Add manifest (metadata.json)

metadata.json

@@ -1,58 +1,58 @@
 {
-  "name": "BenchLMM",
-  "release_date": "2024-07-01",
-  "subsets": {
-    "main": {
-      "language": [
-        "en"
-      ],
-      "modalities": [
-        "single_image_start"
-      ],
-      "task_type": "short_answer",
-      "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"
-          }
-        }
-      },
-      "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)."
-      }
+ "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"
+     }
     }
+   },
+   "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)."
+   }
   }
-}
+ }
+}