flair.datasets.document_classification.GLUE_SST2#

class flair.datasets.document_classification.GLUE_SST2(label_type='sentiment', base_path=None, max_tokens_per_doc=-1, max_chars_per_doc=-1, tokenizer=<flair.tokenization.SegtokTokenizer object>, in_memory=False, encoding='utf-8', sample_missing_splits=True, **datasetargs)View on GitHub#

Bases: CSVClassificationCorpus

__init__(label_type='sentiment', base_path=None, max_tokens_per_doc=-1, max_chars_per_doc=-1, tokenizer=<flair.tokenization.SegtokTokenizer object>, in_memory=False, encoding='utf-8', sample_missing_splits=True, **datasetargs)View on GitHub#

Instantiates a Corpus for text classification from CSV column formatted data.

Parameters:

  • data_folder – base folder with the task data

  • column_name_map – a column name map that indicates which column is text and which the label(s)

  • label_type (str) – name of the label

  • 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

  • max_tokens_per_doc – If set, truncates each Sentence to a maximum number of Tokens

  • max_chars_per_doc – If set, truncates each Sentence to a maximum number of chars

  • tokenizer (Tokenizer) – Tokenizer for dataset, default is SegtokTokenizer

  • in_memory (bool) – If True, keeps dataset as Sentences in memory, otherwise only keeps strings

  • skip_header – If True, skips first line because it is header

  • encoding (str) – Default is ‘utf-8’ but some datasets are in ‘latin-1

  • fmtparams – additional parameters for the CSV file reader

Returns:

a Corpus with annotated train, dev and test data

Methods

__init__([label_type, base_path, ...])

Instantiates a Corpus for text classification from CSV column formatted data.

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.

tsv_from_eval_dataset(folder_path)

Create eval prediction file.

Attributes

dev

The dev split as a torch.utils.data.Dataset object.

label_map

test

The test split as a torch.utils.data.Dataset object.

train

The training split as a torch.utils.data.Dataset object.
label_map = {0: 'negative', 1: 'positive'}#
tsv_from_eval_dataset(folder_path)View on GitHub#

Create eval prediction file.