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flat/objects/03/02/0302704f-ccbd-416a-aabe-1d34b8dad5bc.json
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|
| 170 |
+
},
|
| 171 |
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"evaluation_result_id": "PwP-Bench/Claude-Sonnet-4.0/1771591481.616601#pwp_bench#overall_average_performance_on_pwp_bench_lite_cua_with_apis"
|
| 172 |
+
},
|
| 173 |
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{
|
| 174 |
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"evaluation_name": "PwP-Bench",
|
| 175 |
+
"source_data": {
|
| 176 |
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"dataset_name": "PwP-Bench",
|
| 177 |
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"source_type": "url",
|
| 178 |
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"url": [
|
| 179 |
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"https://www.alphaxiv.org/abs/2502.18525"
|
| 180 |
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]
|
| 181 |
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|
| 182 |
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"metric_config": {
|
| 183 |
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|
| 184 |
+
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|
| 185 |
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"min_score": 0.0,
|
| 186 |
+
"max_score": 100.0,
|
| 187 |
+
"evaluation_description": "Overall average performance of pure computer-use agents on the PwP-Bench-Lite benchmark. These agents interact with the IDE solely through visual inputs (screenshots) and primitive actions (keyboard/mouse), without access to file or bash APIs. The score is the macro average of task-specific metrics.",
|
| 188 |
+
"additional_details": {
|
| 189 |
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"alphaxiv_y_axis": "Overall Avg (%) - Pure CUA",
|
| 190 |
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"alphaxiv_is_primary": "False",
|
| 191 |
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"raw_evaluation_name": "Overall Average Performance on PwP-Bench-Lite - Pure Computer-Use Agents"
|
| 192 |
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},
|
| 193 |
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"metric_id": "overall_average_performance_on_pwp_bench_lite_pure_computer_use_agents",
|
| 194 |
+
"metric_name": "Overall Average Performance on PwP-Bench-Lite - Pure Computer-Use Agents",
|
| 195 |
+
"metric_kind": "score",
|
| 196 |
+
"metric_unit": "points"
|
| 197 |
+
},
|
| 198 |
+
"score_details": {
|
| 199 |
+
"score": 22.9
|
| 200 |
+
},
|
| 201 |
+
"evaluation_result_id": "PwP-Bench/Claude-Sonnet-4.0/1771591481.616601#pwp_bench#overall_average_performance_on_pwp_bench_lite_pure_computer_use_agents"
|
| 202 |
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}
|
| 203 |
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],
|
| 204 |
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"eval_library": {
|
| 205 |
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"name": "alphaxiv",
|
| 206 |
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"version": "unknown"
|
| 207 |
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}
|
| 208 |
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}
|
flat/objects/03/0b/030b16ab-f0fe-4854-8dc1-beeb7fa41232.json
ADDED
|
@@ -0,0 +1,148 @@
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|
|
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|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
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"schema_version": "0.2.2",
|
| 3 |
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"evaluation_id": "EffiBench/CodeLlama-13b-hf/1771591481.616601",
|
| 4 |
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|
| 5 |
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"source_metadata": {
|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
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|
| 11 |
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|
| 12 |
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| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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"model_info": {
|
| 18 |
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"id": "CodeLlama-13b-hf",
|
| 19 |
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"name": "CodeLlama-13b-hf",
|
| 20 |
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|
| 21 |
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|
| 22 |
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"evaluation_results": [
|
| 23 |
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{
|
| 24 |
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"evaluation_name": "EffiBench",
|
| 25 |
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|
| 26 |
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"dataset_name": "EffiBench",
|
| 27 |
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"source_type": "url",
|
| 28 |
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"url": [
|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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|
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| 34 |
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|
| 35 |
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| 36 |
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|
| 37 |
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|
| 38 |
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|
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| 40 |
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|
| 42 |
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|
| 43 |
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"metric_id": "overall_code_efficiency_on_effibench_normalized_execution_time",
|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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"score": 2.71
|
| 50 |
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|
| 51 |
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"evaluation_result_id": "EffiBench/CodeLlama-13b-hf/1771591481.616601#effibench#overall_code_efficiency_on_effibench_normalized_execution_time"
|
| 52 |
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},
|
| 53 |
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{
|
| 54 |
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"evaluation_name": "EffiBench",
|
| 55 |
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|
| 56 |
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|
| 57 |
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|
| 58 |
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"url": [
|
| 59 |
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|
| 60 |
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|
| 61 |
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|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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"evaluation_description": "Normalized Max Memory Usage (NMU) measures the peak memory consumption of generated code relative to a human-written canonical solution. A value > 1 means the generated code uses more peak memory. This metric assesses the spatial efficiency of the generated algorithms. Lower is better.",
|
| 68 |
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"additional_details": {
|
| 69 |
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|
| 70 |
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|
| 71 |
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|
| 72 |
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},
|
| 73 |
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"metric_id": "overall_code_efficiency_on_effibench_normalized_max_memory_usage",
|
| 74 |
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"metric_name": "Overall Code Efficiency on EffiBench: Normalized Max Memory Usage",
|
| 75 |
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|
| 76 |
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|
| 77 |
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|
| 78 |
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|
| 79 |
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"score": 1.85
|
| 80 |
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|
| 81 |
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"evaluation_result_id": "EffiBench/CodeLlama-13b-hf/1771591481.616601#effibench#overall_code_efficiency_on_effibench_normalized_max_memory_usage"
|
| 82 |
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},
|
| 83 |
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{
|
| 84 |
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"evaluation_name": "EffiBench",
|
| 85 |
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|
| 86 |
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"dataset_name": "EffiBench",
|
| 87 |
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"source_type": "url",
|
| 88 |
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"url": [
|
| 89 |
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"https://huggingface.co/spaces/EffiBench/effibench-leaderboard"
|
| 90 |
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|
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|
| 92 |
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|
| 93 |
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|
| 94 |
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|
| 95 |
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"min_score": 0.0,
|
| 96 |
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"max_score": 100.0,
|
| 97 |
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"evaluation_description": "Normalized Total Memory Usage (NTMU) compares the dynamic memory efficiency (area under the memory usage curve) of generated code to a canonical solution. A value > 1 means the code is less efficient in its overall memory management. This metric captures both magnitude and duration of memory use. Lower is better.",
|
| 98 |
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|
| 99 |
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"alphaxiv_y_axis": "Normalized Total Memory Usage (NTMU)",
|
| 100 |
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|
| 101 |
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|
| 102 |
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|
| 103 |
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|
| 104 |
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"metric_name": "Overall Code Efficiency on EffiBench: Normalized Total Memory Usage",
|
| 105 |
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|
| 106 |
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|
| 107 |
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},
|
| 108 |
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|
| 109 |
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"score": 5.32
|
| 110 |
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},
|
| 111 |
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"evaluation_result_id": "EffiBench/CodeLlama-13b-hf/1771591481.616601#effibench#overall_code_efficiency_on_effibench_normalized_total_memory_usage"
|
| 112 |
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},
|
| 113 |
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{
|
| 114 |
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"evaluation_name": "EffiBench",
|
| 115 |
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"source_data": {
|
| 116 |
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"dataset_name": "EffiBench",
|
| 117 |
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"source_type": "url",
|
| 118 |
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"url": [
|
| 119 |
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"https://huggingface.co/spaces/EffiBench/effibench-leaderboard"
|
| 120 |
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]
|
| 121 |
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},
|
| 122 |
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"metric_config": {
|
| 123 |
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"lower_is_better": false,
|
| 124 |
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"score_type": "continuous",
|
| 125 |
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"min_score": 0.0,
|
| 126 |
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"max_score": 100.0,
|
| 127 |
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"evaluation_description": "Pass@1 measures the percentage of problems for which a model generates a functionally correct solution on the first attempt. Only code that passes this correctness check is evaluated for efficiency. Higher is better.",
|
| 128 |
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"additional_details": {
|
| 129 |
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"alphaxiv_y_axis": "Pass@1 (%)",
|
| 130 |
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"alphaxiv_is_primary": "False",
|
| 131 |
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"raw_evaluation_name": "Overall Code Correctness on EffiBench (Pass@1)"
|
| 132 |
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},
|
| 133 |
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"metric_id": "overall_code_correctness_on_effibench_pass_1",
|
| 134 |
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"metric_name": "Overall Code Correctness on EffiBench (Pass@1)",
|
| 135 |
+
"metric_kind": "score",
|
| 136 |
+
"metric_unit": "points"
|
| 137 |
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},
|
| 138 |
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"score_details": {
|
| 139 |
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"score": 1.1
|
| 140 |
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},
|
| 141 |
+
"evaluation_result_id": "EffiBench/CodeLlama-13b-hf/1771591481.616601#effibench#overall_code_correctness_on_effibench_pass_1"
|
| 142 |
+
}
|
| 143 |
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],
|
| 144 |
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"eval_library": {
|
| 145 |
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"name": "alphaxiv",
|
| 146 |
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"version": "unknown"
|
| 147 |
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}
|
| 148 |
+
}
|
flat/objects/03/0b/030b8c6e-e3f5-403d-8382-db08ad060c75.json
ADDED
|
@@ -0,0 +1,328 @@
|
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|
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|
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{
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| 8 |
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| 9 |
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| 10 |
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|
| 11 |
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| 12 |
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"alphaxiv_dataset_org": "University of Graz",
|
| 13 |
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| 14 |
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"scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
|
| 15 |
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|
| 16 |
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|
| 17 |
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"model_info": {
|
| 18 |
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"id": "Qwen 2.5 14B",
|
| 19 |
+
"name": "Qwen 2.5 14B",
|
| 20 |
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|
| 21 |
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|
| 22 |
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| 23 |
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{
|
| 24 |
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"evaluation_name": "MedBench-IT",
|
| 25 |
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|
| 26 |
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"dataset_name": "MedBench-IT",
|
| 27 |
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"source_type": "url",
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| 28 |
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"url": [
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| 29 |
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"https://www.alphaxiv.org/abs/2509.07135"
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| 30 |
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|
| 31 |
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| 32 |
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| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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"evaluation_description": "Measures the overall percentage of correctly answered questions from the MedBench-IT benchmark using a standard, direct-answering prompt. This is the primary metric for evaluating model performance on the Italian medical entrance examination questions.",
|
| 38 |
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"additional_details": {
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| 39 |
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"alphaxiv_y_axis": "Overall Accuracy (%)",
|
| 40 |
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"alphaxiv_is_primary": "True",
|
| 41 |
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"raw_evaluation_name": "Overall Accuracy on MedBench-IT (Standard Prompt)"
|
| 42 |
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|
| 43 |
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"metric_id": "overall_accuracy_on_medbench_it_standard_prompt",
|
| 44 |
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"metric_name": "Overall Accuracy on MedBench-IT (Standard Prompt)",
|
| 45 |
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"metric_kind": "score",
|
| 46 |
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"metric_unit": "points"
|
| 47 |
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},
|
| 48 |
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"score_details": {
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| 49 |
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"score": 72.6
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| 50 |
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| 51 |
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"evaluation_result_id": "MedBench-IT/Qwen 2.5 14B/1771591481.616601#medbench_it#overall_accuracy_on_medbench_it_standard_prompt"
|
| 52 |
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},
|
| 53 |
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{
|
| 54 |
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"evaluation_name": "MedBench-IT",
|
| 55 |
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"source_data": {
|
| 56 |
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"dataset_name": "MedBench-IT",
|
| 57 |
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"source_type": "url",
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| 58 |
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"url": [
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| 59 |
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"https://www.alphaxiv.org/abs/2509.07135"
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| 60 |
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]
|
| 61 |
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| 62 |
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| 63 |
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"lower_is_better": false,
|
| 64 |
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"score_type": "continuous",
|
| 65 |
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"min_score": 0.0,
|
| 66 |
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"max_score": 100.0,
|
| 67 |
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"evaluation_description": "Measures accuracy on the Chemistry subset of MedBench-IT using a standard prompt. This is a knowledge-intensive subject where most models performed well.",
|
| 68 |
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"additional_details": {
|
| 69 |
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"alphaxiv_y_axis": "Accuracy (%) - Chemistry (Std.)",
|
| 70 |
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"alphaxiv_is_primary": "False",
|
| 71 |
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"raw_evaluation_name": "Accuracy on Chemistry Questions (Standard Prompt)"
|
| 72 |
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},
|
| 73 |
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"metric_id": "accuracy_on_chemistry_questions_standard_prompt",
|
| 74 |
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"metric_name": "Accuracy on Chemistry Questions (Standard Prompt)",
|
| 75 |
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"metric_kind": "score",
|
| 76 |
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"metric_unit": "points"
|
| 77 |
+
},
|
| 78 |
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"score_details": {
|
| 79 |
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"score": 73.5
|
| 80 |
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},
|
| 81 |
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"evaluation_result_id": "MedBench-IT/Qwen 2.5 14B/1771591481.616601#medbench_it#accuracy_on_chemistry_questions_standard_prompt"
|
| 82 |
+
},
|
| 83 |
+
{
|
| 84 |
+
"evaluation_name": "MedBench-IT",
|
| 85 |
+
"source_data": {
|
| 86 |
+
"dataset_name": "MedBench-IT",
|
| 87 |
+
"source_type": "url",
|
| 88 |
+
"url": [
|
| 89 |
+
"https://www.alphaxiv.org/abs/2509.07135"
|
| 90 |
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]
|
| 91 |
+
},
|
| 92 |
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"metric_config": {
|
| 93 |
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"lower_is_better": false,
|
| 94 |
+
"score_type": "continuous",
|
| 95 |
+
"min_score": 0.0,
|
| 96 |
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"max_score": 100.0,
|
| 97 |
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"evaluation_description": "Measures accuracy on the General Culture subset of MedBench-IT using a standard prompt.",
|
| 98 |
+
"additional_details": {
|
| 99 |
+
"alphaxiv_y_axis": "Accuracy (%) - General Culture (Std.)",
|
| 100 |
+
"alphaxiv_is_primary": "False",
|
| 101 |
+
"raw_evaluation_name": "Accuracy on General Culture Questions (Standard Prompt)"
|
| 102 |
+
},
|
| 103 |
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"metric_id": "accuracy_on_general_culture_questions_standard_prompt",
|
| 104 |
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"metric_name": "Accuracy on General Culture Questions (Standard Prompt)",
|
| 105 |
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"metric_kind": "score",
|
| 106 |
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"metric_unit": "points"
|
| 107 |
+
},
|
| 108 |
+
"score_details": {
|
| 109 |
+
"score": 76.6
|
| 110 |
+
},
|
| 111 |
+
"evaluation_result_id": "MedBench-IT/Qwen 2.5 14B/1771591481.616601#medbench_it#accuracy_on_general_culture_questions_standard_prompt"
|
| 112 |
+
},
|
| 113 |
+
{
|
| 114 |
+
"evaluation_name": "MedBench-IT",
|
| 115 |
+
"source_data": {
|
| 116 |
+
"dataset_name": "MedBench-IT",
|
| 117 |
+
"source_type": "url",
|
| 118 |
+
"url": [
|
| 119 |
+
"https://www.alphaxiv.org/abs/2509.07135"
|
| 120 |
+
]
|
| 121 |
+
},
|
| 122 |
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"metric_config": {
|
| 123 |
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"lower_is_better": false,
|
| 124 |
+
"score_type": "continuous",
|
| 125 |
+
"min_score": 0.0,
|
| 126 |
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|
| 127 |
+
"evaluation_description": "Measures accuracy on the Logic subset of MedBench-IT using a reasoning-eliciting prompt. This subject was identified as particularly challenging for most models.",
|
| 128 |
+
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|
| 129 |
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"alphaxiv_y_axis": "Accuracy (%) - Logic (Reas.)",
|
| 130 |
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"alphaxiv_is_primary": "False",
|
| 131 |
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"raw_evaluation_name": "Accuracy on Logic Questions (Reasoning Prompt)"
|
| 132 |
+
},
|
| 133 |
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"metric_id": "accuracy_on_logic_questions_reasoning_prompt",
|
| 134 |
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"metric_name": "Accuracy on Logic Questions (Reasoning Prompt)",
|
| 135 |
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|
| 136 |
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"metric_unit": "points"
|
| 137 |
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},
|
| 138 |
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"score_details": {
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| 139 |
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"score": 62.1
|
| 140 |
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},
|
| 141 |
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"evaluation_result_id": "MedBench-IT/Qwen 2.5 14B/1771591481.616601#medbench_it#accuracy_on_logic_questions_reasoning_prompt"
