flair.datasets.sequence_labeling.NER_MULTI_XTREME#

class flair.datasets.sequence_labeling.NER_MULTI_XTREME(languages='en', base_path=None, in_memory=False, **corpusargs)View on GitHub #

__init__(languages='en', base_path=None, in_memory=False, **corpusargs)View on GitHub #

Xtreme corpus for cross-lingual NER consisting of datasets of a total of 40 languages.

The data comes from the google research work XTREME google-research/xtreme. The data is derived from the wikiann dataset https://elisa-ie.github.io/wikiann/ (license: https://opendatacommons.org/licenses/by/)

Parameters:

languages (Union[str, list[str]], optional) – Specify the languages you want to load. Provide an empty list or string to select all languages.
base_path (Union[str, Path], optional) – Default is None, meaning that corpus gets auto-downloaded and loaded. You can override this to point to a different folder but typically this should not be necessary.
in_memory (bool, optional) – Specify that the dataset should be loaded in memory, which speeds up the training process but takes increases the RAM usage significantly.

Methods

`__init__`([languages, base_path, in_memory])	Xtreme corpus for cross-lingual NER consisting of datasets of a total of 40 languages.
`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.