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

metadata.json

@@ -1,52 +1,52 @@
 {
- "name": "Geometry3K",
- "release_date": "2021-05-30",
- "subsets": {
-  "main": {
-   "language": [
-    "en"
-   ],
-   "modalities": [
-    "single_image_start"
-   ],
-   "task_type": "short_answer_qa",
-   "score_type": "none",
-   "score_protocol": {
-    "reference": "Verified absence, checked 2026-07-07: the published copy is row-identical to hiyouga/geometry3k (matching problem/answer text and split sizes 2101/300/601), an RL-TRAINING reformat 'converted from https://github.com/lupantech/InterGPS' whose dataset card publishes NO eval protocol or metric; it is the tutorial training set of EasyR1 (hiyouga/EasyR1 README: 'Run Qwen2.5-VL GRPO on Geometry3K Dataset'), where rule-based mathruler boxed-extraction reward is a training signal, not an eval protocol. The official Inter-GPS (ACL 2021) protocol and lmms-eval geometry3k (geometry3k.yaml: dataset_path Yang130/geometry3k_4choices_mixed; utils.py:66-125 A-D letter extraction) both grade 4-choice MCQ variants and are inapplicable to this free-form copy.",
-    "note": "No official grading protocol exists for this variant; the scorer's default exact,template chain applies and results must be stamped non-official (config.official_protocol=false). Even then, LaTeX ground truths ('2 \\sqrt { 221 }') need math-expression equivalence to grade correctly — exact/template matching under-scores them. This is an RL training dataset (train/validation/test), not an eval-only benchmark; numbers are not comparable to official Inter-GPS Geometry3K results."
-   },
-   "prompt_template": "{{ question }}",
-   "mapping_from_source": {
-    "media": {
-     "from": "images",
-     "type": "list",
-     "min_items": 1,
-     "max_items": 1
-    },
-    "id": {
-     "from": "id"
-    },
-    "question": {
-     "from": "problem"
-    },
-    "answer": {
-     "from": "answer",
-     "optional": true
-    },
-    "source": {
-     "format": "json",
-     "url": {
-      "train": "https://huggingface.co/datasets/hiyouga/geometry3k",
-      "validation": "https://huggingface.co/datasets/hiyouga/geometry3k",
-      "test": "https://huggingface.co/datasets/hiyouga/geometry3k"
-     }
+  "name": "Geometry3K",
+  "release_date": "2021-05-30",
+  "subsets": {
+    "main": {
+      "language": [
+        "en"
+      ],
+      "modalities": [
+        "single_image_start"
+      ],
+      "task_type": "short_answer_qa",
+      "score_pipeline": [],
+      "score_protocol": {
+        "reference": "Verified absence, checked 2026-07-07: the published copy is row-identical to hiyouga/geometry3k (matching problem/answer text and split sizes 2101/300/601), an RL-TRAINING reformat 'converted from https://github.com/lupantech/InterGPS' whose dataset card publishes NO eval protocol or metric; it is the tutorial training set of EasyR1 (hiyouga/EasyR1 README: 'Run Qwen2.5-VL GRPO on Geometry3K Dataset'), where rule-based mathruler boxed-extraction reward is a training signal, not an eval protocol. The official Inter-GPS (ACL 2021) protocol and lmms-eval geometry3k (geometry3k.yaml: dataset_path Yang130/geometry3k_4choices_mixed; utils.py:66-125 A-D letter extraction) both grade 4-choice MCQ variants and are inapplicable to this free-form copy.",
+        "note": "No official grading protocol exists for this variant; the scorer's default exact,template chain applies and results must be stamped non-official (config.official_protocol=false). Even then, LaTeX ground truths ('2 \\sqrt { 221 }') need math-expression equivalence to grade correctly — exact/template matching under-scores them. This is an RL training dataset (train/validation/test), not an eval-only benchmark; numbers are not comparable to official Inter-GPS Geometry3K results."
+      },
+      "prompt_template": "{{ question }}",
+      "mapping_from_source": {
+        "media": {
+          "from": "images",
+          "type": "list",
+          "min_items": 1,
+          "max_items": 1
+        },
+        "id": {
+          "from": "id"
+        },
+        "question": {
+          "from": "problem"
+        },
+        "answer": {
+          "from": "answer",
+          "optional": true
+        },
+        "source": {
+          "format": "json",
+          "url": {
+            "train": "https://huggingface.co/datasets/hiyouga/geometry3k",
+            "validation": "https://huggingface.co/datasets/hiyouga/geometry3k",
+            "test": "https://huggingface.co/datasets/hiyouga/geometry3k"
+          }
+        }
+      },
+      "prompt_template_source": {
+        "origin": "official",
+        "reference": "https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/geometry3k/utils.py (geometry3k_doc_to_text — bare question; choices and trailer baked into source question column)",
+        "notes": "Tier 4: lmms-eval Geometry3K canonical evaluation prompt. The question column already contains <image> placeholders and problem+choices+trailer baked in from the source dataset, so the template is bare {{ question }} to avoid duplicating the placeholder."
+      }
     }
-   },
-   "prompt_template_source": {
-    "origin": "official",
-    "reference": "https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/geometry3k/utils.py (geometry3k_doc_to_text — bare question; choices and trailer baked into source question column)",
-    "notes": "Tier 4: lmms-eval Geometry3K canonical evaluation prompt. The question column already contains <image> placeholders and problem+choices+trailer baked in from the source dataset, so the template is bare {{ question }} to avoid duplicating the placeholder."
-   }
   }
- }
-}
+}