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π LMNT (Partner): https://lmnt.com/\n\n","updatedAt":"2024-06-09T02:49:57.737Z","author":{"avatarUrl":"/avatars/716b6a7d1094c8036b2a8a7b9063e8aa.svg","fullname":"Julien BLANCHON","name":"blanchon","type":"user","isPro":true,"isHf":false,"isMod":false}},"numEdits":0,"editors":["blanchon"],"reactions":[],"identifiedLanguage":{"language":"en","probability":0.5468137860298157},"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"1810.04805","authors":[{"_id":"6411c77a6b75ddced388ecff","name":"Jacob Devlin","hidden":false},{"_id":"6411c77a6b75ddced388ed00","name":"Ming-Wei Chang","hidden":false},{"_id":"6411c77a6b75ddced388ed01","name":"Kenton Lee","hidden":false},{"_id":"6411c77a6b75ddced388ed02","name":"Kristina Toutanova","hidden":false}],"publishedAt":"2018-10-11T00:50:01.000Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language\n Understanding","summary":"We introduce a new language representation model called BERT, which stands\nfor Bidirectional Encoder Representations from Transformers. Unlike recent\nlanguage representation models, BERT is designed to pre-train deep\nbidirectional representations from unlabeled text by jointly conditioning on\nboth left and right context in all layers. As a result, the pre-trained BERT\nmodel can be fine-tuned with just one additional output layer to create\nstate-of-the-art models for a wide range of tasks, such as question answering\nand language inference, without substantial task-specific architecture\nmodifications.\n BERT is conceptually simple and empirically powerful. It obtains new\nstate-of-the-art results on eleven natural language processing tasks, including\npushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI\naccuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering\nTest F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1\n(5.1 point absolute improvement).","upvotes":14,"discussionId":"6411922f3ea54b1aa7e2ea9c"},"canReadDatabase":false,"canManageCommunity":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"63d10d4e8eaa4831005e92b5","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63d10d4e8eaa4831005e92b5/7p7-OmWM6PqqCs7ZStPGD.jpeg","isPro":false,"fullname":"Aymeric Roucher","user":"m-ric","type":"user"},{"_id":"5e67bdd61009063689407479","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1583857146757-5e67bdd61009063689407479.jpeg","isPro":true,"fullname":"Clem π€","user":"clem","type":"user"},{"_id":"6551fce8ea9be8f1e6facf99","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6551fce8ea9be8f1e6facf99/yWHIimvBNxvLpEdKAMT2x.jpeg","isPro":false,"fullname":"James Aymer","user":"qwerty87","type":"user"},{"_id":"6538119803519fddb4a17e10","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6538119803519fddb4a17e10/ffJMkdx-rM7VvLTCM6ri_.jpeg","isPro":false,"fullname":"samusenps","user":"samusenps","type":"user"},{"_id":"6340651b388c3fa40f9a5bc0","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6340651b388c3fa40f9a5bc0/av1C4_S7bHGxAzOu8lOmG.jpeg","isPro":false,"fullname":"Adam Molnar","user":"lunarflu","type":"user"},{"_id":"6032802e1f993496bc14d9e3","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6032802e1f993496bc14d9e3/w6hr-DEQot4VVkoyRIBiy.png","isPro":false,"fullname":"Omar Sanseviero","user":"osanseviero","type":"user"},{"_id":"64b695dcd3df8086e5ed7c89","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64b695dcd3df8086e5ed7c89/06Toh65jDEz3WJbIM6ZmZ.jpeg","isPro":true,"fullname":"Adam Fields","user":"adamelliotfields","type":"user"},{"_id":"655ac762cb17ec19ef82719b","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/655ac762cb17ec19ef82719b/1kDncYrGLYS_2SR8cNdAL.png","isPro":false,"fullname":"Welcome to matlok","user":"matlok","type":"user"},{"_id":"61646b4f83e3ea36633da1d7","avatarUrl":"/avatars/e72c7faaf130ea982328805e8a4a491e.svg","isPro":false,"fullname":"Yusuf AKDAS","user":"yusufakdas","type":"user"},{"_id":"66118dd7e84ec1efec9f63b7","avatarUrl":"/avatars/e8ef491b6a92a63a22cb9da806f5ff01.svg","isPro":false,"fullname":"Ng","user":"inspire1","type":"user"},{"_id":"5ead1b914e876668a0c37772","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/5ead1b914e876668a0c37772/ftW3bs6hy2Q_J63_OUKKW.png","isPro":false,"fullname":"PenutChen","user":"penut85420","type":"user"},{"_id":"636b608d96ac7131feca5b0d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1667981437501-noauth.png","isPro":false,"fullname":"alesmaeel","user":"abdalmohaymen","type":"user"}],"acceptLanguages":["*"]}">
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Abstract
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).
Community
BERT: Transforming NLP with Deep Bidirectional Transformers
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