--- 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: The net interest income for the first quarter of 2023 was $14,448 million. sentences: - What was the fair value of investments in fixed maturity securities at the end of 2023 after a hypothetical 100 basis point increase in interest rates? - What was the net interest income for the first quarter of 2023? - What are the expected consequences of the EMIR 3.0 proposals for ICE Futures Europe and ICE Clear Europe? - source_sentence: The consolidated financial statements and accompanying notes are listed in Part IV, Item 15(a)(1) of the Annual Report on Form 10-K sentences: - What was the total amount invested in purchases from Vebu during the year ended December 31, 2023? - What section of the Annual Report on Form 10-K includes the consolidated financial statements and accompanying notes? - What is the purpose of using constant currency to measure financial performance? - source_sentence: Cash provided by operating activities was impacted by the provision from the Tax Cuts and Jobs Act of 2017 which became effective in fiscal 2023 and requires the capitalization and amortization of research and development costs. The change increased our cash taxes paid in fiscal 2023. sentences: - How much did the provision from the Tax Cuts and Jobs Act increase the cash taxes paid in fiscal 2023? - What is the principal amount of debt maturing in fiscal year 2023? - What is the projected increase in effective tax rate starting from fiscal 2024? - source_sentence: Item 8. Financial Statements and Supplementary Data. sentences: - How does FedEx Express primarily fulfill its jet fuel needs? - What legislative act in the United States established a new corporate alternative minimum tax of 15% on large corporations? - What is the title of Item 8 that covers financial data in the report? - source_sentence: Electronic Arts paid cash dividends totaling $210 million during the fiscal year ended March 31, 2023. sentences: - What was the total cash dividend paid by Electronic Arts in the fiscal year ended March 31, 2023? - What was the SRO's accrued amount as a receivable for CAT implementation expenses as of December 31, 2023? - What percentage of our total U.S. dialysis patients in 2023 was covered under some form of government-based program? 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: 0.6842857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8128571428571428 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.86 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8985714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6842857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27095238095238094 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.172 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08985714285714284 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6842857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8128571428571428 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.86 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8985714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7929325221389678 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7588820861678003 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7629563080276819 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6857142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8585714285714285 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9057142857142857 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: 0.1717142857142857 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09057142857142857 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: 0.8585714285714285 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9057142857142857 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7963845502294126 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7614115646258502 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7648837754793252 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6771428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8042857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8571428571428571 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.89 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6771428571428572 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2680952380952381 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17142857142857137 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08899999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6771428571428572 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8042857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8571428571428571 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.89 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.784627431591255 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7506218820861676 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7549970210504993 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6614285714285715 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7957142857142857 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8271428571428572 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.88 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6614285714285715 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2652380952380952 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1654285714285714 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.088 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6614285714285715 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7957142857142857 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8271428571428572 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.88 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7728766261768507 