flair.datasets.document_classification.SENTEVAL_CR

class flair.datasets.document_classification.SENTEVAL_CR(tokenizer=<flair.tokenization.SpaceTokenizer object>, memory_mode='full', **corpusargs)View on GitHub

Bases: ClassificationCorpus

The customer reviews dataset of SentEval, classified into NEGATIVE or POSITIVE sentiment.

see facebookresearch/SentEval

__init__(tokenizer=<flair.tokenization.SpaceTokenizer object>, memory_mode='full', **corpusargs)View on GitHub

Instantiates SentEval customer reviews dataset.

Parameters:

• corpusargs – Other args for ClassificationCorpus.

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

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

Methods

__init__([tokenizer, memory_mode])	Instantiates SentEval customer reviews dataset.
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.