--- 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: Walmart Connect provides house advertising offerings. sentences: - What was the fair value per performance-based share granted for the fiscal years 2023, 2022, and 2021? - What services does Walmart Connect offer? - By how much did membership fees increase in 2023? - source_sentence: The total revenue for 2023 was reported as $371,620 million. sentences: - What was the percentage increase in Humalog revenue from 2022 to 2023? - What was the total revenue for the year 2023? - What were the primary factors influencing profitability in the automotive market in 2023? - source_sentence: •LinkedIn revenue increased 10%. sentences: - By what percentage did LinkedIn's revenue increase in fiscal year 2023? - What factors influence the recording of the Company's credit-related contingent features in financial statements? - What is the average tenure of associates at the company as of December 31, 2023? - source_sentence: Cash flows from operating activities in 2023 were primarily generated from management and franchise fee revenue and operating income from owned and leased hotels. sentences: - What is the significance of the Company’s trademarks to their businesses? - By what percentage did the S&P 500 Index increase in 2023 compared to the end of 2022? - What were the primary sources of operating activities cash flow in 2023? - source_sentence: The par call date for the 7% Notes due 2029 is August 15, 2025, allowing for redemption at par from this date onward. sentences: - What is the earliest date on which the 7% Notes due 2029 can be redeemed at par? - What are some of the initiatives managed by Visa for supporting underrepresented communities? - Who are the competitors for Microsoft's server applications in PC-based environments? 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: 0.6942857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8314285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8728571428571429 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9071428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6942857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27714285714285714 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17457142857142854 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09071428571428569 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6942857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8314285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8728571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9071428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8042383857063928 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7708656462585032 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7746128511093645 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6985714285714286 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8371428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.87 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9114285714285715 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6985714285714286 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27904761904761904 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.174 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09114285714285714 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6985714285714286 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8371428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.87 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9114285714285715 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8075815858913178 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7741315192743762 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7776656953157759 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.83 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.86 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9071428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17199999999999996 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0907142857142857 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.83 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.86 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9071428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8048199967282856 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7720073696145123 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.775510167698765 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.67 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8185714285714286 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8571428571428571 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8971428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.67 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27285714285714285 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1714285714285714 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0897142857142857 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.67 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8185714285714286 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8571428571428571 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8971428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7867880427582347 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7511031746031744 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7551868866444579 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.65 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7914285714285715 