Comments (3)
Are you using the BERT-large or BERT-base model type? With BERT-base, you should get very good results with a seq len of 256 and batch size of 16 (I did, anyway...).
Google's recommended seq/batch combos are at https://github.com/google-research/bert#out-of-memory-issues .
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I am using BERT-large uncased. Did you get your results after only 4 epochs?
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@tombriles I changed the model from large to base (uncased), and now a max seq len of 256 doesn't cause the out of memory error (it did before when I used the large model). I will report back on the performance once training is done!
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Related Issues (17)
- Change `PreTrainedBertModel` to `BertPreTrainedModel` HOT 2
- Wrong number of labels
- Unable to freeze/unfreeze HOT 1
- Is there a way to do the fitting on an already trained model?
- Does some one know the Segmentation fault (core dumped)
- FileNotFoundError: [Errno 2] No such file or directory: 'BERT\\pytorch_model.bin' HOT 1
- Target size is not matching with the input size HOT 1
- Re: Installation of apex
- AttributeError: 'BertForMultiLabelSequenceClassification' object has no attribute 'module' HOT 3
- What is in the classes.txt file? HOT 1
- Confused details about label_ids HOT 2
- Tips how to use this for multi-class multi-label classification
- Issue in importing Apex HOT 6
- What is in the 'val.csv' file?
- FileNotFoundError: File val.csv does not exist HOT 2
- Problem Loading Bert Weights HOT 3
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