--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:378558 - loss:MultipleNegativesRankingLoss base_model: intfloat/e5-base-v2 widget: - source_sentence: Is intraoperative ketorolac an effective substitute for fentanyl in children undergoing outpatient adenotonsillectomy? sentences: - Ketorolac showed no advantage over fentanyl in reducing the incidence of PONV in children undergoing ADLAT. - The patients with IgAN and their first relatives showed significant higher Gal deficient IgA1 level than healthy controls, whereas patients spouses were the same as healthy controls. It can be suggested that the Gal deficient IgA1 might be inherited in Chinese patients with IgAN. - Our results indicated that triptolide enhanced and enriched the stemness in the PDAC cell lines at a low dose of 12.5 nM, but also resulted in the regression of tumors derived from these cells. - source_sentence: Is task specific fall prevention training effective for warfighters with transtibial amputations? sentences: - These results indicate that task specific fall prevention training is an effective rehabilitation method to reduce falls in persons with lower extremity transtibial amputations. - Don t press on the eye. For pain, give acetaminophen Tylenol . Don t give aspirin or ibuprofen Advil, Motrin , because they can increase bleeding. - Dermatophytes Trichophyton skin ,hair, ,nail Tri all Three Microsporum skin, hair My head on head we have skin and hair Epidermophyton skin, nails - source_sentence: Left horn of sinus venosus forms sentences: - Ki 67 expression is predictive of prognosis, and our prognostic model may become a useful tool for predicting prognosis in patients with stage I II extranodal NK T cell lymphoma, nasal type. - Evidence described here suggests that IFN λ is a good candidate inhibitor of viral replication in dengue infection. Mechanisms for the cellular and organismal interplay between DENV and IFN λ need to be further studied as they could provide insights into strategies to treat this disease. Furthermore, we report a novel epithelial model to study dengue infection in vitro. - Ans. A Coronary sinusRef Netter s Atlas of Human Embryology 2012 ed. pg. 96Heart tube embryonic derivativesembryonic structureGives rise to Proximal 1 3rd of bulbus cordisPrimitive trabeculated left ventricle Middle 1 3rd of bulbus cordisRight and left ventricular outflow tract Distal 1 3rd of bulbus cordis truncus arteriosus Ascending aorta and pulmonary trunk Left horn of sinus venosusCoronary sinus Right horn of sinus venosusSmooth part of right atrium Right common cardinal nerve and right anterior cardinal nerveSVC superior vena cava - source_sentence: Is implementation of national diabetes retinal screening programme associated with a lower proportion of patients referred to ophthalmology? sentences: - Introduction of a systematic retinal screening programme can reduce the proportion of patients referred to the ophthalmology clinic, and use ophthalmology services more efficiently. - A Obesity Medications for the treatment of obesity can be classified as catecholaminergic or serotonergic. Catecholaminergic medications include Amphetamines with high abuse potential The Non Amphetamine schedule IV appetite suppressants Phentermine, Diethyl propion Mazindol. The September 1997 withdrawal from the market of Flenfluramine Defenfluramine has made true serotonergic appetite medications unavailable. The SSRI antidepressants, E.g.., Fluoxetine Setraline, also have serotonergic activity but are not approved by the FDA for weight loss. - A i.e. Protein linked with glycosidic bond - source_sentence: Does amyloid peptide regulate calcium homoeostasis and arrhythmogenesis in pulmonary vein cardiomyocytes? sentences: - Hydroxy ethyl methaacrylate