flair.data.MultiCorpus#

class flair.data.MultiCorpus(corpora, task_ids=None, name='multicorpus', **corpusargs)View on GitHub#

Bases: Corpus

A Corpus composed of multiple individual Corpus objects, often for multi-task learning.

__init__(corpora, task_ids=None, name='multicorpus', **corpusargs)View on GitHub#

Initializes a MultiCorpus by concatenating splits from individual corpora.

Parameters:

  • corpora (list[Corpus]) – List of Corpus objects to combine.

  • task_ids (Optional[list[str]], optional) – List of string IDs for each corpus/task. If None, generates default IDs like “Task_0”, “Task_1”. Defaults to None.

  • name (str, optional) – Name for the combined corpus. Defaults to “multicorpus”.

  • **corpusargs – Additional arguments passed to the parent Corpus constructor (e.g., sample_missing_splits).

Methods

__init__(corpora[, task_ids, name])

Initializes a MultiCorpus by concatenating splits from individual corpora.

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

Adds artificial label noise to a specified split (in-place).

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 for a specific label type from the corpus.

make_tag_dictionary(tag_type)

DEPRECATED: Creates tag dictionary ensuring 'O', '<START>', '<STOP>'.

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.