{"url":"/sota/natural-language-inference-on-qnli","task":{"name":"Natural Language Inference","url":"/task/natural-language-inference","note":null},"dataset":{"name":"QNLI","url":"/dataset/qnli"},"category":"Natural Language Processing","categories":["Natural Language Processing","Reasoning"],"category_note":null,"description":"**Natural language inference (NLI)** is the task of determining whether a \"hypothesis\" is \r\ntrue (entailment), false (contradiction), or undetermined (neutral) given a \"premise\".\r\n\r\nExample:\r\n\r\n| Premise | Label | Hypothesis |\r\n| --- | ---| --- |\r\n| A man inspects the uniform of a figure in some East Asian country. | contradiction | The man is sleeping. |\r\n| An older and younger man smiling. | neutral  | Two men are smiling and laughing at the cats playing on the floor. |\r\n| A soccer game with multiple males playing. | entailment | Some men are playing a sport. |\r\n\r\nApproaches used for NLI include earlier symbolic and statistical approaches to more recent deep learning approaches. Benchmark datasets used for NLI include [SNLI](/dataset/snli), [MultiNLI](/dataset/multinli), [SciTail](/dataset/scitail), among others. You can get hands-on practice on the SNLI task by following this [d2l.ai chapter](https://d2l.ai/chapter_natural-language-processing-applications/natural-language-inference-and-dataset.html).\r\n\r\nFurther readings:\r\n\r\n- [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://arxiv.org/abs/1904.01172)","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Accuracy"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher"}},"counts":{"rows":43,"rows_with_code":37,"rows_with_paper_page":42,"rows_dated":42,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"ALBERT","metrics":{"Accuracy":"99.2%"},"uses_additional_data":false,"paper_date":"2019-09-26","paper":"/paper/albert-a-lite-bert-for-self-supervised","paper_url":"https://arxiv.org/abs/1909.11942v6","paper_title":"ALBERT: A Lite BERT for Self-supervised Learning of Language Representations","code":"https://github.com/huggingface/transformers","n_code_links":48,"syntology":{"n_ran":46,"n_unverified":80,"n_samples":126,"n_pointer_only_licence":22}},{"rank_in_archive_order":2,"model":"StructBERTRoBERTa ensemble","metrics":{"Accuracy":"99.2%"},"uses_additional_data":false,"paper_date":"2019-08-13","paper":"/paper/structbert-incorporating-language-structures","paper_url":"https://arxiv.org/abs/1908.04577v3","paper_title":"StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":3,"model":"ALICE","metrics":{"Accuracy":"99.2%"},"uses_additional_data":false,"paper_date":"2019-11-08","paper":"/paper/smart-robust-and-efficient-fine-tuning-for","paper_url":"https://arxiv.org/abs/1911.03437v5","paper_title":"SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization","code":"https://github.com/namisan/mt-dnn","n_code_links":6,"syntology":{"n_ran":6,"n_unverified":2,"n_samples":8,"n_pointer_only_licence":1}},{"rank_in_archive_order":4,"model":"MT-DNN-SMART","metrics":{"Accuracy":"99.2%"},"uses_additional_data":false,"paper_date":"2019-11-08","paper":"/paper/smart-robust-and-efficient-fine-tuning-for","paper_url":"https://arxiv.org/abs/1911.03437v5","paper_title":"SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization","code":"https://github.com/namisan/mt-dnn","n_code_links":6,"syntology":{"n_ran":6,"n_unverified":2,"n_samples":8,"n_pointer_only_licence":1}},{"rank_in_archive_order":5,"model":"RoBERTa (ensemble)","metrics":{"Accuracy":"98.9%"},"uses_additional_data":false,"paper_date":"2019-07-26","paper":"/paper/roberta-a-robustly-optimized-bert-pretraining","paper_url":"https://arxiv.org/abs/1907.11692v1","paper_title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","code":"https://github.com/huggingface/transformers","n_code_links":67,"syntology":{"n_ran":22,"n_unverified":26,"n_samples":48,"n_pointer_only_licence":23}},{"rank_in_archive_order":6,"model":"T5-11B","metrics":{"Accuracy":"96.7%"},"uses_additional_data":false,"paper_date":"2019-10-23","paper":"/paper/exploring-the-limits-of-transfer-learning","paper_url":"https://arxiv.org/abs/1910.10683v4","paper_title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","code":"https://github.com/huggingface/transformers","n_code_links":57,"syntology":{"n_ran":2,"n_unverified":29,"n_samples":31,"n_pointer_only_licence":0}},{"rank_in_archive_order":7,"model":"T5-3B","metrics":{"Accuracy":"96.3%"},"uses_additional_data":false,"paper_date":"2019-10-23","paper":"/paper/exploring-the-limits-of-transfer-learning","paper_url":"https://arxiv.org/abs/1910.10683v4","paper_title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","code":"https://github.com/huggingface/transformers","n_code_links":57,"syntology":{"n_ran":2,"n_unverified":29,"n_samples":31,"n_pointer_only_licence":0}},{"rank_in_archive_order":8,"model":"DeBERTaV3large","metrics":{"Accuracy":"96%"},"uses_additional_data":false,"paper_date":"2021-11-18","paper":"/paper/debertav3-improving-deberta-using-electra","paper_url":"https://arxiv.org/abs/2111.09543v4","paper_title":"DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing","code":"https://github.com/microsoft/DeBERTa","n_code_links":3,"syntology":{"n_ran":0,"n_unverified":7,"n_samples":7,"n_pointer_only_licence":0}},{"rank_in_archive_order":9,"model":"ELECTRA","metrics":{"Accuracy":"95.4%"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":10,"model":"DeBERTa (large)","metrics":{"Accuracy":"95.3%"},"uses_additional_data":false,"paper_date":"2020-06-05","paper":"/paper/deberta-decoding-enhanced-bert-with","paper_url":"https://arxiv.org/abs/2006.03654v6","paper_title":"DeBERTa: Decoding-enhanced BERT with Disentangled Attention","code":"https://github.com/huggingface/transformers","n_code_links":14,"syntology":{"n_ran":4,"n_unverified":9,"n_samples":13,"n_pointer_only_licence":3}},{"rank_in_archive_order":11,"model":"XLNet (single model)","metrics":{"Accuracy":"94.9%"},"uses_additional_data":false,"paper_date":"2019-06-19","paper":"/paper/xlnet-generalized-autoregressive-pretraining","paper_url":"https://arxiv.org/abs/1906.08237v2","paper_title":"XLNet: Generalized Autoregressive Pretraining for Language Understanding","code":"https://github.com/huggingface/transformers","n_code_links":27,"syntology":{"n_ran":10,"n_unverified":14,"n_samples":24,"n_pointer_only_licence":3}},{"rank_in_archive_order":12,"model":"T5-Large 770M","metrics":{"Accuracy":"94.8%"},"uses_additional_data":false,"paper_date":"2019-10-23","paper":"/paper/exploring-the-limits-of-transfer-learning","paper_url":"https://arxiv.org/abs/1910.10683v4","paper_title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","code":"https://github.com/huggingface/transformers","n_code_links":57,"syntology":{"n_ran":2,"n_unverified":29,"n_samples":31,"n_pointer_only_licence":0}},{"rank_in_archive_order":13,"model":"RoBERTa-large 355M (MLP quantized vector-wise, fine-tuned)","metrics":{"Accuracy":"94.7%"},"uses_additional_data":false,"paper_date":"2022-08-15","paper":"/paper/llm-int8-8-bit-matrix-multiplication-for","paper_url":"https://arxiv.org/abs/2208.07339v2","paper_title":"LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale","code":"https://github.com/timdettmers/bitsandbytes","n_code_links":4,"syntology":{"n_ran":2,"n_unverified":3,"n_samples":5,"n_pointer_only_licence":0}},{"rank_in_archive_order":14,"model":"ERNIE 2.0 Large","metrics":{"Accuracy":"94.6%"},"uses_additional_data":false,"paper_date":"2019-07-29","paper":"/paper/ernie-20-a-continual-pre-training-framework","paper_url":"https://arxiv.org/abs/1907.12412v2","paper_title":"ERNIE 2.0: A Continual Pre-training Framework for Language Understanding","code":"https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-1.0","n_code_links":3,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":15,"model":"PSQ (Chen et al., 2020)","metrics":{"Accuracy":"94.5"},"uses_additional_data":false,"paper_date":"2020-10-27","paper":"/paper/a-statistical-framework-for-low-bitwidth","paper_url":"https://arxiv.org/abs/2010.14298v1","paper_title":"A Statistical Framework for Low-bitwidth Training of Deep Neural Networks","code":"https://github.com/cjf00000/StatQuant","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":3,"n_samples":4,"n_pointer_only_licence":1}},{"rank_in_archive_order":16,"model":"RoBERTa-large 355M + Entailment as Few-shot Learner","metrics":{"Accuracy":"94.5%"},"uses_additional_data":false,"paper_date":"2021-04-29","paper":"/paper/entailment-as-few-shot-learner","paper_url":"https://arxiv.org/abs/2104.14690v1","paper_title":"Entailment