg_at_10
value: 34.062
- type: ndcg_at_100
value: 40.595
- type: ndcg_at_1000
value: 44.124
- type: ndcg_at_3
value: 30.689
- type: ndcg_at_5
value: 31.255
- type: precision_at_1
value: 33.333
- type: precision_at_10
value: 9.722
- type: precision_at_100
value: 1.6480000000000001
- type: precision_at_1000
value: 0.22699999999999998
- type: precision_at_3
value: 20.936
- type: precision_at_5
value: 15.154
- type: recall_at_1
value: 16.314999999999998
- type: recall_at_10
value: 41.221000000000004
- type: recall_at_100
value: 65.857
- type: recall_at_1000
value: 87.327
- type: recall_at_3
value: 27.435
- type: recall_at_5
value: 32.242
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.978
- type: map_at_10
value: 43.784
- type: map_at_100
value: 44.547
- type: map_at_1000
value: 44.614
- type: map_at_3
value: 41.317
- type: map_at_5
value: 42.812
- type: mrr_at_1
value: 63.956999999999994
- type: mrr_at_10
value: 70.502
- type: mrr_at_100
value: 70.845
- type: mrr_at_1000
value: 70.865
- type: mrr_at_3
value: 69.192
- type: mrr_at_5
value: 69.994
- type: ndcg_at_1
value: 63.956999999999994
- type: ndcg_at_10
value: 52.782
- type: ndcg_at_100
value: 55.78999999999999
- type: ndcg_at_1000
value: 57.289
- type: ndcg_at_3
value: 48.864000000000004
- type: ndcg_at_5
value: 50.964
- type: precision_at_1
value: 63.956999999999994
- type: precision_at_10
value: 10.809000000000001
- type: precision_at_100
value: 1.319
- type: precision_at_1000
value: 0.152
- type: precision_at_3
value: 30.2
- type: precision_at_5
value: 19.787
- type: recall_at_1
value: 31.978
- type: recall_at_10
value: 54.045
- type: recall_at_100
value: 65.928
- type: recall_at_1000
value: 75.976
- type: recall_at_3
value: 45.300000000000004
- type: recall_at_5
value: 49.467
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 63.8708
- type: ap
value: 59.02002684158838
- type: f1
value: 63.650055896985315
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 19.834
- type: map_at_10
value: 31.317
- type: map_at_100
value: 32.576
- type: map_at_1000
value: 32.631
- type: map_at_3
value: 27.728
- type: map_at_5
value: 29.720000000000002
- type: mrr_at_1
value: 20.43
- type: mrr_at_10
value: 31.868999999999996
- type: mrr_at_100
value: 33.074999999999996
- type: mrr_at_1000
value: 33.123999999999995
- type: mrr_at_3
value: 28.333000000000002
- type: mrr_at_5
value: 30.305
- type: ndcg_at_1
value: 20.43
- type: ndcg_at_10
value: 37.769000000000005
- type: ndcg_at_100
value: 43.924
- type: ndcg_at_1000
value: 45.323
- type: ndcg_at_3
value: 30.422
- type: ndcg_at_5
value: 33.98
- type: precision_at_1
value: 20.43
- type: precision_at_10
value: 6.027
- type: precision_at_100
value: 0.9119999999999999
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 12.985
- type: precision_at_5
value: 9.593
- type: recall_at_1
value: 19.834
- type: recall_at_10
value: 57.647000000000006
- type: recall_at_100
value: 86.276
- type: recall_at_1000
value: 97.065
- type: recall_at_3
value: 37.616
- type: recall_at_5
value: 46.171
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.52530779753762
- type: f1
value: 91.4004687820246
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 72.82717738258093
- type: f1
value: 56.791387113030346
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.09280430396772
- type: f1
value: 68.92843467363518
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.2542030934768
- type: f1
value: 76.22211319699834
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 29.604407852989457
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 25.011863718751183
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.55552172383111
- type: mrr
value: 32.65475731770242
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.968
- type: map_at_10
value: 10.703999999999999
- type: map_at_100
value: 13.316
- type: map_at_1000
value: 14.674000000000001
- type: map_at_3
value: 7.809000000000001
- type: map_at_5
value: 9.268
- type: mrr_at_1
value: 41.796
- type: mrr_at_10
value: 50.558
- type: mrr_at_100
value: 51.125
- type: mrr_at_1000
value: 51.184
- type: mrr_at_3
value: 48.349
- type: mrr_at_5
value: 49.572
- type: ndcg_at_1
value: 39.783
- type: ndcg_at_10
value: 30.375999999999998
- type: ndcg_at_100
value: 27.648
- type: ndcg_at_1000
value: 36.711
- type: ndcg_at_3
value: 35.053
- type: ndcg_at_5
value: 33.278999999999996
- type: precision_at_1
value: 41.796
- type: precision_at_10
