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import json
import os
import datasets
from tqdm import tqdm
_ARTICLE_ID = "article_id"
_ARTICLE_WORDS = "article_words"
_ARTICLE_BBOXES = "article_bboxes"
_ARTICLE_NORM_BBOXES = "article_norm_bboxes"
_ABSTRACT = "abstract"
_ARTICLE_PDF_URL = "article_pdf_url"
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]),
]
class HALSummarizationConfig(datasets.BuilderConfig):
"""BuilderConfig for HALSummarization."""
def __init__(self, **kwargs):
"""BuilderConfig for ArxivSummarization.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(HALSummarizationConfig, self).__init__(**kwargs)
class HALSummarizationDataset(datasets.GeneratorBasedBuilder):
"""HALSummarization Dataset."""
_TRAIN_ARCHIVE = "train.tar.gz"
_VAL_ARCHIVE = "val.tar.gz"
_TEST_ARCHIVE = "test.tar.gz"
_TRAIN_ABSTRACTS = "train.txt"
_VAL_ABSTRACTS = "validation.txt"
_TEST_ABSTRACTS = "test.txt"
BUILDER_CONFIGS = [
HALSummarizationConfig(
name="hal",
version=datasets.Version("1.0.0"),
description="HAL dataset for summarization",
),
]
def _info(self):
# Should return a datasets.DatasetInfo object
return datasets.DatasetInfo(
features=datasets.Features(
{
_ARTICLE_ID: datasets.Value("string"),
_ARTICLE_WORDS: datasets.Sequence(datasets.Value("string")),
_ARTICLE_BBOXES: datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
_ARTICLE_NORM_BBOXES: datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
_ABSTRACT: datasets.Value("string"),
_ARTICLE_PDF_URL: datasets.Value("string"),
}
),
supervised_keys=None,
)
def _split_generators(self, dl_manager):
train_dir = os.path.join(dl_manager.download_and_extract(self._TRAIN_ARCHIVE), "train")
val_dir = os.path.join(dl_manager.download_and_extract(self._VAL_ARCHIVE), "val")
test_dir = os.path.join(dl_manager.download_and_extract(self._TEST_ARCHIVE), "test")
train_abstracts = dl_manager.download_and_extract(self._TRAIN_ABSTRACTS)
val_abstracts = dl_manager.download_and_extract(self._VAL_ABSTRACTS)
test_abstracts = dl_manager.download_and_extract(self._TEST_ABSTRACTS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"data_path": train_dir, "abstract_path": train_abstracts}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"data_path": val_dir, "abstract_path": val_abstracts}
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"data_path": test_dir, "abstract_path": test_abstracts}
),
]
def _generate_examples(self, data_path, abstract_path):
"""Generate HALSummarization examples."""
filenames = sorted(os.listdir(data_path))
guid = 0
with open(abstract_path, 'r') as abstract_file:
for line in tqdm(abstract_file, total=len(filenames), desc=f"Reading files in {data_path}"):
guid += 1
item = json.loads(line)
fname = item["id"] + ".txt"
filepath = os.path.join(data_path, fname)
words = []
bboxes = []
norm_bboxes = []
with open(filepath, encoding="utf-8") as f:
for line in f:
splits = line.split("\t")
word = splits[0]
bbox = splits[1:5]
bbox = [int(b) for b in bbox]
page_width, page_height = int(splits[5]), int(splits[6])
norm_bbox = normalize_bbox(bbox, (page_width, page_height))
words.append(word)
bboxes.append(bbox)
norm_bboxes.append(norm_bbox)
assert len(words) == len(bboxes)
assert len(bboxes) == len(norm_bboxes)
yield guid, {
_ARTICLE_ID: item["id"],
_ARTICLE_WORDS: words,
_ARTICLE_BBOXES: bboxes,
_ARTICLE_NORM_BBOXES: norm_bboxes,
_ABSTRACT: item["abstract"],
_ARTICLE_PDF_URL: item["pdf_url"],
}
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