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@@ -3,8 +3,8 @@ pretty_name: Evaluation run of harborwater/wizard-orca-3b
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  dataset_summary: "Dataset automatically created during the evaluation run of model\
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  \ [harborwater/wizard-orca-3b](https://huggingface.co/harborwater/wizard-orca-3b)\
5
  \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
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- \nThe dataset is composed of 61 configuration, each one coresponding to one of the\
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- \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
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  \ found as a specific split in each configuration, the split being named using the\
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  \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
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  \nAn additional configuration \"results\" store all the aggregated results of the\
@@ -12,172 +12,20 @@ dataset_summary: "Dataset automatically created during the evaluation run of mod
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  \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
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  \nTo load the details from a run, you can for instance do the following:\n```python\n\
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  from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_harborwater__wizard-orca-3b\"\
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- ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
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- \nThese are the [latest results from run 2023-10-08T19:21:18.723038](https://huggingface.co/datasets/open-llm-leaderboard/details_harborwater__wizard-orca-3b/blob/main/results_2023-10-08T19-21-18.723038.json)(note\
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  \ that their might be results for other tasks in the repos if successive evals didn't\
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  \ cover the same tasks. You find each in the results and the \"latest\" split for\
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- \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2523974583502166,\n\
20
- \ \"acc_stderr\": 0.03129775621975096,\n \"acc_norm\": 0.25584668997848853,\n\
21
- \ \"acc_norm_stderr\": 0.031292926782164415,\n \"mc1\": 0.26193390452876375,\n\
22
- \ \"mc1_stderr\": 0.015392118805015027,\n \"mc2\": 0.40035714074695034,\n\
23
- \ \"mc2_stderr\": 0.014298393496028232\n },\n \"harness|arc:challenge|25\"\
24
- : {\n \"acc\": 0.3848122866894198,\n \"acc_stderr\": 0.014218371065251102,\n\
25
- \ \"acc_norm\": 0.41723549488054607,\n \"acc_norm_stderr\": 0.01440982551840308\n\
26
- \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5467038438558056,\n\
27
- \ \"acc_stderr\": 0.00496796581019999,\n \"acc_norm\": 0.7177853017327226,\n\
28
- \ \"acc_norm_stderr\": 0.004491574539441884\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
29
- : {\n \"acc\": 0.16,\n \"acc_stderr\": 0.03684529491774709,\n \
30
- \ \"acc_norm\": 0.16,\n \"acc_norm_stderr\": 0.03684529491774709\n \
31
- \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.17777777777777778,\n\
32
- \ \"acc_stderr\": 0.033027898599017176,\n \"acc_norm\": 0.17777777777777778,\n\
33
- \ \"acc_norm_stderr\": 0.033027898599017176\n },\n \"harness|hendrycksTest-astronomy|5\"\
34
- : {\n \"acc\": 0.21710526315789475,\n \"acc_stderr\": 0.03355045304882924,\n\
35
- \ \"acc_norm\": 0.21710526315789475,\n \"acc_norm_stderr\": 0.03355045304882924\n\
36
- \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.33,\n\
37
- \ \"acc_stderr\": 0.04725815626252604,\n \"acc_norm\": 0.33,\n \
38
- \ \"acc_norm_stderr\": 0.04725815626252604\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
39
- : {\n \"acc\": 0.2339622641509434,\n \"acc_stderr\": 0.02605529690115292,\n\
40
- \ \"acc_norm\": 0.2339622641509434,\n \"acc_norm_stderr\": 0.02605529690115292\n\
41
- \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.25,\n\
42
- \ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.25,\n \
43
- \ \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
44
- : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \
45
- \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n \
46
- \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
47
- acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
48
- : 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
49
- : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \
50
- \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \
51
- \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2023121387283237,\n\
52
- \ \"acc_stderr\": 0.03063114553919882,\n \"acc_norm\": 0.2023121387283237,\n\
53
- \ \"acc_norm_stderr\": 0.03063114553919882\n },\n \"harness|hendrycksTest-college_physics|5\"\
54
- : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237655,\n\
55
- \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237655\n\
56
- \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
57
- \ 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n\
58
- \ \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
