metadata: migrate score_type -> score_pipeline (atomic stage contract; see mm-eval scorer docs/en/SCORING.md)

metadata.json

@@ -1,66 +1,69 @@
 {
- "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"
-     }
+  "name": "We-Math",
+  "release_date": "2024-07-01",
+  "subsets": {
+    "main": {
+      "language": [
+        "en"
+      ],
+      "modalities": [
+        "single_image_start"
+      ],
+      "task_type": "multiple_choice_qa",
+      "score_pipeline": [
+        "exact-match",
+        "rule-match"
+      ],
+      "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."
+      }
     }
-   },
-   "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."
-   }
   }
- }
-}
+}