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d635307
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Parent(s):
1f46994
Upload README.md with huggingface_hub
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README.md
ADDED
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|
1 |
+
---
|
2 |
+
base_model: nomic-ai/nomic-embed-text-v1.5
|
3 |
+
language:
|
4 |
+
- en
|
5 |
+
library_name: sentence-transformers
|
6 |
+
license: apache-2.0
|
7 |
+
pipeline_tag: sentence-similarity
|
8 |
+
tags:
|
9 |
+
- feature-extraction
|
10 |
+
- sentence-similarity
|
11 |
+
- mteb
|
12 |
+
- transformers
|
13 |
+
- transformers.js
|
14 |
+
- llama-cpp
|
15 |
+
- gguf-my-repo
|
16 |
+
model-index:
|
17 |
+
- name: epoch_0_model
|
18 |
+
results:
|
19 |
+
- task:
|
20 |
+
type: Classification
|
21 |
+
dataset:
|
22 |
+
name: MTEB AmazonCounterfactualClassification (en)
|
23 |
+
type: mteb/amazon_counterfactual
|
24 |
+
config: en
|
25 |
+
split: test
|
26 |
+
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
|
27 |
+
metrics:
|
28 |
+
- type: accuracy
|
29 |
+
value: 75.20895522388058
|
30 |
+
- type: ap
|
31 |
+
value: 38.57605549557802
|
32 |
+
- type: f1
|
33 |
+
value: 69.35586565857854
|
34 |
+
- task:
|
35 |
+
type: Classification
|
36 |
+
dataset:
|
37 |
+
name: MTEB AmazonPolarityClassification
|
38 |
+
type: mteb/amazon_polarity
|
39 |
+
config: default
|
40 |
+
split: test
|
41 |
+
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
|
42 |
+
metrics:
|
43 |
+
- type: accuracy
|
44 |
+
value: 91.8144
|
45 |
+
- type: ap
|
46 |
+
value: 88.65222882032363
|
47 |
+
- type: f1
|
48 |
+
value: 91.80426301643274
|
49 |
+
- task:
|
50 |
+
type: Classification
|
51 |
+
dataset:
|
52 |
+
name: MTEB AmazonReviewsClassification (en)
|
53 |
+
type: mteb/amazon_reviews_multi
|
54 |
+
config: en
|
55 |
+
split: test
|
56 |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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57 |
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metrics:
|
58 |
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- type: accuracy
|
59 |
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value: 47.162000000000006
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60 |
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- type: f1
|
61 |
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value: 46.59329642263158
|
62 |
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- task:
|
63 |
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type: Retrieval
|
64 |
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dataset:
|
65 |
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name: MTEB ArguAna
|
66 |
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type: arguana
|
67 |
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config: default
|
68 |
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split: test
|
69 |
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revision: None
|
70 |
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metrics:
|
71 |
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- type: map_at_1
|
72 |
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value: 24.253
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73 |
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- type: map_at_10
|
74 |
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value: 38.962
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75 |
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76 |
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77 |
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78 |
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value: 40.089000000000006
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79 |
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80 |
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value: 33.499
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81 |
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|
82 |
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value: 36.351
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83 |
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84 |
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value: 24.609
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85 |
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86 |
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value: 39.099000000000004
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87 |
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88 |
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value: 40.211000000000006
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90 |
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value: 40.219
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91 |
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92 |
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value: 33.677
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93 |
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|
94 |
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value: 36.469
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95 |
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96 |
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value: 24.253
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97 |
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98 |
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value: 48.010999999999996
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99 |
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|
100 |
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value: 52.756
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101 |
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102 |
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value: 52.964999999999996
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103 |
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104 |
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value: 36.564
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105 |
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106 |
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value: 41.711999999999996
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107 |
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|
108 |
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value: 24.253
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109 |
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- type: precision_at_10
|
110 |
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value: 7.738
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111 |
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- type: precision_at_100
|
112 |
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value: 0.98
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113 |
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- type: precision_at_1000
|
114 |
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value: 0.1
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115 |
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116 |
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value: 15.149000000000001
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117 |
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118 |
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value: 11.593
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119 |
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|
120 |
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value: 24.253
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121 |
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- type: recall_at_10
|
122 |
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value: 77.383
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123 |
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|
124 |
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value: 98.009
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125 |
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|
126 |
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value: 99.644
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127 |
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|
128 |
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value: 45.448
|
129 |
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- type: recall_at_5
|
130 |
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value: 57.965999999999994
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131 |
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- task:
|
132 |
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type: Clustering
|
133 |
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dataset:
|
134 |
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name: MTEB ArxivClusteringP2P
|
135 |
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type: mteb/arxiv-clustering-p2p
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136 |
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config: default
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137 |
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split: test
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138 |
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
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139 |
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metrics:
|
140 |
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- type: v_measure
|
141 |
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value: 45.69069567851087
|
142 |
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- task:
|
143 |
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type: Clustering
|
144 |
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dataset:
|
145 |
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name: MTEB ArxivClusteringS2S
|
146 |
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type: mteb/arxiv-clustering-s2s
|
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config: default
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148 |
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split: test
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149 |
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revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
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150 |
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metrics:
|
151 |
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- type: v_measure
|
152 |
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value: 36.35185490976283
|
153 |
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- task:
|
154 |
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type: Reranking
|
155 |
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dataset:
|
156 |
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name: MTEB AskUbuntuDupQuestions
|
157 |
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type: mteb/askubuntudupquestions-reranking
|
158 |
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config: default
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159 |
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split: test
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revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
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161 |
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metrics:
|
162 |
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- type: map
|
163 |
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value: 61.71274951450321
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164 |
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- type: mrr
|
165 |
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value: 76.06032625423207
|
166 |
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- task:
|
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type: STS
|
168 |
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dataset:
|
169 |
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name: MTEB BIOSSES
|
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type: mteb/biosses-sts
|
171 |
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config: default
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172 |
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split: test
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173 |
