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

metadata.json

@@ -1,72 +1,75 @@
 {
- "name": "VisualMRC",
- "release_date": "2026-05-26",
- "subsets": {
-  "main": {
-   "language": [
-    "en"
-   ],
-   "modalities": [
-    "single_image_start"
-   ],
-   "task_type": "open_ended_qa",
-   "score_type": "caption_metrics",
-   "score_params": {
-    "metrics": [
-     "rouge_l",
-     "bleu4",
-     "meteor",
-     "cider"
-    ]
-   },
-   "score_protocol": {
-    "reference": "Official VisualMRC paper (arXiv:2101.11272, AAAI 2021, 'Evaluation metrics' §: 'we used BLEU, METEOR, ROUGE-L, and CIDEr' following generative MRC/captioning; BERTScore additionally reported in result tables; ROUGE-L used for model selection). No lmms-eval or VLMEvalKit task exists (re-confirmed 2026-07-07: no visualmrc task in either local clone).",
-    "note": "Generative reading-comprehension answers graded with captioning-style reference metrics over a single reference answer per question; paper declares no single headline — ROUGE-L listed first here since the paper selects models by ROUGE-L. Official tables also report BERTScore (now representable in the caption_metrics enum but kept out of params to match the paper's core four). metadata.json prompt citation of lmms_eval/tasks/visualmrc is bogus — must be corrected."
-   },
-   "prompt_template": "<image>{{ question }}",
-   "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": {
-     "qa_idx": {
-      "from": "qa_idx"
-     },
-     "url": {
-      "from": "url"
-     },
-     "screenshot_filename": {
-      "from": "screenshot_filename"
-     },
-     "image_filename": {
-      "from": "image_filename"
-     }
-    },
-    "source": {
-     "format": "huggingface",
-     "url": {
-      "test": "https://huggingface.co/datasets/jeepliu/VisualMRC"
-     }
+  "name": "VisualMRC",
+  "release_date": "2026-05-26",
+  "subsets": {
+    "main": {
+      "language": [
+        "en"
+      ],
+      "modalities": [
+        "single_image_start"
+      ],
+      "task_type": "open_ended_qa",
+      "score_pipeline": {
+        "unsupported": "caption-metrics",
+        "reason": "requires corpus-level caption metrics (CIDEr/BLEU/ROUGE/BERTScore); use the benchmark's own evaluator"
+      },
+      "score_params": {
+        "metrics": [
+          "rouge_l",
+          "bleu4",
+          "meteor",
+          "cider"
+        ]
+      },
+      "score_protocol": {
+        "reference": "Official VisualMRC paper (arXiv:2101.11272, AAAI 2021, 'Evaluation metrics' §: 'we used BLEU, METEOR, ROUGE-L, and CIDEr' following generative MRC/captioning; BERTScore additionally reported in result tables; ROUGE-L used for model selection). No lmms-eval or VLMEvalKit task exists (re-confirmed 2026-07-07: no visualmrc task in either local clone).",
+        "note": "Generative reading-comprehension answers graded with captioning-style reference metrics over a single reference answer per question; paper declares no single headline — ROUGE-L listed first here since the paper selects models by ROUGE-L. Official tables also report BERTScore (now representable in the caption_metrics enum but kept out of params to match the paper's core four). metadata.json prompt citation of lmms_eval/tasks/visualmrc is bogus — must be corrected."
+      },
+      "prompt_template": "<image>{{ question }}",
+      "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": {
+          "qa_idx": {
+            "from": "qa_idx"
+          },
+          "url": {
+            "from": "url"
+          },
+          "screenshot_filename": {
+            "from": "screenshot_filename"
+          },
+          "image_filename": {
+            "from": "image_filename"
+          }
+        },
+        "source": {
+          "format": "huggingface",
+          "url": {
+            "test": "https://huggingface.co/datasets/jeepliu/VisualMRC"
+          }
+        }
+      },
+      "prompt_template_source": {
+        "origin": "fallback",
+        "reference": "T2",
+        "notes": "Tier 5: no higher tier publishes a prompt. T1: nttmdlab-nlp/VisualMRC is a data-only repo (AAAI 2021; LayoutT5/LayoutBART fine-tuned seq2seq baselines, pre-LLM, no prompt template); T2: no prompt column in jeepliu/VisualMRC; T3: no VLMEvalKit visualmrc; T4: no lmms-eval tasks/visualmrc (clone @047ec52). Bare-question T2 chosen because answers are abstractive full sentences graded with reference text metrics — a short-answer cue would suppress the expected long-form output."
+      }
     }
-   },
-   "prompt_template_source": {
-    "origin": "fallback",
-    "reference": "T2",
-    "notes": "Tier 5: no higher tier publishes a prompt. T1: nttmdlab-nlp/VisualMRC is a data-only repo (AAAI 2021; LayoutT5/LayoutBART fine-tuned seq2seq baselines, pre-LLM, no prompt template); T2: no prompt column in jeepliu/VisualMRC; T3: no VLMEvalKit visualmrc; T4: no lmms-eval tasks/visualmrc (clone @047ec52). Bare-question T2 chosen because answers are abstractive full sentences graded with reference text metrics — a short-answer cue would suppress the expected long-form output."
-   }
   }
- }
-}
+}