flair.datasets.text_text.GLUE_RTE

class flair.datasets.text_text.GLUE_RTE(label_type='entailment', 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='entailment', 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 Glue Recognizing Textual Entailment (RTE) data.

See https://gluebenchmark.com/tasks Additionally to the Corpus we have a eval_dataset containing the test file of the Glue data. This file contains unlabeled test data to evaluate models on the Glue RTE task.

Methods

__init__([label_type, base_path, ...])	Creates a DataPairCorpus for the Glue Recognizing Textual Entailment (RTE) data.
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