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@@ -4,6 +4,2603 @@ inference: true
4
  license: apache-2.0
5
  datasets:
6
  - GritLM/tulu2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7
  ---
8
 
9
  # Model Summary
 
4
  license: apache-2.0
5
  datasets:
6
  - GritLM/tulu2
7
+ tags:
8
+ - mteb
9
+ model-index:
10
+ - name: GritLM-8x7B
11
+ results:
12
+ - task:
13
+ type: Classification
14
+ dataset:
15
+ type: mteb/amazon_counterfactual
16
+ name: MTEB AmazonCounterfactualClassification (en)
17
+ config: en
18
+ split: test
19
+ revision: e8379541af4e31359cca9fbcf4b00f2671dba205
20
+ metrics:
21
+ - type: accuracy
22
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23
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24
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25
+ - type: f1
26
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27
+ - task:
28
+ type: Classification
29
+ dataset:
30
+ type: mteb/amazon_polarity
31
+ name: MTEB AmazonPolarityClassification
32
+ config: default
33
+ split: test
34
+ revision: e2d317d38cd51312af73b3d32a06d1a08b442046
35
+ metrics:
36
+ - type: accuracy
37
+ value: 96.32155000000002
38
+ - type: ap
39
+ value: 94.8026654593679
40
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41
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42
+ - task:
43
+ type: Classification
44
+ dataset:
45
+ type: mteb/amazon_reviews_multi
46
+ name: MTEB AmazonReviewsClassification (en)
47
+ config: en
48
+ split: test
49
+ revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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
+ dataset:
58
+ type: arguana
59
+ name: MTEB ArguAna
60
+ config: default
61
+ split: test
62
+ revision: None
63
+ metrics:
64
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65
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66
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107
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108
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110
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112
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114
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116
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118
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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
+ type: mteb/arxiv-clustering-p2p
128
+ name: MTEB ArxivClusteringP2P
129
+ config: default
130
+ split: test
131
+ revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
132
+ metrics:
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
+ type: mteb/arxiv-clustering-s2s
139
+ name: MTEB ArxivClusteringS2S
140
+ config: default
141
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142
+ revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
143
+ metrics:
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
+ type: mteb/askubuntudupquestions-reranking
150
+ name: MTEB AskUbuntuDupQuestions
151
+ config: default
152
+ split: test
153
+ revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
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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160
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161
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162
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163
+ name: MTEB BIOSSES
164
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165
+ split: test
166
+ revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
167
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168
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169
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170
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172
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174
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178
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180
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181
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182
+ dataset:
183
+ type: mteb/banking77
184
+ name: MTEB Banking77Classification
185
+ config: default
186
