--- base_model: BAAI/bge-base-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:6300 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: The U.S. International Trade Commission (ITC) has become a significant forum to litigate intellectual property disputes. An adverse result in an ITC action can lead to a prohibition on importing infringing products, which, given the importance of the U.S. market, could significantly impact a company including preventing the importation of many important products or necessitating workarounds that may limit certain features of their products. sentences: - What was the overall impact of foreign currencies on net sales in 2023? - What potential consequences could result from intellectual property disputes in the U.S. International Trade Commission for the company? - What was the total purchase consideration for the VMware acquisition? - source_sentence: Reinsurance contracts are normally classified as treaty or facultative contracts. Treaty reinsurance refers to reinsurance coverage for all or a portion of a specified group or class of risks ceded by a direct insurer or reinsurer, while facultative reinsurance involves coverage of specific individual underlying risks. Reinsurance contracts are further classified as quota-share or excess. sentences: - What type of information will you find under 'Note 13 — Commitments and Contingencies' in an Annual Report on Form 10-K? - What type of reinsurance contracts are offered by Berkshire Hathaway Reinsurance Group? - What are the consequences for a company violating anti-bribery laws in the U.S.? - source_sentence: Commitments and contingencies related to legal proceedings are detailed in Part II, Item 8, under 'Financial Statements and Supplementary Data – Note 14'. sentences: - Where can one find commitments and contingencies related to legal proceedings in the context provided? - What is discussed in Item 3. Legal Proceedings of a company's report? - How are net realized capital gains and losses treated in the financial statements according to the Company? - source_sentence: The “Glossary of Terms and Acronyms” is included on pages 315-321 in the set of financial documents. sentences: - What are the principles used in preparing the discussed financial statements? - What is the total remaining budget for future common stock repurchases under the company's stock repurchase programs as of December 31, 2023? - Where is the “Glossary of Terms and Acronyms” located in a set of financial documents? - source_sentence: The table presents our market risk by asset category for positions accounted for at fair value or accounted for at the lower of cost or fair value, that are not included in VaR. As of December 2023, equity was at $1,562 million and debt was at $2,446 million. sentences: - What are the market risk values for Goldman Sachs' equity and debt positions not included in VaR as of December 2023? - What was the conclusion of the Company's review regarding the impact of the American Rescue Plan, the Consolidated Appropriations Act, 2021, and related tax provisions on its business for the fiscal year ended June 30, 2023? - How much did the company's finance lease obligations total as of December 31, 2023? model-index: - name: BGE base Financial Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.6957142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8371428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8714285714285714 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9242857142857143 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6957142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27904761904761904 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17428571428571424 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09242857142857142 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6957142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8371428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8714285714285714 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9242857142857143 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8105294489003092 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7741910430839002 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7773317927980538 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.7 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8285714285714286 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8671428571428571 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9185714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27619047619047615 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1734285714285714 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09185714285714283 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8285714285714286 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8671428571428571 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9185714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8090367290103152 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7740351473922898 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7776494145961331 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6928571428571428 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8185714285714286 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8585714285714285 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.91 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6928571428571428 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27285714285714285 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17171428571428568 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09099999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6928571428571428 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8185714285714286 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8585714285714285 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.91 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8016663265681359 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7669977324263035 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7711841838569463 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6871428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8071428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8585714285714285 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8985714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6871428571428572 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26904761904761904 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1717142857142857 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08985714285714283 