flair.datasets.biomedical.HUNER_CELL_LINE_JNLPBA#
- class flair.datasets.biomedical.HUNER_CELL_LINE_JNLPBA(*args, **kwargs)View on GitHub#
Bases:
HUNER_JNLPBA
HUNER version of the JNLPBA corpus containing cell line 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 (defaultSciSpacySentenceSplitter
)
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.