flair.datasets.text_text.GLUE_MRPC#

class flair.datasets.text_text.GLUE_MRPC(label_type='paraphrase', base_path=None, max_tokens_per_doc=-1, max_chars_per_doc=-1, use_tokenizer=True, in_memory=True, sample_missing_splits=True)View on GitHub#

Bases: DataPairCorpus

__init__(label_type='paraphrase', base_path=None, max_tokens_per_doc=-1, max_chars_per_doc=-1, use_tokenizer=True, in_memory=True, sample_missing_splits=True)View on GitHub#

Creates a DataPairCorpus for the Microsoft Research Paraphrase Corpus (MRPC) from Glue benchmark.

See https://gluebenchmark.com/tasks MRPC includes annotated train and test sets. Dev set is sampled each time when creating this corpus.

Methods

__init__([label_type, base_path, ...])

Creates a DataPairCorpus for the Microsoft Research Paraphrase Corpus (MRPC) from Glue benchmark.

add_label_noise(label_type, labels[, ...])

Generates uniform label noise distribution in the chosen dataset split.

downsample([percentage, downsample_train, ...])

Randomly downsample the corpus to the given percentage (by removing data points).

filter_empty_sentences()

A method that filters all sentences consisting of 0 tokens.

filter_long_sentences(max_charlength)

A method that filters all sentences for which the plain text is longer than a specified number of characters.

get_all_sentences()

Returns all sentences (spanning all three splits) in the Corpus.

get_label_distribution()

Counts occurrences of each label in the corpus and returns them as a dictionary object.

make_label_dictionary(label_type[, ...])

Creates a dictionary of all labels assigned to the sentences in the corpus.

make_tag_dictionary(tag_type)

Create a tag dictionary of a given label type.

make_vocab_dictionary([max_tokens, min_freq])

Creates a Dictionary of all tokens contained in the corpus.

obtain_statistics([label_type, pretty_print])

Print statistics about the corpus, including the length of the sentences and the labels in the corpus.

tsv_from_eval_dataset(folder_path)

Attributes

dev

The dev split as a torch.utils.data.Dataset object.

test

The test split as a torch.utils.data.Dataset object.

train

The training split as a torch.utils.data.Dataset object.
tsv_from_eval_dataset(folder_path)View on GitHub#