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

metadata.json

@@ -1,68 +1,71 @@
 {
- "name": "VSI-Bench",
- "release_date": "2024-12-18",
- "subsets": {
-  "mc": {
-   "language": [
-    "en"
-   ],
-   "modalities": [
-    "single_video_start"
-   ],
-   "task_type": "multiple_choice_qa",
-   "score_type": "rule",
-   "score_protocol": {
-    "reference": "lmms-eval@lmms_eval/tasks/vsibench/utils.py:87-89,115-121 — MCA question types graded by fuzzy_matching (first token, strip trailing period) + case-insensitive exact match against the ground-truth letter; no LLM.",
-    "note": "The org ships ONLY the multiple-choice question types (mc config, 2490 rows; confirmed in rows file). The official benchmark also contains NA (numerical) types graded with Mean Relative Accuracy MRA:.5:.95:.05 (utils.py:95-100,29-31), and the official headline 'overall' averages 8 per-question-type scores after averaging object_rel_direction easy/medium/hard (utils.py:158-170) — neither is reproducible from this copy or from a per-sample mean. Rows carry extra 'question_type' with VSI type names (object_rel_direction_hard, ...) that are not scorer vocabulary."
-   },
-   "prompt_template": "<video>These are frames of a video.\n{{ question }}\nOptions:\n{% for o in options %}{{ o }}\n{% endfor %}Answer with the option's letter from the given choices directly.",
-   "prompt_template_source": {
-    "origin": "official",
-    "reference": "https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/vsibench/utils.py (vsibench_doc_to_text — canonical MCA prompt) + _default_template_yaml",
-    "notes": "Tier 4: lmms-eval VSI-Bench canonical MCA prompt for the MC question types. SAT paper (arXiv 2412.07755, Appendix A.3, p.20) restricts VSI-Bench evaluation to the MC split — numerical splits (object_counting, object_size_estimation, room_size_estimation, object_abs_distance) are excluded. Each MC row carries the source `options` list verbatim from nyu-visionx/VSI-Bench under `choices`."
-   },
-   "video_storage": {
-    "format": "files",
-    "media_root": "videos",
-    "notes": "512 source videos (243 referenced by the MC split): arkitscenes (121), scannet (79), scannetpp (43). Videos are mirrored from lccshunli/VSIBench at 5.7 GB total; nyu-visionx/VSI-Bench provides only QA metadata. Each scene_name resolves to videos/<dataset_source>/<scene_name>.mp4. Frame sampling is the inference backend's responsibility."
-   },
-   "mapping_from_source": {
-    "media": {
-     "from": "video_path",
-     "type": "list",
-     "min_items": 1,
-     "max_items": 1
-    },
-    "id": {
-     "from": "id"
-    },
-    "question": {
-     "from": "question"
-    },
-    "answer": {
-     "from": "ground_truth"
-    },
-    "choices": {
-     "from": "options"
-    },
-    "extra": {
-     "scene_name": {
-      "from": "scene_name"
-     },
-     "dataset_source": {
-      "from": "dataset"
-     },
-     "question_type": {
-      "from": "question_type"
-     }
-    },
-    "source": {
-     "format": "huggingface",
-     "url": {
-      "test": "https://huggingface.co/datasets/nyu-visionx/VSI-Bench (QA metadata, 'full' config); videos mirrored from https://huggingface.co/datasets/lccshunli/VSIBench"
-     }
+  "name": "VSI-Bench",
+  "release_date": "2024-12-18",
+  "subsets": {
+    "mc": {
+      "language": [
+        "en"
+      ],
+      "modalities": [
+        "single_video_start"
+      ],
+      "task_type": "multiple_choice_qa",
+      "score_pipeline": [
+        "exact-match",
+        "rule-match"
+      ],
+      "score_protocol": {
+        "reference": "lmms-eval@lmms_eval/tasks/vsibench/utils.py:87-89,115-121 — MCA question types graded by fuzzy_matching (first token, strip trailing period) + case-insensitive exact match against the ground-truth letter; no LLM.",
+        "note": "The org ships ONLY the multiple-choice question types (mc config, 2490 rows; confirmed in rows file). The official benchmark also contains NA (numerical) types graded with Mean Relative Accuracy MRA:.5:.95:.05 (utils.py:95-100,29-31), and the official headline 'overall' averages 8 per-question-type scores after averaging object_rel_direction easy/medium/hard (utils.py:158-170) — neither is reproducible from this copy or from a per-sample mean. Rows carry extra 'question_type' with VSI type names (object_rel_direction_hard, ...) that are not scorer vocabulary."
+      },
+      "prompt_template": "<video>These are frames of a video.\n{{ question }}\nOptions:\n{% for o in options %}{{ o }}\n{% endfor %}Answer with the option's letter from the given choices directly.",
+      "prompt_template_source": {
+        "origin": "official",
+        "reference": "https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/vsibench/utils.py (vsibench_doc_to_text — canonical MCA prompt) + _default_template_yaml",
+        "notes": "Tier 4: lmms-eval VSI-Bench canonical MCA prompt for the MC question types. SAT paper (arXiv 2412.07755, Appendix A.3, p.20) restricts VSI-Bench evaluation to the MC split — numerical splits (object_counting, object_size_estimation, room_size_estimation, object_abs_distance) are excluded. Each MC row carries the source `options` list verbatim from nyu-visionx/VSI-Bench under `choices`."
+      },
+      "video_storage": {
+        "format": "files",
+        "media_root": "videos",
+        "notes": "512 source videos (243 referenced by the MC split): arkitscenes (121), scannet (79), scannetpp (43). Videos are mirrored from lccshunli/VSIBench at 5.7 GB total; nyu-visionx/VSI-Bench provides only QA metadata. Each scene_name resolves to videos/<dataset_source>/<scene_name>.mp4. Frame sampling is the inference backend's responsibility."
+      },
+      "mapping_from_source": {
+        "media": {
+          "from": "video_path",
+          "type": "list",
+          "min_items": 1,
+          "max_items": 1
+        },
+        "id": {
+          "from": "id"
+        },
+        "question": {
+          "from": "question"
+        },
+        "answer": {
+          "from": "ground_truth"
+        },
+        "choices": {
+          "from": "options"
+        },
+        "extra": {
+          "scene_name": {
+            "from": "scene_name"
+          },
+          "dataset_source": {
+            "from": "dataset"
+          },
+          "question_type": {
+            "from": "question_type"
+          }
+        },
+        "source": {
+          "format": "huggingface",
+          "url": {
+            "test": "https://huggingface.co/datasets/nyu-visionx/VSI-Bench (QA metadata, 'full' config); videos mirrored from https://huggingface.co/datasets/lccshunli/VSIBench"
+          }
+        }
+      }
     }
-   }
   }
- }
-}
+}