flair.datasets.sequence_labeling.ONTONOTES#

class flair.datasets.sequence_labeling.ONTONOTES(base_path=None, version='v4', language='english', domain=None, in_memory=True, **corpusargs)View on GitHub#

Bases: MultiFileColumnCorpus

__init__(base_path=None, version='v4', language='english', domain=None, in_memory=True, **corpusargs)View on GitHub#

Instantiates a Corpus from CoNLL column-formatted task data such as CoNLL03 or CoNLL2000.

Parameters:

  • data_folder – base folder with the task data

  • column_format – a map specifying the column format

  • train_files – the name of the train files

  • test_files – the name of the test files

  • dev_files – the name of the dev files, if empty, dev data is sampled from train

  • column_delimiter – default is to split on any separatator, but you can overwrite for instance with “t” to split only on tabs

  • comment_symbol – if set, lines that begin with this symbol are treated as comments

  • encoding – file encoding (default “utf-8”)

  • document_separator_token – If provided, sentences that function as document boundaries are so marked

  • skip_first_line – set to True if your dataset has a header line

  • in_memory (bool) – If set to True, the dataset is kept in memory as Sentence objects, otherwise does disk reads

  • label_name_map – Optionally map tag names to different schema.

  • banned_sentences – Optionally remove sentences from the corpus. Works only if in_memory is true

Methods

__init__([base_path, version, language, ...])

Instantiates a Corpus from CoNLL column-formatted task data such as CoNLL03 or CoNLL2000.

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

Generates uniform label noise distribution in the chosen dataset split.

dataset_document_iterator(file_path)

An iterator over CONLL formatted files which yields documents, regardless of the number of document annotations in a particular file.

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_available_domains([base_path, version, ...])

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.

sentence_iterator(file_path)

An iterator over the sentences in an individual CONLL formatted file.

Attributes

archive_url

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.
archive_url = 'https://data.mendeley.com/public-files/datasets/zmycy7t9h9/files/b078e1c4-f7a4-4427-be7f-9389967831ef/file_downloaded'#
classmethod get_available_domains(base_path=None, version='v4', language='english', split='train')View on GitHub#
Return type:

list[str]

classmethod dataset_document_iterator(file_path)View on GitHub#

An iterator over CONLL formatted files which yields documents, regardless of the number of document annotations in a particular file.

This is useful for conll data which has been preprocessed, such as the preprocessing which takes place for the 2012 CONLL Coreference Resolution task.

Return type:

Iterator[list[dict]]

classmethod sentence_iterator(file_path)View on GitHub#

An iterator over the sentences in an individual CONLL formatted file.

Return type:

Iterator