| --- |
| 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 |
| |
| ```python |
| 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`](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](LICENSE). |
|
|
