--- language: - en library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/stsb-distilbert-base metrics: - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - dot_accuracy - dot_accuracy_threshold - dot_f1 - dot_f1_threshold - dot_precision - dot_recall - dot_ap - manhattan_accuracy - manhattan_accuracy_threshold - manhattan_f1 - manhattan_f1_threshold - manhattan_precision - manhattan_recall - manhattan_ap - euclidean_accuracy - euclidean_accuracy_threshold - euclidean_f1 - euclidean_f1_threshold - euclidean_precision - euclidean_recall - euclidean_ap - max_accuracy - max_accuracy_threshold - max_f1 - max_f1_threshold - max_precision - max_recall - max_ap - average_precision - f1 - precision - recall - threshold - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 widget: - source_sentence: How metro works? sentences: - How can Turing machine works? - What are the best C++ books? - What should I learn first in PHP? - source_sentence: How fast is fast? sentences: - How does light travel so fast? - How could I become an actor? - Was Muhammad a pedophile? - source_sentence: What is a kernel? sentences: - What is a tensor? - What does copyright protect? - Can we increase height after 23? - source_sentence: What is a tensor? sentences: - What is reliance jio? - What are the reasons of war? - Does speed reading really work? - source_sentence: Is Cicret a scam? sentences: - Is the Cicret Bracelet a scam? - Can you eat only once a day? - What books should every man read? pipeline_tag: sentence-similarity co2_eq_emissions: emissions: 15.153912802318576 energy_consumed: 0.038985939877640395 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.169 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: SentenceTransformer based on sentence-transformers/stsb-distilbert-base results: - task: type: binary-classification name: Binary Classification dataset: name: quora duplicates type: quora-duplicates metrics: - type: cosine_accuracy value: 0.816 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7866689562797546 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.7285714285714286 name: Cosine F1 - type: cosine_f1_threshold value: 0.735264778137207 name: Cosine F1 Threshold - type: cosine_precision value: 0.6746031746031746 name: Cosine Precision - type: cosine_recall value: 0.7919254658385093 name: Cosine Recall - type: cosine_ap value: 0.7731120768804719 name: Cosine Ap - type: dot_accuracy value: 0.807 name: Dot Accuracy - type: dot_accuracy_threshold value: 150.97946166992188 name: Dot Accuracy Threshold - type: dot_f1 value: 0.7223796033994335 name: Dot F1 - type: dot_f1_threshold value: 137.3444366455078 name: Dot F1 Threshold - type: dot_precision value: 0.6640625 name: Dot Precision - type: dot_recall value: 0.7919254658385093 name: Dot Recall - type: dot_ap value: 0.749212069604305 name: Dot Ap - type: manhattan_accuracy value: 0.81 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 195.88662719726562 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.7246376811594203 name: Manhattan F1 - type: manhattan_f1_threshold value: 237.68594360351562 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.6292906178489702 name: Manhattan Precision - type: manhattan_recall value: 0.8540372670807453 name: Manhattan Recall - type: manhattan_ap value: 0.7610544151599187 name: Manhattan Ap - type: euclidean_accuracy value: 0.81 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 8.773942947387695 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.7260812581913498 name: Euclidean F1 - type: euclidean_f1_threshold value: 10.843769073486328 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.6281179138321995 name: Euclidean Precision - type: euclidean_recall value: 0.860248447204969 name: Euclidean Recall - type: euclidean_ap value: 0.7611533877712096 name: Euclidean Ap - type: max_accuracy value: 0.816 name: Max Accuracy - type: max_accuracy_threshold value: 195.88662719726562 name: Max Accuracy Threshold - type: max_f1 value: 0.7285714285714286 name: Max F1 - type: max_f1_threshold value: 237.68594360351562 name: Max F1 Threshold - type: max_precision value: 0.6746031746031746 name: Max Precision - type: max_recall value: 0.860248447204969 name: Max Recall - type: max_ap value: 0.7731120768804719 name: Max Ap - task: type: paraphrase-mining name: Paraphrase Mining dataset: name: quora duplicates dev type: quora-duplicates-dev