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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`
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