flair.datasets.ocr.SROIE#
- class flair.datasets.ocr.SROIE(base_path=None, encoding='utf-8', label_type='ner', in_memory=True, load_images=False, normalize_coords_to_thousands=True, label_name_map=None, **corpusargs)View on GitHub#
Bases:
OcrCorpus
- __init__(base_path=None, encoding='utf-8', label_type='ner', in_memory=True, load_images=False, normalize_coords_to_thousands=True, label_name_map=None, **corpusargs)View on GitHub#
Instantiates the SROIE corpus with perfect ocr boxes.
- Parameters:
base_path (
Union
[str
,Path
,None
]) – the path to store the dataset or load it fromlabel_type (
str
) – the label_type to add the ocr labels toencoding (
str
) – the encoding to load the .json files withload_images (
bool
) – if True, the pillow images will be added as metadatanormalize_coords_to_thousands (
bool
) – if True, the coordinates will be ranged from 0 to 1000in_memory (
bool
) – If set to True, the dataset is kept in memory as Sentence objects, otherwise does disk readslabel_name_map (
Optional
[dict
[str
,str
]]) – Optionally map tag names to different schema.
- Returns:
a Corpus with Sentences that contain OCR information
Methods
__init__
([base_path, encoding, label_type, ...])Instantiates the SROIE corpus with perfect ocr boxes.
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.