flair.datasets.biomedical.HUNER_SPECIES_CRAFT_V4

class flair.datasets.biomedical.HUNER_SPECIES_CRAFT_V4(*args, **kwargs)View on GitHub

Bases: HUNER_CRAFT_V4

HUNER version of the CRAFT corpus containing (only) species annotations.

__init__(*args, **kwargs)View on GitHub

Initialize the HUNER corpus.

Parameters:

• base_path – Path to the corpus on your machine

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

• sentence_splitter – Custom implementation of SentenceSplitter which segments the text into sentences and tokens (default SciSpacySentenceSplitter)

Methods

__init__(*args, **kwargs)	Initialize the HUNER 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_corpus_sentence_splitter()	Return the pre-defined sentence splitter if defined, otherwise return None.
get_entity_type_mapping()
get_label_distribution()	Counts occurrences of each label in the corpus and returns them as a dictionary object.
get_subset(dataset, split, split_dir)
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.
split_url()
to_internal(data_dir)

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.