flair.datasets.sequence_labeling.NER_MULTI_CONER_V2#
- class flair.datasets.sequence_labeling.NER_MULTI_CONER_V2(task='multi', base_path=None, in_memory=True, use_dev_as_test=True, **corpusargs)View on GitHub#
Bases:
MultiFileColumnCorpus
- __init__(task='multi', base_path=None, in_memory=True, use_dev_as_test=True, **corpusargs)View on GitHub#
Initialize the MultiCoNer V2 corpus for the Semeval2023 workshop.
This is only possible if you’ve applied and downloaded it to your machine. Apply for the corpus from here https://multiconer.github.io/dataset and unpack the .zip file’s content into a folder called ‘ner_multi_coner_v2’. Then set the base_path parameter in the constructor to the path to the parent directory where the ner_multi_coner_v2 folder resides. You can also create the multiconer in the {FLAIR_CACHE_ROOT}/datasets folder to leave the path empty. :type base_path:
Union
[str
,Path
,None
] :param base_path: Path to the ner_multi_coner_v2 corpus (i.e. ‘ner_multi_coner_v2’ folder) on your machine POS tags or chunks respectively :type in_memory:bool
:param in_memory: If True, keeps dataset in memory giving speedups in training. :type use_dev_as_test:bool
:param use_dev_as_test: If True, it uses the dev set as test set and samples random training data for a dev split. :type task:str
:param task: either ‘multi’, ‘code-switch’, or the language code for one of the mono tasks.
Methods
__init__
([task, base_path, in_memory, ...])Initialize the MultiCoNer V2 corpus for the Semeval2023 workshop.
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.