Text Classification
Transformers
Safetensors
English
qwen3_5_text
text-generation
system-one
typed-decisions
decision-model
calibrated-probabilities
knowledge-distillation
jev
noul
choice
score
lora
qwen3_5
dual-head
Eval Results (legacy)
Instructions to use autotrust/JEV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autotrust/JEV with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autotrust/JEV")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("autotrust/JEV") model = AutoModelForCausalLM.from_pretrained("autotrust/JEV", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| [build-system] | |
| requires = ["setuptools>=68", "wheel"] | |
| build-backend = "setuptools.build_meta" | |
| [project] | |
| name = "jev-judge" | |
| version = "0.8.0" | |
| description = "Jev-API compatible typed-decision judge distilled onto Qwen3.5/3.8 (qwen3_5 code path)" | |
| requires-python = ">=3.11" | |
| license = { text = "Apache-2.0" } | |
| dependencies = [ | |
| "torch>=2.13", | |
| "transformers>=5.16", | |
| "peft>=0.21", | |
| "safetensors>=0.4", | |
| "pyarrow>=15", | |
| "pandas>=2.0", | |
| "numpy>=1.26", | |
| "pyyaml>=6", | |
| "fastapi>=0.110", | |
| "uvicorn>=0.29", | |
| "tqdm>=4.66", | |
| "scikit-learn>=1.4", | |
| ] | |
| [project.optional-dependencies] | |
| dev = ["pytest>=8", "httpx>=0.27"] | |
| [tool.setuptools.packages.find] | |
| where = ["src"] | |
| [tool.pytest.ini_options] | |
| testpaths = ["tests"] | |
| addopts = "-ra" | |
| markers = ["gpu: requires a CUDA device and the base model on disk"] | |