59
- : {\n \"acc\": 0.31063829787234043,\n \"acc_stderr\": 0.03025123757921317,\n\
60
- \ \"acc_norm\": 0.31063829787234043,\n \"acc_norm_stderr\": 0.03025123757921317\n\
61
- \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.21052631578947367,\n\
62
- \ \"acc_stderr\": 0.038351539543994194,\n \"acc_norm\": 0.21052631578947367,\n\
63
- \ \"acc_norm_stderr\": 0.038351539543994194\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
64
- : {\n \"acc\": 0.2620689655172414,\n \"acc_stderr\": 0.036646663372252565,\n\
65
- \ \"acc_norm\": 0.2620689655172414,\n \"acc_norm_stderr\": 0.036646663372252565\n\
66
- \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
67
- : 0.21693121693121692,\n \"acc_stderr\": 0.02122708244944508,\n \"\
68
- acc_norm\": 0.21693121693121692,\n \"acc_norm_stderr\": 0.02122708244944508\n\
69
- \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2777777777777778,\n\
70
- \ \"acc_stderr\": 0.04006168083848876,\n \"acc_norm\": 0.2777777777777778,\n\
71
- \ \"acc_norm_stderr\": 0.04006168083848876\n },\n \"harness|hendrycksTest-global_facts|5\"\
72
- : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \
73
- \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \
74
- \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.1870967741935484,\n\
75
- \ \"acc_stderr\": 0.02218571009225225,\n \"acc_norm\": 0.1870967741935484,\n\
76
- \ \"acc_norm_stderr\": 0.02218571009225225\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
77
- : {\n \"acc\": 0.15270935960591134,\n \"acc_stderr\": 0.02530890453938063,\n\
78
- \ \"acc_norm\": 0.15270935960591134,\n \"acc_norm_stderr\": 0.02530890453938063\n\
79
- \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
80
- \ \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
81
- : 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
82
- : {\n \"acc\": 0.24242424242424243,\n \"acc_stderr\": 0.03346409881055953,\n\
83
- \ \"acc_norm\": 0.24242424242424243,\n \"acc_norm_stderr\": 0.03346409881055953\n\
84
- \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
85
- : 0.16666666666666666,\n \"acc_stderr\": 0.02655220782821529,\n \"\
86
- acc_norm\": 0.16666666666666666,\n \"acc_norm_stderr\": 0.02655220782821529\n\
87
- \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
88
- \ \"acc\": 0.20207253886010362,\n \"acc_stderr\": 0.02897908979429673,\n\
89
- \ \"acc_norm\": 0.20207253886010362,\n \"acc_norm_stderr\": 0.02897908979429673\n\
90
- \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
91
- \ \"acc\": 0.2205128205128205,\n \"acc_stderr\": 0.021020672680827912,\n\
92
- \ \"acc_norm\": 0.2205128205128205,\n \"acc_norm_stderr\": 0.021020672680827912\n\
93
- \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
94
- acc\": 0.2111111111111111,\n \"acc_stderr\": 0.024882116857655075,\n \
95
- \ \"acc_norm\": 0.2111111111111111,\n \"acc_norm_stderr\": 0.024882116857655075\n\
96
- \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
97
- \ \"acc\": 0.24789915966386555,\n \"acc_stderr\": 0.028047967224176892,\n\
98
- \ \"acc_norm\": 0.24789915966386555,\n \"acc_norm_stderr\": 0.028047967224176892\n\
99
- \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
100
- : 0.24503311258278146,\n \"acc_stderr\": 0.03511807571804726,\n \"\
101
- acc_norm\": 0.24503311258278146,\n \"acc_norm_stderr\": 0.03511807571804726\n\
102
- \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
103
- : 0.21834862385321102,\n \"acc_stderr\": 0.01771260052872273,\n \"\
104
- acc_norm\": 0.21834862385321102,\n \"acc_norm_stderr\": 0.01771260052872273\n\
105
- \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
106
- : 0.18055555555555555,\n \"acc_stderr\": 0.026232878971491656,\n \"\
107
- acc_norm\": 0.18055555555555555,\n \"acc_norm_stderr\": 0.026232878971491656\n\
108
- \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
109
- : 0.19117647058823528,\n \"acc_stderr\": 0.027599174300640766,\n \"\
110
- acc_norm\": 0.19117647058823528,\n \"acc_norm_stderr\": 0.027599174300640766\n\
111
- \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
112
- acc\": 0.2911392405063291,\n \"acc_stderr\": 0.029571601065753374,\n \
113
- \ \"acc_norm\": 0.2911392405063291,\n \"acc_norm_stderr\": 0.029571601065753374\n\
114
- \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.336322869955157,\n\
115
- \ \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.336322869955157,\n\
116
- \ \"acc_norm_stderr\": 0.031708824268455\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
117
- : {\n \"acc\": 0.2824427480916031,\n \"acc_stderr\": 0.03948406125768361,\n\
118
- \ \"acc_norm\": 0.2824427480916031,\n \"acc_norm_stderr\": 0.03948406125768361\n\
119
- \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
120
- \ 0.23140495867768596,\n \"acc_stderr\": 0.03849856098794089,\n \"\
121
- acc_norm\": 0.23140495867768596,\n \"acc_norm_stderr\": 0.03849856098794089\n\
122
- \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.24074074074074073,\n\
123
- \ \"acc_stderr\": 0.04133119440243839,\n \"acc_norm\": 0.24074074074074073,\n\
124
- \ \"acc_norm_stderr\": 0.04133119440243839\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
125
- : {\n \"acc\": 0.2147239263803681,\n \"acc_stderr\": 0.03226219377286774,\n\
126
- \ \"acc_norm\": 0.2147239263803681,\n \"acc_norm_stderr\": 0.03226219377286774\n\
