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README.md

@@ -1,48 +1,13 @@
 ---
-dataset_info:
-  features:
-  - name: task_id
-    dtype: string
-  - name: question
-    dtype: string
-  - name: answer
-    dtype: string
-  - name: reward_mode
-    dtype: string
-  - name: atol
-    dtype: float64
-  - name: rtol
-    dtype: float64
-  - name: difficulty_level
-    dtype: int64
-  - name: difficulty_tier
-    dtype: string
-  - name: kaggle_dataset
-    dtype: string
-  - name: hf_bucket
-    dtype: string
-  - name: bucket_prefix
-    dtype: string
-  - name: files
-    list: string
-  - name: instruction
-    dtype: string
-  - name: source_row_id
-    dtype: string
-  - name: package_tier
-    dtype: int64
-  splits:
-  - name: train
-    num_bytes: 6058592
-    num_examples: 5000
-  - name: test
-    num_bytes: 304849
-    num_examples: 250
-  - name: eval
-    num_bytes: 174695
-    num_examples: 144
-  download_size: 1228811
-  dataset_size: 6538136
+license: mit
+task_categories:
+- question-answering
+- table-question-answering
+tags:
+- data-analysis
+- agent
+- reinforcement-learning
+- code-agent
 configs:
 - config_name: default
   data_files:
@@ -53,3 +18,49 @@ configs:
   - 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`