--- base_model: NeuML/pubmedbert-base-embeddings datasets: [] language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:530 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: If you receive a BharatPe speaker that you didn't order, please contact BharatPe support immediately. They will assist in resolving the issue and advise on the next steps. sentences: - Can I control multiple BharatPe speakers from one app? - What to do if the BharatPe speaker's transaction announcements are intermittently silent? - What should I do if I receive a BharatPe speaker without ordering it? - source_sentence: Remote control capabilities depend on the model of the BharatPe speaker. Check if your model supports remote control through the BharatPe app or a connected device. sentences: - How do I update my personal details in my Bharatpe account? - What are the benefits of the BharatPe speaker? - Can I control the BharatPe speaker remotely? - source_sentence: If the announcements are not clear, check the speaker's volume settings and ensure it's not placed near noisy equipment. If clarity doesn't improve, the speaker may need servicing. sentences: - What to do if my BharatPe speaker is not syncing with the transaction history in the app? - What should I do if the speaker is not announcing payments clearly? - The speaker doesn't produce any sound, what can be done? - source_sentence: If the speaker is causing interference, try relocating it or other devices to reduce the interference. Ensure there's a reasonable distance between the speaker and other wireless equipment. sentences: - Can I use my Bharatpe device for international transactions? - How do I know if my BharatPe speaker is under warranty? - What should I do if the BharatPe speaker is causing interference with other wireless devices? - source_sentence: I can understand and respond in multiple Indian regional languages. Feel free to communicate with me in the language you're most comfortable with. sentences: - How can I check if the BharatPe speaker is receiving a network signal? - Bharti, can you provide tips for effective online communication? - Bharti, what languages can you understand and respond to? model-index: - name: pubmedbert-base-embedding Chatbot Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.7674418604651163 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9069767441860465 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9302325581395349 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9302325581395349 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7674418604651163 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3023255813953489 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18604651162790697 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09302325581395349 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7674418604651163 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9069767441860465 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9302325581395349 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9302325581395349 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8563596702043667 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8313953488372093 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8349894291754757 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6976744186046512 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8837209302325582 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9302325581395349 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9302325581395349 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6976744186046512 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.29457364341085274 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18604651162790697 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09302325581395349 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6976744186046512 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8837209302325582 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9302325581395349 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9302325581395349 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8320432881662091 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7984496124031009 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8017447288993117 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7906976744186046 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8837209302325582 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9069767441860465 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9069767441860465 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7906976744186046 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.29457364341085274 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1813953488372093 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09069767441860466 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7906976744186046 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8837209302325582 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9069767441860465 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9069767441860465 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8533147922143328 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8352713178294573 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8392285023210497 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6744186046511628 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.813953488372093 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8837209302325582 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9069767441860465 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6744186046511628 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2713178294573643 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17674418604651165 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09069767441860466 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6744186046511628 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.813953488372093 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8837209302325582 