flair.datasets.sequence_labeling.NER_ENGLISH_SEC_FILLINGS#

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

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

Initialize corpus of SEC-fillings annotated with English NER tags.

See paper “Domain Adaption of Named Entity Recognition to Support Credit Risk Assessment” by Alvarado et al, 2015: https://aclanthology.org/U15-1010/

Parameters:

base_path (Union[str, Path, None]) – Path to the CoNLL-03 corpus (i.e. ‘conll_03’ folder) on your machine
in_memory (bool) – If True, keeps dataset in memory giving speedups in training.

Methods

`__init__`([base_path, in_memory])	Initialize corpus of SEC-fillings annotated with English NER tags.
`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.