flair.datasets.text_text.ParallelTextCorpus

class flair.datasets.text_text.ParallelTextCorpus(source_file, target_file, name, use_tokenizer=True, max_tokens_per_doc=-1, max_chars_per_doc=-1, in_memory=True, **corpusargs)View on GitHub

Bases: Corpus

__init__(source_file, target_file, name, use_tokenizer=True, max_tokens_per_doc=-1, max_chars_per_doc=-1, in_memory=True, **corpusargs)View on GitHub

Instantiates a Corpus for text classification from CSV column formatted data.

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

Returns:

a Corpus with annotated train, dev and test data

Methods

__init__(source_file, target_file, name[, ...])	Instantiates a Corpus for text classification from CSV column formatted data.
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.

is_in_memory()View on GitHub

Return type:

bool