--- base_model: BAAI/bge-base-en-v1.5 datasets: [] language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6300 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: As of December 31, 2023, deferred revenues for unsatisfied performance obligations consisted of $769 million related to Hilton Honors that will be recognized as revenue over approximately the next two years. sentences: - How many shares of common stock were issued in both 2022 and 2023? - What is the projected timeline for recognizing revenue from deferred revenues related to Hilton Honors as of December 31, 2023? - What acquisitions did CVS Health Corporation complete in 2023 to enhance their care delivery strategy? - source_sentence: If a good or service does not qualify as distinct, it is combined with the other non-distinct goods or services within the arrangement and these combined goods or services are treated as a single performance obligation for accounting purposes. The arrangement's transaction price is then allocated to each performance obligation based on the relative standalone selling price of each performance obligation. sentences: - What does the summary table indicate about the company's activities at the end of 2023? - What governs the treatment of goods or services that are not distinct within a contractual arrangement? - What is the basis for the Company to determine the Standalone Selling Price (SSP) for each distinct performance obligation in contracts with multiple performance obligations? - source_sentence: As of January 2023, the maximum daily borrowing capacity under the commercial paper program was approximately $2.75 billion. sentences: - What is the maximum daily borrowing capacity under the commercial paper program as of January 2023? - When does the Company's fiscal year end? - How much cash did acquisition activities use in 2023? - source_sentence: Federal Home Loan Bank borrowings had an interest rate of 4.59% in 2022, which increased to 5.14% in 2023. sentences: - By what percentage did the company's capital expenditures increase in fiscal 2023 compared to fiscal 2022? - What is the significance of Note 13 in the context of legal proceedings described in the Annual Report on Form 10-K? - How much did the Federal Home Loan Bank borrowings increase in terms of interest rates from 2022 to 2023? - source_sentence: The design of the Annual Report, with the consolidated financial statements placed immediately after Part IV, enhances the integration of financial data by maintaining a coherent structure. sentences: - How does the structure of the Annual Report on Form 10-K facilitate the integration of the consolidated financial statements? - Where can one find the Glossary of Terms and Acronyms in Item 8? - What part of the annual report contains the consolidated financial statements and accompanying notes? 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: 0.6957142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8171428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8628571428571429 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6957142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2723809523809524 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17257142857142854 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08999999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6957142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8171428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8628571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7971144469297426 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7641831065759639 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7681728985040082 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6942857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.81 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8514285714285714 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6942857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17028571428571426 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6942857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.81 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8514285714285714 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7951260604161544 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7617998866213151 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7658003405075238 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7014285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7971428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.85 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8885714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7014285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26571428571428574 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16999999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08885714285714284 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7014285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7971428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.85 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8885714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.793266992460996 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7629580498866213 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7678096436855835 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6957142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8014285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8357142857142857 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8842857142857142 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6957142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2671428571428571 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16714285714285712 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08842857142857141 