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187
+ revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
188
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191
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193
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195
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196
+ type: mteb/biorxiv-clustering-p2p
197
+ name: MTEB BiorxivClusteringP2P
198
+ config: default
199
+ split: test
200
+ revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
201
+ metrics:
202
+ - type: v_measure
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204
+ - task:
205
+ type: Clustering
206
+ dataset:
207
+ type: mteb/biorxiv-clustering-s2s
208
+ name: MTEB BiorxivClusteringS2S
209
+ config: default
210
+ split: test
211
+ revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
212
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213
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214
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215
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217
+ dataset:
218
+ type: BeIR/cqadupstack
219
+ name: MTEB CQADupstackAndroidRetrieval
220
+ config: default
221
+ split: test
222
+ revision: None
223
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224
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225
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289
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2212
+ dataset:
2213
+ type: mteb/sprintduplicatequestions-pairclassification
2214
+ name: MTEB SprintDuplicateQuestions
2215
+ config: default
2216
+ split: test
2217
+ revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
2218
+ metrics:
2219
+ - type: cos_sim_accuracy
2220
+ value: 99.68415841584158
2221
+ - type: cos_sim_ap
2222
+ value: 91.23713949701548
2223
+ - type: cos_sim_f1
2224
+ value: 83.70221327967808
2225
+ - type: cos_sim_precision
2226
+ value: 84.21052631578947
2227
+ - type: cos_sim_recall
2228
+ value: 83.2
2229
+ - type: dot_accuracy
2230
+ value: 99.5
2231
+ - type: dot_ap
2232
+ value: 79.46312132270363
2233
+ - type: dot_f1
2234
+ value: 72.75320970042794
2235
+ - type: dot_precision
2236
+ value: 69.35630099728014
2237
+ - type: dot_recall
2238
+ value: 76.5
2239
+ - type: euclidean_accuracy
2240
+ value: 99.69108910891089
2241
+ - type: euclidean_ap
2242
+ value: 90.9016163254649
2243
+ - type: euclidean_f1
2244
+ value: 83.91752577319586
2245
+ - type: euclidean_precision
2246
+ value: 86.59574468085106
2247
+ - type: euclidean_recall
2248
+ value: 81.39999999999999
2249
+ - type: manhattan_accuracy
2250
+ value: 99.7039603960396
2251
+ - type: manhattan_ap
2252
+ value: 91.5593806619311
2253
+ - type: manhattan_f1
2254
+ value: 85.08124076809453
2255
+ - type: manhattan_precision
2256
+ value: 83.80213385063045
2257
+ - type: manhattan_recall
2258
+ value: 86.4
2259
+ - type: max_accuracy
2260
+ value: 99.7039603960396
2261
+ - type: max_ap
2262
+ value: 91.5593806619311
2263
+ - type: max_f1
2264
+ value: 85.08124076809453
2265
+ - task:
2266
+ type: Clustering
2267
+ dataset:
2268
+ type: mteb/stackexchange-clustering
2269
+ name: MTEB StackExchangeClustering
2270
+ config: default
2271
+ split: test
2272
+ revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
2273
+ metrics:
2274
+ - type: v_measure
2275
+ value: 74.40806543281603
2276
+ - task:
2277
+ type: Clustering
2278
+ dataset:
2279
+ type: mteb/stackexchange-clustering-p2p
2280
+ name: MTEB StackExchangeClusteringP2P
2281
+ config: default
2282
+ split: test
2283
+ revision: 815ca46b2622cec33ccafc3735d572c266efdb44
2284
+ metrics:
2285
+ - type: v_measure
2286
+ value: 38.51757703316821
2287
+ - task:
2288
+ type: Reranking
2289
+ dataset:
2290
+ type: mteb/stackoverflowdupquestions-reranking
2291
+ name: MTEB StackOverflowDupQuestions
2292
+ config: default
2293
+ split: test
2294
+ revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
2295
+ metrics:
2296
+ - type: map
2297
+ value: 54.33475593449746
2298
+ - type: mrr
2299
+ value: 55.3374474789916
2300
+ - task:
2301
+ type: Summarization
2302
+ dataset:
2303
+ type: mteb/summeval
2304
+ name: MTEB SummEval
