Doc new front (#14590)
* Convert PretrainedConfig doc to Markdown * Use syntax * Add necessary doc files (#14496) * Doc fixes (#14499) * Fixes for the new front * Convert DETR file for table * Title is needed * Simplify a bit * Even simpler * Remove imports * Fix typo in toctree (#14516) * Fix checkpoints badge * Update versions.yml format (#14517) * Doc new front github actions (#14512) * Doc new front github actions * Fix docstring * Fix feature extraction utils import (#14515) * Address Julien's comments * Push to doc-builder * Ready for merge * Remove old build and deploy * Doc misc fixes (#14583) * Rm versions.yml from doc * Fix converting.rst * Rm pretrained_models from toctree * Fix index links (#14567) * Fix links in README * Localized READMEs * Fix copy script * Fix find doc script * Update README_ko.md Co-authored-by: Julien Chaumond <julien@huggingface.co> Co-authored-by: Julien Chaumond <julien@huggingface.co> * Adapt build command to new CLI tools (#14578) * Fix typo * Fix doc interlinks (#14589) * Convert PretrainedConfig doc to Markdown * Use syntax * Rm pattern <[a-z]+(.html).*> * Rm huggingface.co/transformers/master * Rm .html * Rm .html from index.mdx * Rm .html from model_summary.rst * Update index.mdx rm html * Update remove .html * Fix inner doc links * Fix interlink in preprocssing.rst * Update pr_checks Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Convert PretrainedConfig doc to Markdown * Use syntax * Add necessary doc files (#14496) * Doc fixes (#14499) * Fixes for the new front * Convert DETR file for table * Title is needed * Simplify a bit * Even simpler * Remove imports * Fix checkpoints badge * Fix typo in toctree (#14516) * Update versions.yml format (#14517) * Doc new front github actions (#14512) * Doc new front github actions * Fix docstring * Fix feature extraction utils import (#14515) * Address Julien's comments * Push to doc-builder * Ready for merge * Remove old build and deploy * Doc misc fixes (#14583) * Rm versions.yml from doc * Fix converting.rst * Rm pretrained_models from toctree * Fix index links (#14567) * Fix links in README * Localized READMEs * Fix copy script * Fix find doc script * Update README_ko.md Co-authored-by: Julien Chaumond <julien@huggingface.co> Co-authored-by: Julien Chaumond <julien@huggingface.co> * Adapt build command to new CLI tools (#14578) * Fix typo * Fix doc interlinks (#14589) * Convert PretrainedConfig doc to Markdown * Use syntax * Rm pattern <[a-z]+(.html).*> * Rm huggingface.co/transformers/master * Rm .html * Rm .html from index.mdx * Rm .html from model_summary.rst * Update index.mdx rm html * Update remove .html * Fix inner doc links * Fix interlink in preprocssing.rst * Update pr_checks Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Styling Co-authored-by: Mishig Davaadorj <mishig.davaadorj@coloradocollege.edu> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Julien Chaumond <julien@huggingface.co>
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Sylvain Gugger
@@ -26,22 +26,22 @@ BERT
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google
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<https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the
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<https://github.com/google-research/bert#pre-trained-models>`_) in a PyTorch save file by using the
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:prefix_link:`convert_bert_original_tf_checkpoint_to_pytorch.py
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<src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py>` script.
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This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated
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configuration file (\ ``bert_config.json``\ ), and creates a PyTorch model for this configuration, loads the weights
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from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that
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can be imported using ``from_pretrained()`` (see example in :doc:`quicktour` , :prefix_link:`run_glue.py
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<examples/pytorch/text-classification/run_glue.py>` \ ).
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This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``) and the associated
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configuration file (``bert_config.json``), and creates a PyTorch model for this configuration, loads the weights from
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the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can
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be imported using ``from_pretrained()`` (see example in :doc:`quicktour` , :prefix_link:`run_glue.py
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<examples/pytorch/text-classification/run_glue.py>` ).
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You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow
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checkpoint (the three files starting with ``bert_model.ckpt``\ ) but be sure to keep the configuration file (\
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``bert_config.json``\ ) and the vocabulary file (\ ``vocab.txt``\ ) as these are needed for the PyTorch model too.
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checkpoint (the three files starting with ``bert_model.ckpt``) but be sure to keep the configuration file (\
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``bert_config.json``) and the vocabulary file (``vocab.txt``) as these are needed for the PyTorch model too.
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To run this specific conversion script you will need to have TensorFlow and PyTorch installed (\ ``pip install
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tensorflow``\ ). The rest of the repository only requires PyTorch.
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To run this specific conversion script you will need to have TensorFlow and PyTorch installed (``pip install
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tensorflow``). The rest of the repository only requires PyTorch.
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Here is an example of the conversion process for a pre-trained ``BERT-Base Uncased`` model:
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@@ -64,9 +64,9 @@ Convert TensorFlow model checkpoints of ALBERT to PyTorch using the
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:prefix_link:`convert_albert_original_tf_checkpoint_to_pytorch.py
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<src/transformers/models/albert/convert_albert_original_tf_checkpoint_to_pytorch.py>` script.
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The CLI takes as input a TensorFlow checkpoint (three files starting with ``model.ckpt-best``\ ) and the accompanying
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configuration file (\ ``albert_config.json``\ ), then creates and saves a PyTorch model. To run this conversion you
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will need to have TensorFlow and PyTorch installed.
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The CLI takes as input a TensorFlow checkpoint (three files starting with ``model.ckpt-best``) and the accompanying
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configuration file (``albert_config.json``), then creates and saves a PyTorch model. To run this conversion you will
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need to have TensorFlow and PyTorch installed.
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Here is an example of the conversion process for the pre-trained ``ALBERT Base`` model:
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@@ -104,7 +104,7 @@ OpenAI GPT-2
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Here is an example of the conversion process for a pre-trained OpenAI GPT-2 model (see `here
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<https://github.com/openai/gpt-2>`__\ )
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<https://github.com/openai/gpt-2>`__)
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.. code-block:: shell
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@@ -120,7 +120,7 @@ Transformer-XL
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Here is an example of the conversion process for a pre-trained Transformer-XL model (see `here
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<https://github.com/kimiyoung/transformer-xl/tree/master/tf#obtain-and-evaluate-pretrained-sota-models>`__\ )
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<https://github.com/kimiyoung/transformer-xl/tree/master/tf#obtain-and-evaluate-pretrained-sota-models>`__)
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.. code-block:: shell
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