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
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metrics:
|
175 |
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- type: cos_sim_pearson
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176 |
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value: 86.73980520022269
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177 |
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- type: cos_sim_spearman
|
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value: 84.24649792685918
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- type: euclidean_pearson
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value: 85.85197641158186
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- type: euclidean_spearman
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value: 84.24649792685918
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- type: manhattan_pearson
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value: 86.26809552711346
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- type: manhattan_spearman
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value: 84.56397504030865
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187 |
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- task:
|
188 |
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type: Classification
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189 |
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dataset:
|
190 |
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name: MTEB Banking77Classification
|
191 |
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type: mteb/banking77
|
192 |
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config: default
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193 |
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split: test
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194 |
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
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metrics:
|
196 |
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- type: accuracy
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197 |
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value: 84.25324675324674
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198 |
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- type: f1
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199 |
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value: 84.17872280892557
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200 |
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- task:
|
201 |
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type: Clustering
|
202 |
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dataset:
|
203 |
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name: MTEB BiorxivClusteringP2P
|
204 |
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type: mteb/biorxiv-clustering-p2p
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205 |
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config: default
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206 |
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split: test
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207 |
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
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208 |
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metrics:
|
209 |
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- type: v_measure
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210 |
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value: 38.770253446400886
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211 |
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- task:
|
212 |
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type: Clustering
|
213 |
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dataset:
|
214 |
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name: MTEB BiorxivClusteringS2S
|
215 |
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type: mteb/biorxiv-clustering-s2s
|
216 |
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config: default
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217 |
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split: test
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218 |
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
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219 |
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metrics:
|
220 |
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221 |
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value: 32.94307095497281
|
222 |
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- task:
|
223 |
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type: Retrieval
|
224 |
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dataset:
|
225 |
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name: MTEB CQADupstackAndroidRetrieval
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226 |
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type: BeIR/cqadupstack
|
227 |
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config: default
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228 |
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split: test
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229 |
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revision: None
|
230 |
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metrics:
|
231 |
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- type: map_at_1
|
232 |
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value: 32.164
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233 |
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- type: map_at_10
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234 |
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value: 42.641
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235 |
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value: 43.947
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value: 44.074999999999996
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value: 39.592
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value: 41.204
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value: 39.628
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246 |
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value: 48.625
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value: 49.368
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value: 49.413000000000004
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value: 46.400000000000006
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value: 47.68
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value: 39.628
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257 |
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value: 48.564
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value: 53.507000000000005
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value: 55.635999999999996
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value: 44.471
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value: 46.137
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267 |
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268 |
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value: 39.628
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269 |
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270 |
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value: 8.856
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271 |
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value: 1.429
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273 |
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value: 0.191
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275 |
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value: 21.268
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277 |
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value: 14.649000000000001
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279 |
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value: 32.164
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281 |
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value: 59.609
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283 |
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value: 80.521
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value: 94.245
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value: 46.521
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value: 52.083999999999996
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value: 31.526
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value: 41.581
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299 |
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value: 38.605000000000004
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301 |
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302 |
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303 |
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value: 39.489999999999995
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305 |
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306 |
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308 |
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value: 48.552
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311 |
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value: 45.754
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313 |
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314 |
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value: 46.986
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315 |
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value: 39.489999999999995
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value: 47.269
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319 |
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value: 51.564
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321 |
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325 |
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326 |
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value: 45.239000000000004
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327 |
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328 |
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value: 39.489999999999995
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329 |
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330 |
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value: 8.93
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331 |
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value: 1.415
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335 |
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value: 31.526
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341 |
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value: 56.76
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value: 40.987
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353 |
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355 |
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403 |
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449 |
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451 |
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455 |
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457 |
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|
458 |
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459 |
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465 |
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|
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|
467 |
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|
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469 |
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|
470 |
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471 |
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- type: map_at_1
|
472 |
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value: 16.715
|
473 |
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|
474 |
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475 |
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|
476 |
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|
477 |
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|
