flair.datasets.text_text.OpusParallelCorpus

class flair.datasets.text_text.OpusParallelCorpus(dataset, l1, l2, use_tokenizer=True, max_tokens_per_doc=-1, max_chars_per_doc=-1, in_memory=True, **corpusargs)View on GitHub

Bases: ParallelTextCorpus

__init__(dataset, l1, l2, use_tokenizer=True, max_tokens_per_doc=-1, max_chars_per_doc=-1, in_memory=True, **corpusargs)View on GitHub

Instantiates a Parallel Corpus from OPUS.

see http://opus.nlpl.eu/ :type dataset: str :param dataset: Name of the dataset (one of “tatoeba”) :type l1: str :param l1: Language code of first language in pair (“en”, “de”, etc.) :type l2: str :param l2: Language code of second language in pair (“en”, “de”, etc.) :type use_tokenizer: bool :param use_tokenizer: Whether or not to use in-built tokenizer :type max_tokens_per_doc: :param max_tokens_per_doc: If set, shortens sentences to this maximum number of tokens :type max_chars_per_doc: :param max_chars_per_doc: If set, shortens sentences to this maximum number of characters :type in_memory: bool :param in_memory: If True, keeps dataset fully in memory

Methods

__init__(dataset, l1, l2[, use_tokenizer, ...])	Instantiates a Parallel Corpus from OPUS.
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.
is_in_memory()
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.