--- base_model: BAAI/bge-base-en-v1.5 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: Chevron regularly conducts employee surveys throughout the year to assess the health of the company’s culture, allowing them to gain insights into employee well-being. sentences: - What was the net cash provided by operating activities for the year ended December 31, 2023? - How often does Chevron conduct employee surveys to assess the health of its culture? - What were the total future minimum lease payments for Comcast's operating leases as of December 31, 2023? - source_sentence: Gross margin for the fiscal year decreased 250 basis points to 43.5% primarily driven by higher product costs, higher markdowns and unfavorable changes in foreign currency exchange rates, partially offset by strategic pricing actions. sentences: - How does the company maintain high standards of product quality and safety? - What were the main factors that negatively impacted NIKE's gross margin in fiscal 2023? - What was the growth rate of Visa Inc.'s commercial payments volume internationally between 2021 and 2022? - source_sentence: Mr. Teter holds a B.S. degree in Mechanical Engineering from the University of California at Davis and a J.D. degree from Stanford Law School. sentences: - What degrees does Timothy S. Teter hold and from which institutions? - What regulations are in place in Europe regarding interactions between pharmaceutical companies and physicians? - What economic factors particularly affected Garmin's consumer behavior in 2023? - source_sentence: Our Office of Diversity, Equity and Inclusion supports our focus on associate diversity, supplier diversity, and engagement with our communities. sentences: - What are the three segments of alcohol ready-to-drink beverages the company is focusing on? - How much net cash was provided by operating activities in 2023? - What is the focus of The Home Depot's Office of Diversity, Equity and Inclusion? - source_sentence: Net cash used in financing activities totaled $2,614 in 2023, compared to $4,283 in 2022. sentences: - What was the net cash used in financing activities in 2023 and how does it compare to 2022? - What are Chipotle's key strategies for business growth as discussed in their strategy? - What are the primary regulatory authorities that supervise and regulate JPMorgan Chase in the U.S.? 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: 0.6971428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8685714285714285 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: 0.2733333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1737142857142857 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: 0.82 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8685714285714285 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9057142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.803607128355984 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.770687641723356 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.77485834386751 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6957142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8228571428571428 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8642857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9042857142857142 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6957142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2742857142857143 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17285714285714285 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0904285714285714 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6957142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8228571428571428 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8642857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9042857142857142 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.802840202489837 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7701360544217687 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7744106258164117 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6871428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8185714285714286 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8528571428571429 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: 0.27285714285714285 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17057142857142854 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08985714285714284 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6871428571428572 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8185714285714286 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8528571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8985714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.795190594370522 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7619773242630383 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7664081914180308 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6685714285714286 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8128571428571428 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8428571428571429 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8942857142857142 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6685714285714286 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27095238095238094 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16857142857142854 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08942857142857143 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6685714285714286 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8128571428571428 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8428571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8942857142857142 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7840862792892018 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7486655328798184 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7527149388922518 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6471428571428571 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7828571428571428 