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added save_directories for _psave_pretrained_pt and _tf, changed model to tf_model and pt_model, enable the notebook to run cleanly from top to bottom without error #14529

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merged 6 commits into from
Nov 26, 2021
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added save_directories for _psave_pretrained_pt and _tf, changed model to tf_model and pt_model, enable the notebook to run cleanly from top to bottom without error #14529

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added save_directories for _psave_pretrained_pt and _tf, changed mode…
cfregly Nov 25, 2021
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Update quicktour.rst
cfregly Nov 25, 2021
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Merge branch 'master' of github.com:cfregly/transformers
cfregly Nov 26, 2021
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22 changes: 14 additions & 8 deletions docs/source/quicktour.rst
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Expand Up @@ -337,8 +337,14 @@ Once your model is fine-tuned, you can save it with its tokenizer in the followi

.. code-block::

tokenizer.save_pretrained(save_directory)
model.save_pretrained(save_directory)
>>> pt_save_directory = './pt_save_pretrained'
>>> tokenizer.save_pretrained(pt_save_directory)
>>> pt_model.save_pretrained(pt_save_directory)

.. code-block::
>>> tf_save_directory = './tf_save_pretrained'
>>> tokenizer.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
directory name instead of the model name. One cool feature of 🤗 Transformers is that you can easily switch between
Expand All @@ -347,17 +353,17 @@ loading a saved PyTorch model in a TensorFlow model, use :func:`~transformers.TF

.. code-block::

from transformers import TFAutoModel
tokenizer = AutoTokenizer.from_pretrained(save_directory)
model = TFAutoModel.from_pretrained(save_directory, from_pt=True)
>>> from transformers import TFAutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
>>> tf_model = TFAutoModel.from_pretrained(pt_save_directory, from_pt=True)

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
tokenizer = AutoTokenizer.from_pretrained(save_directory)
model = AutoModel.from_pretrained(save_directory, from_tf=True)
>>> from transformers import AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
>>> 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:

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