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 DataProcessor. The processor returns a list of InputExample. These InputExample can be converted to InputFeatures in order to be fed to the model.

class transformers.DataProcessor < >

( )

Base class for data converters for sequence classification data sets.

get_dev_examples < >

( data_dir )

Gets a collection of InputExample for the dev set.

get_example_from_tensor_dict < >

( tensor_dict )

Gets an example from a dict with tensorflow tensors.

get_labels < >

( )

Gets the list of labels for this data set.

get_test_examples < >

( data_dir )

Gets a collection of InputExample for the test set.

get_train_examples < >

( data_dir )

Gets a collection of InputExample for the train set.

tfds_map < >

( example )

Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts examples to the correct format.

class transformers.InputExample < >

( guid: str text_a: str text_b: typing.Optional[str] = None label: typing.Optional[str] = None )

A single training/test example for simple sequence classification.

to_json_string < >

( )

Serializes this instance to a JSON string.

class transformers.InputFeatures < >

( input_ids: typing.List[int] attention_mask: typing.Optional[typing.List[int]] = None token_type_ids: typing.Optional[typing.List[int]] = None label: typing.Union[int, float, NoneType] = None )

A single set of features of data. Property names are the same names as the corresponding inputs to a model.

to_json_string < >

( )

Serializes this instance to a JSON string.

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:

Additionally, the following method can be used to load values from a data file and convert them to a list of 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:

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:

They both inherit from the abstract class SquadProcessor

class transformers.data.processors.squad.SquadProcessor < >

( )

Processor for the SQuAD data set. overridden by SquadV1Processor and SquadV2Processor, used by the version 1.1 and version 2.0 of SQuAD, respectively.

get_dev_examples < >

( data_dir filename = None )

Returns the evaluation example from the data directory.

get_examples_from_dataset < >

( dataset evaluate = False )

Returns

List of SquadExample

Creates a list of SquadExampleusing a TFDS dataset.

Examples:

>>> import tensorflow_datasets as tfds

>>> dataset = tfds.load("squad")

>>> training_examples = get_examples_from_dataset(dataset, evaluate=False)
>>> evaluation_examples = get_examples_from_dataset(dataset, evaluate=True)
get_train_examples < >

( data_dir filename = None )

Returns the training examples from the data directory.

Additionally, the following method can be used to convert SQuAD examples into 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:

# 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:

# 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.