|
| 142 |
+
},
|
| 143 |
+
{
|
| 144 |
+
"evaluation_name": "MedBench-IT",
|
| 145 |
+
"source_data": {
|
| 146 |
+
"dataset_name": "MedBench-IT",
|
| 147 |
+
"source_type": "url",
|
| 148 |
+
"url": [
|
| 149 |
+
"https://www.alphaxiv.org/abs/2509.07135"
|
| 150 |
+
]
|
| 151 |
+
},
|
| 152 |
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"metric_config": {
|
| 153 |
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"lower_is_better": false,
|
| 154 |
+
"score_type": "continuous",
|
| 155 |
+
"min_score": 0.0,
|
| 156 |
+
"max_score": 100.0,
|
| 157 |
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"evaluation_description": "Measures accuracy on the Logic subset of MedBench-IT using a standard prompt. This subject was identified as particularly challenging for most models, testing abstract and multi-step reasoning.",
|
| 158 |
+
"additional_details": {
|
| 159 |
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"alphaxiv_y_axis": "Accuracy (%) - Logic (Std.)",
|
| 160 |
+
"alphaxiv_is_primary": "False",
|
| 161 |
+
"raw_evaluation_name": "Accuracy on Logic Questions (Standard Prompt)"
|
| 162 |
+
},
|
| 163 |
+
"metric_id": "accuracy_on_logic_questions_standard_prompt",
|
| 164 |
+
"metric_name": "Accuracy on Logic Questions (Standard Prompt)",
|
| 165 |
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"metric_kind": "score",
|
| 166 |
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"metric_unit": "points"
|
| 167 |
+
},
|
| 168 |
+
"score_details": {
|
| 169 |
+
"score": 56.3
|
| 170 |
+
},
|
| 171 |
+
"evaluation_result_id": "MedBench-IT/Qwen 2.5 14B/1771591481.616601#medbench_it#accuracy_on_logic_questions_standard_prompt"
|
| 172 |
+
},
|
| 173 |
+
{
|
| 174 |
+
"evaluation_name": "MedBench-IT",
|
| 175 |
+
"source_data": {
|
| 176 |
+
"dataset_name": "MedBench-IT",
|
| 177 |
+
"source_type": "url",
|
| 178 |
+
"url": [
|
| 179 |
+
"https://www.alphaxiv.org/abs/2509.07135"
|
| 180 |
+
]
|
| 181 |
+
},
|
| 182 |
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"metric_config": {
|
| 183 |
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"lower_is_better": false,
|
| 184 |
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"score_type": "continuous",
|
| 185 |
+
"min_score": 0.0,
|
| 186 |
+
"max_score": 100.0,
|
| 187 |
+
"evaluation_description": "Measures accuracy on the Biology subset of MedBench-IT using a standard prompt. This is a knowledge-intensive subject where most models performed well.",
|
| 188 |
+
"additional_details": {
|
| 189 |
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"alphaxiv_y_axis": "Accuracy (%) - Biology (Std.)",
|
| 190 |
+
"alphaxiv_is_primary": "False",
|
| 191 |
+
"raw_evaluation_name": "Accuracy on Biology Questions (Standard Prompt)"
|
| 192 |
+
},
|
| 193 |
+
"metric_id": "accuracy_on_biology_questions_standard_prompt",
|
| 194 |
+
"metric_name": "Accuracy on Biology Questions (Standard Prompt)",
|
| 195 |
+
"metric_kind": "score",
|
| 196 |
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"metric_unit": "points"
|
| 197 |
+
},
|
| 198 |
+
"score_details": {
|
| 199 |
+
"score": 81.2
|
| 200 |
+
},
|
| 201 |
+
"evaluation_result_id": "MedBench-IT/Qwen 2.5 14B/1771591481.616601#medbench_it#accuracy_on_biology_questions_standard_prompt"
|
| 202 |
+
},
|
| 203 |
+
{
|
| 204 |
+
"evaluation_name": "MedBench-IT",
|
| 205 |
+
"source_data": {
|
| 206 |
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"dataset_name": "MedBench-IT",
|
| 207 |
+
"source_type": "url",
|
| 208 |
+
"url": [
|
| 209 |
+
"https://www.alphaxiv.org/abs/2509.07135"
|
| 210 |
+
]
|
| 211 |
+
},
|
| 212 |
+
"metric_config": {
|
| 213 |
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"lower_is_better": false,
|
| 214 |
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"score_type": "continuous",
|
| 215 |
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"min_score": 0.0,
|
| 216 |
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"max_score": 100.0,
|
| 217 |
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"evaluation_description": "Measures accuracy on the Mathematics subset of MedBench-IT using a standard prompt. This subject was identified as particularly challenging for most models, testing quantitative reasoning.",
|
| 218 |
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"additional_details": {
|
| 219 |
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"alphaxiv_y_axis": "Accuracy (%) - Mathematics (Std.)",
|
| 220 |
+
"alphaxiv_is_primary": "False",
|
| 221 |
+
"raw_evaluation_name": "Accuracy on Mathematics Questions (Standard Prompt)"
|
| 222 |
+
},
|
| 223 |
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"metric_id": "accuracy_on_mathematics_questions_standard_prompt",
|
| 224 |
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"metric_name": "Accuracy on Mathematics Questions (Standard Prompt)",
|
| 225 |
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"metric_kind": "score",
|
| 226 |
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"metric_unit": "points"
|
| 227 |
+
},
|
| 228 |
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"score_details": {
|
| 229 |
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"score": 69.5
|
| 230 |
+
},
|
| 231 |
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"evaluation_result_id": "MedBench-IT/Qwen 2.5 14B/1771591481.616601#medbench_it#accuracy_on_mathematics_questions_standard_prompt"
|
| 232 |
+
},
|
| 233 |
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{
|
| 234 |
+
"evaluation_name": "MedBench-IT",
|
| 235 |
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"source_data": {
|
| 236 |
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"dataset_name": "MedBench-IT",
|
| 237 |
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"source_type": "url",
|
| 238 |
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"url": [
|
| 239 |
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"https://www.alphaxiv.org/abs/2509.07135"
|
| 240 |
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]
|
| 241 |
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},
|
| 242 |
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"metric_config": {
|
| 243 |
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"lower_is_better": false,
|
| 244 |
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"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
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"max_score": 100.0,
|
| 247 |
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"evaluation_description": "Measures accuracy on the Physics subset of MedBench-IT using a standard prompt.",
|
| 248 |
+
"additional_details": {
|
| 249 |
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"alphaxiv_y_axis": "Accuracy (%) - Physics (Std.)",
|
| 250 |
+
"alphaxiv_is_primary": "False",
|
| 251 |
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"raw_evaluation_name": "Accuracy on Physics Questions (Standard Prompt)"
|
| 252 |
+
},
|
| 253 |
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"metric_id": "accuracy_on_physics_questions_standard_prompt",
|
| 254 |
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"metric_name": "Accuracy on Physics Questions (Standard Prompt)",
|
| 255 |
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"metric_kind": "score",
|
| 256 |
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"metric_unit": "points"
|
| 257 |
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},
|
| 258 |
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"score_details": {
|
| 259 |
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"score": 72.8
|
| 260 |
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},
|
| 261 |
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"evaluation_result_id": "MedBench-IT/Qwen 2.5 14B/1771591481.616601#medbench_it#accuracy_on_physics_questions_standard_prompt"
|
| 262 |
+
},
|
| 263 |
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{
|
| 264 |
+
"evaluation_name": "MedBench-IT",
|
| 265 |
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"source_data": {
|
| 266 |
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"dataset_name": "MedBench-IT",
|
| 267 |
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"source_type": "url",
|
| 268 |
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"url": [
|
| 269 |
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"https://www.alphaxiv.org/abs/2509.07135"
|
| 270 |
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]
|
| 271 |
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},
|
| 272 |
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"metric_config": {
|
| 273 |
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"lower_is_better": false,
|
| 274 |
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"score_type": "continuous",
|
| 275 |
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"min_score": 0.0,
|
| 276 |
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"max_score": 100.0,
|
| 277 |
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"evaluation_description": "Measures the overall percentage of correctly answered questions from the MedBench-IT benchmark using a reasoning-eliciting (Chain-of-Thought style) prompt. This evaluates the impact of explicit reasoning on model accuracy.",
|
| 278 |
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"additional_details": {
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| 279 |
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"alphaxiv_y_axis": "Overall Accuracy (%) - Reasoning Prompt",
|
| 280 |
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"alphaxiv_is_primary": "False",
|
| 281 |
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"raw_evaluation_name": "Overall Accuracy on MedBench-IT (Reasoning-Eliciting Prompt)"
|
| 282 |
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},
|
| 283 |
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"metric_id": "overall_accuracy_on_medbench_it_reasoning_eliciting_prompt",
|
| 284 |
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"metric_name": "Overall Accuracy on MedBench-IT (Reasoning-Eliciting Prompt)",
|
| 285 |
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|
| 286 |
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"metric_unit": "points"
|
| 287 |
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},
|
| 288 |
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"score_details": {
|
| 289 |
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"score": 76.9
|
| 290 |
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},
|
| 291 |
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"evaluation_result_id": "MedBench-IT/Qwen 2.5 14B/1771591481.616601#medbench_it#overall_accuracy_on_medbench_it_reasoning_eliciting_prompt"
|
| 292 |
+
},
|
| 293 |
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{
|
| 294 |
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"evaluation_name": "MedBench-IT",
|
| 295 |
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"source_data": {
|
| 296 |
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"dataset_name": "MedBench-IT",
|
| 297 |
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"source_type": "url",
|
| 298 |
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"url": [
|
| 299 |
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"https://www.alphaxiv.org/abs/2509.07135"
|
| 300 |
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]
|
| 301 |
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},
|
| 302 |
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"metric_config": {
|
| 303 |
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"lower_is_better": false,
|
| 304 |
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"score_type": "continuous",
|
| 305 |
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"min_score": 0.0,
|
| 306 |
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"max_score": 100.0,
|
| 307 |
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"evaluation_description": "Measures accuracy on the Mathematics subset of MedBench-IT using a reasoning-eliciting prompt. This subject was identified as particularly challenging for most models.",
|
| 308 |
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"additional_details": {
|
| 309 |
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"alphaxiv_y_axis": "Accuracy (%) - Mathematics (Reas.)",
|
| 310 |
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"alphaxiv_is_primary": "False",
|
| 311 |
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"raw_evaluation_name": "Accuracy on Mathematics Questions (Reasoning Prompt)"
|
| 312 |
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|
| 313 |
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"metric_id": "accuracy_on_mathematics_questions_reasoning_prompt",
|
| 314 |
+
"metric_name": "Accuracy on Mathematics Questions (Reasoning Prompt)",
|
| 315 |
+
"metric_kind": "score",
|
| 316 |
+
"metric_unit": "points"
|
| 317 |
+
},
|
| 318 |
+
"score_details": {
|
| 319 |
+
"score": 71.7
|
| 320 |
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},
|
| 321 |
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"evaluation_result_id": "MedBench-IT/Qwen 2.5 14B/1771591481.616601#medbench_it#accuracy_on_mathematics_questions_reasoning_prompt"
|
| 322 |
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|
| 323 |
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|
| 324 |
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|
| 325 |
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|
| 326 |
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|
| 327 |
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|
| 328 |
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}
|
flat/objects/03/0b/030bb1a7-28d6-43d9-add5-f1eeb3380dab.json
ADDED
|
@@ -0,0 +1,88 @@
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|
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|
|
|
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|
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|
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|
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|
|
|
|
|
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|
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|
|
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|
|
|
|
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|
|
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|
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|
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|
|
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|
| 1 |
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|
| 2 |
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| 5 |
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| 6 |
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| 7 |
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| 8 |
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| 10 |
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| 11 |
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| 12 |
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| 13 |
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| 14 |
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|
| 15 |
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| 16 |
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| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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| 25 |
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|
| 26 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 37 |
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| 48 |
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| 49 |
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| 51 |
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{
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| 54 |
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| 58 |
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| 59 |
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| 66 |
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|
| 67 |
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|
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| 69 |
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| 70 |
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| 71 |
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| 72 |
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| 75 |
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| 76 |
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| 77 |
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| 78 |
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| 79 |
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"score": 79.61
|
| 80 |
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| 81 |
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| 84 |
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| 85 |
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| 88 |
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|
flat/objects/03/0c/030c24dc-a020-4e44-8e10-c7c6982cb43c.json
ADDED
|
@@ -0,0 +1,208 @@
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| 1 |
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flat/objects/03/11/03116601-2283-44fb-a722-5b7dee328057.json
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flat/objects/03/16/03164b5b-c557-4a32-bbed-99d8b0e1555c.json
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"evaluation_description": "Overall performance on the ImgEdit-Full benchmark, a general-purpose image editing benchmark. This evaluation was performed to show that specializing on structured visuals did not degrade general editing capabilities. Higher is better.",
|
| 218 |
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|
| 219 |
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|
| 220 |
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|
| 221 |
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"raw_evaluation_name": "Overall Performance on ImgEdit-Full (General Editing)"
|
| 222 |
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},
|
| 223 |
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"metric_id": "overall_performance_on_imgedit_full_general_editing",
|
| 224 |
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"metric_name": "Overall Performance on ImgEdit-Full (General Editing)",
|
| 225 |
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"metric_kind": "score",
|
| 226 |
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"metric_unit": "points"
|
| 227 |
+
},
|
| 228 |
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"score_details": {
|
| 229 |
+
"score": 4.27
|
| 230 |
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},
|
| 231 |
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"evaluation_result_id": "StructBench/Qwen-Image/1771591481.616601#structbench#overall_performance_on_imgedit_full_general_editing"
|
| 232 |
+
},
|
| 233 |
+
{
|
| 234 |
+
"evaluation_name": "StructBench",
|
| 235 |
+
"source_data": {
|
| 236 |
+
"dataset_name": "StructBench",
|
| 237 |
+
"source_type": "url",
|
| 238 |
+
"url": [
|
| 239 |
+
"https://www.alphaxiv.org/abs/2510.05091"
|
| 240 |
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]
|
| 241 |
+
},
|
| 242 |
+
"metric_config": {
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
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"min_score": 0.0,
|
| 246 |
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"max_score": 100.0,
|
| 247 |
+
"evaluation_description": "Performance on the Table domain of the StructT2IBench benchmark for structured image generation. The metric is StructScore (reported as Accuracy %).",
|
| 248 |
+
"additional_details": {
|
| 249 |
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"alphaxiv_y_axis": "StructScore (%) - Table (Generation)",
|
| 250 |
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"alphaxiv_is_primary": "False",
|
| 251 |
+
"raw_evaluation_name": "Table Performance on StructT2IBench (Image Generation)"
|
| 252 |
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},
|
| 253 |
+
"metric_id": "table_performance_on_structt2ibench_image_generation",
|
| 254 |
+
"metric_name": "Table Performance on StructT2IBench (Image Generation)",
|
| 255 |
+
"metric_kind": "score",
|
| 256 |
+
"metric_unit": "points"
|
| 257 |
+
},
|
| 258 |
+
"score_details": {
|
| 259 |
+
"score": 73.65
|
| 260 |
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},
|
| 261 |
+
"evaluation_result_id": "StructBench/Qwen-Image/1771591481.616601#structbench#table_performance_on_structt2ibench_image_generation"
|
| 262 |
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}
|
| 263 |
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],
|
| 264 |
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"eval_library": {
|
| 265 |
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"name": "alphaxiv",
|
| 266 |
+
"version": "unknown"
|
| 267 |
+
}
|
| 268 |
+
}
|
flat/objects/03/1b/031b3333-f8b6-4f59-a0fc-3fb00c0c907d.json
ADDED
|
@@ -0,0 +1,79 @@
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|
|
|
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|
|
|
|
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|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
{
|
| 2 |
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"schema_version": "0.2.2",
|
| 3 |
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"evaluation_id": "multi-swe-bench/typescript/anthropic_Claude-3.5-Sonnet_Oct_/20250329_MopenHands_Claude-3.5-Sonnet_Oct_/1776704403.994647",
|
| 4 |
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"evaluation_timestamp": "2025-03-29",
|
| 5 |
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|
| 6 |
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|
| 7 |
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| 8 |
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| 9 |
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|
| 10 |
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|
| 11 |
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"evaluator_relationship": "third_party"
|
| 12 |
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| 13 |
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|
| 14 |
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"name": "multi-swe-bench",
|
| 15 |
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"version": "unknown"
|
| 16 |
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},
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| 17 |
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"model_info": {
|
| 18 |
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"name": "Claude-3.5-Sonnet(Oct)",
|
| 19 |
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"id": "anthropic/Claude-3.5-Sonnet(Oct)",
|
| 20 |
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"developer": "anthropic",
|
| 21 |
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"additional_details": {
|
| 22 |
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"submission_name": "MopenHands + Claude-3.5-Sonnet(Oct)",
|
| 23 |
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"language": "typescript",
|
| 24 |
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"oss": "True",
|
| 25 |
+
"site": "https://github.com/multi-swe-bench/MopenHands",
|
| 26 |
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"verified": "True",
|
| 27 |
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"submission_dir": "20250329_MopenHands_Claude-3.5-Sonnet(Oct)",
|
| 28 |
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"agent": "MopenHands"
|
| 29 |
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}
|
| 30 |
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},
|
| 31 |
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"evaluation_results": [
|
| 32 |
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{
|
| 33 |
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"evaluation_name": "Multi-SWE-Bench (typescript)",
|
| 34 |
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"source_data": {
|
| 35 |
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"dataset_name": "Multi-SWE-bench (typescript)",
|
| 36 |
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"source_type": "url",
|
| 37 |
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"url": [
|
| 38 |
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"https://huggingface.co/datasets/ByteDance-Seed/Multi-SWE-bench"
|
| 39 |
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]
|
| 40 |
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},
|
| 41 |
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"evaluation_timestamp": "2025-03-29",
|
| 42 |
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|
| 43 |
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"evaluation_description": "Fraction of typescript GitHub issues resolved (0.0–1.0)",
|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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"metric_id": "multi_swe_bench_leaderboard.score",
|
| 49 |
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"metric_name": "Score",
|
| 50 |
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|