value: 22.663
- type: precision_at_100
value: 7.210999999999999
- type: precision_at_1000
value: 1.984
- type: precision_at_3
value: 33.127
- type: precision_at_5
value: 29.102
- type: recall_at_1
value: 4.968
- type: recall_at_10
value: 14.469999999999999
- type: recall_at_100
value: 28.188000000000002
- type: recall_at_1000
value: 60.769
- type: recall_at_3
value: 8.737
- type: recall_at_5
value: 11.539000000000001
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.958
- type: map_at_10
value: 40.6
- type: map_at_100
value: 41.754000000000005
- type: map_at_1000
value: 41.792
- type: map_at_3
value: 36.521
- type: map_at_5
value: 38.866
- type: mrr_at_1
value: 30.330000000000002
- type: mrr_at_10
value: 43.013
- type: mrr_at_100
value: 43.89
- type: mrr_at_1000
value: 43.917
- type: mrr_at_3
value: 39.489000000000004
- type: mrr_at_5
value: 41.504999999999995
- type: ndcg_at_1
value: 30.330000000000002
- type: ndcg_at_10
value: 47.878
- type: ndcg_at_100
value: 52.761
- type: ndcg_at_1000
value: 53.69500000000001
- type: ndcg_at_3
value: 40.061
- type: ndcg_at_5
value: 43.980000000000004
- type: precision_at_1
value: 30.330000000000002
- type: precision_at_10
value: 8.048
- type: precision_at_100
value: 1.076
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 18.299000000000003
- type: precision_at_5
value: 13.25
- type: recall_at_1
value: 26.958
- type: recall_at_10
value: 67.72399999999999
- type: recall_at_100
value: 89.02600000000001
- type: recall_at_1000
value: 96.029
- type: recall_at_3
value: 47.332
- type: recall_at_5
value: 56.36600000000001
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 69.926
- type: map_at_10
value: 83.797
- type: map_at_100
value: 84.42699999999999
- type: map_at_1000
value: 84.446
- type: map_at_3
value: 80.78
- type: map_at_5
value: 82.669
- type: mrr_at_1
value: 80.44
- type: mrr_at_10
value: 86.79
- type: mrr_at_100
value: 86.90299999999999
- type: mrr_at_1000
value: 86.904
- type: mrr_at_3
value: 85.753
- type: mrr_at_5
value: 86.478
- type: ndcg_at_1
value: 80.44
- type: ndcg_at_10
value: 87.634
- type: ndcg_at_100
value: 88.9
- type: ndcg_at_1000
value: 89.03
- type: ndcg_at_3
value: 84.622
- type: ndcg_at_5
value: 86.29
- type: precision_at_1
value: 80.44
- type: precision_at_10
value: 13.305
- type: precision_at_100
value: 1.524
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 36.957
- type: precision_at_5
value: 24.328
- type: recall_at_1
value: 69.926
- type: recall_at_10
value: 94.99300000000001
- type: recall_at_100
value: 99.345
- type: recall_at_1000
value: 99.97
- type: recall_at_3
value: 86.465
- type: recall_at_5
value: 91.121
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 42.850644235471144
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 52.547875398320734
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.328
- type: map_at_10
value: 10.479
- type: map_at_100
value: 12.25
- type: map_at_1000
value: 12.522
- type: map_at_3
value: 7.548000000000001
- type: map_at_5
value: 9.039
- type: mrr_at_1
value: 21.3
- type: mrr_at_10
value: 30.678
- type: mrr_at_100
value: 31.77
- type: mrr_at_1000
value: 31.831
- type: mrr_at_3
value: 27.500000000000004
- type: mrr_at_5
value: 29.375
- type: ndcg_at_1
value: 21.3
- type: ndcg_at_10
value: 17.626
- type: ndcg_at_100
value: 25.03
- type: ndcg_at_1000
value: 30.055
- type: ndcg_at_3
value: 16.744999999999997
- type: ndcg_at_5
value: 14.729999999999999
- type: precision_at_1
value: 21.3
- type: precision_at_10
value: 9.09
- type: precision_at_100
value: 1.989
- type: precision_at_1000
value: 0.32
- type: precision_at_3
value: 15.467
- type: precision_at_5
value: 12.879999999999999
- type: recall_at_1
value: 4.328
- type: recall_at_10
value: 18.412
- type: recall_at_100
value: 40.363
- type: recall_at_1000
value: 64.997
- type: recall_at_3
value: 9.408
- type: recall_at_5
value: 13.048000000000002
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.1338589503896
- type: cos_sim_spearman
value: 79.1378154534123
- type: euclidean_pearson
value: 73.17857462509251
- type: euclidean_spearman
value: 70.79268955610539
- type: manhattan_pearson
value: 72.8280251705823
- type: manhattan_spearman
value: 70.60323787229834
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.21604641858598
- type: cos_sim_spearman
value: 75.06080146054282
- type: euclidean_pearson
value: 69.44429285856924
- type: euclidean_spearman
