基于BertForTokenClassification算法的长文本实体识别

上传者: 38349344 | 上传时间: 2022-09-21 18:07:14 | 文件大小: 22KB | 文件类型: ZIP
Bert 模型采取了两个预训练任务:Masked Language Model和Next Sentence Prediction,而这两个任务都是基于BertPreTrainedModel抽象基类。 2.1 BertPreTrainedModel 所有Bert-based的模型,包括预训练模型和下游任务模型都是基于BertPreTrainedModel类,用于初始化权重参数和加载预训练描述。同时也继承了PreTrainedModel的变量和方法。

文件下载

资源详情

[{"title":"( 23 个子文件 22KB ) 基于BertForTokenClassification算法的长文本实体识别","children":[{"title":"命名实体识别","children":[{"title":"strong_long.sh <span style='color:#111;'> 1.83KB </span>","children":null,"spread":false},{"title":"label2json.py <span style='color:#111;'> 4.36KB </span>","children":null,"spread":false},{"title":"config","children":[{"title":"global_config.py <span style='color:#111;'> 229B </span>","children":null,"spread":false},{"title":"logging.conf <span style='color:#111;'> 597B </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 85B </span>","children":null,"spread":false}],"spread":true},{"title":"hello.sh <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"utils.py <span style='color:#111;'> 14.36KB </span>","children":null,"spread":false},{"title":"readme.md <span style='color:#111;'> 1.47KB </span>","children":null,"spread":false},{"title":"train.sh <span style='color:#111;'> 249B </span>","children":null,"spread":false},{"title":"train.py <span style='color:#111;'> 5.98KB </span>","children":null,"spread":false},{"title":"predict.py <span style='color:#111;'> 8.81KB </span>","children":null,"spread":false},{"title":"test.sh <span style='color:#111;'> 49B </span>","children":null,"spread":false},{"title":"nlp.sh <span style='color:#111;'> 143B </span>","children":null,"spread":false},{"title":"run.py <span style='color:#111;'> 6.30KB </span>","children":null,"spread":false},{"title":"strong.py <span style='color:#111;'> 2.85KB </span>","children":null,"spread":false},{"title":"args.py <span style='color:#111;'> 2.23KB </span>","children":null,"spread":false},{"title":"common","children":[{"title":"common.py <span style='color:#111;'> 220B </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 129B </span>","children":null,"spread":false}],"spread":false},{"title":"case.sh <span style='color:#111;'> 477B </span>","children":null,"spread":false},{"title":"strong.sh <span style='color:#111;'> 160B </span>","children":null,"spread":false},{"title":"run.sh <span style='color:#111;'> 101B </span>","children":null,"spread":false},{"title":"modeling.py <span style='color:#111;'> 3.42KB </span>","children":null,"spread":false},{"title":"strong_short.sh <span style='color:#111;'> 375B </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