| --- |
| license: mit |
| task_categories: |
| - question-answering |
| - table-question-answering |
| tags: |
| - data-analysis |
| - agent |
| - reinforcement-learning |
| - code-agent |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: test |
| path: data/test-* |
| - split: eval |
| path: data/eval-* |
| --- |
| |
| # data_agent |
| |
| Plain, Harbor-free version of the data-analysis agent tasks — usable directly via `load_dataset`. |
| Splits: **train 5000**, **test 250**, **eval 144**. Deterministic grading, no LLM judge. |
|
|
| ## Columns |
| - `task_id`, `source_row_id` — ids |
| - `question` — the question to answer |
| - `answer` — gold answer; `reward_mode` (`numeric`/`exact_short`/`exact_bool`/`list`/`list_csv`/`flexible`), `atol`/`rtol` — how to grade |
| - `difficulty_level` (1-5), `difficulty_tier` (easy/medium/hard) |
| - `kaggle_dataset` — source Kaggle dataset |
| - `hf_bucket`, `bucket_prefix` — where the input files live on the HF Hub (fetch without Harbor) |
| - `files` — input filenames; `instruction` — the full agent prompt |
| - `package_tier` |
|
|
| ## Usage |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("AdithyaSK/data_agent", split="train") |
| row = ds[0] |
| print(row["question"], row["answer"], row["reward_mode"]) |
| ``` |
|
|
| ## Getting the data files (no Harbor needed) |
| Files live in the HF bucket `hf_bucket` under `bucket_prefix/`: |
| ```python |
| from huggingface_hub import HfApi |
| api = HfApi() |
| api.snapshot_download(repo_id=row["hf_bucket"], repo_type="dataset", |
| allow_patterns=f"{row['bucket_prefix']}/*", local_dir="input") |
| ``` |
|
|
| ## Grading (deterministic, no LLM) |
| Use the bundled `grader.py`: |
| ```python |
| from grader import grade |
| r = grade(row["answer"], my_prediction, reward_mode=row["reward_mode"], |
| abs_tol=row["atol"], rel_tol=row["rtol"]) |
| print(r.reward) # 1.0 if correct |
| ``` |
| Tiers: exact -> numeric(atol/rtol) -> list/percent normalization -> symbolic (math-verify). |
|
|
| ## Companion datasets |
| - Harbor task suites (to run as environments via OpenEnv): `AdithyaSK/data_agent_harbor_{train,test,eval}` |
| - SFT traces: `AdithyaSK/data_agent_harbor_train_sft` |
|
|
