flair.datasets.biomedical.HunerDataset#

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

Base class for HUNER datasets.

Every subclass has to implement the following methods:

“to_internal”, which reads the complete data set (incl. train, dev, test) and returns the corpus as InternalBioNerDataset
“split_url”, which returns the base url (i.e. without ‘.train’, ‘.dev’, ‘.test’) to the HUNER split files

For further information see:

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

Initialize the HUNER 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]) – Custom implementation of SentenceSplitter which segments the text into sentences and tokens (default SciSpacySentenceSplitter)

Methods

`__init__`([base_path, in_memory, ...])	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_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_folder)

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.

abstract to_internal(data_folder)View on GitHub #

abstract static split_url()View on GitHub #

get_corpus_sentence_splitter()View on GitHub #

Return the pre-defined sentence splitter if defined, otherwise return None.