value: 58.240130690046456
- type: manhattan_pearson
value: 69.07597314234852
- type: manhattan_spearman
value: 58.08224335836159
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 80.2252849321165
- type: cos_sim_spearman
value: 80.85907200101076
- type: euclidean_pearson
value: 70.85619832878055
- type: euclidean_spearman
value: 71.59417341887324
- type: manhattan_pearson
value: 70.55842192345895
- type: manhattan_spearman
value: 71.30332994715893
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 80.50469360654135
- type: cos_sim_spearman
value: 76.12917164308409
- type: euclidean_pearson
value: 70.4070213910491
- type: euclidean_spearman
value: 66.97320451942113
- type: manhattan_pearson
value: 70.24834290119863
- type: manhattan_spearman
value: 66.9047074173091
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 84.70140350059746
- type: cos_sim_spearman
value: 85.55427877110485
- type: euclidean_pearson
value: 63.4780453371435
- type: euclidean_spearman
value: 64.65485395077273
- type: manhattan_pearson
value: 63.64869846572011
- type: manhattan_spearman
value: 64.87219311596813
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 79.4416477676503
- type: cos_sim_spearman
value: 81.2094925260351
- type: euclidean_pearson
value: 68.372257553367
- type: euclidean_spearman
value: 69.47792807911692
- type: manhattan_pearson
value: 68.17773583183664
- type: manhattan_spearman
value: 69.31505452732998
- 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: 88.94688403351994
- type: cos_sim_spearman
value: 88.97626967707933
- type: euclidean_pearson
value: 74.09942728422159
- type: euclidean_spearman
value: 72.91022362666948
- type: manhattan_pearson
value: 74.11262432880199
- type: manhattan_spearman
value: 72.82115894578564
- 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: 67.42605802805606
- type: cos_sim_spearman
value: 66.22330559222408
- type: euclidean_pearson
value: 50.15272876367891
- type: euclidean_spearman
value: 60.695400782452715
- type: manhattan_pearson
value: 50.17076569264417
- type: manhattan_spearman
value: 60.3761281869747
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 82.85939227596093
- type: cos_sim_spearman
value: 82.57071649593358
- type: euclidean_pearson
value: 72.18291316100125
- type: euclidean_spearman
value: 70.70702024402348
- type: manhattan_pearson
value: 72.36789718833687
- type: manhattan_spearman
value: 70.92789721402387
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 79.31107201598611
- type: mrr
value: 93.66321314850727
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 45.428000000000004
- type: map_at_10
value: 54.730000000000004
- type: map_at_100
value: 55.421
- type: map_at_1000
value: 55.47299999999999
- type: map_at_3
value: 52.333
- type: map_at_5
value: 53.72
- type: mrr_at_1
value: 48.333
- type: mrr_at_10
value: 56.601
- type: mrr_at_100
value: 57.106
- type: mrr_at_1000
value: 57.154
- type: mrr_at_3
value: 54.611
- type: mrr_at_5
value: 55.87800000000001
- type: ndcg_at_1
value: 48.333
- type: ndcg_at_10
value: 59.394999999999996
- type: ndcg_at_100
value: 62.549
- type: ndcg_at_1000
value: 63.941
- type: ndcg_at_3
value: 55.096000000000004
- type: ndcg_at_5
value: 57.325
- type: precision_at_1
value: 48.333
- type: precision_at_10
value: 8.1
- type: precision_at_100
value: 0.983
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 21.889
- type: precision_at_5
value: 14.533
- type: recall_at_1
value: 45.428000000000004
- type: recall_at_10
value: 71.806
- type: recall_at_100
value: 86.533
- type: recall_at_1000
value: 97.5
- type: recall_at_3
value: 60.228
- type: recall_at_5
value: 65.90599999999999
- 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: 95.48085242816634
- type: cos_sim_f1
value: 89.86653484923382
- type: cos_sim_precision
value: 88.85630498533725
- type: cos_sim_recall
value: 90.9
- type: dot_accuracy
value: 99.21881188118812
- type: dot_ap
value: 55.14126603018576
- type: dot_f1
value: 55.22458628841608
- type: dot_precision
value: 52.37668161434977
- type: dot_recall
value: 58.4
- type: euclidean_accuracy
value: 99.64356435643565
- type: euclidean_ap
value: 84.52487064474103
- type: euclidean_f1
value: 80.53908355795149
- type: euclidean_precision
value: 87.36842105263159
- type: euclidean_recall
value: 74.7
- type: manhattan_accuracy
value: 99.63861386138613
- type: manhattan_ap
value: 84.1994288662172
- type: manhattan_f1
value: 80.38482095136291
