flair.datasets.sequence_labeling.NER_ESTONIAN_NOISY#
- class flair.datasets.sequence_labeling.NER_ESTONIAN_NOISY(version=0, base_path=None, in_memory=True, **corpusargs)View on GitHub#
Bases:
ColumnCorpus
- __init__(version=0, base_path=None, in_memory=True, **corpusargs)View on GitHub#
Initialize the NoisyNER corpus.
- Parameters:
version (int) – Chooses the labelset for the data. v0 (default): Clean labels v1 to v7: Different kinds of noisy labelsets (details: https://ojs.aaai.org/index.php/AAAI/article/view/16938)
base_path (Optional[Union[str, Path]]) – Path to the data. Default is None, meaning the corpus gets automatically downloaded and saved. You can override this by passing a path to a directory containing the unprocessed files but typically this should not be necessary.
in_memory (bool) – If True the dataset is kept in memory achieving speedups in training.
**corpusargs – The arguments propagated to :meth:’flair.datasets.ColumnCorpus.__init__’.
Methods
__init__
([version, base_path, in_memory])Initialize the NoisyNER 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.- data_url = 'https://storage.googleapis.com/google-code-archive-downloads/v2/code.google.com/patnlp/estner.cnll.zip'#
- label_url = 'https://raw.githubusercontent.com/uds-lsv/NoisyNER/master/data/only_labels'#