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#

Bases: MultiCorpus

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

Constructor method to initialize a Corpus. You can define the train, dev and test split by passing the corresponding Dataset object to the constructor. At least one split should be defined. If the option sample_missing_splits is set to True, missing splits will be randomly sampled from the train split.

In most cases, you will not use the constructor yourself. Rather, you will create a corpus using one of our helper methods that read common NLP filetypes. For instance, you can use flair.datasets.sequence_labeling.ColumnCorpus to read CoNLL-formatted files directly into a Corpus.

Parameters:

  • train – The split you use for model training.

  • dev – A holdout split typically used for model selection or early stopping.

  • test – The final test data to compute the score of the model.

  • name – A name that identifies the corpus.

  • sample_missing_splits – If set to True, missing splits are sampled from train. If set to False, missing splits are not sampled and left empty. Default: True.

  • random_seed – Set a random seed to control the sampling of missing splits.

Methods

__init__([languages, base_path, in_memory])

Constructor method to initialize a 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.