{"action":"created","issue":{"url":"https://api.github.com/repos/google-research/bert/issues/140","repository_url":"https://api.github.com/repos/google-research/bert","labels_url":"https://api.github.com/repos/google-research/bert/issues/140/labels{/name}","comments_url":"https://api.github.com/repos/google-research/bert/issues/140/comments","events_url":"https://api.github.com/repos/google-research/bert/issues/140/events","html_url":"https://github.com/google-research/bert/issues/140","id":381954761,"node_id":"MDU6SXNzdWUzODE5NTQ3NjE=","number":140,"title":"fine tuning on chinese dataset getting unwell result","user":{"login":"voidful","id":10904842,"node_id":"MDQ6VXNlcjEwOTA0ODQy","avatar_url":"https://avatars2.githubusercontent.com/u/10904842?v=4","gravatar_id":"","url":"https://api.github.com/users/voidful","html_url":"https://github.com/voidful","followers_url":"https://api.github.com/users/voidful/followers","following_url":"https://api.github.com/users/voidful/following{/other_user}","gists_url":"https://api.github.com/users/voidful/gists{/gist_id}","starred_url":"https://api.github.com/users/voidful/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/voidful/subscriptions","organizations_url":"https://api.github.com/users/voidful/orgs","repos_url":"https://api.github.com/users/voidful/repos","events_url":"https://api.github.com/users/voidful/events{/privacy}","received_events_url":"https://api.github.com/users/voidful/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":9,"created_at":"2018-11-18T12:16:39Z","updated_at":"2018-11-20T06:37:22Z","closed_at":null,"author_association":"NONE","body":"i tried to train a mc model on Chinese dataset \r\non traditional and simplified Chinese \r\nin word and char level\r\nboth multilang-bert and Chinese-bert\r\nbut all of the experiment getting not very well result\r\n\r\ncompare to BIDAF - speed and accuracy\r\nin bert, i got\r\n0 em for both word and char level\r\n~60% f1 in word level\r\n~4% f1 in char level\r\n\r\nhere are my config ：\r\ntrain_batch_size 12\r\nlearning_rate 3e-5 \r\nnum_train_epochs 1.0\r\nmax_seq_length 512\r\ndoc_stride 128\r\n\r\ndue to the length of sequence in Chinese will longer then in English \r\nit might not doing well in char level chinese ?"},"comment":{"url":"https://api.github.com/repos/google-research/bert/issues/comments/440161074","html_url":"https://github.com/google-research/bert/issues/140#issuecomment-440161074","issue_url":"https://api.github.com/repos/google-research/bert/issues/140","id":440161074,"node_id":"MDEyOklzc3VlQ29tbWVudDQ0MDE2MTA3NA==","user":{"login":"voidful","id":10904842,"node_id":"MDQ6VXNlcjEwOTA0ODQy","avatar_url":"https://avatars2.githubusercontent.com/u/10904842?v=4","gravatar_id":"","url":"https://api.github.com/users/voidful","html_url":"https://github.com/voidful","followers_url":"https://api.github.com/users/voidful/followers","following_url":"https://api.github.com/users/voidful/following{/other_user}","gists_url":"https://api.github.com/users/voidful/gists{/gist_id}","starred_url":"https://api.github.com/users/voidful/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/voidful/subscriptions","organizations_url":"https://api.github.com/users/voidful/orgs","repos_url":"https://api.github.com/users/voidful/repos","events_url":"https://api.github.com/users/voidful/events{/privacy}","received_events_url":"https://api.github.com/users/voidful/received_events","type":"User","site_admin":false},"created_at":"2018-11-20T06:37:22Z","updated_at":"2018-11-20T06:37:22Z","author_association":"NONE","body":"in my dataset there is lots of unknown char such as '小 金 毛 [UNK] 並' in '小 金 毛 鼴 並'\r\nmoreover i found that : \r\nBecause Chinese does not have whitespace characters, we add spaces around every character in the CJK Unicode range before applying WordPiece. This means that Chinese is effectively character-tokenized. Note that the CJK Unicode block only includes Chinese-origin characters and does not include Hangul Korean or Katakana/Hiragana Japanese, which are tokenized with whitespace+WordPiece like all other languages.\r\nand this one :\r\nhttps://github.com/google-research/bert/issues/66\r\n\r\nmaybe that's the reason why bert still have work to do\r\nseems that ELMo is a better choice for now"}}
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