--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6300 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-base-en-v1.5 widget: - source_sentence: What year do the patent families related to DARZALEX expire in the United States? sentences: - Amortization for owned content predominantly monetized on an individual basis and accrued costs associated with participations and residuals payments are recorded using the individual film forecast computation method, which recognizes the costs in the same ratio as the associated ultimate revenue. - The two patent families both expire in the United States in 2029. - For the year ended December 31, 2022, net cash used in investing activities of $371.9 million was primarily from the purchase of $247.3 million marketable securities, net of sale and maturities, $62.2 million net cash used to acquire GreenCom, SolarLeadFactory and ClipperCreek, $46.4 million used in purchases of test and assembly equipment to expand our supply capacity, related facility improvements and information technology enhancements, including capitalized costs related to internal-use software and $16.0 million used to invest in private companies. - source_sentence: What legal claims does Fortis Advisors LLC allege against Ethicon Inc. in the lawsuit related to the acquisition of Auris Health Inc.? sentences: - Payments include a single lump-sum per treatment, referred to as bundled rates, or in other cases separate payments for dialysis treatments and pharmaceuticals, referred to as FFS rates. - In October 2020, Fortis Advisors LLC filed a complaint against Ethicon Inc. and others in Delaware's Court of Chancery. The lawsuit alleges breach of contract and fraud related to Ethicon's acquisition of Auris Health Inc. in 2019. The case underwent a partial dismissal in December 2021, and as of January 2024, the trial's decision is pending. - On September 5, 2023, ICE acquired 100% of Black Knight for aggregate transaction consideration of approximately $11.8 billion, or $76 per share of Black Knight common stock, with cash comprising 90% of the value of the aggregate transaction consideration. The aggregate cash component of the transaction consideration was $10.5 billion. ICE issued 10.9 million shares of its common stock to Black Knight stockholders, which was based on the market price of the common stock and the average of the volume weighted averages of the trading prices of the common stock on each of the ten consecutive trading days ending three trading days prior to the closing of the merger. - source_sentence: What caused the increase in net cash provided by operating activities between 2022 and 2023? sentences: - Net cash provided by operating activities was $712.2 million and $223.7 million for the year ended December 31, 2023 and 2022, respectively. The increase was primarily driven by timing of payments to vendors and timing of the receipt of payments from our customers, as well as an increase in interest income. - Joanne D. Smith held the position of Vice President - Marketing at Delta from November 2005 to February 2007. - Experienced management team with a proven track in the gaming and resort industry. Mr. Robert G. Goldstein, our Chairman and Chief Executive Officer, has been an integral part of our executive team from the beginning, joining our founder and previous Chairman and Chief Executive Officer, Mr. Sheldon G. Adelson, before The Venetian Resort Las Vegas was constructed. Mr. Goldstein is one of the most respected and experienced executives in our industry today. - source_sentence: What does the company believe adds significant value to its business regarding intellectual property? sentences: - In 2022, the net interest expense on pre-acquisition-related debt was $59 million and additional adjustments included costs of $30 million associated with the May and June 2022 extinguishment of four series of senior notes. - Fluctuations in foreign currency exchange rates decreased our consolidated net operating revenues by 4%. - We believe that, to varying degrees, our trademarks, trade names, copyrights, proprietary processes, trade secrets, trade dress, domain names and similar intellectual property add significant value to our business - source_sentence: What does it mean for financial statements to be incorporated by reference? sentences: - The consolidated financial statements are incorporated by reference in the Annual Report on Form 10-K, indicating they are treated as part of the document for legal and reporting purposes. - The Consolidated Financial Statements, together with the Notes thereto and the report thereon dated February 16, 2024, of PricewaterhouseCoopers LLP, the Firm’s independent registered public accounting firm (PCAOB ID 238), appear on pages 163–309. - 'The Goldman Sachs Group, Inc. manages and reports its activities in three business segments: Global Banking & Markets, Asset & Wealth Samantha Management and Platform Solutions.' pipeline_tag: sentence-similarity 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 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: 0.7 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8285714285714286 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: 0.7 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2761904761904762 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: 0.7 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8285714285714286 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: 0.8045805359515339 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7714971655328795 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.775178941729297 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.7014285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.83 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8671428571428571 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9042857142857142 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7014285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1734285714285714 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09042857142857141 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7014285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.83 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8671428571428571 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9042857142857142 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8036464537429646 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.771175736961451 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7751075563277001 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6928571428571428 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8185714285714286 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8628571428571429 