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add new model results
Browse files- static/eval_results/all_model_keywords_stats.json +778 -76
- static/eval_results/all_summary.json +89 -11
- utils.py +10 -11
static/eval_results/all_model_keywords_stats.json
CHANGED
@@ -1,4 +1,238 @@
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{
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"GPT_4o_mini": {
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"skills": {
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"Object Recognition and Classification": {
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"count": 43,
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"tasks": [],
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-
"average_score": 0.45508480503584553
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},
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"app": {
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@@ -187,113 +655,113 @@
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"average_score": 0.
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"input_format": {
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@@ -301,43 +769,43 @@
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"output_format": {
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@@ -345,37 +813,37 @@
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"input_num": {
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@@ -383,37 +851,37 @@
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"video": {
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"app": {
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@@ -421,49 +889,49 @@
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"Scene and Event Understanding": {
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@@ -1267,7 +1735,7 @@
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"average_score": 0.
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"video": {
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"Metrics": {
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@@ -2807,6 +3275,240 @@
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2810 |
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3530 |
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3533 |
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|
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|
3608 |
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|
3609 |
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3610 |
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"average_score": 0.5096772701625744
|
3611 |
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|
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|
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3624 |
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"average_score": 0.5066797418318023
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3626 |
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3627 |
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|
|
3671 |
"count": 315,
|
3672 |
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|
3673 |
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3674 |
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"average_score": 0.5373019912310933
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3675 |
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|
3676 |
"video": {
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3677 |
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|
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"count": 145,
|
3716 |
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|
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|
3719 |
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|
3720 |
"Metrics": {
|
3721 |
"count": 20,
|
static/eval_results/all_summary.json
CHANGED
@@ -5,16 +5,16 @@
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|
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"num_eval_samples": 6539,
|
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"num_not_eval_samples": 0,
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"num_total_samples": 6961,
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"micro_mean_score": 0.
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|
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|
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|
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"macro_mean_score": 0.
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17 |
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"micro_mean_score": 0.
|
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|
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"open": {
|
20 |
"num_eval_tasks": 65,
|
@@ -23,7 +23,7 @@
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|
23 |
"macro_mean_score": 0.6478225794744895,
|
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"micro_mean_score": 0.665391229578676
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|
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"overall_score": 0.
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"Gemini_1.5_pro_002": {
|
29 |
"core_noncot": {
|
@@ -39,8 +39,8 @@
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|
39 |
"num_eval_samples": 6539,
|
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"num_not_eval_samples": 0,
|
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"num_total_samples": 6961,
|
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"micro_mean_score": 0.
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44 |
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45 |
"open": {
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|
@@ -49,7 +49,7 @@
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|
49 |
"macro_mean_score": 0.5858190649927173,
|
50 |
"micro_mean_score": 0.6104901117798793
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"overall_score": 0.
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53 |
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"Gemini_1.5_flash_002": {
|
55 |
"core_noncot": {
|
@@ -91,8 +91,8 @@
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|
91 |
"num_eval_samples": 6539,
|
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"num_not_eval_samples": 0,
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"num_total_samples": 6961,
|
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"macro_mean_score": 0.
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"micro_mean_score": 0.