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value: 25.455
|
479 |
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- type: map_at_3
|
480 |
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881 |
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887 |
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900 |
+
value: 28.924
|
901 |
+
- type: map_at_5
|
902 |
+
value: 30.017
|
903 |
+
- type: mrr_at_1
|
904 |
+
value: 27.668
|
905 |
+
- type: mrr_at_10
|
906 |
+
value: 35.524
|
907 |
+
- type: mrr_at_100
|
908 |
+
value: 36.699
|
909 |
+
- type: mrr_at_1000
|
910 |
+
value: 36.759
|
911 |
+
- type: mrr_at_3
|
912 |
+
value: 33.366
|
913 |
+
- type: mrr_at_5
|
914 |
+
value: 34.552
|
915 |
+
- type: ndcg_at_1
|
916 |
+
value: 27.668
|
917 |
+
- type: ndcg_at_10
|
918 |
+
value: 36.381
|
919 |
+
- type: ndcg_at_100
|
920 |
+
value: 43.062
|
921 |
+
- type: ndcg_at_1000
|
922 |
+
value: 45.656
|
923 |
+
- type: ndcg_at_3
|
924 |
+
value: 32.501999999999995
|
925 |
+
- type: ndcg_at_5
|
926 |
+
value: 34.105999999999995
|
927 |
+
- type: precision_at_1
|
928 |
+
value: 27.668
|
929 |
+
- type: precision_at_10
|
930 |
+
value: 6.798
|
931 |
+
- type: precision_at_100
|
932 |
+
value: 1.492
|
933 |
+
- type: precision_at_1000
|
934 |
+
value: 0.234
|
935 |
+
- type: precision_at_3
|
936 |
+
value: 15.152
|
937 |
+
- type: precision_at_5
|
938 |
+
value: 10.791
|
939 |
+
- type: recall_at_1
|
940 |
+
value: 23.244
|
941 |
+
- type: recall_at_10
|
942 |
+
value: 45.979
|
943 |
+
- type: recall_at_100
|
944 |
+
value: 74.822
|
945 |
+
- type: recall_at_1000
|
946 |
+
value: 91.078
|
947 |
+
- type: recall_at_3
|
948 |
+
value: 34.925
|
949 |
+
- type: recall_at_5
|
950 |
+
value: 39.126
|
951 |
+
- type: map_at_1
|
952 |
+
value: 19.945
|
953 |
+
- type: map_at_10
|
954 |
+
value: 27.517999999999997
|
955 |
+
- type: map_at_100
|
956 |
+
value: 28.588
|
957 |
+
- type: map_at_1000
|
958 |
+
value: 28.682000000000002
|
959 |
+
- type: map_at_3
|
960 |
+
value: 25.345000000000002
|
961 |
+
- type: map_at_5
|
962 |
+
value: 26.555
|
963 |
+
- type: mrr_at_1
|
964 |
+
value: 21.996
|
965 |
+
- type: mrr_at_10
|
966 |
+
value: 29.845
|
967 |
+
- type: mrr_at_100
|
968 |
+
value: 30.775999999999996
|
969 |
+
- type: mrr_at_1000
|
970 |
+
value: 30.845
|
971 |
+
- type: mrr_at_3
|
972 |
+
value: 27.726
|
973 |
+
- type: mrr_at_5
|
974 |
+
value: 28.882
|
975 |
+
- type: ndcg_at_1
|
976 |
+
value: 21.996
|
977 |
+
- type: ndcg_at_10
|
978 |
+
value: 32.034
|
979 |
+
- type: ndcg_at_100
|
980 |
+
value: 37.185
|
981 |
+
- type: ndcg_at_1000
|
982 |
+
value: 39.645
|
983 |
+
- type: ndcg_at_3
|
984 |
+
value: 27.750999999999998
|
985 |
+
- type: ndcg_at_5
|
986 |
+
value: 29.805999999999997
|
987 |
+
- type: precision_at_1
|
988 |
+
value: 21.996
|
989 |
+
- type: precision_at_10
|
990 |
+
value: 5.065
|
991 |
+
- type: precision_at_100
|
992 |
+
value: 0.819
|
993 |
+
- type: precision_at_1000
|
994 |
+
value: 0.11399999999999999
|
995 |
+
- type: precision_at_3
|
996 |
+
value: 12.076
|
997 |
+
- type: precision_at_5
|
998 |
+
value: 8.392
|
999 |
+
- type: recall_at_1
|
1000 |
+
value: 19.945
|
1001 |
+
- type: recall_at_10
|
1002 |
+
value: 43.62
|
1003 |
+
- type: recall_at_100
|
1004 |
+
value: 67.194
|
1005 |
+
- type: recall_at_1000
|
1006 |
+
value: 85.7
|
1007 |
+
- type: recall_at_3
|
1008 |
+
value: 32.15
|
1009 |
+
- type: recall_at_5
|
1010 |
+
value: 37.208999999999996
|
1011 |
+
- task:
|
1012 |
+
type: Retrieval
|
1013 |
+
dataset:
|
1014 |
+
name: MTEB ClimateFEVER
|
1015 |
+
type: climate-fever
|
1016 |
+
config: default
|
1017 |
+
split: test
|
1018 |
+
revision: None
|
1019 |
+
metrics:
|
1020 |
+
- type: map_at_1
|
1021 |
+
value: 18.279
|
1022 |
+
- type: map_at_10
|
1023 |
+
value: 31.052999999999997
|
1024 |
+
- type: map_at_100
|
1025 |
+
value: 33.125
|
1026 |
+
- type: map_at_1000
|
1027 |
+
value: 33.306000000000004
|
1028 |
+
- type: map_at_3
|
1029 |
+
value: 26.208
|
1030 |
+
- type: map_at_5
|
1031 |
+
value: 28.857
|
1032 |
+
- type: mrr_at_1
|
1033 |
+
value: 42.671
|
1034 |
+
- type: mrr_at_10
|
1035 |
+
value: 54.557
|
1036 |
+
- type: mrr_at_100
|
1037 |
+
value: 55.142
|
1038 |
+
- type: mrr_at_1000
|
1039 |
+
value: 55.169000000000004
|
1040 |
+
- type: mrr_at_3
|
1041 |
+
value: 51.488
|
1042 |
+
- type: mrr_at_5
|
1043 |
+
value: 53.439
|
1044 |
+
- type: ndcg_at_1
|
1045 |
+
value: 42.671
|
1046 |
+
- type: ndcg_at_10
|
1047 |
+
value: 41.276
|
1048 |
+
- type: ndcg_at_100
|
1049 |
+
value: 48.376000000000005
|
1050 |
+
- type: ndcg_at_1000
|
1051 |
+
value: 51.318
|
1052 |
+
- type: ndcg_at_3
|
1053 |
+
value: 35.068
|
1054 |
+
- type: ndcg_at_5
|
1055 |
+
value: 37.242
|
1056 |
+
- type: precision_at_1
|
1057 |
+
value: 42.671
|
1058 |
+
- type: precision_at_10
|
1059 |
+
value: 12.638
|
1060 |
+
- type: precision_at_100
|
1061 |
+
value: 2.045
|
1062 |
+
- type: precision_at_1000
|
1063 |
+
value: 0.26
|
1064 |
+
- type: precision_at_3
|
1065 |
+
value: 26.08
|
1066 |
+
- type: precision_at_5
|
1067 |
+
value: 19.805
|
1068 |
+
- type: recall_at_1
|
1069 |
+
value: 18.279
|
1070 |
+
- type: recall_at_10
|
1071 |
+
value: 46.946
|
1072 |
+
- type: recall_at_100
|
1073 |
+
value: 70.97200000000001
|
1074 |
+
- type: recall_at_1000
|
1075 |
+
value: 87.107
|
1076 |
+
- type: recall_at_3
|
1077 |
+
value: 31.147999999999996
|
1078 |
+
- type: recall_at_5
|
1079 |
+
value: 38.099
|
1080 |
+
- task:
|
1081 |
+
type: Retrieval
|
1082 |
+
dataset:
|
1083 |
+
name: MTEB DBPedia
|
1084 |
+
type: dbpedia-entity
|
1085 |
+
config: default
|
1086 |
+
split: test
|
1087 |
+
revision: None
|
1088 |
+
metrics:
|
1089 |
+
- type: map_at_1
|
1090 |
+
value: 8.573
|
1091 |
+
- type: map_at_10
|
1092 |
+
value: 19.747
|
1093 |
+
- type: map_at_100
|
1094 |
+
value: 28.205000000000002
|
1095 |
+
- type: map_at_1000
|
1096 |
+
value: 29.831000000000003
|
1097 |
+
- type: map_at_3
|
1098 |
+
value: 14.109
|
1099 |
+
- type: map_at_5
|
1100 |
+
value: 16.448999999999998
|
1101 |
+
- type: mrr_at_1
|
1102 |
+
value: 71
|
1103 |
+
- type: mrr_at_10
|
1104 |
+
value: 77.68599999999999
|
1105 |
+
- type: mrr_at_100
|
1106 |
+
value: 77.995
|
1107 |
+
- type: mrr_at_1000
|
1108 |
+
value: 78.00200000000001
|
1109 |
+
- type: mrr_at_3
|
1110 |
+
value: 76.292
|
1111 |
+
- type: mrr_at_5
|
1112 |
+
value: 77.029
|
1113 |
+
- type: ndcg_at_1
|
1114 |
+
value: 59.12500000000001
|
1115 |
+
- type: ndcg_at_10
|
1116 |
+
value: 43.9
|
1117 |
+
- type: ndcg_at_100
|
1118 |
+
value: 47.863
|
1119 |
+
- type: ndcg_at_1000
|
1120 |
+
value: 54.848
|
1121 |
+
- type: ndcg_at_3
|
1122 |
+
value: 49.803999999999995
|
1123 |
+
- type: ndcg_at_5
|
1124 |
+
value: 46.317
|
1125 |
+
- type: precision_at_1
|
1126 |
+
value: 71
|
1127 |
+
- type: precision_at_10
|
1128 |
+
value: 34.4
|
1129 |
+
- type: precision_at_100
|
1130 |
+
value: 11.063
|
1131 |
+
- type: precision_at_1000
|
1132 |
+
value: 1.989
|
1133 |
+
- type: precision_at_3
|
1134 |
+
value: 52.333
|
1135 |
+
- type: precision_at_5
|
1136 |
+
value: 43.7
|
1137 |
+
- type: recall_at_1
|
1138 |
+
value: 8.573
|
1139 |
+
- type: recall_at_10
|
1140 |
+
value: 25.615
|
1141 |
+
- type: recall_at_100
|
1142 |
+
value: 53.385000000000005
|
1143 |
+
- type: recall_at_1000
|
1144 |
+
value: 75.46000000000001
|
1145 |
+
- type: recall_at_3
|
1146 |
+
value: 15.429
|
1147 |
+
- type: recall_at_5
|
1148 |
+
value: 19.357
|
1149 |
+
- task:
|
1150 |
+
type: Classification
|
1151 |
+
dataset:
|
1152 |
+
name: MTEB EmotionClassification
|
1153 |
+
type: mteb/emotion
|
1154 |
+
config: default
|
1155 |
+
split: test
|
1156 |
+
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
1157 |
+
metrics:
|
1158 |
+
- type: accuracy
|
1159 |
+
value: 47.989999999999995
|
1160 |
+
- type: f1
|
1161 |
+
value: 42.776314451497555
|
1162 |
+
- task:
|
1163 |
+
type: Retrieval
|
1164 |
+
dataset:
|
1165 |
+
name: MTEB FEVER
|
1166 |
+
type: fever
|
1167 |
+
config: default
|
1168 |
+
split: test
|
1169 |
+
revision: None
|
1170 |
+
metrics:
|
1171 |
+
- type: map_at_1
|
1172 |
+
value: 74.13499999999999
|
1173 |
+
- type: map_at_10
|
1174 |
+
value: 82.825
|
1175 |
+
- type: map_at_100
|
1176 |
+
value: 83.096
|
1177 |
+
- type: map_at_1000
|
1178 |
+
value: 83.111
|
1179 |
+
- type: map_at_3
|
1180 |
+
value: 81.748
|
1181 |
+
- type: map_at_5
|
1182 |
+
value: 82.446
|
1183 |
+
- type: mrr_at_1
|
1184 |
+
value: 79.553
|
1185 |
+
- type: mrr_at_10
|
1186 |
+
value: 86.654
|
1187 |
+
- type: mrr_at_100
|
1188 |
+
value: 86.774
|
1189 |
+
- type: mrr_at_1000
|
1190 |
+
value: 86.778
|
1191 |
+
- type: mrr_at_3
|
1192 |
+
value: 85.981
|
1193 |
+
- type: mrr_at_5
|
1194 |
+
value: 86.462
|
1195 |
+
- type: ndcg_at_1
|
1196 |
+
value: 79.553
|
1197 |
+
- type: ndcg_at_10
|
1198 |
+
value: 86.345
|
1199 |
+
- type: ndcg_at_100
|
1200 |
+
value: 87.32
|
1201 |
+
- type: ndcg_at_1000
|
1202 |
+
value: 87.58200000000001
|
1203 |
+
- type: ndcg_at_3
|
1204 |
+
value: 84.719
|
1205 |
+
- type: ndcg_at_5
|
1206 |
+
value: 85.677
|
1207 |
+
- type: precision_at_1
|
1208 |
+
value: 79.553
|
1209 |
+
- type: precision_at_10
|
1210 |
+
value: 10.402000000000001
|
1211 |
+
- type: precision_at_100
|
1212 |
+
value: 1.1119999999999999
|
1213 |
+
- type: precision_at_1000
|
1214 |
+
value: 0.11499999999999999
|
1215 |
+
- type: precision_at_3
|
1216 |
+
value: 32.413
|
1217 |
+
- type: precision_at_5
|
1218 |
+
value: 20.138
|
1219 |
+
- type: recall_at_1
|
1220 |
+
value: 74.13499999999999
|
1221 |
+
- type: recall_at_10
|
1222 |
+
value: 93.215
|
1223 |
+
- type: recall_at_100
|
1224 |
+
value: 97.083
|
1225 |
+
- type: recall_at_1000
|
1226 |
+
value: 98.732
|
1227 |
+
- type: recall_at_3
|
1228 |
+
value: 88.79
|
1229 |
+
- type: recall_at_5
|
1230 |
+
value: 91.259
|
1231 |
+
- task:
|
1232 |
+
type: Retrieval
|
1233 |
+
dataset:
|
1234 |
+
name: MTEB FiQA2018
|
1235 |
+
type: fiqa
|
1236 |
+
config: default
|
1237 |
+
split: test
|
1238 |
+
revision: None
|
1239 |
+
metrics:
|
1240 |
+
- type: map_at_1
|
1241 |
+
value: 18.298000000000002
|
1242 |
+
- type: map_at_10
|
1243 |
+
value: 29.901
|
1244 |
+
- type: map_at_100
|
1245 |
+
value: 31.528
|
1246 |
+
- type: map_at_1000
|
1247 |
+
value: 31.713
|
1248 |
+
- type: map_at_3
|
1249 |
+
value: 25.740000000000002
|
1250 |
+
- type: map_at_5
|
1251 |
+
value: 28.227999999999998
|
1252 |
+
- type: mrr_at_1
|
1253 |
+
value: 36.728
|
1254 |
+
- type: mrr_at_10
|
1255 |
+
value: 45.401
|
1256 |
+
- type: mrr_at_100
|
1257 |
+
value: 46.27
|
1258 |
+
- type: mrr_at_1000
|
1259 |
+
value: 46.315
|
1260 |
+
- type: mrr_at_3
|
1261 |
+
value: 42.978
|
1262 |
+
- type: mrr_at_5
|
1263 |
+
value: 44.29
|
1264 |
+
- type: ndcg_at_1
|
1265 |
+
value: 36.728
|
1266 |
+
- type: ndcg_at_10
|
1267 |
+
value: 37.456
|
1268 |
+
- type: ndcg_at_100
|
1269 |
+
value: 43.832
|
1270 |
+
- type: ndcg_at_1000
|
1271 |
+
value: 47
|
1272 |
+
- type: ndcg_at_3
|
1273 |
+
value: 33.694
|
1274 |
+
- type: ndcg_at_5
|
1275 |
+
value: 35.085
|
1276 |
+
- type: precision_at_1
|
1277 |
+
value: 36.728
|
1278 |
+
- type: precision_at_10
|
1279 |
+
value: 10.386
|
1280 |
+
- type: precision_at_100
|
1281 |
+
value: 1.701
|
1282 |
+
- type: precision_at_1000
|
1283 |
+
value: 0.22599999999999998
|
1284 |
+
- type: precision_at_3
|
1285 |
+
value: 22.479
|
1286 |
+
- type: precision_at_5
|
1287 |
+
value: 16.605
|
1288 |
+
- type: recall_at_1
|
1289 |
+
value: 18.298000000000002
|
1290 |
+
- type: recall_at_10
|
1291 |
+
value: 44.369
|
1292 |
+
- type: recall_at_100
|
1293 |
+
value: 68.098
|
1294 |
+
- type: recall_at_1000
|
1295 |
+
value: 87.21900000000001
|
1296 |
+
- type: recall_at_3
|
1297 |
+
value: 30.215999999999998
|
1298 |
+
- type: recall_at_5
|
1299 |
+
value: 36.861
|
1300 |
+
- task:
|
1301 |
+
type: Retrieval
|
1302 |
+
dataset:
|
1303 |
+
name: MTEB HotpotQA
|
1304 |
+
type: hotpotqa
|
1305 |
+
config: default
|
1306 |
+
split: test
|
1307 |
+
revision: None
|
1308 |
+
metrics:
|
1309 |
+
- type: map_at_1
|
1310 |
+
value: 39.568
|
1311 |
+
- type: map_at_10
|
1312 |
+
value: 65.061
|
1313 |
+
- type: map_at_100
|
1314 |
+
value: 65.896
|
1315 |
+
- type: map_at_1000
|
1316 |
+
value: 65.95100000000001
|
1317 |
+
- type: map_at_3
|
1318 |
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value: 61.831
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1324 |
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1332 |
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1333 |
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1334 |
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1335 |
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1336 |
