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) `__ 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 `__
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 `__ script.
XNLI
~~~~~~~~~~~~~~~~~~~~~
`The Cross-Lingual NLI Corpus (XNLI) `__ is a benchmark that evaluates
the quality of cross-lingual text representations.
XNLI is crowd-sourced dataset based on `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 `__
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 `__ script.
SQuAD
~~~~~~~~~~~~~~~~~~~~~
`The Stanford Question Answering Dataset (SQuAD) `__ 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 `__. The second version (v2.0) was released alongside
the paper `Know What You Don't Know: Unanswerable Questions for SQuAD `__.
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 `__ script.