as Few-Shot Learner","code":"https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/few_shot/efl","n_code_links":3,"syntology":{"n_ran":1,"n_unverified":2,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":17,"model":"SpanBERT","metrics":{"Accuracy":"94.3%"},"uses_additional_data":false,"paper_date":"2019-07-24","paper":"/paper/spanbert-improving-pre-training-by","paper_url":"https://arxiv.org/abs/1907.10529v3","paper_title":"SpanBERT: Improving Pre-training by Representing and Predicting Spans","code":"https://github.com/facebookresearch/SpanBERT","n_code_links":6,"syntology":{"n_ran":3,"n_unverified":12,"n_samples":15,"n_pointer_only_licence":4}},{"rank_in_archive_order":18,"model":"TRANS-BLSTM","metrics":{"Accuracy":"94.08%"},"uses_additional_data":false,"paper_date":"2020-03-16","paper":"/paper/trans-blstm-transformer-with-bidirectional","paper_url":"https://arxiv.org/abs/2003.07000v1","paper_title":"TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":19,"model":"T5-Base","metrics":{"Accuracy":"93.7%"},"uses_additional_data":false,"paper_date":"2019-10-23","paper":"/paper/exploring-the-limits-of-transfer-learning","paper_url":"https://arxiv.org/abs/1910.10683v4","paper_title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","code":"https://github.com/huggingface/transformers","n_code_links":57,"syntology":{"n_ran":2,"n_unverified":29,"n_samples":31,"n_pointer_only_licence":0}},{"rank_in_archive_order":20,"model":"ASA + RoBERTa","metrics":{"Accuracy":"93.6%"},"uses_additional_data":false,"paper_date":"2022-06-25","paper":"/paper/adversarial-self-attention-for-language","paper_url":"https://arxiv.org/abs/2206.12608v3","paper_title":"Adversarial Self-Attention for Language Understanding","code":"https://github.com/gingasan/adversarialsa","n_code_links":1,"syntology":null},{"rank_in_archive_order":21,"model":"MLM+ subs+ del-span","metrics":{"Accuracy":"93.4%"},"uses_additional_data":false,"paper_date":"2020-12-31","paper":"/paper/clear-contrastive-learning-for-sentence","paper_url":"https://arxiv.org/abs/2012.15466v1","paper_title":"CLEAR: Contrastive Learning for Sentence Representation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":22,"model":"Q-BERT (Shen et al., 2020)","metrics":{"Accuracy":"93.0"},"uses_additional_data":false,"paper_date":"2019-09-12","paper":"/paper/q-bert-hessian-based-ultra-low-precision","paper_url":"https://arxiv.org/abs/1909.05840v2","paper_title":"Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":23,"model":"Q8BERT (Zafrir et al., 2019)","metrics":{"Accuracy":"93.0"},"uses_additional_data":false,"paper_date":"2019-10-14","paper":"/paper/q8bert-quantized-8bit-bert","paper_url":"https://arxiv.org/abs/1910.06188v2","paper_title":"Q8BERT: Quantized 8Bit BERT","code":"https://github.com/NervanaSystems/nlp-architect/blob/master/nlp_architect/models/transformers/quantized_bert.py","n_code_links":5,"syntology":{"n_ran":3,"n_unverified":8,"n_samples":11,"n_pointer_only_licence":3}},{"rank_in_archive_order":24,"model":"ERNIE 2.0 Base","metrics":{"Accuracy":"92.9%"},"uses_additional_data":false,"paper_date":"2019-07-29","paper":"/paper/ernie-20-a-continual-pre-training-framework","paper_url":"https://arxiv.org/abs/1907.12412v2","paper_title":"ERNIE 2.0: A Continual Pre-training Framework for Language Understanding","code":"https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-1.0","n_code_links":3,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":25,"model":"BERT-LARGE","metrics":{"Accuracy":"92.7%"},"uses_additional_data":false,"paper_date":"2018-10-11","paper":"/paper/bert-pre-training-of-deep-bidirectional","paper_url":"https://arxiv.org/abs/1810.04805v2","paper_title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","code":"https://github.com/huggingface/transformers","n_code_links":534,"syntology":{"n_ran":204,"n_unverified":455,"n_samples":659,"n_pointer_only_licence":149}},{"rank_in_archive_order":26,"model":"BigBird","metrics":{"Accuracy":"92.2%"},"uses_additional_data":false,"paper_date":"2020-07-28","paper":"/paper/big-bird-transformers-for-longer-sequences","paper_url":"https://arxiv.org/abs/2007.14062v2","paper_title":"Big Bird: