Text Classification
Transformers
Safetensors
English
qwen3_5_text
text-generation
system-one
system-two
blocks-of-experts
typed-decisions
decision-model
calibrated-probabilities
knowledge-distillation
jev
noul
choice
score
lora
qwen3_5
dual-head
vllm
multimodal
vision
computer-use
robotics
Eval Results (legacy)
Instructions to use autotrust/JEV-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autotrust/JEV-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autotrust/JEV-9B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("autotrust/JEV-9B") model = AutoModelForCausalLM.from_pretrained("autotrust/JEV-9B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download reports/eval_v08_bundle.md from autotrust/JEV-9B: direct link, hf CLI and curl.
- Browser
- Download file 3.77 kB
-
https://huggingface.co/autotrust/JEV-9B/resolve/main/reports/eval_v08_bundle.md
- Command line
-
hf download hf://autotrust/JEV-9B/reports/eval_v08_bundle.md
-
curl -L -o eval_v08_bundle.md https://huggingface.co/autotrust/JEV-9B/resolve/main/reports/eval_v08_bundle.md
3.77 kB
| # Evaluation — export exports/jev-judge-qwen35-9b-v0.8 | |
| base: `/root/models/Qwen3.5-9B` · temperature: yes · max_seq_len 1024 | |
| ## Acceptance (DESIGN §8.3, main = test_set_30k) | |
| **all rows** | |
| | metric | target | value | B0 | pass | | |
| |---|---|---|---|---| | |
| | noul_auroc | >= 0.95 | 0.9958 | nan | ✅ | | |
| | noul_brier_soft | <= 0.1 | 0.0015 | nan | ✅ | | |
| | choice_top1 | >= 0.9 | 0.8975 | nan | ❌ | | |
| | score_mae_expected | <= 0.35 | 0.1030 | nan | ✅ | | |
| | kl | <= 0.15 | 0.0211 | nan | ✅ | | |
| | ece | <= 0.03 | 0.0008 | nan | ✅ | | |
| **excluding yuri_v1 exact-uniform placeholders (D1)** | |
| | metric | target | value | B0 | pass | | |
| |---|---|---|---|---| | |
| | noul_auroc | >= 0.95 | 0.9958 | nan | ✅ | | |
| | noul_brier_soft | <= 0.1 | 0.0019 | nan | ✅ | | |
| | choice_top1 | >= 0.9 | 0.8975 | nan | ❌ | | |
| | score_mae_expected | <= 0.35 | 0.1030 | nan | ✅ | | |
| | kl | <= 0.15 | 0.0228 | nan | ✅ | | |
| | ece | <= 0.03 | 0.0010 | nan | ✅ | | |
| ## test_set_30k | |
| all rows: `n=29955 · kl=0.0211 · js=0.0054 · top1=0.9176 · ece=0.0008 · mce=0.0557 · noul_auroc=0.9958 · noul_brier_soft=0.0015 · noul_brier_hard=0.0744 · score_mae_expected=0.1030 · score_rps=0.0084 · choice_top1=0.8975 · choice_kl=0.0423` | |
| excluding yuri_v1 placeholders (2260 rows): `n=27695 · kl=0.0228 · js=0.0059 · top1=0.9176 · ece=0.0010 · mce=0.0557 · noul_auroc=0.9958 · noul_brier_soft=0.0019 · noul_brier_hard=0.0744 · score_mae_expected=0.1030 · score_rps=0.0084 · choice_top1=0.8975 · choice_kl=0.0423` | |
| throughput: 43375 tok/s · 406.7 rows/s | |
| ### test_set_30k by kind | |
| | kind | n | kl | top1 | ece | noul_auroc | noul_brier_soft | score_mae_expected | choice_top1 | | |
| |---|---|---|---|---|---|---|---|---| | |
| | choice | 9199 | 0.042 | 0.897 | 0.002 | — | — | — | 0.897 | | |
| | noul | 12229 | 0.004 | 0.967 | 0.002 | 0.996 | 0.002 | — | — | | |
| | score | 8527 | 0.023 | 0.882 | 0.002 | — | — | 0.103 | — | | |
| ### test_set_30k by source × kind | |
| | source | kind | n | kl | top1 | ece | noul_auroc | noul_brier_soft | score_mae_expected | choice_top1 | | |
| |---|---|---|---|---|---|---|---|---|---| | |
| | openjev_v2 | choice | 887 | 0.176 | 0.858 | 0.021 | — | — | — | 0.858 | | |
| | openjev_v2 | noul | 1432 | 0.004 | 0.997 | 0.003 | 1.000 | 0.001 | — | — | | |
| | yuri_v1 | noul | 2260 | 0.000 | — | 0.005 | — | 0.000 | — | — | | |
| | yuri_v3 | choice | 8312 | 0.028 | 0.902 | 0.002 | — | — | — | 0.902 | | |
| | yuri_v3 | noul | 8537 | 0.005 | 0.962 | 0.001 | 0.994 | 0.002 | — | — | | |
| | yuri_v3 | score | 8527 | 0.023 | 0.882 | 0.002 | — | — | 0.103 | — | | |
| ### test_set_30k by family | |
| | family | n | kl | top1 | ece | noul_auroc | noul_brier_soft | score_mae_expected | choice_top1 | | |
| |---|---|---|---|---|---|---|---|---| | |
| | agent | 2583 | 0.018 | 0.915 | 0.003 | 0.996 | 0.002 | 0.104 | 0.897 | | |
| | biology | 1654 | 0.018 | 0.912 | 0.006 | 0.999 | 0.002 | 0.130 | 0.922 | | |
| | business | 2352 | 0.017 | 0.944 | 0.002 | 0.998 | 0.001 | 0.074 | 0.920 | | |
| | chemistry | 1333 | 0.015 | 0.932 | 0.003 | 0.997 | 0.002 | 0.099 | 0.939 | | |
| | genomics | 1914 | 0.019 | 0.907 | 0.003 | 0.995 | 0.002 | 0.104 | 0.892 | | |
| | knowledge | 4807 | 0.011 | 0.908 | 0.004 | 0.992 | 0.001 | 0.116 | 0.893 | | |
| | medical | 2120 | 0.018 | 0.922 | 0.005 | 0.996 | 0.002 | 0.080 | 0.891 | | |
| | openjev | 2319 | 0.070 | 0.945 | 0.008 | 1.000 | 0.001 | — | 0.858 | | |
| | physics | 1656 | 0.018 | 0.896 | 0.002 | 0.994 | 0.002 | 0.101 | 0.875 | | |
| | science | 1667 | 0.019 | 0.902 | 0.006 | 0.992 | 0.002 | 0.118 | 0.913 | | |
| | spatial | 1647 | 0.019 | 0.903 | 0.005 | 0.989 | 0.002 | 0.102 | 0.874 | | |
| | structured | 1668 | 0.020 | 0.907 | 0.005 | 0.988 | 0.003 | 0.135 | 0.899 | | |
| | technical | 2581 | 0.019 | 0.925 | 0.003 | 0.994 | 0.002 | 0.077 | 0.896 | | |
| | theology | 1654 | 0.018 | 0.913 | 0.003 | 0.995 | 0.002 | 0.124 | 0.926 | | |