riaz.somc/transformers
1
0
Fork 0
mirror of https://github.com/huggingface/transformers.git synced 2025-08-01 02:31:11 +06:00
Code Issues Actions 3 Packages Projects Releases Wiki Activity

Fix broken links in the converting tf ckpt document (#9791)

Browse Source 
* Fix broken links in the converting tf ckpt document

* Update docs/source/converting_tensorflow_models.rst

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Reflect the review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Yusuke Mori 2021-01-26 17:37:57 +09:00 committed by GitHub
parent d94cc2f904
commit cb73ab5a38
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 4 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
docs/source/converting_tensorflow_models.rst
View File

@ -28,17 +28,13 @@ BERT
You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google
<https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the
:prefix_link:`convert_bert_original_tf_checkpoint_to_pytorch.py :prefix_link:`convert_bert_original_tf_checkpoint_to_pytorch.py
<src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>` script. <src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py>` script.
This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated 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 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 from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that
can be imported using ``torch.load()`` (see examples in `run_bert_extract_features.py can be imported using ``from_pretrained()`` (see example in :doc:`quicktour` , `run_glue.py
<https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_extract_features.py>`_\ , <https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_glue.py>`_\ ).
`run_bert_classifier.py
<https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_classifier.py>`_ and
`run_bert_squad.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_squad.py>`_\
).
You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow 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 (\ checkpoint (the three files starting with ``bert_model.ckpt``\ ) but be sure to keep the configuration file (\
@ -66,7 +62,7 @@ ALBERT
Convert TensorFlow model checkpoints of ALBERT to PyTorch using the Convert TensorFlow model checkpoints of ALBERT to PyTorch using the
:prefix_link:`convert_albert_original_tf_checkpoint_to_pytorch.py :prefix_link:`convert_albert_original_tf_checkpoint_to_pytorch.py
<src/transformers/convert_bert_original_tf_checkpoint_to_pytorch.py>` script. <src/transformers/models/albert/convert_albert_original_tf_checkpoint_to_pytorch.py>` script.
The CLI takes as input a TensorFlow checkpoint (three files starting with ``model.ckpt-best``\ ) and the accompanying The CLI takes as input a TensorFlow checkpoint (three files starting with ``model.ckpt-best``\ ) and the accompanying
configuration file (\ ``albert_config.json``\ ), then creates and saves a PyTorch model. To run this conversion you configuration file (\ ``albert_config.json``\ ), then creates and saves a PyTorch model. To run this conversion you