--- 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: Mergers and acquisitions, joint ventures and strategic investments complement our internal development and enhance our partnerships to align with Visa’s priorities. sentences: - How much did the unbilled accounts receivable amount to as of December 30, 2023? - What was the main reason for Visa to engage in mergers and acquisitions, joint ventures, and strategic investments? - What is the mission of Intuit? - source_sentence: Garmin’s audio brands, Fusion and JL Audio, offer premium audio products and accessories, including head units, speakers, amplifiers, subwoofers, and other audio components. These products are designed specifically for the marine, powersports, aftermarket automotive, home, or RV environments, offering premium sound quality and supporting many connectivity options for integrating with MFDs, smartphones, and Garmin wearables. sentences: - What type of insurance policies cover some of the defense and settlement costs associated with litigation mentioned? - What types of audio products does Garmin's Fusion and JL Audio brands offer? - What should investors consider when comparing Adjusted EBITDA across different companies? - source_sentence: Medical device products that are marketed in the European Union must comply with the requirements of the Medical Device Regulation (the MDR), which came into effect in May 2021. The MDR provides for regulatory oversight with respect to the design, manufacture, clinical trials, labeling and adverse event reporting for medical devices. sentences: - What are the requirements for medical devices to be marketed in the European Union under the MDR? - By what percentage did the pre-tax earnings increase from 2021 to 2022 in the manufacturing sector? - What were the cash and cash equivalents at the end of 2023? - source_sentence: In March 2023, the Board of Directors sanctioned a restructuring plan concentrated on investment prioritization towards significant growth prospects and the optimization of the company's real estate assets. This includes substantial organizational changes such as reductions in office space and workforce. sentences: - How many physicians are part of the domestic Office of the Chief Medical Officer at DaVita as of December 31, 2023? - What changes in expenses did Delta Air Lines' ancillary businesses and refinery segment encounter in 2023 compared to 2022? - What are the restructuring targets of the company's Board of Directors as of 2023? - source_sentence: The quality of GM dealerships and our relationship with our dealers are critical to our success, now, and as we transition to our all-electric future, given that they maintain the primary sales and service interface with the end consumer of our products. In addition to the terms of our contracts with our dealers, we are regulated by various country and state franchise laws and regulations that may supersede those contractual terms and impose specific regulatory sentences: - How does General[39 chars] Motors ensure quality in their dealership network? - How can the public access the company's financial and legal reports? - Is the outcome of the investigation into Tesla's waste segregation practices currently determinable? 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: 0.6785714285714286 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8171428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8671428571428571 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.91 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6785714285714286 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2723809523809524 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1734285714285714 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09099999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6785714285714286 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8171428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8671428571428571 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.91 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7949318413045188 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7579920634920636 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.761780829563342 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6714285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8171428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8642857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9028571428571428 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6714285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2723809523809524 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17285714285714285 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09028571428571427 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6714285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8171428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8642857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9028571428571428 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7892232861723367 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7524767573696142 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7566816338836445 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6671428571428571 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8142857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8657142857142858 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9028571428571428 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6671428571428571 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2714285714285714 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17314285714285713 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09028571428571427 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6671428571428571 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8142857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8657142857142858 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9028571428571428 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.786715703830093 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.749225056689342 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7532686203724872 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6542857142857142 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8071428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8428571428571429 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6542857142857142 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26904761904761904 