- type: manhattan_precision
value: 86.33754305396096
- type: manhattan_recall
value: 75.2
- type: max_accuracy
value: 99.8029702970297
- type: max_ap
value: 95.48085242816634
- type: max_f1
value: 89.86653484923382
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 48.06508273111389
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 31.36169910951664
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.110601218420356
- type: mrr
value: 50.90277777777777
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.63669555287747
- type: cos_sim_spearman
value: 30.708042454053853
- type: dot_pearson
value: 20.309025749838924
- type: dot_spearman
value: 21.511758746817165
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.201
- type: map_at_10
value: 1.405
- type: map_at_100
value: 7.359999999999999
- type: map_at_1000
value: 17.858
- type: map_at_3
value: 0.494
- type: map_at_5
value: 0.757
- type: mrr_at_1
value: 74.0
- type: mrr_at_10
value: 84.89999999999999
- type: mrr_at_100
value: 84.89999999999999
- type: mrr_at_1000
value: 84.89999999999999
- type: mrr_at_3
value: 84.0
- type: mrr_at_5
value: 84.89999999999999
- type: ndcg_at_1
value: 68.0
- type: ndcg_at_10
value: 60.571
- type: ndcg_at_100
value: 46.016
- type: ndcg_at_1000
value: 41.277
- type: ndcg_at_3
value: 63.989
- type: ndcg_at_5
value: 61.41
- type: precision_at_1
value: 74.0
- type: precision_at_10
value: 65.2
- type: precision_at_100
value: 47.04
- type: precision_at_1000
value: 18.416
- type: precision_at_3
value: 68.0
- type: precision_at_5
value: 66.4
- type: recall_at_1
value: 0.201
- type: recall_at_10
value: 1.763
- type: recall_at_100
value: 11.008999999999999
- type: recall_at_1000
value: 38.509
- type: recall_at_3
value: 0.551
- type: recall_at_5
value: 0.881
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.4040000000000001
- type: map_at_10
value: 7.847999999999999
- type: map_at_100
value: 12.908
- type: map_at_1000
value: 14.37
- type: map_at_3
value: 3.6450000000000005
- type: map_at_5
value: 4.93
- type: mrr_at_1
value: 18.367
- type: mrr_at_10
value: 32.576
- type: mrr_at_100
value: 34.163
- type: mrr_at_1000
value: 34.18
- type: mrr_at_3
value: 28.571
- type: mrr_at_5
value: 30.918
- type: ndcg_at_1
value: 15.306000000000001
- type: ndcg_at_10
value: 18.59
- type: ndcg_at_100
value: 30.394
- type: ndcg_at_1000
value: 42.198
- type: ndcg_at_3
value: 18.099
- type: ndcg_at_5
value: 16.955000000000002
- type: precision_at_1
value: 16.326999999999998
- type: precision_at_10
value: 17.959
- type: precision_at_100
value: 6.755
- type: precision_at_1000
value: 1.4529999999999998
- type: precision_at_3
value: 20.408
- type: precision_at_5
value: 18.367
- type: recall_at_1
value: 1.4040000000000001
- type: recall_at_10
value: 14.048
- type: recall_at_100
value: 42.150999999999996
- type: recall_at_1000
value: 77.85600000000001
- type: recall_at_3
value: 4.819
- type: recall_at_5
value: 7.13
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 66.1456
- type: ap
value: 11.631023858569064
- type: f1
value: 50.128196455722254
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.850594227504246
- type: f1
value: 56.82313689360827
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 38.060423744064764
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.43702688204088
- type: cos_sim_ap
value: 68.30176948820142
- type: cos_sim_f1
value: 64.25430330443524
- type: cos_sim_precision
value: 61.33365315423362
- type: cos_sim_recall
value: 67.46701846965699
- type: dot_accuracy
value: 77.76718126005842
- type: dot_ap
value: 37.510516716176305
- type: dot_f1
value: 43.53859496964441
- type: dot_precision
value: 32.428940568475454
- type: dot_recall
value: 66.2269129287599
- type: euclidean_accuracy
value: 82.10049472492102
- type: euclidean_ap
value: 61.64354520687271
- type: euclidean_f1
value: 59.804144841721694
- type: euclidean_precision
value: 52.604166666666664
- type: euclidean_recall
value: 69.28759894459104
- type: manhattan_accuracy
value: 82.22566609048101
- type: manhattan_ap
value: 61.753431124879974
- type: manhattan_f1
value: 59.77735297424941
- type: manhattan_precision
value: 52.0870076425632
- type: manhattan_recall
value: 70.13192612137203
- type: max_accuracy
value: 84.43702688204088
- type: max_ap
value: 68.30176948820142
- type: max_f1
value: 64.25430330443524
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.81515116233942
