primeqa.mrc.processors.preprocessors.tydiboolqa_bpes.TyDiBoolQAPreprocessor#

class primeqa.mrc.processors.preprocessors.tydiboolqa_bpes.TyDiBoolQAPreprocessor(*args, **kwargs)#

Bases: primeqa.mrc.processors.preprocessors.tydiqa.TyDiQAPreprocessor

Methods

adapt_dataset

Convert dataset into standardized format accepted by the preprocessor.

label_features_for_subsampling

Annotate each training feature with a 'subsample_type' of type SubsampleType for subsampling.

process_eval

Process eval examples into features.

process_train

sample comparison of features and updated_features on Tydi train start and end positions are introduced on the two items with target_type==3 (YES) and not on other items

subsample_features

Subsample training features according to 'subsample_type':

validate_schema

Validate the data schema is correct for this preprocessor.
adapt_dataset(dataset: datasets.arrow_dataset.Dataset, is_train: bool) → datasets.arrow_dataset.Dataset#

Convert dataset into standardized format accepted by the preprocessor. This method will likely need to be overridden when subclassing.

Parameters

  • dataset – data to adapt.

  • is_train – whether the dataset is for training.

Returns

Adapted dataset.

label_features_for_subsampling(tokenized_examples: transformers.tokenization_utils_base.BatchEncoding, examples: datasets.arrow_dataset.Batch) → transformers.tokenization_utils_base.BatchEncoding#

Annotate each training feature with a ‘subsample_type’ of type SubsampleType for subsampling.

Parameters

  • tokenized_examples – featurized examples to annotate.

  • examples – original examples corresponding to the tokenized_examples features.

Returns: tokenized_examples annotated with ‘subsample_type’ for subsampling.

process_eval(examples: datasets.arrow_dataset.Dataset) → Tuple[datasets.arrow_dataset.Dataset, datasets.arrow_dataset.Dataset]#

Process eval examples into features.

Parameters

examples – examples to process into features.

Returns

tuple (examples, features) comprising examples adapted into standardized format and processed input features for model.

process_train(examples: datasets.arrow_dataset.Dataset) → Tuple[datasets.arrow_dataset.Dataset, datasets.arrow_dataset.Dataset]#

sample comparison of features and updated_features on Tydi train start and end positions are introduced on the two items with target_type==3 (YES) and not on other items

In [28]: features[0:20][‘start_positions’] Out[28]: [0, 0, 0, 0, 0, 0, 14, 0, 0, 291, 0, 246, 0, 189, 0, 101, 0, 0, 0, 0] In [29]: updated_features[0:20][‘start_positions’] Out[29]: [0, 0, 0, 0, 0, 0, 14, 0, 0, 291, 0, 246, 0, 189, 0, 101, 0, 191, 17, 0] In [30]: features[0:20][‘end_positions’] Out[30]: [0, 0, 0, 0, 0, 0, 26, 0, 0, 302, 0, 249, 0, 193, 0, 106, 0, 0, 0, 0] In [31]: updated_features[0:20][‘end_positions’] Out[31]: [0, 0, 0, 0, 0, 0, 26, 0, 0, 302, 0, 249, 0, 193, 0, 106, 0, 511, 278, 0] In [32]: features[0:20][‘target_type’] Out[32]: [0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 2, 1, 2, 1, 0, 1, 0, 3, 3, 0] In [33]: updated_features[0:20][‘target_type’] Out[33]: [0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 2, 1, 2, 1, 0, 1, 0, 3, 3, 0]

subsample_features(dataset: datasets.arrow_dataset.Dataset) → datasets.arrow_dataset.Dataset#

Subsample training features according to ‘subsample_type’:

  • All positive features are kept.

  • All negative features from an example that has an answer are kept with probability self._negative_sampling_prob_when_has_answer.

  • All negative features from an example that has no answer are kept with probability self._negative_sampling_prob_when_no_answer.

Parameters

dataset – features to subsample.

Returns

subsampled features.

validate_schema(dataset: datasets.arrow_dataset.Dataset, is_train: bool, pre_adaptation: bool = True) → None#

Validate the data schema is correct for this preprocessor.

Parameters

  • dataset – data to validate schema of

  • is_train – whether the data is for training

  • pre_adaptation – whether adapt_dataset has been called. This allows for optional fields (e.g. example_id) to be imputed during adaptation.

Returns

None

Raises

ValueError – The data is not in the correct schema.