| 51 |
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|
| 52 |
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| 53 |
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| 54 |
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"score": 0.11607142857142858,
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| 55 |
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| 56 |
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| 57 |
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| 60 |
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| 61 |
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| 62 |
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| 63 |
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| 64 |
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"generation_config": {
|
| 65 |
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"generation_args": {
|
| 66 |
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"agentic_eval_config": {
|
| 67 |
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"available_tools": [
|
| 68 |
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{
|
| 69 |
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"name": "bash"
|
| 70 |
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}
|
| 71 |
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|
| 72 |
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},
|
| 73 |
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"max_attempts": 1
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| 74 |
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}
|
| 75 |
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},
|
| 76 |
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"evaluation_result_id": "multi-swe-bench/typescript/anthropic_Claude-3.5-Sonnet_Oct_/20250329_MopenHands_Claude-3.5-Sonnet_Oct_/1776704403.994647#multi_swe_bench_typescript#multi_swe_bench_leaderboard_score"
|
| 77 |
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}
|
| 78 |
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]
|
| 79 |
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|
flat/objects/03/1b/031b77c2-34a7-43a0-bb8b-d08644d35f62.json
ADDED
|
@@ -0,0 +1,328 @@
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
|
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|
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|
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|
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|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
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|
| 1 |
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{
|
| 2 |
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"schema_version": "0.2.2",
|
| 3 |
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"evaluation_id": "KILT/BERT + DPR/1771591481.616601",
|
| 4 |
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"retrieved_timestamp": "1771591481.616601",
|
| 5 |
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|
| 6 |
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|
| 7 |
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| 8 |
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|
| 9 |
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"source_organization_url": "https://alphaxiv.org",
|
| 10 |
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"evaluator_relationship": "third_party",
|
| 11 |
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"additional_details": {
|
| 12 |
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"alphaxiv_dataset_org": "University of Amsterdam",
|
| 13 |
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"alphaxiv_dataset_type": "text",
|
| 14 |
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"scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
|
| 15 |
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}
|
| 16 |
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},
|
| 17 |
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"model_info": {
|
| 18 |
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"id": "BERT + DPR",
|
| 19 |
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"name": "BERT + DPR",
|
| 20 |
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"developer": "unknown"
|
| 21 |
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},
|
| 22 |
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"evaluation_results": [
|
| 23 |
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{
|
| 24 |
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"evaluation_name": "KILT",
|
| 25 |
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|
| 26 |
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"dataset_name": "KILT",
|
| 27 |
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"source_type": "url",
|
| 28 |
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"url": [
|
| 29 |
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"https://huggingface.co/datasets?search=kilt"
|
| 30 |
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]
|
| 31 |
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},
|
| 32 |
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"metric_config": {
|
| 33 |
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|
| 34 |
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"score_type": "continuous",
|
| 35 |
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"min_score": 0.0,
|
| 36 |
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"max_score": 100.0,
|
| 37 |
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"evaluation_description": "Measures KILT Exact Match on the Natural Questions (NQ) task. This metric only awards points for a correct answer if the model also provides correct provenance (R-Precision = 1), jointly evaluating accuracy and explainability.",
|
| 38 |
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"additional_details": {
|
| 39 |
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"alphaxiv_y_axis": "KILT Exact Match (%)",
|
| 40 |
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"alphaxiv_is_primary": "True",
|
| 41 |
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"raw_evaluation_name": "KILT Score on Natural Questions (Open Domain QA)"
|
| 42 |
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},
|
| 43 |
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"metric_id": "kilt_score_on_natural_questions_open_domain_qa",
|
| 44 |
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"metric_name": "KILT Score on Natural Questions (Open Domain QA)",
|
| 45 |
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"metric_kind": "score",
|
| 46 |
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"metric_unit": "points"
|
| 47 |
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},
|
| 48 |
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"score_details": {
|
| 49 |
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"score": 31.99
|
| 50 |
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},
|
| 51 |
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"evaluation_result_id": "KILT/BERT + DPR/1771591481.616601#kilt#kilt_score_on_natural_questions_open_domain_qa"
|
| 52 |
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},
|
| 53 |
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{
|
| 54 |
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"evaluation_name": "KILT",
|
| 55 |
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"source_data": {
|
| 56 |
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"dataset_name": "KILT",
|
| 57 |
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"source_type": "url",
|
| 58 |
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"url": [
|
| 59 |
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"https://huggingface.co/datasets?search=kilt"
|
| 60 |
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]
|
| 61 |
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},
|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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"min_score": 0.0,
|
| 66 |
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"max_score": 100.0,
|
| 67 |
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"evaluation_description": "Measures classification accuracy on the FEVER fact-checking task from the KILT benchmark. Models are evaluated on their ability to classify claims as 'Supported' or 'Refuted' based on evidence from a Wikipedia snapshot.",
|
| 68 |
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"additional_details": {
|
| 69 |
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"alphaxiv_y_axis": "Accuracy (%)",
|
| 70 |
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|
| 71 |
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"raw_evaluation_name": "Downstream Performance on FEVER (Fact Checking)"
|
| 72 |
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|
| 73 |
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|
| 74 |
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"metric_name": "Downstream Performance on FEVER (Fact Checking)",
|
| 75 |
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"metric_kind": "score",
|
| 76 |
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"metric_unit": "points"
|
| 77 |
+
},
|
| 78 |
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"score_details": {
|
| 79 |
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"score": 69.68
|
| 80 |
+
},
|
| 81 |
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"evaluation_result_id": "KILT/BERT + DPR/1771591481.616601#kilt#downstream_performance_on_fever_fact_checking"
|
| 82 |
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},
|
| 83 |
+
{
|
| 84 |
+
"evaluation_name": "KILT",
|
| 85 |
+
"source_data": {
|
| 86 |
+
"dataset_name": "KILT",
|
| 87 |
+
"source_type": "url",
|
| 88 |
+
"url": [
|
| 89 |
+
"https://huggingface.co/datasets?search=kilt"
|
| 90 |
+
]
|
| 91 |
+
},
|
| 92 |
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"metric_config": {
|
| 93 |
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"lower_is_better": false,
|
| 94 |
+
"score_type": "continuous",
|
| 95 |
+
"min_score": 0.0,
|
| 96 |
+
"max_score": 100.0,
|
| 97 |
+
"evaluation_description": "Measures KILT Accuracy on the FEVER fact-checking task. This metric only awards points for a correct classification if the model also provides correct provenance (R-Precision = 1), jointly evaluating accuracy and explainability.",
|
| 98 |
+
"additional_details": {
|
| 99 |
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"alphaxiv_y_axis": "KILT Accuracy (%)",
|
| 100 |
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"alphaxiv_is_primary": "False",
|
| 101 |
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"raw_evaluation_name": "KILT Score on FEVER (Fact Checking)"
|
| 102 |
+
},
|
| 103 |
+
"metric_id": "kilt_score_on_fever_fact_checking",
|
| 104 |
+
"metric_name": "KILT Score on FEVER (Fact Checking)",
|
| 105 |
+
"metric_kind": "score",
|
| 106 |
+
"metric_unit": "points"
|
| 107 |
+
},
|
| 108 |
+
"score_details": {
|
| 109 |
+
"score": 58.58
|
| 110 |
+
},
|
| 111 |
+
"evaluation_result_id": "KILT/BERT + DPR/1771591481.616601#kilt#kilt_score_on_fever_fact_checking"
|
| 112 |
+
},
|
| 113 |
+
{
|
| 114 |
+
"evaluation_name": "KILT",
|
| 115 |
+
"source_data": {
|
| 116 |
+
"dataset_name": "KILT",
|
| 117 |
+
"source_type": "url",
|
| 118 |
+
"url": [
|
| 119 |
+
"https://huggingface.co/datasets?search=kilt"
|
| 120 |
+
]
|
| 121 |
+
},
|
| 122 |
+
"metric_config": {
|
| 123 |
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"lower_is_better": false,
|
| 124 |
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"score_type": "continuous",
|
| 125 |
+
"min_score": 0.0,
|
| 126 |
+
"max_score": 100.0,
|
| 127 |
+
"evaluation_description": "Measures Exact Match (EM) score on the HotpotQA (HoPo) task from the KILT benchmark. This is a multi-hop open-domain QA task requiring reasoning over multiple documents.",
|
| 128 |
+
"additional_details": {
|
| 129 |
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"alphaxiv_y_axis": "Exact Match (%)",
|
| 130 |
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"alphaxiv_is_primary": "False",
|
| 131 |
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"raw_evaluation_name": "Downstream Performance on HotpotQA (Open Domain QA)"
|
| 132 |
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},
|
| 133 |
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"metric_id": "downstream_performance_on_hotpotqa_open_domain_qa",
|
| 134 |
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"metric_name": "Downstream Performance on HotpotQA (Open Domain QA)",
|
| 135 |
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"metric_kind": "score",
|
| 136 |
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"metric_unit": "points"
|
| 137 |
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},
|
| 138 |
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"score_details": {
|
| 139 |
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"score": 11.29
|
| 140 |
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},
|
| 141 |
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"evaluation_result_id": "KILT/BERT + DPR/1771591481.616601#kilt#downstream_performance_on_hotpotqa_open_domain_qa"
|
| 142 |
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},
|
| 143 |
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{
|
| 144 |
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"evaluation_name": "KILT",
|
| 145 |
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"source_data": {
|
| 146 |
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"dataset_name": "KILT",
|
| 147 |
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"source_type": "url",
|
| 148 |
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"url": [
|
| 149 |
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"https://huggingface.co/datasets?search=kilt"
|
| 150 |
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]
|
| 151 |
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},
|
| 152 |
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"metric_config": {
|
| 153 |
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"lower_is_better": false,
|
| 154 |
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"score_type": "continuous",
|
| 155 |
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"min_score": 0.0,
|
| 156 |
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"max_score": 100.0,
|
| 157 |
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"evaluation_description": "Measures KILT Exact Match on the multi-hop HotpotQA (HoPo) task. This metric only awards points for a correct answer if the model also provides correct provenance (R-Precision = 1).",
|
| 158 |
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"additional_details": {
|
| 159 |
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"alphaxiv_y_axis": "KILT Exact Match (%)",
|
| 160 |
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"alphaxiv_is_primary": "False",
|
| 161 |
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"raw_evaluation_name": "KILT Score on HotpotQA (Open Domain QA)"
|
| 162 |
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},
|
| 163 |
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"metric_id": "kilt_score_on_hotpotqa_open_domain_qa",
|
| 164 |
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"metric_name": "KILT Score on HotpotQA (Open Domain QA)",
|
| 165 |
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"metric_kind": "score",
|
| 166 |
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"metric_unit": "points"
|
| 167 |
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},
|
| 168 |
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"score_details": {
|
| 169 |
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"score": 0.74
|
| 170 |
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},
|
| 171 |
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"evaluation_result_id": "KILT/BERT + DPR/1771591481.616601#kilt#kilt_score_on_hotpotqa_open_domain_qa"
|
| 172 |
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},
|
| 173 |
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{
|
| 174 |
+
"evaluation_name": "KILT",
|
| 175 |
+
"source_data": {
|
| 176 |
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"dataset_name": "KILT",
|
| 177 |
+
"source_type": "url",
|
| 178 |
+
"url": [
|
| 179 |
+
"https://huggingface.co/datasets?search=kilt"
|
| 180 |
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]
|
| 181 |
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},
|
| 182 |
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"metric_config": {
|
| 183 |
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"lower_is_better": false,
|
| 184 |
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"score_type": "continuous",
|
| 185 |
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"min_score": 0.0,
|
| 186 |
+
"max_score": 100.0,
|
| 187 |
+
"evaluation_description": "Measures Exact Match (EM) score on the Natural Questions (NQ) task from the KILT benchmark, an open-domain extractive question answering task.",
|
| 188 |
+
"additional_details": {
|
| 189 |
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"alphaxiv_y_axis": "Exact Match (%)",
|
| 190 |
+
"alphaxiv_is_primary": "False",
|
| 191 |
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"raw_evaluation_name": "Downstream Performance on Natural Questions (Open Domain QA)"
|
| 192 |
+
},
|
| 193 |
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"metric_id": "downstream_performance_on_natural_questions_open_domain_qa",
|
| 194 |
+
"metric_name": "Downstream Performance on Natural Questions (Open Domain QA)",
|
| 195 |
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"metric_kind": "score",
|
| 196 |
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"metric_unit": "points"
|
| 197 |
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},
|
| 198 |
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"score_details": {
|
| 199 |
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"score": 38.64
|
| 200 |
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},
|
| 201 |
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"evaluation_result_id": "KILT/BERT + DPR/1771591481.616601#kilt#downstream_performance_on_natural_questions_open_domain_qa"
|
| 202 |
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},
|
| 203 |
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{
|
| 204 |
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"evaluation_name": "KILT",
|
| 205 |
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"source_data": {
|
| 206 |
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"dataset_name": "KILT",
|
| 207 |
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"source_type": "url",
|
| 208 |
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"url": [
|
| 209 |
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"https://huggingface.co/datasets?search=kilt"
|
| 210 |
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]
|
| 211 |
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|
| 212 |
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|
| 213 |
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|
| 214 |
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"score_type": "continuous",
|
| 215 |
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"min_score": 0.0,
|
| 216 |
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"max_score": 100.0,
|
| 217 |
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"evaluation_description": "Measures Exact Match (EM) score on the TriviaQA (TQA) task from the KILT benchmark, an open-domain extractive question answering task with a large number of answer aliases.",
|
| 218 |
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"additional_details": {
|
| 219 |
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|
| 220 |
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|
| 221 |
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|
| 222 |
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},
|
| 223 |
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"metric_id": "downstream_performance_on_triviaqa_open_domain_qa",
|
| 224 |
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"metric_name": "Downstream Performance on TriviaQA (Open Domain QA)",
|
| 225 |
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|
| 226 |
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|
| 227 |
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|
| 228 |
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|
| 229 |
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"score": 70.38
|
| 230 |
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},
|
| 231 |
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|
| 232 |
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|
| 233 |
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{
|
| 234 |
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"evaluation_name": "KILT",
|
| 235 |
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"source_data": {
|
| 236 |
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"dataset_name": "KILT",
|
| 237 |
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"source_type": "url",
|
| 238 |
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"url": [
|
| 239 |
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"https://huggingface.co/datasets?search=kilt"
|
| 240 |
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]
|
| 241 |
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|
| 242 |
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|
| 243 |
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"lower_is_better": false,
|
| 244 |
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|
| 245 |
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"min_score": 0.0,
|
| 246 |
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"max_score": 100.0,
|
| 247 |
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"evaluation_description": "Measures KILT Exact Match on the TriviaQA (TQA) task. This metric only awards points for a correct answer if the model also provides correct provenance (R-Precision = 1).",
|
| 248 |
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"additional_details": {
|
| 249 |
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"alphaxiv_y_axis": "KILT Exact Match (%)",
|
| 250 |
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"alphaxiv_is_primary": "False",
|
| 251 |
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"raw_evaluation_name": "KILT Score on TriviaQA (Open Domain QA)"
|
| 252 |
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},
|
| 253 |
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"metric_id": "kilt_score_on_triviaqa_open_domain_qa",
|
| 254 |
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"metric_name": "KILT Score on TriviaQA (Open Domain QA)",
|
| 255 |
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"metric_kind": "score",
|
| 256 |
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|
| 257 |
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},
|
| 258 |
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"score_details": {
|
| 259 |
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"score": 34.48
|
| 260 |
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},
|
| 261 |
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"evaluation_result_id": "KILT/BERT + DPR/1771591481.616601#kilt#kilt_score_on_triviaqa_open_domain_qa"
|
| 262 |
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},
|
| 263 |
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{
|
| 264 |
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"evaluation_name": "KILT",
|
| 265 |
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"source_data": {
|
| 266 |
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"dataset_name": "KILT",
|
| 267 |
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"source_type": "url",
|
| 268 |
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"url": [
|
| 269 |
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"https://huggingface.co/datasets?search=kilt"
|
| 270 |
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]
|
| 271 |
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},
|
| 272 |
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|
| 273 |
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"lower_is_better": false,
|
| 274 |
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"score_type": "continuous",
|
| 275 |
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"min_score": 0.0,
|
| 276 |
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"max_score": 100.0,
|
| 277 |
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"evaluation_description": "Measures accuracy on the Zero Shot RE (zsRE) slot filling task from the KILT benchmark. This task evaluates generalization to unseen relations.",
|
| 278 |
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|
| 279 |
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"alphaxiv_y_axis": "Accuracy (%)",
|
| 280 |
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|
| 281 |