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1338 |
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1339 |
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1340 |
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1342 |
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value: 68.065
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1344 |
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1345 |
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1346 |
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value: 79.136
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1347 |
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1348 |
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value: 15.215
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1349 |
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1350 |
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value: 1.7500000000000002
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value: 0.189
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1353 |
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1354 |
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value: 44.011
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1355 |
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1356 |
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value: 28.388999999999996
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1357 |
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1358 |
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value: 39.568
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1359 |
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1360 |
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value: 76.077
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1362 |
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value: 87.481
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1363 |
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value: 94.56400000000001
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1368 |
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1369 |
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- task:
|
1370 |
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type: Classification
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1371 |
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dataset:
|
1372 |
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name: MTEB ImdbClassification
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1373 |
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type: mteb/imdb
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1374 |
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config: default
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split: test
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1376 |
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1377 |
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|
1378 |
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- type: accuracy
|
1379 |
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value: 85.312
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1380 |
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- type: ap
|
1381 |
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value: 80.36296867333715
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1384 |
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- task:
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1385 |
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type: Retrieval
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1386 |
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dataset:
|
1387 |
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name: MTEB MSMARCO
|
1388 |
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type: msmarco
|
1389 |
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config: default
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1390 |
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split: dev
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1391 |
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revision: None
|
1392 |
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metrics:
|
1393 |
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- type: map_at_1
|
1394 |
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value: 23.363999999999997
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1395 |
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1400 |
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1401 |
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1402 |
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value: 32.034
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1403 |
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1404 |
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value: 34.159
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1405 |
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1406 |
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value: 24.04
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1407 |
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1408 |
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value: 36.345
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1409 |
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1410 |
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value: 37.441
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1411 |
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1412 |
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value: 37.480000000000004
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1414 |
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1415 |
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1416 |
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value: 34.824
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1417 |
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1418 |
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value: 24.026
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1419 |
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1420 |
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value: 42.531
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1421 |
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1422 |
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value: 48.081
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1423 |
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1424 |
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value: 49.213
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1425 |
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1426 |
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value: 35.044
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1428 |
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value: 38.834
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1429 |
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1430 |
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value: 24.026
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1431 |
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1432 |
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value: 6.622999999999999
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1433 |
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|
1434 |
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value: 0.941
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1435 |
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1436 |
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value: 0.104
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1437 |
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1438 |
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value: 14.909
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1439 |
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- type: precision_at_5
|
1440 |
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value: 10.871
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1441 |
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- type: recall_at_1
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1442 |
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value: 23.363999999999997
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1443 |
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- type: recall_at_10
|
1444 |
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value: 63.426
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1445 |
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- type: recall_at_100
|
1446 |
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value: 88.96300000000001
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1447 |
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- type: recall_at_1000
|
1448 |
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value: 97.637
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1449 |
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- type: recall_at_3
|
1450 |
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value: 43.095
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1451 |
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- type: recall_at_5
|
1452 |
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value: 52.178000000000004
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1453 |
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- task:
|
1454 |
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type: Classification
|
1455 |
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dataset:
|
1456 |
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name: MTEB MTOPDomainClassification (en)
|
1457 |
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type: mteb/mtop_domain
|
1458 |
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config: en
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1459 |
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split: test
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1460 |
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revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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1461 |
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metrics:
|
1462 |
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- type: accuracy
|
1463 |
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value: 93.0095759233926
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1464 |
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- type: f1
|
1465 |
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value: 92.78387794667408
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1466 |
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- task:
|
1467 |
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type: Classification
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1468 |
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dataset:
|
1469 |
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name: MTEB MTOPIntentClassification (en)
|
1470 |
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type: mteb/mtop_intent
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1471 |
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config: en
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1472 |
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split: test
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1473 |
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revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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1474 |
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metrics:
|
1475 |
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- type: accuracy
|
1476 |
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value: 75.0296397628819
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1477 |
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- type: f1
|
1478 |
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value: 58.45699589820874
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1479 |
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- task:
|
1480 |
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type: Classification
|
1481 |
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dataset:
|
1482 |
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name: MTEB MassiveIntentClassification (en)