metrics: - type: average_precision value: 0.5348666252858723 name: Average Precision - type: f1 value: 0.5395064090300363 name: F1 - type: precision value: 0.5174549291251892 name: Precision - type: recall value: 0.5635210071439276 name: Recall - type: threshold value: 0.762035459280014 name: Threshold - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.9646 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9926 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9956 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9986 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9646 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.4293333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.2754 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.14515999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.830104138622815 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9609072390452685 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9808022997296821 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9934541226453286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9795490191788223 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9789640476190478 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.971751123151301 name: Cosine Map@100 - type: dot_accuracy@1 value: 0.9574 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.9876 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9924 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9978 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.9574 name: Dot Precision@1 - type: dot_precision@3 value: 0.4257333333333334 name: Dot Precision@3 - type: dot_precision@5 value: 0.27368000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.14468000000000003 name: Dot Precision@10 - type: dot_recall@1 value: 0.8237692901379665 name: Dot Recall@1 - type: dot_recall@3 value: 0.9538191510221804 name: Dot Recall@3 - type: dot_recall@5 value: 0.9764249670623496 name: Dot Recall@5 - type: dot_recall@10 value: 0.9918117957075603 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.9740754474178193 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.9731360317460321 name: Dot Mrr@10 - type: dot_map@100 value: 0.9646398037726347 name: Dot Map@100 --- # SentenceTransformer based on sentence-transformers/stsb-distilbert-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/stsb-distilbert-base](https://huggingface.co/sentence-transformers/stsb-distilbert-base) on the [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/stsb-distilbert-base](https://huggingface.co/sentence-transformers/stsb-distilbert-base) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) - **Language:** en ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tomaarsen/stsb-distilbert-base-mnrl") # Run inference sentences = [ 'Is Cicret a scam?', 'Is the Cicret Bracelet a scam?', 'Can you eat only once a day?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Binary Classification * Dataset: `quora-duplicates` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.816 | | cosine_accuracy_threshold | 0.7867 | | cosine_f1 | 0.7286 | | cosine_f1_threshold | 0.7353 | | cosine_precision | 0.6746 | | cosine_recall | 0.7919 | | cosine_ap | 0.7731 | | dot_accuracy | 0.807 | | dot_accuracy_threshold | 150.9795 | | dot_f1 | 0.7224 | | dot_f1_threshold | 137.3444 | | dot_precision | 0.6641 | | dot_recall | 0.7919 | | dot_ap | 0.7492 | | manhattan_accuracy | 0.81 | | manhattan_accuracy_threshold | 195.8866 | | manhattan_f1 | 0.7246 | | manhattan_f1_threshold | 237.6859 | | manhattan_precision | 0.6293 | | manhattan_recall | 0.854 | | manhattan_ap | 0.7611 | | euclidean_accuracy | 0.81 | | euclidean_accuracy_threshold | 8.7739 | | euclidean_f1 | 0.7261 | | euclidean_f1_threshold | 10.8438 | | euclidean_precision | 0.6281 | | euclidean_recall | 0.8602 | | euclidean_ap | 0.7612 | | max_accuracy | 0.816 | | max_accuracy_threshold | 195.8866 | | max_f1 | 0.7286 | | max_f1_threshold | 237.6859 | | max_precision | 0.6746 | | max_recall | 0.8602 | | **max_ap** | **0.7731** | #### Paraphrase Mining * Dataset: `quora-duplicates-dev` * Evaluated with [ParaphraseMiningEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator) | Metric | Value | |:----------------------|:-----------| | **average_precision** | **0.5349** | | f1 | 0.5395 | | precision | 0.5175 | | recall | 0.5635 | | threshold | 0.762 | #### Information