flair.datasets.entity_linking.NEL_ENGLISH_AIDA#

class flair.datasets.entity_linking.NEL_ENGLISH_AIDA(base_path=None, in_memory=True, use_ids_and_check_existence=False, **corpusargs)View on GitHub#

Bases: ColumnCorpus

__init__(base_path=None, in_memory=True, use_ids_and_check_existence=False, **corpusargs)View on GitHub#

Initialize AIDA CoNLL-YAGO Entity Linking corpus.

The corpus got introduced here https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/ambiverse-nlu/aida/downloads. License: https://creativecommons.org/licenses/by-sa/3.0/deed.en_US If you call the constructor the first time the dataset gets automatically downloaded.

Parameters:

  • 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) – If True, keeps dataset in memory giving speedups in training.

  • use_ids_and_check_existence (bool) – If True the existence of the given wikipedia ids/pagenames is checked and non existent ids/names will be ignored. This also means that one works with current wikipedia-arcticle names and possibly alter some of the out-dated ones in the original dataset

Methods

__init__([base_path, in_memory, ...])

Initialize AIDA CoNLL-YAGO Entity Linking 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.