{"action":"created","issue":{"url":"https://api.github.com/repos/allenai/allennlp/issues/2219","repository_url":"https://api.github.com/repos/allenai/allennlp","labels_url":"https://api.github.com/repos/allenai/allennlp/issues/2219/labels{/name}","comments_url":"https://api.github.com/repos/allenai/allennlp/issues/2219/comments","events_url":"https://api.github.com/repos/allenai/allennlp/issues/2219/events","html_url":"https://github.com/allenai/allennlp/issues/2219","id":392752583,"node_id":"MDU6SXNzdWUzOTI3NTI1ODM=","number":2219,"title":"Replicating CoNLL 2003 NER training","user":{"login":"pvcastro","id":12713359,"node_id":"MDQ6VXNlcjEyNzEzMzU5","avatar_url":"https://avatars0.githubusercontent.com/u/12713359?v=4","gravatar_id":"","url":"https://api.github.com/users/pvcastro","html_url":"https://github.com/pvcastro","followers_url":"https://api.github.com/users/pvcastro/followers","following_url":"https://api.github.com/users/pvcastro/following{/other_user}","gists_url":"https://api.github.com/users/pvcastro/gists{/gist_id}","starred_url":"https://api.github.com/users/pvcastro/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/pvcastro/subscriptions","organizations_url":"https://api.github.com/users/pvcastro/orgs","repos_url":"https://api.github.com/users/pvcastro/repos","events_url":"https://api.github.com/users/pvcastro/events{/privacy}","received_events_url":"https://api.github.com/users/pvcastro/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":3,"created_at":"2018-12-19T19:49:46Z","updated_at":"2018-12-20T08:36:45Z","closed_at":null,"author_association":"NONE","body":"**System (please complete the following information):**\r\n - OS: Linux\r\n - Python version: 3.6.3\r\n - AllenNLP version: I installed from master\r\n - PyTorch version: 0.4.1\r\n\r\n**Question**\r\nWhat is the exact jsonnet config you used for the CoNLL 2003 NER training, as described in the ELMo paper? I just used the same setup as is in training_config/ner.jsonnet and training_config/ner_elmo.jsonnet and the best validation F1 Score are too high. For plain ner.jsonnet was 94.32% and for ner_elmo.jsonnet was 96%. Maybe I'm interpreting the results wrong, but I believe the dataset is correct, it's the same ones I got from [Lample's LSTM-CRF repository](https://github.com/glample/tagger/tree/master/dataset)\r\n\r\nBy default the validation metric \"+f1-measure-overall\" Is the same as the conll validation script, right?"},"comment":{"url":"https://api.github.com/repos/allenai/allennlp/issues/comments/448916675","html_url":"https://github.com/allenai/allennlp/issues/2219#issuecomment-448916675","issue_url":"https://api.github.com/repos/allenai/allennlp/issues/2219","id":448916675,"node_id":"MDEyOklzc3VlQ29tbWVudDQ0ODkxNjY3NQ==","user":{"login":"pvcastro","id":12713359,"node_id":"MDQ6VXNlcjEyNzEzMzU5","avatar_url":"https://avatars0.githubusercontent.com/u/12713359?v=4","gravatar_id":"","url":"https://api.github.com/users/pvcastro","html_url":"https://github.com/pvcastro","followers_url":"https://api.github.com/users/pvcastro/followers","following_url":"https://api.github.com/users/pvcastro/following{/other_user}","gists_url":"https://api.github.com/users/pvcastro/gists{/gist_id}","starred_url":"https://api.github.com/users/pvcastro/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/pvcastro/subscriptions","organizations_url":"https://api.github.com/users/pvcastro/orgs","repos_url":"https://api.github.com/users/pvcastro/repos","events_url":"https://api.github.com/users/pvcastro/events{/privacy}","received_events_url":"https://api.github.com/users/pvcastro/received_events","type":"User","site_admin":false},"created_at":"2018-12-20T08:36:45Z","updated_at":"2018-12-20T08:36:45Z","author_association":"NONE","body":"OK, great! Even if it was just a single run, now it got consistent with the reported results in the paper, 92.27% F1 Score.\r\n\r\n> Recommend best practice is to leave evaluate_on_test=False, and instead run allennlp evaluate with the best model evaluated on the dev set.\r\n\r\nLooks like setting `evaluate_on_test=True` already does exactly this, picking the best model evaluated on the dev set. Any reason why doing it with the evaluate command would be a better approach? \r\n\r\nThanks!"}}
{"actor":{"display_login":"pvcastro"}}