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

metadata.json

@@ -1,116 +1,122 @@
 {
- "name": "SAT",
- "release_date": "2025-12-04",
- "subsets": {
-  "real": {
-   "language": [
-    "en"
-   ],
-   "modalities": [
-    "multi_image_start"
-   ],
-   "task_type": "multiple_choice_qa",
-   "score_type": "rule",
-   "score_protocol": {
-    "reference": "lmms-eval@lmms_eval/tasks/sat/sat.yaml:17-31 + utils.py (MultiChoiceRegexFilter) — deterministic filter maps the response to a choice letter by regex / choice-text matching, then exact_match; no LLM at any step. Official repo (github.com/arijitray1993/SAT) has not published a dedicated scoring script (README: 'work in progress').",
-    "note": "lmms-eval letters the options (A., B.) and grades letters; the mm-eval copy renders un-lettered choice texts ('Choose between the following options: X, or Y.') and stores the full choice text as answer, so grading is deterministic matching of the chosen option text rather than a letter compare. Rows carry extra field 'question_type' with SAT category names (obj_movement, action_sequence, ...) that are NOT scorer vocabulary — the scorer must ignore them (unrecognized values fall through to gt-shape per spec)."
-   },
-   "prompt_template": "{% for i in range(n_images) %}<image>{% endfor %}{{ question }} Choose between the following options: {% for c in choices %}{{ c }}{% if not loop.last %}, or {% endif %}{% endfor %}.",
-   "prompt_template_source": {
-    "origin": "official",
-    "reference": "https://arxiv.org/pdf/2412.07755 (SAT paper, Appendix A.3, Question-answer format, p.21)",
-    "notes": "Tier 1: SAT paper Appendix specifies the binary-choice text format with <image> tokens at start; LLaVA conversation scaffolding is dropped (handled by inference backend)."
-   },
-   "mapping_from_source": {
-    "media": {
-     "from": "media",
-     "type": "list",
-     "min_items": 1,
-     "max_items": 2
+  "name": "SAT",
+  "release_date": "2025-12-04",
+  "subsets": {
+    "real": {
+      "language": [
+        "en"
+      ],
+      "modalities": [
+        "multi_image_start"
+      ],
+      "task_type": "multiple_choice_qa",
+      "score_pipeline": [
+        "exact-match",
+        "rule-match"
+      ],
+      "score_protocol": {
+        "reference": "lmms-eval@lmms_eval/tasks/sat/sat.yaml:17-31 + utils.py (MultiChoiceRegexFilter) — deterministic filter maps the response to a choice letter by regex / choice-text matching, then exact_match; no LLM at any step. Official repo (github.com/arijitray1993/SAT) has not published a dedicated scoring script (README: 'work in progress').",
+        "note": "lmms-eval letters the options (A., B.) and grades letters; the mm-eval copy renders un-lettered choice texts ('Choose between the following options: X, or Y.') and stores the full choice text as answer, so grading is deterministic matching of the chosen option text rather than a letter compare. Rows carry extra field 'question_type' with SAT category names (obj_movement, action_sequence, ...) that are NOT scorer vocabulary — the scorer must ignore them (unrecognized values fall through to gt-shape per spec)."
+      },
+      "prompt_template": "{% for i in range(n_images) %}<image>{% endfor %}{{ question }} Choose between the following options: {% for c in choices %}{{ c }}{% if not loop.last %}, or {% endif %}{% endfor %}.",
+      "prompt_template_source": {
+        "origin": "official",
+        "reference": "https://arxiv.org/pdf/2412.07755 (SAT paper, Appendix A.3, Question-answer format, p.21)",
+        "notes": "Tier 1: SAT paper Appendix specifies the binary-choice text format with <image> tokens at start; LLaVA conversation scaffolding is dropped (handled by inference backend)."
