--- base_model: BAAI/bge-large-en-v1.5 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: BGE large Chatbot Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.8837209302325582 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9534883720930233 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9534883720930233 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9534883720930233 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8837209302325582 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3178294573643411 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19069767441860463 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09534883720930232 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8837209302325582 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9534883720930233 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9534883720930233 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9534883720930233 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9246944071428586 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9147286821705425 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9186317558410582 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.8837209302325582 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9534883720930233 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9534883720930233 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9534883720930233 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8837209302325582 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3178294573643411 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19069767441860463 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09534883720930232 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8837209302325582 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9534883720930233 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9534883720930233 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9534883720930233 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9246944071428586 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9147286821705425 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9186317558410582 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.8837209302325582 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9302325581395349 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9534883720930233 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9534883720930233 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8837209302325582 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.31007751937984496 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19069767441860463 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09534883720930232 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8837209302325582 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9302325581395349 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9534883720930233 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9534883720930233 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9220630770785455 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9116279069767442 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9147848047984846 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.9069767441860465 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9302325581395349 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9302325581395349 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9534883720930233 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9069767441860465 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.31007751937984496 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18604651162790697 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09534883720930232 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.9069767441860465 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9302325581395349 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9302325581395349 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9534883720930233 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9299334172251043 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9224806201550388 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.92549351912877 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.8604651162790697 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9534883720930233 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9767441860465116 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9767441860465116 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8604651162790697 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3178294573643411 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1953488372093023 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09767441860465115 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8604651162790697 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9534883720930233 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9767441860465116 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9767441860465116 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9261271120648318 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9089147286821706 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9089147286821704 name: Cosine Map@100 --- # BGE large Chatbot Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 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': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("MANMEET75/bge-large-Chatbot-matryoshka") # 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, 1024] # 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 | 0.8837 | | cosine_accuracy@3 | 0.9535 | | cosine_accuracy@5 | 0.9535 | | cosine_accuracy@10 | 0.9535 | | cosine_precision@1 | 0.8837 | | cosine_precision@3 | 0.3178 | | cosine_precision@5 | 0.1907 | | cosine_precision@10 | 0.0953 | | cosine_recall@1 | 0.8837 | | cosine_recall@3 | 0.9535 | | cosine_recall@5 | 0.9535 | | cosine_recall@10 | 0.9535 | | cosine_ndcg@10 | 0.9247 | | cosine_mrr@10 | 0.9147 | | **cosine_map@100** | **0.9186** | #### 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 | 0.8837 | | cosine_accuracy@3 | 0.9535 | | cosine_accuracy@5 | 0.9535 | | cosine_accuracy@10 | 0.9535 | | cosine_precision@1 | 0.8837 | | cosine_precision@3 | 0.3178 | | cosine_precision@5 | 0.1907 | | cosine_precision@10 | 0.0953 | | cosine_recall@1 | 0.8837 | | cosine_recall@3 | 0.9535 | | cosine_recall@5 | 0.9535 | | cosine_recall@10 | 0.9535 | | cosine_ndcg@10 | 0.9247 | | cosine_mrr@10 | 0.9147 | | **cosine_map@100** | **0.9186** | #### 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 | 0.8837 | | cosine_accuracy@3 | 0.9302 | | cosine_accuracy@5 | 0.9535 | | cosine_accuracy@10 | 0.9535 | | cosine_precision@1 | 0.8837 | | cosine_precision@3 | 0.3101 | | cosine_precision@5 | 0.1907 | | cosine_precision@10 | 0.0953 | | cosine_recall@1 | 0.8837 | | cosine_recall@3 | 0.9302 | | cosine_recall@5 | 0.9535 | | cosine_recall@10 | 0.9535 | | cosine_ndcg@10 | 0.9221 | | cosine_mrr@10 | 0.9116 | | **cosine_map@100** | **0.9148** | #### 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 | 0.907 | | cosine_accuracy@3 | 0.9302 | | cosine_accuracy@5 | 0.9302 | | cosine_accuracy@10 | 0.9535 | | cosine_precision@1 | 0.907 | | cosine_precision@3 | 0.3101 | | cosine_precision@5 | 0.186 | | cosine_precision@10 | 0.0953 | | cosine_recall@1 | 0.907 | | cosine_recall@3 | 0.9302 | | cosine_recall@5 | 0.9302 | | cosine_recall@10 | 0.9535 | | cosine_ndcg@10 | 0.9299 | | cosine_mrr@10 | 0.9225 | | **cosine_map@100** | **0.9255** | #### 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 | 0.8605 | | cosine_accuracy@3 | 0.9535 | | cosine_accuracy@5 | 0.9767 | | cosine_accuracy@10 | 0.9767 | | cosine_precision@1 | 0.8605 | | cosine_precision@3 | 0.3178 | | cosine_precision@5 | 0.1953 | | cosine_precision@10 | 0.0977 | | cosine_recall@1 | 0.8605 | | cosine_recall@3 | 0.9535 | | cosine_recall@5 | 0.9767 | | cosine_recall@10 | 0.9767 | | cosine_ndcg@10 | 0.9261 | | cosine_mrr@10 | 0.9089 | | **cosine_map@100** | **0.9089** | ## 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 | - | 0.7980 | 0.8251 | 0.8141 | 0.7124 | 0.8260 | | 1.8824 | 2 | - | 0.8624 | 0.8619 | 0.8691 | 0.7637 | 0.8557 | | 2.8235 | 3 | - | 0.8763 | 0.8792 | 0.8770 | 0.8588 | 0.8832 | | 3.7647 | 4 | - | 0.9007 | 0.9014 | 0.9115 | 0.8820 | 0.9130 | | 4.7059 | 5 | - | 0.9014 | 0.9146 | 0.9186 | 0.9053 | 0.9185 | | 5.6471 | 6 | - | 0.9134 | 0.9146 | 0.9186 | 0.9205 | 0.9183 | | **6.5882** | **7** | **-** | **0.9255** | **0.9146** | **0.9186** | **0.9089** | **0.9185** | | 7.5294 | 8 | - | 0.9255 | 0.9147 | 0.9186 | 0.9089 | 0.9185 | | 8.4706 | 9 | - | 0.9255 | 0.9147 | 0.9186 | 0.9089 | 0.9186 | | 9.4118 | 10 | 2.0337 | 0.9255 | 0.9148 | 0.9186 | 0.9089 | 0.9186 | * 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} } ```