SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Updates the default branch from master to main (#16326)

Browse Source 
* Updates the default branch from master to main

* Links from `master` to `main`

* Typo

* Update examples/flax/README.md

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

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Lysandre Debut
2022-03-23 08:46:59 +01:00
committed by GitHub
parent 7732148124
commit eca77f4719
101 changed files with 401 additions and 402 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
docs/source/converting_tensorflow_models.mdx
View File

@@ -27,12 +27,12 @@ The documentation below reflects the **transformers-cli convert** command format
## BERT
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
[convert_bert_original_tf_checkpoint_to_pytorch.py](https://github.com/huggingface/transformers/tree/master/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) script.
[convert_bert_original_tf_checkpoint_to_pytorch.py](https://github.com/huggingface/transformers/tree/main/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
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 [quicktour](quicktour) , [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification/run_glue.py) ).
be imported using `from_pretrained()` (see example in [quicktour](quicktour) , [run_glue.py](https://github.com/huggingface/transformers/tree/main/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 (\
@@ -56,7 +56,7 @@ You can download Google's pre-trained models for the conversion [here](https://g
## ALBERT
Convert TensorFlow model checkpoints of ALBERT to PyTorch using the
[convert_albert_original_tf_checkpoint_to_pytorch.py](https://github.com/huggingface/transformers/tree/master/src/transformers/models/albert/convert_albert_original_tf_checkpoint_to_pytorch.py) script.
[convert_albert_original_tf_checkpoint_to_pytorch.py](https://github.com/huggingface/transformers/tree/main/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
configuration file (`albert_config.json`), then creates and saves a PyTorch model. To run this conversion you will