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|
| 282 |
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},
|
| 283 |
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"metric_id": "downstream_performance_on_zero_shot_re_slot_filling",
|
| 284 |
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"metric_name": "Downstream Performance on Zero Shot RE (Slot Filling)",
|
| 285 |
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"metric_kind": "score",
|
| 286 |
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|
| 287 |
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|
| 288 |
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|
| 289 |
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"score": 6.93
|
| 290 |
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|
| 291 |
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|
| 292 |
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},
|
| 293 |
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{
|
| 294 |
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"evaluation_name": "KILT",
|
| 295 |
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"source_data": {
|
| 296 |
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"dataset_name": "KILT",
|
| 297 |
+
"source_type": "url",
|
| 298 |
+
"url": [
|
| 299 |
+
"https://huggingface.co/datasets?search=kilt"
|
| 300 |
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]
|
| 301 |
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},
|
| 302 |
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"metric_config": {
|
| 303 |
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"lower_is_better": false,
|
| 304 |
+
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|
| 305 |
+
"min_score": 0.0,
|
| 306 |
+
"max_score": 100.0,
|
| 307 |
+
"evaluation_description": "Measures KILT Accuracy on the Zero Shot RE (zsRE) slot filling task. This metric only awards points for a correct prediction if the model also provides correct provenance (R-Precision = 1).",
|
| 308 |
+
"additional_details": {
|
| 309 |
+
"alphaxiv_y_axis": "KILT Accuracy (%)",
|
| 310 |
+
"alphaxiv_is_primary": "False",
|
| 311 |
+
"raw_evaluation_name": "KILT Score on Zero Shot RE (Slot Filling)"
|
| 312 |
+
},
|
| 313 |
+
"metric_id": "kilt_score_on_zero_shot_re_slot_filling",
|
| 314 |
+
"metric_name": "KILT Score on Zero Shot RE (Slot Filling)",
|
| 315 |
+
"metric_kind": "score",
|
| 316 |
+
"metric_unit": "points"
|
| 317 |
+
},
|
| 318 |
+
"score_details": {
|
| 319 |
+
"score": 4.47
|
| 320 |
+
},
|
| 321 |
+
"evaluation_result_id": "KILT/BERT + DPR/1771591481.616601#kilt#kilt_score_on_zero_shot_re_slot_filling"
|
| 322 |
+
}
|
| 323 |
+
],
|
| 324 |
+
"eval_library": {
|
| 325 |
+
"name": "alphaxiv",
|
| 326 |
+
"version": "unknown"
|
| 327 |
+
}
|
| 328 |
+
}
|
flat/objects/03/1f/031f0ea2-6c95-460f-bf25-bcfafad2a1f0.json
ADDED
|
@@ -0,0 +1,72 @@
|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
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|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
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|
|
|
|
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|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
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"evaluation_id": "mmlu-pro/unknown_hunyuanturbos/self-reported/1777613486.918081",
|
| 4 |
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"retrieved_timestamp": "1777613486.918081",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
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"source_type": "documentation",
|
| 8 |
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"source_organization_name": "TIGER-Lab",
|
| 9 |
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"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
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"evaluator_relationship": "third_party",
|
| 11 |
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"additional_details": {
|
| 12 |
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"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
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"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
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"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
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"leaderboard_data_source": "Self-Reported"
|
| 17 |
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}
|
| 18 |
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},
|
| 19 |
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"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "HunyuanTurboS",
|
| 25 |
+
"id": "unknown/hunyuanturbos",
|
| 26 |
+
"developer": "unknown",
|
| 27 |
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"additional_details": {
|
| 28 |
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"raw_model_name": "HunyuanTurboS",
|
| 29 |
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"size_billions_parameters": "560.0",
|
| 30 |
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"leaderboard_data_source": "Self-Reported"
|
| 31 |
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}
|
| 32 |
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},
|
| 33 |
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"evaluation_results": [
|
| 34 |
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{
|
| 35 |
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"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
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"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
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"source_data": {
|
| 38 |
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"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
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"source_type": "hf_dataset",
|
| 40 |
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|
| 41 |
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|
| 42 |
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| 43 |
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| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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|
| 52 |
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| 53 |
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|
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|
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|
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|
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ADDED
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@@ -0,0 +1,298 @@
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|
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|
| 1 |
+
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| 2 |
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| 5 |
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| 11 |
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| 16 |
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|
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|
| 18 |
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|
| 19 |
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|
| 20 |
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| 21 |
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| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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| 27 |
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| 28 |
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| 29 |
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|
| 35 |
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| 36 |
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|
| 37 |
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|
| 38 |
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|
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|
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|
| 41 |
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|
| 42 |
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|
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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|
| 52 |
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|
| 53 |
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{
|
| 54 |
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"evaluation_name": "DocMath-Eval",
|
| 55 |
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|
| 56 |
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|
| 57 |
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|
| 58 |
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|
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|
| 60 |
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|
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|
| 63 |
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|
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|
| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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|
| 69 |
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|
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|
| 71 |
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|
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|
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|
| 74 |
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|
| 75 |
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|
| 76 |
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|
| 77 |
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|
| 78 |
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| 79 |
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|
| 80 |
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|
| 81 |
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|
| 82 |
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|
| 83 |
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{
|
| 84 |
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|
| 85 |
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|
| 86 |
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|
| 87 |
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|
| 88 |
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|
| 89 |
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| 90 |
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|
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| 92 |
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|
| 93 |
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|
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|
| 95 |
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|
| 96 |
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|
| 97 |
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|
| 98 |
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|
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| 100 |
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|
| 101 |
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|
| 102 |
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|
| 103 |
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|
| 104 |
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|
| 105 |
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|
| 106 |
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|
| 107 |
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|
| 108 |
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|
| 109 |
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|
| 110 |
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|
| 111 |
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|
| 112 |
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|
| 113 |
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{
|
| 114 |
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"evaluation_name": "DocMath-Eval",
|
| 115 |
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|
| 116 |
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"dataset_name": "DocMath-Eval",
|
| 117 |
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|
| 118 |
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|
| 119 |
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|
| 120 |
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|
| 121 |
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|
| 122 |
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| 123 |
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|
| 124 |
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|
| 125 |
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|
| 126 |
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|
| 127 |
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|
| 128 |
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|
| 129 |
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|
| 130 |
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|
| 131 |
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|
| 132 |
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|
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|
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|
| 135 |
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|
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|
| 137 |
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|
| 138 |
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|
| 139 |
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|
| 140 |
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|
| 141 |
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|
| 142 |
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|
| 143 |
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{
|
| 144 |
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"evaluation_name": "DocMath-Eval",
|
| 145 |
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|
| 146 |
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"dataset_name": "DocMath-Eval",
|
| 147 |
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|
| 148 |
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|
| 149 |
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|
| 150 |
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|
| 151 |
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|
| 152 |
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|
| 153 |
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|
| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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|
| 158 |
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|
| 159 |
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"alphaxiv_y_axis": "DM_CompLong Accuracy (%) - PoT",
|
| 160 |
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|
| 161 |
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|
| 162 |
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|
| 163 |
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|
| 164 |
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"metric_name": "DocMath-Eval (Test Set): Accuracy on DM_CompLong with Program-of-Thought (PoT)",
|
| 165 |
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"metric_kind": "score",
|
| 166 |
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"metric_unit": "points"
|
| 167 |
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|
| 168 |
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|
| 169 |
+
"score": 37.5
|
| 170 |
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|
| 171 |
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"evaluation_result_id": "DocMath-Eval/DeepSeek-Coder-V2/1771591481.616601#docmath_eval#docmath_eval_test_set_accuracy_on_dm_complong_with_program_of_thought_pot"
|
| 172 |
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|
| 173 |
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|
| 174 |
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|
| 175 |
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|
| 176 |
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|
| 177 |
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|
| 178 |
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|
flat/objects/03/22/032213d0-942a-4f61-a615-427a3bf31eea.json
ADDED
|
@@ -0,0 +1,328 @@
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|
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|
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|
|
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|
|
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|
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|
|
|
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|
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|
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| 299 |
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|
| 300 |
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|
| 301 |
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| 302 |
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| 305 |
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|
| 306 |
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|
| 307 |
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| 314 |
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|
| 315 |
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|
| 316 |
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| 318 |
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|
| 319 |
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|
| 320 |
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|
| 321 |
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ADDED
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@@ -0,0 +1,268 @@
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| 215 |
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| 216 |
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| 217 |
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|
| 218 |
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| 219 |
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| 220 |
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| 229 |
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|
| 230 |
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|
| 231 |
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|
| 232 |
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|
| 233 |
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|
| 234 |
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|
| 235 |
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|
| 236 |
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|
| 237 |
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| 238 |
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| 239 |
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|
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|
| 245 |
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|
| 246 |
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|
| 247 |
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|
| 248 |
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| 249 |
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|
| 250 |
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| 251 |
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| 255 |
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|
| 256 |
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|
| 257 |
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| 258 |
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| 259 |
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|
| 260 |
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|
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| 262 |
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| 265 |
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|
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| 267 |
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|
| 268 |
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flat/objects/03/28/03288721-9eaa-413e-9b54-a5185b923cfd.json
ADDED
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| 163 |
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|
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| 169 |
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|
flat/objects/03/28/03289397-c487-4641-aed1-77a16ced2097.json
ADDED
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@@ -0,0 +1,388 @@
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| 98 |
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| 99 |
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| 100 |
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| 101 |
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| 102 |
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| 103 |
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| 104 |
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| 105 |
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| 106 |
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| 107 |
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| 108 |
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| 115 |
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| 116 |
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| 117 |
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| 118 |
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| 119 |
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| 121 |
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| 122 |
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{
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| 123 |
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| 124 |
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| 125 |
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| 126 |
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| 128 |
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| 129 |
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| 130 |
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| 131 |
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|
| 132 |
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| 133 |
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| 137 |
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| 138 |
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|
| 139 |
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|
| 140 |
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|
| 141 |
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|
| 142 |
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|
| 143 |
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"evaluation_result_id": "hfopenllm_v2/paloalma_ECE-TW3-JRGL-V2/1773936498.240187#musr#accuracy"
|
| 144 |
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|
| 145 |
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{
|
| 146 |
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"evaluation_name": "MMLU-PRO",
|
| 147 |
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|
| 148 |
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|
| 149 |
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| 153 |
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|
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|
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| 159 |
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|
| 160 |
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|
| 161 |
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|
| 162 |
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|
| 163 |
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"score_details": {
|
| 164 |
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"score": 0.4588
|
| 165 |
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|
| 166 |
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"evaluation_result_id": "hfopenllm_v2/paloalma_ECE-TW3-JRGL-V2/1773936498.240187#mmlu_pro#accuracy"
|
| 167 |
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|
| 168 |
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|
| 169 |
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}
|
flat/objects/03/2d/032d6068-e6a5-4174-9321-13c185f7378e.json
ADDED
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@@ -0,0 +1,508 @@
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|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "CORAL/Qwen2.5-0.5B-SFT/1771591481.616601",
|
| 4 |
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"retrieved_timestamp": "1771591481.616601",
|
| 5 |
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"source_metadata": {
|
| 6 |
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"source_name": "alphaXiv State of the Art",
|
| 7 |
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"source_type": "documentation",
|
| 8 |
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"source_organization_name": "alphaXiv",
|
| 9 |
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"source_organization_url": "https://alphaxiv.org",
|
| 10 |
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"evaluator_relationship": "third_party",
|
| 11 |
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"additional_details": {
|
| 12 |
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"alphaxiv_dataset_org": "Beijing Academy of Artificial Intelligence",