+      },
+      "mapping_from_source": {
+        "media": {
+          "from": "media",
+          "type": "list",
+          "min_items": 1,
+          "max_items": 2
+        },
+        "id": {
+          "from": "id"
+        },
+        "question": {
+          "from": "question"
+        },
+        "answer": {
+          "from": "answer",
+          "optional": true
+        },
+        "choices": {
+          "from": "choices"
+        },
+        "extra": {
+          "question_type": {
+            "from": "question_type"
+          },
+          "n_images": {
+            "from": "n_images"
+          }
+        },
+        "source": {
+          "format": "huggingface",
+          "url": {
+            "real_test": "https://huggingface.co/datasets/array/SAT"
+          }
+        }
+      }
     },
-    "id": {
-     "from": "id"
-    },
-    "question": {
-     "from": "question"
-    },
-    "answer": {
-     "from": "answer",
-     "optional": true
-    },
-    "choices": {
-     "from": "choices"
-    },
-    "extra": {
-     "question_type": {
-      "from": "question_type"
-     },
-     "n_images": {
-      "from": "n_images"
-     }
-    },
-    "source": {
-     "format": "huggingface",
-     "url": {
-      "real_test": "https://huggingface.co/datasets/array/SAT"
-     }
-    }
-   }
-  },
-  "synthetic": {
-   "language": [
-    "en"
-   ],
-   "modalities": [
-    "multi_image_start"
-   ],
-   "task_type": "multiple_choice_qa",
-   "score_type": "rule",
-   "score_protocol": {
-    "reference": "lmms-eval@lmms_eval/tasks/sat/sat.yaml:17-31 + utils.py (MultiChoiceRegexFilter) — same deterministic choice-matching protocol as the real subset; no LLM.",
-    "note": "Same as SAT/real: full-text answers instead of letters; extra 'question_type' field carries SAT category names, not scorer vocabulary. lmms-eval's sat task evaluates the (nv-njb/SAT) test set; the org ships real (150 rows) and synthetic (4001 rows) as separate configs from array/SAT."
-   },
-   "prompt_template": "{% for i in range(n_images) %}<image>{% endfor %}{{ question }} Choose between the following options: {% for c in choices %}{{ c }}{% if not loop.last %}, or {% endif %}{% endfor %}.",
-   "prompt_template_source": {
-    "origin": "official",
-    "reference": "https://arxiv.org/pdf/2412.07755 (SAT paper, Appendix A.3, Question-answer format, p.21)",
-    "notes": "Tier 1: SAT paper Appendix specifies the binary-choice text format with <image> tokens at start; LLaVA conversation scaffolding is dropped (handled by inference backend)."
-   },
-   "mapping_from_source": {
-    "media": {
-     "from": "media",
-     "type": "list",
-     "min_items": 1,
-     "max_items": 2
-    },
-    "id": {
-     "from": "id"
-    },
-    "question": {
-     "from": "question"
-    },
-    "answer": {
-     "from": "answer",
-     "optional": true
-    },
-    "choices": {
-     "from": "choices"
-    },
-    "extra": {
-     "question_type": {
-      "from": "question_type"
-     },
-     "n_images": {
-      "from": "n_images"
-     }
-    },
-    "source": {
-     "format": "huggingface",
-     "url": {
-      "synthetic_test": "https://huggingface.co/datasets/array/SAT"
-     }
+    "synthetic": {
+      "language": [
+        "en"
+      ],
+      "modalities": [
+        "multi_image_start"
+      ],
+      "task_type": "multiple_choice_qa",
+      "score_pipeline": [
+        "exact-match",
+        "rule-match"
+      ],
+      "score_protocol": {
+        "reference": "lmms-eval@lmms_eval/tasks/sat/sat.yaml:17-31 + utils.py (MultiChoiceRegexFilter) — same deterministic choice-matching protocol as the real subset; no LLM.",
+        "note": "Same as SAT/real: full-text answers instead of letters; extra 'question_type' field carries SAT category names, not scorer vocabulary. lmms-eval's sat task evaluates the (nv-njb/SAT) test set; the org ships real (150 rows) and synthetic (4001 rows) as separate configs from array/SAT."
+      },
+      "prompt_template": "{% for i in range(n_images) %}<image>{% endfor %}{{ question }} Choose between the following options: {% for c in choices %}{{ c }}{% if not loop.last %}, or {% endif %}{% endfor %}.",
+      "prompt_template_source": {
+        "origin": "official",
+        "reference": "https://arxiv.org/pdf/2412.07755 (SAT paper, Appendix A.3, Question-answer format, p.21)",
+        "notes": "Tier 1: SAT paper Appendix specifies the binary-choice text format with <image> tokens at start; LLaVA conversation scaffolding is dropped (handled by inference backend)."
+      },
+      "mapping_from_source": {
+        "media": {
+          "from": "media",
+          "type": "list",
+          "min_items": 1,
+          "max_items": 2
+        },
+        "id": {
+          "from": "id"
+        },
+        "question": {
+          "from": "question"
+        },
+        "answer": {
+          "from": "answer",
+          "optional": true
+        },
+        "choices": {
+          "from": "choices"
+        },
+        "extra": {
+          "question_type": {
+            "from": "question_type"
+          },
+          "n_images": {
+            "from": "n_images"
+          }
+        },
+        "source": {
+          "format": "huggingface",
+          "url": {
+            "synthetic_test": "https://huggingface.co/datasets/array/SAT"
+          }
+        }
+      }
     }
-   }
   }
- }
-}
+}