2305
+ config: default
2306
+ split: test
2307
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2308
+ metrics:
2309
+ - type: cos_sim_pearson
2310
+ value: 30.249926396023596
2311
+ - type: cos_sim_spearman
2312
+ value: 29.820375700458158
2313
+ - type: dot_pearson
2314
+ value: 28.820307635930355
2315
+ - type: dot_spearman
2316
+ value: 28.824273052746825
2317
+ - task:
2318
+ type: Retrieval
2319
+ dataset:
2320
+ type: trec-covid
2321
+ name: MTEB TRECCOVID
2322
+ config: default
2323
+ split: test
2324
+ revision: None
2325
+ metrics:
2326
+ - type: map_at_1
2327
+ value: 0.233
2328
+ - type: map_at_10
2329
+ value: 2.061
2330
+ - type: map_at_100
2331
+ value: 12.607
2332
+ - type: map_at_1000
2333
+ value: 30.031000000000002
2334
+ - type: map_at_3
2335
+ value: 0.6669999999999999
2336
+ - type: map_at_5
2337
+ value: 1.091
2338
+ - type: mrr_at_1
2339
+ value: 88.0
2340
+ - type: mrr_at_10
2341
+ value: 93.067
2342
+ - type: mrr_at_100
2343
+ value: 93.067
2344
+ - type: mrr_at_1000
2345
+ value: 93.067
2346
+ - type: mrr_at_3
2347
+ value: 92.667
2348
+ - type: mrr_at_5
2349
+ value: 93.067
2350
+ - type: ndcg_at_1
2351
+ value: 84.0
2352
+ - type: ndcg_at_10
2353
+ value: 81.072
2354
+ - type: ndcg_at_100
2355
+ value: 62.875
2356
+ - type: ndcg_at_1000
2357
+ value: 55.641
2358
+ - type: ndcg_at_3
2359
+ value: 85.296
2360
+ - type: ndcg_at_5
2361
+ value: 84.10499999999999
2362
+ - type: precision_at_1
2363
+ value: 88.0
2364
+ - type: precision_at_10
2365
+ value: 83.39999999999999
2366
+ - type: precision_at_100
2367
+ value: 63.7
2368
+ - type: precision_at_1000
2369
+ value: 24.622
2370
+ - type: precision_at_3
2371
+ value: 88.0
2372
+ - type: precision_at_5
2373
+ value: 87.2
2374
+ - type: recall_at_1
2375
+ value: 0.233
2376
+ - type: recall_at_10
2377
+ value: 2.188
2378
+ - type: recall_at_100
2379
+ value: 15.52
2380
+ - type: recall_at_1000
2381
+ value: 52.05499999999999
2382
+ - type: recall_at_3
2383
+ value: 0.6859999999999999
2384
+ - type: recall_at_5
2385
+ value: 1.1440000000000001
2386
+ - task:
2387
+ type: Retrieval
2388
+ dataset:
2389
+ type: webis-touche2020
2390
+ name: MTEB Touche2020
2391
+ config: default
2392
+ split: test
2393
+ revision: None
2394
+ metrics:
2395
+ - type: map_at_1
2396
+ value: 3.19
2397
+ - type: map_at_10
2398
+ value: 11.491999999999999
2399
+ - type: map_at_100
2400
+ value: 17.251
2401
+ - type: map_at_1000
2402
+ value: 18.795
2403
+ - type: map_at_3
2404
+ value: 6.146
2405
+ - type: map_at_5
2406
+ value: 8.113
2407
+ - type: mrr_at_1
2408
+ value: 44.897999999999996
2409
+ - type: mrr_at_10
2410
+ value: 56.57
2411
+ - type: mrr_at_100
2412
+ value: 57.348
2413
+ - type: mrr_at_1000
2414
+ value: 57.357
2415
+ - type: mrr_at_3
2416
+ value: 52.041000000000004
2417
+ - type: mrr_at_5
2418
+ value: 55.408
2419
+ - type: ndcg_at_1
2420
+ value: 40.816
2421
+ - type: ndcg_at_10
2422
+ value: 27.968
2423
+ - type: ndcg_at_100
2424
+ value: 39.0
2425
+ - type: ndcg_at_1000
2426
+ value: 50.292
2427
+ - type: ndcg_at_3
2428
+ value: 31.256
2429
+ - type: ndcg_at_5
2430
+ value: 28.855999999999998
2431
+ - type: precision_at_1
2432
+ value: 44.897999999999996
2433
+ - type: precision_at_10
2434
+ value: 24.285999999999998
2435
+ - type: precision_at_100
2436
+ value: 7.898
2437
+ - type: precision_at_1000
2438
+ value: 1.541
2439
+ - type: precision_at_3
2440
+ value: 30.612000000000002
2441
+ - type: precision_at_5
2442
+ value: 27.346999999999998
2443
+ - type: recall_at_1
2444
+ value: 3.19
2445
+ - type: recall_at_10
2446
+ value: 17.954
2447
+ - type: recall_at_100
2448
+ value: 48.793
2449
+ - type: recall_at_1000
2450
+ value: 83.357
2451
+ - type: recall_at_3
2452
+ value: 6.973999999999999
2453
+ - type: recall_at_5
2454
+ value: 10.391
2455
+ - task:
2456
+ type: Classification
2457
+ dataset:
2458
+ type: mteb/toxic_conversations_50k
2459
+ name: MTEB ToxicConversationsClassification
2460
+ config: default
2461
+ split: test
2462
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2463
+ metrics:
2464
+ - type: accuracy
2465
+ value: 70.89139999999999
2466
+ - type: ap
2467
