BERT-keras:采用预先训练的权重的BERT的Keras实现

上传者: 42103128 | 上传时间: 2022-05-28 20:02:02 | 文件大小: 43KB | 文件类型: ZIP
状态:存档(代码按原样提供,预计无更新) 伯特·凯拉斯 Google BERT(来自Transformers的双向编码器表示)的Keras实现和OpenAI的Transformer LM能够使用微调API加载预训练的模型。 更新:得益于 TPU支持进行推理和训练 如何使用它? # this is a pseudo code you can read an actual working example in tutorial.ipynb or the colab notebook text_encoder = MyTextEncoder ( ** my_text_encoder_params ) # you create a text encoder (sentence piece and openai's bpe are included) lm_generator = lm_generator ( text_encoder , ** lm_generator_params ) # this is essentially your data reader (single sente

文件下载

资源详情

[{"title":"( 20 个子文件 43KB ) BERT-keras:采用预先训练的权重的BERT的Keras实现","children":[{"title":"BERT-keras-master","children":[{"title":".gitignore <span style='color:#111;'> 1.22KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 4.19KB </span>","children":null,"spread":false},{"title":".gitmodules <span style='color:#111;'> 196B </span>","children":null,"spread":false},{"title":"tests","children":[{"title":"test_bert.py <span style='color:#111;'> 5.10KB </span>","children":null,"spread":false},{"title":"test_data.py <span style='color:#111;'> 11.76KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 62B </span>","children":null,"spread":false},{"title":"test_transformer.py <span style='color:#111;'> 10.18KB </span>","children":null,"spread":false}],"spread":true},{"title":"transformer","children":[{"title":"train.py <span style='color:#111;'> 8.13KB </span>","children":null,"spread":false},{"title":"load.py <span style='color:#111;'> 8.04KB </span>","children":null,"spread":false},{"title":"model.py <span style='color:#111;'> 4.28KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 2.38KB </span>","children":null,"spread":false},{"title":"embedding.py <span style='color:#111;'> 3.90KB </span>","children":null,"spread":false},{"title":"layers.py <span style='color:#111;'> 2.96KB </span>","children":null,"spread":false},{"title":"funcs.py <span style='color:#111;'> 3.42KB </span>","children":null,"spread":false}],"spread":true},{"title":"LICENSE <span style='color:#111;'> 34.33KB </span>","children":null,"spread":false},{"title":"google_bert","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"openai","children":null,"spread":false},{"title":"data","children":[{"title":"vocab.py <span style='color:#111;'> 3.83KB </span>","children":null,"spread":false},{"title":"dataset.py <span style='color:#111;'> 7.12KB </span>","children":null,"spread":false},{"title":"lm_dataset.py <span style='color:#111;'> 11.47KB </span>","children":null,"spread":false}],"spread":true},{"title":"tutorial.ipynb <span style='color:#111;'> 13.23KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明