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7384614512471652 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.74301468254304 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6128571428571429 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7628571428571429 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7957142857142857 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8471428571428572 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6128571428571429 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2542857142857143 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.15914285714285714 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0847142857142857 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6128571428571429 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7628571428571429 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7957142857142857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8471428571428572 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7315764159717033 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6946094104308389 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7001749041654559 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("elsayovita/bge-base-financial-matryoshka-testing") # Run inference sentences = [ 'Electronic Arts paid cash dividends totaling $210 million during the fiscal year ended March 31, 2023.', 'What was the total cash dividend paid by Electronic Arts in the fiscal year ended March 31, 2023?', "What was the SRO's accrued amount as a receivable for CAT implementation expenses as of December 31, 2023?", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_768` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:----------| | cosine_accuracy@1 | 0.6843 | | cosine_accuracy@3 | 0.8129 | | cosine_accuracy@5 | 0.86 | | cosine_accuracy@10 | 0.8986 | | cosine_precision@1 | 0.6843 | | cosine_precision@3 | 0.271 | | cosine_precision@5 | 0.172 | | cosine_precision@10 | 0.0899 | | cosine_recall@1 | 0.6843 | | cosine_recall@3 | 0.8129 | | cosine_recall@5 | 0.86 | | cosine_recall@10 | 0.8986 | | cosine_ndcg@10 | 0.7929 | | cosine_mrr@10 | 0.7589 | | **cosine_map@100** | **0.763** | #### 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 | 0.8586 | | cosine_accuracy@10 | 0.9057 | | cosine_precision@1 | 0.6857 | | cosine_precision@3 | 0.2733 | | cosine_precision@5 | 0.1717 | | cosine_precision@10 | 0.0906 | | cosine_recall@1 | 0.6857 | | cosine_recall@3 | 0.82 | | cosine_recall@5 | 0.8586 | | cosine_recall@10 | 0.9057 | | cosine_ndcg@10 | 0.7964 | | cosine_mrr@10 | 0.7614 | | **cosine_map@100** | **0.7649** | #### 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 | 0.6771 | | cosine_accuracy@3 | 0.8043 | | cosine_accuracy@5 | 0.8571 | | cosine_accuracy@10 | 0.89 | | cosine_precision@1 | 0.6771 | | cosine_precision@3 | 0.2681 | | cosine_precision@5 | 0.1714 | | cosine_precision@10 | 0.089 | | cosine_recall@1 | 0.6771 | | cosine_recall@3 | 0.8043 | | cosine_recall@5 | 0.8571 | | cosine_recall@10 | 0.89 | | cosine_ndcg@10 | 0.7846 | | cosine_mrr@10 | 0.7506 | | **cosine_map@100** | **0.755** | #### 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 | 0.6614 | | cosine_accuracy@3 | 0.7957 | | cosine_accuracy@5 | 0.8271 | | cosine_accuracy@10 | 0.88 | | cosine_precision@1 | 0.6614 | | cosine_precision@3 | 0.2652 | | cosine_precision@5 | 0.1654 | | cosine_precision@10 | 0.088 | | cosine_recall@1 | 0.6614 | | cosine_recall@3 | 0.7957 | | cosine_recall@5 | 0.8271 | | cosine_recall@10 | 0.88 | | cosine_ndcg@10 | 0.7729 | | cosine_mrr@10 | 0.7385 | | **cosine_map@100** | **0.743** | #### 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 | 0.6129 | | cosine_accuracy@3 | 0.7629 | | cosine_accuracy@5 | 0.7957 | | cosine_accuracy@10 | 0.8471 | | cosine_precision@1 | 0.6129 | | cosine_precision@3 | 0.2543 | | cosine_precision@5 | 0.1591 | | cosine_precision@10 | 0.0847 | | cosine_recall@1 | 0.6129 | | cosine_recall@3 | 0.7629 | | cosine_recall@5 | 0.7957 | | cosine_recall@10 | 0.8471 | | cosine_ndcg@10 | 0.7316 | | cosine_mrr@10 | 0.6946 | | **cosine_map@100** | **0.7002** | ## 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 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------| | For the year ended December 31, 2023, the average balance for savings and transaction accounts was $86,102 and the interest expense for these accounts was $3,357. | What was the average balance and interest expense for savings and transaction accounts in the year 2023? | | Limits are used at various levels and types to manage the size of liquidity exposures, relative to acceptable risk levels according the the organization's liquidity risk tolerance. | What is the purpose of the liquidity risk limits used by the organization? | | Value-Based Care refers to the goal of incentivizing healthcare providers to simultaneously increase quality while lowering the cost of care for patients. | What is the primary goal of value-based care according to the company? | * 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`: 2 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: 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`: 2 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: False - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: 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 | 1.4746 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7378 | 0.7470 | 0.7589 | 0.6941 | 0.7563 | | 1.6244 | 20 | 0.6694 | - | - | - | - | - | | **1.9492** | **24** | **-** | **0.743** | **0.755** | **0.7649** | **0.7002** | **0.763** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.4.0+cu121 - Accelerate: 0.32.1 - Datasets: 2.21.0 - 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} } ```