flair.datasets.sequence_labeling.NER_HIPE_2022

class flair.datasets.sequence_labeling.NER_HIPE_2022(dataset_name, language, base_path=None, in_memory=True, version='v2.1', branch_name='main', dev_split_name='dev', add_document_separator=False, sample_missing_splits=False, preproc_fn=None, **corpusargs)View on GitHub

Bases: ColumnCorpus

__init__(dataset_name, language, base_path=None, in_memory=True, version='v2.1', branch_name='main', dev_split_name='dev', add_document_separator=False, sample_missing_splits=False, preproc_fn=None, **corpusargs)View on GitHub

Initialize the CLEF-HIPE 2022 NER dataset.

The first time you call this constructor it will automatically download the specified dataset (by given a language). :dataset_name: Supported datasets are: ajmc, hipe2020, letemps, newseye, sonar and topres19th. :language: Language for a supported dataset. :base_path: 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: If True, keeps dataset in memory giving speedups in training. :version: Version of CLEF-HIPE dataset. Currently only v1.0 is supported and available. :branch_name: Defines git branch name of HIPE data repository (main by default). :dev_split_name: Defines default name of development split (dev by default). Only the NewsEye dataset has currently two development splits: dev and dev2. :add_document_separator: If True, a special document seperator will be introduced. This is highly recommended when using our FLERT approach. :sample_missing_splits: If True, data is automatically sampled when certain data splits are None. :preproc_fn: Function that is used for dataset preprocessing. If None, default preprocessing will be performed.

Methods

__init__(dataset_name, language[, ...])	Initialize the CLEF-HIPE 2022 NER dataset.
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.