|
| 13 |
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"alphaxiv_dataset_type": "text",
|
| 14 |
+
"scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
|
| 15 |
+
}
|
| 16 |
+
},
|
| 17 |
+
"model_info": {
|
| 18 |
+
"id": "Qwen2.5-0.5B-SFT",
|
| 19 |
+
"name": "Qwen2.5-0.5B-SFT",
|
| 20 |
+
"developer": "unknown"
|
| 21 |
+
},
|
| 22 |
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"evaluation_results": [
|
| 23 |
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{
|
| 24 |
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"evaluation_name": "CORAL",
|
| 25 |
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"source_data": {
|
| 26 |
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"dataset_name": "CORAL",
|
| 27 |
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"source_type": "url",
|
| 28 |
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"url": [
|
| 29 |
+
"https://www.alphaxiv.org/abs/2410.23090"
|
| 30 |
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]
|
| 31 |
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|
| 32 |
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"metric_config": {
|
| 33 |
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"lower_is_better": false,
|
| 34 |
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"score_type": "continuous",
|
| 35 |
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"min_score": 0.0,
|
| 36 |
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"max_score": 100.0,
|
| 37 |
+
"evaluation_description": "Citation Precision for citation labeling on CORAL using the 'LLM Summarization' strategy. This metric measures the proportion of generated citations that are correct after condensing the conversation history. This metric is highlighted as a key finding, showing that context compression can improve citation accuracy.",
|
| 38 |
+
"additional_details": {
|
| 39 |
+
"alphaxiv_y_axis": "Citation Precision (LLM Summarization)",
|
| 40 |
+
"alphaxiv_is_primary": "True",
|
| 41 |
+
"raw_evaluation_name": "Citation Labeling (Precision) with Summarized Context"
|
| 42 |
+
},
|
| 43 |
+
"metric_id": "citation_labeling_precision_with_summarized_context",
|
| 44 |
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"metric_name": "Citation Labeling (Precision) with Summarized Context",
|
| 45 |
+
"metric_kind": "score",
|
| 46 |
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"metric_unit": "points"
|
| 47 |
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},
|
| 48 |
+
"score_details": {
|
| 49 |
+
"score": 17.4
|
| 50 |
+
},
|
| 51 |
+
"evaluation_result_id": "CORAL/Qwen2.5-0.5B-SFT/1771591481.616601#coral#citation_labeling_precision_with_summarized_context"
|
| 52 |
+
},
|
| 53 |
+
{
|
| 54 |
+
"evaluation_name": "CORAL",
|
| 55 |
+
"source_data": {
|
| 56 |
+
"dataset_name": "CORAL",
|
| 57 |
+
"source_type": "url",
|
| 58 |
+
"url": [
|
| 59 |
+
"https://www.alphaxiv.org/abs/2410.23090"
|
| 60 |
+
]
|
| 61 |
+
},
|
| 62 |
+
"metric_config": {
|
| 63 |
+
"lower_is_better": false,
|
| 64 |
+
"score_type": "continuous",
|
| 65 |
+
"min_score": 0.0,
|
| 66 |
+
"max_score": 100.0,
|
| 67 |
+
"evaluation_description": "BLEU-1 score for response generation on CORAL using the 'LLM Summarization' strategy, where the conversation history is summarized by an LLM to create a condensed context. Higher is better.",
|
| 68 |
+
"additional_details": {
|
| 69 |
+
"alphaxiv_y_axis": "BLEU-1 (LLM Summarization)",
|
| 70 |
+
"alphaxiv_is_primary": "False",
|
| 71 |
+
"raw_evaluation_name": "Response Generation (BLEU-1) with Summarized Context"
|
| 72 |
+
},
|
| 73 |
+
"metric_id": "response_generation_bleu_1_with_summarized_context",
|
| 74 |
+
"metric_name": "Response Generation (BLEU-1) with Summarized Context",
|
| 75 |
+
"metric_kind": "score",
|
| 76 |
+
"metric_unit": "points"
|
| 77 |
+
},
|
| 78 |
+
"score_details": {
|
| 79 |
+
"score": 21.4
|
| 80 |
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| 88 |
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| 89 |
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|
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|
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|
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| 173 |
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|
| 174 |
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|
| 175 |
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| 176 |
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| 177 |
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| 178 |
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| 179 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
| 306 |
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|
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|
| 308 |
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|
| 309 |
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|
| 310 |
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|
| 311 |
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|
| 312 |
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|
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|
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|
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|
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|
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|
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|
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|
| 323 |
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{
|
| 324 |
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|
| 325 |
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|
| 326 |
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|
| 327 |
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|
| 328 |
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|
| 329 |
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|
| 330 |
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|
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|
| 337 |
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|
| 338 |
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|
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|
| 340 |
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|
| 341 |
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|
| 342 |
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|
| 343 |
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|
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|
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|
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|
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|
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|
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|
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|
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| 356 |
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| 357 |
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| 358 |
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|
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|
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|
| 365 |
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|
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|
| 367 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
| 383 |
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|
| 384 |
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|
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|
| 386 |
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|
| 387 |
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|
| 388 |
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|
| 389 |
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|
| 390 |
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|
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|
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|
| 396 |
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|
| 397 |
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|
| 398 |
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|
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|
| 400 |
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|
| 401 |
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|
| 402 |
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|
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|
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|
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|
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|
| 413 |
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|
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|
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|
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|
| 418 |
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|
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| 427 |
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| 474 |
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|
| 486 |
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|
| 487 |
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|
| 488 |
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|
| 489 |
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|
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ADDED
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"evaluation_description": "Single-attempt whole-trace accuracy on the 'Long' data split of L0-Bench. This split contains programs requiring an average of ~164 execution steps. This level of difficulty tests the model's ability to maintain procedural correctness over extended sequences.",
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| 147 |
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| 148 |
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| 149 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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"evaluation_description": "This metric (pass@31) calculates the probability that at least one of 31 independently generated program execution traces is perfectly correct. It serves as a soft upper-bound on performance, indicating the model's capability to produce a correct solution, even if not consistently. The large gap between this metric and single-attempt accuracy highlights significant room for improving model reliability.",
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| 162 |
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| 163 |
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|
| 164 |
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|
| 165 |
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| 174 |
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|
| 185 |
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|
| 186 |
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"max_score": 100.0,
|
| 187 |
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"evaluation_description": "Single-attempt whole-trace accuracy on the 'Short' data split of L0-Bench. This split contains programs requiring an average of ~13 execution steps. It evaluates baseline procedural correctness on the simplest tasks.",
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| 188 |
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| 189 |
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| 190 |
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| 191 |
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|
| 192 |
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|
| 193 |
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|
| 194 |
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"metric_name": "L0-Bench: Performance on Short Traces (~13 steps)",
|
| 195 |
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|
| 204 |
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"evaluation_name": "L0-Bench",
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| 205 |
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| 206 |
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"dataset_name": "L0-Bench",
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| 207 |
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| 208 |
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|
| 215 |
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|
| 216 |
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"max_score": 100.0,
|
| 217 |
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"evaluation_description": "This metric quantifies partial procedural correctness by counting the average number of correct steps a model generates before the first error occurs. Scores are based on single generation attempts, averaged across 31 runs and four data splits. A higher value indicates the model can maintain correctness for longer procedural sequences.",
|
| 218 |
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| 219 |
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{
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| 234 |
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"evaluation_name": "L0-Bench",
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| 236 |
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"dataset_name": "L0-Bench",
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| 237 |
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|
| 238 |
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| 239 |
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|
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|
| 245 |
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|
| 246 |
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|
| 247 |
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"evaluation_description": "This metric measures the average percentage of perfectly correct program execution traces generated in a single attempt, without aggregation methods like majority voting. The score is averaged over 31 independent runs (each with different few-shot examples) and across four data splits of increasing difficulty. It represents a model's baseline reliability for procedural reasoning.",
|
| 248 |
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|
| 249 |
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|
| 250 |
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| 251 |
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| 252 |
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|
| 253 |
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|
| 254 |
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"metric_name": "L0-Bench: Overall Procedural Correctness in a Single Attempt",
|
| 255 |
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|
| 256 |
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"metric_unit": "points"
|
| 257 |
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|
| 258 |
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|
| 259 |
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"score": 17.1
|
| 260 |
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|
| 261 |
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"name": "alphaxiv",
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flat/objects/03/2e/032e68a1-76c5-4dea-abae-a0ac3637a4af.json
ADDED
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@@ -0,0 +1,178 @@
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{
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"evaluation_name": "NT-VOT211",
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"https://www.alphaxiv.org/abs/2410.20421"
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"max_score": 100.0,
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"evaluation_description": "Measures the percentage of frames where the Intersection over Union (IoU) between the predicted bounding box and the ground truth bounding box is greater than 0.5 on the NT-VOT211 benchmark. Higher scores are better. Results are from Table 2 of the paper.",
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"alphaxiv_y_axis": "Overlap Precision 50 (OP50)",
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"score": 51.77
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{
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"evaluation_name": "NT-VOT211",
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|
| 126 |
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"max_score": 100.0,
|
| 127 |
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"evaluation_description": "Measures the percentage of frames where the Intersection over Union (IoU) between the predicted bounding box and the ground truth bounding box is greater than 0.75 on the NT-VOT211 benchmark. This is a stricter metric than OP50. Higher scores are better. Results are from Table 2 of the paper.",
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"alphaxiv_y_axis": "Overlap Precision 75 (OP75)",
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"alphaxiv_is_primary": "False",
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"raw_evaluation_name": "Overlap Precision 75 (OP75) on the NT-VOT211 Benchmark"
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"metric_name": "Overlap Precision 75 (OP75) on the NT-VOT211 Benchmark",
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"metric_kind": "score",
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| 141 |
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| 142 |
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| 143 |
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{
|
| 144 |
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| 145 |
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| 147 |
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| 148 |
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| 149 |
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|
| 157 |
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| 165 |
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| 166 |
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| 167 |
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| 168 |
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| 169 |
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| 170 |
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"evaluation_result_id": "NT-VOT211/TATrack-L/1771591481.616601#nt_vot211#precision_on_the_nt_vot211_benchmark"
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ADDED
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@@ -0,0 +1,328 @@
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"evaluation_name": "TRIG-Bench",
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|
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|
| 156 |
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|
| 157 |
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"evaluation_description": "Evaluation of model performance on the 'Knowledge' dimension for the Subject-driven Generation task. Knowledge measures the ability to generate images with complex or specialized knowledge. Scores are calculated using TRIGScore. Higher scores indicate better performance.",
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"additional_details": {
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"score": 0.55
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|
| 171 |
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|
| 172 |
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|
| 173 |
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|
| 174 |
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| 175 |
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| 176 |
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| 177 |
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| 178 |
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|
| 179 |
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|
| 180 |
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| 182 |
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| 183 |
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| 184 |
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|
| 185 |
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|
| 186 |
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|
| 187 |
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|
| 188 |
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|
| 189 |
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|
| 190 |
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|
| 191 |
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| 192 |
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| 194 |
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| 195 |
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| 196 |
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| 197 |
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| 198 |
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| 199 |
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|
| 200 |
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|
| 201 |
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| 202 |
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| 203 |
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|
| 204 |
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| 206 |
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| 216 |
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|
| 217 |
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| 218 |
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| 220 |
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| 221 |
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| 222 |
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| 224 |
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| 226 |
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| 232 |
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| 233 |
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|
| 234 |
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| 235 |
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| 236 |
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| 245 |
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| 246 |
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|
| 247 |
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|
| 248 |
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|
| 249 |
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| 251 |
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| 252 |
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| 254 |
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| 255 |
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| 256 |
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| 262 |
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| 263 |
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|
| 264 |
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| 265 |
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| 266 |
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| 267 |
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| 269 |
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| 275 |
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|
| 276 |
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|
| 277 |
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"evaluation_description": "Evaluation of model performance on the 'Style' dimension for the Subject-driven Generation task. Style measures the alignment of the image’s aesthetic and scheme with that specified in the prompt. Scores are calculated using TRIGScore. Higher scores indicate better performance.",
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| 278 |
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|
| 279 |
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| 280 |
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| 282 |
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| 285 |
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| 286 |
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| 287 |
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| 288 |
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|
| 289 |
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"score": 0.58
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| 290 |
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| 291 |
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| 292 |
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| 293 |
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|
| 294 |
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"evaluation_name": "TRIG-Bench",
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| 295 |
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| 296 |
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| 297 |
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|
| 298 |
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| 299 |
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|
| 304 |
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|
| 305 |
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|
| 306 |