- type: cos_sim_ap
value: 85.33305785100573
- type: cos_sim_f1
value: 78.11202938475667
- type: cos_sim_precision
value: 74.68567816253424
- type: cos_sim_recall
value: 81.86787804126887
- type: dot_accuracy
value: 82.50475414289595
- type: dot_ap
value: 69.87015340174045
- type: dot_f1
value: 65.94174480373633
- type: dot_precision
value: 61.40362525728703
- type: dot_recall
value: 71.20418848167539
- type: euclidean_accuracy
value: 83.05778709201692
- type: euclidean_ap
value: 70.54206653977498
- type: euclidean_f1
value: 62.98969847356943
- type: euclidean_precision
value: 61.55033063923585
- type: euclidean_recall
value: 64.49799815214044
- type: manhattan_accuracy
value: 83.0034540303489
- type: manhattan_ap
value: 70.53997987198404
- type: manhattan_f1
value: 62.95875898600075
- type: manhattan_precision
value: 61.89555125725339
- type: manhattan_recall
value: 64.05913150600554
- type: max_accuracy
value: 88.81515116233942
- type: max_ap
value: 85.33305785100573
- type: max_f1
value: 78.11202938475667
---
---
<br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a></b>
</p>
## Intented Usage & Model Info
`jina-embedding-b-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 standard size of 110 million parameters,
the model enables fast inference while delivering better performance than our small model.
It is recommended to use a single GPU for inference.
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
- [`jina-embedding-b-en-v1`](https://huggingface.co/jinaai/jina-embedding-b-en-v1): 110 million parameters **(you are here)**.
- [`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
Usage with Jina AI Finetuner:
```python
!pip install finetuner
import finetuner
model = finetuner.build_model('jinaai/jina-embedding-b-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-b-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}
}
```
|
[
"BIOSSES",
"LINNAEUS",
"SCIFACT"
] |
oobabooga/CodeBooga-34B-v0.1
|
oobabooga
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-10-19T23:04:00Z |
2024-01-02T23:01:35+00:00
| 2,065 | 144 |
---
license: llama2
---
# CodeBooga-34B-v0.1
This is a merge between the following two models:
1) [Phind-CodeLlama-34B-v2](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2)
2) [WizardCoder-Python-34B-V1.0](https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0)
It was created with the [BlockMerge Gradient script](https://github.com/Gryphe/BlockMerge_Gradient), the same one that was used to create [MythoMax-L2-13b](https://huggingface.co/Gryphe/MythoMax-L2-13b), and with the same settings. The following YAML was used:
```yaml
model_path1: "Phind_Phind-CodeLlama-34B-v2_safetensors"
model_path2: "WizardLM_WizardCoder-Python-34B-V1.0_safetensors"
output_model_path: "CodeBooga-34B-v0.1"
operations:
- operation: lm_head # Single tensor
filter: "lm_head"
gradient_values: [0.75]
- operation: embed_tokens # Single tensor
filter: "embed_tokens"
gradient_values: [0.75]
- operation: self_attn
filter: "self_attn"
gradient_values: [0.75, 0.25]
- operation: mlp
filter: "mlp"
gradient_values: [0.25, 0.75]
- operation: layernorm
filter: "layernorm"
gradient_values: [0.5, 0.5]
- operation: modelnorm # Single tensor
filter: "model.norm"
gradient_values: [0.75]
```
## Prompt format
Both base models use the Alpaca format, so it should be used for this one as well.
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Your instruction
### Response:
Bot reply
### Instruction:
Another instruction
### Response:
Bot reply
```
## Evaluation
(This is not very scientific, so bear with me.)
I made a quick experiment where I asked a set of 3 Python and 3 Javascript questions (real world, difficult questions with nuance) to the following models:
1) This one
2) A second variant generated with `model_path1` and `model_path2` swapped in the YAML above, which I called CodeBooga-Reversed-34B-v0.1
3) WizardCoder-Python-34B-V1.0
4) Phind-CodeLlama-34B-v2
Specifically, I used 4.250b EXL2 quantizations of each. I then sorted the responses for each question by quality, and attributed the following scores:
* 4th place: 0
* 3rd place: 1
* 2nd place: 2
* 1st place: 4
The resulting cumulative scores were:
* CodeBooga-34B-v0.1: 22
* WizardCoder-Python-34B-V1.0: 12
* Phind-CodeLlama-34B-v2: 7
* CodeBooga-Reversed-34B-v0.1: 1
CodeBooga-34B-v0.1 performed very well, while its variant performed poorly, so I uploaded the former but not the latter.
