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README.md
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|
1 |
+
---
|
2 |
+
base_model: intfloat/e5-large-v2
|
3 |
+
language:
|
4 |
+
- en
|
5 |
+
license: mit
|
6 |
+
tags:
|
7 |
+
- mteb
|
8 |
+
- Sentence Transformers
|
9 |
+
- sentence-similarity
|
10 |
+
- sentence-transformers
|
11 |
+
- llama-cpp
|
12 |
+
- gguf-my-repo
|
13 |
+
model-index:
|
14 |
+
- name: e5-large-v2
|
15 |
+
results:
|
16 |
+
- task:
|
17 |
+
type: Classification
|
18 |
+
dataset:
|
19 |
+
name: MTEB AmazonCounterfactualClassification (en)
|
20 |
+
type: mteb/amazon_counterfactual
|
21 |
+
config: en
|
22 |
+
split: test
|
23 |
+
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
|
24 |
+
metrics:
|
25 |
+
- type: accuracy
|
26 |
+
value: 79.22388059701493
|
27 |
+
- type: ap
|
28 |
+
value: 43.20816505595132
|
29 |
+
- type: f1
|
30 |
+
value: 73.27811303522058
|
31 |
+
- task:
|
32 |
+
type: Classification
|
33 |
+
dataset:
|
34 |
+
name: MTEB AmazonPolarityClassification
|
35 |
+
type: mteb/amazon_polarity
|
36 |
+
config: default
|
37 |
+
split: test
|
38 |
+
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
|
39 |
+
metrics:
|
40 |
+
- type: accuracy
|
41 |
+
value: 93.748325
|
42 |
+
- type: ap
|
43 |
+
value: 90.72534979701297
|
44 |
+
- type: f1
|
45 |
+
value: 93.73895874282185
|
46 |
+
- task:
|
47 |
+
type: Classification
|
48 |
+
dataset:
|
49 |
+
name: MTEB AmazonReviewsClassification (en)
|
50 |
+
type: mteb/amazon_reviews_multi
|
51 |
+
config: en
|
52 |
+
split: test
|
53 |
+
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
54 |
+
metrics:
|
55 |
+
- type: accuracy
|
56 |
+
value: 48.612
|
57 |
+
- type: f1
|
58 |
+
value: 47.61157345898393
|
59 |
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- task:
|
60 |
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type: Retrieval
|
61 |
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dataset:
|
62 |
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name: MTEB ArguAna
|
63 |
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type: arguana
|
64 |
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config: default
|
65 |
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split: test
|
66 |
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revision: None
|
67 |
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metrics:
|
68 |
+
- type: map_at_1
|
69 |
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value: 23.541999999999998
|
70 |
+
- type: map_at_10
|
71 |
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value: 38.208
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72 |
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- type: map_at_100
|
73 |
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value: 39.417
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74 |
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|
75 |
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value: 39.428999999999995
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76 |
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77 |
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value: 33.95
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78 |
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|
79 |
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value: 36.329
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80 |
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|
81 |
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value: 23.755000000000003
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82 |
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|
83 |
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value: 38.288
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84 |
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|
85 |
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value: 39.511
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86 |
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- type: mrr_at_1000
|
87 |
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value: 39.523
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88 |
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|
89 |
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value: 34.009
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90 |
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|
91 |
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value: 36.434
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92 |
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|
93 |
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value: 23.541999999999998
|
94 |
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- type: ndcg_at_10
|
95 |
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value: 46.417
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96 |
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|
97 |
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value: 51.812000000000005
|
98 |
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- type: ndcg_at_1000
|
99 |
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value: 52.137
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100 |
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- type: ndcg_at_3
|
101 |
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value: 37.528
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102 |
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- type: ndcg_at_5
|
103 |
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value: 41.81
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104 |
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|
105 |
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value: 23.541999999999998
|
106 |
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- type: precision_at_10
|
107 |
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value: 7.269
|
108 |
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- type: precision_at_100
|
109 |
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value: 0.9690000000000001
|
110 |
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- type: precision_at_1000
|
111 |
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value: 0.099
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112 |
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- type: precision_at_3
|
113 |
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value: 15.979
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114 |
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- type: precision_at_5
|
115 |
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value: 11.664
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116 |
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|
117 |
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value: 23.541999999999998
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118 |
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- type: recall_at_10
|
119 |
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value: 72.688
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120 |
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- type: recall_at_100
|
121 |
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value: 96.871
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122 |
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- type: recall_at_1000
|
123 |
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value: 99.431
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124 |
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- type: recall_at_3
|
125 |
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value: 47.937000000000005
|
126 |
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- type: recall_at_5
|
127 |
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value: 58.321
|
128 |
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- task:
|
129 |
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type: Clustering
|
130 |
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dataset:
|
131 |
+
name: MTEB ArxivClusteringP2P
|
132 |
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type: mteb/arxiv-clustering-p2p
|
133 |
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config: default
|
134 |
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split: test
|
135 |
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
|
136 |
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metrics:
|
137 |
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- type: v_measure
|
138 |
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value: 45.546499570522094
|
139 |
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- task:
|
140 |
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type: Clustering
|
141 |
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dataset:
|
142 |
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name: MTEB ArxivClusteringS2S
|
143 |
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type: mteb/arxiv-clustering-s2s
|
144 |
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config: default
|
145 |
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split: test
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146 |
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revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
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147 |
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metrics:
|
148 |
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- type: v_measure
|
149 |
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value: 41.01607489943561
|
150 |
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- task:
|
151 |
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type: Reranking
|
152 |
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dataset:
|
153 |
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name: MTEB AskUbuntuDupQuestions
|
154 |
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type: mteb/askubuntudupquestions-reranking
|
155 |
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config: default
|
156 |
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split: test
|
157 |
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revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
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158 |
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metrics:
|
159 |
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- type: map
|
160 |
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value: 59.616107510107774
|
161 |
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- type: mrr
|
162 |
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value: 72.75106626214661
|
163 |
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- task:
|
164 |
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type: STS
|
165 |
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dataset:
|
166 |
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name: MTEB BIOSSES
|
167 |
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type: mteb/biosses-sts
|
168 |
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config: default
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169 |
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split: test
|
170 |
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
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171 |
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metrics:
|
172 |
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|
173 |
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value: 84.33018094733868
|
174 |
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- type: cos_sim_spearman
|
175 |
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value: 83.60190492611737
|
176 |
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- type: euclidean_pearson
|
177 |
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value: 82.1492450218961
|
178 |
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- type: euclidean_spearman
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value: 82.70308926526991
|
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- type: manhattan_pearson
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value: 81.93959600076842
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182 |
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- type: manhattan_spearman
|
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value: 82.73260801016369
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184 |
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- task:
|
185 |
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type: Classification
|
186 |
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dataset:
|
187 |
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name: MTEB Banking77Classification
|
188 |
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type: mteb/banking77
|
189 |
