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import json
import tarfile
from datasets import DatasetInfo, DatasetBuilder, DownloadManager
_CITATION = """\
Coming Soon
"""
_DESCRIPTION = """\
American Stories offers high-quality structured data from historical newspapers suitable for pre-training large language models to enhance the understanding of historical English and world knowledge. It can also be integrated into external databases of retrieval-augmented language models, enabling broader access to historical information, including interpretations of political events and intricate details about people's ancestors. Additionally, the structured article texts facilitate the application of transformer-based methods for popular tasks like detecting reproduced content, significantly improving accuracy compared to traditional OCR methods. American Stories serves as a substantial and valuable dataset for advancing multimodal layout analysis models and other multimodal applications.
"""
class YourDatasetName(DatasetBuilder):
VERSION = "0.0.1"
BUILDER_CONFIGS = [
DatasetBuilderConfig(
name="AmericanStories",
version=VERSION,
description=_DESCRIPTION,
),
]
def _info(self) -> DatasetInfo:
features = {
"feature_name": datasets.Value("string"),
# Define the other features of your dataset here
}
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(features),
citation=_CITATION,
)
def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
# No explicit splits defined
return []
def _generate_examples(self, filepath: str) -> Iterator[datasets.Example]:
with tarfile.open(filepath, "r:gz") as tar:
for member in tar.getmembers():
if member.isfile() and member.name.endswith('.json'):
file_name = os.path.basename(member.name)
with tar.extractfile(member) as f:
data = json.load(f)
for idx, example in enumerate(data):
# Process and yield each example
yield idx, {
"feature_name": example["feature_name"],
# Assign values to other features of your dataset
}
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