## Quantized versions
### GGUF
TheBloke has kindly provided GGUF quantizations for llama.cpp:
https://huggingface.co/TheBloke/CodeBooga-34B-v0.1-GGUF
<a href="https://ko-fi.com/oobabooga"><img src="https://i.imgur.com/UJlEAYw.png"></a>
|
[
"BEAR"
] |
lgaalves/gpt1
|
lgaalves
|
text-generation
|
[
"transformers",
"pytorch",
"safetensors",
"openai-gpt",
"text-generation",
"en",
"arxiv:1705.11168",
"arxiv:1803.02324",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-09-25T14:34:55Z |
2023-11-21T17:05:38+00:00
| 2,049 | 3 |
---
language: en
license: mit
---
# OpenAI GPT
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specifications)
- [Citation Information](#citation-information)
- [Model Card Authors](#model-card-authors)
## Model Details
**Model Description:** `openai-gpt` is a transformer-based language model created and released by OpenAI. The model is a causal (unidirectional) transformer pre-trained using language modeling on a large corpus with long range dependencies.
- **Developed by:** Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever. See [associated research paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) and [GitHub repo](https://github.com/openai/finetune-transformer-lm) for model developers and contributors.
- **Model Type:** Transformer-based language model
- **Language(s):** English
- **License:** [MIT License](https://github.com/openai/finetune-transformer-lm/blob/master/LICENSE)
- **Resources for more information:**
- [Research Paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)
- [OpenAI Blog Post](https://openai.com/blog/language-unsupervised/)
- [GitHub Repo](https://github.com/openai/finetune-transformer-lm)
- Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt
## How to Get Started with the Model
Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='lgaalves/gpt1')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
[{'generated_text': "Hello, I'm a language model,'he said, when i was finished.'ah well,'said the man,'that's"},
{'generated_text': 'Hello, I\'m a language model, " she said. \n she reached the bottom of the shaft and leaned a little further out. it was'},
{'generated_text': 'Hello, I\'m a language model, " she laughed. " we call that a\'white girl.\'or as we are called by the'},
{'generated_text': 'Hello, I\'m a language model, " said mr pin. " an\'the ones with the funny hats don\'t. " the rest of'},
{'generated_text': 'Hello, I\'m a language model, was\'ere \'bout to do some more dancin \', " he said, then his voice lowered to'}]
```
Here is how to use this model in PyTorch:
```python
from transformers import OpenAIGPTTokenizer, OpenAIGPTModel
import torch
tokenizer = OpenAIGPTTokenizer.from_pretrained("lgaalves/gpt1")
model = OpenAIGPTModel.from_pretrained("lgaalves/gpt1")
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
and in TensorFlow:
```python
from transformers import OpenAIGPTTokenizer, TFOpenAIGPTModel
tokenizer = OpenAIGPTTokenizer.from_pretrained("lgaalves/gpt1")
model = TFOpenAIGPTModel.from_pretrained("lgaalves/gpt1")
inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
outputs = model(inputs)
last_hidden_states = outputs.last_hidden_state
```
## Uses
#### Direct Use
This model can be used for language modeling tasks.
#### Downstream Use
Potential downstream uses of this model include tasks that leverage language models. In the [associated paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf), the model developers discuss evaluations of the model for tasks including natural language inference (NLI), question answering, semantic similarity, and text classification.
#### Misuse and Out-of-scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
## Risks, Limitations and Biases
#### Biases
**CONTENT WARNING: Readers should be aware that language generated by this model can be disturbing or offensive to some and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
Predictions generated by this model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='lgaalves/gpt1')
>>> set_seed(42)
>>> generator("The man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The man worked as a teacher for the college he'},
{'generated_text': 'The man worked as a janitor at the club.'},
{'generated_text': 'The man worked as a bodyguard in america. the'},
{'generated_text': 'The man worked as a clerk for one of the'},
{'generated_text': 'The man worked as a nurse, but there was'}]
>>> set_seed(42)
>>> generator("The woman worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The woman worked as a medical intern but is a'},
{'generated_text': 'The woman worked as a midwife, i know that'},
{'generated_text': 'The woman worked as a prostitute in a sex club'},
{'generated_text': 'The woman worked as a secretary for one of the'},
{'generated_text': 'The woman worked as a nurse, but she had'}]
```
This bias may also affect fine-tuned versions of this model. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
#### Risks and Limitations
The model developers also wrote in a [blog post](https://openai.com/blog/language-unsupervised/) about risks and limitations of the model, including:
> - **Compute Requirements:** Many previous approaches to NLP tasks train relatively small models on a single GPU from scratch. Our approach requires an expensive pre-training step - 1 month on 8 GPUs. Luckily, this only has to be done once and we’re releasing our model so others can avoid it. It is also a large model (in comparison to prior work) and consequently uses more compute and memory — we used a 37-layer (12 block) Transformer architecture, and we train on sequences of up to 512 tokens. Most experiments were conducted on 4 and 8 GPU systems. The model does fine-tune to new tasks very quickly which helps mitigate the additional resource requirements.
