--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:400 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: Snowflake/snowflake-arctic-embed-l widget: - source_sentence: 'QUESTION #2: What percentage of patients in the study reported experiencing "chills" and "feverish discomfort"?' sentences: - "been proven superior. Annual influenza vaccination is recommended for all people\ \ six months and older who do not have \ncontraindications. ( Am Fam Physician.\ \ 2019; 100(12):751-758. Copyright © 2019 American Academy of Family Physicians.)\n\ BEST PRACTICES IN INFECTIOUS DISEASE \nRecommendations from the Choosing \nWisely\ \ Campaign\nRecommendation Sponsoring organization\nDo not routinely avoid \n\ influenza vaccination in \negg-allergic patients.\nAmerican Academy of Allergy,\ \ \nAsthma, and Immunology\nSource: For more information on the Choosing Wisely\ \ Campaign," - 'Review 722 Vol 5 November 2005 accompanied by fever and some subjects have a transient fall in body temperature during the early stages of common cold. In a study of 272 patients with sore throat associated with URTIs, the mean aural temperature was 36·8ºC and around 35% of these patients said they were suffering from “chills” and “feverish discomfort”.49 The sensation of chilliness may be unrelated to any change in skin or body temperature. In a study of human volunteers, a sensation of chill still develops on administration of exogenous pyrogen even though the' - "ered when the results will modify management or when a \npatient with signs or\ \ symptoms of influenza is hospitalized.19 \nTABLE 2\nComplications of Influenza\n\ Cardiovascular 26\nCerebrovascular accidents\nIschemic heart disease\nMyocarditis\n\ Hematologic 26\nHemolytic uremic syndrome\nHemophagocytic syndrome\nThrombotic\ \ thrombocytope -\nnic purpura\nMusculoskeletal 19,26\nMyositis\nRhabdomyolysis\n\ Neurologic 26\nAcute disseminated \nencephalomyelitis\nEncephalitis\nGuillain-Barré\ \ syndrome\nPostinfluenza encephalopathy \n(neurologic symptoms occur -\nring\ \ after resolution but within" - source_sentence: How do cytokines interact with the body's systems to influence the hypothalamus and affect body temperature? sentences: - 'interleukin 1, interleukin 6, and tumour necrosis factor alpha, as well as the anti-inflammatory cytokines interleukin-1 receptor antagonist and interleukin 10 have been investigated for their pyrogenic or antipyretic action.17 Interleukin 1 and interleukin 6 are believed to be the most important cytokines that induce fever. 55 Cytokines are believed to cross the blood–brain barrier or interact with the vagus nerve endings to signal the temperature control centre of the hypothalamus to increase the thermal set point.55,56 The hypothalamus then initiates shivering, constriction of skin blood' - "mended human dose; possible \nrisk of embryo-fetal toxicity with \ncontinuous\ \ intravenous infusion \nbased on limited animal data\nBaloxavir (Xofluza), \n\ available as oral \ntablets\nNA ($160) Adults and children 12 years \nand older:\ \ \n88 to 174 lb (40 to 79 kg): \nsingle dose of 40 mg \n≥ 175 lb (80 kg):\ \ single dose \nof 80 mg\nTreatment of uncom-\nplicated acute \ninfluenza in\ \ patients \n12 years and older who \nhave been symptom -\natic for no more than\ \ \n48 hours\nContraindicated in people with \na history of hypersensitivity to\ \ \nbaloxavir or any component of the \nproduct" - "CME This clinical content conforms to AAFP criteria for con-\ntinuing medical\ \ education (CME). See CME Quiz on page 271.\nAuthor disclosure: No relevant\ \ financial affiliations.\nPatient information: Handouts on this topic, written\ \ by the \nauthors of this article, are available at https:// www.aafp.org/\n\ afp/2019/0901/p281-s1.html and https:// www.aafp.org/\nafp/2019/0901/p281-s2.html.\n\ Acute upper respiratory tract infections are extremely common in adults and children,\ \ but only a few safe and effective treat-" - source_sentence: What are the limitations of using adamantanes (amantadine and rimantadine) for influenza treatment according to the context? sentences: - "December 15, 2019 ◆ Volume 100, Number 12 www.aafp.org/afp American Family Physician\ \ 755\nINFLUENZA\nClinicians caring for high-risk patients can also be consid\ \ -\nered for treatment.28\nFour antiviral drugs have been approved for the treat\ \ -\nment of influenza (Table 4): the NA inhibitors oseltamivir \n(Tamiflu),\ \ zanamivir (Relenza), and peramivir (Rapivab), \nand the cap-dependent endonuclease\ \ inhibitor baloxa -\nvir (Xofluza). 18,37 Any of these agents can be used in\ \ age- \nappropriate, otherwise healthy outpatients with uncom -\nplicated influenza\ \ and no contraindications. 18 Baloxavir is" - "756 American Family Physician www.aafp.org/afp Volume 100, Number 12 ◆ December\ \ 15, 2019\nINFLUENZA\nthe risk of bronchospasm. 18,28 Adamantanes (amantadine\ \ \nand rimantadine [Flumadine]) are approved for influenza \ntreatment but are\ \ not currently recommended. These med -\nications are not active against influenza\ \ B, and most influ -\nenza A strains have shown adamantane resistance for the\ \ \npast 10 years.18\nThere is no demonstrated benefit to treating patients \n\ with more than one antiviral agent or using higher than \nrecommended dosages.