+ value: 15.562539739828049
2468
+ - type: f1
2469
+ value: 55.38685639741247
2470
+ - task:
2471
+ type: Classification
2472
+ dataset:
2473
+ type: mteb/tweet_sentiment_extraction
2474
+ name: MTEB TweetSentimentExtractionClassification
2475
+ config: default
2476
+ split: test
2477
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2478
+ metrics:
2479
+ - type: accuracy
2480
+ value: 62.48160724391625
2481
+ - type: f1
2482
+ value: 62.76700854121342
2483
+ - task:
2484
+ type: Clustering
2485
+ dataset:
2486
+ type: mteb/twentynewsgroups-clustering
2487
+ name: MTEB TwentyNewsgroupsClustering
2488
+ config: default
2489
+ split: test
2490
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2491
+ metrics:
2492
+ - type: v_measure
2493
+ value: 57.157071531498275
2494
+ - task:
2495
+ type: PairClassification
2496
+ dataset:
2497
+ type: mteb/twittersemeval2015-pairclassification
2498
+ name: MTEB TwitterSemEval2015
2499
+ config: default
2500
+ split: test
2501
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2502
+ metrics:
2503
+ - type: cos_sim_accuracy
2504
+ value: 87.15503367705789
2505
+ - type: cos_sim_ap
2506
+ value: 77.20584529783206
2507
+ - type: cos_sim_f1
2508
+ value: 71.3558088770313
2509
+ - type: cos_sim_precision
2510
+ value: 66.02333931777379
2511
+ - type: cos_sim_recall
2512
+ value: 77.62532981530343
2513
+ - type: dot_accuracy
2514
+ value: 83.10186564940096
2515
+ - type: dot_ap
2516
+ value: 64.34160146443133
2517
+ - type: dot_f1
2518
+ value: 63.23048153342683
2519
+ - type: dot_precision
2520
+ value: 56.75618967687789
2521
+ - type: dot_recall
2522
+ value: 71.37203166226914
2523
+ - type: euclidean_accuracy
2524
+ value: 86.94045419324074
2525
+ - type: euclidean_ap
2526
+ value: 76.08471767931738
2527
+ - type: euclidean_f1
2528
+ value: 71.41248592518455
2529
+ - type: euclidean_precision
2530
+ value: 67.90387818225078
2531
+ - type: euclidean_recall
2532
+ value: 75.30343007915567
2533
+ - type: manhattan_accuracy
2534
+ value: 86.80932228646361
2535
+ - type: manhattan_ap
2536
+ value: 76.03862870753638
2537
+ - type: manhattan_f1
2538
+ value: 71.2660917385327
2539
+ - type: manhattan_precision
2540
+ value: 67.70363334124912
2541
+ - type: manhattan_recall
2542
+ value: 75.22427440633246
2543
+ - type: max_accuracy
2544
+ value: 87.15503367705789
2545
+ - type: max_ap
2546
+ value: 77.20584529783206
2547
+ - type: max_f1
2548
+ value: 71.41248592518455
2549
+ - task:
2550
+ type: PairClassification
2551
+ dataset:
2552
+ type: mteb/twitterurlcorpus-pairclassification
2553
+ name: MTEB TwitterURLCorpus
2554
+ config: default
2555
+ split: test
2556
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2557
+ metrics:
2558
+ - type: cos_sim_accuracy
2559
+ value: 89.42639810610471
2560
+ - type: cos_sim_ap
2561
+ value: 86.45196525133669
2562
+ - type: cos_sim_f1
2563
+ value: 79.25172592977508
2564
+ - type: cos_sim_precision
2565
+ value: 76.50852802063925
2566
+ - type: cos_sim_recall
2567
+ value: 82.19895287958116
2568
+ - type: dot_accuracy
2569
+ value: 87.03768385919976
2570
+ - type: dot_ap
2571
+ value: 80.86465404774172
2572
+ - type: dot_f1
2573
+ value: 74.50351637940457
2574
+ - type: dot_precision
2575
+ value: 70.72293324109305
2576
+ - type: dot_recall
2577
+ value: 78.71111795503542
2578
+ - type: euclidean_accuracy
2579
+ value: 89.29056545193464
2580
+ - type: euclidean_ap
2581
+ value: 86.25102188096191
2582
+ - type: euclidean_f1
2583
+ value: 79.05038057267126
2584
+ - type: euclidean_precision
2585
+ value: 74.681550472538
2586
+ - type: euclidean_recall
2587
+ value: 83.9621188789652
2588
+ - type: manhattan_accuracy
2589
+ value: 89.34877944657896
2590
+ - type: manhattan_ap
2591
+ value: 86.35336214205911
2592
+ - type: manhattan_f1
2593
+ value: 79.20192588269623
2594
+ - type: manhattan_precision
2595
+ value: 75.24951483227058
2596
+ - type: manhattan_recall
2597
+ value: 83.59254696643055
2598
+ - type: max_accuracy
2599
+ value: 89.42639810610471
2600
+ - type: max_ap
2601
+ value: 86.45196525133669
2602
+ - type: max_f1
2603
+ value: 79.25172592977508
2604
  ---
2605
 
2606
  # Model Summary