is a soft, flexible, water absorbing, plastic used to make soft contact lenses. It is a polymer of 2 hydroxyethyl methacrylate HEMA , a clear liquid component. Hard contact lenses are made from polymethyl methacrylate PMMA and Silicon. - Beta carotene has become popular in part because it s an antioxidant a substance that may protect cells from damage. A number of studies show that people who eat lots of fruits and vegetables that are rich in beta carotene and other vitamins and minerals have a lower risk of some cancers and heart disease. However, so far studies have not found that beta carotene supplements have the same health benefits as foods. - Aβ 25 35 has direct electrophysiological effects on PV cardiomyocytes. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy model-index: - name: MPNet base trained on AllNLI triplets results: - task: type: triplet name: Triplet dataset: name: eval dataset type: eval-dataset metrics: - type: cosine_accuracy value: 0.9937447168216399 name: Cosine Accuracy - task: type: triplet name: Triplet dataset: name: test dataset type: test-dataset metrics: - type: cosine_accuracy value: 0.9964285714285714 name: Cosine Accuracy --- # MPNet base trained on AllNLI triplets This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2). 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:** [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **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': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("sentence_transformers_model_id") # Run inference sentences = [ 'Does amyloid peptide regulate calcium homoeostasis and arrhythmogenesis in pulmonary vein cardiomyocytes?', 'Aβ 25 35 has direct electrophysiological effects on PV cardiomyocytes.', 'Beta carotene has become popular in part because it s an antioxidant a substance that may protect cells from damage. A number of studies show that people who eat lots of fruits and vegetables that are rich in beta carotene and other vitamins and minerals have a lower risk of some cancers and heart disease. However, so far studies have not found that beta carotene supplements have the same health benefits as foods.', ] 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 #### Triplet * Datasets: `eval-dataset` and `test-dataset` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | eval-dataset | test-dataset | |:--------------------|:-------------|:-------------| | **cosine_accuracy** | **0.9937** | **0.9964** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 378,558 training samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | label | |:--------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | Does tolbutamide alter glucose transport and metabolism in the embryonic mouse heart? | Tolbutamide stimulates glucose uptake and metabolism in the embryonic heart, as occurs in adult extra pancreatic tissues. Glut 1 and HKI, but not GRP78, are likely involved in tolbutamide induced cardiac dysmorphogenesis. | 1.0 | | Do flk1 cells derived from mouse embryonic stem cells reconstitute hematopoiesis in vivo in SCID mice? | The Flk1 hematopoietic cells derived from ES cells reconstitute hematopoiesis in vivo and may become an alternative donor source for bone marrow transplantation. | 1.0 | | Does systematic aging of degradable nanosuspension ameliorate vibrating mesh nebulizer performance? | Nebulization of purified nanosuspensions resulted in droplet diameters of 7.0 µm. However, electrolyte supplementation and storage, which led to an increase in sample conductivity 10 20 µS cm , were capable of providing smaller droplet diameters during vibrating mesh nebulization 5.0 µm . No relevant change of NP properties i.e. size, morphology, remaining mass and molecular weight of the employed