| import os |
| import json |
| import datasets |
|
|
| logger = datasets.logging.get_logger(__name__) |
|
|
| _CITATION = """\ |
| @dataset{gotthatdata_stargate_2024, |
| title = {STARGATE: CIA Remote Viewing Archive}, |
| author = {GotThatData}, |
| year = {2024}, |
| url = {https://huggingface.co/datasets/GotThatData/STARGATE} |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| STARGATE is a dataset of 12,000+ declassified CIA PDFs related to remote viewing (RV), extrasensory perception (ESP), and anomalous cognition. |
| This loader includes structured metadata and binary access to the original scanned PDFs. |
| """ |
|
|
| _HOMEPAGE = "https://huggingface.co/datasets/GotThatData/STARGATE" |
|
|
| _LICENSE = "CC-BY-4.0" |
|
|
| class StargatePDFConfig(datasets.BuilderConfig): |
| def __init__(self, **kwargs): |
| super(StargatePDFConfig, self).__init__(**kwargs) |
|
|
| class StargatePDFDataset(datasets.GeneratorBasedBuilder): |
| VERSION = datasets.Version("1.0.0") |
| BUILDER_CONFIGS = [ |
| StargatePDFConfig(name="default", version=VERSION, description="STARGATE raw PDFs with metadata") |
| ] |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features({ |
| "filename": datasets.Value("string"), |
| "document_id": datasets.Value("string"), |
| "page_count": datasets.Value("int32"), |
| "image_count": datasets.Value("int32"), |
| "processed_at": datasets.Value("string"), |
| "ocr_status": datasets.Value("string"), |
| "text_extracted": datasets.Value("bool"), |
| "source": datasets.Value("string"), |
| "tags": datasets.Sequence(datasets.Value("string")), |
| "pdf": datasets.Value("binary"), |
| }), |
| supervised_keys=None, |
| homepage=_HOMEPAGE, |
| license=_LICENSE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| |
| archive_path = dl_manager.download_and_extract("./") |
| metadata_path = os.path.join(archive_path, "metadata.json") |
| data_dir = os.path.join(archive_path, "data") |
|
|
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={"metadata_path": metadata_path, "data_dir": data_dir} |
| ) |
| ] |
|
|
| def _generate_examples(self, metadata_path, data_dir): |
| logger.info(f"⏳ Loading metadata from {metadata_path}") |
| with open(metadata_path, "r", encoding="utf-8") as f: |
| records = json.load(f) |
|
|
| for idx, record in enumerate(records): |
| pdf_path = os.path.join(data_dir, record["filename"]) |
| if not os.path.isfile(pdf_path): |
| logger.warning(f"🚫 Missing PDF: {pdf_path}") |
| continue |
|
|
| with open(pdf_path, "rb") as pdf_file: |
| yield idx, { |
| "filename": record.get("filename"), |
| "document_id": record.get("document_id", record["filename"].replace(".pdf", "")), |
| "page_count": record.get("page_count", 0), |
| "image_count": record.get("image_count", 0), |
| "processed_at": record.get("processed_at", ""), |
| "ocr_status": record.get("ocr_status", "pending"), |
| "text_extracted": record.get("text_extracted", False), |
| "source": record.get("source", "CIA Stargate Archive"), |
| "tags": record.get("tags", []), |
| "pdf": pdf_file.read(), |
| } |
|
|
