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/headline_typed.data.json from chaoliangUNSW/Jev-Style-0.8B-Decision-v3: direct link, hf CLI and curl.
- Browser
- Download file 3.82 kB
-
https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3/resolve/656ca59be52fd6100eb5dba7bc9e79cddcd09251/figures/headline_typed.data.json
- Command line
-
hf download hf://chaoliangUNSW/Jev-Style-0.8B-Decision-v3@656ca59be52fd6100eb5dba7bc9e79cddcd09251/figures/headline_typed.data.json
-
curl -L -o headline_typed.data.json https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3/resolve/656ca59be52fd6100eb5dba7bc9e79cddcd09251/figures/headline_typed.data.json
3.82 kB
| { | |
| "figure": "headline_typed", | |
| "panels": { | |
| "accuracy_pct": [ | |
| { | |
| "label": "Jev-Style 2B v1", | |
| "entry": "typed.teacher_agreement.v1", | |
| "raw": 0.5335, | |
| "plotted": 53.4, | |
| "source": "https://huggingface.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2/raw/main/README.md", | |
| "field": "card text: 'The separate typed-decisions group contains 2,000 teacher-reference decisions from 400 states. Teacher agreement is 53.35% for v1, 37.55% for English Laya and 73.45% for v2'" | |
| }, | |
| { | |
| "label": "Jev", | |
| "entry": "typed.accuracy.jev", | |
| "raw": 0.727, | |
| "plotted": 72.7, | |
| "source": "docs/round2_audit/track_jev_results.json", | |
| "field": "string 'Jev typed-decisions (zero-shot)': '0.727 acc; KL 1.442; Brier 0.148; ECE 0.144; soft-acc 0.580' (from LocalLLaMA/typed-decisions dataset card / Laya HF card)" | |
| }, | |
| { | |
| "label": "Jev-Style 2B v2", | |
| "entry": "typed.teacher_agreement.v2", | |
| "raw": 0.7345, | |
| "plotted": 73.5, | |
| "source": "https://huggingface.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2/raw/main/README.md", | |
| "field": "card text: 'The separate typed-decisions group contains 2,000 teacher-reference decisions from 400 states. Teacher agreement is 53.35% for v1, 37.55% for English Laya and 73.45% for v2'" | |
| }, | |
| { | |
| "label": "Laya (typed ckpt)", | |
| "entry": "typed.accuracy.laya_typed", | |
| "raw": 0.766, | |
| "plotted": 76.6, | |
| "source": "runs/macjev/report_r2/scoreboard/scoreboard.json", | |
| "field": "metrics['typed.accuracy'].laya.typed.value" | |
| }, | |
| { | |
| "label": "Jev-Style 0.8B v3", | |
| "entry": "typed.accuracy.v3", | |
| "raw": 0.7915, | |
| "plotted": 79.2, | |
| "source": "runs/macjev/report_r2/scoreboard/scoreboard.json", | |
| "field": "metrics['typed.accuracy'].ours_value" | |
| } | |
| ], | |
| "brier_vs_soft": [ | |
| { | |
| "label": "Jev", | |
| "entry": "typed.Brier_vs_soft_labels.jev", | |
| "raw": 0.148, | |
| "plotted": 0.148, | |
| "source": "docs/round2_audit/track_jev_results.json", | |
| "field": "string 'Jev typed-decisions (zero-shot)': '0.727 acc; KL 1.442; Brier 0.148; ECE 0.144; soft-acc 0.580' (from LocalLLaMA/typed-decisions dataset card / Laya HF card)" | |
| }, | |
| { | |
| "label": "Laya (typed ckpt)", | |
| "entry": "typed.brier_vs_soft.laya_typed", | |
| "raw": 0.06146485330135357, | |
| "plotted": 0.061, | |
| "source": "runs/macjev/report_r2/scoreboard/scoreboard.json", | |
| "field": "metrics['typed.brier_vs_soft'].laya.typed.value" | |
| }, | |
| { | |
| "label": "Jev-Style 0.8B v3", | |
| "entry": "typed.brier_vs_soft.v3", | |
| "raw": 0.04583493309263009, | |
| "plotted": 0.046, | |
| "source": "runs/macjev/report_r2/scoreboard/scoreboard.json", | |
| "field": "metrics['typed.brier_vs_soft'].ours_value" | |
| } | |
| ] | |
| }, | |
| "annotations": { | |
| "delta_vs_jev_pts": 6.4, | |
| "delta_vs_v2_pts": 5.7, | |
| "delta_vs_laya_typed_pts": 2.6, | |
| "brier_ratio_jev_over_v3": 3.229, | |
| "brier_pct_lower_than_laya_typed": 25.4, | |
| "v3_acc_ci95_wilson": [ | |
| 0.7731, | |
| 0.8087 | |
| ], | |
| "v3_minus_laya_typed_paired_ci95": [ | |
| 0.010499999999999954, | |
| 0.04249999999999998 | |
| ] | |
| }, | |
| "protocol": "in-domain for v3 and Laya typed; zero-shot for Jev (dataset card); 2B v1/v2 as reported on the v2 card", | |
| "footnote": "Typed-decisions test set (LocalLLaMA/typed-decisions), 2,000 decisions from 400 states. In-domain for v3 and Laya typed (both trained on its\ntrain split); zero-shot for Jev (dataset-card numbers, Jev API, all 2,000 decisions). Laya: official typed-decisions checkpoint re-run by us on\nidentical rows with its shipped temperature. 2B v1/v2: teacher agreement as reported on the v2 card (same 2,000 decisions, that card's harness;\nv1 was not trained on typed decisions, v2's pool included typed workflow decisions). v3 95% CI 77.3\u201380.9% (Wilson);\nv3 minus Laya typed, paired bootstrap 95% CI +1.0 to +4.2 pts. Plotted values: figures/headline_typed.data.json." | |
| } |