127
- \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.30357142857142855,\n\
128
- \ \"acc_stderr\": 0.04364226155841043,\n \"acc_norm\": 0.30357142857142855,\n\
129
- \ \"acc_norm_stderr\": 0.04364226155841043\n },\n \"harness|hendrycksTest-management|5\"\
130
- : {\n \"acc\": 0.13592233009708737,\n \"acc_stderr\": 0.03393295729761013,\n\
131
- \ \"acc_norm\": 0.13592233009708737,\n \"acc_norm_stderr\": 0.03393295729761013\n\
132
- \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.3076923076923077,\n\
133
- \ \"acc_stderr\": 0.030236389942173116,\n \"acc_norm\": 0.3076923076923077,\n\
134
- \ \"acc_norm_stderr\": 0.030236389942173116\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
135
- : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542129,\n \
136
- \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542129\n \
137
- \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.24265644955300128,\n\
138
- \ \"acc_stderr\": 0.01532988894089987,\n \"acc_norm\": 0.24265644955300128,\n\
139
- \ \"acc_norm_stderr\": 0.01532988894089987\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
140
- : {\n \"acc\": 0.25722543352601157,\n \"acc_stderr\": 0.02353292543104428,\n\
141
- \ \"acc_norm\": 0.25722543352601157,\n \"acc_norm_stderr\": 0.02353292543104428\n\
142
- \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23687150837988827,\n\
143
- \ \"acc_stderr\": 0.014219570788103982,\n \"acc_norm\": 0.23687150837988827,\n\
144
- \ \"acc_norm_stderr\": 0.014219570788103982\n },\n \"harness|hendrycksTest-nutrition|5\"\
145
- : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.02355083135199509,\n\
146
- \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.02355083135199509\n\
147
- \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.27009646302250806,\n\
148
- \ \"acc_stderr\": 0.02521804037341063,\n \"acc_norm\": 0.27009646302250806,\n\
149
- \ \"acc_norm_stderr\": 0.02521804037341063\n },\n \"harness|hendrycksTest-prehistory|5\"\
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- : {\n \"acc\": 0.24074074074074073,\n \"acc_stderr\": 0.023788583551658533,\n\
151
- \ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.023788583551658533\n\
152
- \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
153
- acc\": 0.2730496453900709,\n \"acc_stderr\": 0.026577860943307857,\n \
154
- \ \"acc_norm\": 0.2730496453900709,\n \"acc_norm_stderr\": 0.026577860943307857\n\
155
- \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2392438070404172,\n\
156
- \ \"acc_stderr\": 0.01089612365267665,\n \"acc_norm\": 0.2392438070404172,\n\
157
- \ \"acc_norm_stderr\": 0.01089612365267665\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
158
- : {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n\
159
- \ \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n\
160
- \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
161
- acc\": 0.25163398692810457,\n \"acc_stderr\": 0.017555818091322263,\n \
162
- \ \"acc_norm\": 0.25163398692810457,\n \"acc_norm_stderr\": 0.017555818091322263\n\
163
- \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.2636363636363636,\n\
164
- \ \"acc_stderr\": 0.04220224692971987,\n \"acc_norm\": 0.2636363636363636,\n\
165
- \ \"acc_norm_stderr\": 0.04220224692971987\n },\n \"harness|hendrycksTest-security_studies|5\"\
166
- : {\n \"acc\": 0.21224489795918366,\n \"acc_stderr\": 0.026176967197866767,\n\
167
- \ \"acc_norm\": 0.21224489795918366,\n \"acc_norm_stderr\": 0.026176967197866767\n\
168
- \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24378109452736318,\n\
169
- \ \"acc_stderr\": 0.030360490154014652,\n \"acc_norm\": 0.24378109452736318,\n\
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- \ \"acc_norm_stderr\": 0.030360490154014652\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
171
- : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
172
- \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
173
- \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3132530120481928,\n\
174
- \ \"acc_stderr\": 0.03610805018031023,\n \"acc_norm\": 0.3132530120481928,\n\
175
- \ \"acc_norm_stderr\": 0.03610805018031023\n },\n \"harness|hendrycksTest-world_religions|5\"\
176
- : {\n \"acc\": 0.3216374269005848,\n \"acc_stderr\": 0.03582529442573122,\n\
177
- \ \"acc_norm\": 0.3216374269005848,\n \"acc_norm_stderr\": 0.03582529442573122\n\
178
- \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.26193390452876375,\n\
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- \ \"mc1_stderr\": 0.015392118805015027,\n \"mc2\": 0.40035714074695034,\n\
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- \ \"mc2_stderr\": 0.014298393496028232\n }\n}\n```"
181
  repo_url: https://huggingface.co/harborwater/wizard-orca-3b
182
  leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
183
  point_of_contact: [email protected]
@@ -190,6 +38,22 @@ configs:
190
  - split: latest
191
  path:
192
  - '**/details_harness|arc:challenge|25_2023-10-08T19-21-18.723038.parquet'
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
193
  - config_name: harness_hellaswag_10
194
  data_files:
195
  - split: 2023_10_08T19_21_18.723038
@@ -782,14 +646,25 @@ configs:
782
  - split: latest
783
  path:
784
  - '**/details_harness|truthfulqa:mc|0_2023-10-08T19-21-18.723038.parquet'
 
