--- language: - en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6300 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-base-en-v1.5 datasets: [] 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 widget: - source_sentence: 'Forward-looking statements may appear throughout this report, including without limitation, the following sections: “Management''s Discussion and Analysis,” “Risk Factors” and "Notes 4, 8 and 13 to the Consolidated Financial Statements."' sentences: - How does a one-year adjustment in the 2023 expected retirement age for U.S. plans affect income before income taxes? - Which sections of the report might contain forward-looking statements according to the text? - What was the allowance for loan and lease losses at Bank of America as of December 31, 2022? - source_sentence: Interest income | $ | 267 | | | $ | 29 | | $ | 238 | | 821 | % sentences: - What are the key risks and uncertainties mentioned that could impact the validity of DaVita's forward-looking statements? - How did the interest income change in fiscal year 2023 compared to the previous year? - What are some of the main competitive factors in the interactive entertainment industry? - source_sentence: Veklury received U.S. Food and Drug Administration (FDA) and European Commission (EC) approval to treat COVID-19 in patients with mild to severe hepatic impairment and those with severe renal impairment, including those on dialysis. sentences: - What significant regulatory approvals did Gilead's Veklury receive? - What type of information is included under the caption "Legal Proceedings" in an Annual Report on Form 10-K? - What was the cash change related to changes in operating assets and liabilities, including working capital, in 2022? - source_sentence: The net value of property, plant, and equipment for the consolidated group increased from $12,028 million in 2022 to $12,680 million in 2023. sentences: - What steps does the company plan to take next after discussing data with regulators and key opinion leaders? - How does the company manage fluctuations in foreign currency exchange rates? - What was the increase in property, plant, and equipment net value from 2022 to 2023 for the consolidated group? - source_sentence: The effective duration of our total AFS and HTM investments securities as of December 31, 2023 is approximately 3.9 years. sentences: - What are the effective durations of the total Available-for-Sale (AFS) and Held-to-Maturity (HTM) investment securities as of December 31, 2023? - What was the net unit growth percentage for Hilton in the year ended December 31, 2023? - What does goodwill represent in accounting? pipeline_tag: sentence-similarity 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: 0.7285714285714285 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8485714285714285 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8885714285714286 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9214285714285714 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7285714285714285 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28285714285714286 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17771428571428569 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09214285714285712 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7285714285714285 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8485714285714285 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8885714285714286 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9214285714285714 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8274202252845575 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7969903628117911 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7998523047098398 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.72 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8442857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8785714285714286 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.92 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.72 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2814285714285714 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17571428571428568 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09199999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.72 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8442857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8785714285714286 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.92 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8213589464095679 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7896825396825394 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7926726035572866 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7214285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8385714285714285 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8742857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9128571428571428 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7214285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27952380952380956 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17485714285714282 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09128571428571428 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7214285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8385714285714285 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8742857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9128571428571428 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8190844047519252 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7888673469387758 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7921199469128796 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6971428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8328571428571429 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8671428571428571 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9057142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6971428571428572 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2776190476190476 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1734285714285714 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09057142857142855 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6971428571428572 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8328571428571429 