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config: default
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190 |
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split: test
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191 |
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
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192 |
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metrics:
|
193 |
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- type: accuracy
|
194 |
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value: 84.54545454545455
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195 |
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- type: f1
|
196 |
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value: 84.49582530928923
|
197 |
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- task:
|
198 |
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type: Clustering
|
199 |
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dataset:
|
200 |
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name: MTEB BiorxivClusteringP2P
|
201 |
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type: mteb/biorxiv-clustering-p2p
|
202 |
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config: default
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203 |
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split: test
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204 |
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
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205 |
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metrics:
|
206 |
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- type: v_measure
|
207 |
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value: 37.362725540120096
|
208 |
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- task:
|
209 |
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type: Clustering
|
210 |
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dataset:
|
211 |
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name: MTEB BiorxivClusteringS2S
|
212 |
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type: mteb/biorxiv-clustering-s2s
|
213 |
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config: default
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214 |
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split: test
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215 |
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
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216 |
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metrics:
|
217 |
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- type: v_measure
|
218 |
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value: 34.849509608178145
|
219 |
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- task:
|
220 |
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type: Retrieval
|
221 |
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dataset:
|
222 |
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name: MTEB CQADupstackAndroidRetrieval
|
223 |
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type: BeIR/cqadupstack
|
224 |
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config: default
|
225 |
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split: test
|
226 |
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revision: None
|
227 |
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metrics:
|
228 |
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- type: map_at_1
|
229 |
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value: 31.502999999999997
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230 |
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|
231 |
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value: 43.323
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232 |
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233 |
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value: 44.708999999999996
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value: 44.838
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value: 38.987
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238 |
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value: 41.516999999999996
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value: 38.769999999999996
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243 |
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value: 49.13
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244 |
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245 |
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value: 49.697
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246 |
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247 |
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value: 49.741
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248 |
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249 |
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value: 45.804
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251 |
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value: 47.842
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252 |
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253 |
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value: 38.769999999999996
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254 |
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value: 50.266999999999996
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value: 54.967
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258 |
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value: 56.976000000000006
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260 |
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261 |
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value: 43.823
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262 |
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263 |
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value: 47.12
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264 |
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265 |
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value: 38.769999999999996
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266 |
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267 |
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value: 10.057
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268 |
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269 |
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value: 1.554
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270 |
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271 |
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value: 0.202
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272 |
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273 |
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value: 21.125
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274 |
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275 |
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value: 15.851
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276 |
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277 |
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value: 31.502999999999997
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278 |
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279 |
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value: 63.715999999999994
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280 |
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value: 83.61800000000001
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283 |
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value: 96.63199999999999
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284 |
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285 |
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value: 45.403
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286 |
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value: 54.481
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288 |
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value: 27.833000000000002
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290 |
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value: 37.330999999999996
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value: 38.580999999999996
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value: 38.708
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296 |
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297 |
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value: 34.713
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298 |
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299 |
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value: 36.104
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300 |
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301 |
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value: 35.223
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302 |
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303 |
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value: 43.419000000000004
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304 |
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305 |
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306 |
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307 |
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value: 44.249
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308 |
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309 |
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value: 41.614000000000004
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310 |
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311 |
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value: 42.553000000000004
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312 |
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313 |
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314 |
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315 |
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316 |
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317 |
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318 |
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319 |
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320 |
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321 |
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value: 39.162
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322 |
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323 |
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value: 40.557
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324 |
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325 |
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value: 35.223
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326 |
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327 |
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value: 7.962
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328 |
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329 |
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value: 1.304
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330 |
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331 |
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value: 0.18
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332 |
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333 |
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value: 19.023
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334 |
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335 |
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336 |
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348 |
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350 |
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352 |
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357 |
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455 |
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458 |
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|
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460 |
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|
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value: 33.126
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|
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value: 40.073
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|
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value: 13.309999999999999
|
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472 |
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473 |
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|
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476 |
