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