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Examples reorg (#11350)

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* Base move

* Examples reorganization

* Update references

* Put back test data

* Move conftest

* More fixes

* Move test data to test fixtures

* Update path

* Apply suggestions from code review

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Address review comments and clean

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
This commit is contained in:
Sylvain Gugger
2021-04-21 11:11:20 -04:00
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parent ca7ff64f5b
commit dabeb15292
105 changed files with 1062 additions and 560 deletions
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docs/source/converting_tensorflow_models.rst
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@@ -33,8 +33,8 @@ You can convert any TensorFlow checkpoint for BERT (in particular `the pre-train
This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated
configuration file (\ ``bert_config.json``\ ), and creates a PyTorch model for this configuration, loads the weights
from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that
can be imported using ``from_pretrained()`` (see example in :doc:`quicktour` , `run_glue.py
<https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_glue.py>`_\ ).
can be imported using ``from_pretrained()`` (see example in :doc:`quicktour` , :prefix_link:`run_glue.py
<examples/pytorch/text-classification/run_glue.py>` \ ).
You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow
checkpoint (the three files starting with ``bert_model.ckpt``\ ) but be sure to keep the configuration file (\