\ \ 28 However, extended treatment" - "distress syndrome\nDiffuse alveolar \nhemorrhage\nHypoxic respiratory \nfailure\n\ Primary viral pneumonia\nSecondary bacterial \npneumonia\nRenal 26\nAcute kidney\ \ injury \n(e.g., acute tubulo- \ninterstitial nephritis, \nglomerulonephritis,\ \ \nminimal change disease)\nMultiorgan failure\nInformation from references 8,\ \ 19, and 25-27.\nSORT: KEY RECOMMENDATIONS FOR PRACTICE\nClinical recommendation\n\ Evidence \nrating Comments\nAnnual influenza vaccination is recommended for all\ \ people 6 months and older. 15,16 A Reports of expert committees" - source_sentence: Which symptoms of colds and flu are now better understood due to new knowledge in molecular biology? sentences: - 'mechanisms that generate the familiar symptoms is poor compared with the amount of knowledge available on the molecular biology of the viruses involved. New knowledge of the effects of cytokines in human beings now helps to explain some of the symptoms of colds and flu that were previously in the realm of folklore rather than medicine— eg, fever, anorexia, malaise, chilliness, headache, and muscle aches and pains. The mechanisms of symptoms of sore throat, rhinorrhoea, sneezing, nasal congestion, cough, watery eyes, and sinus pain are discussed, since these' - 'medicines such as ipratropium. These studies have demonstrated that nasal secretions in the first 4 days of a common cold are inhibited by intranasal administration of ipratropium.25 The nasal discharge also consists of a protein-rich plasma exudate derived from subepithelial capillaries,28 which may explain why anticholinergics only partly inhibit nasal discharge associated with URTIs.27 The colour of nasal discharge and sputum is often used as a clinical marker to determine whether or not to prescribe antibiotics but there is no evidence from the' - "ing diffuse alveolar hemorrhage in immunocompetent patients: a state-\nof-the-art\ \ review. Lung. 2013; 191(1): 9-18.\n 28. Uyeki TM, Bernstein HH, Bradley JS,\ \ et al. Clinical practice guidelines by \nthe Infectious Diseases Society of\ \ America: 2018 update on diagnosis, \ntreatment, chemoprophylaxis, and institutional\ \ outbreak management \nof seasonal influenza. Clin Infect Dis. 2019; 68(6): 895-902.\n\ \ 29. Ebell MH, Afonso AM, Gonzales R, et al. Development and validation of \n\ a clinical decision rule for the diagnosis of influenza. J Am Board Fam \nMed.\ \ 2012; 25(1): 55-62." - source_sentence: 'QUESTION #2: How does the sneeze centre in the brainstem coordinate the actions involved in sneezing?' sentences: - "stroke, seizure disorder, dementia)\nAsthma or other chronic pulmonary disease\n\ Chronic kidney disease\nChronic liver disease\nHeart disease (acquired or congenital)\n\ Immunosuppression (e.g., HIV infection, cancer, transplant \nrecipients, use of\ \ immunosuppressive medications)\nLong-term aspirin therapy in patients younger\ \ than 19 years\nMetabolic disorders (acquired [e.g., diabetes mellitus] or \n\ inherited [e.g., mitochondrial disorders])\nMorbid obesity\nSickle cell anemia\ \ and other hemoglobinopathies\nSpecial groups\nAdults 65 years and older\nAmerican\ \ Indians and Alaska Natives" - 'causes sneezing.23 The trigeminal nerves relay information to the sneeze centre in the brainstem and cause reflex activation of motor and parasympathetic branches of the facial nerve and activate respiratory muscles. A model of the sneeze reflex is illustrated in figure 1. The sneeze centre coordinates the inspiratory and expiratory actions of sneezing via respiratory muscles, and lacrimation and nasal congestion via parasympathetic branches of the facial nerve. The eyes are always closed during sneezing by activation of facial muscles, indicating a close relation between the' - 'during experimental rhinovirus infections have not been able to find any morphological changes in the nasal epithelium of infected volunteers, apart from a substantial increase in polymorphonuclear leucocytes early in the course of the infection.11 The major cell monitoring the host for the invasion of pathogens is the macrophage, which has the ability to trigger an acute phase response when stimulated with components of viruses or bacteria—eg, viral RNA and bacterial cell wall components.12 The surface of the macrophage exhibits toll-like receptors that combine' 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: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.6122448979591837 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8877551020408163 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9387755102040817 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9897959183673469 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6122448979591837 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.29591836734693877 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1877551020408163 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09897959183673469 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6122448979591837 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8877551020408163 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9387755102040817 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9897959183673469 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8165441473931409 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7593091998704244 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7600380628441854 name: Cosine Map@100 --- # SentenceTransformer based on Snowflake/snowflake-arctic-embed-l