--- base_model: BAAI/bge-base-en-v1.5 datasets: [] language: [] library_name: sentence-transformers 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: As of January 31, 2023, the weighted average remaining lease term for operating leases was 7 years and for finance leases was 3 years. sentences: - What was the Company's net deferred tax assets as of December 30, 2023, and December 31, 2022? - What were the weighted average remaining lease terms for operating and finance leases as of January 31, 2023? - How much did the net investment income change from 2021 to 2023? - source_sentence: The 4.500% notes due in August 2034 have an interest rate of 4.55%. sentences: - What types of insurance coverage does the company provide to its employees at no premium cost, as part of their general employee benefits package? - What is the interest rate for the 4.500% notes due in August 2034? - How much did the company's revenues decrease in 2023 compared to 2022? - source_sentence: In 2023, other income (expense), net included $376 million of interest income, partially offset by $167 million of net unrealized losses on equity investments. Other income (expense), net in 2022 included $657 million of net unrealized losses on equity investments, partially offset by $106 million of interest income. sentences: - What contributed to the net other income (expense) in 2023? - What types of products does the Canada operation offer? - What was the net change in cash and cash equivalents in 2022? - source_sentence: We believe the claims in these cases are without merit and are vigorously defending these lawsuits. sentences: - Where in the Annual Report can one find a description of certain legal matters and their impact on the company? - What is the goal of the company regarding its global corporate operations by 2030? - What is the stance of the defending airlines on the claims made against them in the capacity antitrust litigation? - source_sentence: North America's total net revenues for the fiscal year ended October 1, 2023, were $26,569.6 million. sentences: - What was the total net revenue for North America in fiscal 2023? - What are the consequences of impermissible use or disclosure of PHI according to the HITECH Act? - What does the index in a financial report indicate? model-index: - name: SentenceTransformer based on BAAI/bge-base-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.6171428571428571 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7457142857142857 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8114285714285714 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8585714285714285 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6171428571428571 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.24857142857142858 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16228571428571428 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08585714285714285 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6171428571428571 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7457142857142857 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8114285714285714 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8585714285714285 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7357204832416036 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6965260770975052 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7015509951793545 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6214285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.74 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8642857142857143 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6214285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.24666666666666665 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.15999999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08642857142857142 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6214285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.74 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8642857142857143 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.738181682287809 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6983236961451246 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7027820040111107 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7271428571428571 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7928571428571428 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8442857142857143 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.24238095238095236 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.15857142857142856 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08442857142857142 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7271428571428571 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7928571428571428 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8442857142857143 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7182448637999702 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6782879818594099 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.683606591058064 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.5728571428571428 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7014285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7557142857142857 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8157142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5728571428571428 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2338095238095238 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1511428571428571 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08157142857142856 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5728571428571428 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7014285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7557142857142857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8157142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6915163160852085 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6521536281179136 