flair.datasets.document_classification.WASSA_JOY#
- class flair.datasets.document_classification.WASSA_JOY(base_path=None, tokenizer=<flair.tokenization.SegtokTokenizer object>, **corpusargs)View on GitHub#
Bases:
ClassificationCorpus
WASSA-2017 joy emotion-intensity dataset corpus.
see https://saifmohammad.com/WebPages/EmotionIntensity-SharedTask.html
- __init__(base_path=None, tokenizer=<flair.tokenization.SegtokTokenizer object>, **corpusargs)View on GitHub#
Instantiates WASSA-2017 joy emotion-intensity corpus.
- Parameters:
base_path (
Union
[str
,Path
,None
]) – Provide this only if you store the WASSA corpus in a specific folder, otherwise use default.tokenizer (
Tokenizer
) – Custom tokenizer to use (default is SegtokTokenizer)corpusargs – Other args for ClassificationCorpus.
Methods
__init__
([base_path, tokenizer])Instantiates WASSA-2017 joy emotion-intensity corpus.
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.