Doc styling (#8067)
* Important files * Styling them all * Revert "Styling them all" This reverts commit 7d029395fdae8513b8281cbc2a6c239f8093503e. * Syling them for realsies * Fix syntax error * Fix benchmark_utils * More fixes * Fix modeling auto and script * Remove new line * Fixes * More fixes * Fix more files * Style * Add FSMT * More fixes * More fixes * More fixes * More fixes * Fixes * More fixes * More fixes * Last fixes * Make sphinx happy
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Sylvain Gugger
@@ -15,8 +15,8 @@ Prepare your model for uploading
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We have seen in the :doc:`training tutorial <training>`: how to fine-tune a model on a given task. You have probably
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done something similar on your task, either using the model directly in your own training loop or using the
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:class:`~.transformers.Trainer`/:class:`~.transformers.TFTrainer` class. Let's see how you can share the result on
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the `model hub <https://huggingface.co/models>`__.
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:class:`~.transformers.Trainer`/:class:`~.transformers.TFTrainer` class. Let's see how you can share the result on the
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`model hub <https://huggingface.co/models>`__.
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Basic steps
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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@@ -60,22 +60,20 @@ Make your model work on all frameworks
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You probably have your favorite framework, but so will other users! That's why it's best to upload your model with both
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PyTorch `and` TensorFlow checkpoints to make it easier to use (if you skip this step, users will still be able to load
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your model in another framework, but it will be slower, as it will have to be converted on the fly). Don't worry, it's super easy to do (and in a future version,
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it will all be automatic). You will need to install both PyTorch and TensorFlow for this step, but you don't need to
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worry about the GPU, so it should be very easy. Check the
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`TensorFlow installation page <https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available>`__
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and/or the `PyTorch installation page <https://pytorch.org/get-started/locally/#start-locally>`__ to see how.
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your model in another framework, but it will be slower, as it will have to be converted on the fly). Don't worry, it's
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super easy to do (and in a future version, it will all be automatic). You will need to install both PyTorch and
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TensorFlow for this step, but you don't need to worry about the GPU, so it should be very easy. Check the `TensorFlow
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installation page <https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available>`__ and/or the `PyTorch
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installation page <https://pytorch.org/get-started/locally/#start-locally>`__ to see how.
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First check that your model class exists in the other framework, that is try to import the same model by either adding
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or removing TF. For instance, if you trained a :class:`~transformers.DistilBertForSequenceClassification`, try to
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type
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or removing TF. For instance, if you trained a :class:`~transformers.DistilBertForSequenceClassification`, try to type
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.. code-block::
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from transformers import TFDistilBertForSequenceClassification
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and if you trained a :class:`~transformers.TFDistilBertForSequenceClassification`, try to
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type
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and if you trained a :class:`~transformers.TFDistilBertForSequenceClassification`, try to type
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.. code-block::
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@@ -112,7 +110,8 @@ Make sure there are no garbage files in the directory you'll upload. It should o
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- a `tf_model.h5` file, which is the TensorFlow checkpoint (unless you can't have it for some reason) ;
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- a `special_tokens_map.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save;
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- a `tokenizer_config.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save;
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- files named `vocab.json`, `vocab.txt`, `merges.txt`, or similar, which contain the vocabulary of your tokenizer, part of your :doc:`tokenizer <main_classes/tokenizer>` save;
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- files named `vocab.json`, `vocab.txt`, `merges.txt`, or similar, which contain the vocabulary of your tokenizer, part
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of your :doc:`tokenizer <main_classes/tokenizer>` save;
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- maybe a `added_tokens.json`, which is part of your :doc:`tokenizer <main_classes/tokenizer>` save.
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Other files can safely be deleted.
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@@ -135,7 +134,8 @@ Then log in using the same credentials as on huggingface.co. To upload your mode
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This will upload the folder containing the weights, tokenizer and configuration we prepared in the previous section.
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By default you will be prompted to confirm that you want these files to be uploaded. If you are uploading multiple models and need to script that process, you can add `-y` to bypass the prompt. For example:
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By default you will be prompted to confirm that you want these files to be uploaded. If you are uploading multiple
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models and need to script that process, you can add `-y` to bypass the prompt. For example:
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.. code-block::
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@@ -179,15 +179,15 @@ Add a model card
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To make sure everyone knows what your model can do, what its limitations and potential bias or ethetical
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considerations, please add a README.md model card to the 🤗 Transformers repo under `model_cards/`. It should then be
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placed in a subfolder with your username or organization, then another subfolder named like your model
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(`awesome-name-you-picked`). Or just click on the "Create a model card on GitHub" button on the model page, it will
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get you directly to the right location. If you need one, `here <https://github.com/huggingface/model_card>`__ is a
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model card template (meta-suggestions are welcome).
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(`awesome-name-you-picked`). Or just click on the "Create a model card on GitHub" button on the model page, it will get
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you directly to the right location. If you need one, `here <https://github.com/huggingface/model_card>`__ is a model
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card template (meta-suggestions are welcome).
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If your model is fine-tuned from another model coming from the model hub (all 🤗 Transformers pretrained models do),
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don't forget to link to its model card so that people can fully trace how your model was built.
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If you have never made a pull request to the 🤗 Transformers repo, look at the
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:doc:`contributing guide <contributing>` to see the steps to follow.
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If you have never made a pull request to the 🤗 Transformers repo, look at the :doc:`contributing guide <contributing>`
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to see the steps to follow.
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.. Note::
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