Add manifest (metadata.json)

metadata.json

@@ -1,66 +1,66 @@
 {
-  "name": "We-Math",
-  "release_date": "2024-07-01",
-  "subsets": {
-    "main": {
-      "language": [
-        "en"
-      ],
-      "modalities": [
-        "single_image_start"
-      ],
-      "task_type": "multiple_choice",
-      "score_type": "rule",
-      "score_protocol": {
-        "reference": "official We-Math/We-Math@README + evaluation/four_dimensional_metrics.py & evaluation/accuracy.py — 'we use string matching to directly extract answers, which eliminates the high cost of using additional models for further answer extraction' (rule-based option-letter extraction, exact compare). [Quote re-verified verbatim in the official README 2026-07-07; rule pipeline re-verified vendored at VLMEvalKit@vlmeval/dataset/utils/wemath.py:28-41 (split on 'Answer', strip, first char in A-H, compare).]",
-        "note": "Official headline is the four-dimensional metric set (IK/IG/CM/RM) plus step-wise accuracy, computed by grouping one-step sub-problems with their multi-step composite problem (problem_id/step_key, vendored in VLMEvalKit vlmeval/dataset/utils/wemath.py:16-238); a per-sample accuracy scorer will not reproduce those group scores (grouping keys ARE shipped in extra). VLMEvalKit grades WeMath via mcq_vanilla_eval (image_mcq.py WeMath.evaluate -> utils/multiple_choice.py mcq_vanilla_eval, rule can_infer + GPT extraction fallback), i.e. llm_extract — disagrees with the official rule-only protocol; official preferred."
-      },
-      "prompt_template": "<image>{{ question }}\nOptions:\n{% for k, v in options.items() %}{{ k }}. {{ v }}{% if not loop.last %}\n{% endif %}{% endfor %}\nPlease select the correct answer from the options above. \n",
-      "mapping_from_source": {
-        "media": {
-          "from": "image",
-          "type": "list",
-          "min_items": 1,
-          "max_items": 1
-        },
-        "id": {
-          "from": "id"
-        },
-        "question": {
-          "from": "question"
-        },
-        "answer": {
-          "from": "answer",
-          "optional": true
-        },
-        "options": {
-          "from": "options",
-          "optional": true,
-          "note": "list source values are normalized to {A,B,...} dict"
-        },
-        "extra": {
-          "knowledge_concept": {
-            "from": "knowledge_concept"
-          },
-          "problem_id": {
-            "from": "problem_id"
-          },
-          "step_key": {
-            "from": "step_key"
-          }
-        },
-        "source": {
-          "format": "huggingface",
-          "url": {
-            "testmini": "https://huggingface.co/datasets/We-Math/We-Math"
-          }
-        }
-      },
-      "prompt_template_source": {
-        "origin": "official",
-        "reference": "https://github.com/open-compass/VLMEvalKit/blob/main/vlmeval/dataset/image_mcq.py (ImageMCQDataset.build_prompt — canonical MCQ trailer with options bullets)",
-        "notes": "Tier 3: VLMEvalKit ImageMCQDataset.build_prompt; We-Math is evaluated via VLMEvalKit's standard MCQ flow."
-      }
+ "name": "We-Math",
+ "release_date": "2024-07-01",
+ "subsets": {
+  "main": {
+   "language": [
+    "en"
+   ],
+   "modalities": [
+    "single_image_start"
+   ],
+   "task_type": "multiple_choice_qa",
+   "score_type": "rule",
+   "score_protocol": {
+    "reference": "official We-Math/We-Math@README + evaluation/four_dimensional_metrics.py & evaluation/accuracy.py — 'we use string matching to directly extract answers, which eliminates the high cost of using additional models for further answer extraction' (rule-based option-letter extraction, exact compare). [Quote re-verified verbatim in the official README 2026-07-07; rule pipeline re-verified vendored at VLMEvalKit@vlmeval/dataset/utils/wemath.py:28-41 (split on 'Answer', strip, first char in A-H, compare).]",
+    "note": "Official headline is the four-dimensional metric set (IK/IG/CM/RM) plus step-wise accuracy, computed by grouping one-step sub-problems with their multi-step composite problem (problem_id/step_key, vendored in VLMEvalKit vlmeval/dataset/utils/wemath.py:16-238); a per-sample accuracy scorer will not reproduce those group scores (grouping keys ARE shipped in extra). VLMEvalKit grades WeMath via mcq_vanilla_eval (image_mcq.py WeMath.evaluate -> utils/multiple_choice.py mcq_vanilla_eval, rule can_infer + GPT extraction fallback), i.e. llm_extract — disagrees with the official rule-only protocol; official preferred."
+   },
+   "prompt_template": "<image>{{ question }}\nOptions:\n{% for k, v in options.items() %}{{ k }}. {{ v }}{% if not loop.last %}\n{% endif %}{% endfor %}\nPlease select the correct answer from the options above. \n",
+   "mapping_from_source": {
+    "media": {
+     "from": "image",
+     "type": "list",
+     "min_items": 1,
+     "max_items": 1
+    },
+    "id": {
+     "from": "id"
+    },
+    "question": {
+     "from": "question"
+    },
+    "answer": {
+     "from": "answer",
+     "optional": true
+    },
+    "options": {
+     "from": "options",
+     "optional": true,
+     "note": "list source values are normalized to {A,B,...} dict"
+    },
+    "extra": {
+     "knowledge_concept": {
+      "from": "knowledge_concept"
+     },
+     "problem_id": {
+      "from": "problem_id"
+     },
+     "step_key": {
+      "from": "step_key"
+     }
+    },
+    "source": {
+     "format": "huggingface",
+     "url": {
+      "testmini": "https://huggingface.co/datasets/We-Math/We-Math"
+     }
     }
+   },
+   "prompt_template_source": {
+    "origin": "official",
+    "reference": "https://github.com/open-compass/VLMEvalKit/blob/main/vlmeval/dataset/image_mcq.py (ImageMCQDataset.build_prompt — canonical MCQ trailer with options bullets)",
+    "notes": "Tier 3: VLMEvalKit ImageMCQDataset.build_prompt; We-Math is evaluated via VLMEvalKit's standard MCQ flow."
+   }
   }
-}
+ }
+}