|
1483 |
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type: mteb/amazon_massive_intent
|
1484 |
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config: en
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1485 |
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split: test
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1486 |
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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1487 |
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metrics:
|
1488 |
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- type: accuracy
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1489 |
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value: 73.45662407531944
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1490 |
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- type: f1
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1491 |
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value: 71.42364781421813
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1492 |
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- task:
|
1493 |
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type: Classification
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1494 |
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dataset:
|
1495 |
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name: MTEB MassiveScenarioClassification (en)
|
1496 |
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type: mteb/amazon_massive_scenario
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1497 |
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config: en
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1498 |
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1499 |
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
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1500 |
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metrics:
|
1501 |
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- type: accuracy
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1502 |
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value: 77.07800941492937
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1503 |
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- type: f1
|
1504 |
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value: 77.22799045640845
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1505 |
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- task:
|
1506 |
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type: Clustering
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1507 |
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dataset:
|
1508 |
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name: MTEB MedrxivClusteringP2P
|
1509 |
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type: mteb/medrxiv-clustering-p2p
|
1510 |
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config: default
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1511 |
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split: test
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1512 |
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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1513 |
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metrics:
|
1514 |
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- type: v_measure
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1515 |
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value: 34.531234379250606
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1516 |
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- task:
|
1517 |
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type: Clustering
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1518 |
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dataset:
|
1519 |
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name: MTEB MedrxivClusteringS2S
|
1520 |
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type: mteb/medrxiv-clustering-s2s
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1521 |
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config: default
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1522 |
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split: test
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1523 |
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revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
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1524 |
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metrics:
|
1525 |
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- type: v_measure
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1526 |
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value: 30.941490381193802
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1527 |
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- task:
|
1528 |
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type: Reranking
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1529 |
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dataset:
|
1530 |
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name: MTEB MindSmallReranking
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1531 |
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type: mteb/mind_small
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1532 |
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config: default
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1533 |
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split: test
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1534 |
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revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
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1535 |
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metrics:
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1536 |
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- type: map
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1537 |
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value: 30.3115090856725
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1538 |
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- type: mrr
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1539 |
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value: 31.290667638675757
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1540 |
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- task:
|
1541 |
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1542 |
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dataset:
|
1543 |
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name: MTEB NFCorpus
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1544 |
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type: nfcorpus
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1545 |
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config: default
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1546 |
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split: test
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1547 |
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revision: None
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1548 |
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metrics:
|
1549 |
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- type: map_at_1
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1550 |
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value: 5.465
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1551 |
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- type: map_at_10
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1552 |
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value: 13.03
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1553 |
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1554 |
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value: 16.057
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1555 |
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1556 |
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value: 17.49
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1557 |
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1558 |
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1559 |
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1560 |
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value: 11.204
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1561 |
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1562 |
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value: 43.653
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1563 |
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1564 |
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value: 53.269
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1565 |
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1566 |
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1567 |
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1568 |
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1571 |
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1572 |
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1573 |
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1574 |
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1575 |
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1576 |
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1577 |
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1578 |
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1579 |
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1580 |
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1582 |
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value: 40.349000000000004
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1583 |
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value: 37.915
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1585 |
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1586 |
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value: 43.653
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1587 |
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1588 |
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value: 25.789
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1589 |
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1590 |
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value: 7.754999999999999
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1591 |
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1592 |
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value: 2.07
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1593 |
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1594 |
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value: 38.596000000000004
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1595 |
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- type: precision_at_5
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1596 |
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value: 33.251
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1597 |
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- type: recall_at_1
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1598 |
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value: 5.465
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1599 |
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- type: recall_at_10
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1600 |
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value: 17.148
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1601 |
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1602 |
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value: 29.768
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1603 |
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- type: recall_at_1000
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1604 |
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value: 62.239
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1605 |
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1606 |
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value: 10.577
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1607 |
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- type: recall_at_5
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1608 |
