File size: 8,660 Bytes
1c6463b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | import json
import os
from PIL import Image
import datasets
from datasets import load_dataset
def load_image(image_path):
image = Image.open(image_path).convert("RGB")
w, h = image.size
return image, (w, h)
def normalize_bbox(bbox, size):
return [
int(1000 * bbox[0] / size[0]),
int(1000 * bbox[1] / size[1]),
int(1000 * bbox[2] / size[0]),
int(1000 * bbox[3] / size[1]),
]
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
}
"""
_DESCRIPTION = """\
"""
class FunsdConfig(datasets.BuilderConfig):
"""BuilderConfig for FUNSD"""
def __init__(self, **kwargs):
"""BuilderConfig for FUNSD.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(FunsdConfig, self).__init__(**kwargs)
class Funsd(datasets.GeneratorBasedBuilder):
"""Conll2003 dataset."""
BUILDER_CONFIGS = [
FunsdConfig(name="funsd", version=datasets.Version("1.0.0"), description="FUNSD dataset"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"bboxes": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names = [ "S-ANSWER_EXP",
"S-ANSWER_FECHA_SERVICIO",
"S-ANSWER_HORA_SERVICIO",
"S-ANSWER_SALA",
"B-ANSWER_NOMBRE1",
"I-ANSWER_NOMBRE1",
"E-ANSWER_NOMBRE1",
"B-ANSWER_DIRECCION",
"I-ANSWER_DIRECCION",
"E-ANSWER_DIRECCION",
"B-ANSWER_POBLACION",
"I-ANSWER_POBLACION",
"E-ANSWER_POBLACION",
"S-ANSWER_DNI",
"S-ANSWER_TELEFONO",
"S-ANSWER_EDAD",
"S-ANSWER_NACIMIENTO_DIF",
"S-ANSWER_ESTADO_CIVIL_DIF",
"S-ANSWER_FECHA_DEF",
"B-ANSWER_LUGAR_DEF",
"I-ANSWER_LUGAR_DEF",
"E-ANSWER_LUGAR_DEF",
"S-ANSWER_NATURAL_DE_DIF",
"B-ANSWER_PADRES_DIF",
"I-ANSWER_PADRES_DIF",
"E-ANSWER_PADRES_DIF",
"B-ANSWER_NOMBRE_TITULAR",
"I-ANSWER_NOMBRE_TITULAR",
"E-ANSWER_NOMBRE_TITULAR",
"S-ANSWER_AUT_DNI_TITULAR",
"B-ANSWER_DIRECCION_TITULAR",
"I-ANSWER_DIRECCION_TITULAR",
"E-ANSWER_DIRECCION_TITULAR",
"B-ANSWER_POBLACION_TITULAR",
"I-ANSWER_POBLACION_TITULAR",
"E-ANSWER_POBLACION_TITULAR",
"B-ANSWER_AUTORIZACION_TITULAR",
"I-ANSWER_AUTORIZACION_TITULAR",
"E-ANSWER_AUTORIZACION_TITULAR",
"S-ANSWER_DNI_TITULAR",
"S-ANSWER_HORA_DEFUNCION",
"B-ANSWER_DESCRIPCION",
"I-ANSWER_DESCRIPCION",
"E-ANSWER_DESCRIPCION",
"B-ANSWER_NOMBRE",
"I-ANSWER_NOMBRE",
"E-ANSWER_NOMBRE",
"S-ANSWER_CANTIDAD",
"S-ANSWER_IMPORTE"]
)
),
"image": datasets.features.Image(),
}
),
supervised_keys=None,
homepage="",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
downloaded_file = dl_manager.download_and_extract("https://huggingface.co/datasets/LauraExp/LILT2/resolve/main/Data.zip")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/Data/training_data/"}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/Data/testing_data/"}
),
]
def get_line_bbox(self, bboxs):
x = [bboxs[i][j] for i in range(len(bboxs)) for j in range(0, len(bboxs[i]), 2)]
y = [bboxs[i][j] for i in range(len(bboxs)) for j in range(1, len(bboxs[i]), 2)]
x0, y0, x1, y1 = min(x), min(y), max(x), max(y)
assert x1 >= x0 and y1 >= y0
bbox = [[x0, y0, x1, y1] for _ in range(len(bboxs))]
return bbox
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
ann_dir = os.path.join(filepath, "annotations")
img_dir = os.path.join(filepath, "images")
for guid, file in enumerate(sorted(os.listdir(ann_dir))):
tokens = []
bboxes = []
ner_tags = []
file_path = os.path.join(ann_dir, file)
with open(file_path, "r", encoding="utf8") as f:
data = json.load(f)
image_path = os.path.join(img_dir, file)
image_path = image_path.replace("json", "png")
image, size = load_image(image_path)
for item in data["form"]:
words_example, label = item["words"], item["label"]
words_example = [w for w in words_example if w["text"].strip() != ""]
if len(words_example) == 0:
continue
if label == "other":
for w in words_example:
tokens.append(w["text"])
ner_tags.append("O")
bboxes.append(normalize_bbox(w["box"], size))
else:
if len(words_example) == 1:
tokens.append(words_example[0]["text"])
ner_tags.append("S-" + label.upper())
bboxes.append(normalize_bbox(words_example[0]["box"], size))
else:
tokens.append(words_example[0]["text"])
ner_tags.append("B-" + label.upper())
bboxes.append(normalize_bbox(words_example[0]["box"], size))
for w in words_example[1:]:
tokens.append(w["text"])
ner_tags.append("I-" + label.upper())
bboxes.append(normalize_bbox(w["box"], size))
tokens.append(words_example[-1]["text"])
ner_tags.append("E-" + label.upper())
bboxes.append(normalize_bbox(words_example[-1]["box"], size))
cur_line_bboxes = self.get_line_bbox(cur_line_bboxes)
bboxes.extend(cur_line_bboxes)
yield guid, {"id": str(guid), "tokens": tokens, "bboxes": bboxes, "ner_tags": ner_tags,
"image": image}
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