huggingface-transformers/docs

Generating the documentation

To generate the documentation, you first have to build it. Several packages are necessary to build the doc, you can install them with the following command, at the root of the code repository:

pip install -e ".[docs]"

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NOTE

You only need to generate the documentation to inspect it locally (if you're planning changes and want to check how they look like before committing for instance). You don't have to commit the built documentation.

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Packages installed

Here's an overview of all the packages installed. If you ran the previous command installing all packages from requirements.txt, you do not need to run the following commands.

Building it requires the package sphinx that you can install using:

pip install -U sphinx

You would also need the custom installed theme by Read The Docs. You can install it using the following command:

pip install sphinx_rtd_theme

The third necessary package is the recommonmark package to accept Markdown as well as Restructured text:

pip install recommonmark

Building the documentation

Once you have setup sphinx, you can build the documentation by running the following command in the /docs folder:

make html

A folder called _build/html should have been created. You can now open the file _build/html/index.html in your browser.

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NOTE

If you are adding/removing elements from the toc-tree or from any structural item, it is recommended to clean the build directory before rebuilding. Run the following command to clean and build:

make clean && make html

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It should build the static app that will be available under /docs/_build/html

Adding a new element to the tree (toc-tree)

Accepted files are reStructuredText (.rst) and Markdown (.md). Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting the filename without the extension.

Preview the documentation in a pull request

Once you have made your pull request, you can check what the documentation will look like after it's merged by following these steps:

• Look at the checks at the bottom of the conversation page of your PR (you may need to click on "show all checks" to expand them).
• Click on "details" next to the ci/circleci: build_doc check.
• In the new window, click on the "Artifacts" tab.
• Locate the file "docs/_build/html/index.html" (or any specific page you want to check) and click on it to get a preview.

Writing Documentation - Specification

The huggingface/transformers documentation follows the Google documentation style. It is mostly written in ReStructuredText (Sphinx simple documentation, Sourceforge complete documentation).

Adding a new tutorial

Adding a new tutorial or section is done in two steps:

• Add a new file under ./source. This file can either be ReStructuredText (.rst) or Markdown (.md).
• Link that file in ./source/index.rst on the correct toc-tree.

Make sure to put your new file under the proper section. It's unlikely to go in the first section (Get Started), so depending on the intended targets (beginners, more advanced users or researchers) it should go in section two, three or four.

Adding a new model

When adding a new model:

• Create a file xxx.rst under ./source/model_doc (don't hesitate to copy an existing file as template).
• Link that file in ./source/index.rst on the model_doc toc-tree.
• Write a short overview of the model:
  • Overview with paper & authors
  • Paper abstract
  • Tips and tricks and how to use it best
• Add the classes that should be linked in the model. This generally includes the configuration, the tokenizer, and every model of that class (the base model, alongside models with additional heads), both in PyTorch and TensorFlow. The order is generally:
  • Configuration,
  • Tokenizer
  • PyTorch base model
  • PyTorch head models
  • TensorFlow base model
  • TensorFlow head models

These classes should be added using the RST syntax. Usually as follows:

XXXConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.XXXConfig
    :members:

This will include every public method of the configuration that is documented. If for some reason you wish for a method not to be displayed in the documentation, you can do so by specifying which methods should be in the docs:

XXXTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.XXXTokenizer
    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
        create_token_type_ids_from_sequences, save_vocabulary

Writing source documentation

Values that should be put in code should either be surrounded by double backticks: ``like so`` or be written as an object using the :obj: syntax: :obj:`like so`. Note that argument names and objects like True, None or any strings should usually be put in code.

When mentionning a class, it is recommended to use the :class: syntax as the mentioned class will be automatically linked by Sphinx: :class:`~transformers.XXXClass`

When mentioning a function, it is recommended to use the :func: syntax as the mentioned function will be automatically linked by Sphinx: :func:`~transformers.function`.

When mentioning a method, it is recommended to use the :meth: syntax as the mentioned method will be automatically linked by Sphinx: :meth:`~transformers.XXXClass.method`.

Links should be done as so (note the double underscore at the end): `text for the link <./local-link-or-global-link#loc>`__

Defining arguments in a method

Arguments should be defined with the Args: prefix, followed by a line return and an indentation. The argument should be followed by its type, with its shape if it is a tensor, and a line return. Another indentation is necessary before writing the description of the argument.

Here's an example showcasing everything so far:

    Args:
        input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using :class:`~transformers.AlbertTokenizer`.
            See :meth:`~transformers.PreTrainedTokenizer.encode` and
            :meth:`~transformers.PreTrainedTokenizer.__call__` for details.

            `What are input IDs? <../glossary.html#input-ids>`__

For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the following signature:

def my_function(x: str = None, a: float = 1):

then its documentation should look like this:

    Args:
        x (:obj:`str`, `optional`):
            This argument controls ...
        a (:obj:`float`, `optional`, defaults to 1):
            This argument is used to ...

Note that we always omit the "defaults to :obj:`None`" when None is the default for any argument. Also note that even if the first line describing your argument type and its default gets long, you can't break it on several lines. You can however write as many lines as you want in the indented description (see the example above with input_ids).

Writing a multi-line code block

Multi-line code blocks can be useful for displaying examples. They are done like so:

Example::

    # first line of code
    # second line
    # etc

The Example string at the beginning can be replaced by anything as long as there are two semicolons following it.

We follow the doctest syntax for the examples to automatically test the results stay consistent with the library.

Writing a return block

Arguments should be defined with the Args: prefix, followed by a line return and an indentation. The first line should be the type of the return, followed by a line return. No need to indent further for the elements building the return.

Here's an example for tuple return, comprising several objects:

    Returns:
        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
        loss (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
            Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
        prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

Here's an example for a single value return:

    Returns:
        :obj:`List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.