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9069767441860465 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.794152105183587 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7575858250276855 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7600321150655651 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6046511627906976 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7441860465116279 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7906976744186046 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8604651162790697 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6046511627906976 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.24806201550387597 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.15813953488372093 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08604651162790698 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6046511627906976 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7441860465116279 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7906976744186046 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8604651162790697 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7220252449949186 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6786083425618308 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6823125300680127 name: Cosine Map@100 --- # pubmedbert-base-embedding Chatbot Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings). 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:** [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': 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, '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("MANMEET75/pubmedbert-base-embedding-Chatbot-Matryoshk") # Run inference sentences = [ "I can understand and respond in multiple Indian regional languages. Feel free to communicate with me in the language you're most comfortable with.", 'Bharti, what languages can you understand and respond to?', 'Bharti, can you provide tips for effective online communication?', ] 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] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_768` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.7674 | | cosine_accuracy@3 | 0.907 | | cosine_accuracy@5 | 0.9302 | | cosine_accuracy@10 | 0.9302 | | cosine_precision@1 | 0.7674 | | cosine_precision@3 | 0.3023 | | cosine_precision@5 | 0.186 | | cosine_precision@10 | 0.093 | | cosine_recall@1 | 0.7674 | | cosine_recall@3 | 0.907 | | cosine_recall@5 | 0.9302 | | cosine_recall@10 | 0.9302 | | cosine_ndcg@10 | 0.8564 | | cosine_mrr@10 | 0.8314 | | **cosine_map@100** | **0.835** | #### Information Retrieval * Dataset: `dim_512` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.6977 | | cosine_accuracy@3 | 0.8837 | | cosine_accuracy@5 | 0.9302 | | cosine_accuracy@10 | 0.9302 | | cosine_precision@1 | 0.6977 | | cosine_precision@3 | 0.2946 | | cosine_precision@5 | 0.186 | | cosine_precision@10 | 0.093 | | cosine_recall@1 | 0.6977 | | cosine_recall@3 | 0.8837 | | cosine_recall@5 | 0.9302 | | cosine_recall@10 | 0.9302 | | cosine_ndcg@10 | 0.832 | | cosine_mrr@10 | 0.7984 | | **cosine_map@100** | **0.8017** | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.7907 | | cosine_accuracy@3 | 0.8837 | | cosine_accuracy@5 | 0.907 | | cosine_accuracy@10 | 0.907 | | cosine_precision@1 | 0.7907 | | cosine_precision@3 | 0.2946 | | cosine_precision@5 | 0.1814 | | cosine_precision@10 | 0.0907 | | cosine_recall@1 | 0.7907 | | cosine_recall@3 | 0.8837 | | cosine_recall@5 | 0.907 | | cosine_recall@10 | 0.907 | | cosine_ndcg@10 | 0.8533 | | cosine_mrr@10 | 0.8353 | | **cosine_map@100** | **0.8392** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:---------| | cosine_accuracy@1 | 0.6744 | | cosine_accuracy@3 | 0.814 | | cosine_accuracy@5 | 0.8837 | | cosine_accuracy@10 | 0.907 | | cosine_precision@1 | 0.6744 | | cosine_precision@3 | 0.2713 | | cosine_precision@5 | 0.1767 | | cosine_precision@10 | 0.0907 | | cosine_recall@1 | 0.6744 | | cosine_recall@3 | 0.814 | | cosine_recall@5 | 0.8837 | | cosine_recall@10 | 0.907 | | cosine_ndcg@10 | 0.7942 | | cosine_mrr@10 | 0.7576 | | **cosine_map@100** | **0.76** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.6047 | | cosine_accuracy@3 | 0.7442 | | cosine_accuracy@5 | 0.7907 | | cosine_accuracy@10 | 0.8605 | | cosine_precision@1 | 0.6047 | | cosine_precision@3 | 0.2481 | | cosine_precision@5 | 0.1581 | | cosine_precision@10 | 0.086 | | cosine_recall@1 | 0.6047 | | cosine_recall@3 | 0.7442 | | cosine_recall@5 | 0.7907 | | cosine_recall@10 | 0.8605 | | cosine_ndcg@10 | 0.722 | | cosine_mrr@10 | 0.6786 | | **cosine_map@100** | **0.6823** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 530 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------| | BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker. | What are the benefits of the BharatPe speaker? | | BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker. | What advantages does the BharatPe speaker offer? | | BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker. | Can you outline the benefits of using the BharatPe speaker? | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 10 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `tf32`: False - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: False - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |:----------:|:-----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.9412 | 1 | - | 0.4829 | 0.5338 | 0.5921 | 0.3235 | 0.6100 | | 1.8824 | 2 | - | 0.5767 | 0.6175 | 0.6588 | 0.4176 | 0.6793 | | 2.8235 | 3 | - | 0.6337 | 0.6776 | 0.6979 | 0.5083 | 0.7263 | | 3.7647 | 4 | - | 0.6588 | 0.7257 | 0.7297 | 0.5840 | 0.7612 | | 4.7059 | 5 | - | 0.7049 | 0.7766 | 0.7643 | 0.6151 | 0.7902 | | 5.6471 | 6 | - | 0.7374 | 0.8257 | 0.7890 | 0.6519 | 0.7956 | | 6.5882 | 7 | - | 0.7573 | 0.8261 | 0.7912 | 0.6689 | 0.7978 | | 7.5294 | 8 | - | 0.7590 | 0.8275 | 0.7958 | 0.6811 | 0.8233 | | **8.4706** | **9** | **-** | **0.76** | **0.8392** | **0.7998** | **0.6823** | **0.8234** | | 9.4118 | 10 | 4.944 | 0.7600 | 0.8392 | 0.8017 | 0.6823 | 0.8350 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.32.1 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```