polymer was observed when incubated at 22 C for two weeks. | 1.0 | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 47,320 evaluation samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | label | |:-------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | Does thrombospondin 2 gene silencing in human aortic smooth muscle cells improve cell attachment? | siRNA mediated TSP 2 silencing of human aortic HAoSMCs improved cell attachment but had no effect on cell migration or proliferation. The effect on cell attachment was unrelated to changes in MMP activity. | 1.0 | | What can you do to manage polycythemia vera? | Most people with polycythemia vera take low dose aspirin. There are a lot of ways you can keep yourself comfortable and as healthy as possible Don t smoke or chew tobacco. Tobacco makes blood vessels narrow, which can make blood clots more likely. Get some light exercise, such as walking, to help your circulation and keep your heart healthy. Do leg and ankle exercises to stop clots from forming in the veins of your legs. Your doctor or a physical therapist can show you how. Bathe or shower in cool water if warm water makes you itch. Keep your skin moist with lotion, and try not to scratch. | 1.0 | | Is weekly nab paclitaxel safe and effective in 65 years old patients with metastatic breast cancer a post hoc analysis? | Weekly nab paclitaxel was safe and more efficacious compared with the q3w schedule and with solvent based taxanes in older patients with MBC. | 1.0 | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `do_predict`: True - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `load_best_model_at_end`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: True - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `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`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `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`: 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`: 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 - `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 - `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
Click to expand | Epoch | Step | Training Loss | Validation Loss | eval-dataset_cosine_accuracy | test-dataset_cosine_accuracy | |:----------:|:--------:|:-------------:|:---------------:|:----------------------------:|:----------------------------:| | 0 | 0 | - | - | 0.9813 | - | | 0.0085 | 50 | 1.8471 | - | - | - | | 0.0169 | 100 | 0.5244 | - | - | - | | 0.0254 | 150 | 0.2175 | - | - | - | | 0.0338 | 200 | 0.1392 | - | - | - | | 0.0423 | 250 | 0.1437 | - | - | - | | 0.0507 | 300 | 0.142 | - | - | - | | 0.0592 | 350 | 0.1295 | - | - | - | | 0.0676 | 400 | 0.1238 | - | - | - | | 0.0761 | 450 | 0.14 | - | - | - | | 0.0845 | 500 | 0.1173 | 0.1006 | 0.9931 | - | | 0.0930 | 550 | 0.1236 | - | - | - | | 0.1014 | 600 | 0.1127 | - | - | - | | 0.1099 | 650 | 0.1338 | - | - | - | | 0.1183 | 700 | 0.1071 | - | - | - | | 0.1268 | 750 | 0.1149 | - | - | - | | 0.1352 | 800 | 0.1072 | - | - | - | | 0.1437 | 850 | 0.1117 | - | - | - | | 0.1522 | 900 | 0.1087 | - | - | - | | 0.1606 | 950 | 0.1242 | - | - | - | | **0.1691** | **1000** | **0.1039** | **0.091** | **0.9965** | **-** | | 0.1775 | 1050 | 0.1043 | - | - | - | | 0.1860 | 1100 | 0.1193 | - | - | - | | 0.1944 | 1150 | 0.1028 | - | - | - | | 0.2029 | 1200 | 0.1027 | - | - | - | | 0.2113 | 1250 | 0.1075 | - | - | - | | 0.2198 | 1300 | 0.1177 | - | - | - | | 0.2282 | 1350 | 0.0937 | - | - | - | | 0.2367 | 1400 | 0.1095 | - | - | - | | 0.2451 | 1450 | 0.1054 | - | - | - | | 0.2536 | 1500 | 0.1003 | 0.0798 | 0.9958 | - | | 0.2620 | 1550 | 0.0952 | - | - | - | | 0.2705 | 1600 | 0.1028 | - | - | - | | 0.2790 | 1650 | 0.0988 | - | - | - | | 0.2874 | 