First version of the AmericanStories dataset.

.gitattributes

@@ -53,3 +53,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
 .tar.gz filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+faro_1799.tar.gz filter=lfs diff=lfs merge=lfs -text

AmericanStories.py

@@ -0,0 +1,52 @@
+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
+ }

README.md

@@ -0,0 +1,3 @@
+---
+license: cc
+---