Transformers for Longer Sequences","code":"https://github.com/huggingface/transformers","n_code_links":14,"syntology":{"n_ran":10,"n_unverified":5,"n_samples":15,"n_pointer_only_licence":11}},{"rank_in_archive_order":27,"model":"RealFormer","metrics":{"Accuracy":"91.89%"},"uses_additional_data":false,"paper_date":"2020-12-21","paper":"/paper/informer-transformer-likes-informed-attention","paper_url":"https://arxiv.org/abs/2012.11747v3","paper_title":"RealFormer: Transformer Likes Residual Attention","code":"https://github.com/google-research/google-research","n_code_links":5,"syntology":null},{"rank_in_archive_order":28,"model":"ASA + BERT-base","metrics":{"Accuracy":"91.4%"},"uses_additional_data":false,"paper_date":"2022-06-25","paper":"/paper/adversarial-self-attention-for-language","paper_url":"https://arxiv.org/abs/2206.12608v3","paper_title":"Adversarial Self-Attention for Language Understanding","code":"https://github.com/gingasan/adversarialsa","n_code_links":1,"syntology":null},{"rank_in_archive_order":29,"model":"ERNIE","metrics":{"Accuracy":"91.3%"},"uses_additional_data":false,"paper_date":"2019-05-17","paper":"/paper/ernie-enhanced-language-representation-with","paper_url":"https://arxiv.org/abs/1905.07129v3","paper_title":"ERNIE: Enhanced Language Representation with Informative Entities","code":"https://github.com/thunlp/ERNIE","n_code_links":2,"syntology":{"n_ran":3,"n_unverified":0,"n_samples":3,"n_pointer_only_licence":1}},{"rank_in_archive_order":30,"model":"data2vec","metrics":{"Accuracy":"91.1%"},"uses_additional_data":false,"paper_date":"2022-02-07","paper":"/paper/data2vec-a-general-framework-for-self-1","paper_url":"https://arxiv.org/abs/2202.03555v3","paper_title":"data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language","code":"https://github.com/huggingface/transformers/tree/main/src/transformers/models/data2vec","n_code_links":12,"syntology":{"n_ran":0,"n_unverified":6,"n_samples":6,"n_pointer_only_licence":0}},{"rank_in_archive_order":31,"model":"Charformer-Tall","metrics":{"Accuracy":"91.0%"},"uses_additional_data":false,"paper_date":"2021-06-23","paper":"/paper/charformer-fast-character-transformers-via","paper_url":"https://arxiv.org/abs/2106.12672v3","paper_title":"Charformer: Fast Character Transformers via Gradient-based Subword Tokenization","code":"https://github.com/google-research/google-research","n_code_links":2,"syntology":{"n_ran":7,"n_unverified":3,"n_samples":10,"n_pointer_only_licence":0}},{"rank_in_archive_order":32,"model":"24hBERT","metrics":{"Accuracy":"90.6"},"uses_additional_data":false,"paper_date":"2021-04-15","paper":"/paper/how-to-train-bert-with-an-academic-budget","paper_url":"https://arxiv.org/abs/2104.07705v2","paper_title":"How to Train BERT with an Academic Budget","code":"https://github.com/peteriz/academic-budget-bert","n_code_links":4,"syntology":null},{"rank_in_archive_order":33,"model":"SenseBERT-base 110M","metrics":{"Accuracy":"90.6%"},"uses_additional_data":false,"paper_date":"2019-08-15","paper":"/paper/sensebert-driving-some-sense-into-bert","paper_url":"https://arxiv.org/abs/1908.05646v2","paper_title":"SenseBERT: Driving Some Sense into BERT","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":34,"model":"TinyBERT-6 67M","metrics":{"Accuracy":"90.4%"},"uses_additional_data":false,"paper_date":"2019-09-23","paper":"/paper/190910351","paper_url":"https://arxiv.org/abs/1909.10351v5","paper_title":"TinyBERT: Distilling BERT for Natural Language Understanding","code":"https://github.com/PaddlePaddle/PaddleNLP/tree/develop/paddlenlp/transformers/tinybert","n_code_links":10,"syntology":{"n_ran":0,"n_unverified":4,"n_samples":4,"n_pointer_only_licence":4}},{"rank_in_archive_order":35,"model":"T5-Small","metrics":{"Accuracy":"90.3%"},"uses_additional_data":false,"paper_date":"2019-10-23","paper":"/paper/exploring-the-limits-of-transfer-learning","paper_url":"https://arxiv.org/abs/1910.10683v4","paper_title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","code":"https://github.com/huggingface/transformers","n_code_links":57,"syntology":{"n_ran":2,"n_unverified":29,"n_samples":31,"n_pointer_only_licence":0}},{"rank_in_archive_order":36,"model":"DistilBERT 