payload: {"action":"created","issue":{"url":"https://api.github.com/repos/macanv/BERT-BiLSTM-CRF-NER/issues/58","repository_url":"https://api.github.com/repos/macanv/BERT-BiLSTM-CRF-NER","labels_url":"https://api.github.com/repos/macanv/BERT-BiLSTM-CRF-NER/issues/58/labels{/name}","comments_url":"https://api.github.com/repos/macanv/BERT-BiLSTM-CRF-NER/issues/58/comments","events_url":"https://api.github.com/repos/macanv/BERT-BiLSTM-CRF-NER/issues/58/events","html_url":"https://github.com/macanv/BERT-BiLSTM-CRF-NER/issues/58","id":412902275,"node_id":"MDU6SXNzdWU0MTI5MDIyNzU=","number":58,"title":"训练自己数据模型问题","user":{"login":"lotushacker","id":16223730,"node_id":"MDQ6VXNlcjE2MjIzNzMw","avatar_url":"https://avatars0.githubusercontent.com/u/16223730?v=4","gravatar_id":"","url":"https://api.github.com/users/lotushacker","html_url":"https://github.com/lotushacker","followers_url":"https://api.github.com/users/lotushacker/followers","following_url":"https://api.github.com/users/lotushacker/following{/other_user}","gists_url":"https://api.github.com/users/lotushacker/gists{/gist_id}","starred_url":"https://api.github.com/users/lotushacker/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/lotushacker/subscriptions","organizations_url":"https://api.github.com/users/lotushacker/orgs","repos_url":"https://api.github.com/users/lotushacker/repos","events_url":"https://api.github.com/users/lotushacker/events{/privacy}","received_events_url":"https://api.github.com/users/lotushacker/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2019-02-21T12:33:21Z","updated_at":"2019-02-22T09:59:35Z","closed_at":"2019-02-22T09:59:35Z","author_association":"NONE","body":"您好，使用的您这个ner模型做主干提取(删字)的训练，标签集：[\"Y\", \"N\", \"X\", \"[CLS]\", \"[SEP]\"], 训练数据句子对每个字打上Y或者N来标识是否作为提取之后的句子主干内容，train9w/dev3w/test3w，训练完后效果如下：\r\n![2019-02-21 20 27 47](https://user-images.githubusercontent.com/16223730/53169201-90067200-3617-11e9-8d2b-06b59bb6f99a.png)\r\n![2019-02-21 20 31 20](https://user-images.githubusercontent.com/16223730/53169244-aad8e680-3617-11e9-845f-b03721f34633.png)\r\n![2019-02-21 20 55 31](https://user-images.githubusercontent.com/16223730/53170595-79fab080-361b-11e9-93ee-7253ff318c85.png)\r\n\r\n\r\n请问下精确召回都0这是什么原因的，这个是在测试集上验证的吗? 怎样修改可以输出单label的精确和召回，Y/N\r\n\r\n另外请教下terminal_predict.py是不是不用修改像上图那样就可以预测Y/N是否保留字？如果要修改大致怎样去修改，谢谢了。\r\n\r\n(我是用的38be088这个之前的版本，按照博客里源码训练的方式做的）"},"comment":{"url":"https://api.github.com/repos/macanv/BERT-BiLSTM-CRF-NER/issues/comments/466342203","html_url":"https://github.com/macanv/BERT-BiLSTM-CRF-NER/issues/58#issuecomment-466342203","issue_url":"https://api.github.com/repos/macanv/BERT-BiLSTM-CRF-NER/issues/58","id":466342203,"node_id":"MDEyOklzc3VlQ29tbWVudDQ2NjM0MjIwMw==","user":{"login":"macanv","id":16610116,"node_id":"MDQ6VXNlcjE2NjEwMTE2","avatar_url":"https://avatars1.githubusercontent.com/u/16610116?v=4","gravatar_id":"","url":"https://api.github.com/users/macanv","html_url":"https://github.com/macanv","followers_url":"https://api.github.com/users/macanv/followers","following_url":"https://api.github.com/users/macanv/following{/other_user}","gists_url":"https://api.github.com/users/macanv/gists{/gist_id}","starred_url":"https://api.github.com/users/macanv/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/macanv/subscriptions","organizations_url":"https://api.github.com/users/macanv/orgs","repos_url":"https://api.github.com/users/macanv/repos","events_url":"https://api.github.com/users/macanv/events{/privacy}","received_events_url":"https://api.github.com/users/macanv/received_events","type":"User","site_admin":false},"created_at":"2019-02-22T09:59:35Z","updated_at":"2019-02-22T09:59:35Z","author_association":"OWNER","body":"使用的验证效果的脚本只能识别以B- I-开头的这种标注格式，对于单一的N,Y这种标注识别不了，你可以通过对test_labels.txt中得结果进行评测，第一列是token,第二列是标注结果，第三列是预测结果。可以使用scikit-learn中的metric package"}}
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