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6580414471513885 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.5142857142857142 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6371428571428571 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6728571428571428 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7357142857142858 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5142857142857142 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.21238095238095234 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.13457142857142856 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07357142857142857 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5142857142857142 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6371428571428571 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6728571428571428 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7357142857142858 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6197107516374883 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5832369614512468 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5907376271746598 name: Cosine Map@100 --- # SentenceTransformer based on BAAI/bge-base-en-v1.5 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 ### 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("ethan-ky/bge-base-financial-matryoshka") # Run inference sentences = [ "North America's total net revenues for the fiscal year ended October 1, 2023, were $26,569.6 million.", 'What was the total net revenue for North America in fiscal 2023?', 'What are the consequences of impermissible use or disclosure of PHI according to the HITECH Act?', ] 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 | 0.6171 | | cosine_accuracy@3 | 0.7457 | | cosine_accuracy@5 | 0.8114 | | cosine_accuracy@10 | 0.8586 | | cosine_precision@1 | 0.6171 | | cosine_precision@3 | 0.2486 | | cosine_precision@5 | 0.1623 | | cosine_precision@10 | 0.0859 | | cosine_recall@1 | 0.6171 | | cosine_recall@3 | 0.7457 | | cosine_recall@5 | 0.8114 | | cosine_recall@10 | 0.8586 | | cosine_ndcg@10 | 0.7357 | | cosine_mrr@10 | 0.6965 | | **cosine_map@100** | **0.7016** | #### 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 | 0.6214 | | cosine_accuracy@3 | 0.74 | | cosine_accuracy@5 | 0.8 | | cosine_accuracy@10 | 0.8643 | | cosine_precision@1 | 0.6214 | | cosine_precision@3 | 0.2467 | | cosine_precision@5 | 0.16 | | cosine_precision@10 | 0.0864 | | cosine_recall@1 | 0.6214 | | cosine_recall@3 | 0.74 | | cosine_recall@5 | 0.8 | | cosine_recall@10 | 0.8643 | | cosine_ndcg@10 | 0.7382 | | cosine_mrr@10 | 0.6983 | | **cosine_map@100** | **0.7028** | #### 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 | 0.6 | | cosine_accuracy@3 | 0.7271 | | cosine_accuracy@5 | 0.7929 | | cosine_accuracy@10 | 0.8443 | | cosine_precision@1 | 0.6 | | cosine_precision@3 | 0.2424 | | cosine_precision@5 | 0.1586 | | cosine_precision@10 | 0.0844 | | cosine_recall@1 | 0.6 | | cosine_recall@3 | 0.7271 | | cosine_recall@5 | 0.7929 | | cosine_recall@10 | 0.8443 | | cosine_ndcg@10 | 0.7182 | | cosine_mrr@10 | 0.6783 | | **cosine_map@100** | **0.6836** | #### 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 | 0.5729 | | cosine_accuracy@3 | 0.7014 | | cosine_accuracy@5 | 0.7557 | | cosine_accuracy@10 | 0.8157 | | cosine_precision@1 | 0.5729 | | cosine_precision@3 | 0.2338 | | cosine_precision@5 | 0.1511 | | cosine_precision@10 | 0.0816 | | cosine_recall@1 | 0.5729 | | cosine_recall@3 | 0.7014 | | cosine_recall@5 | 0.7557 | | cosine_recall@10 | 0.8157 | | cosine_ndcg@10 | 0.6915 | | cosine_mrr@10 | 0.6522 | | **cosine_map@100** | **0.658** | #### 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 | 0.5143 | | cosine_accuracy@3 | 0.6371 | | cosine_accuracy@5 | 0.6729 | | cosine_accuracy@10 | 0.7357 | | cosine_precision@1 | 0.5143 | | cosine_precision@3 | 0.2124 | | cosine_precision@5 | 0.1346 | | cosine_precision@10 | 0.0736 | | cosine_recall@1 | 0.5143 | | cosine_recall@3 | 0.6371 | | cosine_recall@5 | 0.6729 | | cosine_recall@10 | 0.7357 | | cosine_ndcg@10 | 0.6197 | | cosine_mrr@10 | 0.5832 | | **cosine_map@100** | **0.5907** | ## 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 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------| | Our ability to develop and operate units at the right locations and to deliver a customer-centric omni-channel experience largely determines our competitive position within the retail industry. We believe price leadership is a critical part of our business model and we continue to focus on moving our markets towards an EDLP approach. Additionally, our ability to operate food departments effectively has a significant impact on our competitive position in the markets where we operate. | What factors contribute to Walmart International's competitive position? | | tax annual aggregate losses incurred in any year from U.S. hurricane events could be in excess of $3,827 million (or 6.4 percent of total Chubb shareholders’ equity at December 31, 2023). | What is the expected maximum potential loss from hurricane events for Chubb as of the end of 2023? | | The 'Glossary of Terms and Acronyms’ is included on pages 315-321. | What is included on pages 315 to 321 of the document? | * 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 - `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 - `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 | 1.3939 | - | - | - | - | - | | **0.9746** | **12** | **-** | **0.658** | **0.6836** | **0.7028** | **0.5907** | **0.7016** | | 1.6244 | 20 | 1.3574 | - | - | - | - | - | | 1.9492 | 24 | - | 0.6580 | 0.6836 | 0.7028 | 0.5907 | 0.7016 | | 2.4365 | 30 | 1.3485 | - | - | - | - | - | | 2.9239 | 36 | - | 0.6580 | 0.6836 | 0.7028 | 0.5907 | 0.7016 | | 3.2487 | 40 | 1.3606 | - | - | - | - | - | | 3.8985 | 48 | - | 0.6580 | 0.6836 | 0.7028 | 0.5907 | 0.7016 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.9.19 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.33.0 - 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} } ```