66M","metrics":{"Accuracy":"90.2%"},"uses_additional_data":false,"paper_date":"2019-10-02","paper":"/paper/distilbert-a-distilled-version-of-bert","paper_url":"https://arxiv.org/abs/1910.01108v4","paper_title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","code":"https://github.com/huggingface/transformers","n_code_links":37,"syntology":{"n_ran":19,"n_unverified":8,"n_samples":27,"n_pointer_only_licence":0}},{"rank_in_archive_order":37,"model":"SqueezeBERT","metrics":{"Accuracy":"90.1%"},"uses_additional_data":false,"paper_date":"2020-06-19","paper":"/paper/squeezebert-what-can-computer-vision-teach","paper_url":"https://arxiv.org/abs/2006.11316v1","paper_title":"SqueezeBERT: What can computer vision teach NLP about efficient neural networks?","code":"https://github.com/huggingface/transformers/blob/main/src/transformers/models/squeezebert/modeling_squeezebert.py","n_code_links":6,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":38,"model":"Nyströmformer","metrics":{"Accuracy":"88.7%"},"uses_additional_data":false,"paper_date":"2021-02-07","paper":"/paper/nystromformer-a-nystrom-based-algorithm-for","paper_url":"https://arxiv.org/abs/2102.03902v3","paper_title":"Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention","code":"https://github.com/facebookresearch/xformers","n_code_links":10,"syntology":{"n_ran":1,"n_unverified":1,"n_samples":2,"n_pointer_only_licence":1}},{"rank_in_archive_order":39,"model":"TinyBERT-4 14.5M","metrics":{"Accuracy":"87.7%"},"uses_additional_data":false,"paper_date":"2019-09-23","paper":"/paper/190910351","paper_url":"https://arxiv.org/abs/1909.10351v5","paper_title":"TinyBERT: Distilling BERT for Natural Language Understanding","code":"https://github.com/PaddlePaddle/PaddleNLP/tree/develop/paddlenlp/transformers/tinybert","n_code_links":10,"syntology":{"n_ran":0,"n_unverified":4,"n_samples":4,"n_pointer_only_licence":4}},{"rank_in_archive_order":40,"model":"FNet-Large","metrics":{"Accuracy":"85%"},"uses_additional_data":false,"paper_date":"2021-05-09","paper":"/paper/fnet-mixing-tokens-with-fourier-transforms","paper_url":"https://arxiv.org/abs/2105.03824v4","paper_title":"FNet: Mixing Tokens with Fourier Transforms","code":"https://github.com/labmlai/annotated_deep_learning_paper_implementations","n_code_links":12,"syntology":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":1}},{"rank_in_archive_order":41,"model":"LM-CPPF RoBERTa-base","metrics":{"Accuracy":"70.2%"},"uses_additional_data":false,"paper_date":"2023-05-29","paper":"/paper/lm-cppf-paraphrasing-guided-data-augmentation","paper_url":"https://arxiv.org/abs/2305.18169v3","paper_title":"LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning","code":"https://github.com/amirabaskohi/lm-cppf","n_code_links":1,"syntology":null},{"rank_in_archive_order":42,"model":"SMART-BERT","metrics":{},"uses_additional_data":false,"paper_date":"2019-11-08","paper":"/paper/smart-robust-and-efficient-fine-tuning-for","paper_url":"https://arxiv.org/abs/1911.03437v5","paper_title":"SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization","code":"https://github.com/namisan/mt-dnn","n_code_links":6,"syntology":{"n_ran":6,"n_unverified":2,"n_samples":8,"n_pointer_only_licence":1}},{"rank_in_archive_order":43,"model":"SMARTRoBERTa","metrics":{},"uses_additional_data":false,"paper_date":"2019-11-08","paper":"/paper/smart-robust-and-efficient-fine-tuning-for","paper_url":"https://arxiv.org/abs/1911.03437v5","paper_title":"SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization","code":"https://github.com/namisan/mt-dnn","n_code_links":6,"syntology":{"n_ran":6,"n_unverified":2,"n_samples":8,"n_pointer_only_licence":1}}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,795 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6795,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":2785},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":32,"rows_with_any_sample_ran":25,"distinct_papers_with_graph_line":23,"distinct_papers_with_any_sample_ran":18,"samples_over_distinct_papers":{"n_ran":346,"n_unverified":679,"n_samples":1025,"n_pointer_only_licence":228,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":372,"n_unverified":806,"n_samples":1178,"n_pointer_only_licence":236,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}