This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-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:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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("Gonalb/flucold-ft-v1") # Run inference sentences = [ 'QUESTION #2: How does the sneeze centre in the brainstem coordinate the actions involved in sneezing?', 'causes sneezing.23 The trigeminal nerves relay\ninformation to the sneeze centre in the brainstem and\ncause reflex activation of motor and parasympathetic\nbranches of the facial nerve and activate respiratory\nmuscles. A model of the sneeze reflex is illustrated in\nfigure 1. The sneeze centre coordinates the inspiratory\nand expiratory actions of sneezing via respiratory\nmuscles, and lacrimation and nasal congestion via\nparasympathetic branches of the facial nerve. The eyes\nare always closed during sneezing by activation of facial\nmuscles, indicating a close relation between the', 'stroke, seizure disorder, dementia)\nAsthma or other chronic pulmonary disease\nChronic kidney disease\nChronic liver disease\nHeart disease (acquired or congenital)\nImmunosuppression (e.g., HIV infection, cancer, transplant \nrecipients, use of immunosuppressive medications)\nLong-term aspirin therapy in patients younger than 19 years\nMetabolic disorders (acquired [e.g., diabetes mellitus] or \ninherited [e.g., mitochondrial disorders])\nMorbid obesity\nSickle cell anemia and other hemoglobinopathies\nSpecial groups\nAdults 65 years and older\nAmerican Indians and Alaska Natives', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.6122 | | cosine_accuracy@3 | 0.8878 | | cosine_accuracy@5 | 0.9388 | | cosine_accuracy@10 | 0.9898 | | cosine_precision@1 | 0.6122 | | cosine_precision@3 | 0.2959 | | cosine_precision@5 | 0.1878 | | cosine_precision@10 | 0.099 | | cosine_recall@1 | 0.6122 | | cosine_recall@3 | 0.8878 | | cosine_recall@5 | 0.9388 | | cosine_recall@10 | 0.9898 | | **cosine_ndcg@10** | **0.8165** | | cosine_mrr@10 | 0.7593 | | cosine_map@100 | 0.76 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 400 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 400 samples: | | sentence_0 | sentence_1 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:----------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What is the recommended age for annual influenza vaccination according to the context? | recommend annual influenza vaccination for all people six
months and older who do not have contraindications. 15,16
Vaccination efforts should target people at increased risk of
complicated or severe influenza (Table 117-19) and those who
care for or live with high-risk individuals, including health
care professionals. 15 Two previous FPM articles provided
communication strategies and tools for increasing influenza
vaccination rates in practice. 20,21
Multiple formulations of the influenza vaccine are avail -
able, including inactivated influenza vaccines (IIV); a recom-
| | Who should vaccination efforts specifically target to prevent complicated or severe influenza? | recommend annual influenza vaccination for all people six
months and older who do not have contraindications. 15,16
Vaccination efforts should target people at increased risk of
complicated or severe influenza (Table 117-19) and those who
care for or live with high-risk individuals, including health
care professionals. 15 Two previous FPM articles provided
communication strategies and tools for increasing influenza
vaccination rates in practice. 20,21
Multiple formulations of the influenza vaccine are avail -
able, including inactivated influenza vaccines (IIV); a recom-
| | What types of studies were included in the search regarding influenza complications and treatment? | enza complications American Indians, influenza treatment, and
influenza universal vaccine. The search included meta-analyses,
randomized controlled trials, clinical trials, and reviews. Search
dates: December 1, 2018, to October 5, 2019.
The Authors
DAVID Y. GAITONDE, MD, is a core clinical faculty member
and chief of endocrinology service at Dwight D. Eisenhower
Army Medical Center, Fort Gordon, Ga.
CPT. FAITH C. MOORE, USA, MC, is a resident in the Depart -
ment of Internal Medicine at Dwight D. Eisenhower Army
Medical Center.
MAJ. MACKENZIE K. MORGAN, USA, MC, is chief of infec-
| * 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`: steps - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `num_train_epochs`: 10 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `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`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `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`: False - `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 - `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`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | cosine_ndcg@10 | |:-----:|:----:|:--------------:| | 1.0 | 40 | 0.8359 | | 1.25 | 50 | 0.8312 | | 2.0 | 80 | 0.8304 | | 2.5 | 100 | 0.8156 | | 3.0 | 120 | 0.8016 | | 3.75 | 150 | 0.7952 | | 4.0 | 160 | 0.7880 | | 5.0 | 200 | 0.8021 | | 6.0 | 240 | 0.8215 | | 6.25 | 250 | 0.8286 | | 7.0 | 280 | 0.8079 | | 7.5 | 300 | 0.8043 | | 8.0 | 320 | 0.8126 | | 8.75 | 350 | 0.8099 | | 9.0 | 360 | 0.8126 | | 10.0 | 400 | 0.8165 | ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.4.1 - Transformers: 4.48.3 - PyTorch: 2.5.1+cu124 - Accelerate: 1.3.0 - Datasets: 3.3.2 - 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} } ```