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"open": {
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98 |
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|
@@ -101,7 +101,33 @@
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101 |
"macro_mean_score": 0.6373907158949892,
|
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"micro_mean_score": 0.6569647463456579
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104 |
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"overall_score": 0.
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|
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"GPT_4o_mini": {
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"core_noncot": {
|
@@ -414,5 +440,57 @@
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|
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415 |
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|
416 |
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|
418 |
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"macro_mean_score": 0.5203470034386184,
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20 |
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29 |
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"num_eval_samples": 6539,
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"num_eval_samples": 6539,
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"macro_mean_score": 0.5029618079901714,
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"macro_mean_score": 0.6373907158949892,
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"overall_score": 0.5202645387105935
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},
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"Claude_3.5_new": {
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"num_eval_samples": 6539,
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},
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"num_total_samples": 6961,
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"micro_mean_score": 0.5230784020211157
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},
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"num_eval_samples": 1163,
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"num_total_samples": 1224,
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},
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|
495 |
}
|
496 |
}
|
utils.py
CHANGED
@@ -17,15 +17,18 @@ with open("./static/eval_results/all_summary.json", "r") as f:
|
|
17 |
|
18 |
# Define model name mapping
|
19 |
MODEL_NAME_MAP = {
|
|
|
20 |
"GPT_4o": "GPT-4o (0513)",
|
21 |
-
"Claude_3.5": "Claude-3.5-Sonnet",
|
22 |
"Gemini_1.5_pro_002": "Gemini-1.5-Pro-002",
|
23 |
"InternVL2_76B": "InternVL2-Llama3-76B",
|
24 |
"Qwen2_VL_72B": "Qwen2-VL-72B",
|
25 |
"llava_onevision_72B": "Llava-OneVision-72B",
|
|
|
26 |
"GPT_4o_mini": "GPT-4o mini",
|
27 |
"Gemini_1.5_flash_002": "Gemini-1.5-Flash-002",
|
28 |
"Pixtral_12B": "Pixtral 12B",
|
|
|
29 |
"Qwen2_VL_7B": "Qwen2-VL-7B",
|
30 |
"InternVL2_8B": "InternVL2-8B",
|
31 |
"llava_onevision_7B": "Llava-OneVision-7B",
|
@@ -92,10 +95,6 @@ KEYWORD_NAME_MAP = {
|
|
92 |
SUPER_GROUPS = {DIMENSION_NAME_MAP[dim]: [KEYWORD_NAME_MAP.get(k, k) for k in MODEL_DATA[next(iter(MODEL_DATA))][dim].keys()]
|
93 |
for dim in MODEL_DATA[next(iter(MODEL_DATA))]}
|
94 |
|
95 |
-
SUBMISSION_NAME = "test_leaderboard_submission"
|
96 |
-
SUBMISSION_URL = os.path.join("https://huggingface.co/datasets/cccjc/", SUBMISSION_NAME)
|
97 |
-
CSV_DIR = "./test_leaderboard_submission/results.csv"
|
98 |
-
|
99 |
def get_original_dimension(mapped_dimension):
|
100 |
return next(k for k, v in DIMENSION_NAME_MAP.items() if v == mapped_dimension)
|
101 |
|
@@ -105,12 +104,12 @@ def get_original_keyword(mapped_keyword):
|
|
105 |
# Define model groups
|
106 |
MODEL_GROUPS = {
|
107 |
"All": list(MODEL_DATA.keys()),
|
108 |
-
"Flagship Models": ['GPT_4o', 'Claude_3.5', 'Gemini_1.5_pro_002', 'Qwen2_VL_72B', 'InternVL2_76B', 'llava_onevision_72B'],
|
109 |
-
"Efficiency Models": ['Gemini_1.5_flash_002', 'GPT_4o_mini', 'Qwen2_VL_7B', 'Pixtral_12B', 'InternVL2_8B', 'Phi-3.5-vision', 'MiniCPM_v2.6', 'llava_onevision_7B', 'Llama_3_2_11B', 'Idefics3'],
|
110 |
-
"Proprietary Flagship models": ['GPT_4o', 'Claude_3.5', 'Gemini_1.5_pro_002'],
|
111 |
-
"
|
112 |
-
"Open-source Flagship Models": ['Qwen2_VL_72B', 'InternVL2_76B', 'llava_onevision_72B'],
|
113 |
-
"
|
114 |
}
|
115 |
|
116 |
def get_display_model_name(model_name):
|
|
|
17 |
|
18 |
# Define model name mapping
|
19 |
MODEL_NAME_MAP = {
|
20 |
+
"Claude_3.5_new": "Claude-3.5-Sonnet (1022)",
|
21 |
"GPT_4o": "GPT-4o (0513)",
|
22 |
+
"Claude_3.5": "Claude-3.5-Sonnet (0622)",
|
23 |
"Gemini_1.5_pro_002": "Gemini-1.5-Pro-002",
|
24 |
"InternVL2_76B": "InternVL2-Llama3-76B",
|
25 |
"Qwen2_VL_72B": "Qwen2-VL-72B",
|
26 |
"llava_onevision_72B": "Llava-OneVision-72B",
|
27 |
+
"NVLM": "NVLM-72B",
|
28 |
"GPT_4o_mini": "GPT-4o mini",
|
29 |
"Gemini_1.5_flash_002": "Gemini-1.5-Flash-002",
|
30 |
"Pixtral_12B": "Pixtral 12B",
|
31 |
+
"Aria": "Aria-MoE-25B",
|
32 |
"Qwen2_VL_7B": "Qwen2-VL-7B",
|
33 |
"InternVL2_8B": "InternVL2-8B",
|
34 |
"llava_onevision_7B": "Llava-OneVision-7B",
|
|
|
95 |
SUPER_GROUPS = {DIMENSION_NAME_MAP[dim]: [KEYWORD_NAME_MAP.get(k, k) for k in MODEL_DATA[next(iter(MODEL_DATA))][dim].keys()]
|
96 |
for dim in MODEL_DATA[next(iter(MODEL_DATA))]}
|
97 |
|
|
|
|
|
|
|
|
|
98 |
def get_original_dimension(mapped_dimension):
|
99 |
return next(k for k, v in DIMENSION_NAME_MAP.items() if v == mapped_dimension)
|
100 |
|
|
|
104 |
# Define model groups
|
105 |
MODEL_GROUPS = {
|
106 |
"All": list(MODEL_DATA.keys()),
|
107 |
+
"Flagship Models": ['Claude_3.5_new', 'GPT_4o', 'Claude_3.5', 'Gemini_1.5_pro_002', 'Qwen2_VL_72B', 'InternVL2_76B', 'llava_onevision_72B', 'NVLM-72B'],
|
108 |
+
"Efficiency Models": ['Gemini_1.5_flash_002', 'GPT_4o_mini', 'Qwen2_VL_7B', 'Pixtral_12B', 'Aria', 'InternVL2_8B', 'Phi-3.5-vision', 'MiniCPM_v2.6', 'llava_onevision_7B', 'Llama_3_2_11B', 'Idefics3'],
|
109 |
+
"Proprietary Flagship models": ['Claude_3.5_new', 'GPT_4o', 'Claude_3.5', 'Gemini_1.5_pro_002'],
|
110 |
+
"Proprietary Efficiency Models": ['Gemini_1.5_flash_002', 'GPT_4o_mini'],
|
111 |
+
"Open-source Flagship Models": ['Qwen2_VL_72B', 'InternVL2_76B', 'llava_onevision_72B', 'NVLM'],
|
112 |
+
"Open-source Efficiency Models": ['Qwen2_VL_7B', 'Pixtral_12B', 'Aria', 'InternVL2_8B', 'Phi-3.5-vision', 'MiniCPM_v2.6', 'llava_onevision_7B', 'Llama_3_2_11B', 'Idefics3'],
|
113 |
}
|
114 |
|
115 |
def get_display_model_name(model_name):
|