flair.datasets.sequence_labeling.NER_MASAKHANE

class flair.datasets.sequence_labeling.NER_MASAKHANE(languages='luo', version='v2', base_path=None, in_memory=True, **corpusargs)View on GitHub

Bases: MultiCorpus

__init__(languages='luo', version='v2', base_path=None, in_memory=True, **corpusargs)View on GitHub

Initialize the Masakhane corpus available on masakhane-io/masakhane-ner.

It consists of ten African languages. Pass a language code or a list of language codes to initialize the corpus with the languages you require. If you pass “all”, all languages will be initialized. :version: Specifies version of the dataset. Currently, only “v1” and “v2” are supported, using “v2” as default. :type base_path: Union[str, Path, None] :param base_path: Default is None, meaning that corpus gets auto-downloaded and loaded. You can override this to point to a different folder but typically this should not be necessary. POS tags instead :type in_memory: bool :param in_memory: If True, keeps dataset in memory giving speedups in training.

Methods

__init__([languages, version, base_path, ...])	Initialize the Masakhane corpus available on masakhane-io/masakhane-ner.
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.