Update dataset card for CRAG-MM multi-turn v0.1.2 (master)

README.md

@@ -1,85 +1,5 @@
 ---
-configs:
-- config_name: default
-  data_files:
-  - split: validation
-    path: data/validation-*
-  - split: public_test
-    path: data/public_test-*
-dataset_info:
-  features:
-  - name: session_id
-    dtype: string
-  - name: image
-    dtype: image
-  - name: image_url
-    dtype: string
-  - name: turns
-    sequence:
-    - name: interaction_id
-      dtype: string
-    - name: domain
-      dtype:
-        class_label:
-          names:
-            '0': animal
-            '1': other
-            '2': shopping
-            '3': book
-            '4': general object recognition
-            '5': local
-            '6': vehicle
-            '7': plants and gardening
-            '8': style and fashion
-            '9': food
-            '10': text understanding
-            '11': sports and games
-            '12': math and science
-    - name: query_category
-      dtype:
-        class_label:
-          names:
-            '0': reasoning
-            '1': simple-knowledge
-            '2': comparison
-            '3': simple-recognition
-            '4': multi-hop
-            '5': aggregation
-    - name: dynamism
-      dtype:
-        class_label:
-          names:
-            '0': fast-changing
-            '1': static
-            '2': slow-changing
-            '3': real-time
-    - name: query
-      dtype: string
-    - name: image_quality
-      dtype:
-        class_label:
-          names:
-            '0': rotated
-            '1': truncated
-            '2': occluded
-            '3': low light
-            '4': blurred
-            '5': normal
-  - name: answers
-    sequence:
-    - name: interaction_id
-      dtype: string
-    - name: ans_full
-      dtype: string
-  splits:
-  - name: validation
-    num_bytes: 867851844.0
-    num_examples: 586
-  - name: public_test
-    num_bytes: 813099225.0
-    num_examples: 587
-  download_size: 1677258759
-  dataset_size: 1680951069.0
+{}
 ---
 # CRAG-MM: Comprehensive multi-modal, multi-turn RAG Benchmark 
 
@@ -111,10 +31,10 @@ You can easily load and explore the dataset using the Hugging Face `datasets` li
 from datasets import load_dataset
 
 # For single-turn dataset
-dataset = load_dataset("crag-mm-2025/crag-mm-single_turn-public", revision="v0.1.1")
+dataset = load_dataset("crag-mm-2025/crag-mm-single_turn-public", revision="v0.1.2")
 
 # For multi-turn dataset
-dataset = load_dataset("crag-mm-2025/crag-mm-multi_turn-public", revision="v0.1.1")
+dataset = load_dataset("crag-mm-2025/crag-mm-multi_turn-public", revision="v0.1.2")
 
