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

metadata.json

@@ -1,69 +1,72 @@
 {
- "name": "DynaMath",
- "release_date": "2024-05-01",
- "subsets": {
-  "main": {
-   "language": [
-    "en"
-   ],
-   "modalities": [
-    "single_image_start"
-   ],
-   "task_type": "short_answer_qa",
-   "score_type": "llm_extract",
-   "score_protocol": {
-    "reference": "VLMEvalKit@vlmeval/dataset/dynamath.py:58-109 — rule parse of the answer first (JSON 'short answer' / direct parse_answer), auxiliary LLM extraction only on parse failure, then rule compare: float abs(diff)<=0.001, MC letter equality, text containment. Official DynaMath@evaluation/gpt4o/gpt_4o_json_eval.py:98-123 uses the same JSON 'short answer' parse + abs(diff)<=0.001 rule compare. [Re-verified in clone 2026-07-07: DynaMath_auxeval at dynamath.py:58-110 — abs(diff)<=0.001 at :92.]",
-    "note": "Official grading is rule-based over a JSON-formatted answer forced by their inference prompt; the mm-eval prompt (lmms-eval style question+Options trailer) does not force JSON, so LLM extraction fallback (VLMEvalKit protocol) is required. Official float tolerance is ABSOLUTE 0.001 (score_params.numeric_abs_tol), not relative. Official headline metrics are average-case AND worst-case accuracy over 10 generated variants per seed question; the mm-eval copy ships 501 rows (seed variant only), so worst-case accuracy is not reproducible."
-   },
-   "prompt_template": "<image>{{ question }}{% if options %}\nOptions:\n{% for k, v in options.items() %}{{ k }}. {{ v }}{% if not loop.last %}\n{% endif %}{% endfor %}{% endif %}\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": {
-     "subject": {
-      "from": "subject"
-     },
-     "level": {
-      "from": "level"
-     },
-     "answer_type": {
-      "from": "answer_type"
-     }
-    },
-    "source": {
-     "format": "json",
-     "url": {
-      "test": "https://github.com/DynaMath/DynaMath/tree/main/dataset"
-     }
+  "name": "DynaMath",
+  "release_date": "2024-05-01",
+  "subsets": {
+    "main": {
+      "language": [
+        "en"
+      ],
+      "modalities": [
+        "single_image_start"
+      ],
+      "task_type": "short_answer_qa",
+      "score_pipeline": [
+        "rule-match",
+        "llm-match"
+      ],
+      "score_protocol": {
+        "reference": "VLMEvalKit@vlmeval/dataset/dynamath.py:58-109 — rule parse of the answer first (JSON 'short answer' / direct parse_answer), auxiliary LLM extraction only on parse failure, then rule compare: float abs(diff)<=0.001, MC letter equality, text containment. Official DynaMath@evaluation/gpt4o/gpt_4o_json_eval.py:98-123 uses the same JSON 'short answer' parse + abs(diff)<=0.001 rule compare. [Re-verified in clone 2026-07-07: DynaMath_auxeval at dynamath.py:58-110 — abs(diff)<=0.001 at :92.]",
+        "note": "Official grading is rule-based over a JSON-formatted answer forced by their inference prompt; the mm-eval prompt (lmms-eval style question+Options trailer) does not force JSON, so LLM extraction fallback (VLMEvalKit protocol) is required. Official float tolerance is ABSOLUTE 0.001 (score_params.numeric_abs_tol), not relative. Official headline metrics are average-case AND worst-case accuracy over 10 generated variants per seed question; the mm-eval copy ships 501 rows (seed variant only), so worst-case accuracy is not reproducible."
+      },
+      "prompt_template": "<image>{{ question }}{% if options %}\nOptions:\n{% for k, v in options.items() %}{{ k }}. {{ v }}{% if not loop.last %}\n{% endif %}{% endfor %}{% endif %}\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": {
+          "subject": {
+            "from": "subject"
+          },
+          "level": {
+            "from": "level"
+          },
+          "answer_type": {
+            "from": "answer_type"
+          }
+        },
+        "source": {
+          "format": "json",
+          "url": {
+            "test": "https://github.com/DynaMath/DynaMath/tree/main/dataset"
+          }
+        }
+      },
+      "prompt_template_source": {
+        "origin": "official",
+        "reference": "https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/dynamath/utils.py (dynamath_doc_to_text — image+question+optional Options:/A./B. trailer)",
+        "notes": "Tier 4: lmms-eval DynaMath canonical evaluation prompt."
+      },
+      "score_params": {
+        "numeric_abs_tol": 0.001
+      }
     }
-   },
-   "prompt_template_source": {
-    "origin": "official",
-    "reference": "https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/dynamath/utils.py (dynamath_doc_to_text — image+question+optional Options:/A./B. trailer)",
-    "notes": "Tier 4: lmms-eval DynaMath canonical evaluation prompt."
-   },
-   "score_params": {
-    "numeric_abs_tol": 0.001
-   }
   }
- }
-}
+}