Text Classification
jev-style
Safetensors
Transformers
qwen3_5_text
text-generation
decision-model
system-one
calibration
classification
long-context
multilingual
qwen3.5
on-device
llm-routing
guardrails
Instructions to use chaoliangUNSW/Jev-Style-0.8B-Decision-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- jev-style
How to use chaoliangUNSW/Jev-Style-0.8B-Decision-v3 with jev-style:
pip install "jev-style[torch]"
from jev_style import JevStyle, noul, choice js = JevStyle.from_pretrained("chaoliangUNSW/Jev-Style-0.8B-Decision-v3") out = js.decide("I was charged twice for one order.", { "billing": noul("This message is about billing."), "team": choice("Which team should handle it?", ["billing", "shipping", "tech"]), }) print(out["answers"]["team"]["choice"]) - Transformers
How to use chaoliangUNSW/Jev-Style-0.8B-Decision-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chaoliangUNSW/Jev-Style-0.8B-Decision-v3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("chaoliangUNSW/Jev-Style-0.8B-Decision-v3") model = AutoModelForCausalLM.from_pretrained("chaoliangUNSW/Jev-Style-0.8B-Decision-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download figures/jevbench.data.json from chaoliangUNSW/Jev-Style-0.8B-Decision-v3: direct link, hf CLI and curl.
- Browser
- Download file 2.65 kB
-
https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3/resolve/main/figures/jevbench.data.json
- Command line
-
hf download hf://chaoliangUNSW/Jev-Style-0.8B-Decision-v3/figures/jevbench.data.json
-
curl -L -o jevbench.data.json https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3/resolve/main/figures/jevbench.data.json
2.65 kB
| { | |
| "chart": "jevbench", | |
| "metric": "JevBench v1.4.1 public accuracy (231 items)", | |
| "rows": [ | |
| { | |
| "label": "Jev-Style 0.8B v3", | |
| "key": "v3", | |
| "accuracy": 0.6406926406926406, | |
| "correct": 148, | |
| "n": 231, | |
| "colour": "#3456F0", | |
| "source": "runs/macjev/received/ext_evals/main/jevbench/results.json :: public_accuracy" | |
| }, | |
| { | |
| "label": "Qwen3.5-0.8B Decision Model (M. Ghafiri)", | |
| "key": "mghafiri-qwen3.5-0.8b-decision-model", | |
| "accuracy": 0.5930735930735931, | |
| "correct": 137, | |
| "n": 231, | |
| "colour": "#C4C4CB", | |
| "source": "data/external/jevbench_v1.4.1/results/v1.4.1/jevbench-v1.4.1-results.json :: systems[key=mghafiri-qwen3.5-0.8b-decision-model].public_accuracy", | |
| "v3_lead_pts": 4.76 | |
| }, | |
| { | |
| "label": "Laya (ModernBERT-large, 421M)", | |
| "key": "laya", | |
| "accuracy": 0.5844155844155844, | |
| "correct": 135, | |
| "n": 231, | |
| "colour": "#D0D0D6", | |
| "source": "data/external/jevbench_v1.4.1/results/v1.4.1/jevbench-v1.4.1-results.json :: systems[key=laya].public_accuracy", | |
| "v3_lead_pts": 5.63 | |
| }, | |
| { | |
| "label": "SimpleJev (Qwen3.5-0.8B)", | |
| "key": "simplejev-qwen3.5-0.8b", | |
| "accuracy": 0.5454545454545454, | |
| "correct": 126, | |
| "n": 231, | |
| "colour": "#C4C4CB", | |
| "source": "data/external/jevbench_v1.4.1/results/v1.4.1/jevbench-v1.4.1-results.json :: systems[key=simplejev-qwen3.5-0.8b].public_accuracy", | |
| "v3_lead_pts": 9.52 | |
| } | |
| ], | |
| "footnote": "JevBench v1.4.1, public items only (231). v3: self-run zero-shot with the vendored official harness (commit 24b9b5c); training-pool contamination scan: 0 hits; not an official leaderboard entry. Other rows: public accuracy as published in the board's v1.4.1 results file (github.com/fstandhartinger/jevbench). Shown: Laya plus every Qwen3.5-0.8B-based system on the board. Laya and M. Ghafiri lie inside v3's 95% CI (57.7\u201370.0%, Wilson): those two leads are point estimates, not significant at n = 231.", | |
| "v3_run": { | |
| "source_path": "runs/macjev/received/ext_evals/main/jevbench/results.json", | |
| "field": "public_accuracy (148/231; independent check equal)", | |
| "protocol_label": "v3 self-run with the vendored official harness on the 231 public items, zero-shot (contamination scan of the training pool: 0 hits, runs/macjev/external/jevbench/contamination.json); other rows as published in jevbench v1.4.1 results.json (full runs by the board); not an official leaderboard entry", | |
| "ci95": [ | |
| 0.577, | |
| 0.6998 | |
| ], | |
| "ci_method": "Wilson 95%" | |
| }, | |
| "board": { | |
| "source_path": "data/external/jevbench_v1.4.1/results/v1.4.1/jevbench-v1.4.1-results.json", | |
| "protocol_label": "as published by the board (commit 24b9b5c1609a, tag v1.4.1)" | |
| } | |
| } |