flair.datasets.document_classification.IMDB#

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

Bases: ClassificationCorpus

Corpus of IMDB movie reviews labeled by sentiment (POSITIVE, NEGATIVE).

Downloaded from and documented at http://ai.stanford.edu/~amaas/data/sentiment/.

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

Initialize the IMDB move review sentiment 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)

  • rebalance_corpus (bool) – Weather to use a 80/10/10 data split instead of the original 50/0/50 split.

  • memory_mode –

    Set to ‘partial’ because this is a huge 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, rebalance_corpus, ...])

Initialize the IMDB move review sentiment 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.