Retrieval * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.9646 | | cosine_accuracy@3 | 0.9926 | | cosine_accuracy@5 | 0.9956 | | cosine_accuracy@10 | 0.9986 | | cosine_precision@1 | 0.9646 | | cosine_precision@3 | 0.4293 | | cosine_precision@5 | 0.2754 | | cosine_precision@10 | 0.1452 | | cosine_recall@1 | 0.8301 | | cosine_recall@3 | 0.9609 | | cosine_recall@5 | 0.9808 | | cosine_recall@10 | 0.9935 | | cosine_ndcg@10 | 0.9795 | | cosine_mrr@10 | 0.979 | | **cosine_map@100** | **0.9718** | | dot_accuracy@1 | 0.9574 | | dot_accuracy@3 | 0.9876 | | dot_accuracy@5 | 0.9924 | | dot_accuracy@10 | 0.9978 | | dot_precision@1 | 0.9574 | | dot_precision@3 | 0.4257 | | dot_precision@5 | 0.2737 | | dot_precision@10 | 0.1447 | | dot_recall@1 | 0.8238 | | dot_recall@3 | 0.9538 | | dot_recall@5 | 0.9764 | | dot_recall@10 | 0.9918 | | dot_ndcg@10 | 0.9741 | | dot_mrr@10 | 0.9731 | | dot_map@100 | 0.9646 | ## Training Details ### Training Dataset #### sentence-transformers/quora-duplicates * Dataset: [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) * Size: 100,000 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------| | Why in India do we not have one on one political debate as in USA? | Why cant we have a public debate between politicians in India like the one in US? | Can people on Quora stop India Pakistan debate? We are sick and tired seeing this everyday in bulk? | | What is OnePlus One? | How is oneplus one? | Why is OnePlus One so good? | | Does our mind control our emotions? | How do smart and successful people control their emotions? | How can I control my positive emotions for the people whom I love but they don't care about me? | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### sentence-transformers/quora-duplicates * Dataset: [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) * Size: 1,000 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:---------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Which programming language is best for developing low-end games? | What coding language should I learn first for making games? | I am entering the world of video game programming and want to know what language I should learn? Because there are so many languages ​​I do not know which one to start with. Can you recommend a language that's easy to learn and can be used with many platforms? | | Was it appropriate for Meryl Streep to use her Golden Globes speech to attack Donald Trump? | Should Meryl Streep be using her position to attack the president? | Why did Kelly Ann Conway say that Meryl Streep incited peoples worst feelings? | | Where can I found excellent commercial fridges in Sydney? | Where can I found impressive range of commercial fridges in Sydney? | What is the best grocery delivery service in Sydney? | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: False - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: None - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | cosine_map@100 | quora-duplicates-dev_average_precision | quora-duplicates_max_ap | |:------:|:----:|:-------------:|:------:|:--------------:|:--------------------------------------:|:-----------------------:| | 0 | 0 | - | - | 0.9245 | 0.4200 | 0.6890 | | 0.0640 | 100 | 0.2535 | - | - | - | - | | 0.1280 | 200 | 0.1732 | - | - | - | - | | 0.1599 | 250 | - | 0.1021 | 0.9601 | 0.5033 | 0.7342 | | 0.1919 | 300 | 0.1465 | - | - | - | - | | 0.2559 | 400 | 0.1186 | - | - | - | - | | 0.3199 | 500 | 0.1159 | 0.0773 | 0.9653 | 0.5247 | 0.7453 | | 0.3839 | 600 | 0.1088 | - | - | - | - | | 0.4479 | 700 | 0.0993 | - | - | - | - | | 0.4798 | 750 | - | 0.0665 | 0.9666 | 0.5264 | 0.7655 | | 0.5118 | 800 | 0.0952 | - | - | - | - | | 0.5758 | 900 | 0.0799 | - | - | - | - | | 0.6398 | 1000 | 0.0855 | 0.0570 | 0.9709 | 0.5391 | 0.7717 | | 0.7038 | 1100 | 0.0804 | - | - | - | - | | 0.7678 | 1200 | 0.073 | - | - | - | - | | 0.7997 | 1250 | - | 0.0513 | 0.9719 | 0.5329 | 0.7662 | | 0.8317 | 1300 | 0.0741 | - | - | - | - | | 0.8957 | 1400 | 0.0699 | - | - | - | - | | 0.9597 | 1500 | 0.0755 | 0.0476 | 0.9718 | 0.5349 | 0.7731 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.039 kWh - **Carbon Emitted**: 0.015 kg of CO2 - **Hours Used**: 0.169 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.0.0.dev0 - Transformers: 4.41.0.dev0 - PyTorch: 2.3.0+cu121 - Accelerate: 0.26.1 - Datasets: 2.18.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```