Datasets:
license: apache-2.0
language:
- en
pretty_name: EvalSafe O*NET
tags:
- evaluation
- synthetic
- benchmark
configs:
- config_name: cases
default: true
data_files:
- split: test
path: data/cases.parquet
- config_name: questions
data_files:
- split: test
path: data/questions.parquet
- config_name: run_results
data_files:
- split: test
path: data/run_results.parquet
EvalSafe O*NET
150 documents · 7,500 consensus-labeled questions · 9 candidate models. Snapshot: 2026-09-29. Default reference: consensus.
Only questions with an available consensus target and their corresponding documents
and final model results are included. The documents are synthetic workplace examples.
The reference targets are model-generated, using Astra (gpt-6-astra) and Fable
(claude-fable-5-1). The default reference is their consensus.
Load
from datasets import load_dataset
repo = "typesafe/evalsafe-onet"
cases = load_dataset(repo, "cases", split="test")
questions = load_dataset(repo, "questions", split="test")
results = load_dataset(repo, "run_results", split="test")
Use revision="<commit SHA>" to pin the data. If access requires authentication,
sign in to Hugging Face first. All three configurations have one test split.
| Configuration | Rows | Contents |
|---|---|---|
cases |
150 | Complete documents, descriptive metadata, and question IDs |
questions |
7,500 | Question/input and independent OpenAI, Anthropic, and consensus labels |
run_results |
67,500 | Final prediction, embedded consensus target, score, cost and usage for each model/question |
Each document has 27 noul, 16 score, and 7 choice questions, totaling 4,050 noul,
2,400 score, and 1,050 choice questions. Coverage, scoring definitions, and model
summaries are recorded in dataset.json.
Join tables with case_id and question_instance_id; run_id joins model results
to the summaries in dataset.json. Fields ending in _json contain JSON-encoded
text. Every model-result row includes its reference target.
All 7,500 questions have a consensus target. Both reference sources are available for 7,468 questions; 32 have one available source.
Metrics
- Noul/choice intelligence:
1 - Jensen-Shannon divergence, using base-2 logarithms. This uses divergence, not its square root. - Score agreement:
1 - abs(model_mean - reference_mean) / rubric_range. - Overall performance: equal-weight mean of the noul, score, and choice type means. This is a composite of two metrics, not a pure JSD score.
Failed candidates score zero. The final table has 66,528/67,500 valid
candidate results. Reference models are excluded from candidate rankings.
Jev is reported as jev-1.13.0; the DeepSeek models were served by Together.
Results
Dashed frontiers connect nondominated models and do not predict intermediate configurations. Cost and latency axes are logarithmic; the chart uses all nine candidates.
| Model | Performance | Valid results | USD/1,000 questions | sec/question |
|---|---|---|---|---|
| claude-opus-5 | 0.94330 | 7,439/7,500 | $19.44560 | 3.184 |
| gpt-5.6-sol | 0.93450 | 7,438/7,500 | $5.67873 | 2.720 |
| typesafe/jev-1.13.0 | 0.93386 | 7,500/7,500 | $0.07463 | 0.141 |
| gpt-5.6-terra | 0.91759 | 7,445/7,500 | $2.72939 | 2.300 |
| claude-sonnet-5 | 0.90798 | 7,436/7,500 | $7.59240 | 2.125 |
| claude-haiku-4-5 | 0.90350 | 7,468/7,500 | $7.91799 | 12.008 |
| gpt-5.6-luna | 0.88725 | 7,423/7,500 | $0.32674 | 2.281 |
| together/deepseek-ai/DeepSeek-V4-Pro-0813 | 0.86267 | 7,245/7,500 | $5.60110 | 9.227 |
| together/deepseek-ai/DeepSeek-V4-Flash-0731 | 0.83574 | 7,134/7,500 | $0.47691 | 7.094 |
These are generated workplace documents and questions, not the original O*NET database tables.
Targets are model-generated rather than human-verified ground truth. This benchmark is not a held-out evaluation of generalization.
License
This dataset is licensed under the Apache License 2.0.