Comments (4)
Thanks for your interest in finetune.
The api for loading base model files saved in this way is.
new_model = Classifier(base_model=<base model object>, base_model_path=<model_file_name>)
where the base model object is whatever you set base_model to when you originally trained the model, for example Bert, RoBERTa etc. If you did not set this originally, you can ignore this argument as the default (RoBERTa) will be used.
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If you wanted to use this new model frequently and wanted a cleaner api you could also do:
class NewBaseModel(RoBERTa): # replace RoBERTa with whatever model this is based on
settings = {**RoBERTa.settings, "base_model_path": <model file name>}
new_model = Classifier(base_model=NewBaseModel)
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Thank you so much!
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When I use create_base_model(filename, exists_ok=False)
, it only allows me to save the file in the default /Finetune/finetune/model/
folder (docker version). It would be nice if I can save it to a custom path.
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Related Issues (20)
- Add chunk long sequences support to featurize_sequence HOT 1
- Make tqdm behavior more consistent between train / predict
- ModuleNotFoundError: No module named 'tensorflow.contrib' HOT 1
- Make try/except in visible devices search less broad and print traceback to enable debugging deployed finetune
- Soft targets for sequence labeling models (with use_crf=False) HOT 2
- Add chunk long sequences support to featurize
- Add "download=False" argument to optionally prevent automatic download on instantiation
- Check md5sums of hashes base model file hashes
- McDonalds dataset link is broken HOT 1
- Document tensorflow 2.0 requirement and removal of 1.x support
- How to save checkpoint when finetune?
- Pin Spacy Version to < 3.0 HOT 1
- LayoutLM documentation
- Add absl-py to requirements.txt
- Question: Does the Bert base model support multiple languages?
- Use on Colab Fails HOT 2
- Finetune a model Q/A Response
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