flair.datasets.biomedical.HUNER_SPECIES

class flair.datasets.biomedical.HUNER_SPECIES(sentence_splitter=None)View on GitHub

Bases: HunerMultiCorpus

Union of all HUNER species data sets.

__init__(sentence_splitter=None)View on GitHub

Initialize a Corpus.

You can define the train, dev and test split by passing the corresponding Dataset object to the constructor. At least one split should be defined. If the option sample_missing_splits is set to True, missing splits will be randomly sampled from the train split. In most cases, you will not use the constructor yourself. Rather, you will create a corpus using one of our helper methods that read common NLP filetypes. For instance, you can use flair.datasets.sequence_labeling.ColumnCorpus to read CoNLL-formatted files directly into a Corpus.

Parameters:

• train – The split you use for model training.

• dev – A holdout split typically used for model selection or early stopping.

• test – The final test data to compute the score of the model.

• name – A name that identifies the corpus.

• sample_missing_splits – If set to True, missing splits are sampled from train. If set to False, missing splits are not sampled and left empty. Default: True.

• random_seed – Set a random seed to control the sampling of missing splits.

Methods

__init__([sentence_splitter])	Initialize a 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_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.

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.