{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "heman10x-ngu-openjev-verdict-2-0--gate-typed-decisions-by-confidence",
 "repo": "Heman10x-NGU/openJev-verdict-2.0",
 "repo_url": "https://github.com/Heman10x-NGU/openJev-verdict-2.0",
 "repo_slug": "heman10x-ngu-openjev-verdict-2-0",
 "title": "Auto-approve confident decisions and escalate the rest",
 "page_title": "How to auto-approve confident decisions and escalate the rest with Open Jev Verdict 2 0",
 "summary": "Use calibrated confidence to automate high-confidence typed decisions and route unclear ones to people.",
 "who": "An operations or platform engineer who handles a high volume of support, security, or invoice decisions and wants fewer manual reviews without silent errors.",
 "problem": "Automated classifiers either act on every answer, including wrong ones, or send everything to humans. Without calibrated confidence there is no reliable threshold.",
 "outcome": "A workflow that acts automatically above a chosen confidence threshold and sends lower-confidence cases to a human queue, using the selective classification figures in the README as a guide.",
 "steps": [
  "Clone the repository with `git clone https://github.com/Heman10x-NGU/openJev-verdict-2.0` and run `git lfs pull` so the model weights are fetched. (inferred, check the README)",
  "Load the tokenizer with `from transformers import AutoTokenizer` and `tokenizer = AutoTokenizer.from_pretrained(\"heman10x/openJev-verdict-2.0\")`, then follow the README sequence layout for your candidate options.",
  "Run `python verdict2/evaluate.py` to reproduce the test receipt and confirm the coverage and accuracy figures in `reports/verdict2_base_test.json`. (inferred, check the README)",
  "Pick a confidence threshold from the coverage table, for example 80% coverage where accuracy is 83.44% on the held-out test set.",
  "Wire the threshold into your workflow so decisions above it are applied automatically and the rest are queued for a human reviewer."
 ],
 "agent_prompt": "In the repo openJev-verdict-2.0, read reports/verdict2_base_test.json and the selective classification section of the README. Build a function that takes a decision from <input decision file> and returns either an automatic action or a human-review flag based on a confidence threshold of 0.8. Expected output: a short report listing the coverage and accuracy at that threshold, and verify that it matches the 80% coverage row of 83.44% accuracy.",
 "agents": [
  "any"
 ],
 "difficulty": "Advanced",
 "time": "1 hour",
 "basis": "readme",
 "caveat": "The README says the Verdict 2.0 weights are tracked via Git LFS pointers, and the public Hugging Face checkpoint it names belongs to the other model, so confirm you have the correct weights before relying on the reported numbers.",
 "category": "Work and operations",
 "tags": [
  "Work and operations"
 ],
 "lang": "python",
 "requirements": [
  "Python with the transformers library installed",
  "Access to the model files; the Verdict 2.0 weights are tracked via Git LFS pointers in artifacts/verdict2-base/model.pt",
  "Chrome or Edge for the in-browser WebGPU playground",
  "A GPU is optional; the README reports training on a 6 GB laptop GPU"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/heman10x-ngu-openjev-verdict-2-0/gate-typed-decisions-by-confidence/",
 "md": "https://dedupedai.com/use-cases/heman10x-ngu-openjev-verdict-2-0/gate-typed-decisions-by-confidence/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/heman10x-ngu-openjev-verdict-2-0--gate-typed-decisions-by-confidence.json",
 "api_version": "v1"
}