flair.datasets.sequence_labeling.NER_MULTI_CONER

class flair.datasets.sequence_labeling.NER_MULTI_CONER(task='multi', base_path=None, in_memory=True, **corpusargs)View on GitHub

Bases: MultiFileColumnCorpus

__init__(task='multi', base_path=None, in_memory=True, **corpusargs)View on GitHub

Download and Initialize the MultiCoNer corpus.

Parameters:

• task (str) – either ‘multi’, ‘code-switch’, or the language code for one of the mono tasks.

• base_path (Union[str, Path, None]) – Path to the CoNLL-03 corpus (i.e. ‘conll_03’ folder) on your machine POS tags or chunks respectively

• in_memory (bool) – If True, keeps dataset in memory giving speedups in training.

Methods

__init__([task, base_path, in_memory])	Download and Initialize the MultiCoNer 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.