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|
| 307 |
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"evaluation_description": "Evaluation of model performance on the 'Toxicity' dimension for the Subject-driven Generation task. Toxicity measures the extent to which generated images contain harmful or offensive content. Scores are calculated using TRIGScore. Higher scores indicate less toxic content.",
|
| 308 |
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|
| 309 |
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|
| 310 |
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|
| 311 |
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| 312 |
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| 315 |
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| 316 |
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| 317 |
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| 318 |
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| 319 |
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"score": 0.52
|
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|
| 321 |
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"evaluation_result_id": "TRIG-Bench/OminiControl/1771591481.616601#trig_bench#trig_bench_toxicity_robustness_in_subject_driven_generation"
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| 322 |
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| 323 |
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|
| 328 |
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flat/objects/03/30/03307425-0836-46a2-aec2-40a9bfcc1f8f.json
ADDED
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@@ -0,0 +1,88 @@
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flat/objects/03/31/03317d0e-9a25-4c0f-8d41-5505f40b1a12.json
ADDED
|
@@ -0,0 +1,508 @@
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flat/objects/03/31/0331ce52-e200-4acc-acea-18369b0da5ab.json
ADDED
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|
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|
|
|
|
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|
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|
|
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|
|
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|
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|
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|
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|
| 1 |
+
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|
| 2 |
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|
| 3 |
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| 4 |
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
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|
| 30 |
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{
|
| 31 |
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"evaluation_name": "IFEval",
|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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|
| 40 |
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|
| 41 |
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| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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"score": 0.6517
|
| 50 |
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|
| 51 |
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|
| 52 |
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|
| 53 |
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{
|
| 54 |
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"evaluation_name": "BBH",
|
| 55 |
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|
| 56 |
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"dataset_name": "BBH",
|
| 57 |
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"source_type": "hf_dataset",
|
| 58 |
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|
| 59 |
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|
| 60 |
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|
| 61 |
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|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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|
| 69 |
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|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
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"evaluation_result_id": "hfopenllm_v2/spow12_ChatWaifu_v2.0_22B/1773936498.240187#bbh#accuracy"
|
| 75 |
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|
| 76 |
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{
|
| 77 |
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"evaluation_name": "MATH Level 5",
|
| 78 |
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"source_data": {
|
| 79 |
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"dataset_name": "MATH Level 5",
|
| 80 |
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"source_type": "hf_dataset",
|
| 81 |
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"hf_repo": "DigitalLearningGmbH/MATH-lighteval"
|
| 82 |
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|
| 83 |
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|
| 84 |
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"evaluation_description": "Exact Match on MATH Level 5",
|
| 85 |
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|
| 86 |
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|
| 87 |
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|
| 88 |
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|
| 89 |
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|
| 90 |
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|
| 91 |
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|
| 92 |
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|
| 93 |
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|
| 94 |
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|
| 95 |
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|
| 96 |
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|
| 97 |
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|
| 98 |
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|
| 99 |
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{
|
| 100 |
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"evaluation_name": "GPQA",
|
| 101 |
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|
| 102 |
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|
| 103 |
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|
| 104 |
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|
| 105 |
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|
| 106 |
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|
| 107 |
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|
| 108 |
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|
| 109 |
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|
| 110 |
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|
| 111 |
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|
| 112 |
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|
| 113 |
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|
| 114 |
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|
| 115 |
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|
| 116 |
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|
| 117 |
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|
| 118 |
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|
| 119 |
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|
| 120 |
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"evaluation_result_id": "hfopenllm_v2/spow12_ChatWaifu_v2.0_22B/1773936498.240187#gpqa#accuracy"
|
| 121 |
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|
| 122 |
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{
|
| 123 |
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"evaluation_name": "MUSR",
|
| 124 |
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"source_data": {
|
| 125 |
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"dataset_name": "MUSR",
|
| 126 |
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|
| 127 |
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|
| 128 |
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|
| 129 |
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"metric_config": {
|
| 130 |
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"evaluation_description": "Accuracy on MUSR",
|
| 131 |
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|
| 132 |
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|
| 133 |
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|
| 134 |
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|
| 135 |
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|
| 136 |
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|
| 137 |
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|
| 138 |
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|
| 139 |
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|
| 140 |
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|
| 141 |
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"score": 0.3842
|
| 142 |
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|
| 143 |
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"evaluation_result_id": "hfopenllm_v2/spow12_ChatWaifu_v2.0_22B/1773936498.240187#musr#accuracy"
|
| 144 |
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|
| 145 |
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{
|
| 146 |
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"evaluation_name": "MMLU-PRO",
|
| 147 |
+
"source_data": {
|
| 148 |
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"dataset_name": "MMLU-PRO",
|
| 149 |
+
"source_type": "hf_dataset",
|
| 150 |
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"hf_repo": "TIGER-Lab/MMLU-Pro"
|
| 151 |
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|
| 152 |
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|
| 153 |
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"evaluation_description": "Accuracy on MMLU-PRO",
|
| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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|
| 158 |
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|
| 159 |
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"metric_name": "Accuracy",
|
| 160 |
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|
| 161 |
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"metric_unit": "proportion"
|
| 162 |
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|
| 163 |
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"score_details": {
|
| 164 |
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"score": 0.3812
|
| 165 |
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|
| 166 |
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"evaluation_result_id": "hfopenllm_v2/spow12_ChatWaifu_v2.0_22B/1773936498.240187#mmlu_pro#accuracy"
|
| 167 |
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}
|
| 168 |
+
]
|
| 169 |
+
}
|
flat/objects/03/33/0333211e-390c-441f-aafa-8b304eef5cf3.json
ADDED
|
@@ -0,0 +1,69 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
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"evaluation_id": "fibble2_arena/openai/o4-mini/1773248706",
|
| 4 |
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"retrieved_timestamp": "1773248706",
|
| 5 |
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|
| 6 |
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"source_name": "Fibble2 Arena (2 lies)",
|
| 7 |
+
"source_type": "evaluation_run",
|
| 8 |
+
"source_organization_name": "Dr. Chang Liu's Lab",
|
| 9 |
+
"source_organization_url": "https://drchangliu.github.io/WordleArenas/",
|
| 10 |
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"evaluator_relationship": "first_party"
|
| 11 |
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|
| 12 |
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"eval_library": {
|
| 13 |
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"name": "wordle_arena",
|
| 14 |
+
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|
| 15 |
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|
| 16 |
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|
| 17 |
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"num_lies": "2",
|
| 18 |
+
"max_guesses": "8"
|
| 19 |
+
}
|
| 20 |
+
},
|
| 21 |
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"model_info": {
|
| 22 |
+
"name": "o4 Mini",
|
| 23 |
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"id": "openai/o4-mini",
|
| 24 |
+
"developer": "OpenAI",
|
| 25 |
+
"inference_platform": "openai"
|
| 26 |
+
},
|
| 27 |
+
"evaluation_results": [
|
| 28 |
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{
|
| 29 |
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"evaluation_name": "fibble2_arena",
|
| 30 |
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"evaluation_result_id": "fibble2_arena/openai/o4-mini/1773248706#fibble2_arena#fibble2_arena_win_rate",
|
| 31 |
+
"source_data": {
|
| 32 |
+
"dataset_name": "Fibble2 Arena (2 lies) Word Set",
|
| 33 |
+
"source_type": "url",
|
| 34 |
+
"url": [
|
| 35 |
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|
| 36 |
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]
|
| 37 |
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},
|
| 38 |
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|
| 39 |
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"evaluation_description": "Win rate on Fibble2 Arena (2 lies) puzzles (2 lies, 8 max guesses)",
|
| 40 |
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"metric_id": "fibble2_arena.win_rate",
|
| 41 |
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"metric_name": "Win Rate",
|
| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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},
|
| 52 |
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|
| 53 |
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"score": 0.0,
|
| 54 |
+
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|
| 55 |
+
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|
| 56 |
+
},
|
| 57 |
+
"details": {
|
| 58 |
+
"games_played": "30",
|
| 59 |
+
"games_won": "0"
|
| 60 |
+
}
|
| 61 |
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}
|
| 62 |
+
}
|
| 63 |
+
],
|
| 64 |
+
"detailed_evaluation_results": {
|
| 65 |
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"format": "jsonl",
|
| 66 |
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"file_path": "0333211e-390c-441f-aafa-8b304eef5cf3_samples.jsonl",
|
| 67 |
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"total_rows": 30
|
| 68 |
+
}
|
| 69 |
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}
|
flat/objects/03/35/03353542-aae9-41ee-905c-7d8514e4087d.json
ADDED
|
@@ -0,0 +1,178 @@
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
| 1 |
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| 2 |
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| 5 |
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| 6 |
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|
| 7 |
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| 8 |
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| 10 |
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| 11 |
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| 14 |
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|
| 15 |
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|
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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| 40 |
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|
| 41 |
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|
| 42 |
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|
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
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|
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|
| 49 |
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| 53 |
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|
| 54 |
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|
| 55 |
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| 56 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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| 68 |
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| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
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| 75 |
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| 76 |
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| 82 |
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|
| 83 |
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|
| 84 |
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|
| 85 |
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|
| 86 |
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| 87 |
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| 88 |
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|
| 89 |
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| 93 |
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|
| 94 |
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|
| 95 |
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|
| 96 |
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|
| 97 |
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"evaluation_description": "Measures the percentage of predictions that exactly match the ground truth answer across all MMDocBench tasks. This is a strict metric for answer accuracy. Data is from the official project leaderboard.",
|
| 98 |
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|
| 99 |
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| 100 |
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| 101 |
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| 102 |
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|
| 103 |
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|
| 104 |
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| 105 |
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|
| 106 |
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| 107 |
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| 108 |
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| 109 |
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| 110 |
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| 111 |
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| 112 |
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| 113 |
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|
| 114 |
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|
| 115 |
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|
| 116 |
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| 117 |
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| 118 |
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| 119 |
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|
| 125 |
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|
| 126 |
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|
| 127 |
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|
| 128 |
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| 129 |
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| 130 |
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| 131 |
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| 134 |
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| 135 |
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| 136 |
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| 139 |
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| 142 |
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| 143 |
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|
| 144 |
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|
| 145 |
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|
| 146 |
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|
| 147 |
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|
| 148 |
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|
| 149 |
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| 150 |
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|
| 151 |
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| 152 |
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| 153 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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|
| 158 |
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|
| 159 |
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|
| 160 |
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|
| 161 |
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|
| 162 |
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|
| 163 |
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|
| 164 |
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|
| 165 |
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|
| 166 |
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|
| 167 |
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|
| 168 |
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|
| 169 |
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"score": 1.27
|
| 170 |
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|
| 171 |
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"evaluation_result_id": "MMDocBench/InternVL2-8B/1771591481.616601#mmdocbench#overall_iou_score_for_region_prediction_on_mmdocbench"
|
| 172 |
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| 173 |
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| 174 |
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| 175 |
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| 178 |
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|
flat/objects/03/35/0335df57-f8a0-4076-8ccf-8b298d24e3bf.json
ADDED
|
@@ -0,0 +1,148 @@
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|
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|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
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|
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|
|
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|
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|
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|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
| 1 |
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| 45 |
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|
| 54 |
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| 55 |
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|
| 66 |
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|
| 67 |
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"evaluation_description": "Measures the accuracy of models on multiple-choice questions about general world knowledge, presented in the Basque language. This eval tests models' ability to process a low-resource language for topics they are generally familiar with.",
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| 68 |
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| 69 |
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"alphaxiv_y_axis": "Accuracy (%) on Global Questions (Basque)",
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| 88 |
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|
| 97 |
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| 98 |
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| 99 |
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|
| 114 |
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|
| 115 |
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| 116 |
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| 117 |
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| 118 |
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|
| 126 |
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|
| 127 |
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|
| 128 |
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|
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|
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"score": 49.45
|
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|
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|
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flat/objects/03/37/033749a5-172b-4778-be39-e1da233f086c.json
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flat/objects/03/37/03376628-21b7-4a2b-a24d-a42dbb062d14.json
ADDED
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|
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|
|
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|
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|
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|
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|
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|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "SpatialBench/Bunny-Phi2-3B-RGBD/1771591481.616601",
|
| 4 |
+
"retrieved_timestamp": "1771591481.616601",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "alphaXiv State of the Art",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "alphaXiv",
|
| 9 |
+
"source_organization_url": "https://alphaxiv.org",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"alphaxiv_dataset_org": "BAAI",
|
| 13 |
+
"alphaxiv_dataset_type": "image",
|
| 14 |
+
"scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
|
| 15 |
+
}
|
| 16 |
