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What you can do with Open Jev Verdict 2 0

A 149.6M parameter non-autoregressive decision model that classifies typed enterprise decisions (Choice, Score, Noul) and returns calibrated confidence estimates.

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Heman10x-NGU/openJev-verdict-2.0 on GitHub

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What do you want to do?

3 use cases

  • Auto-approve confident decisions and escalate the rest

    Use calibrated confidence to automate high-confidence typed decisions and route unclear ones to people.

    View 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.

  • Test typed decisions in the browser with no backend

    Run the WebGPU playground to try customer service, security, and invoice decisions locally.

    View prompt
    In the repo openJev-verdict-2.0, open the webgpu-demo folder and start a local server on port 8080 with python3 -m http.server 8080. Then test three sample decisions from <sample file> in the page at http://localhost:8080. Expected output: the results for each sample, and verify that the page loads in Chrome or Edge with WebGPU enabled.

  • Measure how often answers flip when options are reordered

    Test whether a decision model gives the same answer when option order changes.

    View prompt
    In the repo openJev-verdict-2.0, read the Symmetric Permutation-KL section and the evaluation script at verdict2/evaluate.py. Write a script that shuffles option order for each decision in <decision file> and reports the percentage of answers that flip. Expected output: a flip rate percentage, and verify that the script reports the same option when the input order is unchanged.

How to use Open Jev Verdict 2 0

  1. Check you have what it needs: 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.
  2. Open Heman10x-NGU/openJev-verdict-2.0 on GitHub and read the README and license.
  3. 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)
  4. 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.
  5. 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)
  6. Pick a confidence threshold from the coverage table, for example 80% coverage where accuracy is 83.44% on the held-out test set.

These steps come from our top use case, Auto-approve confident decisions and escalate the rest, which also has a copy-paste agent prompt.

Open Jev Verdict 2 0 alternatives · Best AI repos for Work and operations

About this repo

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
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Questions about Open Jev Verdict 2 0

What is Open Jev Verdict 2 0?

A 149.6M parameter non-autoregressive decision model that classifies typed enterprise decisions (Choice, Score, Noul) and returns calibrated confidence estimates. It is an open-source project on GitHub: https://github.com/Heman10x-NGU/openJev-verdict-2.0.

Who is Open Jev Verdict 2 0 for?

Software engineers and ops teams who need a fast, calibrated classifier for typed workflow decisions and a clear rule for when to escalate to a human.

What can I do with Open Jev Verdict 2 0?

3 use cases so far, for example: Auto-approve confident decisions and escalate the rest; Test typed decisions in the browser with no backend; Measure how often answers flip when options are reordered. Each has steps and a copy-paste agent prompt.

What do I need to run Open Jev Verdict 2 0?

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.

What are alternatives to Open Jev Verdict 2 0?

Other hot AI repos in Work and operations: AI Job Search, Solidcraft, Fastbrowse, Awesome AI Agent Platforms, Nautilo.

Is Open Jev Verdict 2 0 safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.