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8671428571428571 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9057142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8054254319689889 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7729421768707481 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.776216648701894 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6614285714285715 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7985714285714286 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8442857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8814285714285715 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6614285714285715 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26619047619047614 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16885714285714284 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08814285714285712 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6614285714285715 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7985714285714286 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8442857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8814285714285715 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7728992637054746 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.737815759637188 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7417951294330247 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("Hritikmore/bge-base-financial-matryoshka") # Run inference sentences = [ 'The effective duration of our total AFS and HTM investments securities as of December 31, 2023 is approximately 3.9 years.', 'What are the effective durations of the total Available-for-Sale (AFS) and Held-to-Maturity (HTM) investment securities as of December 31, 2023?', 'What was the net unit growth percentage for Hilton in the year ended December 31, 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 | 0.7286 | | cosine_accuracy@3 | 0.8486 | | cosine_accuracy@5 | 0.8886 | | cosine_accuracy@10 | 0.9214 | | cosine_precision@1 | 0.7286 | | cosine_precision@3 | 0.2829 | | cosine_precision@5 | 0.1777 | | cosine_precision@10 | 0.0921 | | cosine_recall@1 | 0.7286 | | cosine_recall@3 | 0.8486 | | cosine_recall@5 | 0.8886 | | cosine_recall@10 | 0.9214 | | cosine_ndcg@10 | 0.8274 | | cosine_mrr@10 | 0.797 | | **cosine_map@100** | **0.7999** | #### 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 | 0.72 | | cosine_accuracy@3 | 0.8443 | | cosine_accuracy@5 | 0.8786 | | cosine_accuracy@10 | 0.92 | | cosine_precision@1 | 0.72 | | cosine_precision@3 | 0.2814 | | cosine_precision@5 | 0.1757 | | cosine_precision@10 | 0.092 | | cosine_recall@1 | 0.72 | | cosine_recall@3 | 0.8443 | | cosine_recall@5 | 0.8786 | | cosine_recall@10 | 0.92 | | cosine_ndcg@10 | 0.8214 | | cosine_mrr@10 | 0.7897 | | **cosine_map@100** | **0.7927** | #### 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 | 0.7214 | | cosine_accuracy@3 | 0.8386 | | cosine_accuracy@5 | 0.8743 | | cosine_accuracy@10 | 0.9129 | | cosine_precision@1 | 0.7214 | | cosine_precision@3 | 0.2795 | | cosine_precision@5 | 0.1749 | | cosine_precision@10 | 0.0913 | | cosine_recall@1 | 0.7214 | | cosine_recall@3 | 0.8386 | | cosine_recall@5 | 0.8743 | | cosine_recall@10 | 0.9129 | | cosine_ndcg@10 | 0.8191 | | cosine_mrr@10 | 0.7889 | | **cosine_map@100** | **0.7921** | #### 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 | 0.6971 | | cosine_accuracy@3 | 0.8329 | | cosine_accuracy@5 | 0.8671 | | cosine_accuracy@10 | 0.9057 | | cosine_precision@1 | 0.6971 | | cosine_precision@3 | 0.2776 | | cosine_precision@5 | 0.1734 | | cosine_precision@10 | 0.0906 | | cosine_recall@1 | 0.6971 | | cosine_recall@3 | 0.8329 | | cosine_recall@5 | 0.8671 | | cosine_recall@10 | 0.9057 | | cosine_ndcg@10 | 0.8054 | | cosine_mrr@10 | 0.7729 | | **cosine_map@100** | **0.7762** | #### 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 | 0.6614 | | cosine_accuracy@3 | 0.7986 | | cosine_accuracy@5 | 0.8443 | | cosine_accuracy@10 | 0.8814 | | cosine_precision@1 | 0.6614 | | cosine_precision@3 | 0.2662 | | cosine_precision@5 | 0.1689 | | cosine_precision@10 | 0.0881 | | cosine_recall@1 | 0.6614 | | cosine_recall@3 | 0.7986 | | cosine_recall@5 | 0.8443 | | cosine_recall@10 | 0.8814 | | cosine_ndcg@10 | 0.7729 | | cosine_mrr@10 | 0.7378 | | **cosine_map@100** | **0.7418** | ## 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 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------| | Significant judgment is required in evaluating our tax positions and during the ordinary course of business, there are many transactions and calculations for which the ultimate tax settlement is uncertain. As a result, we recognize the effect of this uncertainty on our tax attributes or taxes payable based on our estimates of the eventual outcome. | Why might the company's tax settlements vary? | | OPSUMIT is used for the treatment of pediatric pulmonary arterial hypertension. | What medical condition does OPSUMIT treat? | | Tangible equity ratios and tangible book value per share of common stock are non-GAAP financial measures. For more information on these ratios and corresponding reconciliations to GAAP financial measures, see Supplemental Financial Data and Non-GAAP Reconciliations. | What is the tangible equity ratio considered according to standard financial measures? | * 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 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 2 - `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`: 8 - `per_device_eval_batch_size`: 8 - `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`: 2 - `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 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.2030 | 10 | 0.7168 | - | - | - | - | - | | 0.4061 | 20 | 0.3345 | - | - | - | - | - | | 0.6091 | 30 | 0.2234 | - | - | - | - | - | | 0.8122 | 40 | 0.2126 | - | - | - | - | - | | **0.9949** | **49** | **-** | **0.7796** | **0.7844** | **0.7905** | **0.7293** | **0.7973** | | 1.0152 | 50 | 0.2301 | - | - | - | - | - | | 1.2183 | 60 | 0.1595 | - | - | - | - | - | | 1.4213 | 70 | 0.1082 | - | - | - | - | - | | 1.6244 | 80 | 0.0911 | - | - | - | - | - | | 1.8274 | 90 | 0.1068 | - | - | - | - | - | | 1.9898 | 98 | - | 0.7762 | 0.7921 | 0.7927 | 0.7418 | 0.7999 | * 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: 0.31.0 - 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} } ```