+
},
|
| 17 |
+
"model_info": {
|
| 18 |
+
"id": "Bunny-Phi2-3B-RGBD",
|
| 19 |
+
"name": "Bunny-Phi2-3B-RGBD",
|
| 20 |
+
"developer": "unknown"
|
| 21 |
+
},
|
| 22 |
+
"evaluation_results": [
|
| 23 |
+
{
|
| 24 |
+
"evaluation_name": "SpatialBench",
|
| 25 |
+
"source_data": {
|
| 26 |
+
"dataset_name": "SpatialBench",
|
| 27 |
+
"source_type": "url",
|
| 28 |
+
"url": [
|
| 29 |
+
"https://www.alphaxiv.org/abs/2406.13642"
|
| 30 |
+
]
|
| 31 |
+
},
|
| 32 |
+
"metric_config": {
|
| 33 |
+
"lower_is_better": false,
|
| 34 |
+
"score_type": "continuous",
|
| 35 |
+
"min_score": 0.0,
|
| 36 |
+
"max_score": 100.0,
|
| 37 |
+
"evaluation_description": "This metric assesses a model's ability to accurately determine the metric depth of specific points or objects from either RGB (Monocular Depth Estimation) or RGB-D images. A higher score indicates better performance.",
|
| 38 |
+
"additional_details": {
|
| 39 |
+
"alphaxiv_y_axis": "Accuracy (%)",
|
| 40 |
+
"alphaxiv_is_primary": "True",
|
| 41 |
+
"raw_evaluation_name": "SpatialBench Depth Understanding"
|
| 42 |
+
},
|
| 43 |
+
"metric_id": "spatialbench_depth_understanding",
|
| 44 |
+
"metric_name": "SpatialBench Depth Understanding",
|
| 45 |
+
"metric_kind": "score",
|
| 46 |
+
"metric_unit": "points"
|
| 47 |
+
},
|
| 48 |
+
"score_details": {
|
| 49 |
+
"score": 85.8
|
| 50 |
+
},
|
| 51 |
+
"evaluation_result_id": "SpatialBench/Bunny-Phi2-3B-RGBD/1771591481.616601#spatialbench#spatialbench_depth_understanding"
|
| 52 |
+
},
|
| 53 |
+
{
|
| 54 |
+
"evaluation_name": "SpatialBench",
|
| 55 |
+
"source_data": {
|
| 56 |
+
"dataset_name": "SpatialBench",
|
| 57 |
+
"source_type": "url",
|
| 58 |
+
"url": [
|
| 59 |
+
"https://www.alphaxiv.org/abs/2406.13642"
|
| 60 |
+
]
|
| 61 |
+
},
|
| 62 |
+
"metric_config": {
|
| 63 |
+
"lower_is_better": false,
|
| 64 |
+
"score_type": "continuous",
|
| 65 |
+
"min_score": 0.0,
|
| 66 |
+
"max_score": 100.0,
|
| 67 |
+
"evaluation_description": "Tests the model's ability to count objects based on visual and spatial attributes from images. Higher scores are better.",
|
| 68 |
+
"additional_details": {
|
| 69 |
+
"alphaxiv_y_axis": "Accuracy (%)",
|
| 70 |
+
"alphaxiv_is_primary": "False",
|
| 71 |
+
"raw_evaluation_name": "SpatialBench Counting & Enumeration"
|
| 72 |
+
},
|
| 73 |
+
"metric_id": "spatialbench_counting_enumeration",
|
| 74 |
+
"metric_name": "SpatialBench Counting & Enumeration",
|
| 75 |
+
"metric_kind": "score",
|
| 76 |
+
"metric_unit": "points"
|
| 77 |
+
},
|
| 78 |
+
"score_details": {
|
| 79 |
+
"score": 90.4
|
| 80 |
+
},
|
| 81 |
+
"evaluation_result_id": "SpatialBench/Bunny-Phi2-3B-RGBD/1771591481.616601#spatialbench#spatialbench_counting_enumeration"
|
| 82 |
+
},
|
| 83 |
+
{
|
| 84 |
+
"evaluation_name": "SpatialBench",
|
| 85 |
+
"source_data": {
|
| 86 |
+
"dataset_name": "SpatialBench",
|
| 87 |
+
"source_type": "url",
|
| 88 |
+
"url": [
|
| 89 |
+
"https://www.alphaxiv.org/abs/2406.13642"
|
| 90 |
+
]
|
| 91 |
+
},
|
| 92 |
+
"metric_config": {
|
| 93 |
+
"lower_is_better": false,
|
| 94 |
+
"score_type": "continuous",
|
| 95 |
+
"min_score": 0.0,
|
| 96 |
+
"max_score": 100.0,
|
| 97 |
+
"evaluation_description": "Determines if a model can correctly identify the presence or absence of specified objects or conditions in the scene. Higher scores are better.",
|
| 98 |
+
"additional_details": {
|
| 99 |
+
"alphaxiv_y_axis": "Accuracy (%)",
|
| 100 |
+
"alphaxiv_is_primary": "False",
|
| 101 |
+
"raw_evaluation_name": "SpatialBench Object Existence"
|
| 102 |
+
},
|
| 103 |
+
"metric_id": "spatialbench_object_existence",
|
| 104 |
+
"metric_name": "SpatialBench Object Existence",
|
| 105 |
+
"metric_kind": "score",
|
| 106 |
+
"metric_unit": "points"
|
| 107 |
+
},
|
| 108 |
+
"score_details": {
|
| 109 |
+
"score": 75
|
| 110 |
+
},
|
| 111 |
+
"evaluation_result_id": "SpatialBench/Bunny-Phi2-3B-RGBD/1771591481.616601#spatialbench#spatialbench_object_existence"
|
| 112 |
+
},
|
| 113 |
+
{
|
| 114 |
+
"evaluation_name": "SpatialBench",
|
| 115 |
+
"source_data": {
|
| 116 |
+
"dataset_name": "SpatialBench",
|
| 117 |
+
"source_type": "url",
|
| 118 |
+
"url": [
|
| 119 |
+
"https://www.alphaxiv.org/abs/2406.13642"
|
| 120 |
+
]
|
| 121 |
+
},
|
| 122 |
+
"metric_config": {
|
| 123 |
+
"lower_is_better": false,
|
| 124 |
+
"score_type": "continuous",
|
| 125 |
+
"min_score": 0.0,
|
| 126 |
+
"max_score": 100.0,
|
| 127 |
+
"evaluation_description": "Evaluates the understanding of relative object positions (e.g., left/right, above/below) and proximity relationships (which object is closer/further). Higher scores are better.",
|
| 128 |
+
"additional_details": {
|
| 129 |
+
"alphaxiv_y_axis": "Accuracy (%)",
|
| 130 |
+
"alphaxiv_is_primary": "False",
|
| 131 |
+
"raw_evaluation_name": "SpatialBench Positional Reasoning"
|
| 132 |
+
},
|
| 133 |
+
"metric_id": "spatialbench_positional_reasoning",
|
| 134 |
+
"metric_name": "SpatialBench Positional Reasoning",
|
| 135 |
+
"metric_kind": "score",
|
| 136 |
+
"metric_unit": "points"
|
| 137 |
+
},
|
| 138 |
+
"score_details": {
|
| 139 |
+
"score": 50
|
| 140 |
+
},
|
| 141 |
+
"evaluation_result_id": "SpatialBench/Bunny-Phi2-3B-RGBD/1771591481.616601#spatialbench#spatialbench_positional_reasoning"
|
| 142 |
+
},
|
| 143 |
+
{
|
| 144 |
+
"evaluation_name": "SpatialBench",
|
| 145 |
+
"source_data": {
|
| 146 |
+
"dataset_name": "SpatialBench",
|
| 147 |
+
"source_type": "url",
|
| 148 |
+
"url": [
|
| 149 |
+
"https://www.alphaxiv.org/abs/2406.13642"
|
| 150 |
+
]
|
| 151 |
+
},
|
| 152 |
+
"metric_config": {
|
| 153 |
+
"lower_is_better": false,
|
| 154 |
+
"score_type": "continuous",
|
| 155 |
+
"min_score": 0.0,
|
| 156 |
+
"max_score": 100.0,
|
| 157 |
+
"evaluation_description": "Assesses whether a model can determine if one object has physically contacted or reached another, requiring precise metric depth understanding. Higher scores are better.",
|
| 158 |
+
"additional_details": {
|
| 159 |
+
"alphaxiv_y_axis": "Accuracy (%)",
|
| 160 |
+
"alphaxiv_is_primary": "False",
|
| 161 |
+
"raw_evaluation_name": "SpatialBench Reaching/Touching"
|
| 162 |
+
},
|
| 163 |
+
"metric_id": "spatialbench_reaching_touching",
|
| 164 |
+
"metric_name": "SpatialBench Reaching/Touching",
|
| 165 |
+
"metric_kind": "score",
|
| 166 |
+
"metric_unit": "points"
|
| 167 |
+
},
|
| 168 |
+
"score_details": {
|
| 169 |
+
"score": 43.3
|
| 170 |
+
},
|
| 171 |
+
"evaluation_result_id": "SpatialBench/Bunny-Phi2-3B-RGBD/1771591481.616601#spatialbench#spatialbench_reaching_touching"
|
| 172 |
+
},
|
| 173 |
+
{
|
| 174 |
+
"evaluation_name": "SpatialBench",
|
| 175 |
+
"source_data": {
|
| 176 |
+
"dataset_name": "SpatialBench",
|
| 177 |
+
"source_type": "url",
|
| 178 |
+
"url": [
|
| 179 |
+
"https://www.alphaxiv.org/abs/2406.13642"
|
| 180 |
+
]
|
| 181 |
+
},
|
| 182 |
+
"metric_config": {
|
| 183 |
+
"lower_is_better": false,
|
| 184 |
+
"score_type": "continuous",
|
| 185 |
+
"min_score": 0.0,
|
| 186 |
+
"max_score": 100.0,
|
| 187 |
+
"evaluation_description": "Evaluates the model's capacity to compare the sizes of objects in a scene, which can be influenced by depth information to account for perspective. Higher scores are better.",
|
| 188 |
+
"additional_details": {
|
| 189 |
+
"alphaxiv_y_axis": "Accuracy (%)",
|
| 190 |
+
"alphaxiv_is_primary": "False",
|
| 191 |
+
"raw_evaluation_name": "SpatialBench Size Comparison"
|
| 192 |
+
},
|
| 193 |
+
"metric_id": "spatialbench_size_comparison",
|
| 194 |
+
"metric_name": "SpatialBench Size Comparison",
|
| 195 |
+
"metric_kind": "score",
|
| 196 |
+
"metric_unit": "points"
|
| 197 |
+
},
|
| 198 |
+
"score_details": {
|
| 199 |
+
"score": 28.3
|
| 200 |
+
},
|
| 201 |
+
"evaluation_result_id": "SpatialBench/Bunny-Phi2-3B-RGBD/1771591481.616601#spatialbench#spatialbench_size_comparison"
|
| 202 |
+
}
|
| 203 |
+
],
|
| 204 |
+
"eval_library": {
|
| 205 |
+
"name": "alphaxiv",
|
| 206 |
+
"version": "unknown"
|
| 207 |
+
}
|
| 208 |
+
}
|
flat/objects/03/38/0338283e-d589-4549-814b-4592af0a1f8e.json
ADDED
|
@@ -0,0 +1,1108 @@
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
|
|
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|
|
|
|
|
|
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|
|
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|
|
|
|
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|
|
|
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|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
|
|
|
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|
|
|
|
|
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|
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|
|
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|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "IMAGINE-E/Stable Diffusion 3/1771591481.616601",
|
| 4 |
+
"retrieved_timestamp": "1771591481.616601",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "alphaXiv State of the Art",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "alphaXiv",
|
| 9 |
+
"source_organization_url": "https://alphaxiv.org",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"alphaxiv_dataset_org": "Shanghai AI Laboratory",
|
| 13 |
+
"alphaxiv_dataset_type": "image",
|
| 14 |
+
"scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
|
| 15 |
+
}
|
| 16 |
+
},
|
| 17 |
+
"model_info": {
|
| 18 |
+
"id": "Stable Diffusion 3",
|
| 19 |
+
"name": "Stable Diffusion 3",
|
| 20 |
+
"developer": "unknown"
|
| 21 |
+
},
|
| 22 |
+
"evaluation_results": [
|
| 23 |
+
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| 24 |
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| 53 |
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| 54 |
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| 69 |
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| 71 |
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| 72 |
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| 73 |
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| 74 |
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| 81 |
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"evaluation_result_id": "IMAGINE-E/Stable Diffusion 3/1771591481.616601#imagine_e#imagine_e_human_evaluation_on_autonomous_driving_scene_generation"
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| 83 |
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| 84 |
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"evaluation_name": "IMAGINE-E",
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| 85 |
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| 86 |
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| 88 |
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|
| 89 |
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| 95 |
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| 96 |
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| 97 |
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| 99 |
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| 101 |
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|
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| 111 |
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| 113 |
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| 114 |
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| 126 |
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| 141 |
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| 143 |
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| 144 |
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| 146 |
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| 148 |
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| 149 |
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| 155 |
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|
| 156 |
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|
| 157 |
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| 159 |
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|
| 161 |
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|
| 162 |
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|
| 163 |
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|
| 164 |
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|
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|
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|
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| 170 |
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|
| 171 |
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|
| 172 |
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| 173 |
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|
| 174 |
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| 175 |
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| 176 |
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| 177 |
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|
| 178 |
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| 179 |
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| 185 |
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|
| 186 |
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|
| 187 |
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| 190 |
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|
| 191 |
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|
| 192 |
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|
| 193 |
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|
| 194 |
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|
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| 196 |
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|
| 197 |
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|
| 198 |
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|
| 199 |
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|
| 200 |
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|
| 201 |
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|
| 202 |
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|
| 203 |
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{
|
| 204 |
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|
| 205 |
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| 206 |
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|
| 207 |
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|
| 208 |
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|
| 209 |
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|
| 210 |
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|
| 214 |
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|
| 215 |
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|
| 216 |
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|
| 217 |
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| 218 |
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|
| 220 |
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|
| 221 |
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|
| 222 |
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|
| 223 |
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|
| 224 |
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|
| 225 |
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|
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| 228 |
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|
| 229 |
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|
| 230 |
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|
| 231 |
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"evaluation_result_id": "IMAGINE-E/Stable Diffusion 3/1771591481.616601#imagine_e#imagine_e_human_evaluation_on_code_generation"
|
| 232 |
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|
| 233 |
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|
| 234 |
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| 236 |
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|
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|
| 238 |
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|
| 239 |
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| 244 |
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|
| 245 |
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|
| 246 |
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|
| 247 |
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| 248 |
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| 249 |
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|
| 250 |
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|
| 251 |
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|
| 252 |
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},
|
| 253 |
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|
| 254 |
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|
| 255 |
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|
| 256 |
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|
| 257 |
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|
| 258 |
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|
| 259 |
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|
| 260 |
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},
|
| 261 |
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|
| 262 |
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},
|
| 263 |
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{
|
| 264 |
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|
| 265 |
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|
| 266 |
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|
| 267 |
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|
| 268 |
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|
| 269 |
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|
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|
| 272 |
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|
| 273 |
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|
| 274 |
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|
| 275 |
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|
| 276 |
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|
| 277 |
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"evaluation_description": "Human evaluation scores for Text-to-Image models on the Emoji task, assessing the ability to interpret and generate images from combinations of emojis. The score is calculated based on aesthetic appeal, realism, safety, and text alignment, with a maximum of 10.",
|
| 278 |
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|
| 279 |
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|
| 280 |
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|
| 281 |
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|
| 282 |
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},
|
| 283 |
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|
| 284 |
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|
| 285 |
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|
| 286 |
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|
| 287 |
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},
|
| 288 |
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|
| 289 |
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|
| 290 |
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|
| 291 |
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"evaluation_result_id": "IMAGINE-E/Stable Diffusion 3/1771591481.616601#imagine_e#imagine_e_human_evaluation_on_emoji_prompt_interpretation"
|
| 292 |
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|
| 293 |
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|
| 294 |
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|
| 295 |
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|
| 296 |
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|
| 297 |
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|
| 298 |
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|
| 299 |
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|
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|
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|
| 305 |
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|
| 306 |
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|
| 307 |
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| 308 |
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| 309 |
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|
| 310 |
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|
| 311 |
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|
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|
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|
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|
| 318 |
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| 319 |
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|
| 320 |
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|
| 321 |
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|
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| 323 |
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|
| 324 |
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| 326 |
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| 327 |
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|
| 328 |
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|
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|
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|
| 335 |
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|
| 336 |
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|
| 337 |
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| 338 |
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| 339 |
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|
| 340 |
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|
| 341 |
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|
| 342 |
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|
| 343 |
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| 344 |
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|
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|
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|
| 348 |
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|
| 349 |
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|
| 350 |
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|
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flat/objects/03/3c/033c8a36-cda7-4405-914a-82809d34ab20.json
ADDED
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flat/objects/03/3d/033db36f-2faa-41be-8520-f3a18ab1b6d8.json
ADDED
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@@ -0,0 +1,88 @@
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| 1 |
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flat/objects/03/42/03420115-7b3e-4eac-8bf4-8ae8cca46fe4.json
ADDED
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@@ -0,0 +1,148 @@
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| 1 |
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"alphaxiv_y_axis": "Correctness (%) - Recognition Q&A",
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"evaluation_result_id": "ChineseSimpleVQA/InterVL2.5-8B/1771591481.616601#chinesesimplevqa#chinesesimplevqa_correctness_on_recognition_object_id_questions"
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| 112 |
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| 113 |
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{
|
| 114 |
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| 115 |
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| 116 |
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| 117 |
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"url": [
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| 119 |
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| 125 |
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|
| 126 |
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|
| 127 |
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"evaluation_description": "F-score on the object recognition questions (Recognition Q&A) in the ChineseSimpleVQA benchmark. This task evaluates the model's fundamental visual perception and object identification capabilities, which is the first step in the multi-hop reasoning process.",
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| 128 |
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| 129 |
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"alphaxiv_y_axis": "F-score (%) - Recognition Q&A",
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| 131 |
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|
| 132 |
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|
| 133 |
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"metric_id": "chinesesimplevqa_f_score_on_recognition_object_id_questions",
|
| 134 |
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"metric_name": "ChineseSimpleVQA: F-score on Recognition (Object ID) Questions",
|
| 135 |
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"metric_kind": "score",
|
| 136 |
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"metric_unit": "points"
|
| 137 |
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|
| 138 |
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|
| 139 |
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"score": 26
|
| 140 |
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|
| 141 |
