Rename configs to VFIG-Bench / VFIG-Bench-OOD (proper capitalization)

README.md

@@ -12,7 +12,7 @@ size_categories:
 - n<1K
 arxiv: 2603.24575
 dataset_info:
-- config_name: vfig-bench
+- config_name: VFIG-Bench
   features:
   - name: filename
     dtype: string
@@ -24,7 +24,7 @@ dataset_info:
     num_examples: 400
   download_size: 756845
   dataset_size: 2532483
-- config_name: vfig-bench-ood
+- config_name: VFIG-Bench-OOD
   features:
   - name: filename
     dtype: string
@@ -37,14 +37,14 @@ dataset_info:
   download_size: 23321600
   dataset_size: 23528361.0
 configs:
-- config_name: vfig-bench
+- config_name: VFIG-Bench
   data_files:
   - split: test
-    path: vfig-bench/test-*
-- config_name: vfig-bench-ood
+    path: VFIG-Bench/test-*
+- config_name: VFIG-Bench-OOD
   data_files:
   - split: test
-    path: vfig-bench-ood/test-*
+    path: VFIG-Bench-OOD/test-*
 ---
 
 # VFIG-Bench Dataset Card
@@ -53,13 +53,13 @@ configs:
 
 **VFIG-Bench** is the evaluation benchmark accompanying [VFIG-Data](https://huggingface.co/datasets/QijiaHe/VFIG-Data), used to assess vision-language models on the task of vectorizing complex academic figures into SVG. It comprises two complementary test sets:
 
-- **vfig-bench** — 400 in-distribution figure–SVG pairs (held-out from VFIG-Data).
-- **vfig-bench-ood** — 198 out-of-distribution figures (image-only, no SVG ground truth) sourced from well-known academic figures, used to probe generalization.
+- **VFIG-Bench** — 400 in-distribution figure–SVG pairs (held-out from VFIG-Data).
+- **VFIG-Bench-OOD** — 198 out-of-distribution figures (image-only, no SVG ground truth) sourced from well-known academic figures, used to probe generalization.
 
 | Config | Split | Rows | Schema |
 |---|---|---|---|
-| `vfig-bench` | test | 400 | `{filename: string, svg: string}` |
-| `vfig-bench-ood` | test | 198 | `{filename: string, image: Image}` |
+| `VFIG-Bench` | test | 400 | `{filename: string, svg: string}` |
+| `VFIG-Bench-OOD` | test | 198 | `{filename: string, image: Image}` |
 
 - **License:** ODC-BY 1.0
 - **Paper:** [VFIG: Vectorizing Complex Figures in SVG with Vision-Language Models](https://arxiv.org/abs/2603.24575)
@@ -70,17 +70,17 @@ configs:
 from datasets import load_dataset
 
 # In-distribution test (figure-SVG pairs)
-ds = load_dataset("QijiaHe/VFIG-Bench", "vfig-bench", split="test")
+ds = load_dataset("QijiaHe/VFIG-Bench", "VFIG-Bench", split="test")
 print(ds[0]["filename"], len(ds[0]["svg"]))
 
 # Out-of-distribution test (image only)
-ds_ood = load_dataset("QijiaHe/VFIG-Bench", "vfig-bench-ood", split="test")
+ds_ood = load_dataset("QijiaHe/VFIG-Bench", "VFIG-Bench-OOD", split="test")
 print(ds_ood[0]["filename"], ds_ood[0]["image"].size)
 ```
 
 ## Notes on the OOD config
 
-The `vfig-bench-ood` config contains **only PNG images** — there is no SVG ground truth. It is intended for evaluating a model's ability to vectorize unseen, real-world academic figures, judged via downstream metrics (rendering fidelity, code cleanliness, etc.) rather than direct SVG comparison.
+The `VFIG-Bench-OOD` config contains **only PNG images** — there is no SVG ground truth. It is intended for evaluating a model's ability to vectorize unseen, real-world academic figures, judged via downstream metrics (rendering fidelity, code cleanliness, etc.) rather than direct SVG comparison.
 
 ## License