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6957142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8014285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8357142857142857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8842857142857142 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.787378246207931 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7566984126984126 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7613545312565108 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6571428571428571 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7871428571428571 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8285714285714286 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8757142857142857 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6571428571428571 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2623809523809524 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1657142857142857 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08757142857142856 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6571428571428571 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7871428571428571 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8285714285714286 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8757142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7655516319615892 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7303951247165531 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7349875161463472 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("rbhatia46/bge-base-financial-nvidia-matryoshka") # Run inference sentences = [ 'The design of the Annual Report, with the consolidated financial statements placed immediately after Part IV, enhances the integration of financial data by maintaining a coherent structure.', 'How does the structure of the Annual Report on Form 10-K facilitate the integration of the consolidated financial statements?', 'Where can one find the Glossary of Terms and Acronyms in Item 8?', ] 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 | 0.6957 | | cosine_accuracy@3 | 0.8171 | | cosine_accuracy@5 | 0.8629 | | cosine_accuracy@10 | 0.9 | | cosine_precision@1 | 0.6957 | | cosine_precision@3 | 0.2724 | | cosine_precision@5 | 0.1726 | | cosine_precision@10 | 0.09 | | cosine_recall@1 | 0.6957 | | cosine_recall@3 | 0.8171 | | cosine_recall@5 | 0.8629 | | cosine_recall@10 | 0.9 | | cosine_ndcg@10 | 0.7971 | | cosine_mrr@10 | 0.7642 | | **cosine_map@100** | **0.7682** | #### 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 | 0.6943 | | cosine_accuracy@3 | 0.81 | | cosine_accuracy@5 | 0.8514 | | cosine_accuracy@10 | 0.9 | | cosine_precision@1 | 0.6943 | | cosine_precision@3 | 0.27 | | cosine_precision@5 | 0.1703 | | cosine_precision@10 | 0.09 | | cosine_recall@1 | 0.6943 | | cosine_recall@3 | 0.81 | | cosine_recall@5 | 0.8514 | | cosine_recall@10 | 0.9 | | cosine_ndcg@10 | 0.7951 | | cosine_mrr@10 | 0.7618 | | **cosine_map@100** | **0.7658** | #### 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 | 0.7014 | | cosine_accuracy@3 | 0.7971 | | cosine_accuracy@5 | 0.85 | | cosine_accuracy@10 | 0.8886 | | cosine_precision@1 | 0.7014 | | cosine_precision@3 | 0.2657 | | cosine_precision@5 | 0.17 | | cosine_precision@10 | 0.0889 | | cosine_recall@1 | 0.7014 | | cosine_recall@3 | 0.7971 | | cosine_recall@5 | 0.85 | | cosine_recall@10 | 0.8886 | | cosine_ndcg@10 | 0.7933 | | cosine_mrr@10 | 0.763 | | **cosine_map@100** | **0.7678** | #### 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 | 0.6957 | | cosine_accuracy@3 | 0.8014 | | cosine_accuracy@5 | 0.8357 | | cosine_accuracy@10 | 0.8843 | | cosine_precision@1 | 0.6957 | | cosine_precision@3 | 0.2671 | | cosine_precision@5 | 0.1671 | | cosine_precision@10 | 0.0884 | | cosine_recall@1 | 0.6957 | | cosine_recall@3 | 0.8014 | | cosine_recall@5 | 0.8357 | | cosine_recall@10 | 0.8843 | | cosine_ndcg@10 | 0.7874 | | cosine_mrr@10 | 0.7567 | | **cosine_map@100** | **0.7614** | #### 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 | 0.6571 | | cosine_accuracy@3 | 0.7871 | | cosine_accuracy@5 | 0.8286 | | cosine_accuracy@10 | 0.8757 | | cosine_precision@1 | 0.6571 | | cosine_precision@3 | 0.2624 | | cosine_precision@5 | 0.1657 | | cosine_precision@10 | 0.0876 | | cosine_recall@1 | 0.6571 | | cosine_recall@3 | 0.7871 | | cosine_recall@5 | 0.8286 | | cosine_recall@10 | 0.8757 | | cosine_ndcg@10 | 0.7656 | | cosine_mrr@10 | 0.7304 | | **cosine_map@100** | **0.735** | ## 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 | |:---------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------| | Acquisition activity used cash of $765 million in 2023, primarily related to a Beauty acquisition. | How much cash did acquisition activities use in 2023? | | In a financial report, Part IV Item 15 includes Exhibits and Financial Statement Schedules as mentioned. | What content can be expected under Part IV Item 15 in a financial report? | | we had more than 8.3 million fiber consumer wireline broadband customers, adding 1.1 million during the year. | How many fiber consumer wireline broadband customers did the company have at the end of the year? | * 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 | 1.5751 | - | - | - | - | - | | 0.9746 | 12 | - | - | - | - | - | 0.7580 | | 0.8122 | 10 | 0.6362 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7503 | 0.7576 | 0.7653 | 0.7282 | 0.7638 | | 1.6244 | 20 | 0.4426 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7544 | 0.7662 | 0.7640 | 0.7311 | 0.7676 | | 2.4365 | 30 | 0.3217 | - | - | - | - | - | | 2.9239 | 36 | - | 0.7608 | 0.7684 | 0.7662 | 0.7341 | 0.7686 | | 3.2487 | 40 | 0.2761 | - | - | - | - | - | | **3.8985** | **48** | **-** | **0.7614** | **0.7678** | **0.7658** | **0.735** | **0.7682** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.6 - 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} } ```