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|
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value: 17.954
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|
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value: 19.439
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481 |
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value: 16.294
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|
483 |
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value: 24.479
|
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812 |
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814 |
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816 |
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868 |
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869 |
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872 |
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874 |
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876 |
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877 |
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878 |
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879 |
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880 |
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881 |
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882 |
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883 |
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884 |
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885 |
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886 |
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887 |
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888 |
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889 |
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890 |
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894 |
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896 |
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897 |
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898 |
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900 |
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901 |
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value: 29.051
|
902 |
+
- type: mrr_at_10
|
903 |
+
value: 38.013000000000005
|
904 |
+
- type: mrr_at_100
|
905 |
+
value: 38.997
|
906 |
+
- type: mrr_at_1000
|
907 |
+
value: 39.055
|
908 |
+
- type: mrr_at_3
|
909 |
+
value: 34.947
|
910 |
+
- type: mrr_at_5
|
911 |
+
value: 36.815
|
912 |
+
- type: ndcg_at_1
|
913 |
+
value: 29.051
|
914 |
+
- type: ndcg_at_10
|
915 |
+
value: 39.361000000000004
|
916 |
+
- type: ndcg_at_100
|
917 |
+
value: 45.186
|
918 |
+
- type: ndcg_at_1000
|
919 |
+
value: 47.867
|
920 |
+
- type: ndcg_at_3
|
921 |
+
value: 33.797
|
922 |
+
- type: ndcg_at_5
|
923 |
+
value: 36.456
|
924 |
+
- type: precision_at_1
|
925 |
+
value: 29.051
|
926 |
+
- type: precision_at_10
|
927 |
+
value: 7.668
|
928 |
+
- type: precision_at_100
|
929 |
+
value: 1.532
|
930 |
+
- type: precision_at_1000
|
931 |
+
value: 0.247
|
932 |
+
- type: precision_at_3
|
933 |
+
value: 15.876000000000001
|
934 |
+
- type: precision_at_5
|
935 |
+
value: 11.779
|
936 |
+
- type: recall_at_1
|
937 |
+
value: 23.666
|
938 |
+
- type: recall_at_10
|
939 |
+
value: 51.858000000000004
|
940 |
+
- type: recall_at_100
|
941 |
+
value: 77.805
|
942 |
+
- type: recall_at_1000
|
943 |
+
value: 94.504
|
944 |
+
- type: recall_at_3
|
945 |
+
value: 36.207
|
946 |
+
- type: recall_at_5
|
947 |
+
value: 43.094
|
948 |
+
- type: map_at_1
|
949 |
+
value: 15.662
|
950 |
+
- type: map_at_10
|
951 |
+
value: 23.594
|
952 |
+
- type: map_at_100
|
953 |
+
value: 24.593999999999998
|
954 |
+
- type: map_at_1000
|
955 |
+
value: 24.694
|
956 |
+
- type: map_at_3
|
957 |
+
value: 20.925
|
958 |
+
- type: map_at_5
|
959 |
+
value: 22.817999999999998
|
960 |
+
- type: mrr_at_1
|
961 |
+
value: 17.375
|
962 |
+
- type: mrr_at_10
|
963 |
+
value: 25.734
|
964 |
+
- type: mrr_at_100
|
965 |
+
value: 26.586
|
966 |
+
- type: mrr_at_1000
|
967 |
+
value: 26.671
|
968 |
+
- type: mrr_at_3
|
969 |
+
value: 23.044
|
970 |
+
- type: mrr_at_5
|
971 |
+
value: 24.975
|
972 |
+
- type: ndcg_at_1
|
973 |
+
value: 17.375
|
974 |
+
- type: ndcg_at_10
|
975 |
+
value: 28.186
|
976 |
+
- type: ndcg_at_100
|
977 |
+
value: 33.436
|
978 |
+
- type: ndcg_at_1000
|
979 |
+
value: 36.203
|
980 |
+
- type: ndcg_at_3
|
981 |
+
value: 23.152
|
982 |
+
- type: ndcg_at_5
|
983 |
+
value: 26.397
|
984 |
+
- type: precision_at_1
|
985 |
+
value: 17.375
|
986 |
+
- type: precision_at_10
|
987 |
+
value: 4.677
|
988 |
+
- type: precision_at_100
|
989 |
+
value: 0.786
|
990 |
+
- type: precision_at_1000
|
991 |
+
value: 0.109
|
992 |
+
- type: precision_at_3
|
993 |
+
value: 10.351
|
994 |
+
- type: precision_at_5
|
995 |
+
value: 7.985
|
996 |
+
- type: recall_at_1
|
997 |
+
value: 15.662
|
998 |
+
- type: recall_at_10
|
999 |
+
value: 40.066
|
1000 |
+
- type: recall_at_100
|
1001 |
+
value: 65.006
|
1002 |
+
- type: recall_at_1000
|
1003 |
+
value: 85.94000000000001
|
1004 |
+
- type: recall_at_3
|
1005 |
+
value: 27.400000000000002
|
1006 |
+
- type: recall_at_5
|
1007 |
+
value: 35.002
|
1008 |
+
- task:
|
1009 |
+
type: Retrieval
|
1010 |
+
dataset:
|
1011 |
+
name: MTEB ClimateFEVER
|
1012 |
+
type: climate-fever
|
1013 |
+
config: default
|
1014 |
+
split: test
|
1015 |
+
revision: None
|
1016 |
+
metrics:
|
1017 |
+
- type: map_at_1
|
1018 |
+
value: 8.853
|
1019 |
+
- type: map_at_10
|
1020 |
+
value: 15.568000000000001
|
1021 |
+
- type: map_at_100
|
1022 |
+
value: 17.383000000000003
|
1023 |
+
- type: map_at_1000
|
1024 |
+
value: 17.584
|
1025 |
+
- type: map_at_3
|
1026 |
+
value: 12.561
|
1027 |
+
- type: map_at_5
|
1028 |
+
value: 14.056
|
1029 |
+
- type: mrr_at_1
|
1030 |
+
value: 18.958
|
1031 |
+
- type: mrr_at_10
|
1032 |
+
value: 28.288000000000004
|
1033 |
+
- type: mrr_at_100
|
1034 |
+
value: 29.432000000000002
|
1035 |
+
- type: mrr_at_1000
|
1036 |
+
value: 29.498
|
1037 |
+
- type: mrr_at_3
|
1038 |
+
value: 25.049
|
1039 |
+
- type: mrr_at_5
|
1040 |
+
value: 26.857
|
1041 |
+
- type: ndcg_at_1
|
1042 |
+
value: 18.958
|
1043 |
+
- type: ndcg_at_10
|
1044 |
+
value: 22.21
|
1045 |
+
- type: ndcg_at_100
|
1046 |
+
value: 29.596
|
1047 |
+
- type: ndcg_at_1000
|
1048 |
+
value: 33.583
|
1049 |
+
- type: ndcg_at_3
|
1050 |
+
value: 16.994999999999997
|
1051 |
+
- type: ndcg_at_5
|
1052 |
+
value: 18.95
|
1053 |
+
- type: precision_at_1
|
1054 |
+
value: 18.958
|
1055 |
+
- type: precision_at_10
|
1056 |
+
value: 7.192
|
1057 |
+
- type: precision_at_100
|
1058 |
+
value: 1.5
|
1059 |
+
- type: precision_at_1000
|
1060 |
+
value: 0.22399999999999998
|
1061 |
+
- type: precision_at_3
|
1062 |
+
value: 12.573
|
1063 |
+
- type: precision_at_5
|
1064 |
+
value: 10.202
|
1065 |
+
- type: recall_at_1
|
1066 |
+
value: 8.853
|
1067 |
+
- type: recall_at_10
|
1068 |
+
value: 28.087
|
1069 |
+
- type: recall_at_100
|
1070 |
+
value: 53.701
|
1071 |
+
- type: recall_at_1000
|
1072 |
+
value: 76.29899999999999
|
1073 |
+
- type: recall_at_3
|
1074 |
+
value: 15.913
|
1075 |
+
- type: recall_at_5
|
1076 |
+
value: 20.658
|
1077 |
+
- task:
|
1078 |
+
type: Retrieval
|
1079 |
+
dataset:
|
1080 |
+
name: MTEB DBPedia
|
1081 |
+
type: dbpedia-entity
|
1082 |
+
config: default
|
1083 |
+
split: test
|
1084 |
+
revision: None
|
1085 |
+
metrics:
|
1086 |
+
- type: map_at_1
|
1087 |
+
value: 9.077
|
1088 |
+
- type: map_at_10
|
1089 |
+
value: 20.788999999999998
|
1090 |
+
- type: map_at_100
|
1091 |
+
value: 30.429000000000002
|
1092 |
+
- type: map_at_1000
|
1093 |
+
value: 32.143
|
1094 |
+
- type: map_at_3
|
1095 |
+
value: 14.692
|
1096 |
+
- type: map_at_5
|
1097 |
+
value: 17.139
|
1098 |
+
- type: mrr_at_1
|
1099 |
+
value: 70.75
|
1100 |
+
- type: mrr_at_10
|
1101 |
+
value: 78.036
|
1102 |
+
- type: mrr_at_100
|
1103 |
+
value: 78.401
|
1104 |
+
- type: mrr_at_1000
|
1105 |
+
value: 78.404
|
1106 |
+
- type: mrr_at_3
|
1107 |
+
value: 76.75
|
1108 |
+
- type: mrr_at_5
|
1109 |
+
value: 77.47500000000001
|
1110 |
+
- type: ndcg_at_1
|
1111 |
+
value: 58.12500000000001
|
1112 |
+
- type: ndcg_at_10
|
1113 |
+
value: 44.015
|
1114 |
+
- type: ndcg_at_100
|
1115 |
+
value: 49.247
|
1116 |
+
- type: ndcg_at_1000
|
1117 |
+
value: 56.211999999999996
|
1118 |
+
- type: ndcg_at_3
|
1119 |
+
value: 49.151
|
1120 |
+
- type: ndcg_at_5
|
1121 |
+
value: 46.195
|
1122 |
+
- type: precision_at_1
|
1123 |
+
value: 70.75
|
1124 |
+
- type: precision_at_10
|
1125 |
+
value: 35.5
|
1126 |
+
- type: precision_at_100
|
1127 |
+
value: 11.355
|
1128 |
+
- type: precision_at_1000
|
1129 |
+
value: 2.1950000000000003
|
1130 |
+
- type: precision_at_3
|
1131 |
+
value: 53.083000000000006
|
1132 |
+
- type: precision_at_5
|
1133 |
+
value: 44.800000000000004
|
1134 |
+
- type: recall_at_1
|
1135 |
+
value: 9.077
|
1136 |
+
- type: recall_at_10
|
1137 |
+
value: 26.259
|
1138 |
+
- type: recall_at_100
|
1139 |
+
value: 56.547000000000004
|
1140 |
+
- type: recall_at_1000
|
1141 |
+
value: 78.551
|
1142 |
+
- type: recall_at_3
|
1143 |
+
value: 16.162000000000003
|
1144 |
+
- type: recall_at_5
|
1145 |
+
value: 19.753999999999998
|
1146 |
+
- task:
|
1147 |
+
type: Classification
|
1148 |
+
dataset:
|
1149 |
+
name: MTEB EmotionClassification
|
1150 |
+
type: mteb/emotion
|
1151 |
+
config: default
|
1152 |
+
split: test
|
1153 |
+
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
1154 |
+
metrics:
|
1155 |
+
- type: accuracy
|
1156 |
+
value: 49.44500000000001
|
1157 |
+
- type: f1
|
1158 |
+
value: 44.67067691783401
|
1159 |
+
- task:
|
1160 |
+
type: Retrieval
|
1161 |
+
dataset:
|
1162 |
+
name: MTEB FEVER
|
1163 |
+
type: fever
|
1164 |
+
config: default
|
1165 |
+
split: test
|
1166 |
+
revision: None
|
1167 |
+
metrics:
|
1168 |
+
- type: map_at_1
|
1169 |
+
value: 68.182
|
1170 |
+
- type: map_at_10
|
1171 |
+
value: 78.223
|
1172 |
+
- type: map_at_100
|
1173 |
+
value: 78.498
|
1174 |
+
- type: map_at_1000
|
1175 |
+
value: 78.512
|
1176 |
+
- type: map_at_3
|
1177 |
+
value: 76.71
|
1178 |
+
- type: map_at_5
|
1179 |
+
value: 77.725
|
1180 |
+
- type: mrr_at_1
|
1181 |
+
value: 73.177
|
1182 |
+
- type: mrr_at_10
|
1183 |
+
value: 82.513
|
1184 |
+
- type: mrr_at_100
|
1185 |
+
value: 82.633
|
1186 |
+
- type: mrr_at_1000
|
1187 |
+
value: 82.635
|
1188 |
+
- type: mrr_at_3
|
1189 |
+
value: 81.376
|
1190 |
+
- type: mrr_at_5
|
1191 |
+
value: 82.182
|
1192 |
+
- type: ndcg_at_1
|
1193 |
+
value: 73.177
|
1194 |
+
- type: ndcg_at_10
|
1195 |
+
value: 82.829
|
1196 |
+
- type: ndcg_at_100
|
1197 |
+
value: 83.84
|
1198 |
+
- type: ndcg_at_1000
|
1199 |
+
value: 84.07900000000001
|
1200 |
+
- type: ndcg_at_3
|
1201 |
+
value: 80.303
|
1202 |
+
- type: ndcg_at_5
|
1203 |
+
value: 81.846
|
1204 |
+
- type: precision_at_1
|
1205 |
+
value: 73.177
|
1206 |
+
- type: precision_at_10
|
1207 |
+
value: 10.241999999999999
|
1208 |
+
- type: precision_at_100
|
1209 |
+
value: 1.099
|
1210 |
+
- type: precision_at_1000
|
1211 |
+
value: 0.11399999999999999
|
1212 |
+
- type: precision_at_3
|
1213 |
+
value: 31.247999999999998
|
1214 |
+
- type: precision_at_5
|
1215 |
+
value: 19.697
|
1216 |
+
- type: recall_at_1
|
1217 |
+
value: 68.182
|
1218 |
+
- type: recall_at_10
|
1219 |
+
value: 92.657
|
1220 |
+
- type: recall_at_100
|
1221 |
+
value: 96.709
|
1222 |
+
- type: recall_at_1000
|
1223 |
+
value: 98.184
|
1224 |
+
- type: recall_at_3
|
1225 |
+
value: 85.9
|
1226 |
+
- type: recall_at_5
|
1227 |
+
value: 89.755
|
1228 |
+
- task:
|
1229 |
+
type: Retrieval
|
1230 |
+
dataset:
|
1231 |
+
name: MTEB FiQA2018
|
1232 |
+
type: fiqa
|
1233 |
+
config: default
|
1234 |
+
split: test
|
1235 |
+
revision: None
|
1236 |
+
metrics:
|
1237 |
+
- type: map_at_1
|
1238 |
+
value: 21.108
|
1239 |
+
- type: map_at_10
|
1240 |
+
value: 33.342
|
1241 |
+
- type: map_at_100
|
1242 |
+
value: 35.281
|
1243 |
+
- type: map_at_1000
|
1244 |
+
value: 35.478
|
1245 |
+
- type: map_at_3
|
1246 |
+
value: 29.067
|
1247 |
+
- type: map_at_5
|
1248 |
+
value: 31.563000000000002
|
1249 |
+
- type: mrr_at_1
|
1250 |
+
value: 41.667
|
1251 |
+
- type: mrr_at_10
|
1252 |
+
value: 49.913000000000004
|
1253 |
+
- type: mrr_at_100
|
1254 |
+
value: 50.724000000000004
|
1255 |
+
- type: mrr_at_1000
|
1256 |
+
value: 50.766
|
1257 |
+
- type: mrr_at_3
|
1258 |
+
value: 47.504999999999995
|
1259 |
+
- type: mrr_at_5
|
1260 |
+
value: 49.033
|
1261 |
+
- type: ndcg_at_1
|
1262 |
+
value: 41.667
|
1263 |
+
- type: ndcg_at_10
|
1264 |
+
value: 41.144
|
1265 |
+
- type: ndcg_at_100
|
1266 |
+
value: 48.326
|
1267 |
+
- type: ndcg_at_1000
|
1268 |
+
value: 51.486
|
1269 |
+
- type: ndcg_at_3
|
1270 |
+
value: 37.486999999999995
|
1271 |
+
- type: ndcg_at_5
|
1272 |
+
value: 38.78
|
1273 |
+
- type: precision_at_1
|
1274 |
+
value: 41.667
|
1275 |
+
- type: precision_at_10
|
1276 |
+
value: 11.358
|
1277 |
+
- type: precision_at_100
|
1278 |
+
value: 1.873
|
1279 |
+
- type: precision_at_1000
|
1280 |
+
value: 0.244
|
1281 |
+
- type: precision_at_3
|
1282 |
+
value: 25
|
1283 |
+
- type: precision_at_5
|
1284 |
+
value: 18.519
|
