Update README.md

README.md

@@ -47,6 +47,14 @@ Each archive follows the ABC dataset structure: files are organized as `{modalit
 | `text_annotations.zip` | Natural language annotations describing the geometry, topology, and design intent of each model, obtained with GPT-OSS-120b. |
 | `step.zip` | STEP files with B-rep geometry. Rendered from `featurescript_fp.zip` where possible, or `featurescript_rp.zip` otherwise. |
 
+### `misc/` — Helpers
+| File | Description |
+|------|-------------|
+| `DeepCAD_train_val_test_split.json` | DeepCAD train/val/test split. |
+| `stage1_indices.json` | Models with only sketch/extrude operations used in train stage 1. |
+| `unique_models.json` | Subset of 382609 CADFS models selected by filtering out duplicate models (same FeatureScript or geometry). |
+
+
 ### `raw/` — Raw Source Data
 
 Source data used to create the processed FeatureScript representation.
@@ -63,15 +71,15 @@ Source data used to create the processed FeatureScript representation.
 
 Minimal data required to perform evaluation, without the need to download the full dataset. A `.json` metadata file and a `.zip` archive are provided for each benchmark:
 
-| Benchmark | Contents |
-|-----------|----------|
-| **CADFS** | jsonl, fs, annotations, images, step, stl |
-| **DeepCAD** | jsonl, fs, annotations, images, step, stl |
-| **CADParser** | jsonl, images, step, stl |
+| Benchmark | Contents | Description |
+|-----------|----------|-------------|
+| **CADFS** | jsonl, fs, annotations, images, step, stl | 9347 models sampled from the full CADFS dataset, excluding DeepCAD train/val/test split. |
+| **DeepCAD** | jsonl, fs, annotations, images, step, stl | 7278 models from the original DeepCAD test set. |
+| **CADParser** | jsonl, images, step, stl | 6764 models from the CADParser, the subset is selected by `index_1` in the model name. |
 
 ### `train_data/` — Training Splits
 
-Training data is split into two stages following the CADFS two-stage fine-tuning strategy, and two input modalities each. We filtered out duplicates and kept only samples whose input and output fit within a context size of 8192 tokens.
+Training data is split into two stages following the two-stage fine-tuning strategy described in the paper, and two input modalities each. We filtered out duplicates and kept only samples whose input and output fit within a context size of 8192 tokens.
 
 | File | Stage | Modality | Description |
 |------|-------|----------|-------------|