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value: 13.315
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1609 |
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- task:
|
1610 |
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type: Retrieval
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1611 |
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dataset:
|
1612 |
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name: MTEB NQ
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1613 |
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type: nq
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1614 |
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config: default
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1615 |
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split: test
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1616 |
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revision: None
|
1617 |
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metrics:
|
1618 |
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- type: map_at_1
|
1619 |
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value: 37.008
|
1620 |
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- type: map_at_10
|
1621 |
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value: 52.467
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1622 |
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1623 |
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value: 53.342999999999996
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1624 |
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1625 |
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value: 53.366
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1626 |
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1627 |
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value: 48.412
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1628 |
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1629 |
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value: 50.875
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1630 |
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1631 |
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value: 41.541
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1632 |
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1633 |
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value: 54.967
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1634 |
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1635 |
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value: 55.611
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1636 |
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1637 |
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value: 55.627
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1638 |
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1639 |
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value: 51.824999999999996
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1640 |
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|
1641 |
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value: 53.763000000000005
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1642 |
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- type: ndcg_at_1
|
1643 |
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value: 41.541
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1644 |
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1645 |
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value: 59.724999999999994
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1646 |
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1647 |
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value: 63.38700000000001
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1648 |
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- type: ndcg_at_1000
|
1649 |
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value: 63.883
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1650 |
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|
1651 |
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value: 52.331
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1652 |
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1653 |
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value: 56.327000000000005
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1654 |
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- type: precision_at_1
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1655 |
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value: 41.541
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1656 |
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|
1657 |
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value: 9.447
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1658 |
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|
1659 |
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value: 1.1520000000000001
|
1660 |
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|
1661 |
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value: 0.12
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1662 |
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- type: precision_at_3
|
1663 |
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value: 23.262
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1664 |
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- type: precision_at_5
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1665 |
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value: 16.314999999999998
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1666 |
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- type: recall_at_1
|
1667 |
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value: 37.008
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1668 |
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- type: recall_at_10
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1669 |
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value: 79.145
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1670 |
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- type: recall_at_100
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1671 |
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value: 94.986
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1672 |
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- type: recall_at_1000
|
1673 |
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value: 98.607
|
1674 |
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- type: recall_at_3
|
1675 |
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value: 60.277
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1676 |
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- type: recall_at_5
|
1677 |
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value: 69.407
|
1678 |
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- task:
|
1679 |
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type: Retrieval
|
1680 |
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dataset:
|
1681 |
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name: MTEB QuoraRetrieval
|
1682 |
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type: quora
|
1683 |
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config: default
|
1684 |
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split: test
|
1685 |
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revision: None
|
1686 |
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metrics:
|
1687 |
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- type: map_at_1
|
1688 |
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value: 70.402
|
1689 |
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1690 |
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value: 84.181
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1691 |
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1692 |
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1693 |
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1694 |
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value: 84.81400000000001
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1695 |
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1696 |
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1697 |
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1698 |
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value: 83.085
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1699 |
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1700 |
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value: 81.02000000000001
|
1701 |
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|
1702 |
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value: 87.263
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1703 |
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1704 |
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value: 87.36
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1705 |
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- type: mrr_at_1000
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1706 |
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value: 87.36
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1707 |
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1708 |
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value: 86.235
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1709 |
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1710 |
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value: 86.945
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1711 |
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1712 |
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value: 81.01
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1713 |
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- type: ndcg_at_10
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1714 |
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value: 87.99900000000001
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1715 |
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- type: ndcg_at_100
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1716 |
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value: 89.217
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1717 |
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- type: ndcg_at_1000
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1718 |
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value: 89.33
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1719 |
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- type: ndcg_at_3
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1720 |
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value: 85.053
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1721 |
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- type: ndcg_at_5
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1722 |
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value: 86.703
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1723 |
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- type: precision_at_1
|
1724 |
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value: 81.01
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1725 |
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- type: precision_at_10
|
1726 |
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value: 13.336
|
1727 |
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- type: precision_at_100
|
1728 |
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value: 1.52
|
1729 |
+
- type: precision_at_1000
|
1730 |
+
value: 0.156
|
1731 |
+
- type: precision_at_3
|
1732 |
+
value: 37.14
|
1733 |
+
- type: precision_at_5
|
1734 |
+
value: 24.44
|
1735 |
+
- type: recall_at_1
|
1736 |
+
value: 70.402
|
1737 |
+
- type: recall_at_10
|
1738 |
+
value: 95.214
|
1739 |
+
- type: recall_at_100
|
1740 |
+
value: 99.438
|
1741 |
+
- type: recall_at_1000
|
1742 |
+
value: 99.928
|
1743 |
+
- type: recall_at_3
|
1744 |
+
value: 86.75699999999999
|
1745 |
+
- type: recall_at_5
|
1746 |
+
value: 91.44099999999999
|
1747 |
+
- task:
|
1748 |
+
type: Clustering
|
1749 |
+
dataset:
|
1750 |
+
name: MTEB RedditClustering
|
1751 |
+
type: mteb/reddit-clustering
|
1752 |
+
config: default
|
1753 |
+
split: test
|
1754 |
+