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16857142857142854 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6542857142857142 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8071428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8428571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7763972670750712 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7369308390022671 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7407041984815913 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.62 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7671428571428571 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8171428571428572 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8785714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.62 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2557142857142857 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16342857142857142 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08785714285714284 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.62 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7671428571428571 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8171428571428572 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8785714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7482796784963641 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7067517006802718 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7110201251131743 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("uhoffmann/bge-base-financial-matryoshka") # Run inference sentences = [ 'The quality of GM dealerships and our relationship with our dealers are critical to our success, now, and as we transition to our all-electric future, given that they maintain the primary sales and service interface with the end consumer of our products. In addition to the terms of our contracts with our dealers, we are regulated by various country and state franchise laws and regulations that may supersede those contractual terms and impose specific regulatory', 'How does General[39 chars] Motors ensure quality in their dealership network?', "How can the public access the company's financial and legal reports?", ] 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 | 0.6786 | | cosine_accuracy@3 | 0.8171 | | cosine_accuracy@5 | 0.8671 | | cosine_accuracy@10 | 0.91 | | cosine_precision@1 | 0.6786 | | cosine_precision@3 | 0.2724 | | cosine_precision@5 | 0.1734 | | cosine_precision@10 | 0.091 | | cosine_recall@1 | 0.6786 | | cosine_recall@3 | 0.8171 | | cosine_recall@5 | 0.8671 | | cosine_recall@10 | 0.91 | | cosine_ndcg@10 | 0.7949 | | cosine_mrr@10 | 0.758 | | **cosine_map@100** | **0.7618** | #### 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 | 0.6714 | | cosine_accuracy@3 | 0.8171 | | cosine_accuracy@5 | 0.8643 | | cosine_accuracy@10 | 0.9029 | | cosine_precision@1 | 0.6714 | | cosine_precision@3 | 0.2724 | | cosine_precision@5 | 0.1729 | | cosine_precision@10 | 0.0903 | | cosine_recall@1 | 0.6714 | | cosine_recall@3 | 0.8171 | | cosine_recall@5 | 0.8643 | | cosine_recall@10 | 0.9029 | | cosine_ndcg@10 | 0.7892 | | cosine_mrr@10 | 0.7525 | | **cosine_map@100** | **0.7567** | #### 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 | 0.6671 | | cosine_accuracy@3 | 0.8143 | | cosine_accuracy@5 | 0.8657 | | cosine_accuracy@10 | 0.9029 | | cosine_precision@1 | 0.6671 | | cosine_precision@3 | 0.2714 | | cosine_precision@5 | 0.1731 | | cosine_precision@10 | 0.0903 | | cosine_recall@1 | 0.6671 | | cosine_recall@3 | 0.8143 | | cosine_recall@5 | 0.8657 | | cosine_recall@10 | 0.9029 | | cosine_ndcg@10 | 0.7867 | | cosine_mrr@10 | 0.7492 | | **cosine_map@100** | **0.7533** | #### 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 | 0.6543 | | cosine_accuracy@3 | 0.8071 | | cosine_accuracy@5 | 0.8429 | | cosine_accuracy@10 | 0.9 | | cosine_precision@1 | 0.6543 | | cosine_precision@3 | 0.269 | | cosine_precision@5 | 0.1686 | | cosine_precision@10 | 0.09 | | cosine_recall@1 | 0.6543 | | cosine_recall@3 | 0.8071 | | cosine_recall@5 | 0.8429 | | cosine_recall@10 | 0.9 | | cosine_ndcg@10 | 0.7764 | | cosine_mrr@10 | 0.7369 | | **cosine_map@100** | **0.7407** | #### 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 | 0.62 | | cosine_accuracy@3 | 0.7671 | | cosine_accuracy@5 | 0.8171 | | cosine_accuracy@10 | 0.8786 | | cosine_precision@1 | 0.62 | | cosine_precision@3 | 0.2557 | | cosine_precision@5 | 0.1634 | | cosine_precision@10 | 0.0879 | | cosine_recall@1 | 0.62 | | cosine_recall@3 | 0.7671 | | cosine_recall@5 | 0.8171 | | cosine_recall@10 | 0.8786 | | cosine_ndcg@10 | 0.7483 | | cosine_mrr@10 | 0.7068 | | **cosine_map@100** | **0.711** | ## 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 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------| | Walmart Inc. reported total revenues of $611,289 million for the fiscal year ended January 31, 2023. | What was Walmart Inc.'s total revenue in the fiscal year ended January 31, 2023? | | The total equity balance of Visa Inc. as of September 30, 2023 was $38,733 million. | What was the total equity of Visa Inc. as of September 30, 2023? | | Nike incorporates new technologies in its product design by using market intelligence and research, which helps its design teams identify opportunities to leverage these technologies in existing categories to respond to consumer preferences. | How does Nike incorporate new technologies in its product design? | * 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 - `tf32`: 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 - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 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`: True - `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 - `eval_on_start`: False - `eval_use_gather_object`: 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 | 1.5521 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7178 | 0.7352 | 0.7404 | 0.6833 | 0.7422 | | 1.6244 | 20 | 0.6753 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7340 | 0.7452 | 0.7524 | 0.7057 | 0.7561 | | 2.4365 | 30 | 0.4611 | - | - | - | - | - | | 2.9239 | 36 | - | 0.7392 | 0.7509 | 0.7560 | 0.7103 | 0.7588 | | 3.2487 | 40 | 0.3763 | - | - | - | - | - | | **3.8985** | **48** | **-** | **0.7407** | **0.7533** | **0.7567** | **0.711** | **0.7618** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.44.2 - PyTorch: 2.4.0+cu121 - Accelerate: 0.32.1 - Datasets: 2.21.0 - 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", } ``` #### 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} } ```