README.md

metadata

title: Jevhakim
emoji: ⚖️
colorFrom: green
colorTo: gray
sdk: gradio
sdk_version: 6.28.0
python_version: '3.12'
app_file: app.py
short_description: Typed decisions for Arabic text, MSA and dialects
startup_duration_timeout: 1h
license: cc-by-nc-sa-4.0
models:
  - TigreGotico/jevhakim-4b
  - TigreGotico/jevhakim-0.8b
  - Qwen/Qwen3.5-0.8B-Base
  - Qwen/Qwen3.5-4B-Base
datasets:
  - TigreGotico/jevhakim-decisions
tags:
  - decision-model
  - arabic
  - arabic-dialects

Jevhakim

Jevhakim is a decision model for Arabic, both Modern Standard Arabic and the dialects. It reads a state, which is a text or a JSON object, and a set of typed questions about it. It returns a probability for each option of each question in one forward pass. It does not generate text.

This Space runs two models on ZeroGPU, in bf16. Pick one with the model selector. Jevhakim-4B is more accurate, and Jevhakim-0.8B is smaller. Both are Kev fine-tuned on the TigreGotico/jevhakim-decisions dataset.

Request shape

A request follows Kev's System One format (POST /v1/systemone). Keys are English code identifiers: the state's field names, the question ids and the option names. Values are Arabic.

{
  "state": {"message": "اتخصم مني فلوس الباقة مرتين الشهر ده، وعايز الفلوس ترجعلي."},
  "questions": {
    "category": {"type": "choice", "instructions": "إلى أي قسم ينتمي هذا الطلب؟",
                 "criteria": {"billing_payment": "الفواتير والدفع", "home_internet": "الإنترنت المنزلي", "devices": "الأجهزة"}},
    "needs_human": {"type": "noul", "instructions": "هل يحتاج هذا الطلب إلى موظف بشري؟"},
    "urgency": {"type": "score", "instructions": "كم هو ملحّ طلب العميل؟",
                "criteria": ["غير مستعجل", "مستعجل قليلًا", "مستعجل", "عاجل جدًا"]}
  }
}

There are three question types. A choice question picks one of its named options, and each option can have a description. A noul question is yes or no, and the answer is p(yes). A score question picks a level on an ordered list, and the answer is the expected level.

The Questions tab builds a request from a form. The Raw JSON tab takes a raw JSON request and shows the raw JSON answer. The same two functions are on the Gradio API as /decide and /systemone.

Measured results

On the test split of 141,217 questions, Jevhakim-4B has accuracy 0.765, ECE 0.061 and Brier score 0.328. Jevhakim-0.8B has accuracy 0.661, ECE 0.063 and Brier score 0.438. Stock Kev-0.8B has accuracy 0.503. On the evaluation-only sources, which give no training rows, the accuracy is 0.727 for Jevhakim-4B, 0.585 for Jevhakim-0.8B and 0.490 for stock Kev-0.8B.

How it runs

The Space installs Kev from GitHub at commit 1f04cfa28e6a708485b28a9585010b9e436b776e and loads the model with Kev's own loader, as python -m kev.serve --run TigreGotico/jevhakim-0.8b does. The probabilities use the calibration temperature stored in the model's head.pt.

JEVHAKIM_MODELS names the models to serve, for example JEVHAKIM_MODELS=Jevhakim-0.8B. Off ZeroGPU, JEVHAKIM_DEVICE_4B and JEVHAKIM_DEVICE_08B place each model, for example JEVHAKIM_DEVICE_4B=cpu.

Licence and limits

The models and the dataset are under CC BY-NC-SA 4.0, for non-commercial research only. Several training sources are research-only. The instruction wording is model-written Modern Standard Arabic that no native speaker has reviewed. Do not use the model for decisions with legal, medical, financial or employment consequences without human review.