Comments (2)
If you want to continue training the model from a previous finetuned checkpoint you can use the
'load_at_start flag' in fit and set it to False. This isn't documented since I envisioned people would mostly use the finetuned model for inference.
So this should work for you now:
new_model.fit(X_train, y_train, load_at_start=False)
Regardless, what you found is a bug which I will fix. Will get back to you when its done. Thanks!
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Thanks Charles!, it worked.
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Related Issues (17)
- pytorch_pretrained_bert/modeling.py name error -- PreTrainedBertModel HOT 1
- GPU memory leak ? HOT 2
- Unable to load GPU trained model on CPU HOT 2
- Is there a way to access the embeddings of the training set of a fitted model?
- Redesigning the fit function
- Issue with Multi-GPU HOT 1
- How to change hidden_dropout_prob value?
- Had problem with using load_model() // no issue of training a model HOT 2
- Tokenization for Bio NER
- PyPI package
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- which version of scikit-learn do you use for this project? HOT 1
- some error in chinese NER HOT 6
- URL for IMDb notebook HOT 2
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