1285 |
+
- type: recall_at_1
|
1286 |
+
value: 21.108
|
1287 |
+
- type: recall_at_10
|
1288 |
+
value: 47.249
|
1289 |
+
- type: recall_at_100
|
1290 |
+
value: 74.52
|
1291 |
+
- type: recall_at_1000
|
1292 |
+
value: 93.31
|
1293 |
+
- type: recall_at_3
|
1294 |
+
value: 33.271
|
1295 |
+
- type: recall_at_5
|
1296 |
+
value: 39.723000000000006
|
1297 |
+
- task:
|
1298 |
+
type: Retrieval
|
1299 |
+
dataset:
|
1300 |
+
name: MTEB HotpotQA
|
1301 |
+
type: hotpotqa
|
1302 |
+
config: default
|
1303 |
+
split: test
|
1304 |
+
revision: None
|
1305 |
+
metrics:
|
1306 |
+
- type: map_at_1
|
1307 |
+
value: 40.317
|
1308 |
+
- type: map_at_10
|
1309 |
+
value: 64.861
|
1310 |
+
- type: map_at_100
|
1311 |
+
value: 65.697
|
1312 |
+
- type: map_at_1000
|
1313 |
+
value: 65.755
|
1314 |
+
- type: map_at_3
|
1315 |
+
value: 61.258
|
1316 |
+
- type: map_at_5
|
1317 |
+
value: 63.590999999999994
|
1318 |
+
- type: mrr_at_1
|
1319 |
+
value: 80.635
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1320 |
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|
1321 |
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value: 86.528
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1322 |
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- type: mrr_at_100
|
1323 |
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value: 86.66199999999999
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1324 |
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1325 |
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1326 |
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1327 |
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value: 85.744
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1328 |
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1329 |
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value: 86.24300000000001
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1330 |
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1331 |
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value: 80.635
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1332 |
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|
1333 |
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value: 73.13199999999999
|
1334 |
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|
1335 |
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value: 75.927
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1336 |
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|
1337 |
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value: 76.976
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1338 |
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- type: ndcg_at_3
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1339 |
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value: 68.241
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1340 |
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1341 |
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value: 71.071
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1342 |
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1343 |
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value: 80.635
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1344 |
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- type: precision_at_10
|
1345 |
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value: 15.326
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1346 |
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|
1347 |
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value: 1.7500000000000002
|
1348 |
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1349 |
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value: 0.189
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1350 |
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|
1351 |
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value: 43.961
|
1352 |
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- type: precision_at_5
|
1353 |
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value: 28.599999999999998
|
1354 |
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- type: recall_at_1
|
1355 |
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value: 40.317
|
1356 |
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- type: recall_at_10
|
1357 |
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value: 76.631
|
1358 |
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- type: recall_at_100
|
1359 |
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value: 87.495
|
1360 |
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- type: recall_at_1000
|
1361 |
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value: 94.362
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1362 |
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- type: recall_at_3
|
1363 |
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value: 65.94200000000001
|
1364 |
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- type: recall_at_5
|
1365 |
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value: 71.499
|
1366 |
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- task:
|
1367 |
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type: Classification
|
1368 |
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dataset:
|
1369 |
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name: MTEB ImdbClassification
|
1370 |
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type: mteb/imdb
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1371 |
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config: default
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1372 |
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split: test
|
1373 |
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revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
1374 |
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metrics:
|
1375 |
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- type: accuracy
|
1376 |
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value: 91.686
|
1377 |
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- type: ap
|
1378 |
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value: 87.5577120393173
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1379 |
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value: 91.6629447355139
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1381 |
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- task:
|
1382 |
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type: Retrieval
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1383 |
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dataset:
|
1384 |
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name: MTEB MSMARCO
|
1385 |
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type: msmarco
|
1386 |
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config: default
|
1387 |
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split: dev
|
1388 |
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revision: None
|
1389 |
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metrics:
|
1390 |
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- type: map_at_1
|
1391 |
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value: 23.702
|
1392 |
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- type: map_at_10
|
1393 |
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value: 36.414
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1394 |
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1395 |
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value: 37.561
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1396 |
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1397 |
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value: 37.605
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1398 |
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1399 |
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value: 32.456
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1400 |
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|
1401 |
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value: 34.827000000000005
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1402 |
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1403 |
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value: 24.355
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1404 |
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1405 |
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value: 37.01
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1406 |
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- type: mrr_at_100
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1407 |
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value: 38.085
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1408 |
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- type: mrr_at_1000
|
1409 |
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value: 38.123000000000005
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1410 |
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- type: mrr_at_3
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1411 |
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value: 33.117999999999995
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1412 |
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- type: mrr_at_5
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1413 |
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value: 35.452
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1414 |
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- type: ndcg_at_1
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1415 |
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value: 24.384
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1416 |
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- type: ndcg_at_10
|
1417 |
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value: 43.456
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1418 |
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- type: ndcg_at_100
|
1419 |
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value: 48.892
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1420 |
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- type: ndcg_at_1000
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1421 |
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value: 49.964
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1422 |
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- type: ndcg_at_3
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1423 |
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value: 35.475
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1424 |
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1425 |
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value: 39.711
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1426 |
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|
1427 |
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value: 24.384
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1428 |
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|
1429 |
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value: 6.7940000000000005
|
1430 |
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- type: precision_at_100
|
1431 |
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value: 0.951
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1432 |
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- type: precision_at_1000
|
1433 |
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value: 0.104
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1434 |
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- type: precision_at_3
|
1435 |
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value: 15.052999999999999
|
1436 |
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- type: precision_at_5
|
1437 |
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value: 11.189
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1438 |
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- type: recall_at_1
|
1439 |
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value: 23.702
|
1440 |
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- type: recall_at_10
|
1441 |
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value: 65.057
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1442 |
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- type: recall_at_100
|
1443 |
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value: 90.021
|
1444 |
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- type: recall_at_1000
|
1445 |
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value: 98.142
|
1446 |
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- type: recall_at_3
|
1447 |
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value: 43.551
|
1448 |
