flair.datasets.sequence_labeling.WNUT_17#
- class flair.datasets.sequence_labeling.WNUT_17(base_path=None, in_memory=True, **corpusargs)View on GitHub#
Bases:
ColumnCorpus
- __init__(base_path=None, in_memory=True, **corpusargs)View on GitHub#
Instantiates a Corpus from CoNLL column-formatted task data such as CoNLL03 or CoNLL2000.
- Parameters:
data_folder – base folder with the task data
column_format – a map specifying the column format
train_file – the name of the train file
test_file – the name of the test file
dev_file – the name of the dev file, if None, dev data is sampled from train
column_delimiter – default is to split on any separatator, but you can overwrite for instance with “t” to split only on tabs
comment_symbol – if set, lines that begin with this symbol are treated as comments
document_separator_token – If provided, sentences that function as document boundaries are so marked
skip_first_line – set to True if your dataset has a header line
in_memory (
bool
) – If set to True, the dataset is kept in memory as Sentence objects, otherwise does disk readslabel_name_map – Optionally map tag names to different schema.
banned_sentences – Optionally remove sentences from the corpus. Works only if in_memory is true
Methods
__init__
([base_path, in_memory])Instantiates a Corpus from CoNLL column-formatted task data such as CoNLL03 or CoNLL2000.
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.