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
1755 |
+
metrics:
|
1756 |
+
- type: v_measure
|
1757 |
+
value: 56.51721502758904
|
1758 |
+
- task:
|
1759 |
+
type: Clustering
|
1760 |
+
dataset:
|
1761 |
+
name: MTEB RedditClusteringP2P
|
1762 |
+
type: mteb/reddit-clustering-p2p
|
1763 |
+
config: default
|
1764 |
+
split: test
|
1765 |
+
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
1766 |
+
metrics:
|
1767 |
+
- type: v_measure
|
1768 |
+
value: 61.054808572333016
|
1769 |
+
- task:
|
1770 |
+
type: Retrieval
|
1771 |
+
dataset:
|
1772 |
+
name: MTEB SCIDOCS
|
1773 |
+
type: scidocs
|
1774 |
+
config: default
|
1775 |
+
split: test
|
1776 |
+
revision: None
|
1777 |
+
metrics:
|
1778 |
+
- type: map_at_1
|
1779 |
+
value: 4.578
|
1780 |
+
- type: map_at_10
|
1781 |
+
value: 11.036999999999999
|
1782 |
+
- type: map_at_100
|
1783 |
+
value: 12.879999999999999
|
1784 |
+
- type: map_at_1000
|
1785 |
+
value: 13.150999999999998
|
1786 |
+
- type: map_at_3
|
1787 |
+
value: 8.133
|
1788 |
+
- type: map_at_5
|
1789 |
+
value: 9.559
|
1790 |
+
- type: mrr_at_1
|
1791 |
+
value: 22.6
|
1792 |
+
- type: mrr_at_10
|
1793 |
+
value: 32.68
|
1794 |
+
- type: mrr_at_100
|
1795 |
+
value: 33.789
|
1796 |
+
- type: mrr_at_1000
|
1797 |
+
value: 33.854
|
1798 |
+
- type: mrr_at_3
|
1799 |
+
value: 29.7
|
1800 |
+
- type: mrr_at_5
|
1801 |
+
value: 31.480000000000004
|
1802 |
+
- type: ndcg_at_1
|
1803 |
+
value: 22.6
|
1804 |
+
- type: ndcg_at_10
|
1805 |
+
value: 18.616
|
1806 |
+
- type: ndcg_at_100
|
1807 |
+
value: 25.883
|
1808 |
+
- type: ndcg_at_1000
|
1809 |
+
value: 30.944
|
1810 |
+
- type: ndcg_at_3
|
1811 |
+
value: 18.136
|
1812 |
+
- type: ndcg_at_5
|
1813 |
+
value: 15.625
|
1814 |
+
- type: precision_at_1
|
1815 |
+
value: 22.6
|
1816 |
+
- type: precision_at_10
|
1817 |
+
value: 9.48
|
1818 |
+
- type: precision_at_100
|
1819 |
+
value: 1.991
|
1820 |
+
- type: precision_at_1000
|
1821 |
+
value: 0.321
|
1822 |
+
- type: precision_at_3
|
1823 |
+
value: 16.8
|
1824 |
+
- type: precision_at_5
|
1825 |
+
value: 13.54
|
1826 |
+
- type: recall_at_1
|
1827 |
+
value: 4.578
|
1828 |
+
- type: recall_at_10
|
1829 |
+
value: 19.213
|
1830 |
+
- type: recall_at_100
|
1831 |
+
value: 40.397
|
1832 |
+
- type: recall_at_1000
|
1833 |
+
value: 65.2
|
1834 |
+
- type: recall_at_3
|
1835 |
+
value: 10.208
|
1836 |
+
- type: recall_at_5
|
1837 |
+
value: 13.718
|
1838 |
+
- task:
|
1839 |
+
type: STS
|
1840 |
+
dataset:
|
1841 |
+
name: MTEB SICK-R
|
1842 |
+
type: mteb/sickr-sts
|
1843 |
+
config: default
|
1844 |
+
split: test
|
1845 |
+
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
1846 |
+
metrics:
|
1847 |
+
- type: cos_sim_pearson
|
1848 |
+
value: 83.44288351714071
|
1849 |
+
- type: cos_sim_spearman
|
1850 |
+
value: 79.37995604564952
|
1851 |
+
- type: euclidean_pearson
|
1852 |
+
value: 81.1078874670718
|
1853 |
+
- type: euclidean_spearman
|
1854 |
+
value: 79.37995905980499
|
1855 |
+
- type: manhattan_pearson
|
1856 |
+
value: 81.03697527288986
|
1857 |
+
- type: manhattan_spearman
|
1858 |
+
value: 79.33490235296236
|
1859 |
+
- task:
|
1860 |
+
type: STS
|
1861 |
+
dataset:
|
1862 |
+
name: MTEB STS12
|
1863 |
+
type: mteb/sts12-sts
|
1864 |
+
config: default
|
1865 |
+
split: test
|
1866 |
+
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
1867 |
+
metrics:
|
1868 |
+
- type: cos_sim_pearson
|
1869 |
+
value: 84.95557650436523
|
1870 |
+
- type: cos_sim_spearman
|
1871 |
+
value: 78.5190672399868
|
1872 |
+
- type: euclidean_pearson
|
1873 |
+
value: 81.58064025904707
|
1874 |
+
- type: euclidean_spearman
|
1875 |
+
value: 78.5190672399868
|
1876 |
+
- type: manhattan_pearson
|
1877 |
+
value: 81.52857930619889
|
1878 |
+
- type: manhattan_spearman
|
1879 |
+
value: 78.50421361308034
|
1880 |
+
- task:
|
1881 |
+
type: STS
|
1882 |
+
dataset:
|
1883 |
+
name: MTEB STS13
|
1884 |
+
type: mteb/sts13-sts
|
1885 |
+
config: default
|
1886 |
+
split: test
|
1887 |
+
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
1888 |
+
metrics:
|
1889 |
+
- type: cos_sim_pearson
|
1890 |
+
value: 84.79128416228737
|
1891 |
+
- type: cos_sim_spearman
|
1892 |
+
value: 86.05402451477147
|
1893 |
+
- type: euclidean_pearson
|
1894 |
+
value: 85.46280267054289
|
1895 |
+
- type: euclidean_spearman
|
1896 |
+
value: 86.05402451477147
|
1897 |
+
- type: manhattan_pearson
|
1898 |
+
value: 85.46278563858236
|
1899 |
+
- type: manhattan_spearman
|
1900 |
+
value: 86.08079590861004
|
1901 |
+
- task:
|
1902 |
+
type: STS
|
1903 |
+
dataset:
|
1904 |
+
name: MTEB STS14
|
1905 |
+
type: mteb/sts14-sts
|
1906 |
+
config: default
|
1907 |
+
split: test
|
1908 |
+
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
1909 |
+
metrics:
|
1910 |
+
- type: cos_sim_pearson
|
1911 |
+
value: 83.20623089568763
|
1912 |
+
- type: cos_sim_spearman
|
1913 |
+
value: 81.53786907061009
|
1914 |
+
- type: euclidean_pearson
|
1915 |
+
value: 82.82272250091494
|
1916 |
+
- type: euclidean_spearman
|
1917 |
+
value: 81.53786907061009
|
1918 |
+
- type: manhattan_pearson
|
1919 |
+
value: 82.78850494027013
|
1920 |
+
- type: manhattan_spearman
|
1921 |
+
value: 81.5135618083407
|
1922 |
+
- task:
|
1923 |
+
type: STS
|
1924 |
+
dataset:
|
1925 |
+
name: MTEB STS15
|
1926 |
+
type: mteb/sts15-sts
|
1927 |
+
config: default
|
1928 |
+
split: test
|
1929 |
+
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
1930 |
+
metrics:
|
1931 |
+
- type: cos_sim_pearson
|
1932 |
+
value: 85.46366618397936
|
1933 |
+
- type: cos_sim_spearman
|
1934 |
+
value: 86.96566013336908
|
1935 |
+
- type: euclidean_pearson
|
1936 |
+
value: 86.62651697548931
|
1937 |
+
- type: euclidean_spearman
|
1938 |
+
value: 86.96565526364454
|
1939 |
+
- type: manhattan_pearson
|
1940 |
+
value: 86.58812160258009
|
1941 |
+
- type: manhattan_spearman
|
1942 |
+
value: 86.9336484321288
|
1943 |
+
- task:
|
1944 |
+
type: STS
|
1945 |
+
dataset:
|
1946 |
+
name: MTEB STS16
|
1947 |
+
type: mteb/sts16-sts
|
1948 |
+
config: default
|
1949 |
+
split: test
|
1950 |
+
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
1951 |
+
metrics:
|
1952 |
+
- type: cos_sim_pearson
|
1953 |
+
value: 82.51858358641559
|
1954 |
+
- type: cos_sim_spearman
|
1955 |
+
value: 84.7652527954999
|
1956 |
+
- type: euclidean_pearson
|
1957 |
+
value: 84.23914783766861
|
1958 |
+
- type: euclidean_spearman
|
1959 |
+
value: 84.7652527954999
|
1960 |
+
- type: manhattan_pearson
|
1961 |
+
value: 84.22749648503171
|
1962 |
+
- type: manhattan_spearman
|
1963 |
+
value: 84.74527996746386
|
1964 |
+
- task:
|
1965 |
+
type: STS
|
1966 |
+
dataset:
|
1967 |
+
name: MTEB STS17 (en-en)
|
1968 |
+
type: mteb/sts17-crosslingual-sts
|
1969 |
+
config: en-en
|
1970 |
+
split: test
|
1971 |
+
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
1972 |
+
metrics:
|
1973 |
+
- type: cos_sim_pearson
|
1974 |
+
value: 87.28026563313065
|
1975 |
+
- type: cos_sim_spearman
|
1976 |
+
value: 87.46928143824915
|
1977 |
+
- type: euclidean_pearson
|
1978 |
+
value: 88.30558762000372
|
1979 |
+
- type: euclidean_spearman
|
1980 |
+
value: 87.46928143824915
|
1981 |
+
- type: manhattan_pearson
|
1982 |
+
value: 88.10513330809331
|
1983 |
+
- type: manhattan_spearman
|
1984 |
+
value: 87.21069787834173
|
1985 |
+
- task:
|
1986 |
+
type: STS
|
1987 |
+
dataset:
|
1988 |
+
name: MTEB STS22 (en)
|
1989 |
+
type: mteb/sts22-crosslingual-sts
|
1990 |
+
config: en
|
1991 |
+
split: test
|
1992 |
+
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
1993 |
+
metrics:
|
1994 |
+
- type: cos_sim_pearson
|
1995 |
+
value: 62.376497134587375
|
1996 |
+
- type: cos_sim_spearman
|
1997 |
+
value: 65.0159550112516
|
1998 |
+
- type: euclidean_pearson
|
1999 |
+
value: 65.64572120879598
|
2000 |
+
- type: euclidean_spearman
|
2001 |
+
value: 65.0159550112516
|
2002 |
+
- type: manhattan_pearson
|
2003 |
+
value: 65.88143604989976
|
2004 |
+
- type: manhattan_spearman
|
2005 |
+
value: 65.17547297222434
|
2006 |
+
- task:
|
2007 |
+
type: STS
|
2008 |
+
dataset:
|
2009 |
+
name: MTEB STSBenchmark
|
2010 |
+
type: mteb/stsbenchmark-sts
|
2011 |
+
config: default
|
2012 |
+
split: test
|
2013 |
+
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
2014 |
+
metrics:
|
2015 |
+
- type: cos_sim_pearson
|
2016 |
+
value: 84.22876368947644
|
2017 |
+
- type: cos_sim_spearman
|
2018 |
+
value: 85.46935577445318
|
2019 |
+
- type: euclidean_pearson
|
2020 |
+
value: 85.32830231392005
|
2021 |
+
- type: euclidean_spearman
|
2022 |
+
value: 85.46935577445318
|
2023 |
+
- type: manhattan_pearson
|
2024 |
+
value: 85.30353211758495
|
2025 |
+
- type: manhattan_spearman
|
2026 |
+
value: 85.42821085956945
|
2027 |
+
- task:
|
2028 |
+
type: Reranking
|
2029 |
+
dataset:
|
2030 |
+
name: MTEB SciDocsRR
|
2031 |
+
type: mteb/scidocs-reranking
|
2032 |
+
config: default
|
2033 |
+
split: test
|
2034 |
+
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
2035 |
+
metrics:
|
2036 |
+
- type: map
|
2037 |
+
value: 80.60986667767133
|
2038 |
+
- type: mrr
|
2039 |
+
value: 94.29432314236236
|
2040 |
+
- task:
|
2041 |
+
type: Retrieval
|
2042 |
+
dataset:
|
2043 |
+
name: MTEB SciFact
|
2044 |
+
type: scifact
|
2045 |
+
config: default
|
2046 |
+
split: test
|
2047 |
+
revision: None
|
2048 |
+
metrics:
|
2049 |
+
- type: map_at_1
|
2050 |
+
value: 54.528
|
2051 |
+
- type: map_at_10
|
2052 |
+
value: 65.187
|
2053 |
+
- type: map_at_100
|
2054 |
+
value: 65.62599999999999
|
2055 |
+
- type: map_at_1000
|
2056 |
+
value: 65.657
|
2057 |
+
- type: map_at_3
|
2058 |
+
value: 62.352
|
2059 |
+
- type: map_at_5
|
2060 |
+
value: 64.025
|
2061 |
+
- type: mrr_at_1
|
2062 |
+
value: 57.333
|
2063 |
+
- type: mrr_at_10
|
2064 |
+
value: 66.577
|
2065 |
+
- type: mrr_at_100
|
2066 |
+
value: 66.88
|
2067 |
+
- type: mrr_at_1000
|
2068 |
+
value: 66.908
|
2069 |
+
- type: mrr_at_3
|
2070 |
+
value: 64.556
|
2071 |
+
- type: mrr_at_5
|
2072 |
+
value: 65.739
|
2073 |
+
- type: ndcg_at_1
|
2074 |
+
value: 57.333
|
2075 |
+
- type: ndcg_at_10
|
2076 |
+
value: 70.275
|
2077 |
+
- type: ndcg_at_100
|
2078 |
+
value: 72.136
|
2079 |
+
- type: ndcg_at_1000
|
2080 |
+
value: 72.963
|
2081 |
+
- type: ndcg_at_3
|
2082 |
+
value: 65.414
|
2083 |
+
- type: ndcg_at_5
|
2084 |
+
value: 67.831
|
2085 |
+
- type: precision_at_1
|
2086 |
+
value: 57.333
|
2087 |
+
- type: precision_at_10
|
2088 |
+
value: 9.5
|
2089 |
+
- type: precision_at_100
|
2090 |
+
value: 1.057
|
2091 |
+
- type: precision_at_1000
|
2092 |
+
value: 0.11199999999999999
|
2093 |
+
- type: precision_at_3
|
2094 |
+
value: 25.778000000000002
|
2095 |
+
- type: precision_at_5
|
2096 |
+
value: 17.2
|
2097 |
+
- type: recall_at_1
|
2098 |
+
value: 54.528
|
2099 |
+
- type: recall_at_10
|
2100 |
+
value: 84.356
|
2101 |
+
- type: recall_at_100
|
2102 |
+
value: 92.833
|
2103 |
+
- type: recall_at_1000
|
2104 |
+
value: 99.333
|
2105 |
+
- type: recall_at_3
|
2106 |
+
value: 71.283
|
2107 |
+
- type: recall_at_5
|
2108 |
+
value: 77.14999999999999
|
2109 |
+
- task:
|
2110 |
+
type: PairClassification
|
2111 |
+
dataset:
|
2112 |
+
name: MTEB SprintDuplicateQuestions
|
2113 |
+
type: mteb/sprintduplicatequestions-pairclassification
|
2114 |
+
config: default
|
2115 |
+
split: test
|
2116 |
+
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
2117 |
+
metrics:
|
2118 |
+
- type: cos_sim_accuracy
|
2119 |
+
value: 99.74158415841585
|
2120 |
+
- type: cos_sim_ap
|
2121 |
+
value: 92.90048959850317
|
2122 |
+
- type: cos_sim_f1
|
2123 |
+
value: 86.35650810245687
|
2124 |
+
- type: cos_sim_precision
|
2125 |
+
value: 90.4709748083242
|
2126 |
+
- type: cos_sim_recall
|
2127 |
+
value: 82.6
|
2128 |
+
- type: dot_accuracy
|
2129 |
+
value: 99.74158415841585
|
2130 |
+
- type: dot_ap
|
2131 |
+
value: 92.90048959850317
|
2132 |
+
- type: dot_f1
|
2133 |
+
value: 86.35650810245687
|
2134 |
+
- type: dot_precision
|
2135 |
+
value: 90.4709748083242
|
2136 |
+
- type: dot_recall
|
2137 |
+
value: 82.6
|
2138 |
+
- type: euclidean_accuracy
|
2139 |
+
value: 99.74158415841585
|
2140 |
+
- type: euclidean_ap
|
2141 |
+
value: 92.90048959850317
|
2142 |
+
- type: euclidean_f1
|
2143 |
+
value: 86.35650810245687
|
2144 |
+
- type: euclidean_precision
|
2145 |
+
value: 90.4709748083242
|
2146 |
+
- type: euclidean_recall
|
2147 |
+
value: 82.6
|
2148 |
+
- type: manhattan_accuracy
|
2149 |
+
value: 99.74158415841585
|
2150 |
+
- type: manhattan_ap
|
2151 |
+
value: 92.87344692947894
|
2152 |
+
- type: manhattan_f1
|
2153 |
+
value: 86.38497652582159
|
2154 |
+
- type: manhattan_precision
|
2155 |
+
value: 90.29443838604145
|
2156 |
+
- type: manhattan_recall
|
2157 |
+
value: 82.8
