Comments (2)
Does this model apply to Chinese data?
hi, I'm applying this model(fine tune bert base) to Chinese OneNote data, using a pytorch version, but I got a very low f1, about 0.2, I don't know what happened, or something wrong with the pytorch version. Did you try the model on Chinese data and could you share your results or parameters?
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Related Issues (20)
- Has anyone got this to work? HOT 3
- How to specify paths correctly? HOT 1
- why not pytorch code for that?
- How to evaluate SpanBERT using sample test data? HOT 3
- assert num_words == np.sum(input_mask), (num_words, np.sum(input_mask))
- Found too many repeated mentions (> 10) in the response, so refusing to score
- Requirements txt is broken HOT 1
- libprotobuf FATAL : CHECK failed: it != end(): key not found
- converting predicted (subtoken) output to normal text HOT 1
- Num_docs and evaluating docs
- Predicting singletons HOT 1
- Too many errors while installing requirements HOT 1
- Custom training data for BERT
- Has anyone reproduced successfully on windows?
- can anyone explain why the execution kept stuck here without generating any error or something else ?
- how can i choose the batch size, in which code file should I modify ?
- can anyone help me with the gpu configuration? it works well on cpu but when i turn to the model to run on gpu it opens succefully all the related libraries but crashes it some step
- Sentence index when splitting long sentences into non-overlapping chunks
- F1 79.96 on ontonotes 5 with your pretrained spanbert_large HOT 1
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