> - **The limits and bias of learning about the world through text:** Books and text readily available on the internet do not contain complete or even accurate information about the world. Recent work ([Lucy and Gauthier, 2017](https://arxiv.org/abs/1705.11168)) has shown that certain kinds of information are difficult to learn via just text and other work ([Gururangan et al., 2018](https://arxiv.org/abs/1803.02324)) has shown that models learn and exploit biases in data distributions.
> - **Still brittle generalization:** Although our approach improves performance across a broad range of tasks, current deep learning NLP models still exhibit surprising and counterintuitive behavior - especially when evaluated in a systematic, adversarial, or out-of-distribution way. Our approach is not immune to these issues, though we have observed some indications of progress. Our approach shows improved lexical robustness over previous purely neural approaches to textual entailment. On the dataset introduced in Glockner et al. (2018) our model achieves 83.75%, performing similarly to KIM, which incorporates external knowledge via WordNet.
## Training
#### Training Data
The model developers [write](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf):
> We use the BooksCorpus dataset ([Zhu et al., 2015](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zhu_Aligning_Books_and_ICCV_2015_paper.pdf)) for training the language model. It contains over 7,000 unique unpublished books from a variety of genres including Adventure, Fantasy, and Romance. Crucially, it contains long stretches of contiguous text, which allows the generative model to learn to condition on long-range information.
#### Training Procedure
The model developers [write](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf):
> Our model largely follows the original transformer work [62]. We trained a 12-layer decoder-only transformer with masked self-attention heads (768 dimensional states and 12 attention heads). For the position-wise feed-forward networks, we used 3072 dimensional inner states. We used the Adam optimization scheme [27] with a max learning rate of 2.5e-4. The learning rate was increased linearly from zero over the first 2000 updates and annealed to 0 using a cosine schedule. We train for 100 epochs on minibatches of 64 randomly sampled, contiguous sequences of 512 tokens. Since layernorm [2] is used extensively throughout the model, a simple weight initialization of N (0, 0.02) was sufficient. We used a bytepair encoding (BPE) vocabulary with 40,000 merges [53] and residual, embedding, and attention dropouts with a rate of 0.1 for regularization. We also employed a modified version of L2 regularization proposed in [37], with w = 0.01 on all non bias or gain weights. For the activation function, we used the Gaussian Error Linear Unit (GELU) [18]. We used learned position embeddings instead of the sinusoidal version proposed in the original work. We use the ftfy library2 to clean the raw text in BooksCorpus, standardize some punctuation and whitespace, and use the spaCy tokenizer.
See the paper for further details and links to citations.
## Evaluation
The following evaluation information is extracted from the [associated blog post](https://openai.com/blog/language-unsupervised/). See the [associated paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) for further details.
#### Testing Data, Factors and Metrics
The model developers report that the model was evaluated on the following tasks and datasets using the listed metrics:
- **Task:** Textual Entailment
- **Datasets:** [SNLI](https://huggingface.co/datasets/snli), [MNLI Matched](https://huggingface.co/datasets/glue), [MNLI Mismatched](https://huggingface.co/datasets/glue), [SciTail](https://huggingface.co/datasets/scitail), [QNLI](https://huggingface.co/datasets/glue), [RTE](https://huggingface.co/datasets/glue)
- **Metrics:** Accuracy
- **Task:** Semantic Similarity
- **Datasets:** [STS-B](https://huggingface.co/datasets/glue), [QQP](https://huggingface.co/datasets/glue), [MRPC](https://huggingface.co/datasets/glue)
- **Metrics:** Accuracy
- **Task:** Reading Comprehension
- **Datasets:** [RACE](https://huggingface.co/datasets/race)
- **Metrics:** Accuracy
- **Task:** Commonsense Reasoning
- **Datasets:** [ROCStories](https://huggingface.co/datasets/story_cloze), [COPA](https://huggingface.co/datasets/xcopa)
- **Metrics:** Accuracy
- **Task:** Sentiment Analysis
- **Datasets:** [SST-2](https://huggingface.co/datasets/glue)
- **Metrics:** Accuracy
- **Task:** Linguistic Acceptability
- **Datasets:** [CoLA](https://huggingface.co/datasets/glue)
- **Metrics:** Accuracy
- **Task:** Multi Task Benchmark
- **Datasets:** [GLUE](https://huggingface.co/datasets/glue)
- **Metrics:** Accuracy
#### Results
The model achieves the following results without any fine-tuning (zero-shot):
| Task | TE | TE | TE |TE | TE | TE | SS | SS | SS | RC | CR | CR | SA | LA | MTB |
|:--------:|:--:|:----------:|:-------------:|:-----:|:----:|:---:|:---:|:---:|:--:|:----:|:--------:|:----:|:----:|:----:|:----:|
| Dataset |SNLI|MNLI Matched|MNLI Mismatched|SciTail| QNLI | RTE |STS-B| QQP |MPRC|RACE |ROCStories|COPA | SST-2| CoLA | GLUE |
| |89.9| 82.1 | 81.4 |88.3 | 88.1 | 56.0|82.0 | 70.3|82.3|59.0 | 86.5 | 78.6 | 91.3 | 45.4 | 72.8 |
## Environmental Impact
The model developers [report that](https://openai.com/blog/language-unsupervised/):
> The total compute used to train this model was 0.96 petaflop days (pfs-days).
