{
 "id": "Cyber-security-final-project/Evaluation_of_OpenSource_Models_for_PDF_Injection_Recognition",
 "owner": "Cyber-security-final-project",
 "name": "Evaluation_of_OpenSource_Models_for_PDF_Injection_Recognition",
 "downloads": 134,
 "likes": 0,
 "last_modified": "2026-08-03T16:27:39.000Z",
 "stated_licence": "mit",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "text-classification",
  "feature-extraction"
 ],
 "citation": "The real half of the EDA's Part 1 comparison is the CIC-Evasive-PDFMal2022 dataset from the Canadian\nInstitute for Cybersecurity, University of New Brunswick. Models evaluated: Nomic AI, Google,\nAlibaba, Xiaomi and Microsoft, each under its own licence.",
 "signal": "permissive",
 "reason": "The declared licence (mit) is classified as permissive.",
 "revision": "646273630823cd7543ebb6a5446e491455460d9c",
 "stated_evidence": "https://huggingface.co/datasets/Cyber-security-final-project/Evaluation_of_OpenSource_Models_for_PDF_Injection_Recognition/blob/646273630823cd7543ebb6a5446e491455460d9c/README.md",
 "file_evidence": "https://huggingface.co/datasets/Cyber-security-final-project/Evaluation_of_OpenSource_Models_for_PDF_Injection_Recognition/tree/646273630823cd7543ebb6a5446e491455460d9c",
 "card_nc": false
}