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8971428571428571 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6928571428571428 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27285714285714285 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17257142857142854 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0897142857142857 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6928571428571428 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8185714285714286 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8628571428571429 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8971428571428571 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7963364154792727 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7638741496598634 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7683107318753077 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6771428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8142857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8514285714285714 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8885714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6771428571428572 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2714285714285714 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17028571428571426 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08885714285714284 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6771428571428572 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8142857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8514285714285714 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8885714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.786332288682679 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7531507936507934 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7576033800206036 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: 0.7814285714285715 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8171428571428572 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.86 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6571428571428571 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2604761904761905 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16342857142857142 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08599999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6571428571428571 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7814285714285715 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8171428571428572 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.86 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7602042820067257 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7281371882086165 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7334805218687248 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) on the json dataset. 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 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json - **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("Fe2x/bge-base-financial-matryoshka") # Run inference sentences = [ 'What does it mean for financial statements to be incorporated by reference?', 'The consolidated financial statements are incorporated by reference in the Annual Report on Form 10-K, indicating they are treated as part of the document for legal and reporting purposes.', 'The Consolidated Financial Statements, together with the Notes thereto and the report thereon dated February 16, 2024, of PricewaterhouseCoopers LLP, the Firm’s independent registered public accounting firm (PCAOB ID 238), appear on pages 163–309.', ] 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 * Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------| | cosine_accuracy@1 | 0.7 | 0.7014 | 0.6929 | 0.6771 | 0.6571 | | cosine_accuracy@3 | 0.8286 | 0.83 | 0.8186 | 0.8143 | 0.7814 | | cosine_accuracy@5 | 0.8729 | 0.8671 | 0.8629 | 0.8514 | 0.8171 | | cosine_accuracy@10 | 0.9071 | 0.9043 | 0.8971 | 0.8886 | 0.86 | | cosine_precision@1 | 0.7 | 0.7014 | 0.6929 | 0.6771 | 0.6571 | | cosine_precision@3 | 0.2762 | 0.2767 | 0.2729 | 0.2714 | 0.2605 | | cosine_precision@5 | 0.1746 | 0.1734 | 0.1726 | 0.1703 | 0.1634 | | cosine_precision@10 | 0.0907 | 0.0904 | 0.0897 | 0.0889 | 0.086 | | cosine_recall@1 | 0.7 | 0.7014 | 0.6929 | 0.6771 | 0.6571 | | cosine_recall@3 | 0.8286 | 0.83 | 0.8186 | 0.8143 | 0.7814 | | cosine_recall@5 | 0.8729 | 0.8671 | 0.8629 | 0.8514 | 0.8171 | | cosine_recall@10 | 0.9071 | 0.9043 | 0.8971 | 0.8886 | 0.86 | | **cosine_ndcg@10** | **0.8046** | **0.8036** | **0.7963** | **0.7863** | **0.7602** | | cosine_mrr@10 | 0.7715 | 0.7712 | 0.7639 | 0.7532 | 0.7281 | | cosine_map@100 | 0.7752 | 0.7751 | 0.7683 | 0.7576 | 0.7335 | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 6,300 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:---------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What was the amount of cash generated from operations by the company in fiscal year 2023? | Highlights during fiscal year 2023 include the following: We generated $18,085 million of cash from operations. | | How much were unrealized losses on U.S. government and agency securities for those held for 12 months or greater as of June 30, 2023? | U.S. government and agency securities | $ | 7,950 | | $ | (336 | ) | $ | 45,273 | $ | (3,534 | ) | $ | 53,223 | $ | (3,870 | ) | | How is the impairment of assets assessed for projects still under development? | For assets under development, assets are grouped and assessed for impairment by estimating the undiscounted cash flows, which include remaining construction costs, over the asset's remaining useful life. If cash flows do not exceed the carrying amount, impairment based on fair value versus carrying value is considered. | * 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 - `torch_empty_cache_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`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `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 - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |:---------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| | 0.8122 | 10 | 1.5872 | - | - | - | - | - | | 1.0 | 13 | - | 0.7879 | 0.7860 | 0.7782 | 0.7698 | 0.7320 | | 1.5685 | 20 | 0.6329 | - | - | - | - | - | | 2.0 | 26 | - | 0.7988 | 0.7969 | 0.7923 | 0.7826 | 0.7520 | | 2.3249 | 30 | 0.4465 | - | - | - | - | - | | 3.0 | 39 | - | 0.8046 | 0.8026 | 0.7959 | 0.7855 | 0.7596 | | 3.0812 | 40 | 0.349 | - | - | - | - | - | | **3.731** | **48** | **-** | **0.8046** | **0.8036** | **0.7963** | **0.7863** | **0.7602** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.9.20 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.1.2+cu121 - Accelerate: 1.2.1 - Datasets: 2.19.1 - Tokenizers: 0.21.0 ## 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} } ```