Different understanding about the original paper

[lanzhuzhu]

In README, you said: “The model produces a test F1_score of 90.9 % with ~70 epochs. The results produced in the paper for the given architecture is 91.14 ” In fact, the paper said the result 91.14 is produced under the situation "All other hyper-parameters and features remain the same as our best model in Table 5", that is , lex feature is used, while you do not use that feature, so this architecture can not reach 91.14.

[kamalkraj]

Correct . I didn't use any lex feature so model can't reach 91.4

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On Tue, 12 Jun 2018 at 09:32, lanzhuzhu ***@***.***> wrote: In README, you said: “The model produces a test F1_score of 90.9 % with ~70 epochs. The results produced in the paper for the given architecture is 91.14 ” In fact, the paper said the result 91.14 is produced under the situation "All other hyper-parameters and features remain the same as our best model in Table 5", that is , lex feature is used, while you do not use that feature, so this architecture can not reach 91.14. — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#4>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AQTgmg44TgVmr_guDHMjbgd6hjvMBrc8ks5t7z1XgaJpZM4UjvlI> .

[davidsbatista]

Did you use Viterbi to do the decoding of the best sequence ? That might also explain the different results

[shuiyueche]

Did you use Viterbi to do the decoding of the best sequence ? That might also explain the different results

If I am not wrong, the code here does not include the transition matrix of the tags. So no need to apply Viterbi here. But this is also a big difference.