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
| import os | |
| import sys | |
| import pytest | |
| ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) | |
| sys.path.insert(0, os.path.join(ROOT, "src")) | |
| def pytest_addoption(parser): | |
| parser.addoption("--model-path", default=os.environ.get("JEV_MODEL_PATH", "/root/models/Qwen3.5-9B")) | |
| def model_path(request): | |
| return request.config.getoption("--model-path") | |