flair.datasets.sequence_labeling.NER_ICELANDIC

class flair.datasets.sequence_labeling.NER_ICELANDIC(base_path=None, in_memory=True, **corpusargs)View on GitHub

Bases: ColumnCorpus

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

Initialize the ICELANDIC_NER corpus.

The first time you call this constructor it will automatically download the dataset. :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. :param document_as_sequence: If True, all sentences of a document are read into a single Sentence object

Methods

__init__([base_path, in_memory])	Initialize the ICELANDIC_NER 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.