flair.datasets.document_classification.TREC_6

class flair.datasets.document_classification.TREC_6(base_path=None, tokenizer=<flair.tokenization.SpaceTokenizer object>, memory_mode='full', **corpusargs)View on GitHub

Bases: ClassificationCorpus

The TREC Question Classification Corpus, classifying questions into 6 coarse-grained answer types.

__init__(base_path=None, tokenizer=<flair.tokenization.SpaceTokenizer object>, memory_mode='full', **corpusargs)View on GitHub

Instantiates TREC Question Classification Corpus with 6 classes.

Parameters:

• base_path (Union[str, Path, None]) – Provide this only if you store the TREC corpus in a specific folder, otherwise use default.

• tokenizer (Union[bool, Tokenizer]) – Custom tokenizer to use (default is SpaceTokenizer)

• memory_mode – Set to ‘full’ by default since this is a small corpus. Can also be ‘partial’ or ‘none’.

• corpusargs – Other args for ClassificationCorpus.

Methods

__init__([base_path, tokenizer, memory_mode])	Instantiates TREC Question Classification Corpus with 6 classes.
add_label_noise(label_type, labels[, ...])	Adds artificial label noise to a specified split (in-place).
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 for a specific label type from the corpus.
make_tag_dictionary(tag_type)	DEPRECATED: Creates tag dictionary ensuring 'O', '<START>', '<STOP>'.
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

corpus_tokenizer	Returns the custom tokenizer provided during corpus initialization for retokenization, if any.
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.