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README.md

@@ -53,10 +53,6 @@ dataset_info:
         dtype: string
       - name: abc_notation
         dtype: string
-      - name: pdf
-        dtype: binary
-      - name: images
-        sequence: image
       - name: level
         dtype: int32
       - name: question
@@ -110,30 +106,30 @@ The dataset is available in two configurations:
 
 ### `flat` (default) — 1 question per row, 1,800 rows
 
-Each row contains a single question-answer pair alongside the full score data. Best for evaluation pipelines.
+Each row contains a single question-answer pair with the score's ABC notation. Best for evaluation pipelines.
 
 | Column | Type | Description |
 |--------|------|-------------|
 | `song_id` | `string` | Unique identifier derived from the score filename |
 | `abc_notation` | `string` | Full ABC notation (text-based symbolic representation) |
-| `pdf` | `binary` | The original rendered PDF of the score |
-| `images` | `list[image]` | Individual page images (PNG), 1–35 pages per score |
 | `level` | `int32` | Difficulty level (1–4) |
 | `question` | `string` | The question text |
 | `answer` | `string` | The reference answer |
 
 ### `nested` — 12 questions per score, 150 rows
 
-Each row contains one complete score with all 12 questions nested in a struct. Best for per-score analysis.
+Each row contains one complete score with all 12 questions nested, plus the PDF and page images. Best for per-score or visual analysis.
 
 | Column | Type | Description |
 |--------|------|-------------|
 | `song_id` | `string` | Unique identifier |
 | `abc_notation` | `string` | Full ABC notation |
 | `pdf` | `binary` | The original rendered PDF |
-| `images` | `list[image]` | Individual page images (PNG) |
+| `images` | `list[image]` | Individual page images (PNG), 1–35 pages per score |
 | `questions` | `struct{level, question, answer}` | 12 questions (3 per difficulty level) with reference answers |
 
+> **Note:** PDF and page images are only stored in the `nested` config to avoid duplication. Use `song_id` to join with `flat` if needed.
+
 ### Modalities for Evaluation
 
 | Modality | Input | Target Models |
@@ -155,7 +151,6 @@ The benchmark covers 150 scores from the Western art music canon, spanning:
 ```python
 from datasets import load_dataset
 
-# Default config: flat (1 question per row)
 ds = load_dataset("Krinos/MSU-Bench", split="test")
 print(len(ds))  # 1800
 
@@ -167,11 +162,10 @@ print(f"Level {sample['level']}: {sample['question']} -> {sample['answer']}")
 ### Loading the Nested Config
 
 ```python
-# Nested config: 12 questions per score
-ds = load_dataset("Krinos/MSU-Bench", "nested", split="test")
-print(len(ds))  # 150
+ds_nested = load_dataset("Krinos/MSU-Bench", "nested", split="test")
+print(len(ds_nested))  # 150
 
-sample = ds[0]
+sample = ds_nested[0]
 for lvl, q, a in zip(
     sample["questions"]["level"],
     sample["questions"]["question"],
@@ -183,11 +177,26 @@ for lvl, q, a in zip(
 ### Visual QA with Page Images
 
 ```python
-sample = ds[0]
+ds_nested = load_dataset("Krinos/MSU-Bench", "nested", split="test")
+sample = ds_nested[0]
 for i, img in enumerate(sample["images"]):
     img.save(f"page_{i}.png")
 ```
 
+### Joining Flat + Nested for Visual Evaluation
+
+```python
+ds_flat = load_dataset("Krinos/MSU-Bench", split="test")
+ds_nested = load_dataset("Krinos/MSU-Bench", "nested", split="test")
+
+# Build a lookup from song_id to images
+images_lookup = {row["song_id"]: row["images"] for row in ds_nested}
+
+# Get images for a flat row
+sample = ds_flat[0]
+images = images_lookup[sample["song_id"]]
+```
+
 ## Citation
 
 ```bibtex