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

metadata.json

@@ -1,58 +1,60 @@
 {
- "name": "BenchLMM",
- "release_date": "2024-07-01",
- "subsets": {
-  "main": {
-   "language": [
-    "en"
-   ],
-   "modalities": [
-    "single_image_start"
-   ],
-   "task_type": "short_answer_qa",
-   "score_type": "llm_judge",
-   "score_params": {
-    "rubric": "scale",
-    "scale": [
-     0,
-     1
-    ],
-    "official_judge_model": "gpt-4-0613"
-   },
-   "score_protocol": {
-    "reference": "official@github.com/AIFEG/BenchLMM evaluate/gpt_evaluation_script.py — GPT judge prompt: 'Compare the ground truth and prediction from AI models, to give a correctness score for the prediction' ignoring case/grammar, '/' = multiple acceptable answers, similar meaning gets full marks; judge outputs a score in {0.0,0.1,...,1.0}; example model gpt-4-0613; final metric = mean score (avg_score.py).",
-    "note": "Every sample is LLM-judged with fractional credit on a 0-1 scale (11 discrete values); headline is the mean per-sample score, not binary accuracy. The mm-eval copy has only 96 rows (test split) whereas official BenchLMM spans many style/domain files (CT, MRI, infrared, AD, RS, styles, Robots/Games, each with its own eval variant) — published copy appears to be a small subset."
-   },
-   "prompt_template": "<image>{{ question }}\nAnswer the question using a single word or phrase.",
-   "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
-    },
-    "source": {
-     "format": "json",
-     "url": {
-      "test": "https://huggingface.co/datasets/AIFEG/BenchLMM"
-     }
+  "name": "BenchLMM",
+  "release_date": "2024-07-01",
+  "subsets": {
+    "main": {
+      "language": [
+        "en"
+      ],
+      "modalities": [
+        "single_image_start"
+      ],
+      "task_type": "short_answer_qa",
+      "score_pipeline": [
+        "llm-judge"
+      ],
+      "score_params": {
+        "rubric": "scale",
+        "scale": [
+          0,
+          1
+        ],
+        "official_judge_model": "gpt-4-0613"
+      },
+      "score_protocol": {
+        "reference": "official@github.com/AIFEG/BenchLMM evaluate/gpt_evaluation_script.py — GPT judge prompt: 'Compare the ground truth and prediction from AI models, to give a correctness score for the prediction' ignoring case/grammar, '/' = multiple acceptable answers, similar meaning gets full marks; judge outputs a score in {0.0,0.1,...,1.0}; example model gpt-4-0613; final metric = mean score (avg_score.py).",
+        "note": "Every sample is LLM-judged with fractional credit on a 0-1 scale (11 discrete values); headline is the mean per-sample score, not binary accuracy. The mm-eval copy has only 96 rows (test split) whereas official BenchLMM spans many style/domain files (CT, MRI, infrared, AD, RS, styles, Robots/Games, each with its own eval variant) — published copy appears to be a small subset."
+      },
+      "prompt_template": "<image>{{ question }}\nAnswer the question using a single word or phrase.",
+      "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
+        },
+        "source": {
+          "format": "json",
+          "url": {
+            "test": "https://huggingface.co/datasets/AIFEG/BenchLMM"
+          }
+        }
+      },
+      "prompt_template_source": {
+        "origin": "official",
+        "reference": "https://github.com/AIFEG/BenchLMM/blob/main/baseline/LLaVA/BenchGPT_LLaVA_model_vqa.py#L44-L49 (qs = line['text']; DEFAULT_IMAGE_TOKEN + '\\n' + qs); trailer '\\nAnswer the question using a single word or phrase.' is embedded in every official jsonl 'text' field, e.g. https://github.com/AIFEG/BenchLMM/blob/main/jsonl/Benchmark_style_cartoon.jsonl",
+        "notes": "Tier 1: BenchLMM authors' own inference script feeds <image>\\n + question, and the official jsonl question text carries the '\\nAnswer the question using a single word or phrase.' trailer; the mm-eval template reproduces this (trailer re-appended because the mm-eval question column is trailer-free)."
+      }
     }
-   },
-   "prompt_template_source": {
-    "origin": "official",
-    "reference": "https://github.com/AIFEG/BenchLMM/blob/main/baseline/LLaVA/BenchGPT_LLaVA_model_vqa.py#L44-L49 (qs = line['text']; DEFAULT_IMAGE_TOKEN + '\\n' + qs); trailer '\\nAnswer the question using a single word or phrase.' is embedded in every official jsonl 'text' field, e.g. https://github.com/AIFEG/BenchLMM/blob/main/jsonl/Benchmark_style_cartoon.jsonl",
-    "notes": "Tier 1: BenchLMM authors' own inference script feeds <image>\\n + question, and the official jsonl question text carries the '\\nAnswer the question using a single word or phrase.' trailer; the mm-eval template reproduces this (trailer re-appended because the mm-eval question column is trailer-free)."
-   }
   }
- }
-}
+}