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"evaluation_result_id": "ChineseSimpleVQA/InterVL2.5-8B/1771591481.616601#chinesesimplevqa#chinesesimplevqa_f_score_on_recognition_object_id_questions"
|
| 142 |
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|
| 143 |
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|
| 144 |
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"eval_library": {
|
| 145 |
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"name": "alphaxiv",
|
| 146 |
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|
| 147 |
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|
| 148 |
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|
flat/objects/03/46/0346acab-b5cb-4cdd-ae0b-d71b7e0daf93.json
ADDED
|
@@ -0,0 +1,208 @@
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|
| 1 |
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| 2 |
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| 9 |
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| 10 |
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|
| 11 |
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| 12 |
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| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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| 22 |
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|
| 23 |
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{
|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
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|
| 30 |
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|
| 31 |
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| 32 |
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| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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| 38 |
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|
| 44 |
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| 45 |
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| 46 |
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|
| 47 |
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| 48 |
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| 49 |
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|
| 50 |
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| 51 |
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|
| 52 |
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|
| 53 |
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|
| 54 |
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|
| 55 |
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|
| 56 |
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|
| 57 |
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|
| 58 |
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|
| 59 |
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| 60 |
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| 63 |
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|
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|
| 66 |
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|
| 67 |
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| 68 |
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| 69 |
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| 70 |
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| 74 |
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| 76 |
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|
| 77 |
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| 79 |
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| 80 |
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| 81 |
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| 82 |
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|
| 83 |
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|
| 84 |
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|
| 85 |
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|
| 86 |
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| 87 |
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|
| 88 |
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|
| 89 |
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| 90 |
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| 97 |
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| 99 |
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| 100 |
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| 101 |
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| 105 |
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| 110 |
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| 111 |
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| 112 |
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| 113 |
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|
| 114 |
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| 115 |
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|
| 116 |
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| 126 |
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|
| 127 |
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| 128 |
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| 129 |
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| 130 |
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| 131 |
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| 132 |
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| 134 |
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| 142 |
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| 143 |
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|
| 144 |
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| 145 |
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|
| 146 |
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| 147 |
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| 148 |
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| 156 |
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| 171 |
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|
| 172 |
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|
| 173 |
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|
| 174 |
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|
| 175 |
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|
| 176 |
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|
| 177 |
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|
| 178 |
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|
| 179 |
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| 180 |
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| 186 |
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|
| 187 |
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| 188 |
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|
| 189 |
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| 190 |
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|
| 191 |
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|
| 192 |
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|
| 194 |
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|
| 195 |
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|
| 196 |
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|
| 197 |
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|
| 198 |
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|
| 199 |
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|
| 200 |
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|
| 201 |
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| 202 |
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| 204 |
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| 205 |
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|
| 208 |
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|
flat/objects/03/48/03484c1a-1ab0-4b87-9f0f-281319d90095.json
ADDED
|
@@ -0,0 +1,88 @@
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|
|
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|
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|
|
|
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|
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|
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|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
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|
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|
|
|
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|
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|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
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|
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|
|
|
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|
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|
|
|
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|
|
|
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|
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|
|
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|
|
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|
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|
| 1 |
+
{
|
| 2 |
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|
| 3 |
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| 4 |
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| 5 |
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| 6 |
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| 7 |
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|
| 8 |
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| 9 |
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| 10 |
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|
| 11 |
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| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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| 26 |
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| 27 |
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| 28 |
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|
| 29 |
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| 30 |
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|
| 31 |
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|
| 37 |
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| 50 |
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| 57 |
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"source_type": "url",
|
| 58 |
+
"url": [
|
| 59 |
+
"https://www.alphaxiv.org/abs/2406.15877"
|
| 60 |
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]
|
| 61 |
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|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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"evaluation_description": "Measures the functional correctness (Pass@5) on the BigCodeBench-Complete dataset (1,140 tasks with structured docstrings). This metric is computed by generating N=5 samples with a temperature of 0.8 and top-p of 0.95, and assessing if at least one of the samples passes all test cases. It indicates a model's potential when allowed multiple attempts.",
|
| 68 |
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"additional_details": {
|
| 69 |
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"alphaxiv_y_axis": "Pass@5",
|
| 70 |
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|
| 71 |
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"raw_evaluation_name": "BigCodeBench (Full-Complete) Pass@5"
|
| 72 |
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|
| 73 |
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"metric_id": "bigcodebench_full_complete_pass_5",
|
| 74 |
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"metric_name": "BigCodeBench (Full-Complete) Pass@5",
|
| 75 |
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|
| 76 |
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|
| 77 |
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},
|
| 78 |
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"score_details": {
|
| 79 |
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"score": 0.539
|
| 80 |
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|
| 81 |
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"evaluation_result_id": "BigCodeBench/Mistral Large/1771591481.616601#bigcodebench#bigcodebench_full_complete_pass_5"
|
| 82 |
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|
| 83 |
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],
|
| 84 |
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"eval_library": {
|
| 85 |
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"name": "alphaxiv",
|
| 86 |
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|
| 87 |
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|
| 88 |
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|
flat/objects/03/48/0348fb88-5221-4c4c-8168-f9f766519dda.json
ADDED
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@@ -0,0 +1,58 @@
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|
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|
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|
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|
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|
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|
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|
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|
| 1 |
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{
|
| 2 |
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|
| 3 |
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|
| 4 |
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|
| 5 |
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| 6 |
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| 7 |
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| 8 |
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| 9 |
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"source_organization_url": "https://alphaxiv.org",
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| 10 |
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|
| 11 |
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| 12 |
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| 13 |
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| 14 |
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| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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"name": "CodeGemma-7b-Base",
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| 20 |
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| 21 |
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},
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| 22 |
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"evaluation_results": [
|
| 23 |
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{
|
| 24 |
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"evaluation_name": "LiveCodeBench",
|
| 25 |
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|
| 26 |
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"dataset_name": "LiveCodeBench",
|
| 27 |
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|
| 28 |
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|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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|
| 33 |
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| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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"evaluation_description": "Overall Pass@1 scores for the code generation task, as reported in the original paper. This evaluation uses a contamination-free subset of problems released after September 2023. The task assesses an LLM's ability to translate natural language descriptions into functional Python code.",
|
| 38 |
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"additional_details": {
|
| 39 |
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"alphaxiv_y_axis": "Pass@1 (%) - Code Generation (Paper)",
|
| 40 |
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|
| 41 |
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|
| 42 |
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| 44 |
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"metric_name": "LiveCodeBench: Code Generation Performance (Paper Results)",
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| 45 |
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| 46 |
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| 47 |
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| 48 |
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|
| 49 |
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"score": 12.8
|
| 50 |
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|
| 51 |
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"evaluation_result_id": "LiveCodeBench/CodeGemma-7b-Base/1771591481.616601#livecodebench#livecodebench_code_generation_performance_paper_results"
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| 52 |
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| 54 |
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| 55 |
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"name": "alphaxiv",
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| 56 |
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| 58 |
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flat/objects/03/4c/034c656c-80a3-4aa8-b2ee-baf9308596e3.json
ADDED
|
@@ -0,0 +1,257 @@
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| 1 |
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flat/objects/03/4d/034da237-557b-49df-91f9-5ef0f9834fd6.json
ADDED
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@@ -0,0 +1,238 @@
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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| 48 |
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| 49 |
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| 50 |
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|
| 52 |
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|
| 53 |
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|
| 54 |
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| 55 |
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| 56 |
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| 57 |
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| 58 |
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| 59 |
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| 65 |
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| 66 |
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|
| 67 |
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|
| 68 |
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|
| 69 |
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|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 74 |
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| 75 |
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| 76 |
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| 77 |
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|
| 82 |
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| 83 |
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|
| 84 |
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| 85 |
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| 86 |
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| 87 |
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| 88 |
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| 89 |
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| 96 |
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| 97 |
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| 98 |
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| 99 |
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| 100 |
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| 101 |
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| 112 |
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| 113 |
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| 114 |
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| 115 |
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| 116 |
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| 117 |
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| 118 |
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| 130 |
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| 132 |
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| 143 |
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| 144 |
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| 145 |
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| 146 |
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| 147 |
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| 148 |
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| 172 |
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| 173 |
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| 174 |
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"evaluation_name": "GridPuzzle",
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| 175 |
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| 176 |
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| 177 |
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| 178 |
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| 179 |
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| 180 |
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| 185 |
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| 187 |
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| 188 |
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| 190 |
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| 191 |
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| 192 |
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| 195 |
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| 196 |
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| 197 |
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| 198 |
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| 199 |
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| 202 |
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| 208 |
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flat/objects/03/54/0354fd88-4825-4456-af3e-afaa3ef02b2b.json
ADDED
|
@@ -0,0 +1,58 @@
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| 6 |
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| 11 |
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| 13 |
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| 15 |
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| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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| 27 |
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| 28 |
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| 29 |
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|
| 30 |
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| 31 |
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| 32 |
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| 35 |
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|
| 36 |
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|
| 37 |
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| 38 |
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|
| 40 |
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|
| 42 |
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|
| 44 |
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|
| 45 |
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| 46 |
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|
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| 52 |
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flat/objects/03/5b/035b84ad-84c0-412e-81d2-22e77c5e2410.json
ADDED
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@@ -0,0 +1,169 @@
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| 1 |
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{
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|
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|
| 19 |
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| 20 |
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| 1 |
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ADDED
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@@ -0,0 +1,225 @@
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| 190 |
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"leaderboard_page_url": "https://www.vals.ai/benchmarks/aime"
|
| 191 |
+
}
|
| 192 |
+
},
|
| 193 |
+
"score_details": {
|
| 194 |
+
"score": 13.333,
|
| 195 |
+
"details": {
|
| 196 |
+
"benchmark_slug": "aime",
|
| 197 |
+
"benchmark_name": "AIME",
|
| 198 |
+
"benchmark_updated": "2026-04-16",
|
| 199 |
+
"task_key": "overall",
|
| 200 |
+
"task_name": "Overall",
|
| 201 |
+
"dataset_type": "public",
|
| 202 |
+
"industry": "math",
|
| 203 |
+
"raw_score": "13.333",
|
| 204 |
+
"raw_stderr": "0.945",
|
| 205 |
+
"latency": "23.345",
|
| 206 |
+
"cost_per_test": "0.011732",
|
| 207 |
+
"temperature": "0.3",
|
| 208 |
+
"provider": "Cohere"
|
| 209 |
+
},
|
| 210 |
+
"uncertainty": {
|
| 211 |
+
"standard_error": {
|
| 212 |
+
"value": 0.945,
|
| 213 |
+
"method": "vals_reported"
|
| 214 |
+
}
|
| 215 |
+
}
|
| 216 |
+
},
|
| 217 |
+
"generation_config": {
|
| 218 |
+
"generation_args": {
|
| 219 |
+
"temperature": 0.3,
|
| 220 |
+
"max_attempts": 1
|
| 221 |
+
}
|
| 222 |
+
}
|
| 223 |
+
}
|
| 224 |
+
]
|
| 225 |
+
}
|