Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

autotrust
/
JEV

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
Eval Results (legacy)
Model card Files Files and versions
xet
Community

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
JEV / code
196 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
cloudyu's picture
cloudyu
v0.8.0: continued training to 0.93 epoch with LR annealing — t30k KL 0.0276→0.0210, choice top-1 0.884→0.898, score MAE 0.119→0.103, ECE 0.0014→0.0007
b0140c7 verified about 23 hours ago
  • configs
    Upload JEV v0.7.0 (Qwen3.5-9B distilled from Jev 1.13) 1 day ago
  • reports
    v0.8.0: continued training to 0.93 epoch with LR annealing — t30k KL 0.0276→0.0210, choice top-1 0.884→0.898, score MAE 0.119→0.103, ECE 0.0014→0.0007 about 23 hours ago
  • scripts
    Upload JEV v0.7.0 (Qwen3.5-9B distilled from Jev 1.13) 1 day ago
  • src
    v0.8.0: continued training to 0.93 epoch with LR annealing — t30k KL 0.0276→0.0210, choice top-1 0.884→0.898, score MAE 0.119→0.103, ECE 0.0014→0.0007 about 23 hours ago
  • tests
    Upload JEV v0.7.0 (Qwen3.5-9B distilled from Jev 1.13) 1 day ago
  • DESIGN.md
    16.5 kB
    Upload JEV v0.7.0 (Qwen3.5-9B distilled from Jev 1.13) 1 day ago
  • README.md
    4.43 kB
    Upload JEV v0.7.0 (Qwen3.5-9B distilled from Jev 1.13) 1 day ago
  • pyproject.toml
    799 Bytes
    v0.8.0: continued training to 0.93 epoch with LR annealing — t30k KL 0.0276→0.0210, choice top-1 0.884→0.898, score MAE 0.119→0.103, ECE 0.0014→0.0007 about 23 hours ago