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- type: recall_at_5
|
1449 |
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value: 53.738
|
1450 |
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- task:
|
1451 |
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type: Classification
|
1452 |
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dataset:
|
1453 |
+
name: MTEB MTOPDomainClassification (en)
|
1454 |
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type: mteb/mtop_domain
|
1455 |
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config: en
|
1456 |
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split: test
|
1457 |
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revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
1458 |
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metrics:
|
1459 |
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- type: accuracy
|
1460 |
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value: 94.62380300957591
|
1461 |
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- type: f1
|
1462 |
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value: 94.49871222100734
|
1463 |
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- task:
|
1464 |
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type: Classification
|
1465 |
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dataset:
|
1466 |
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name: MTEB MTOPIntentClassification (en)
|
1467 |
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type: mteb/mtop_intent
|
1468 |
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config: en
|
1469 |
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split: test
|
1470 |
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revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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1471 |
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metrics:
|
1472 |
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- type: accuracy
|
1473 |
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value: 77.14090287277702
|
1474 |
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- type: f1
|
1475 |
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value: 60.32101258220515
|
1476 |
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- task:
|
1477 |
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type: Classification
|
1478 |
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dataset:
|
1479 |
+
name: MTEB MassiveIntentClassification (en)
|
1480 |
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type: mteb/amazon_massive_intent
|
1481 |
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config: en
|
1482 |
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split: test
|
1483 |
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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1484 |
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metrics:
|
1485 |
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- type: accuracy
|
1486 |
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value: 73.84330867518494
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1487 |
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- type: f1
|
1488 |
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value: 71.92248688515255
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1489 |
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- task:
|
1490 |
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type: Classification
|
1491 |
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dataset:
|
1492 |
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name: MTEB MassiveScenarioClassification (en)
|
1493 |
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type: mteb/amazon_massive_scenario
|
1494 |
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config: en
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1495 |
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split: test
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1496 |
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
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1497 |
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metrics:
|
1498 |
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- type: accuracy
|
1499 |
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value: 78.10692669804976
|
1500 |
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- type: f1
|
1501 |
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value: 77.9904839122866
|
1502 |
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- task:
|
1503 |
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type: Clustering
|
1504 |
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dataset:
|
1505 |
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name: MTEB MedrxivClusteringP2P
|
1506 |
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type: mteb/medrxiv-clustering-p2p
|
1507 |
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config: default
|
1508 |
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split: test
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1509 |
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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1510 |
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metrics:
|
1511 |
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- type: v_measure
|
1512 |
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value: 31.822988923078444
|
1513 |
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- task:
|
1514 |
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type: Clustering
|
1515 |
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dataset:
|
1516 |
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name: MTEB MedrxivClusteringS2S
|
1517 |
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type: mteb/medrxiv-clustering-s2s
|
1518 |
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config: default
|
1519 |
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split: test
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1520 |
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revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
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1521 |
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metrics:
|
1522 |
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- type: v_measure
|
1523 |
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value: 30.38394880253403
|
1524 |
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- task:
|
1525 |
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type: Reranking
|
1526 |
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dataset:
|
1527 |
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name: MTEB MindSmallReranking
|
1528 |
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type: mteb/mind_small
|
1529 |
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config: default
|
1530 |
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split: test
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1531 |
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revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
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1532 |
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metrics:
|
1533 |
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- type: map
|
1534 |
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value: 31.82504612539082
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1535 |
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- type: mrr
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1536 |
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value: 32.84462298174977
|
1537 |
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- task:
|
1538 |
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type: Retrieval
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1539 |
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dataset:
|
1540 |
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name: MTEB NFCorpus
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1541 |
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type: nfcorpus
|
1542 |
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config: default
|
1543 |
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split: test
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1544 |
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revision: None
|
1545 |
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metrics:
|
1546 |
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- type: map_at_1
|
1547 |
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value: 6.029
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1548 |
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- type: map_at_10
|
1549 |
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value: 14.088999999999999
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1550 |
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- type: map_at_100
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1551 |
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value: 17.601
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1552 |
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- type: map_at_1000
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1553 |
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value: 19.144
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1554 |
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- type: map_at_3
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1555 |
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value: 10.156
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1556 |
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1557 |
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value: 11.892
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1558 |
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1559 |
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value: 46.44
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1560 |
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1561 |
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value: 56.596999999999994
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1562 |
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1563 |
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value: 57.11000000000001
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1564 |
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1565 |
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value: 57.14
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1566 |
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1567 |
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value: 54.334
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1568 |
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1569 |
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value: 55.774
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1570 |
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1571 |
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1572 |
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1573 |
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value: 37.134
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1574 |
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1575 |
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value: 33.652
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1576 |
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1577 |
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value: 42.548
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1578 |
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1579 |
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value: 41.851
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1580 |
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1581 |
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value: 39.842
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1582 |
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- type: precision_at_1
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1583 |
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value: 46.44
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1584 |
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- type: precision_at_10
|
1585 |
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value: 27.647
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1586 |
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1587 |
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value: 8.309999999999999
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1588 |
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- type: precision_at_1000
|
1589 |
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value: 2.146
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1590 |