|
2158 |
+
- type: max_accuracy
|
2159 |
+
value: 99.74158415841585
|
2160 |
+
- type: max_ap
|
2161 |
+
value: 92.90048959850317
|
2162 |
+
- type: max_f1
|
2163 |
+
value: 86.38497652582159
|
2164 |
+
- task:
|
2165 |
+
type: Clustering
|
2166 |
+
dataset:
|
2167 |
+
name: MTEB StackExchangeClustering
|
2168 |
+
type: mteb/stackexchange-clustering
|
2169 |
+
config: default
|
2170 |
+
split: test
|
2171 |
+
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
2172 |
+
metrics:
|
2173 |
+
- type: v_measure
|
2174 |
+
value: 63.191648770424216
|
2175 |
+
- task:
|
2176 |
+
type: Clustering
|
2177 |
+
dataset:
|
2178 |
+
name: MTEB StackExchangeClusteringP2P
|
2179 |
+
type: mteb/stackexchange-clustering-p2p
|
2180 |
+
config: default
|
2181 |
+
split: test
|
2182 |
+
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
2183 |
+
metrics:
|
2184 |
+
- type: v_measure
|
2185 |
+
value: 34.02944668730218
|
2186 |
+
- task:
|
2187 |
+
type: Reranking
|
2188 |
+
dataset:
|
2189 |
+
name: MTEB StackOverflowDupQuestions
|
2190 |
+
type: mteb/stackoverflowdupquestions-reranking
|
2191 |
+
config: default
|
2192 |
+
split: test
|
2193 |
+
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
2194 |
+
metrics:
|
2195 |
+
- type: map
|
2196 |
+
value: 50.466386167525265
|
2197 |
+
- type: mrr
|
2198 |
+
value: 51.19071492233257
|
2199 |
+
- task:
|
2200 |
+
type: Summarization
|
2201 |
+
dataset:
|
2202 |
+
name: MTEB SummEval
|
2203 |
+
type: mteb/summeval
|
2204 |
+
config: default
|
2205 |
+
split: test
|
2206 |
+
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
2207 |
+
metrics:
|
2208 |
+
- type: cos_sim_pearson
|
2209 |
+
value: 30.198022505886435
|
2210 |
+
- type: cos_sim_spearman
|
2211 |
+
value: 30.40170257939193
|
2212 |
+
- type: dot_pearson
|
2213 |
+
value: 30.198015316402614
|
2214 |
+
- type: dot_spearman
|
2215 |
+
value: 30.40170257939193
|
2216 |
+
- task:
|
2217 |
+
type: Retrieval
|
2218 |
+
dataset:
|
2219 |
+
name: MTEB TRECCOVID
|
2220 |
+
type: trec-covid
|
2221 |
+
config: default
|
2222 |
+
split: test
|
2223 |
+
revision: None
|
2224 |
+
metrics:
|
2225 |
+
- type: map_at_1
|
2226 |
+
value: 0.242
|
2227 |
+
- type: map_at_10
|
2228 |
+
value: 2.17
|
2229 |
+
- type: map_at_100
|
2230 |
+
value: 12.221
|
2231 |
+
- type: map_at_1000
|
2232 |
+
value: 28.63
|
2233 |
+
- type: map_at_3
|
2234 |
+
value: 0.728
|
2235 |
+
- type: map_at_5
|
2236 |
+
value: 1.185
|
2237 |
+
- type: mrr_at_1
|
2238 |
+
value: 94
|
2239 |
+
- type: mrr_at_10
|
2240 |
+
value: 97
|
2241 |
+
- type: mrr_at_100
|
2242 |
+
value: 97
|
2243 |
+
- type: mrr_at_1000
|
2244 |
+
value: 97
|
2245 |
+
- type: mrr_at_3
|
2246 |
+
value: 97
|
2247 |
+
- type: mrr_at_5
|
2248 |
+
value: 97
|
2249 |
+
- type: ndcg_at_1
|
2250 |
+
value: 89
|
2251 |
+
- type: ndcg_at_10
|
2252 |
+
value: 82.30499999999999
|
2253 |
+
- type: ndcg_at_100
|
2254 |
+
value: 61.839999999999996
|
2255 |
+
- type: ndcg_at_1000
|
2256 |
+
value: 53.381
|
2257 |
+
- type: ndcg_at_3
|
2258 |
+
value: 88.877
|
2259 |
+
- type: ndcg_at_5
|
2260 |
+
value: 86.05199999999999
|
2261 |
+
- type: precision_at_1
|
2262 |
+
value: 94
|
2263 |
+
- type: precision_at_10
|
2264 |
+
value: 87
|
2265 |
+
- type: precision_at_100
|
2266 |
+
value: 63.38
|
2267 |
+
- type: precision_at_1000
|
2268 |
+
value: 23.498
|
2269 |
+
- type: precision_at_3
|
2270 |
+
value: 94
|
2271 |
+
- type: precision_at_5
|
2272 |
+
value: 92
|
2273 |
+
- type: recall_at_1
|
2274 |
+
value: 0.242
|
2275 |
+
- type: recall_at_10
|
2276 |
+
value: 2.302
|
2277 |
+
- type: recall_at_100
|
2278 |
+
value: 14.979000000000001
|
2279 |
+
- type: recall_at_1000
|
2280 |
+
value: 49.638
|
2281 |
+
- type: recall_at_3
|
2282 |
+
value: 0.753
|
2283 |
+
- type: recall_at_5
|
2284 |
+
value: 1.226
|
2285 |
+
- task:
|
2286 |
+
type: Retrieval
|
2287 |
+
dataset:
|
2288 |
+
name: MTEB Touche2020
|
2289 |
+
type: webis-touche2020
|
2290 |
+
config: default
|
2291 |
+
split: test
|
2292 |
+
revision: None
|
2293 |
+
metrics:
|
2294 |
+
- type: map_at_1
|
2295 |
+
value: 3.006
|
2296 |
+
- type: map_at_10
|
2297 |
+
value: 11.805
|
2298 |
+
- type: map_at_100
|
2299 |
+
value: 18.146
|
2300 |
+
- type: map_at_1000
|
2301 |
+
value: 19.788
|
2302 |
+
- type: map_at_3
|
2303 |
+
value: 5.914
|
2304 |
+
- type: map_at_5
|
2305 |
+
value: 8.801
|
2306 |
+
- type: mrr_at_1
|
2307 |
+
value: 40.816
|
2308 |
+
- type: mrr_at_10
|
2309 |
+
value: 56.36600000000001
|
2310 |
+
- type: mrr_at_100
|
2311 |
+
value: 56.721999999999994
|
2312 |
+
- type: mrr_at_1000
|
2313 |
+
value: 56.721999999999994
|
2314 |
+
- type: mrr_at_3
|
2315 |
+
value: 52.041000000000004
|
2316 |
+
- type: mrr_at_5
|
2317 |
+
value: 54.796
|
2318 |
+
- type: ndcg_at_1
|
2319 |
+
value: 37.755
|
2320 |
+
- type: ndcg_at_10
|
2321 |
+
value: 29.863
|
2322 |
+
- type: ndcg_at_100
|
2323 |
+
value: 39.571
|
2324 |
+
- type: ndcg_at_1000
|
2325 |
+
value: 51.385999999999996
|
2326 |
+
- type: ndcg_at_3
|
2327 |
+
value: 32.578
|
2328 |
+
- type: ndcg_at_5
|
2329 |
+
value: 32.351
|
2330 |
+
- type: precision_at_1
|
2331 |
+
value: 40.816
|
2332 |
+
- type: precision_at_10
|
2333 |
+
value: 26.531
|
2334 |
+
- type: precision_at_100
|
2335 |
+
value: 7.796
|
2336 |
+
- type: precision_at_1000
|
2337 |
+
value: 1.555
|
2338 |
+
- type: precision_at_3
|
2339 |
+
value: 32.653
|
2340 |
+
- type: precision_at_5
|
2341 |
+
value: 33.061
|
2342 |
+
- type: recall_at_1
|
2343 |
+
value: 3.006
|
2344 |
+
- type: recall_at_10
|
2345 |
+
value: 18.738
|
2346 |
+
- type: recall_at_100
|
2347 |
+
value: 48.058
|
2348 |
+
- type: recall_at_1000
|
2349 |
+
value: 83.41300000000001
|
2350 |
+
- type: recall_at_3
|
2351 |
+
value: 7.166
|
2352 |
+
- type: recall_at_5
|
2353 |
+
value: 12.102
|
2354 |
+
- task:
|
2355 |
+
type: Classification
|
2356 |
+
dataset:
|
2357 |
+
name: MTEB ToxicConversationsClassification
|
2358 |
+
type: mteb/toxic_conversations_50k
|
2359 |
+
config: default
|
2360 |
+
split: test
|
2361 |
+
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
2362 |
+
metrics:
|
2363 |
+
- type: accuracy
|
2364 |
+
value: 71.4178
|
2365 |
+
- type: ap
|
2366 |
+
value: 14.648781342150446
|
2367 |
+
- type: f1
|
2368 |
+
value: 55.07299194946378
|
2369 |
+
- task:
|
2370 |
+
type: Classification
|
2371 |
+
dataset:
|
2372 |
+
name: MTEB TweetSentimentExtractionClassification
|
2373 |
+
type: mteb/tweet_sentiment_extraction
|
2374 |
+
config: default
|
2375 |
+
split: test
|
2376 |
+
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
2377 |
+
metrics:
|
2378 |
+
- type: accuracy
|
2379 |
+
value: 60.919637804187886
|
2380 |
+
- type: f1
|
2381 |
+
value: 61.24122013967399
|
2382 |
+
- task:
|
2383 |
+
type: Clustering
|
2384 |
+
dataset:
|
2385 |
+
name: MTEB TwentyNewsgroupsClustering
|
2386 |
+
type: mteb/twentynewsgroups-clustering
|
2387 |
+
config: default
|
2388 |
+
split: test
|
2389 |
+
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
2390 |
+
metrics:
|
2391 |
+
- type: v_measure
|
2392 |
+
value: 49.207896583685695
|
2393 |
+
- task:
|
2394 |
+
type: PairClassification
|
2395 |
+
dataset:
|
2396 |
+
name: MTEB TwitterSemEval2015
|
2397 |
+
type: mteb/twittersemeval2015-pairclassification
|
2398 |
+
config: default
|
2399 |
+
split: test
|
2400 |
+
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
2401 |
+
metrics:
|
2402 |
+
- type: cos_sim_accuracy
|
2403 |
+
value: 86.23114978840078
|
2404 |
+
- type: cos_sim_ap
|
2405 |
+
value: 74.26624727825818
|
2406 |
+
- type: cos_sim_f1
|
2407 |
+
value: 68.72377190817083
|
2408 |
+
- type: cos_sim_precision
|
2409 |
+
value: 64.56400742115028
|
2410 |
+
- type: cos_sim_recall
|
2411 |
+
value: 73.45646437994723
|
2412 |
+
- type: dot_accuracy
|
2413 |
+
value: 86.23114978840078
|
2414 |
+
- type: dot_ap
|
2415 |
+
value: 74.26624032659652
|
2416 |
+
- type: dot_f1
|
2417 |
+
value: 68.72377190817083
|
2418 |
+
- type: dot_precision
|
2419 |
+
value: 64.56400742115028
|
2420 |
+
- type: dot_recall
|
2421 |
+
value: 73.45646437994723
|
2422 |
+
- type: euclidean_accuracy
|
2423 |
+
value: 86.23114978840078
|
2424 |
+
- type: euclidean_ap
|
2425 |
+
value: 74.26624714480556
|
2426 |
+
- type: euclidean_f1
|
2427 |
+
value: 68.72377190817083
|
2428 |
+
- type: euclidean_precision
|
2429 |
+
value: 64.56400742115028
|
2430 |
+
- type: euclidean_recall
|
2431 |
+
value: 73.45646437994723
|
2432 |
+
- type: manhattan_accuracy
|
2433 |
+
value: 86.16558383501221
|
2434 |
+
- type: manhattan_ap
|
2435 |
+
value: 74.2091943976357
|
2436 |
+
- type: manhattan_f1
|
2437 |
+
value: 68.64221520524654
|
2438 |
+
- type: manhattan_precision
|
2439 |
+
value: 63.59135913591359
|
2440 |
+
- type: manhattan_recall
|
2441 |
+
value: 74.5646437994723
|
2442 |
+
- type: max_accuracy
|
2443 |
+
value: 86.23114978840078
|
2444 |
+
- type: max_ap
|
2445 |
+
value: 74.26624727825818
|
2446 |
+
- type: max_f1
|
2447 |
+
value: 68.72377190817083
|
2448 |
+
- task:
|
2449 |
+
type: PairClassification
|
2450 |
+
dataset:
|
2451 |
+
name: MTEB TwitterURLCorpus
|
2452 |
+
type: mteb/twitterurlcorpus-pairclassification
|
2453 |
+
config: default
|
2454 |
+
split: test
|
2455 |
+
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
2456 |
+
metrics:
|
2457 |
+
- type: cos_sim_accuracy
|
2458 |
+
value: 89.3681841114604
|
2459 |
+
- type: cos_sim_ap
|
2460 |
+
value: 86.65166387498546
|
2461 |
+
- type: cos_sim_f1
|
2462 |
+
value: 79.02581944698774
|
2463 |
+
- type: cos_sim_precision
|
2464 |
+
value: 75.35796605434099
|
2465 |
+
- type: cos_sim_recall
|
2466 |
+
value: 83.06898675700647
|
2467 |
+
- type: dot_accuracy
|
2468 |
+
value: 89.3681841114604
|
2469 |
+
- type: dot_ap
|
2470 |
+
value: 86.65166019802056
|
2471 |
+
- type: dot_f1
|
2472 |
+
value: 79.02581944698774
|
2473 |
+
- type: dot_precision
|
2474 |
+
value: 75.35796605434099
|
2475 |
+
- type: dot_recall
|
2476 |
+
value: 83.06898675700647
|
2477 |
+
- type: euclidean_accuracy
|
2478 |
+
value: 89.3681841114604
|
2479 |
+
- type: euclidean_ap
|
2480 |
+
value: 86.65166462876266
|
2481 |
+
- type: euclidean_f1
|
2482 |
+
value: 79.02581944698774
|
2483 |
+
- type: euclidean_precision
|
2484 |
+
value: 75.35796605434099
|
2485 |
+
- type: euclidean_recall
|
2486 |
+
value: 83.06898675700647
|
2487 |
+
- type: manhattan_accuracy
|
2488 |
+
value: 89.36624364497226
|
2489 |
+
- type: manhattan_ap
|
2490 |
+
value: 86.65076471274106
|
2491 |
+
- type: manhattan_f1
|
2492 |
+
value: 79.07408783532733
|
2493 |
+
- type: manhattan_precision
|
2494 |
+
value: 76.41102972856527
|
2495 |
+
- type: manhattan_recall
|
2496 |
+
value: 81.92947336002464
|
2497 |
+
- type: max_accuracy
|
2498 |
+
value: 89.3681841114604
|
2499 |
+
- type: max_ap
|
2500 |
+
value: 86.65166462876266
|
2501 |
+
- type: max_f1
|
2502 |
+
value: 79.07408783532733
|
2503 |
+
---
|
2504 |
+
|
2505 |
+
# YorkieOH10/nomic-embed-text-v1.5-Q8_0-GGUF
|
2506 |
+
This model was converted to GGUF format from [`nomic-ai/nomic-embed-text-v1.5`](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
|
2507 |
+
Refer to the [original model card](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) for more details on the model.
|
2508 |
+
|
2509 |
+
## Use with llama.cpp
|
2510 |
+
Install llama.cpp through brew (works on Mac and Linux)
|
2511 |
+
|
2512 |
+
```bash
|
2513 |
+
brew install llama.cpp
|
2514 |
+
|
2515 |
+
```
|
2516 |
+
Invoke the llama.cpp server or the CLI.
|
2517 |
+
|
2518 |
+
### CLI:
|
2519 |
+
```bash
|
2520 |
+
llama-cli --hf-repo YorkieOH10/nomic-embed-text-v1.5-Q8_0-GGUF --hf-file nomic-embed-text-v1.5-q8_0.gguf -p "The meaning to life and the universe is"
|
2521 |
+
```
|
2522 |
+
|
2523 |
+
### Server:
|
2524 |
+
```bash
|
2525 |
+
llama-server --hf-repo YorkieOH10/nomic-embed-text-v1.5-Q8_0-GGUF --hf-file nomic-embed-text-v1.5-q8_0.gguf -c 2048
|
2526 |
+
```
|
2527 |
+
|
2528 |
+
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
|
2529 |
+
|
2530 |
+
Step 1: Clone llama.cpp from GitHub.
|
2531 |
+
```
|
2532 |
+
git clone https://github.com/ggerganov/llama.cpp
|
2533 |
+
```
|
2534 |
+
|
2535 |
+
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
|
2536 |
+
```
|
2537 |
+
cd llama.cpp && LLAMA_CURL=1 make
|
2538 |
+
```
|
2539 |
+
|
2540 |
+
Step 3: Run inference through the main binary.
|
2541 |
+
```
|
2542 |
+
./llama-cli --hf-repo YorkieOH10/nomic-embed-text-v1.5-Q8_0-GGUF --hf-file nomic-embed-text-v1.5-q8_0.gguf -p "The meaning to life and the universe is"
|
2543 |
+
```
|
2544 |
+
or
|
2545 |
+
```
|
2546 |
+
./llama-server --hf-repo YorkieOH10/nomic-embed-text-v1.5-Q8_0-GGUF --hf-file nomic-embed-text-v1.5-q8_0.gguf -c 2048
|
2547 |
+
```
|