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8385714285714285 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8785714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.65 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26380952380952377 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16771428571428568 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08785714285714286 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.65 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7914285714285715 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8385714285714285 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8785714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7645553995345995 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.727849206349206 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.73258711812532 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("Jaswanth160/bge-base-financial-matryoshka") # Run inference sentences = [ 'The par call date for the 7% Notes due 2029 is August 15, 2025, allowing for redemption at par from this date onward.', 'What is the earliest date on which the 7% Notes due 2029 can be redeemed at par?', 'What are some of the initiatives managed by Visa for supporting underrepresented communities?', ] 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 | 0.6943 | | cosine_accuracy@3 | 0.8314 | | cosine_accuracy@5 | 0.8729 | | cosine_accuracy@10 | 0.9071 | | cosine_precision@1 | 0.6943 | | cosine_precision@3 | 0.2771 | | cosine_precision@5 | 0.1746 | | cosine_precision@10 | 0.0907 | | cosine_recall@1 | 0.6943 | | cosine_recall@3 | 0.8314 | | cosine_recall@5 | 0.8729 | | cosine_recall@10 | 0.9071 | | cosine_ndcg@10 | 0.8042 | | cosine_mrr@10 | 0.7709 | | **cosine_map@100** | **0.7746** | #### 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 | 0.6986 | | cosine_accuracy@3 | 0.8371 | | cosine_accuracy@5 | 0.87 | | cosine_accuracy@10 | 0.9114 | | cosine_precision@1 | 0.6986 | | cosine_precision@3 | 0.279 | | cosine_precision@5 | 0.174 | | cosine_precision@10 | 0.0911 | | cosine_recall@1 | 0.6986 | | cosine_recall@3 | 0.8371 | | cosine_recall@5 | 0.87 | | cosine_recall@10 | 0.9114 | | cosine_ndcg@10 | 0.8076 | | cosine_mrr@10 | 0.7741 | | **cosine_map@100** | **0.7777** | #### 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 | 0.7 | | cosine_accuracy@3 | 0.83 | | cosine_accuracy@5 | 0.86 | | cosine_accuracy@10 | 0.9071 | | cosine_precision@1 | 0.7 | | cosine_precision@3 | 0.2767 | | cosine_precision@5 | 0.172 | | cosine_precision@10 | 0.0907 | | cosine_recall@1 | 0.7 | | cosine_recall@3 | 0.83 | | cosine_recall@5 | 0.86 | | cosine_recall@10 | 0.9071 | | cosine_ndcg@10 | 0.8048 | | cosine_mrr@10 | 0.772 | | **cosine_map@100** | **0.7755** | #### 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 | 0.67 | | cosine_accuracy@3 | 0.8186 | | cosine_accuracy@5 | 0.8571 | | cosine_accuracy@10 | 0.8971 | | cosine_precision@1 | 0.67 | | cosine_precision@3 | 0.2729 | | cosine_precision@5 | 0.1714 | | cosine_precision@10 | 0.0897 | | cosine_recall@1 | 0.67 | | cosine_recall@3 | 0.8186 | | cosine_recall@5 | 0.8571 | | cosine_recall@10 | 0.8971 | | cosine_ndcg@10 | 0.7868 | | cosine_mrr@10 | 0.7511 | | **cosine_map@100** | **0.7552** | #### 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 | 0.65 | | cosine_accuracy@3 | 0.7914 | | cosine_accuracy@5 | 0.8386 | | cosine_accuracy@10 | 0.8786 | | cosine_precision@1 | 0.65 | | cosine_precision@3 | 0.2638 | | cosine_precision@5 | 0.1677 | | cosine_precision@10 | 0.0879 | | cosine_recall@1 | 0.65 | | cosine_recall@3 | 0.7914 | | cosine_recall@5 | 0.8386 | | cosine_recall@10 | 0.8786 | | cosine_ndcg@10 | 0.7646 | | cosine_mrr@10 | 0.7278 | | **cosine_map@100** | **0.7326** | ## 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 some of our medical membership, we share risk with providers under capitation contracts where physicians and hospitals accept varying levels of financial risk for a defined set of membership, primarily HMO membership. | What is the primary type of membership for which risk is shared with providers under capitation contracts? | | Revenue for Comcast's Theme Parks segment is primarily derived from guest spending at the theme parks, including ticket sales and in-park spending on food, beverages, and merchandise. | What is the primary revenue source for Comcast's Theme Parks segment? | | In August 2022, the Board of Directors authorized a program to repurchase up to $10.0 billion of the Company’s common stock, referred to as the "Share Repurchase Program". In February 2023, the Board of Directors authorized an additional $10.0 billion in repurchases under the Share Repurchase Program, bringing the aggregate total authorized to $20.0 billion. | What was the total authorization amount for the Share Repurchase Program of the Company as of February 2023? | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `fp16`: True - `tf32`: False - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: False - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.8122 | 10 | 1.5811 | - | - | - | - | - | | 0.9746 | 12 | - | 0.7341 | 0.7568 | 0.7632 | 0.7056 | 0.7660 | | 1.6244 | 20 | 0.6854 | - | - | - | - | - | | 1.9492 | 24 | - | 0.7516 | 0.7705 | 0.7722 | 0.7263 | 0.7702 | | 2.4365 | 30 | 0.4874 | - | - | - | - | - | | **2.9239** | **36** | **-** | **0.755** | **0.7747** | **0.7756** | **0.7321** | **0.7739** | | 3.2487 | 40 | 0.3876 | - | - | - | - | - | | 3.8985 | 48 | - | 0.7552 | 0.7755 | 0.7777 | 0.7326 | 0.7746 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - 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} } ```