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- type: precision_at_3
|
1591 |
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value: 39.422000000000004
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1592 |
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- type: precision_at_5
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1593 |
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value: 34.675
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1594 |
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- type: recall_at_1
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1595 |
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value: 6.029
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1596 |
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- type: recall_at_10
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1597 |
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value: 18.907
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1598 |
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- type: recall_at_100
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1599 |
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value: 33.76
|
1600 |
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- type: recall_at_1000
|
1601 |
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value: 65.14999999999999
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1602 |
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- type: recall_at_3
|
1603 |
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value: 11.584999999999999
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1604 |
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- type: recall_at_5
|
1605 |
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value: 14.626
|
1606 |
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- task:
|
1607 |
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type: Retrieval
|
1608 |
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dataset:
|
1609 |
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name: MTEB NQ
|
1610 |
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type: nq
|
1611 |
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config: default
|
1612 |
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split: test
|
1613 |
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revision: None
|
1614 |
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metrics:
|
1615 |
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- type: map_at_1
|
1616 |
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value: 39.373000000000005
|
1617 |
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- type: map_at_10
|
1618 |
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value: 55.836
|
1619 |
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- type: map_at_100
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1620 |
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value: 56.611999999999995
|
1621 |
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1622 |
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value: 56.63
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1623 |
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1624 |
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value: 51.747
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1625 |
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1626 |
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value: 54.337999999999994
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1627 |
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1628 |
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value: 44.147999999999996
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1629 |
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|
1630 |
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value: 58.42699999999999
|
1631 |
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|
1632 |
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value: 58.902
|
1633 |
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- type: mrr_at_1000
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1634 |
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value: 58.914
|
1635 |
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|
1636 |
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value: 55.156000000000006
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1637 |
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|
1638 |
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value: 57.291000000000004
|
1639 |
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- type: ndcg_at_1
|
1640 |
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value: 44.119
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1641 |
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|
1642 |
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value: 63.444
|
1643 |
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- type: ndcg_at_100
|
1644 |
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value: 66.40599999999999
|
1645 |
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- type: ndcg_at_1000
|
1646 |
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value: 66.822
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1647 |
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- type: ndcg_at_3
|
1648 |
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value: 55.962
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1649 |
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- type: ndcg_at_5
|
1650 |
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value: 60.228
|
1651 |
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- type: precision_at_1
|
1652 |
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value: 44.119
|
1653 |
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- type: precision_at_10
|
1654 |
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value: 10.006
|
1655 |
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- type: precision_at_100
|
1656 |
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value: 1.17
|
1657 |
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- type: precision_at_1000
|
1658 |
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value: 0.121
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1659 |
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- type: precision_at_3
|
1660 |
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value: 25.135
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1661 |
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- type: precision_at_5
|
1662 |
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value: 17.59
|
1663 |
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- type: recall_at_1
|
1664 |
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value: 39.373000000000005
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1665 |
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- type: recall_at_10
|
1666 |
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value: 83.78999999999999
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1667 |
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- type: recall_at_100
|
1668 |
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value: 96.246
|
1669 |
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- type: recall_at_1000
|
1670 |
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value: 99.324
|
1671 |
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- type: recall_at_3
|
1672 |
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value: 64.71900000000001
|
1673 |
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- type: recall_at_5
|
1674 |
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value: 74.508
|
1675 |
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- task:
|
1676 |
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type: Retrieval
|
1677 |
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dataset:
|
1678 |
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name: MTEB QuoraRetrieval
|
1679 |
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type: quora
|
1680 |
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config: default
|
1681 |
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split: test
|
1682 |
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revision: None
|
1683 |
+
metrics:
|
1684 |
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- type: map_at_1
|
1685 |
+
value: 69.199
|
1686 |
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1687 |
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value: 82.892
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1688 |
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1689 |
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value: 83.578
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1690 |
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1691 |
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value: 83.598
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1692 |
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1693 |
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value: 79.948
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1694 |
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1695 |
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value: 81.779
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1696 |
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1697 |
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value: 79.67
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1698 |
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1699 |
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value: 86.115
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1700 |
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- type: mrr_at_100
|
1701 |
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value: 86.249
|
1702 |
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- type: mrr_at_1000
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1703 |
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value: 86.251
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1704 |
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- type: mrr_at_3
|
1705 |
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value: 85.08200000000001
|
1706 |
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- type: mrr_at_5
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1707 |
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value: 85.783
|
1708 |
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- type: ndcg_at_1
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1709 |
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value: 79.67
|
1710 |
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- type: ndcg_at_10
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1711 |
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value: 86.839
|
1712 |
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- type: ndcg_at_100
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1713 |
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value: 88.252
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1714 |
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- type: ndcg_at_1000
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1715 |
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value: 88.401
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1716 |
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- type: ndcg_at_3
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1717 |
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value: 83.86200000000001
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1718 |
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- type: ndcg_at_5
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1719 |
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value: 85.473
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1720 |
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- type: precision_at_1
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1721 |
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value: 79.67
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1722 |
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- type: precision_at_10
|
1723 |
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value: 13.19
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1724 |
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- type: precision_at_100
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1725 |
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value: 1.521
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1726 |
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- type: precision_at_1000
|
1727 |
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value: 0.157
|
1728 |
