flair.datasets.sequence_labeling.MultiFileColumnCorpus#

class flair.datasets.sequence_labeling.MultiFileColumnCorpus(column_format, train_files=None, test_files=None, dev_files=None, column_delimiter='\\\\s+', comment_symbol=None, encoding='utf-8', document_separator_token=None, skip_first_line=False, in_memory=True, label_name_map=None, banned_sentences=None, default_whitespace_after=1, **corpusargs)View on GitHub#

Bases: Corpus

__init__(column_format, train_files=None, test_files=None, dev_files=None, column_delimiter='\\\\s+', comment_symbol=None, encoding='utf-8', document_separator_token=None, skip_first_line=False, in_memory=True, label_name_map=None, banned_sentences=None, default_whitespace_after=1, **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 (dict[int, str]) – 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 (str) – default is to split on any separatator, but you can overwrite for instance with “t” to split only on tabs

  • comment_symbol (Optional[str]) – if set, lines that begin with this symbol are treated as comments

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

  • document_separator_token (Optional[str]) – If provided, sentences that function as document boundaries are so marked

  • skip_first_line (bool) – 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 (Optional[dict[str, str]]) – Optionally map tag names to different schema.

  • banned_sentences (Optional[list[str]]) – Optionally remove sentences from the corpus. Works only if in_memory is true

Methods

__init__(column_format[, train_files, ...])

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.

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.