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6871428571428572 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8071428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8585714285714285 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8985714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7921056491431833 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7580946712018135 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7627063166788922 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6642857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7842857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8257142857142857 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8728571428571429 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6642857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26142857142857145 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16514285714285715 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08728571428571427 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6642857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7842857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8257142857142857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8728571428571429 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7689727571743198 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7358214285714282 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7406658506857838 name: Cosine Map@100 --- # BGE base Financial Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) - **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': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("akashmaggon/bge-base-financial-matryoshka") # Run inference sentences = [ 'The table presents our market risk by asset category for positions accounted for at fair value or accounted for at the lower of cost or fair value, that are not included in VaR. As of December 2023, equity was at $1,562 million and debt was at $2,446 million.', "What are the market risk values for Goldman Sachs' equity and debt positions not included in VaR as of December 2023?", "What was the conclusion of the Company's review regarding the impact of the American Rescue Plan, the Consolidated Appropriations Act, 2021, and related tax provisions on its business for the fiscal year ended June 30, 2023?", ] 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 | 0.6957 | | cosine_accuracy@3 | 0.8371 | | cosine_accuracy@5 | 0.8714 | | cosine_accuracy@10 | 0.9243 | | cosine_precision@1 | 0.6957 | | cosine_precision@3 | 0.279 | | cosine_precision@5 | 0.1743 | | cosine_precision@10 | 0.0924 | | cosine_recall@1 | 0.6957 | | cosine_recall@3 | 0.8371 | | cosine_recall@5 | 0.8714 | | cosine_recall@10 | 0.9243 | | cosine_ndcg@10 | 0.8105 | | cosine_mrr@10 | 0.7742 | | **cosine_map@100** | **0.7773** | #### 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 | 0.7 | | cosine_accuracy@3 | 0.8286 | | cosine_accuracy@5 | 0.8671 | | cosine_accuracy@10 | 0.9186 | | cosine_precision@1 | 0.7 | | cosine_precision@3 | 0.2762 | | cosine_precision@5 | 0.1734 | | cosine_precision@10 | 0.0919 | | cosine_recall@1 | 0.7 | | cosine_recall@3 | 0.8286 | | cosine_recall@5 | 0.8671 | | cosine_recall@10 | 0.9186 | | cosine_ndcg@10 | 0.809 | | cosine_mrr@10 | 0.774 | | **cosine_map@100** | **0.7776** | #### 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 | 0.6929 | | cosine_accuracy@3 | 0.8186 | | cosine_accuracy@5 | 0.8586 | | cosine_accuracy@10 | 0.91 | | cosine_precision@1 | 0.6929 | | cosine_precision@3 | 0.2729 | | cosine_precision@5 | 0.1717 | | cosine_precision@10 | 0.091 | | cosine_recall@1 | 0.6929 | | cosine_recall@3 | 0.8186 | | cosine_recall@5 | 0.8586 | | cosine_recall@10 | 0.91 | | cosine_ndcg@10 | 0.8017 | | cosine_mrr@10 | 0.767 | | **cosine_map@100** | **0.7712** | #### 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 | 0.6871 | | cosine_accuracy@3 | 0.8071 | | cosine_accuracy@5 | 0.8586 | | cosine_accuracy@10 | 0.8986 | | cosine_precision@1 | 0.6871 | | cosine_precision@3 | 0.269 | | cosine_precision@5 | 0.1717 | | cosine_precision@10 | 0.0899 | | cosine_recall@1 | 0.6871 | | cosine_recall@3 | 0.8071 | | cosine_recall@5 | 0.8586 | | cosine_recall@10 | 0.8986 | | cosine_ndcg@10 | 0.7921 | | cosine_mrr@10 | 0.7581 | | **cosine_map@100** | **0.7627** | #### 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 | 0.6643 | | cosine_accuracy@3 | 0.7843 | | cosine_accuracy@5 | 0.8257 | | cosine_accuracy@10 | 0.8729 | | cosine_precision@1 | 0.6643 | | cosine_precision@3 | 0.2614 | | cosine_precision@5 | 0.1651 | | cosine_precision@10 | 0.0873 | | cosine_recall@1 | 0.6643 | | cosine_recall@3 | 0.7843 | | cosine_recall@5 | 0.8257 | | cosine_recall@10 | 0.8729 | | cosine_ndcg@10 | 0.769 | | cosine_mrr@10 | 0.7358 | | **cosine_map@100** | **0.7407** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 6,300 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:---------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------| | Johnson & Johnson reported cash and cash equivalents of $21,859 million as of the end of 2023. | What was the amount of cash and cash equivalents reported by Johnson & Johnson at the end of 2023? | | Johnson & Johnson's consolidated statements of earnings for 2023 reported total net earnings of $35,153 million. | What was the total net earnings for Johnson & Johnson in 2023? | | As of December 31, 2023, short-term investments were valued at $236,118 thousand and long-term investments at $86,676 thousand. | What is the total value of short-term and long-term investments held by the company as of December 31, 2023? | * 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`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `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`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: 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_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 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.8122 | 10 | 1.5779 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7388 | 0.7509 | 0.7604 | 0.7081 | 0.7579 | | 1.6244 | 20 | 0.6572 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7612 | 0.7670 | 0.7729 | 0.7269 | 0.7705 | | 2.4365 | 30 | 0.4661 | - | - | - | - | - | | 2.9239 | 36 | - | 0.7623 | 0.7702 | 0.7771 | 0.7386 | 0.7758 | | 3.2487 | 40 | 0.3774 | - | - | - | - | - | | **3.8985** | **48** | **-** | **0.7627** | **0.7712** | **0.7776** | **0.7407** | **0.7773** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.1+cu121 - Accelerate: 0.32.1 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### 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} } ```