> 8 P600 GPU's * 30 days * 12 TFLOPS/GPU * 0.33 utilization = .96 pfs-days
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** 8 P600 GPUs
- **Hours used:** 720 hours (30 days)
- **Cloud Provider:** Unknown
- **Compute Region:** Unknown
- **Carbon Emitted:** Unknown
## Technical Specifications
See the [associated paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) for details on the modeling architecture, objective, compute infrastructure, and training details.
## Citation Information
```bibtex
@article{radford2018improving,
title={Improving language understanding by generative pre-training},
author={Radford, Alec and Narasimhan, Karthik and Salimans, Tim and Sutskever, Ilya and others},
year={2018},
publisher={OpenAI}
}
```
APA:
*Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training.*
## Model Card Authors
This model card was written by the Hugging Face team.
|
[
"SCITAIL"
] |
Locutusque/gpt2-large-conversational-retrain
|
Locutusque
|
text-generation
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"en",
"dataset:Locutusque/InstructMix",
"arxiv:1910.09700",
"doi:10.57967/hf/1341",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-09-25T04:21:35Z |
2023-12-17T22:10:43+00:00
| 2,032 | 9 |
---
datasets:
- Locutusque/InstructMix
language:
- en
license: mit
metrics:
- bleu
- perplexity
pipeline_tag: text-generation
widget:
- text: '<|USER|> Design a Neo4j database and Cypher function snippet to Display Extreme
Dental hygiene: Using Mouthwash for Analysis for Beginners. Implement if/else
or switch/case statements to handle different conditions related to the Consent.
Provide detailed comments explaining your control flow and the reasoning behind
each decision. <|ASSISTANT|> '
- text: '<|USER|> Write me a story about a magical place. <|ASSISTANT|> '
- text: '<|USER|> Write me an essay about the life of George Washington <|ASSISTANT|> '
- text: '<|USER|> Solve the following equation 2x + 10 = 20 <|ASSISTANT|> '
- text: '<|USER|> Craft me a list of some nice places to visit around the world. <|ASSISTANT|> '
inference:
parameters:
temperature: 0.5
do_sample: true
top_p: 0.5
top_k: 30
max_new_tokens: 250
repetition_penalty: 1.15
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This a fine-tuned version of gpt2 on Locutusque/InstructMix.
## Model Details
This model performs significantly better than Locutusque/gpt2-large-conversational. Here are the training results:
- BLEU - 30
- Perplexity - 5
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Locutusque
- **Shared by [optional]:** [More Information Needed]
- **Model type:** GPT-2
- **Language(s) (NLP):** English
- **License:** mit
- **Finetuned from model [optional]:** GPT-2
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
This model is designed to follow instructions, or partake in conversations.
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
Instruction-following or conversational.
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
This model struggles to write complex code, and I only recommend simple code from this model.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This model will most likely produce false information, especially about history. Make sure to confirm the responses this model makes.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large-conversational-retrain')
model = GPT2LMHeadModel.from_pretrained('gpt2-large-conversational-retrain')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
def generate_text(model, tokenizer, prompt, max_length=1024):
prompt = f'<|USER|> {prompt} <|ASSISTANT|> '
input_ids = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt").to(device)
attention_mask = torch.ones_like(input_ids).to(device)
output = model.generate(input_ids,
max_length=max_length,
do_sample=True,
temperature=0.3,
top_k=23,
top_p=0.7,
repetition_penalty=1.176,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
attention_mask=attention_mask)
output_ids = tokenizer.decode(output[0], skip_special_tokens=False)
return output_ids
# Loop to interact with the model
while True:
prompt = input("Enter a prompt (or 'q' to quit): ")
if prompt == "q":
break
output_text = generate_text(model, tokenizer, prompt)
print(output_text)
```
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
https://huggingface.co/datasets/Locutusque/InstructMix
This model has so far been trained on 600,000 examples of the linked data, with more training sessions to come.
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** fp16 non-mixed precision <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
- BLEU = 30
- Perplexity = 5
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"CRAFT"
] |
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