flair.datasets.document_classification.GLUE_COLA

class flair.datasets.document_classification.GLUE_COLA(label_type='acceptability', base_path=None, tokenizer=<flair.tokenization.SegtokTokenizer object>, **corpusargs)View on GitHub

Bases: ClassificationCorpus

Corpus of Linguistic Acceptability from GLUE benchmark.

see https://gluebenchmark.com/tasks

The task is to predict whether an English sentence is grammatically correct. Additionaly to the Corpus we have eval_dataset containing the unlabeled test data for Glue evaluation.

__init__(label_type='acceptability', base_path=None, tokenizer=<flair.tokenization.SegtokTokenizer object>, **corpusargs)View on GitHub

Instantiates CoLA dataset.

Parameters:

• base_path (Union[str, Path, None]) – Provide this only if you store the COLA corpus in a specific folder.

• tokenizer (Tokenizer) – Custom tokenizer to use (default is SegtokTokenizer)

• corpusargs – Other args for ClassificationCorpus.

Methods

__init__([label_type, base_path, tokenizer])	Instantiates CoLA dataset.
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)	Create eval prediction file.

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

Create eval prediction file.

This function creates a tsv file with predictions of the eval_dataset (after calling classifier.predict(corpus.eval_dataset, label_name=’acceptability’)). The resulting file is called CoLA.tsv and is in the format required for submission to the Glue Benchmark.