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- type: precision_at_3
|
1729 |
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value: 36.677
|
1730 |
+
- type: precision_at_5
|
1731 |
+
value: 24.118000000000002
|
1732 |
+
- type: recall_at_1
|
1733 |
+
value: 69.199
|
1734 |
+
- type: recall_at_10
|
1735 |
+
value: 94.321
|
1736 |
+
- type: recall_at_100
|
1737 |
+
value: 99.20400000000001
|
1738 |
+
- type: recall_at_1000
|
1739 |
+
value: 99.947
|
1740 |
+
- type: recall_at_3
|
1741 |
+
value: 85.787
|
1742 |
+
- type: recall_at_5
|
1743 |
+
value: 90.365
|
1744 |
+
- task:
|
1745 |
+
type: Clustering
|
1746 |
+
dataset:
|
1747 |
+
name: MTEB RedditClustering
|
1748 |
+
type: mteb/reddit-clustering
|
1749 |
+
config: default
|
1750 |
+
split: test
|
1751 |
+
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
1752 |
+
metrics:
|
1753 |
+
- type: v_measure
|
1754 |
+
value: 55.82810046856353
|
1755 |
+
- task:
|
1756 |
+
type: Clustering
|
1757 |
+
dataset:
|
1758 |
+
name: MTEB RedditClusteringP2P
|
1759 |
+
type: mteb/reddit-clustering-p2p
|
1760 |
+
config: default
|
1761 |
+
split: test
|
1762 |
+
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
1763 |
+
metrics:
|
1764 |
+
- type: v_measure
|
1765 |
+
value: 63.38132611783628
|
1766 |
+
- task:
|
1767 |
+
type: Retrieval
|
1768 |
+
dataset:
|
1769 |
+
name: MTEB SCIDOCS
|
1770 |
+
type: scidocs
|
1771 |
+
config: default
|
1772 |
+
split: test
|
1773 |
+
revision: None
|
1774 |
+
metrics:
|
1775 |
+
- type: map_at_1
|
1776 |
+
value: 5.127000000000001
|
1777 |
+
- type: map_at_10
|
1778 |
+
value: 12.235
|
1779 |
+
- type: map_at_100
|
1780 |
+
value: 14.417
|
1781 |
+
- type: map_at_1000
|
1782 |
+
value: 14.75
|
1783 |
+
- type: map_at_3
|
1784 |
+
value: 8.906
|
1785 |
+
- type: map_at_5
|
1786 |
+
value: 10.591000000000001
|
1787 |
+
- type: mrr_at_1
|
1788 |
+
value: 25.2
|
1789 |
+
- type: mrr_at_10
|
1790 |
+
value: 35.879
|
1791 |
+
- type: mrr_at_100
|
1792 |
+
value: 36.935
|
1793 |
+
- type: mrr_at_1000
|
1794 |
+
value: 36.997
|
1795 |
+
- type: mrr_at_3
|
1796 |
+
value: 32.783
|
1797 |
+
- type: mrr_at_5
|
1798 |
+
value: 34.367999999999995
|
1799 |
+
- type: ndcg_at_1
|
1800 |
+
value: 25.2
|
1801 |
+
- type: ndcg_at_10
|
1802 |
+
value: 20.509
|
1803 |
+
- type: ndcg_at_100
|
1804 |
+
value: 28.67
|
1805 |
+
- type: ndcg_at_1000
|
1806 |
+
value: 34.42
|
1807 |
+
- type: ndcg_at_3
|
1808 |
+
value: 19.948
|
1809 |
+
- type: ndcg_at_5
|
1810 |
+
value: 17.166
|
1811 |
+
- type: precision_at_1
|
1812 |
+
value: 25.2
|
1813 |
+
- type: precision_at_10
|
1814 |
+
value: 10.440000000000001
|
1815 |
+
- type: precision_at_100
|
1816 |
+
value: 2.214
|
1817 |
+
- type: precision_at_1000
|
1818 |
+
value: 0.359
|
1819 |
+
- type: precision_at_3
|
1820 |
+
value: 18.533
|
1821 |
+
- type: precision_at_5
|
1822 |
+
value: 14.860000000000001
|
1823 |
+
- type: recall_at_1
|
1824 |
+
value: 5.127000000000001
|
1825 |
+
- type: recall_at_10
|
1826 |
+
value: 21.147
|
1827 |
+
- type: recall_at_100
|
1828 |
+
value: 44.946999999999996
|
1829 |
+
- type: recall_at_1000
|
1830 |
+
value: 72.89
|
1831 |
+
- type: recall_at_3
|
1832 |
+
value: 11.277
|
1833 |
+
- type: recall_at_5
|
1834 |
+
value: 15.042
|
1835 |
+
- task:
|
1836 |
+
type: STS
|
1837 |
+
dataset:
|
1838 |
+
name: MTEB SICK-R
|
1839 |
+
type: mteb/sickr-sts
|
1840 |
+
config: default
|
1841 |
+
split: test
|
1842 |
+
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
1843 |
+
metrics:
|
1844 |
+
- type: cos_sim_pearson
|
1845 |
+
value: 83.0373011786213
|
1846 |
+
- type: cos_sim_spearman
|
1847 |
+
value: 79.27889560856613
|
1848 |
+
- type: euclidean_pearson
|
1849 |
+
value: 80.31186315495655
|
1850 |
+
- type: euclidean_spearman
|
1851 |
+
value: 79.41630415280811
|
1852 |
+
- type: manhattan_pearson
|
1853 |
+
value: 80.31755140442013
|
1854 |
+
- type: manhattan_spearman
|
1855 |
+
value: 79.43069870027611
|
1856 |
+
- task:
|
1857 |
+
type: STS
|
1858 |
+
dataset:
|
1859 |
+
name: MTEB STS12
|
1860 |
+
type: mteb/sts12-sts
|
1861 |
+
config: default
|
1862 |
+
split: test
|
1863 |
+
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
1864 |
+
metrics:
|
1865 |
+
- type: cos_sim_pearson
|
1866 |
+
value: 84.8659751342045
|
1867 |
+
- type: cos_sim_spearman
|
1868 |
+
value: 76.95377612997667
|
1869 |
+
- type: euclidean_pearson
|
1870 |
+
value: 81.24552945497848
|
1871 |
+
- type: euclidean_spearman
|
1872 |
+
value: 77.18236963555253
|
1873 |
+
- type: manhattan_pearson
|
1874 |
+
value: 81.26477607759037
|
1875 |
+
- type: manhattan_spearman
|
1876 |
+
value: 77.13821753062756
|
1877 |
+
- task:
|
1878 |
+
type: STS
|
1879 |
+
dataset:
|
1880 |
+
name: MTEB STS13
|
1881 |
+
type: mteb/sts13-sts
|
1882 |
+
config: default
|
1883 |
+
split: test
|
1884 |
+
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
1885 |
+
metrics:
|
1886 |
+
- type: cos_sim_pearson
|
1887 |
+
value: 83.34597139044875
|
1888 |
+
- type: cos_sim_spearman
|
1889 |
+
value: 84.124169425592
|
1890 |
+
- type: euclidean_pearson
|
1891 |
+
value: 83.68590721511401
|
1892 |
+
- type: euclidean_spearman
|
1893 |
+
value: 84.18846190846398
|
1894 |
+
- type: manhattan_pearson
|
1895 |
+
value: 83.57630235061498
|
1896 |
+
- type: manhattan_spearman
|
1897 |
+
value: 84.10244043726902
|
1898 |
+
- task:
|
1899 |
+
type: STS
|
1900 |
+
dataset:
|
1901 |
+
name: MTEB STS14
|
1902 |
+
type: mteb/sts14-sts
|
1903 |
+
config: default
|
1904 |
+
split: test
|
1905 |
+
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
1906 |
+
metrics:
|
1907 |
+
- type: cos_sim_pearson
|
1908 |
+
value: 82.67641885599572
|
1909 |
+
- type: cos_sim_spearman
|
1910 |
+
value: 80.46450725650428
|
1911 |
+
- type: euclidean_pearson
|
1912 |
+
value: 81.61645042715865
|
1913 |
+
- type: euclidean_spearman
|
1914 |
+
value: 80.61418394236874
|
1915 |
+
- type: manhattan_pearson
|
1916 |
+
value: 81.55712034928871
|
1917 |
+
- type: manhattan_spearman
|
1918 |
+
value: 80.57905670523951
|
1919 |
+
- task:
|
1920 |
+
type: STS
|
1921 |
+
dataset:
|
1922 |
+
name: MTEB STS15
|
1923 |
+
type: mteb/sts15-sts
|
1924 |
+
config: default
|
1925 |
+
split: test
|
1926 |
+
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
1927 |
+
metrics:
|
1928 |
+
- type: cos_sim_pearson
|
1929 |
+
value: 88.86650310886782
|
1930 |
+
- type: cos_sim_spearman
|
1931 |
+
value: 89.76081629222328
|
1932 |
+
- type: euclidean_pearson
|
1933 |
+
value: 89.1530747029954
|
1934 |
+
- type: euclidean_spearman
|
1935 |
+
value: 89.80990657280248
|
1936 |
+
- type: manhattan_pearson
|
1937 |
+
value: 89.10640563278132
|
1938 |
+
- type: manhattan_spearman
|
1939 |
+
value: 89.76282108434047
|
1940 |
+
- task:
|
1941 |
+
type: STS
|
1942 |
+
dataset:
|
1943 |
+
name: MTEB STS16
|
1944 |
+
type: mteb/sts16-sts
|
1945 |
+
config: default
|
1946 |
+
split: test
|
1947 |
+
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
1948 |
+
metrics:
|
1949 |
+
- type: cos_sim_pearson
|
1950 |
+
value: 83.93864027911118
|
1951 |
+
- type: cos_sim_spearman
|
1952 |
+
value: 85.47096193999023
|
1953 |
+
- type: euclidean_pearson
|
1954 |
+
value: 85.03141840870533
|
1955 |
+
- type: euclidean_spearman
|
1956 |
+
value: 85.43124029598181
|
1957 |
+
- type: manhattan_pearson
|
1958 |
+
value: 84.99002664393512
|
1959 |
+
- type: manhattan_spearman
|
1960 |
+
value: 85.39169195120834
|
1961 |
+
- task:
|
1962 |
+
type: STS
|
1963 |
+
dataset:
|
1964 |
+
name: MTEB STS17 (en-en)
|
1965 |
+
type: mteb/sts17-crosslingual-sts
|
1966 |
+
config: en-en
|
1967 |
+
split: test
|
1968 |
+
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
1969 |
+
metrics:
|
1970 |
+
- type: cos_sim_pearson
|
1971 |
+
value: 88.7045343749832
|
1972 |
+
- type: cos_sim_spearman
|
1973 |
+
value: 89.03262221146677
|
1974 |
+
- type: euclidean_pearson
|
1975 |
+
value: 89.56078218264365
|
1976 |
+
- type: euclidean_spearman
|
1977 |
+
value: 89.17827006466868
|
1978 |
+
- type: manhattan_pearson
|
1979 |
+
value: 89.52717595468582
|
1980 |
+
- type: manhattan_spearman
|
1981 |
+
value: 89.15878115952923
|
1982 |
+
- task:
|
1983 |
+
type: STS
|
1984 |
+
dataset:
|
1985 |
+
name: MTEB STS22 (en)
|
1986 |
+
type: mteb/sts22-crosslingual-sts
|
1987 |
+
config: en
|
1988 |
+
split: test
|
1989 |
+
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
1990 |
+
metrics:
|
1991 |
+
- type: cos_sim_pearson
|
1992 |
+
value: 64.20191302875551
|
1993 |
+
- type: cos_sim_spearman
|
1994 |
+
value: 64.11446552557646
|
1995 |
+
- type: euclidean_pearson
|
1996 |
+
value: 64.6918197393619
|
1997 |
+
- type: euclidean_spearman
|
1998 |
+
value: 63.440182631197764
|
1999 |
+
- type: manhattan_pearson
|
2000 |
+
value: 64.55692904121835
|
2001 |
+
- type: manhattan_spearman
|
2002 |
+
value: 63.424877742756266
|
2003 |
+
- task:
|
2004 |
+
type: STS
|
2005 |
+
dataset:
|
2006 |
+
name: MTEB STSBenchmark
|
2007 |
+
type: mteb/stsbenchmark-sts
|
2008 |
+
config: default
|
2009 |
+
split: test
|
2010 |
+
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
2011 |
+
metrics:
|
2012 |
+
- type: cos_sim_pearson
|
2013 |
+
value: 86.37793104662344
|
2014 |
+
- type: cos_sim_spearman
|
2015 |
+
value: 87.7357802629067
|
2016 |
+
- type: euclidean_pearson
|
2017 |
+
value: 87.4286301545109
|
2018 |
+
- type: euclidean_spearman
|
2019 |
+
value: 87.78452920777421
|
2020 |
+
- type: manhattan_pearson
|
2021 |
+
value: 87.42445169331255
|
2022 |
+
- type: manhattan_spearman
|
2023 |
+
value: 87.78537677249598
|
2024 |
+
- task:
|
2025 |
+
type: Reranking
|
2026 |
+
dataset:
|
2027 |
+
name: MTEB SciDocsRR
|
2028 |
+
type: mteb/scidocs-reranking
|
2029 |
+
config: default
|
2030 |
+
split: test
|
2031 |
+
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
2032 |
+
metrics:
|
2033 |
+
- type: map
|
2034 |
+
value: 84.31465405081792
|
2035 |
+
- type: mrr
|
2036 |
+
value: 95.7173781193389
|
2037 |
+
- task:
|
2038 |
+
type: Retrieval
|
2039 |
+
dataset:
|
2040 |
+
name: MTEB SciFact
|
2041 |
+
type: scifact
|
2042 |
+
config: default
|
2043 |
+
split: test
|
2044 |
+
revision: None
|
2045 |
+
metrics:
|
2046 |
+
- type: map_at_1
|
2047 |
+
value: 57.760999999999996
|
2048 |
+
- type: map_at_10
|
2049 |
+
value: 67.904
|
2050 |
+
- type: map_at_100
|
2051 |
+
value: 68.539
|
2052 |
+
- type: map_at_1000
|
2053 |
+
value: 68.562
|
2054 |
+
- type: map_at_3
|
2055 |
+
value: 65.415
|
2056 |
+
- type: map_at_5
|
2057 |
+
value: 66.788
|
2058 |
+
- type: mrr_at_1
|
2059 |
+
value: 60.333000000000006
|
2060 |
+
- type: mrr_at_10
|
2061 |
+
value: 68.797
|
2062 |
+
- type: mrr_at_100
|
2063 |
+
value: 69.236
|
2064 |
+
- type: mrr_at_1000
|
2065 |
+
value: 69.257
|
2066 |
+
- type: mrr_at_3
|
2067 |
+
value: 66.667
|
2068 |
+
- type: mrr_at_5
|
2069 |
+
value: 67.967
|
2070 |
+
- type: ndcg_at_1
|
2071 |
+
value: 60.333000000000006
|
2072 |
+
- type: ndcg_at_10
|
2073 |
+
value: 72.24199999999999
|
2074 |
+
- type: ndcg_at_100
|
2075 |
+
value: 74.86
|
2076 |
+
- type: ndcg_at_1000
|
2077 |
+
value: 75.354
|
2078 |
+
- type: ndcg_at_3
|
2079 |
+
value: 67.93400000000001
|
2080 |
+
- type: ndcg_at_5
|
2081 |
+
value: 70.02199999999999
|
2082 |
+
- type: precision_at_1
|
2083 |
+
value: 60.333000000000006
|
2084 |
+
- type: precision_at_10
|
2085 |
+
value: 9.533
|
2086 |
+
- type: precision_at_100
|
2087 |
+
value: 1.09
|
2088 |
+
- type: precision_at_1000
|
2089 |
+
value: 0.11299999999999999
|
2090 |
+
- type: precision_at_3
|
2091 |
+
value: 26.778000000000002
|
2092 |
+
- type: precision_at_5
|
2093 |
+
value: 17.467
|
2094 |
+
- type: recall_at_1
|
2095 |
+
value: 57.760999999999996
|
2096 |
+
- type: recall_at_10
|
2097 |
+
value: 84.383
|
2098 |
+
- type: recall_at_100
|
2099 |
+
value: 96.267
|
2100 |
+
- type: recall_at_1000
|
2101 |
+
value: 100
|
2102 |
+
- type: recall_at_3
|
2103 |
+
value: 72.628
|
2104 |
+
- type: recall_at_5
|
2105 |
+
value: 78.094
|
2106 |
+
- task:
|
2107 |
+
type: PairClassification
|
2108 |
+
dataset:
|
2109 |
+
name: MTEB SprintDuplicateQuestions
|
2110 |
+
type: mteb/sprintduplicatequestions-pairclassification
|
2111 |
+
config: default
|
2112 |
+
split: test
|
2113 |
+
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
2114 |
+
metrics:
|
2115 |
+
- type: cos_sim_accuracy
|
2116 |
+
value: 99.8029702970297
|
2117 |
+
- type: cos_sim_ap
|
2118 |
+
value: 94.9210324173411
|
2119 |
+
- type: cos_sim_f1
|
2120 |
+
value: 89.8521162672106
|
2121 |
+
- type: cos_sim_precision
|
2122 |
+
value: 91.67533818938605
|
2123 |
+
- type: cos_sim_recall
|
2124 |
+
value: 88.1
|
2125 |
+
- type: dot_accuracy
|
2126 |
+
value: 99.69504950495049
|
2127 |
+
- type: dot_ap
|
2128 |
+
value: 90.4919719146181
|
2129 |
+
- type: dot_f1
|
2130 |
+
value: 84.72289156626506
|
2131 |
+
- type: dot_precision
|
2132 |
+
value: 81.76744186046511
|
2133 |
+
- type: dot_recall
|
2134 |
+
value: 87.9
|
2135 |
+
- type: euclidean_accuracy
|
2136 |
+
value: 99.79702970297029
|
2137 |
+
- type: euclidean_ap
|
2138 |
+
value: 94.87827463795753
|
2139 |
+
- type: euclidean_f1
|
2140 |
+
value: 89.55680081507896
|
2141 |
+
- type: euclidean_precision
|
2142 |
+
value: 91.27725856697819
|
2143 |
+
