Add classifier dropout in ALBERT #2679
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## master #2679 +/- ##
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+ Coverage 74.59% 74.59% +<.01%
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Files 89 89
Lines 14971 14972 +1
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+ Hits 11168 11169 +1
Misses 3803 3803
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That's fantastic, thank you for taking the time to do this @peteriz ! |
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The configuration files were updated. The type of GELU activation function used was also changed to "gelu_new", which is the appropriate activation function that is used in the google-research repository. |
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As mentioned in the original paper, they separated the dropout rates of the transformer cells and the classifier, moreover, in V2 the dropouts are 0 (expect for the classifier, again).
Current implementation does not supports this and models are not training well (can't reproduce results of GLUE benchmark using V2 models). I manually updated these values and got V2 models converging.
This issue was raised in #2337 and also mentioned in google-research/albert#23
I added a separate parameter in the config file and update the sequence classification head.
Please also update the configuration of ALBERT V2 models (base, large, xlarge) in your repository.
More specifically, the configuration of the attention and hidden dropout rates of ALBERT V2 models in your repository as well (see as in https://tfhub.dev/google/albert_base/3, https://tfhub.dev/google/albert_large/3, https://tfhub.dev/google/albert_xlarge/3 and https://tfhub.dev/google/albert_xxlarge/3)