Datasets:
|
Download README.md from KKcoding2025/evo-eval_data: direct link, hf CLI and curl.
- Browser
- Download file 1.93 kB
-
https://huggingface.co/datasets/KKcoding2025/evo-eval_data/resolve/main/README.md
- Command line
-
hf download hf://datasets/KKcoding2025/evo-eval_data/README.md
-
curl -L -o README.md https://huggingface.co/datasets/KKcoding2025/evo-eval_data/resolve/main/README.md
1.93 kB
| license: other | |
| task_categories: | |
| - question-answering | |
| language: | |
| - en | |
| tags: | |
| - data-science-agents | |
| - benchmark | |
| - agent-evaluation | |
| size_categories: | |
| - 100<n<1K | |
| configs: | |
| - config_name: krama_full | |
| data_dir: . | |
| # KramaBench (evo-eval `krama_full`) | |
| End-to-end data-science agent benchmark, packaged for the [evo-eval](https://gitcode.com/datagallery/evo-eval) evaluation framework (`evo_eval.dataset.v1` generic dataset schema). | |
| Source: KramaBench (Lai et al., 2025), https://github.com/mitdbg/kramabench. If you use this data, please cite the upstream preprint: | |
| ```bibtex | |
| @misc{lai2025KramaBench, | |
| title = {KramaBench: Evaluating End-to-End Data-Science Agents}, | |
| author = {Eugenie Lai and Gerardo Vitagliano and Ziyu Zhang and *et al.*}, | |
| year = {2025}, | |
| } | |
| ``` | |
| ## Layout (repo root = evo-eval `DATA_ROOT`) | |
| - `krama_full/tasks.jsonl` — 104 top-level tasks, integer ids `1..104` (one per line: `task_id`, `query.text`, `assets_dir`, `meta`) | |
| - `krama_full/references/{task_id}.json` — golden answers with `answer_type` (`numeric_exact` / `string_exact` / `list_exact` / `numeric_approximate` / `list_approximate` / `string_approximate`) | |
| - `krama_full/assets/{domain}/` — per-domain input data (copied from upstream `data/{domain}/input`) | |
| - `krama_full/subtasks/` — 631 per-step subtasks (`N-K` ids derived from parent task `N`), for intermediate-step diagnostics; not part of the official top-level metric | |
| ## Notes | |
| - Original KramaBench task ids (e.g. `legal-hard-1`) are preserved in `meta.source_task_id` (tasks) and `meta.source_subtask_id` (subtasks). | |
| - Deterministic mapping: top-level tasks sorted by `(domain, source_task_id)`, then numbered `1..104`; subtask `K` of task `N` gets id `N-K`. | |
| - Scoring (official metric dispatch by `answer_type` → success / f1 / f1_approximate / rae_score / llm_paraphrase) ships with the evo-eval dataset template `configs/datasets/krama_full/`, not with this data repo. | |