- type: euclidean_recall
|
2144 |
+
value: 87.9
|
2145 |
+
- type: manhattan_accuracy
|
2146 |
+
value: 99.7990099009901
|
2147 |
+
- type: manhattan_ap
|
2148 |
+
value: 94.87587025149682
|
2149 |
+
- type: manhattan_f1
|
2150 |
+
value: 89.76298537569339
|
2151 |
+
- type: manhattan_precision
|
2152 |
+
value: 90.53916581892166
|
2153 |
+
- type: manhattan_recall
|
2154 |
+
value: 89
|
2155 |
+
- type: max_accuracy
|
2156 |
+
value: 99.8029702970297
|
2157 |
+
- type: max_ap
|
2158 |
+
value: 94.9210324173411
|
2159 |
+
- type: max_f1
|
2160 |
+
value: 89.8521162672106
|
2161 |
+
- task:
|
2162 |
+
type: Clustering
|
2163 |
+
dataset:
|
2164 |
+
name: MTEB StackExchangeClustering
|
2165 |
+
type: mteb/stackexchange-clustering
|
2166 |
+
config: default
|
2167 |
+
split: test
|
2168 |
+
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
2169 |
+
metrics:
|
2170 |
+
- type: v_measure
|
2171 |
+
value: 65.92385753948724
|
2172 |
+
- task:
|
2173 |
+
type: Clustering
|
2174 |
+
dataset:
|
2175 |
+
name: MTEB StackExchangeClusteringP2P
|
2176 |
+
type: mteb/stackexchange-clustering-p2p
|
2177 |
+
config: default
|
2178 |
+
split: test
|
2179 |
+
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
2180 |
+
metrics:
|
2181 |
+
- type: v_measure
|
2182 |
+
value: 33.671756975431144
|
2183 |
+
- task:
|
2184 |
+
type: Reranking
|
2185 |
+
dataset:
|
2186 |
+
name: MTEB StackOverflowDupQuestions
|
2187 |
+
type: mteb/stackoverflowdupquestions-reranking
|
2188 |
+
config: default
|
2189 |
+
split: test
|
2190 |
+
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
2191 |
+
metrics:
|
2192 |
+
- type: map
|
2193 |
+
value: 50.677928036739004
|
2194 |
+
- type: mrr
|
2195 |
+
value: 51.56413133435193
|
2196 |
+
- task:
|
2197 |
+
type: Summarization
|
2198 |
+
dataset:
|
2199 |
+
name: MTEB SummEval
|
2200 |
+
type: mteb/summeval
|
2201 |
+
config: default
|
2202 |
+
split: test
|
2203 |
+
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
2204 |
+
metrics:
|
2205 |
+
- type: cos_sim_pearson
|
2206 |
+
value: 30.523589340819683
|
2207 |
+
- type: cos_sim_spearman
|
2208 |
+
value: 30.187407518823235
|
2209 |
+
- type: dot_pearson
|
2210 |
+
value: 29.039713969699015
|
2211 |
+
- type: dot_spearman
|
2212 |
+
value: 29.114740651155508
|
2213 |
+
- task:
|
2214 |
+
type: Retrieval
|
2215 |
+
dataset:
|
2216 |
+
name: MTEB TRECCOVID
|
2217 |
+
type: trec-covid
|
2218 |
+
config: default
|
2219 |
+
split: test
|
2220 |
+
revision: None
|
2221 |
+
metrics:
|
2222 |
+
- type: map_at_1
|
2223 |
+
value: 0.211
|
2224 |
+
- type: map_at_10
|
2225 |
+
value: 1.6199999999999999
|
2226 |
+
- type: map_at_100
|
2227 |
+
value: 8.658000000000001
|
2228 |
+
- type: map_at_1000
|
2229 |
+
value: 21.538
|
2230 |
+
- type: map_at_3
|
2231 |
+
value: 0.575
|
2232 |
+
- type: map_at_5
|
2233 |
+
value: 0.919
|
2234 |
+
- type: mrr_at_1
|
2235 |
+
value: 78
|
2236 |
+
- type: mrr_at_10
|
2237 |
+
value: 86.18599999999999
|
2238 |
+
- type: mrr_at_100
|
2239 |
+
value: 86.18599999999999
|
2240 |
+
- type: mrr_at_1000
|
2241 |
+
value: 86.18599999999999
|
2242 |
+
- type: mrr_at_3
|
2243 |
+
value: 85
|
2244 |
+
- type: mrr_at_5
|
2245 |
+
value: 85.9
|
2246 |
+
- type: ndcg_at_1
|
2247 |
+
value: 74
|
2248 |
+
- type: ndcg_at_10
|
2249 |
+
value: 66.542
|
2250 |
+
- type: ndcg_at_100
|
2251 |
+
value: 50.163999999999994
|
2252 |
+
- type: ndcg_at_1000
|
2253 |
+
value: 45.696999999999996
|
2254 |
+
- type: ndcg_at_3
|
2255 |
+
value: 71.531
|
2256 |
+
- type: ndcg_at_5
|
2257 |
+
value: 70.45
|
2258 |
+
- type: precision_at_1
|
2259 |
+
value: 78
|
2260 |
+
- type: precision_at_10
|
2261 |
+
value: 69.39999999999999
|
2262 |
+
- type: precision_at_100
|
2263 |
+
value: 51.06
|
2264 |
+
- type: precision_at_1000
|
2265 |
+
value: 20.022000000000002
|
2266 |
+
- type: precision_at_3
|
2267 |
+
value: 76
|
2268 |
+
- type: precision_at_5
|
2269 |
+
value: 74.8
|
2270 |
+
- type: recall_at_1
|
2271 |
+
value: 0.211
|
2272 |
+
- type: recall_at_10
|
2273 |
+
value: 1.813
|
2274 |
+
- type: recall_at_100
|
2275 |
+
value: 12.098
|
2276 |
+
- type: recall_at_1000
|
2277 |
+
value: 42.618
|
2278 |
+
- type: recall_at_3
|
2279 |
+
value: 0.603
|
2280 |
+
- type: recall_at_5
|
2281 |
+
value: 0.987
|
2282 |
+
- task:
|
2283 |
+
type: Retrieval
|
2284 |
+
dataset:
|
2285 |
+
name: MTEB Touche2020
|
2286 |
+
type: webis-touche2020
|
2287 |
+
config: default
|
2288 |
+
split: test
|
2289 |
+
revision: None
|
2290 |
+
metrics:
|
2291 |
+
- type: map_at_1
|
2292 |
+
value: 2.2079999999999997
|
2293 |
+
- type: map_at_10
|
2294 |
+
value: 7.777000000000001
|
2295 |
+
- type: map_at_100
|
2296 |
+
value: 12.825000000000001
|
2297 |
+
- type: map_at_1000
|
2298 |
+
value: 14.196
|
2299 |
+
- type: map_at_3
|
2300 |
+
value: 4.285
|
2301 |
+
- type: map_at_5
|
2302 |
+
value: 6.177
|
2303 |
+
- type: mrr_at_1
|
2304 |
+
value: 30.612000000000002
|
2305 |
+
- type: mrr_at_10
|
2306 |
+
value: 42.635
|
2307 |
+
- type: mrr_at_100
|
2308 |
+
value: 43.955
|
2309 |
+
- type: mrr_at_1000
|
2310 |
+
value: 43.955
|
2311 |
+
- type: mrr_at_3
|
2312 |
+
value: 38.435
|
2313 |
+
- type: mrr_at_5
|
2314 |
+
value: 41.088
|
2315 |
+
- type: ndcg_at_1
|
2316 |
+
value: 28.571
|
2317 |
+
- type: ndcg_at_10
|
2318 |
+
value: 20.666999999999998
|
2319 |
+
- type: ndcg_at_100
|
2320 |
+
value: 31.840000000000003
|
2321 |
+
- type: ndcg_at_1000
|
2322 |
+
value: 43.191
|
2323 |
+
- type: ndcg_at_3
|
2324 |
+
value: 23.45
|
2325 |
+
- type: ndcg_at_5
|
2326 |
+
value: 22.994
|
2327 |
+
- type: precision_at_1
|
2328 |
+
value: 30.612000000000002
|
2329 |
+
- type: precision_at_10
|
2330 |
+
value: 17.959
|
2331 |
+
- type: precision_at_100
|
2332 |
+
value: 6.755
|
2333 |
+
- type: precision_at_1000
|
2334 |
+
value: 1.4200000000000002
|
2335 |
+
- type: precision_at_3
|
2336 |
+
value: 23.810000000000002
|
2337 |
+
- type: precision_at_5
|
2338 |
+
value: 23.673
|
2339 |
+
- type: recall_at_1
|
2340 |
+
value: 2.2079999999999997
|
2341 |
+
- type: recall_at_10
|
2342 |
+
value: 13.144
|
2343 |
+
- type: recall_at_100
|
2344 |
+
value: 42.491
|
2345 |
+
- type: recall_at_1000
|
2346 |
+
value: 77.04299999999999
|
2347 |
+
- type: recall_at_3
|
2348 |
+
value: 5.3469999999999995
|
2349 |
+
- type: recall_at_5
|
2350 |
+
value: 9.139
|
2351 |
+
- task:
|
2352 |
+
type: Classification
|
2353 |
+
dataset:
|
2354 |
+
name: MTEB ToxicConversationsClassification
|
2355 |
+
type: mteb/toxic_conversations_50k
|
2356 |
+
config: default
|
2357 |
+
split: test
|
2358 |
+
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
2359 |
+
metrics:
|
2360 |
+
- type: accuracy
|
2361 |
+
value: 70.9044
|
2362 |
+
- type: ap
|
2363 |
+
value: 14.625783489340755
|
2364 |
+
- type: f1
|
2365 |
+
value: 54.814936562590546
|
2366 |
+
- task:
|
2367 |
+
type: Classification
|
2368 |
+
dataset:
|
2369 |
+
name: MTEB TweetSentimentExtractionClassification
|
2370 |
+
type: mteb/tweet_sentiment_extraction
|
2371 |
+
config: default
|
2372 |
+
split: test
|
2373 |
+
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
2374 |
+
metrics:
|
2375 |
+
- type: accuracy
|
2376 |
+
value: 60.94227504244483
|
2377 |
+
- type: f1
|
2378 |
+
value: 61.22516038508854
|
2379 |
+
- task:
|
2380 |
+
type: Clustering
|
2381 |
+
dataset:
|
2382 |
+
name: MTEB TwentyNewsgroupsClustering
|
2383 |
+
type: mteb/twentynewsgroups-clustering
|
2384 |
+
config: default
|
2385 |
+
split: test
|
2386 |
+
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
2387 |
+
metrics:
|
2388 |
+
- type: v_measure
|
2389 |
+
value: 49.602409155145864
|
2390 |
+
- task:
|
2391 |
+
type: PairClassification
|
2392 |
+
dataset:
|
2393 |
+
name: MTEB TwitterSemEval2015
|
2394 |
+
type: mteb/twittersemeval2015-pairclassification
|
2395 |
+
config: default
|
2396 |
+
split: test
|
2397 |
+
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
2398 |
+
metrics:
|
2399 |
+
- type: cos_sim_accuracy
|
2400 |
+
value: 86.94641473445789
|
2401 |
+
- type: cos_sim_ap
|
2402 |
+
value: 76.91572747061197
|
2403 |
+
- type: cos_sim_f1
|
2404 |
+
value: 70.14348097317529
|
2405 |
+
- type: cos_sim_precision
|
2406 |
+
value: 66.53254437869822
|
2407 |
+
- type: cos_sim_recall
|
2408 |
+
value: 74.1688654353562
|
2409 |
+
- type: dot_accuracy
|
2410 |
+
value: 84.80061989628658
|
2411 |
+
- type: dot_ap
|
2412 |
+
value: 70.7952548895177
|
2413 |
+
- type: dot_f1
|
2414 |
+
value: 65.44780728844965
|
2415 |
+
- type: dot_precision
|
2416 |
+
value: 61.53310104529617
|
2417 |
+
- type: dot_recall
|
2418 |
+
value: 69.89445910290237
|
2419 |
+
- type: euclidean_accuracy
|
2420 |
+
value: 86.94641473445789
|
2421 |
+
- type: euclidean_ap
|
2422 |
+
value: 76.80774009393652
|
2423 |
+
- type: euclidean_f1
|
2424 |
+
value: 70.30522503879979
|
2425 |
+
- type: euclidean_precision
|
2426 |
+
value: 68.94977168949772
|
2427 |
+
- type: euclidean_recall
|
2428 |
+
value: 71.71503957783642
|
2429 |
+
- type: manhattan_accuracy
|
2430 |
+
value: 86.8629671574179
|
2431 |
+
- type: manhattan_ap
|
2432 |
+
value: 76.76518632600317
|
2433 |
+
- type: manhattan_f1
|
2434 |
+
value: 70.16056518946692
|
2435 |
+
- type: manhattan_precision
|
2436 |
+
value: 68.360450563204
|
2437 |
+
- type: manhattan_recall
|
2438 |
+
value: 72.0580474934037
|
2439 |
+
- type: max_accuracy
|
2440 |
+
value: 86.94641473445789
|
2441 |
+
- type: max_ap
|
2442 |
+
value: 76.91572747061197
|
2443 |
+
- type: max_f1
|
2444 |
+
value: 70.30522503879979
|
2445 |
+
- task:
|
2446 |
+
type: PairClassification
|
2447 |
+
dataset:
|
2448 |
+
name: MTEB TwitterURLCorpus
|
2449 |
+
type: mteb/twitterurlcorpus-pairclassification
|
2450 |
+
config: default
|
2451 |
+
split: test
|
2452 |
+
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
2453 |
+
metrics:
|
2454 |
+
- type: cos_sim_accuracy
|
2455 |
+
value: 89.10428066907285
|
2456 |
+
- type: cos_sim_ap
|
2457 |
+
value: 86.25114759921435
|
2458 |
+
- type: cos_sim_f1
|
2459 |
+
value: 78.37857884586856
|
2460 |
+
- type: cos_sim_precision
|
2461 |
+
value: 75.60818546078993
|
2462 |
+
- type: cos_sim_recall
|
2463 |
+
value: 81.35971666153372
|
2464 |
+
- type: dot_accuracy
|
2465 |
+
value: 87.41995575736406
|
2466 |
+
- type: dot_ap
|
2467 |
+
value: 81.51838010086782
|
2468 |
+
- type: dot_f1
|
2469 |
+
value: 74.77398015435503
|
2470 |
+
- type: dot_precision
|
2471 |
+
value: 71.53002390662354
|
2472 |
+
- type: dot_recall
|
2473 |
+
value: 78.32614721281182
|
2474 |
+
- type: euclidean_accuracy
|
2475 |
+
value: 89.12368533395428
|
2476 |
+
- type: euclidean_ap
|
2477 |
+
value: 86.33456799874504
|
2478 |
+
- type: euclidean_f1
|
2479 |
+
value: 78.45496750232127
|
2480 |
+
- type: euclidean_precision
|
2481 |
+
value: 75.78388462366364
|
2482 |
+
- type: euclidean_recall
|
2483 |
+
value: 81.32121958731136
|
2484 |
+
- type: manhattan_accuracy
|
2485 |
+
value: 89.10622113556099
|
2486 |
+
- type: manhattan_ap
|
2487 |
+
value: 86.31215061745333
|
2488 |
+
- type: manhattan_f1
|
2489 |
+
value: 78.40684906011539
|
2490 |
+
- type: manhattan_precision
|
2491 |
+
value: 75.89536643366722
|
2492 |
+
- type: manhattan_recall
|
2493 |
+
value: 81.09023714197721
|
2494 |
+
- type: max_accuracy
|
2495 |
+
value: 89.12368533395428
|
2496 |
+
- type: max_ap
|
2497 |
+
value: 86.33456799874504
|
2498 |
+
- type: max_f1
|
2499 |
+
value: 78.45496750232127
|
2500 |
+
---
|
2501 |
+
|
2502 |
+
# Pekarnick/e5-large-v2-Q4_K_M-GGUF
|
2503 |
+
This model was converted to GGUF format from [`intfloat/e5-large-v2`](https://huggingface.co/intfloat/e5-large-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
|
2504 |
+
Refer to the [original model card](https://huggingface.co/intfloat/e5-large-v2) for more details on the model.
|
2505 |
+
|
2506 |
+
## Use with llama.cpp
|
2507 |
+
Install llama.cpp through brew (works on Mac and Linux)
|
2508 |
+
|
2509 |
+
```bash
|
2510 |
+
brew install llama.cpp
|
2511 |
+
|
2512 |
+
```
|
2513 |
+
Invoke the llama.cpp server or the CLI.
|
2514 |
+
|
2515 |
+
### CLI:
|
2516 |
+
```bash
|
2517 |
+
llama-cli --hf-repo Pekarnick/e5-large-v2-Q4_K_M-GGUF --hf-file e5-large-v2-q4_k_m.gguf -p "The meaning to life and the universe is"
|
2518 |
+
```
|
2519 |
+
|
2520 |
+
### Server:
|
2521 |
+
```bash
|
2522 |
+
llama-server --hf-repo Pekarnick/e5-large-v2-Q4_K_M-GGUF --hf-file e5-large-v2-q4_k_m.gguf -c 2048
|
2523 |
+
```
|
2524 |
+
|
2525 |
+
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.
|
2526 |
+
|
2527 |
+
Step 1: Clone llama.cpp from GitHub.
|
2528 |
+
```
|
2529 |
+
git clone https://github.com/ggerganov/llama.cpp
|
2530 |
+
```
|
2531 |
+
|
2532 |
+
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).
|
2533 |
+
```
|
2534 |
+
cd llama.cpp && LLAMA_CURL=1 make
|
2535 |
+
```
|
2536 |
+
|
2537 |
+
Step 3: Run inference through the main binary.
|
2538 |
+
```
|
2539 |
+
./llama-cli --hf-repo Pekarnick/e5-large-v2-Q4_K_M-GGUF --hf-file e5-large-v2-q4_k_m.gguf -p "The meaning to life and the universe is"
|
2540 |
+
```
|
2541 |
+
or
|
2542 |
+
```
|
2543 |
+
./llama-server --hf-repo Pekarnick/e5-large-v2-Q4_K_M-GGUF --hf-file e5-large-v2-q4_k_m.gguf -c 2048
|
2544 |
+
```
|