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dabeb15292
* Base move * Examples reorganization * Update references * Put back test data * Move conftest * More fixes * Move test data to test fixtures * Update path * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Address review comments and clean Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
173 lines
7.4 KiB
ReStructuredText
173 lines
7.4 KiB
ReStructuredText
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Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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Processors
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-----------------------------------------------------------------------------------------------------------------------
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This library includes processors for several traditional tasks. These processors can be used to process a dataset into
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examples that can be fed to a model.
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Processors
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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All processors follow the same architecture which is that of the
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:class:`~transformers.data.processors.utils.DataProcessor`. The processor returns a list of
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:class:`~transformers.data.processors.utils.InputExample`. These
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:class:`~transformers.data.processors.utils.InputExample` can be converted to
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:class:`~transformers.data.processors.utils.InputFeatures` in order to be fed to the model.
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.. autoclass:: transformers.data.processors.utils.DataProcessor
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:members:
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.. autoclass:: transformers.data.processors.utils.InputExample
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:members:
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.. autoclass:: transformers.data.processors.utils.InputFeatures
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:members:
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GLUE
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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`General Language Understanding Evaluation (GLUE) <https://gluebenchmark.com/>`__ is a benchmark that evaluates the
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performance of models across a diverse set of existing NLU tasks. It was released together with the paper `GLUE: A
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multi-task benchmark and analysis platform for natural language understanding
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<https://openreview.net/pdf?id=rJ4km2R5t7>`__
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This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched), CoLA, SST2, STSB,
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QQP, QNLI, RTE and WNLI.
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Those processors are:
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- :class:`~transformers.data.processors.utils.MrpcProcessor`
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- :class:`~transformers.data.processors.utils.MnliProcessor`
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- :class:`~transformers.data.processors.utils.MnliMismatchedProcessor`
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- :class:`~transformers.data.processors.utils.Sst2Processor`
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- :class:`~transformers.data.processors.utils.StsbProcessor`
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- :class:`~transformers.data.processors.utils.QqpProcessor`
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- :class:`~transformers.data.processors.utils.QnliProcessor`
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- :class:`~transformers.data.processors.utils.RteProcessor`
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- :class:`~transformers.data.processors.utils.WnliProcessor`
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Additionally, the following method can be used to load values from a data file and convert them to a list of
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:class:`~transformers.data.processors.utils.InputExample`.
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.. automethod:: transformers.data.processors.glue.glue_convert_examples_to_features
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Example usage
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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An example using these processors is given in the :prefix_link:`run_glue.py
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<examples/legacy/text-classification/run_glue.py>` script.
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XNLI
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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`The Cross-Lingual NLI Corpus (XNLI) <https://www.nyu.edu/projects/bowman/xnli/>`__ is a benchmark that evaluates the
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quality of cross-lingual text representations. XNLI is crowd-sourced dataset based on `MultiNLI
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<http://www.nyu.edu/projects/bowman/multinli/>`: pairs of text are labeled with textual entailment annotations for 15
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different languages (including both high-resource language such as English and low-resource languages such as Swahili).
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It was released together with the paper `XNLI: Evaluating Cross-lingual Sentence Representations
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<https://arxiv.org/abs/1809.05053>`__
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This library hosts the processor to load the XNLI data:
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- :class:`~transformers.data.processors.utils.XnliProcessor`
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Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
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An example using these processors is given in the :prefix_link:`run_xnli.py
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<examples/legacy/text-classification/run_xnli.py>` script.
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SQuAD
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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`The Stanford Question Answering Dataset (SQuAD) <https://rajpurkar.github.io/SQuAD-explorer//>`__ is a benchmark that
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evaluates the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version
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(v1.1) was released together with the paper `SQuAD: 100,000+ Questions for Machine Comprehension of Text
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<https://arxiv.org/abs/1606.05250>`__. The second version (v2.0) was released alongside the paper `Know What You Don't
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Know: Unanswerable Questions for SQuAD <https://arxiv.org/abs/1806.03822>`__.
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This library hosts a processor for each of the two versions:
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Processors
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Those processors are:
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- :class:`~transformers.data.processors.utils.SquadV1Processor`
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- :class:`~transformers.data.processors.utils.SquadV2Processor`
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They both inherit from the abstract class :class:`~transformers.data.processors.utils.SquadProcessor`
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.. autoclass:: transformers.data.processors.squad.SquadProcessor
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:members:
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Additionally, the following method can be used to convert SQuAD examples into
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:class:`~transformers.data.processors.utils.SquadFeatures` that can be used as model inputs.
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.. automethod:: transformers.data.processors.squad.squad_convert_examples_to_features
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These processors as well as the aforementionned method can be used with files containing the data as well as with the
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`tensorflow_datasets` package. Examples are given below.
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Example usage
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Here is an example using the processors as well as the conversion method using data files:
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.. code-block::
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# Loading a V2 processor
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processor = SquadV2Processor()
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examples = processor.get_dev_examples(squad_v2_data_dir)
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# Loading a V1 processor
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processor = SquadV1Processor()
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examples = processor.get_dev_examples(squad_v1_data_dir)
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features = squad_convert_examples_to_features(
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examples=examples,
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tokenizer=tokenizer,
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max_seq_length=max_seq_length,
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doc_stride=args.doc_stride,
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max_query_length=max_query_length,
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is_training=not evaluate,
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)
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Using `tensorflow_datasets` is as easy as using a data file:
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.. code-block::
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# tensorflow_datasets only handle Squad V1.
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tfds_examples = tfds.load("squad")
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examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
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features = squad_convert_examples_to_features(
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examples=examples,
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tokenizer=tokenizer,
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max_seq_length=max_seq_length,
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doc_stride=args.doc_stride,
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max_query_length=max_query_length,
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is_training=not evaluate,
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)
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Another example using these processors is given in the :prefix_link:`run_squad.py
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<examples/legacy/question-answering/run_squad.py>` script.
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