Processors
-----------------------------------------------------------------------------------------------------------------------

This library includes processors for several traditional tasks. These processors can be used to process a dataset into
examples that can be fed to a model.

Processors
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

All processors follow the same architecture which is that of the
:class:`~transformers.data.processors.utils.DataProcessor`. The processor returns a list
of :class:`~transformers.data.processors.utils.InputExample`. These
:class:`~transformers.data.processors.utils.InputExample` can be converted to
:class:`~transformers.data.processors.utils.InputFeatures` in order to be fed to the model.

.. autoclass:: transformers.data.processors.utils.DataProcessor
    :members:


.. autoclass:: transformers.data.processors.utils.InputExample
    :members:


.. autoclass:: transformers.data.processors.utils.InputFeatures
    :members:


GLUE
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

`General Language Understanding Evaluation (GLUE) <https://gluebenchmark.com/>`__ is a benchmark that evaluates
the performance of models across a diverse set of existing NLU tasks. It was released together with the paper
`GLUE: A multi-task benchmark and analysis platform for natural language understanding <https://openreview.net/pdf?id=rJ4km2R5t7>`__

This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched),
CoLA, SST2, STSB, QQP, QNLI, RTE and WNLI.

Those processors are:
    - :class:`~transformers.data.processors.utils.MrpcProcessor`
    - :class:`~transformers.data.processors.utils.MnliProcessor`
    - :class:`~transformers.data.processors.utils.MnliMismatchedProcessor`
    - :class:`~transformers.data.processors.utils.Sst2Processor`
    - :class:`~transformers.data.processors.utils.StsbProcessor`
    - :class:`~transformers.data.processors.utils.QqpProcessor`
    - :class:`~transformers.data.processors.utils.QnliProcessor`
    - :class:`~transformers.data.processors.utils.RteProcessor`
    - :class:`~transformers.data.processors.utils.WnliProcessor`

Additionally, the following method  can be used to load values from a data file and convert them to a list of
:class:`~transformers.data.processors.utils.InputExample`.

.. automethod:: transformers.data.processors.glue.glue_convert_examples_to_features

Example usage
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

An example using these processors is given in the `run_glue.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/text-classification/run_glue.py>`__ script.


XNLI
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

`The Cross-Lingual NLI Corpus (XNLI) <https://www.nyu.edu/projects/bowman/xnli/>`__ is a benchmark that evaluates
the quality of cross-lingual text representations. 
XNLI is crowd-sourced dataset based on `MultiNLI <http://www.nyu.edu/projects/bowman/multinli/>`: pairs of text are labeled with textual entailment 
annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).

It was released together with the paper
`XNLI: Evaluating Cross-lingual Sentence Representations <https://arxiv.org/abs/1809.05053>`__

This library hosts the processor to load the XNLI data:
    - :class:`~transformers.data.processors.utils.XnliProcessor`

Please note that since the gold labels are available on the test set, evaluation is performed on the test set.

An example using these processors is given in the
`run_xnli.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/text-classification/run_xnli.py>`__ script.


SQuAD
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

`The Stanford Question Answering Dataset (SQuAD) <https://rajpurkar.github.io/SQuAD-explorer//>`__ is a benchmark that evaluates
the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version (v1.1) was released together with the paper
`SQuAD: 100,000+ Questions for Machine Comprehension of Text <https://arxiv.org/abs/1606.05250>`__. The second version (v2.0) was released alongside 
the paper `Know What You Don't Know: Unanswerable Questions for SQuAD <https://arxiv.org/abs/1806.03822>`__.

This library hosts a processor for each of the two versions:

Processors
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Those processors are:
    - :class:`~transformers.data.processors.utils.SquadV1Processor`
    - :class:`~transformers.data.processors.utils.SquadV2Processor`

They both inherit from the abstract class :class:`~transformers.data.processors.utils.SquadProcessor`

.. autoclass:: transformers.data.processors.squad.SquadProcessor
    :members:

Additionally, the following method can be used to convert SQuAD examples into :class:`~transformers.data.processors.utils.SquadFeatures`
that can be used as model inputs.

.. automethod:: transformers.data.processors.squad.squad_convert_examples_to_features

These processors as well as the aforementionned method can be used with files containing the data as well as with the `tensorflow_datasets` package.
Examples are given below.


Example usage
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Here is an example using the processors as well as the conversion method using data files:

Example::

    # Loading a V2 processor
    processor = SquadV2Processor()
    examples = processor.get_dev_examples(squad_v2_data_dir)

    # Loading a V1 processor
    processor = SquadV1Processor()
    examples = processor.get_dev_examples(squad_v1_data_dir)

    features = squad_convert_examples_to_features( 
        examples=examples,
        tokenizer=tokenizer,
        max_seq_length=max_seq_length,
        doc_stride=args.doc_stride,
        max_query_length=max_query_length,
        is_training=not evaluate,
    )

Using `tensorflow_datasets` is as easy as using a data file:

Example::

    # tensorflow_datasets only handle Squad V1.
    tfds_examples = tfds.load("squad")
    examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)

    features = squad_convert_examples_to_features( 
        examples=examples,
        tokenizer=tokenizer,
        max_seq_length=max_seq_length,
        doc_stride=args.doc_stride,
        max_query_length=max_query_length,
        is_training=not evaluate,
    )


Another example using these processors is given in the
`run_squad.py <https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py>`__ script.