 
 
 
 
 
 
 
785
  - config_name: results
786
  data_files:
787
  - split: 2023_10_08T19_21_18.723038
788
  path:
789
  - results_2023-10-08T19-21-18.723038.parquet
 
 
 
790
  - split: latest
791
  path:
792
- - results_2023-10-08T19-21-18.723038.parquet
793
  ---
794
 
795
  # Dataset Card for Evaluation run of harborwater/wizard-orca-3b
@@ -806,9 +681,9 @@ configs:
806
 
807
  Dataset automatically created during the evaluation run of model [harborwater/wizard-orca-3b](https://huggingface.co/harborwater/wizard-orca-3b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
808
 
809
- The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
810
 
811
- The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
812
 
813
  An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
814
 
@@ -816,385 +691,37 @@ To load the details from a run, you can for instance do the following:
816
  ```python
817
  from datasets import load_dataset
818
  data = load_dataset("open-llm-leaderboard/details_harborwater__wizard-orca-3b",
819
- "harness_truthfulqa_mc_0",
820
  split="train")
821
  ```
822
 
823
  ## Latest results
824
 
825
- These are the [latest results from run 2023-10-08T19:21:18.723038](https://huggingface.co/datasets/open-llm-leaderboard/details_harborwater__wizard-orca-3b/blob/main/results_2023-10-08T19-21-18.723038.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
826
 
827
  ```python
828
  {
829
  "all": {
830
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1073
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1121
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1157
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1158
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1162
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1163
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1164
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1168
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1169
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1175
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1176
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1179
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1180
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1181
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1182
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1186
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1187
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1188
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1192
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1193
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- "mc2": 0.40035714074695034,
1197
- "mc2_stderr": 0.014298393496028232
1198
  }
1199
  }
1200
  ```
 
3
  dataset_summary: "Dataset automatically created during the evaluation run of model\
4
  \ [harborwater/wizard-orca-3b](https://huggingface.co/harborwater/wizard-orca-3b)\
5
  \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
6
+ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\
7
+ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
8
  \ found as a specific split in each configuration, the split being named using the\
9
  \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
10
  \nAn additional configuration \"results\" store all the aggregated results of the\
 
12
  \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
13
  \nTo load the details from a run, you can for instance do the following:\n```python\n\
14
  from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_harborwater__wizard-orca-3b\"\
15
+ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
16
+ These are the [latest results from run 2023-10-24T08:46:00.865464](https://huggingface.co/datasets/open-llm-leaderboard/details_harborwater__wizard-orca-3b/blob/main/results_2023-10-24T08-46-00.865464.json)(note\
17
  \ that their might be results for other tasks in the repos if successive evals didn't\
18
  \ cover the same tasks. You find each in the results and the \"latest\" split for\
19
+ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0019924496644295304,\n\
20
+ \ \"em_stderr\": 0.00045666764626669333,\n \"f1\": 0.05503670302013434,\n\
21
+ \ \"f1_stderr\": 0.0013533156474354355,\n \"acc\": 0.33995582743378455,\n\
22
+ \ \"acc_stderr\": 0.008022574604695198\n },\n \"harness|drop|3\": {\n\
23
+ \ \"em\": 0.0019924496644295304,\n \"em_stderr\": 0.00045666764626669333,\n\
24
+ \ \"f1\": 0.05503670302013434,\n \"f1_stderr\": 0.0013533156474354355\n\
25
+ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.01061410159211524,\n \
26
+ \ \"acc_stderr\": 0.002822713322387704\n },\n \"harness|winogrande|5\"\
27
+ : {\n \"acc\": 0.6692975532754538,\n \"acc_stderr\": 0.013222435887002691\n\
28
+ \ }\n}\n```"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
29
  repo_url: https://huggingface.co/harborwater/wizard-orca-3b
30
  leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
31
  point_of_contact: [email protected]
 
