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
File size: 799 Bytes
b2f3bf4 b0140c7 b2f3bf4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | [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"]
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