1700 | 0.0887 | - | - | - | | 0.2959 | 1750 | 0.1027 | - | - | - | | 0.3043 | 1800 | 0.0937 | - | - | - | | 0.3128 | 1850 | 0.1031 | - | - | - | | 0.3212 | 1900 | 0.0857 | - | - | - | | 0.3297 | 1950 | 0.094 | - | - | - | | 0.3381 | 2000 | 0.1044 | 0.0721 | 0.9954 | - | | 0.3466 | 2050 | 0.0829 | - | - | - | | 0.3550 | 2100 | 0.0934 | - | - | - | | 0.3635 | 2150 | 0.0785 | - | - | - | | 0.3719 | 2200 | 0.0938 | - | - | - | | 0.3804 | 2250 | 0.0885 | - | - | - | | 0.3888 | 2300 | 0.0907 | - | - | - | | 0.3973 | 2350 | 0.0911 | - | - | - | | 0.4057 | 2400 | 0.0891 | - | - | - | | 0.4142 | 2450 | 0.0798 | - | - | - | | 0.4227 | 2500 | 0.0856 | 0.0655 | 0.9935 | - | | 0.4311 | 2550 | 0.0925 | - | - | - | | 0.4396 | 2600 | 0.0778 | - | - | - | | 0.4480 | 2650 | 0.0871 | - | - | - | | 0.4565 | 2700 | 0.0769 | - | - | - | | 0.4649 | 2750 | 0.0815 | - | - | - | | 0.4734 | 2800 | 0.0697 | - | - | - | | 0.4818 | 2850 | 0.0714 | - | - | - | | 0.4903 | 2900 | 0.0788 | - | - | - | | 0.4987 | 2950 | 0.0772 | - | - | - | | 0.5072 | 3000 | 0.0825 | 0.0618 | 0.9917 | - | | 0.5156 | 3050 | 0.0742 | - | - | - | | 0.5241 | 3100 | 0.0784 | - | - | - | | 0.5325 | 3150 | 0.0697 | - | - | - | | 0.5410 | 3200 | 0.0791 | - | - | - | | 0.5495 | 3250 | 0.0657 | - | - | - | | 0.5579 | 3300 | 0.0779 | - | - | - | | 0.5664 | 3350 | 0.0719 | - | - | - | | 0.5748 | 3400 | 0.0656 | - | - | - | | 0.5833 | 3450 | 0.0698 | - | - | - | | 0.5917 | 3500 | 0.0678 | 0.0578 | 0.9903 | - | | 0.6002 | 3550 | 0.0771 | - | - | - | | 0.6086 | 3600 | 0.0645 | - | - | - | | 0.6171 | 3650 | 0.078 | - | - | - | | 0.6255 | 3700 | 0.064 | - | - | - | | 0.6340 | 3750 | 0.0691 | - | - | - | | 0.6424 | 3800 | 0.0634 | - | - | - | | 0.6509 | 3850 | 0.0732 | - | - | - | | 0.6593 | 3900 | 0.059 | - | - | - | | 0.6678 | 3950 | 0.0671 | - | - | - | | 0.6762 | 4000 | 0.0633 | 0.0552 | 0.9936 | - | | 0.6847 | 4050 | 0.0732 | - | - | - | | 0.6932 | 4100 | 0.0593 | - | - | - | | 0.7016 | 4150 | 0.0639 | - | - | - | | 0.7101 | 4200 | 0.0672 | - | - | - | | 0.7185 | 4250 | 0.0604 | - | - | - | | 0.7270 | 4300 | 0.0666 | - | - | - | | 0.7354 | 4350 | 0.0594 | - | - | - | | 0.7439 | 4400 | 0.0783 | - | - | - | | 0.7523 | 4450 | 0.0654 | - | - | - | | 0.7608 | 4500 | 0.0596 | 0.0520 | 0.9937 | - | | 0.7692 | 4550 | 0.0654 | - | - | - | | 0.7777 | 4600 | 0.0511 | - | - | - | | 0.7861 | 4650 | 0.0641 | - | - | - | | 0.7946 | 4700 | 0.0609 | - | - | - | | 0.8030 | 4750 | 0.0591 | - | - | - | | 0.8115 | 4800 | 0.0496 | - | - | - | | 0.8199 | 4850 | 0.0624 | - | - | - | | 0.8284 | 4900 | 0.0639 | - | - | - | | 0.8369 | 4950 | 0.056 | - | - | - | | 0.8453 | 5000 | 0.0641 | 0.0487 | 0.9947 | - | | 0.8538 | 5050 | 0.0608 | - | - | - | | 0.8622 | 5100 | 0.0725 | - | - | - | | 0.8707 | 5150 | 0.055 | - | - | - | | 0.8791 | 5200 | 0.0556 | - | - | - | | 0.8876 | 5250 | 0.0489 | - | - | - | | 0.8960 | 5300 | 0.0513 | - | - | - | | 0.9045 | 5350 | 0.0493 | - | - | - | | 0.9129 | 5400 | 0.0574 | - | - | - | | 0.9214 | 5450 | 0.0665 | - | - | - | | 0.9298 | 5500 | 0.0588 | 0.0475 | 0.9937 | - | | 0.9383 | 5550 | 0.0557 | - | - | - | | 0.9467 | 5600 | 0.0497 | - | - | - | | 0.9552 | 5650 | 0.0592 | - | - | - | | 0.9637 | 5700 | 0.0526 | - | - | - | | 0.9721 | 5750 | 0.0683 | - | - | - | | 0.9806 | 5800 | 0.0588 | - | - | - | | 0.9890 | 5850 | 0.0541 | - | - | - | | 0.9975 | 5900 | 0.0636 | - | - | - | | 1.0 | 5915 | - | - | - | 0.9964 | * The bold row denotes the saved checkpoint.
### Framework Versions - Python: 3.11.10 - Sentence Transformers: 3.3.0 - Transformers: 4.46.2 - PyTorch: 2.5.1+cu124 - Accelerate: 1.1.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## 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", } ``` #### 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} } ```