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

metadata.json

@@ -1,69 +1,72 @@
 {
- "name": "MMT-Bench",
- "release_date": "2026-05-26",
- "subsets": {
-  "main": {
-   "language": [
-    "en"
-   ],
-   "modalities": [
-    "multi_image_start"
-   ],
-   "task_type": "multiple_choice_qa",
-   "score_type": "llm_extract",
-   "score_protocol": {
-    "reference": "VLMEvalKit@vlmeval/dataset/image_mcq.py:81-85,249-318 (MMT-Bench served by ImageMCQDataset.evaluate -> mcq_vanilla_eval; report_acc_MMT at :317-318) + vlmeval/dataset/utils/multiple_choice.py:359-406,475-508 — rule prefetch via can_infer (option letter / unique option-text), GPT extraction fallback, exact letter compare. Official MMT-Bench (OpenGVLab) evaluates via VLMEvalKit.",
-    "note": "Official headline aggregation is report_acc_MMT: accuracy grouped by category/l2_category (per-sample scorer yields overall accuracy only). mm-eval publishes the VAL split (3126 rows). lmms-eval mmt uses rule-only MMMU-style parsing (no GPT fallback) — VLMEvalKit protocol preferred as the authors' official harness."
-   },
-   "prompt_template": "{% for _ in range(n_images) %}<image>{% endfor %}{{ question }}\nOptions:\n{% for k, v in options.items() %}{{ k }}. {{ v }}\n{% endfor %}Please select the correct answer from the options above. ",
-   "mapping_from_source": {
-    "media": {
-     "from": "images",
-     "type": "list",
-     "min_items": 1,
-     "max_items": 23
-    },
-    "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": {
-     "source_id": {
-      "from": "source_id"
-     },
-     "category": {
-      "from": "category"
-     },
-     "l2_category": {
-      "from": "l2_category"
-     },
-     "n_images": {
-      "from": "n_images"
-     }
-    },
-    "source": {
-     "format": "json",
-     "url": {
-      "val": "https://huggingface.co/datasets/Kaining/MMT-Bench"
-     }
+  "name": "MMT-Bench",
+  "release_date": "2026-05-26",
+  "subsets": {
+    "main": {
+      "language": [
+        "en"
+      ],
+      "modalities": [
+        "multi_image_start"
+      ],
+      "task_type": "multiple_choice_qa",
+      "score_pipeline": [
+        "rule-match",
+        "llm-match"
+      ],
+      "score_protocol": {
+        "reference": "VLMEvalKit@vlmeval/dataset/image_mcq.py:81-85,249-318 (MMT-Bench served by ImageMCQDataset.evaluate -> mcq_vanilla_eval; report_acc_MMT at :317-318) + vlmeval/dataset/utils/multiple_choice.py:359-406,475-508 — rule prefetch via can_infer (option letter / unique option-text), GPT extraction fallback, exact letter compare. Official MMT-Bench (OpenGVLab) evaluates via VLMEvalKit.",
+        "note": "Official headline aggregation is report_acc_MMT: accuracy grouped by category/l2_category (per-sample scorer yields overall accuracy only). mm-eval publishes the VAL split (3126 rows). lmms-eval mmt uses rule-only MMMU-style parsing (no GPT fallback) — VLMEvalKit protocol preferred as the authors' official harness."
+      },
+      "prompt_template": "{% for _ in range(n_images) %}<image>{% endfor %}{{ question }}\nOptions:\n{% for k, v in options.items() %}{{ k }}. {{ v }}\n{% endfor %}Please select the correct answer from the options above. ",
+      "mapping_from_source": {
+        "media": {
+          "from": "images",
+          "type": "list",
+          "min_items": 1,
+          "max_items": 23
+        },
+        "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": {
+          "source_id": {
+            "from": "source_id"
+          },
+          "category": {
+            "from": "category"
+          },
+          "l2_category": {
+            "from": "l2_category"
+          },
+          "n_images": {
+            "from": "n_images"
+          }
+        },
+        "source": {
+          "format": "json",
+          "url": {
+            "val": "https://huggingface.co/datasets/Kaining/MMT-Bench"
+          }
+        }
+      },
+      "prompt_template_source": {
+        "origin": "official",
+        "reference": "https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/mmt/utils.py#L40-L55 (mmt_doc_to_text — multi-image MCQ with 'Question:/Options:/A./B./Please select the correct answer from the options above. ' format)",
+        "notes": "Tier 4: lmms-eval MMT-Bench canonical evaluation prompt."
+      }
     }
-   },
-   "prompt_template_source": {
-    "origin": "official",
-    "reference": "https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/mmt/utils.py#L40-L55 (mmt_doc_to_text — multi-image MCQ with 'Question:/Options:/A./B./Please select the correct answer from the options above. ' format)",
-    "notes": "Tier 4: lmms-eval MMT-Bench canonical evaluation prompt."
-   }
   }
- }
-}
+}