--- 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@10 widget: - source_sentence: The Gross Merchandise Sales (GMS) decreased by 1.2% in 2023 compared to 2022. sentences: - What specific matters did the CFPB investigate concerning Equifax? - What was the percentage decline in GMS for the year ended December 31, 2023 compared to 2022? - What percentage of eBay's 2023 net revenues were attributed to international markets? - source_sentence: Asset management and administration fees vary with changes in the balances of client assets due to market fluctuations and client activity. sentences: - Why was there a net outflow of cash in financing activities in fiscal 2022? - How do asset management and administration fees vary at The Charles Schwab Corporation? - What are some key goals of the corporation related to climate change? - source_sentence: Operating profit margin was 19.3 percent in 2023, compared with 13.3 percent in 2022. sentences: - What was the operating profit margin for 2023? - How do the studios compete in the entertainment industry? - What types of audio products does Garmin's Fusion and JL Audio brands offer? - source_sentence: Subsequent to 2023, on February 12, 2024, AbbVie borrowed $5.0 billion under the term loan credit agreement. sentences: - What percentage of U.S. dialysis patient service revenues in 2023 came from Medicare and Medicare Advantage plans? - What is Peloton Interactive, Inc. known for in the interactive fitness industry? - What was the purpose stated by AbbVie for borrowing $5.0 billion under the term loan credit agreement on February 12, 2024? - source_sentence: Chipotle retains an independent third-party compensation consultant each year to conduct a pay equity analysis of its U.S. and Canadian workforce, including factors of pay such as grade level, tenure in role, and external market conditions like geographic location, to ensure consistency and equitable treatment among employees. sentences: - How does Chipotle ensure pay equity among its employees? - How can one locate information on legal proceedings within the Consolidated Financial Statements? - What criteria did the independent audit use to assess the effectiveness of internal control over financial reporting at the company? 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: 0.6871428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8214285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8585714285714285 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6871428571428572 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27380952380952384 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1717142857142857 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6871428571428572 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8214285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8585714285714285 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7966931280955273 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7633656462585031 name: Cosine Mrr@10 - type: cosine_map@10 value: 0.7633656462585034 name: Cosine Map@10 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6857142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8557142857142858 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9014285714285715 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6857142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2733333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17114285714285712 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09014285714285712 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6857142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.82 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8557142857142858 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9014285714285715 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7951662657569053 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.761045918367347 name: Cosine Mrr@10 - type: cosine_map@10 value: 0.761045918367347 name: Cosine Map@10 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6814285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8171428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8571428571428571 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8885714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6814285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2723809523809524 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17142857142857137 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08885714285714284 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6814285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8171428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8571428571428571 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8885714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7890567420578879 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7567375283446709 name: Cosine Mrr@10 - type: cosine_map@10 value: 0.7567375283446711 name: Cosine Map@10 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6571428571428571 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8071428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8457142857142858 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8742857142857143 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6571428571428571 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26904761904761904 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16914285714285712 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08742857142857141 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6571428571428571 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8071428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8457142857142858 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8742857142857143 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7723888716536037 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7390544217687071 name: Cosine Mrr@10 - type: cosine_map@10 value: 0.7390544217687074 name: Cosine Map@10 