flair.datasets.treebanks.UD_LATVIAN#

class flair.datasets.treebanks.UD_LATVIAN(base_path=None, in_memory=True, split_multiwords=True, revision='master')View on GitHub#

Bases: UniversalDependenciesCorpus

__init__(base_path=None, in_memory=True, split_multiwords=True, revision='master')View on GitHub#

Instantiates a Corpus from CoNLL-U column-formatted task data such as the UD corpora.

Parameters:

  • data_folder – base folder with the task data

  • train_file – the name of the train file

  • test_file – the name of the test file

  • dev_file – the name of the dev file, if None, dev data is sampled from train

  • in_memory (bool) – If set to True, keeps full dataset in memory, otherwise does disk reads

  • split_multiwords (bool) – If set to True, multiwords are split (default), otherwise kept as single tokens

Returns:

a Corpus with annotated train, dev and test data

Methods

__init__([base_path, in_memory, ...])

Instantiates a Corpus from CoNLL-U column-formatted task data such as the UD corpora.

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.