flair.datasets.sequence_labeling.NER_MULTI_WIKINER#

class flair.datasets.sequence_labeling.NER_MULTI_WIKINER(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 #

Initializes a MultiCorpus by concatenating splits from individual corpora.

Parameters:

corpora (list[Corpus]) – List of Corpus objects to combine.
task_ids (Optional[list[str]], optional) – List of string IDs for each corpus/task. If None, generates default IDs like “Task_0”, “Task_1”. Defaults to None.
name (str, optional) – Name for the combined corpus. Defaults to “multicorpus”.
**corpusargs – Additional arguments passed to the parent Corpus constructor (e.g., sample_missing_splits).

Methods

`__init__`([languages, base_path, in_memory])	Initializes a MultiCorpus by concatenating splits from individual corpora.
`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.