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8242857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8685714285714285 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6471428571428571 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26095238095238094 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16485714285714284 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08685714285714284 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6471428571428571 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7828571428571428 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8242857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8685714285714285 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7601900384958588 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.725268707482993 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7302983967510448 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) on the json dataset. 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 - **Training Dataset:** - json - **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("revtestuser/bge-base-financial-matryoshka") # Run inference sentences = [ 'Net cash used in financing activities totaled $2,614 in 2023, compared to $4,283 in 2022.', 'What was the net cash used in financing activities in 2023 and how does it compare to 2022?', "What are Chipotle's key strategies for business growth as discussed in their strategy?", ] 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 | 0.6971 | | cosine_accuracy@3 | 0.82 | | cosine_accuracy@5 | 0.8686 | | cosine_accuracy@10 | 0.9057 | | cosine_precision@1 | 0.6971 | | cosine_precision@3 | 0.2733 | | cosine_precision@5 | 0.1737 | | cosine_precision@10 | 0.0906 | | cosine_recall@1 | 0.6971 | | cosine_recall@3 | 0.82 | | cosine_recall@5 | 0.8686 | | cosine_recall@10 | 0.9057 | | cosine_ndcg@10 | 0.8036 | | cosine_mrr@10 | 0.7707 | | **cosine_map@100** | **0.7749** | #### 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 | 0.6957 | | cosine_accuracy@3 | 0.8229 | | cosine_accuracy@5 | 0.8643 | | cosine_accuracy@10 | 0.9043 | | cosine_precision@1 | 0.6957 | | cosine_precision@3 | 0.2743 | | cosine_precision@5 | 0.1729 | | cosine_precision@10 | 0.0904 | | cosine_recall@1 | 0.6957 | | cosine_recall@3 | 0.8229 | | cosine_recall@5 | 0.8643 | | cosine_recall@10 | 0.9043 | | cosine_ndcg@10 | 0.8028 | | cosine_mrr@10 | 0.7701 | | **cosine_map@100** | **0.7744** | #### 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 | 0.6871 | | cosine_accuracy@3 | 0.8186 | | cosine_accuracy@5 | 0.8529 | | cosine_accuracy@10 | 0.8986 | | cosine_precision@1 | 0.6871 | | cosine_precision@3 | 0.2729 | | cosine_precision@5 | 0.1706 | | cosine_precision@10 | 0.0899 | | cosine_recall@1 | 0.6871 | | cosine_recall@3 | 0.8186 | | cosine_recall@5 | 0.8529 | | cosine_recall@10 | 0.8986 | | cosine_ndcg@10 | 0.7952 | | cosine_mrr@10 | 0.762 | | **cosine_map@100** | **0.7664** | #### 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 | 0.6686 | | cosine_accuracy@3 | 0.8129 | | cosine_accuracy@5 | 0.8429 | | cosine_accuracy@10 | 0.8943 | | cosine_precision@1 | 0.6686 | | cosine_precision@3 | 0.271 | | cosine_precision@5 | 0.1686 | | cosine_precision@10 | 0.0894 | | cosine_recall@1 | 0.6686 | | cosine_recall@3 | 0.8129 | | cosine_recall@5 | 0.8429 | | cosine_recall@10 | 0.8943 | | cosine_ndcg@10 | 0.7841 | | cosine_mrr@10 | 0.7487 | | **cosine_map@100** | **0.7527** | #### 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 | 0.6471 | | cosine_accuracy@3 | 0.7829 | | cosine_accuracy@5 | 0.8243 | | cosine_accuracy@10 | 0.8686 | | cosine_precision@1 | 0.6471 | | cosine_precision@3 | 0.261 | | cosine_precision@5 | 0.1649 | | cosine_precision@10 | 0.0869 | | cosine_recall@1 | 0.6471 | | cosine_recall@3 | 0.7829 | | cosine_recall@5 | 0.8243 | | cosine_recall@10 | 0.8686 | | cosine_ndcg@10 | 0.7602 | | cosine_mrr@10 | 0.7253 | | **cosine_map@100** | **0.7303** | ## Training Details ### Training Dataset #### json * Dataset: json * 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 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Certain provisions of the final rule become effective on April 1, 2024, but the majority of the final rule’s operative provisions (including the revisions to the definition of “limited purpose bank”) become effective on January 1, 2026, with additional data collection and reporting requirements becoming effective on January 1, 2027. | What are the effective dates for the main provisions and additional data collection and reporting requirements of the final rule impacting AENB's compliance obligations? | | Our total revenue for 2023 was $134.90 billion, an increase of 16% compared to 2022. | What was the total revenue for the year 2023 and the percentage increase from 2022? | | As of December 31, 2023, our domestic Chief Medical Officer leads a team of 22 nephrologists in our physician leadership team as part of our domestic Office of the Chief Medical Officer. | How many physicians are part of the domestic Office of the Chief Medical Officer at DaVita 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 - `fp16`: True - `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`: 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`: False - `fp16`: True - `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 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.8122 | 10 | 1.6288 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7384 | 0.7485 | 0.7508 | 0.7013 | 0.7561 | | 1.6244 | 20 | 0.6896 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7499 | 0.7621 | 0.7676 | 0.7220 | 0.7704 | | 2.4365 | 30 | 0.4965 | - | - | - | - | - | | 2.9239 | 36 | - | 0.7529 | 0.7669 | 0.7739 | 0.7302 | 0.7754 | | 3.2487 | 40 | 0.415 | - | - | - | - | - | | **3.8985** | **48** | **-** | **0.7527** | **0.7664** | **0.7744** | **0.7303** | **0.7749** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.1.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.34.2 - 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} } ```