38
  - split: latest
39
  path:
40
  - '**/details_harness|arc:challenge|25_2023-10-08T19-21-18.723038.parquet'
41
+ - config_name: harness_drop_3
42
+ data_files:
43
+ - split: 2023_10_24T08_46_00.865464
44
+ path:
45
+ - '**/details_harness|drop|3_2023-10-24T08-46-00.865464.parquet'
46
+ - split: latest
47
+ path:
48
+ - '**/details_harness|drop|3_2023-10-24T08-46-00.865464.parquet'
49
+ - config_name: harness_gsm8k_5
50
+ data_files:
51
+ - split: 2023_10_24T08_46_00.865464
52
+ path:
53
+ - '**/details_harness|gsm8k|5_2023-10-24T08-46-00.865464.parquet'
54
+ - split: latest
55
+ path:
56
+ - '**/details_harness|gsm8k|5_2023-10-24T08-46-00.865464.parquet'
57
  - config_name: harness_hellaswag_10
58
  data_files:
59
  - split: 2023_10_08T19_21_18.723038
 
646
  - split: latest
647
  path:
648
  - '**/details_harness|truthfulqa:mc|0_2023-10-08T19-21-18.723038.parquet'
649
+ - config_name: harness_winogrande_5
650
+ data_files:
651
+ - split: 2023_10_24T08_46_00.865464
652
+ path:
653
+ - '**/details_harness|winogrande|5_2023-10-24T08-46-00.865464.parquet'
654
+ - split: latest
655
+ path:
656
+ - '**/details_harness|winogrande|5_2023-10-24T08-46-00.865464.parquet'
657
  - config_name: results
658
  data_files:
659
  - split: 2023_10_08T19_21_18.723038
660
  path:
661
  - results_2023-10-08T19-21-18.723038.parquet
662
+ - split: 2023_10_24T08_46_00.865464
663
+ path:
664
+ - results_2023-10-24T08-46-00.865464.parquet
665
  - split: latest
666
  path:
667
+ - results_2023-10-24T08-46-00.865464.parquet
668
  ---
669
 
670
  # Dataset Card for Evaluation run of harborwater/wizard-orca-3b
 
681
 
682
  Dataset automatically created during the evaluation run of model [harborwater/wizard-orca-3b](https://huggingface.co/harborwater/wizard-orca-3b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
683
 
684
+ The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
685
 
686
+ The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
687
 
688
  An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
689
 
 
691
  ```python
692
  from datasets import load_dataset
693
  data = load_dataset("open-llm-leaderboard/details_harborwater__wizard-orca-3b",
694
+ "harness_winogrande_5",
695
  split="train")
696
  ```
697
 
698
  ## Latest results
699
 
700
+ These are the [latest results from run 2023-10-24T08:46:00.865464](https://huggingface.co/datasets/open-llm-leaderboard/details_harborwater__wizard-orca-3b/blob/main/results_2023-10-24T08-46-00.865464.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
701
 
702
  ```python
703
  {
704
  "all": {
705
+ "em": 0.0019924496644295304,
706
+ "em_stderr": 0.00045666764626669333,
707
+ "f1": 0.05503670302013434,
708
+ "f1_stderr": 0.0013533156474354355,
709
+ "acc": 0.33995582743378455,
710
+ "acc_stderr": 0.008022574604695198
711
+ },
712
+ "harness|drop|3": {
713
+ "em": 0.0019924496644295304,
714
+ "em_stderr": 0.00045666764626669333,
715
+ "f1": 0.05503670302013434,
716
+ "f1_stderr": 0.0013533156474354355
717
+ },
718
+ "harness|gsm8k|5": {
719
+ "acc": 0.01061410159211524,
720
+ "acc_stderr": 0.002822713322387704
721
+ },
722
+ "harness|winogrande|5": {
723
+ "acc": 0.6692975532754538,
724
+ "acc_stderr": 0.013222435887002691
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
725
  }
726
  }
727
  ```