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-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FINAL-Bench/ZTC-Judge-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FINAL-Bench/ZTC-Judge-4B")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("FINAL-Bench/ZTC-Judge-4B") model = AutoModelForMultimodalLM.from_pretrained("FINAL-Bench/ZTC-Judge-4B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
card: limitation — score shifts when the answering-model mixture changes (measured)
Browse files
README.md
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@@ -266,6 +266,11 @@ other systems on the independent leaderboard:
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- **Domain coverage.** Scores are meaningful only for the five domains listed. Outside them nothing
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has been measured and no guarantee is published.
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- **Below the surface baseline on disaster and safety.** Stated in the table rather than omitted.
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- **Revision lock.** The probe is fitted to one specific revision of the base model. Running it on a
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different revision produces **no error and silently wrong scores**; this repository ships the
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matching weights so that failure mode cannot occur.
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- **Domain coverage.** Scores are meaningful only for the five domains listed. Outside them nothing
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has been measured and no guarantee is published.
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- **Below the surface baseline on disaster and safety.** Stated in the table rather than omitted.
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- **Sensitive to which model wrote the answer.** The probe is fitted on answers from four models.
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Adding 1,772 answers from a single additional model shifted the mixture and **lowered** the
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size-weighted score from 0.7278 to 0.7177 — professional exams rose to 0.8575 while every other
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domain fell. Treat "works on any model's output" as a design goal, not a measured guarantee: if
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your generator differs sharply from the training mixture, measure before relying on the number.
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- **Revision lock.** The probe is fitted to one specific revision of the base model. Running it on a
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different revision produces **no error and silently wrong scores**; this repository ships the
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matching weights so that failure mode cannot occur.
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