Text Classification
Transformers
Safetensors
English
Korean
qwen3_5
image-text-to-text
ztc
answer-verification
hallucination-detection
zero-token
confidence-estimation
Instructions to use FINAL-Bench/ZTC-Judge-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FINAL-Bench/ZTC-Judge-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FINAL-Bench/ZTC-Judge-9B")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("FINAL-Bench/ZTC-Judge-9B") model = AutoModelForMultimodalLM.from_pretrained("FINAL-Bench/ZTC-Judge-9B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
27 days ago
-
v2 판독기 문서화 — 미지 모델 축 추가
Verified signature0f9d0d9
28 days ago
-
정정: 폐기값(0.7364/0.7282) → 정본(0.7272/0.7289) + 정정 고지 추가
Verified signature05d154a
29 days ago
-
card: limitation — score shifts when the answering-model mixture changes (measured)
Verified signature91d9234 -
card: generate candidates and let the verifier pick (+2.31 pp); majority voting measured as a loss
Verified signature7a79bb6 -
card: JEV-compatible API, calibration, and what to do on a low score
Verified signature2da1014 -
card: ladder position, per-domain vs baseline, both probes
Verified signature91fad24