 # View available splits
 print(f"Available splits: {', '.join(dataset.keys())}")
@@ -135,6 +55,29 @@ import matplotlib.pyplot as plt
 plt.imshow(example['image'])
 ```
 
+## Data Structure
+
+Each example in the dataset contains:
+
+```
+{
+  "session_id": str,          # Unique identifier for the conversation
+  "image": Image,             # Image
+  "image_url": str,           # Image URL where applicable
+  "turns": {                  # Dictionary containing conversation turn data
+    "interaction_id": [str],  # List of unique IDs for each interaction
+    "domain": [int],          # List of domain category indices
+    "query_category": [int],  # List of query category indices
+    "dynamism": [int],        # List of dynamism level indices
+    "query": [str],           # List of questions or prompts
+    "image_quality": [int]    # List of image quality indices
+  },
+  "answers": {                # Dictionary containing answer data
+    "interaction_id": [str],  # List of interaction IDs (matches turns)
+    "ans_full": [str]         # List of complete answer texts
+  }
+}
+```
 
 ## Example Visualization
 
@@ -146,11 +89,12 @@ def _prepare_feature_vocabularies(dataset_split):
     
     These vocabularies allow conversion between integer indices and string labels.
     """
+    turns_feature = dataset_split.features["turns"]
     return {
-        "domain": dataset_split.features["turns"][0]["domain"],
-        "query_category": dataset_split.features["turns"][0]["query_category"],
-        "dynamism": dataset_split.features["turns"][0]["dynamism"],
-        "image_quality": dataset_split.features["image_quality"],
+        "domain": turns_feature.feature["domain"],
+        "query_category": turns_feature.feature["query_category"],
+        "dynamism": turns_feature.feature["dynamism"],
+        "image_quality": turns_feature.feature["image_quality"],
     }
 
 
@@ -172,41 +116,48 @@ def print_conversation(example: Dict[str, Any], feature_vocabularies: Dict[str,
     When the actual image cannot be included, only the image_url will be available.
     The evaluation servers will nevertheless always include the loaded 'image' field.
     """
-    image_quality_str = feature_vocabularies["image_quality"].int2str(example['image_quality'])
-    print(f"Image Quality: {image_quality_str}")
     
-    # Determine if single-turn or multi-turn
-    is_single_turn = len(example['turns']) == 1
-    print(f"Type: {'Single-turn' if is_single_turn else 'Multi-turn'} ({len(example['turns'])} turns)")
+    # Determine if single-turn or multi-turn based on number of queries
+    num_turns = len(example['turns']['query'])
+    is_single_turn = num_turns == 1
+    print(f"Type: {'Single-turn' if is_single_turn else 'Multi-turn'} ({num_turns} turns)")
     
     # Create answer lookup dictionary if answers exist
     answer_lookup = {}
     if 'answers' in example and example['answers'] is not None:
-        answer_lookup = {a["interaction_id"]: a["ans_full"] for a in example["answers"]}
+        answer_lookup = {
+            interaction_id: ans_full 
+            for interaction_id, ans_full in zip(
+                example['answers']['interaction_id'], 
+                example['answers']['ans_full']
+            )
+        }
     
     # Print each turn
     print("\nConversation:")
-    for i, turn in enumerate(example['turns']):
+    for i in range(num_turns):
         # For multi-turn, show turn number
         if not is_single_turn:
             print(f"\tTurn {i+1}:")
         
         # Convert metadata to string representations
-        domain_str = feature_vocabularies["domain"].int2str(turn['domain'])
-        category_str = feature_vocabularies["query_category"].int2str(turn['query_category'])
-        dynamism_str = feature_vocabularies["dynamism"].int2str(turn['dynamism'])
+        domain_str = feature_vocabularies["domain"].int2str(example['turns']['domain'][i])
+        category_str = feature_vocabularies["query_category"].int2str(example['turns']['query_category'][i])
+        dynamism_str = feature_vocabularies["dynamism"].int2str(example['turns']['dynamism'][i])
+        quality_str = feature_vocabularies["image_quality"].int2str(example['turns']['image_quality'][i])
         
         # Print metadata
         prefix = "\t\t" if not is_single_turn else "\t"
-        print(f"{prefix}Domain: {domain_str} | Category: {category_str} | Dynamism: {dynamism_str}")
+        print(f"{prefix}Domain: {domain_str} | Category: {category_str} | Dynamism: {dynamism_str} | Image Quality: {quality_str}")
         
         # Print query and answer with fixed tab indentation
-        print(f"{prefix}Q: {turn['query']}")
+        print(f"{prefix}Q: {example['turns']['query'][i]}")
         
-        ans = answer_lookup.get(turn['interaction_id'], "No answer available")
+        interaction_id = example['turns']['interaction_id'][i]
+        ans = answer_lookup.get(interaction_id, "No answer available")
         print(f"{prefix}A: {ans}")
         
-        if not is_single_turn and i < len(example['turns']) - 1:
+        if not is_single_turn and i < num_turns - 1:
             print()  # Add blank line between turns in multi-turn conversations
     
     print("\n" + "-" * 60 + "\n")  # Add separator between examples
@@ -221,66 +172,12 @@ print_conversation(dataset[split_to_use][0], feature_vocabularies)
 
 The dataset includes the following splits:
 - `validation`: A small subset for quick testing and exploration
+- `public_test`: The test split used in Round 1 of the Meta CRAG 2025 Challenge. 
 - Additional splits may be available depending on the specific version
 
 ## Versions
 
-The dataset is versioned using the `revision` parameter. Latest version: `v0.1.1`
-
-## Dataset Structure
-
-### Single-Turn Format
-```json
-{
-  "session_id": "string",
-  "image": Image(),
-  "image_url": "string",
-  "image_quality": "string",
-  "turns": [
-    {
-      "interaction_id": "string",
-      "domain": "string",
-      "query_category": "string",
-      "dynamism": "string",
-      "query": "string",
-    }
-  ],
-  "answers": [
-    {
-      "interaction_id": "string",
-      "ans_full": "string"
-    }
-  ]
-}
-```
-
-### Multi-Turn Format
-```json
-{
-  "session_id": "string",
-  "image": Image(),
-  "image_url": "string",
-  "image_quality": "string",
-  "turns": [
-    {
-      "interaction_id": "string",
-      "domain": "string", 
-      "query_category": "string",
-      "dynamism": "string",
-      "query": "string",
-    },
-    ...
-  ],
-  "answers": [
-    {
-      "interaction_id": "string",
-      "ans_full": "string"
-    },
-    ...
-  ]
-}
-```
-
+The dataset is versioned using the `revision` parameter. Latest version: `v0.1.2`
 
 ## Citation