# AI use cases for Open Jev Verdict 2 0 (Heman10x-NGU/openJev-verdict-2.0)
Repo: [Heman10x-NGU/openJev-verdict-2.0](https://github.com/Heman10x-NGU/openJev-verdict-2.0) | Use cases: 3 | Page: https://dedupedai.com/use-cases/heman10x-ngu-openjev-verdict-2-0/
A 149.6M parameter non-autoregressive decision model that classifies typed enterprise decisions (Choice, Score, Noul) and returns calibrated confidence estimates.
This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.
- Auto-approve confident decisions and escalate the rest: https://dedupedai.com/use-cases/heman10x-ngu-openjev-verdict-2-0/gate-typed-decisions-by-confidence/
- Test typed decisions in the browser with no backend: https://dedupedai.com/use-cases/heman10x-ngu-openjev-verdict-2-0/demo-typed-decisions-in-browser/
- Measure how often answers flip when options are reordered: https://dedupedai.com/use-cases/heman10x-ngu-openjev-verdict-2-0/check-decision-order-robustness/
## How to auto-approve confident decisions and escalate the rest with Open Jev Verdict 2 0
Repo: [Heman10x-NGU/openJev-verdict-2.0](https://github.com/Heman10x-NGU/openJev-verdict-2.0) | Difficulty: Advanced | Time: 1 hour | Basis: From the README
Use calibrated confidence to automate high-confidence typed decisions and route unclear ones to people.
**Who it is for:** An operations or platform engineer who handles a high volume of support, security, or invoice decisions and wants fewer manual reviews without silent errors.
**The problem:** Automated classifiers either act on every answer, including wrong ones, or send everything to humans. Without calibrated confidence there is no reliable threshold.
**The 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
1. 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)
2. 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.
3. 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)
4. Pick a confidence threshold from the coverage table, for example 80% coverage where accuracy is 83.44% on the held-out test set.
5. Wire the threshold into your workflow so decisions above it are applied automatically and the rest are queued for a human reviewer.
### Prompt to paste into your agent
```text
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 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.
```
### 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.
Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/
URL: https://dedupedai.com/use-cases/heman10x-ngu-openjev-verdict-2-0/gate-typed-decisions-by-confidence/
## How to test typed decisions in the browser with no backend with Open Jev Verdict 2 0
Repo: [Heman10x-NGU/openJev-verdict-2.0](https://github.com/Heman10x-NGU/openJev-verdict-2.0) | Difficulty: Intermediate | Time: 15 minutes | Basis: From the README
Run the WebGPU playground to try customer service, security, and invoice decisions locally.
**Who it is for:** A product manager or engineer who wants to see how the model handles real-looking workflow decisions before committing to an integration.
**The problem:** Evaluating a classifier usually requires setting up a serving stack, which slows down a quick feasibility check.
**The outcome:** A local browser page where you enter sample customer service, security, or invoice decisions and see the model's output.
### Steps
1. Open a terminal in the cloned repository folder.
2. Run `cd webgpu-demo`.
3. Run `python3 -m http.server 8080`.
4. Open `http://localhost:8080` in Chrome or Edge.
5. Enter sample customer service, security, or invoice decisions and review the outputs.
### Prompt to paste into your agent
```text
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 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.
```
### Caveat
The README only says to use Chrome or Edge for the playground, so other browsers may not run it and the README does not describe its output format.
Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/
URL: https://dedupedai.com/use-cases/heman10x-ngu-openjev-verdict-2-0/demo-typed-decisions-in-browser/
## How to measure how often answers flip when options are reordered with Open Jev Verdict 2 0
Repo: [Heman10x-NGU/openJev-verdict-2.0](https://github.com/Heman10x-NGU/openJev-verdict-2.0) | Difficulty: Advanced | Time: 1 hour | Basis: From the README
Test whether a decision model gives the same answer when option order changes.
**Who it is for:** A machine learning engineer who must justify a decision classifier to a risk or compliance team and needs evidence that prompt order does not drive results.
**The problem:** Autoregressive models can change answers when option positions are shuffled, and a team needs a measured flip rate before trusting the output.
**The outcome:** A flip rate for your own shuffled option sets, compared against the 4.76% flip rate and the 7.41% flip rate for Kev-0.5B that the README reports.
### Steps
1. Clone the repository with `git clone https://github.com/Heman10x-NGU/openJev-verdict-2.0`. (inferred, check the README)
2. Read the Symmetric Permutation-KL section and the flip rate definitions in the README.
3. Build a set of decisions where you shuffle the option order for each item, keeping the same correct answer.
4. Run `python verdict2/evaluate.py` and collect the predicted option for each shuffled version. (inferred, check the README)
5. Count the items where the chosen option changes after reordering and divide by the total number of items.
### Prompt to paste into your agent
```text
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 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.
```
### Caveat
The README reports the flip rate on its own 2,918 perturbed decisions, so your own set may give different numbers and the flip rate does not measure accuracy.
Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/
URL: https://dedupedai.com/use-cases/heman10x-ngu-openjev-verdict-2-0/check-decision-order-robustness/