flair.datasets.document_classification.GERMEVAL_2018_OFFENSIVE_LANGUAGE#

class flair.datasets.document_classification.GERMEVAL_2018_OFFENSIVE_LANGUAGE(base_path=None, tokenizer=<flair.tokenization.SegtokTokenizer object>, memory_mode='full', fine_grained_classes=False, **corpusargs)View on GitHub#

Bases: ClassificationCorpus

GermEval 2018 corpus for identification of offensive language.

Classifying German tweets into 2 coarse-grained categories OFFENSIVE and OTHER or 4 fine-grained categories ABUSE, INSULT, PROFATINTY and OTHER.

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

Instantiates GermEval 2018 Offensive Language Classification Corpus.

Parameters:

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

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

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

  • fine_grained_classes (bool) – Set to True to load the dataset with 4 fine-grained classes

  • corpusargs – Other args for ClassificationCorpus.

Methods

__init__([base_path, tokenizer, ...])

Instantiates GermEval 2018 Offensive Language Classification 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.