flair.datasets.document_classification.NEWSGROUPS#

class flair.datasets.document_classification.NEWSGROUPS(base_path=None, tokenizer=<flair.tokenization.SegtokTokenizer object>, memory_mode='partial', **corpusargs)View on GitHub#

Bases: ClassificationCorpus

20 newsgroups corpus, classifying news items into one of 20 categories.

Downloaded from http://qwone.com/~jason/20Newsgroups

Each data point is a full news article so documents may be very long.

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

Instantiates 20 newsgroups corpus.

Parameters:

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

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

  • memory_mode (str) – Set to ‘partial’ because this is a big corpus, but you can also set to ‘full’ for faster processing or ‘none’ for less memory.

  • corpusargs – Other args for ClassificationCorpus.

Methods

__init__([base_path, tokenizer, memory_mode])

Instantiates 20 newsgroups corpus.

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.

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.