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

metadata.json

@@ -1,61 +1,64 @@
 {
- "name": "LogicVista",
- "release_date": "2024-07-01",
- "subsets": {
-  "main": {
-   "language": [
-    "en"
-   ],
-   "modalities": [
-    "single_image_start"
-   ],
-   "task_type": "multiple_choice_qa",
-   "score_type": "llm_extract",
-   "score_protocol": {
-    "reference": "VLMEvalKit@vlmeval/dataset/image_vqa.py:1703-1760 + vlmeval/dataset/utils/logicvista.py:42-72 — every sample sent to GPT extractor (evaluate asserts a working OpenAI API); extracted letters are lowercased+sorted and exact-compared against gold answer split on ', ' (sorted letter-set compare); evaluate_logicvista aggregates Overall + per-skill accuracy. Matches official Yijia-Xiao/LogicVista GPT-extraction protocol.",
-    "note": "Multi-letter answers exist (e.g. gold 'A, C'): official compare is order-insensitive letter-SET equality, not single-letter match. VLMEvalKit runs the LLM extractor on every sample (no rule prefetch); our llm_extract chain adds a deterministic prefetch, which is a superset. Official aggregation reports per-skill (inductive/deductive/numerical/spatial/mechanical) accuracies alongside Overall."
-   },
-   "prompt_template": "<image>{{ question }}\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
-    },
-    "extra": {
-     "skill": {
-      "from": "skill"
-     },
-     "broad_capability": {
-      "from": "broad_capability"
-     },
-     "specific_capability": {
-      "from": "specific_capability"
-     }
-    },
-    "source": {
-     "format": "json",
-     "url": {
-      "test": "https://github.com/Yijia-Xiao/LogicVista/tree/main/data"
-     }
+  "name": "LogicVista",
+  "release_date": "2024-07-01",
+  "subsets": {
+    "main": {
+      "language": [
+        "en"
+      ],
+      "modalities": [
+        "single_image_start"
+      ],
+      "task_type": "multiple_choice_qa",
+      "score_pipeline": [
+        "rule-match",
+        "llm-match"
+      ],
+      "score_protocol": {
+        "reference": "VLMEvalKit@vlmeval/dataset/image_vqa.py:1703-1760 + vlmeval/dataset/utils/logicvista.py:42-72 — every sample sent to GPT extractor (evaluate asserts a working OpenAI API); extracted letters are lowercased+sorted and exact-compared against gold answer split on ', ' (sorted letter-set compare); evaluate_logicvista aggregates Overall + per-skill accuracy. Matches official Yijia-Xiao/LogicVista GPT-extraction protocol.",
+        "note": "Multi-letter answers exist (e.g. gold 'A, C'): official compare is order-insensitive letter-SET equality, not single-letter match. VLMEvalKit runs the LLM extractor on every sample (no rule prefetch); our llm_extract chain adds a deterministic prefetch, which is a superset. Official aggregation reports per-skill (inductive/deductive/numerical/spatial/mechanical) accuracies alongside Overall."
+      },
+      "prompt_template": "<image>{{ question }}\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
+        },
+        "extra": {
+          "skill": {
+            "from": "skill"
+          },
+          "broad_capability": {
+            "from": "broad_capability"
+          },
+          "specific_capability": {
+            "from": "specific_capability"
+          }
+        },
+        "source": {
+          "format": "json",
+          "url": {
+            "test": "https://github.com/Yijia-Xiao/LogicVista/tree/main/data"
+          }
+        }
+      },
+      "prompt_template_source": {
+        "origin": "official",
+        "reference": "https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/logicvista/utils.py (logicvista_doc_to_text — bare image+question; eval logic handled at scoring time)",
+        "notes": "Tier 4: lmms-eval LogicVista canonical evaluation prompt (bare format with eval-time scoring)."
+      }
     }
-   },
-   "prompt_template_source": {
-    "origin": "official",
-    "reference": "https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/logicvista/utils.py (logicvista_doc_to_text — bare image+question; eval logic handled at scoring time)",
-    "notes": "Tier 4: lmms-eval LogicVista canonical evaluation prompt (bare format with eval-time scoring)."
-   }
   }
- }
-}
+}