flair.datasets.biomedical.HUNER_SPECIES_VARIOME#

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

Bases: HunerDataset

HUNER version of the Variome corpus containing 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.
static split_url()View on GitHub#
Return type:

str

to_internal(data_dir)View on GitHub#
Return type:

InternalBioNerDataset

get_entity_type_mapping()View on GitHub#
Return type:

Optional[dict]