Comments (2)
RaymondLi0
commented on July 26, 2024
In our implementation, we do not consider any length-normalization, so the score of a given hypothesis strictly decreases as you add tokens to it.
As a consequence, if a finished hypothesis is in the top-K at some point, it will stay in the top-K until the end of decoding.
However this would not be the case if the score was not strictly decreasing. Then I believe doing what you say would indeed be a better solution. Does that make sense?
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ruiqi-zhong
commented on July 26, 2024
yeah that makes sense. Thanks!
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Related Issues (14)
- Pre-trained model HOT 2
- input token tensor has been truncated HOT 8
- Is this the model that reached 65.4 EM on the test set? （#7，DuoRAT + BERT (DB content used)） HOT 1
- asdl license HOT 2
- How to obtain ActionInfo in duorat/asdl/action_info.py ?
- docker: Error response from daemon: Unknown runtime specified nvidia.
- License for commercial use HOT 2
- Running git submodule update --init HOT 2
- How to get around 69 exact_match performance? HOT 9
- Using float 16 in training? HOT 2
- Is it possible to run BERT Large with Multiple-GPUs? HOT 1
- Requirements HOT 1
- Problems loading jsonnet files HOT 1
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