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#
Bases:
ColumnCorpus
- __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 machinein_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.