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README.md

@@ -1,104 +1,71 @@
 ---
 license: apache-2.0
 task_categories:
-- question-answering
-- visual-question-answering
+  - question-answering
+  - visual-question-answering
 language:
-- en
+  - en
 tags:
-- music
-- music-understanding
-- sheet-music
-- abc-notation
-- benchmark
-- multimodal
-pretty_name: 'MSU-Bench: Musical Score Understanding Benchmark'
+  - music
+  - music-understanding
+  - sheet-music
+  - abc-notation
+  - benchmark
+  - multimodal
+pretty_name: "MSU-Bench: Musical Score Understanding Benchmark"
 size_categories:
-- n<1K
+  - 1K<n<10K
 configs:
-- config_name: default
-  data_files:
-  - split: test
-    path: data/test-*
-- config_name: flat
-  data_files:
-  - split: test
-    path: flat/test-*
-- config_name: nested
-  data_files:
-  - split: test
-    path: nested/test-*
+  - config_name: nested
+    data_files:
+      - split: test
+        path: nested/test-*
+  - config_name: flat
+    data_files:
+      - split: test
+        path: flat/test-*
+    default: true
 dataset_info:
-- config_name: default
-  features:
-  - name: song_id
-    dtype: string
-  - name: abc_notation
-    dtype: string
-  - name: pdf
-    dtype: binary
-  - name: images
-    list: image
-  - name: questions
-    struct:
-    - name: level
-      list: int32
-    - name: question
-      list: string
-    - name: answer
-      list: string
-  splits:
-  - name: test
-    num_bytes: 209552791
-    num_examples: 150
-  download_size: 415977378
-  dataset_size: 209552791
-- config_name: flat
-  features:
-  - name: song_id
-    dtype: string
-  - name: abc_notation
-    dtype: string
-  - name: pdf
-    dtype: binary
-  - name: images
-    list: image
-  - name: level
-    dtype: int32
-  - name: question
-    dtype: string
-  - name: answer
-    dtype: string
-  splits:
-  - name: test
-    num_bytes: 2427705035
-    num_examples: 1800
-  download_size: 2171477267
-  dataset_size: 2427705035
-- config_name: nested
-  features:
-  - name: song_id
-    dtype: string
-  - name: abc_notation
-    dtype: string
-  - name: pdf
-    dtype: binary
-  - name: images
-    list: image
-  - name: questions
-    struct:
-    - name: level
-      list: int32
-    - name: question
-      list: string
-    - name: answer
-      list: string
-  splits:
-  - name: test
-    num_bytes: 209552791
-    num_examples: 150
-  download_size: 207988689
-  dataset_size: 209552791
+  - config_name: nested
+    features:
+      - name: song_id
+        dtype: string
+      - name: abc_notation
+        dtype: string
+      - name: pdf
+        dtype: binary
+      - name: images
+        sequence: image
+      - name: questions
+        struct:
+          - name: level
+            sequence: int32
+          - name: question
+            sequence: string
+          - name: answer
+            sequence: string
+    splits:
+      - name: test
+        num_examples: 150
+  - config_name: flat
+    features:
+      - name: song_id
+        dtype: string
+      - name: abc_notation
+        dtype: string
+      - name: pdf
+        dtype: binary
+      - name: images
+        sequence: image
+      - name: level
+        dtype: int32
+      - name: question
+        dtype: string
+      - name: answer
+        dtype: string
+    splits:
+      - name: test
+        num_examples: 1800
 ---
 
 # MSU-Bench: Musical Score Understanding Benchmark
@@ -108,8 +75,6 @@ dataset_info:
 
 **Evaluating Large Language Models' Comprehension of Complete Musical Scores**
 
-> Accepted to **ACL 2026 Main Conference**
-
 ---
 
 ## Overview
@@ -139,17 +104,35 @@ MSU-Bench is a human-curated benchmark for evaluating the musical score understa
 | **Level 3** - Chord & Harmony | Harmonic structures and progressions | Chord qualities, inversions, cadences, modulations, pedal points |
 | **Level 4** - Texture & Form | Large-scale structural analysis | Melodic motifs, thematic organisation, texture types, formal design |
 
-## Dataset Structure
+## Dataset Configs
+
+The dataset is available in two configurations:
 
-Each of the 150 samples contains:
+### `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.
 
 | Column | Type | Description |
 |--------|------|-------------|
 | `song_id` | `string` | Unique identifier derived from the score filename |
-| `abc_notation` | `string` | Full ABC notation of the score (text-based symbolic representation) |
+| `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) of the score (1-35 pages) |
-| `questions` | `struct{level, question, answer}` | 12 questions per score (3 per difficulty level) with reference answers |
+| `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.
+
+| 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) |
+| `questions` | `struct{level, question, answer}` | 12 questions (3 per difficulty level) with reference answers |
 
 ### Modalities for Evaluation
 
@@ -167,57 +150,44 @@ The benchmark covers 150 scores from the Western art music canon, spanning:
 
 ## Usage
 
-### Loading the Dataset
+### Loading the Flat Config (default)
 
 ```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
 
-# Access a sample
 sample = ds[0]
-print(sample["song_id"])        # '1._Gnomus_The_Gnome__Promenade_...'
-print(sample["abc_notation"][:200])
-print(len(sample["images"]))    # number of page images
-print(sample["questions"])      # {'level': [...], 'question': [...], 'answer': [...]}
+print(sample["song_id"])
+print(f"Level {sample['level']}: {sample['question']} -> {sample['answer']}")
 ```
 
-### Iterating Over Questions
+### Loading the Nested Config
 
 ```python
-# Flatten to one row per question for evaluation
-flat_data = []
-for sample in ds:
-    levels = sample["questions"]["level"]
-    questions = sample["questions"]["question"]
-    answers = sample["questions"]["answer"]
-    for lvl, q, a in zip(levels, questions, answers):
-        flat_data.append({
-            "song_id": sample["song_id"],
-            "level": lvl,
-            "question": q,
-            "answer": a,
-            "abc_notation": sample["abc_notation"],
-        })
-
-print(f"Total QA pairs: {len(flat_data)}")  # 1800
+# Nested config: 12 questions per score
+ds = load_dataset("Krinos/MSU-Bench", "nested", split="test")
+print(len(ds))  # 150
+
+sample = ds[0]
+for lvl, q, a in zip(
+    sample["questions"]["level"],
+    sample["questions"]["question"],
+    sample["questions"]["answer"],
+):
+    print(f"  L{lvl}: {q} -> {a}")
 ```
 
 ### Visual QA with Page Images
 
 ```python
-# Get page images for a score
 sample = ds[0]
 for i, img in enumerate(sample["images"]):
     img.save(f"page_{i}.png")
 ```
 
-## Evaluation Protocol
-
-We adopt an **LLM-as-a-judge** framework using majority voting across three models (ChatGPT-5, Claude Sonnet 4, Gemini 2.5 Pro) to evaluate semantic correctness. This approach handles equivalent musical terminology (e.g., "V-I" vs. "authentic cadence") better than strict string matching.
-
-**Inter-judge agreement:** 83.33% (95% CI: 79.44%-87.22%)
-
 ## Citation
 
 ```bibtex