flair.datasets.biomedical.CRAFT_V4#

class flair.datasets.biomedical.CRAFT_V4(base_path=None, in_memory=True, sentence_splitter=None)View on GitHub#

Bases: ColumnCorpus

Version 4.0.1 of the CRAFT corpus containing all but the co-reference and structural annotations.

For further information see: UCDenver-ccp/CRAFT

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

Initializes version 4.0.1 of the CRAFT corpus.

Parameters:
  • base_path (Union[str, Path, None]) – Path to the corpus on your machine

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

  • sentence_splitter (Optional[SentenceSplitter]) – Implementation of SentenceSplitter which segments documents into sentences and tokens (default SciSpacySentenceSplitter)

Methods

__init__([base_path, in_memory, ...])

Initializes version 4.0.1 of the CRAFT corpus.

add_label_noise(label_type, labels[, ...])

Generates uniform label noise distribution in the chosen dataset split.

download_corpus(data_dir)

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_entities(corpus)

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.

parse_corpus(corpus_dir)

prepare_splits(data_dir, 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.

filter_entities(corpus)View on GitHub#
Return type:

InternalBioNerDataset

classmethod download_corpus(data_dir)View on GitHub#
Return type:

Path

static prepare_splits(data_dir, corpus)View on GitHub#
Return type:

tuple[InternalBioNerDataset, InternalBioNerDataset, InternalBioNerDataset]

static parse_corpus(corpus_dir)View on GitHub#
Return type:

InternalBioNerDataset