Add README.md

README.md

@@ -1,3 +1,125 @@
 ---
 license: mit
+language:
+  - en
+  - zh
+task_categories:
+  - text-generation
+tags:
+  - geometry
+  - geometric-constraint-solving
+  - reasoning
+  - synthetic-data
+  - math
+pretty_name: PyGeoX
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: gcs-sft
+    data_files: data/PyGeoX-GCS-SFT.jsonl
+  - config_name: codegen-sft
+    data_files: data/PyGeoX-CodeGen-SFT.jsonl
+  - config_name: gcs-rl
+    data_files: data/PyGeoX-GCS-RL.jsonl
+  - config_name: bench
+    data_files: data/PyGeoX-Bench.jsonl
+  - config_name: wild
+    data_files: data/PyGeoX-Wild.jsonl
 ---
+
+# PyGeoX
+
+Datasets for training and evaluating language models on **geometric constraint solving** —
+reading a natural-language description of a diagram and producing the point coordinates /
+circle radii that satisfy the stated constraints — plus a supervised set for generating
+PyGeoX scene code.
+
+## Subsets
+
+| Config | Rows | Task |
+|--------|------|------|
+| `gcs-sft` | 15,738 | SFT — solve geometry (NL → coordinates), merged master with per-variant reward labels |
+| `codegen-sft` | 46,977 | SFT — NL diagram description → PyGeoX code |
+| `gcs-rl` | 46,977 | RL — prompts for geometry constraint solving |
+| `bench` | 300 | Evaluation benchmark (self-contained) |
+| `wild` | 200 | Evaluation — real-world school-geometry questions |
+
+```python
+from datasets import load_dataset
+ds = load_dataset("rafaelcabral96/PyGeoX", "gcs-rl")
+```
+
+> **How this data is meant to be used.** Several subsets contain free-form nested objects
+> (e.g. `Objs`, `possible_solution`) whose keys vary per example. The reward / evaluation
+> harness **downloads the raw `.jsonl` files and parses them line-by-line with `json`** —
+> that always works. The Hugging Face viewer may not fully render the nested fields.
+
+## Reward ground truth (`PyGeoX-GCS-RL-Code.zip`)
+
+The problem definitions used to score RL and benchmark outputs are shipped as
+`data/PyGeoX-GCS-RL-Code.zip`. The `source_file` field of each `gcs-rl` row points into
+this folder. After downloading the repo, extract it **inside `data/`** so the relative
+paths resolve:
+
+```bash
+cd data && unzip PyGeoX-GCS-RL-Code.zip      # -> data/PyGeoX-GCS-RL-Code/<id>.json
+```
+
+All `source_file` paths are written relative to the repo root
+(`data/PyGeoX-GCS-RL-Code/<name>.json`), so they are invariant to where the repo lives.
+
+## Subset details
+
+### `gcs-sft` — geometry-solving SFT (merged master)
+One deduplicated file keyed by (problem, completion). Each row's `splits` map records, per
+training variant (`sar`, `sar_sd`, `mse`, `mse_sd`, `sparse`, plus the `full` base),
+whether that variant includes it and with what reward/weight. Reconstruct any variant:
+
+```python
+rows    = [r for r in ds if r["splits"]["mse_sd"]["in"]]
+weights = [r["splits"]["mse_sd"].get("weight", 1.0) for r in rows]
+```
+
+### `codegen-sft` — NL → PyGeoX code
+`messages` = (user: diagram description + "Please generate PyGeoX code…", assistant:
+```python … ``` block), plus `problem_id`, `source_file`, `difficulty`.
+
+### `gcs-rl` — RL prompts
+`messages` (system + user), `source_file` (→ reward ground truth), `difficulty`
+(`easy`/`medium`/`hard` for 1/2/3 primary objects).
+
+### `bench` — evaluation benchmark (self-contained)
+Each record carries everything needed to score a model inline:
+`unique_id, nl_description, pygeox_code, Objs, Rels, Points, extra_rel, possible_solution`.
+Rebuild the scene directly from a record (no file needed) and score predicted coordinates:
+
+```python
+from pygeox.synthetic.llm_client import create_scene_from_json
+scene = create_scene_from_json(domain=10, json_data=record, generate_objective_function=True)
+reward, details = scene.reward.reward_function(pred_points, pred_circles)
+```
+
+### `wild` — real-world school geometry
+200 questions from MathVerse, ZhongKaoGeo, and MathVista (`id, question, source,
+source_id`). Ground truth is executable PyGeoX `full_code`, shipped in
+`data/PyGeoX-Wild-Code.zip` (one `problem_<id>.json` per question, keyed 1:1 by `id`,
+each with `full_code` + `possible_solution`). Extract inside `data/`:
+
+```bash
+cd data && unzip PyGeoX-Wild-Code.zip    # -> data/PyGeoX-Wild-Code/problem_<id>.json
+```
+
+## Citation
+
+```bibtex
+@software{pygeox_datasets,
+  title  = {PyGeoX: Datasets for Geometric Constraint Solving},
+  author = {Rafael Cabral},
+  year   = {2026},
+  url    = {https://huggingface.co/datasets/rafaelcabral96/PyGeoX}
+}
+```
+
+## License
+
+MIT