First version of the AmericanStories dataset.

.gitattributes

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 *.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

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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
+                            }

README.md

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+---
+license: cc
+---