| | 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(), |
| | } |
| |
|
