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Fixes (#14534)

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Lysandre Debut
2021-11-26 04:35:08 -05:00
committed by GitHub
parent c15f4f203f
commit 2318bf77eb
1 changed files with 18 additions and 10 deletions
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@@ -346,35 +346,43 @@ Once your model is fine-tuned, you can save it with its tokenizer in the followi
.. code-block:: .. code-block::
>>> ## PYTORCH CODE
>>> pt_save_directory = './pt_save_pretrained' >>> pt_save_directory = './pt_save_pretrained'
>>> tokenizer.save_pretrained(pt_save_directory) >>> tokenizer.save_pretrained(pt_save_directory)
>>> pt_model.save_pretrained(pt_save_directory) >>> pt_model.save_pretrained(pt_save_directory)
>>> ## TENSORFLOW CODE
.. code-block::
>>> tf_save_directory = './tf_save_pretrained' >>> tf_save_directory = './tf_save_pretrained'
>>> tokenizer.save_pretrained(tf_save_directory) >>> tokenizer.save_pretrained(tf_save_directory)
>>> tf_model.save_pretrained(tf_save_directory) >>> tf_model.save_pretrained(tf_save_directory)
You can then load this model back using the :func:`~transformers.AutoModel.from_pretrained` method by passing the You can then load this model back using the :func:`~transformers.AutoModel.from_pretrained` method by passing the
directory name instead of the model name. One cool feature of 🤗 Transformers is that you can easily switch between directory name instead of the model name. One cool feature of 🤗 Transformers is that you can easily switch between
PyTorch and TensorFlow: any model saved as before can be loaded back either in PyTorch or TensorFlow. If you are PyTorch and TensorFlow: any model saved as before can be loaded back either in PyTorch or TensorFlow.
loading a saved PyTorch model in a TensorFlow model, use :func:`~transformers.TFAutoModel.from_pretrained` like this:
If you would like to load your saved model in the other framework, first make sure it is installed:
.. code-block:: bash
## PYTORCH CODE
pip install tensorflow
## TENSORFLOW CODE
pip install torch
Then, use the corresponding Auto class to load it like this:
.. code-block:: .. code-block::
## PYTORCH CODE
>>> from transformers import TFAutoModel >>> from transformers import TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
>>> tf_model = TFAutoModel.from_pretrained(pt_save_directory, from_pt=True) >>> tf_model = TFAutoModel.from_pretrained(pt_save_directory, from_pt=True)
## TENSORFLOW CODE
and if you are loading a saved TensorFlow model in a PyTorch model, you should use the following code:
.. code-block::
>>> from transformers import AutoModel >>> from transformers import AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory) >>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
>>> pt_model = AutoModel.from_pretrained(tf_save_directory, from_tf=True) >>> pt_model = AutoModel.from_pretrained(tf_save_directory, from_tf=True)
Lastly, you can also ask the model to return all hidden states and all attention weights if you need them: Lastly, you can also ask the model to return all hidden states and all attention weights if you need them: