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Specify from_tf=True to convert a checkpoint from TensorFlow to PyTorch:
pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True)
pt_model.save_pretrained("path/to/awesome-name-you-picked")
``
</pt>
<tf>
Specifyfrom_pt=True` to convert a checkpoint from PyTorch to TensorFlow:
tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True)
Then you can save your new TensorFlow model with its new checkpoint:
tf_model.save_pretrained("path/to/awesome-name-you-picked")
If a model is available in Flax, you can also convert a checkpoint from PyTorch to Flax:
flax_model = FlaxDistilBertForSequenceClassification.from_pretrained(
"path/to/awesome-name-you-picked", from_pt=True
)
Push a model during training
Sharing a model to the Hub is as simple as adding an extra parameter or callback. |