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6157142857142858 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7685714285714286 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8171428571428572 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8557142857142858 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6157142857142858 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2561904761904762 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1634285714285714 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08557142857142856 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6157142857142858 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7685714285714286 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8171428571428572 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8557142857142858 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7405386424360808 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7031672335600904 name: Cosine Mrr@10 - type: cosine_map@10 value: 0.7031672335600907 name: Cosine Map@10 --- # 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("Sailesh9999/bge-base-financial-matryoshka_3") # Run inference sentences = [ 'Chipotle retains an independent third-party compensation consultant each year to conduct a pay equity analysis of its U.S. and Canadian workforce, including factors of pay such as grade level, tenure in role, and external market conditions like geographic location, to ensure consistency and equitable treatment among employees.', 'How does Chipotle ensure pay equity among its employees?', 'How can one locate information on legal proceedings within the Consolidated Financial Statements?', ] 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 | 0.6871 | | cosine_accuracy@3 | 0.8214 | | cosine_accuracy@5 | 0.8586 | | cosine_accuracy@10 | 0.9 | | cosine_precision@1 | 0.6871 | | cosine_precision@3 | 0.2738 | | cosine_precision@5 | 0.1717 | | cosine_precision@10 | 0.09 | | cosine_recall@1 | 0.6871 | | cosine_recall@3 | 0.8214 | | cosine_recall@5 | 0.8586 | | cosine_recall@10 | 0.9 | | cosine_ndcg@10 | 0.7967 | | cosine_mrr@10 | 0.7634 | | **cosine_map@10** | **0.7634** | #### 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 | 0.6857 | | cosine_accuracy@3 | 0.82 | | cosine_accuracy@5 | 0.8557 | | cosine_accuracy@10 | 0.9014 | | cosine_precision@1 | 0.6857 | | cosine_precision@3 | 0.2733 | | cosine_precision@5 | 0.1711 | | cosine_precision@10 | 0.0901 | | cosine_recall@1 | 0.6857 | | cosine_recall@3 | 0.82 | | cosine_recall@5 | 0.8557 | | cosine_recall@10 | 0.9014 | | cosine_ndcg@10 | 0.7952 | | cosine_mrr@10 | 0.761 | | **cosine_map@10** | **0.761** | #### 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 | 0.6814 | | cosine_accuracy@3 | 0.8171 | | cosine_accuracy@5 | 0.8571 | | cosine_accuracy@10 | 0.8886 | | cosine_precision@1 | 0.6814 | | cosine_precision@3 | 0.2724 | | cosine_precision@5 | 0.1714 | | cosine_precision@10 | 0.0889 | | cosine_recall@1 | 0.6814 | | cosine_recall@3 | 0.8171 | | cosine_recall@5 | 0.8571 | | cosine_recall@10 | 0.8886 | | cosine_ndcg@10 | 0.7891 | | cosine_mrr@10 | 0.7567 | | **cosine_map@10** | **0.7567** | #### 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 | 0.6571 | | cosine_accuracy@3 | 0.8071 | | cosine_accuracy@5 | 0.8457 | | cosine_accuracy@10 | 0.8743 | | cosine_precision@1 | 0.6571 | | cosine_precision@3 | 0.269 | | cosine_precision@5 | 0.1691 | | cosine_precision@10 | 0.0874 | | cosine_recall@1 | 0.6571 | | cosine_recall@3 | 0.8071 | | cosine_recall@5 | 0.8457 | | cosine_recall@10 | 0.8743 | | cosine_ndcg@10 | 0.7724 | | cosine_mrr@10 | 0.7391 | | **cosine_map@10** | **0.7391** | #### 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 | 0.6157 | | cosine_accuracy@3 | 0.7686 | | cosine_accuracy@5 | 0.8171 | | cosine_accuracy@10 | 0.8557 | | cosine_precision@1 | 0.6157 | | cosine_precision@3 | 0.2562 | | cosine_precision@5 | 0.1634 | | cosine_precision@10 | 0.0856 | | cosine_recall@1 | 0.6157 | | cosine_recall@3 | 0.7686 | | cosine_recall@5 | 0.8171 | | cosine_recall@10 | 0.8557 | | cosine_ndcg@10 | 0.7405 | | cosine_mrr@10 | 0.7032 | | **cosine_map@10** | **0.7032** | ## 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 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------| | Americas | $ | 7,631,647 | | | $ | 6,817,454 | | 79.3 | % | 84.1 | % | What was the proportion of Americas' net revenue to the company's total net revenue in 2023, and how did it change from 2022? | | Item 1 Business typically includes detailed information about the organization's operations, the nature of the business, and its strategic direction. | What is the title of the section that potentially discusses the operations or nature of a business in a document? | | Operating expenses as a percentage of total revenues decreased to 15.3% in 2023 compared to 15.9% in 2022. | What was the operating expenses as a percentage of total revenues in 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`: 1e-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 - `learning_rate`: 1e-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 - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | dim_128_cosine_map@10 | dim_256_cosine_map@10 | dim_512_cosine_map@10 | dim_64_cosine_map@10 | dim_768_cosine_map@10 | |:----------:|:------:|:-------------:|:---------------------:|:---------------------:|:---------------------:|:--------------------:|:---------------------:| | 0.8122 | 10 | 1.7427 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7118 | 0.7377 | 0.7411 | 0.6774 | 0.7440 | | 1.6244 | 20 | 0.9354 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7353 | 0.7544 | 0.7562 | 0.7008 | 0.7632 | | 2.4365 | 30 | 0.674 | - | - | - | - | - | | 2.9239 | 36 | - | 0.7382 | 0.7569 | 0.7612 | 0.7018 | 0.7625 | | 3.2487 | 40 | 0.5862 | - | - | - | - | - | | **3.8985** | **48** | **-** | **0.7391** | **0.7567** | **0.761** | **0.7032** | **0.7634** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.9.18 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.29.3 - 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} } ```